Instruction: What field is the article from?

Input:
Title: Understanding the Effects of Projectors in Knowledge Distillation
Abstract: Conventionally, during the knowledge distillation process (e.g. feature
distillation), an additional projector is often required to perform feature
transformation due to the dimension mismatch between the teacher and the
student networks. Interestingly, we discovered that even if the student and the
teacher have the same feature dimensions, adding a projector still helps to
improve the distillation performance. In addition, projectors even improve
logit distillation if we add them to the architecture too. Inspired by these
surprising findings and the general lack of understanding of the projectors in
the knowledge distillation process from existing literature, this paper
investigates the implicit role that projectors play but so far have been
overlooked. Our empirical study shows that the student with a projector (1)
obtains a better trade-off between the training accuracy and the testing
accuracy compared to the student without a projector when it has the same
feature dimensions as the teacher, (2) better preserves its similarity to the
teacher beyond shallow and numeric resemblance, from the view of Centered
Kernel Alignment (CKA), and (3) avoids being over-confident as the teacher does
at the testing phase. Motivated by the positive effects of projectors, we
propose a projector ensemble-based feature distillation method to further
improve distillation performance. Despite the simplicity of the proposed
strategy, empirical results from the evaluation of classification tasks on
benchmark datasets demonstrate the superior classification performance of our
method on a broad range of teacher-student pairs and verify from the aspects of
CKA and model calibration that the student's features are of improved quality
with the projector ensemble design.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: LLMs for Science: Usage for Code Generation and Data Analysis
Abstract: Large language models (LLMs) have been touted to enable increased
productivity in many areas of today's work life. Scientific research as an area
of work is no exception: the potential of LLM-based tools to assist in the
daily work of scientists has become a highly discussed topic across
disciplines. However, we are only at the very onset of this subject of study.
It is still unclear how the potential of LLMs will materialise in research
practice. With this study, we give first empirical evidence on the use of LLMs
in the research process. We have investigated a set of use cases for LLM-based
tools in scientific research, and conducted a first study to assess to which
degree current tools are helpful. In this paper we report specifically on use
cases related to software engineering, such as generating application code and
developing scripts for data analytics. While we studied seemingly simple use
cases, results across tools differ significantly. Our results highlight the
promise of LLM-based tools in general, yet we also observe various issues,
particularly regarding the integrity of the output these tools provide.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge
Abstract: Automatic aorta segmentation from 3-D medical volumes is an important yet
difficult task. Several factors make the problem challenging, e.g. the
possibility of aortic dissection or the difficulty with segmenting and
annotating the small branches. This work presents a contribution by the MedGIFT
team to the SEG.A challenge organized during the MICCAI 2023 conference. We
propose a fully automated algorithm based on deep encoder-decoder architecture.
The main assumption behind our work is that data preprocessing and augmentation
are much more important than the deep architecture, especially in low data
regimes. Therefore, the solution is based on a variant of traditional
convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for
all testing cases with the highest stability among all participants. The method
scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative
results, and volumetric meshing quality, respectively. We freely release the
source code, pretrained model, and provide access to the algorithm on the
Grand-Challenge platform.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Abstract: While existing large vision-language multimodal models focus on whole image
understanding, there is a prominent gap in achieving region-specific
comprehension. Current approaches that use textual coordinates or spatial
encodings often fail to provide a user-friendly interface for visual prompting.
To address this challenge, we introduce a novel multimodal model capable of
decoding arbitrary visual prompts. This allows users to intuitively mark images
and interact with the model using natural cues like a "red bounding box" or
"pointed arrow". Our simple design directly overlays visual markers onto the
RGB image, eliminating the need for complex region encodings, yet achieves
state-of-the-art performance on region-understanding tasks like Visual7W,
PointQA, and Visual Commonsense Reasoning benchmark. Furthermore, we present
ViP-Bench, a comprehensive benchmark to assess the capability of models in
understanding visual prompts across multiple dimensions, enabling future
research in this domain. Code, data, and model are publicly available.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A* search algorithm for an optimal investment problem in vehicle-sharing systems
Abstract: We study an optimal investment problem that arises in the context of the
vehicle-sharing system. Given a set of locations to build stations, we need to
determine i) the sequence of stations to be built and the number of vehicles to
acquire in order to obtain the target state where all stations are built, and
ii) the number of vehicles to acquire and their allocation in order to maximize
the total profit returned by operating the system when some or all stations are
open. The profitability associated with operating open stations, measured over
a specific time period, is represented as a linear optimization problem applied
to a collection of open stations. With operating capital, the owner of the
system can open new stations. This property introduces a set-dependent aspect
to the duration required for opening a new station, and the optimal investment
problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with
set-dependent cost. We propose an A* search algorithm to address this
particular variant of the TSP. Computational experiments highlight the benefits
of the proposed algorithm in comparison to the widely recognized Dijkstra
algorithm and propose future research to explore new possibilities and
applications for both exact and approximate A* algorithms.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Revisiting Graph-based Fraud Detection in Sight of Heterophily and Spectrum
Abstract: Graph-based fraud detection (GFD) can be regarded as a challenging
semi-supervised node binary classification task. In recent years, Graph Neural
Networks(GNN) have been widely applied to GFD, characterizing the anomalous
possibility of a node by aggregating neighbor information. However, fraud
graphs are inherently heterophilic, thus most of GNNs perform poorly due to
their assumption of homophily. In addition, due to the existence of heterophily
and class imbalance problem, the existing models do not fully utilize the
precious node label information. To address the above issues, this paper
proposes a semi-supervised GNN-based fraud detector SEC-GFD. This detector
includes a hybrid filtering module and a local environmental constraint module,
the two modules are utilized to solve heterophily and label utilization problem
respectively. The first module starts from the perspective of the spectral
domain, and solves the heterophily problem to a certain extent. Specifically,
it divides the spectrum into multiple mixed frequency bands according to the
correlation between spectrum energy distribution and heterophily. Then in order
to make full use of the node label information, a local environmental
constraint module is adaptively designed. The comprehensive experimental
results on four real-world fraud detection datasets show that SEC-GFD
outperforms other competitive graph-based fraud detectors.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Abstract: In this paper, we address the limitations of the common data annotation and
training methods for objective single-label classification tasks. Typically,
when annotating such tasks annotators are only asked to provide a single label
for each sample and annotator disagreement is discarded when a final hard label
is decided through majority voting. We challenge this traditional approach,
acknowledging that determining the appropriate label can be difficult due to
the ambiguity and lack of context in the data samples. Rather than discarding
the information from such ambiguous annotations, our soft label method makes
use of them for training. Our findings indicate that additional annotator
information, such as confidence, secondary label and disagreement, can be used
to effectively generate soft labels. Training classifiers with these soft
labels then leads to improved performance and calibration on the hard label
test set.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Exploring the Impact of Lay User Feedback for Improving AI Fairness
Abstract: Fairness in AI is a growing concern for high-stakes decision making. Engaging
stakeholders, especially lay users, in fair AI development is promising yet
overlooked. Recent efforts explore enabling lay users to provide AI
fairness-related feedback, but there is still a lack of understanding of how to
integrate users' feedback into an AI model and the impacts of doing so. To
bridge this gap, we collected feedback from 58 lay users on the fairness of a
XGBoost model trained on the Home Credit dataset, and conducted offline
experiments to investigate the effects of retraining models on accuracy, and
individual and group fairness. Our work contributes baseline results of
integrating user fairness feedback in XGBoost, and a dataset and code framework
to bootstrap research in engaging stakeholders in AI fairness. Our discussion
highlights the challenges of employing user feedback in AI fairness and points
the way to a future application area of interactive machine learning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Model-as-a-Service (MaaS): A Survey
Abstract: Due to the increased number of parameters and data in the pre-trained model
exceeding a certain level, a foundation model (e.g., a large language model)
can significantly improve downstream task performance and emerge with some
novel special abilities (e.g., deep learning, complex reasoning, and human
alignment) that were not present before. Foundation models are a form of
generative artificial intelligence (GenAI), and Model-as-a-Service (MaaS) has
emerged as a groundbreaking paradigm that revolutionizes the deployment and
utilization of GenAI models. MaaS represents a paradigm shift in how we use AI
technologies and provides a scalable and accessible solution for developers and
users to leverage pre-trained AI models without the need for extensive
infrastructure or expertise in model training. In this paper, the introduction
aims to provide a comprehensive overview of MaaS, its significance, and its
implications for various industries. We provide a brief review of the
development history of "X-as-a-Service" based on cloud computing and present
the key technologies involved in MaaS. The development of GenAI models will
become more democratized and flourish. We also review recent application
studies of MaaS. Finally, we highlight several challenges and future issues in
this promising area. MaaS is a new deployment and service paradigm for
different AI-based models. We hope this review will inspire future research in
the field of MaaS.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery
Abstract: Assessing the environmental impact of the mineral extraction industry plays a
critical role in understanding and mitigating the ecological consequences of
extractive activities. This paper presents MineSegSAT, a model that presents a
novel approach to predicting environmentally impacted areas of mineral
extraction sites using the SegFormer deep learning segmentation architecture
trained on Sentinel-2 data. The data was collected from non-overlapping regions
over Western Canada in 2021 containing areas of land that have been
environmentally impacted by mining activities that were identified from
high-resolution satellite imagery in 2021. The SegFormer architecture, a
state-of-the-art semantic segmentation framework, is employed to leverage its
advanced spatial understanding capabilities for accurate land cover
classification. We investigate the efficacy of loss functions including Dice,
Tversky, and Lovasz loss respectively. The trained model was utilized for
inference over the test region in the ensuing year to identify potential areas
of expansion or contraction over these same periods. The Sentinel-2 data is
made available on Amazon Web Services through a collaboration with Earth Daily
Analytics which provides corrected and tiled analytics-ready data on the AWS
platform. The model and ongoing API to access the data on AWS allow the
creation of an automated tool to monitor the extent of disturbed areas
surrounding known mining sites to ensure compliance with their environmental
impact goals.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ROSO: Improving Robotic Policy Inference via Synthetic Observations
Abstract: In this paper, we propose the use of generative artificial intelligence (AI)
to improve zero-shot performance of a pre-trained policy by altering
observations during inference. Modern robotic systems, powered by advanced
neural networks, have demonstrated remarkable capabilities on pre-trained
tasks. However, generalizing and adapting to new objects and environments is
challenging, and fine-tuning visuomotor policies is time-consuming. To overcome
these issues we propose Robotic Policy Inference via Synthetic Observations
(ROSO). ROSO uses stable diffusion to pre-process a robot's observation of
novel objects during inference time to fit within its distribution of
observations of the pre-trained policies. This novel paradigm allows us to
transfer learned knowledge from known tasks to previously unseen scenarios,
enhancing the robot's adaptability without requiring lengthy fine-tuning. Our
experiments show that incorporating generative AI into robotic inference
significantly improves successful outcomes, finishing up to 57% of tasks
otherwise unsuccessful with the pre-trained policy.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Detecting and Restoring Non-Standard Hands in Stable Diffusion Generated Images
Abstract: We introduce a pipeline to address anatomical inaccuracies in Stable
Diffusion generated hand images. The initial step involves constructing a
specialized dataset, focusing on hand anomalies, to train our models
effectively. A finetuned detection model is pivotal for precise identification
of these anomalies, ensuring targeted correction. Body pose estimation aids in
understanding hand orientation and positioning, crucial for accurate anomaly
correction. The integration of ControlNet and InstructPix2Pix facilitates
sophisticated inpainting and pixel-level transformation, respectively. This
dual approach allows for high-fidelity image adjustments. This comprehensive
approach ensures the generation of images with anatomically accurate hands,
closely resembling real-world appearances. Our experimental results demonstrate
the pipeline's efficacy in enhancing hand image realism in Stable Diffusion
outputs. We provide an online demo at https://fixhand.yiqun.io

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions for Enhanced Sociability
Abstract: Robots' acceptability among humans and their sociability can be significantly
enhanced by incorporating human-like reactions. Humans can react to
environmental events very quickly and without thinking. An instance where
humans display natural reactions is when they encounter a sudden and loud sound
that startles or frightens them. During such moments, individuals may
instinctively move their hands, turn toward the origin of the sound, and try to
determine the event's cause. This inherent behavior motivated us to explore
this less-studied part of social robotics. In this work, a multi-modal system
composed of an action generator, sound classifier, and YOLO object detector was
designed to sense the environment and, in the presence of sudden loud sounds,
show natural human fear reactions, and finally, locate the fear-causing sound
source in the environment. These unique and valid generated motions and
inferences could imitate intrinsic human reactions and enhance the sociability
of robots. For motion generation, a model based on LSTM and MDN networks was
proposed to synthesize various motions. Also, in the case of sound detection, a
transfer learning model was preferred that used the spectrogram of sound
signals as its input. After developing individual models for sound detection,
motion generation, and image recognition, they were integrated into a
comprehensive fear module that was implemented on the NAO robot. Finally, the
fear module was tested in practical application and two groups of experts and
non-experts filled out a questionnaire to evaluate the performance of the
robot. Given our promising results, this preliminary exploratory research
provides a fresh perspective on social robotics and could be a starting point
for modeling intrinsic human behaviors and emotions in robots.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Multiple Instance Learning for Uplift Modeling
Abstract: Uplift modeling is widely used in performance marketing to estimate effects
of promotion campaigns (e.g., increase of customer retention rate). Since it is
impossible to observe outcomes of a recipient in treatment (e.g., receiving a
certain promotion) and control (e.g., without promotion) groups simultaneously
(i.e., counter-factual), uplift models are mainly trained on instances of
treatment and control groups separately to form two models respectively, and
uplifts are predicted by the difference of predictions from these two models
(i.e., two-model method). When responses are noisy and the treatment effect is
fractional, induced individual uplift predictions will be inaccurate, resulting
in targeting undesirable customers. Though it is impossible to obtain the ideal
ground-truth individual uplifts, known as Individual Treatment Effects (ITEs),
alternatively, an average uplift of a group of users, called Average Treatment
Effect (ATE), can be observed from experimental deliveries. Upon this, similar
to Multiple Instance Learning (MIL) in which each training sample is a bag of
instances, our framework sums up individual user uplift predictions for each
bag of users as its bag-wise ATE prediction, and regularizes it to its ATE
label, thus learning more accurate individual uplifts. Additionally, to amplify
the fractional treatment effect, bags are composed of instances with adjacent
individual uplift predictions, instead of random instances. Experiments
conducted on two datasets show the effectiveness and universality of the
proposed framework.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Efficient Off-Policy Safe Reinforcement Learning Using Trust Region Conditional Value at Risk
Abstract: This paper aims to solve a safe reinforcement learning (RL) problem with risk
measure-based constraints. As risk measures, such as conditional value at risk
(CVaR), focus on the tail distribution of cost signals, constraining risk
measures can effectively prevent a failure in the worst case. An on-policy safe
RL method, called TRC, deals with a CVaR-constrained RL problem using a trust
region method and can generate policies with almost zero constraint violations
with high returns. However, to achieve outstanding performance in complex
environments and satisfy safety constraints quickly, RL methods are required to
be sample efficient. To this end, we propose an off-policy safe RL method with
CVaR constraints, called off-policy TRC. If off-policy data from replay buffers
is directly used to train TRC, the estimation error caused by the
distributional shift results in performance degradation. To resolve this issue,
we propose novel surrogate functions, in which the effect of the distributional
shift can be reduced, and introduce an adaptive trust-region constraint to
ensure a policy not to deviate far from replay buffers. The proposed method has
been evaluated in simulation and real-world environments and satisfied safety
constraints within a few steps while achieving high returns even in complex
robotic tasks.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: GraphTransformers for Geospatial Forecasting of Hurricane Trajectories
Abstract: In this paper we introduce a novel framework for trajectory prediction of
geospatial sequences using GraphTransformers. When viewed across several
sequences, we observed that a graph structure automatically emerges between
different geospatial points that is often not taken into account for such
sequence modeling tasks. We show that by leveraging this graph structure
explicitly, geospatial trajectory prediction can be significantly improved. Our
GraphTransformer approach improves upon state-of-the-art Transformer based
baseline significantly on HURDAT, a dataset where we are interested in
predicting the trajectory of a hurricane on a 6 hourly basis.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
Abstract: In surgical procedures, correct instrument counting is essential. Instance
segmentation is a location method that locates not only an object's bounding
box but also each pixel's specific details. However, obtaining mask-level
annotations is labor-intensive in instance segmentation. To address this issue,
we propose a novel yet effective weakly-supervised surgical instrument instance
segmentation approach, named Point-based Weakly-supervised Instance
Segmentation (PWISeg). PWISeg adopts an FCN-based architecture with
point-to-box and point-to-mask branches to model the relationships between
feature points and bounding boxes, as well as feature points and segmentation
masks on FPN, accomplishing instrument detection and segmentation jointly in a
single model. Since mask level annotations are hard to available in the real
world, for point-to-mask training, we introduce an unsupervised projection
loss, utilizing the projected relation between predicted masks and bboxes as
supervision signal. On the other hand, we annotate a few pixels as the key
pixel for each instrument. Based on this, we further propose a key pixel
association loss and a key pixel distribution loss, driving the point-to-mask
branch to generate more accurate segmentation predictions. To comprehensively
evaluate this task, we unveil a novel surgical instrument dataset with manual
annotations, setting up a benchmark for further research. Our comprehensive
research trial validated the superior performance of our PWISeg. The results
show that the accuracy of surgical instrument segmentation is improved,
surpassing most methods of instance segmentation via weakly supervised bounding
boxes. This improvement is consistently observed in our proposed dataset and
when applied to the public HOSPI-Tools dataset.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: XAI for time-series classification leveraging image highlight methods
Abstract: Although much work has been done on explainability in the computer vision and
natural language processing (NLP) fields, there is still much work to be done
to explain methods applied to time series as time series by nature can not be
understood at first sight. In this paper, we present a Deep Neural Network
(DNN) in a teacher-student architecture (distillation model) that offers
interpretability in time-series classification tasks. The explainability of our
approach is based on transforming the time series to 2D plots and applying
image highlight methods (such as LIME and GradCam), making the predictions
interpretable. At the same time, the proposed approach offers increased
accuracy competing with the baseline model with the trade-off of increasing the
training time.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Continuous 16-bit Training: Accelerating 32-bit Pre-Trained Neural Networks
Abstract: In the field of deep learning, the prevalence of models initially trained
with 32-bit precision is a testament to its robustness and accuracy. However,
the continuous evolution of these models often demands further training, which
can be resource-intensive. This study introduces a novel approach where we
continue the training of these pre-existing 32-bit models using 16-bit
precision. This technique not only caters to the need for efficiency in
computational resources but also significantly improves the speed of additional
training phases. By adopting 16-bit precision for ongoing training, we are able
to substantially decrease memory requirements and computational burden, thereby
accelerating the training process in a resource-limited setting. Our
experiments show that this method maintains the high standards of accuracy set
by the original 32-bit training while providing a much-needed boost in training
speed. This approach is especially pertinent in today's context, where most
models are initially trained in 32-bit and require periodic updates and
refinements. The findings from our research suggest that this strategy of
16-bit continuation training can be a key solution for sustainable and
efficient deep learning, offering a practical way to enhance pre-trained models
rapidly and in a resource-conscious manner.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Abstract: Open-world 3D part segmentation is pivotal in diverse applications such as
robotics and AR/VR. Traditional supervised methods often grapple with limited
3D data availability and struggle to generalize to unseen object categories.
PartSLIP, a recent advancement, has made significant strides in zero- and
few-shot 3D part segmentation. This is achieved by harnessing the capabilities
of the 2D open-vocabulary detection module, GLIP, and introducing a heuristic
method for converting and lifting multi-view 2D bounding box predictions into
3D segmentation masks. In this paper, we introduce PartSLIP++, an enhanced
version designed to overcome the limitations of its predecessor. Our approach
incorporates two major improvements. First, we utilize a pre-trained 2D
segmentation model, SAM, to produce pixel-wise 2D segmentations, yielding more
precise and accurate annotations than the 2D bounding boxes used in PartSLIP.
Second, PartSLIP++ replaces the heuristic 3D conversion process with an
innovative modified Expectation-Maximization algorithm. This algorithm
conceptualizes 3D instance segmentation as unobserved latent variables, and
then iteratively refines them through an alternating process of 2D-3D matching
and optimization with gradient descent. Through extensive evaluations, we show
that PartSLIP++ demonstrates better performance over PartSLIP in both low-shot
3D semantic and instance-based object part segmentation tasks. Code released at
https://github.com/zyc00/PartSLIP2.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models
Abstract: A long-standing challenge for a robotic manipulation system operating in
real-world scenarios is adapting and generalizing its acquired motor skills to
unseen environments. We tackle this challenge employing hybrid skill models
that integrate imitation and reinforcement paradigms, to explore how the
learning and adaptation of a skill, along with its core grounding in the scene
through a learned keypoint, can facilitate such generalization. To that end, we
develop Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models (KIS-GMM)
approach that learns to predict the reference of a dynamical system within the
scene as a 3D keypoint, leveraging visual observations obtained by the robot's
physical interactions during skill learning. Through conducting comprehensive
evaluations in both simulated and real-world environments, we show that our
method enables a robot to gain a significant zero-shot generalization to novel
environments and to refine skills in the target environments faster than
learning from scratch. Importantly, this is achieved without the need for new
ground truth data. Moreover, our method effectively copes with scene
displacements.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: "Do it my way!": Impact of Customizations on Trust perceptions in Human-Robot Collaboration
Abstract: Trust has been shown to be a key factor in effective human-robot
collaboration. In the context of assistive robotics, the effect of trust
factors on human experience is further pronounced. Personalization of assistive
robots is an orthogonal factor positively correlated with robot adoption and
user perceptions. In this work, we investigate the relationship between these
factors through a within-subjects study (N=17). We provide different levels of
customization possibilities over baseline autonomous robot behavior and
investigate its impact on trust. Our findings indicate that increased levels of
customization was associated with higher trust and comfort perceptions. The
assistive robot design process can benefit significantly from our insights for
designing trustworthy and customized robots.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
Abstract: Uncertainty estimation in Neural Networks (NNs) is vital in improving
reliability and confidence in predictions, particularly in safety-critical
applications. Bayesian Neural Networks (BayNNs) with Dropout as an
approximation offer a systematic approach to quantifying uncertainty, but they
inherently suffer from high hardware overhead in terms of power, memory, and
computation. Thus, the applicability of BayNNs to edge devices with limited
resources or to high-performance applications is challenging. Some of the
inherent costs of BayNNs can be reduced by accelerating them in hardware on a
Computation-In-Memory (CIM) architecture with spintronic memories and
binarizing their parameters. However, numerous stochastic units are required to
implement conventional dropout-based BayNN. In this paper, we propose the Scale
Dropout, a novel regularization technique for Binary Neural Networks (BNNs),
and Monte Carlo-Scale Dropout (MC-Scale Dropout)-based BayNNs for efficient
uncertainty estimation. Our approach requires only one stochastic unit for the
entire model, irrespective of the model size, leading to a highly scalable
Bayesian NN. Furthermore, we introduce a novel Spintronic memory-based CIM
architecture for the proposed BayNN that achieves more than $100\times$ energy
savings compared to the state-of-the-art. We validated our method to show up to
a $1\%$ improvement in predictive performance and superior uncertainty
estimates compared to related works.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Honeybee: Locality-enhanced Projector for Multimodal LLM
Abstract: In Multimodal Large Language Models (MLLMs), a visual projector plays a
crucial role in bridging pre-trained vision encoders with LLMs, enabling
profound visual understanding while harnessing the LLMs' robust capabilities.
Despite the importance of the visual projector, it has been relatively less
explored. In this study, we first identify two essential projector properties:
(i) flexibility in managing the number of visual tokens, crucial for MLLMs'
overall efficiency, and (ii) preservation of local context from visual
features, vital for spatial understanding. Based on these findings, we propose
a novel projector design that is both flexible and locality-enhanced,
effectively satisfying the two desirable properties. Additionally, we present
comprehensive strategies to effectively utilize multiple and multifaceted
instruction datasets. Through extensive experiments, we examine the impact of
individual design choices. Finally, our proposed MLLM, Honeybee, remarkably
outperforms previous state-of-the-art methods across various benchmarks,
including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly
higher efficiency. Code and models are available at
https://github.com/kakaobrain/honeybee.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Large Language Models for Mathematicians
Abstract: Large language models (LLMs) such as ChatGPT have received immense interest
for their general-purpose language understanding and, in particular, their
ability to generate high-quality text or computer code. For many professions,
LLMs represent an invaluable tool that can speed up and improve the quality of
work. In this note, we discuss to what extent they can aid professional
mathematicians. We first provide a mathematical description of the transformer
model used in all modern language models. Based on recent studies, we then
outline best practices and potential issues and report on the mathematical
abilities of language models. Finally, we shed light on the potential of LMMs
to change how mathematicians work.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Graph Convolutional Networks for Complex Traffic Scenario Classification
Abstract: A scenario-based testing approach can reduce the time required to obtain
statistically significant evidence of the safety of Automated Driving Systems
(ADS). Identifying these scenarios in an automated manner is a challenging
task. Most methods on scenario classification do not work for complex scenarios
with diverse environments (highways, urban) and interaction with other traffic
agents. This is mirrored in their approaches which model an individual vehicle
in relation to its environment, but neglect the interaction between multiple
vehicles (e.g. cut-ins, stationary lead vehicle). Furthermore, existing
datasets lack diversity and do not have per-frame annotations to accurately
learn the start and end time of a scenario. We propose a method for complex
traffic scenario classification that is able to model the interaction of a
vehicle with the environment, as well as other agents. We use Graph
Convolutional Networks to model spatial and temporal aspects of these
scenarios. Expanding the nuScenes and Argoverse 2 driving datasets, we
introduce a scenario-labeled dataset, which covers different driving
environments and is annotated per frame. Training our method on this dataset,
we present a promising baseline for future research on per-frame complex
scenario classification.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
Abstract: Misunderstandings arise not only in interpersonal communication but also
between humans and Large Language Models (LLMs). Such discrepancies can make
LLMs interpret seemingly unambiguous questions in unexpected ways, yielding
incorrect responses. While it is widely acknowledged that the quality of a
prompt, such as a question, significantly impacts the quality of the response
provided by LLMs, a systematic method for crafting questions that LLMs can
better comprehend is still underdeveloped. In this paper, we present a method
named `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand
questions posed by humans and provide responses in a single prompt. This
approach serves as a simple yet effective prompting method for improving
performance. We also introduce a two-step variant of RaR, where a rephrasing
LLM first rephrases the question and then passes the original and rephrased
questions together to a different responding LLM. This facilitates the
effective utilization of rephrased questions generated by one LLM with another.
Our experiments demonstrate that our methods significantly improve the
performance of different models across a wide range to tasks. We further
provide a comprehensive comparison between RaR and the popular Chain-of-Thought
(CoT) methods, both theoretically and empirically. We show that RaR is
complementary to CoT and can be combined with CoT to achieve even better
performance. Our work not only contributes to enhancing LLM performance
efficiently and effectively but also sheds light on a fair evaluation of LLM
capabilities. Data and codes are available at
https://github.com/uclaml/Rephrase-and-Respond.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Protecting Publicly Available Data With Machine Learning Shortcuts
Abstract: Machine-learning (ML) shortcuts or spurious correlations are artifacts in
datasets that lead to very good training and test performance but severely
limit the model's generalization capability. Such shortcuts are insidious
because they go unnoticed due to good in-domain test performance. In this
paper, we explore the influence of different shortcuts and show that even
simple shortcuts are difficult to detect by explainable AI methods. We then
exploit this fact and design an approach to defend online databases against
crawlers: providers such as dating platforms, clothing manufacturers, or used
car dealers have to deal with a professionalized crawling industry that grabs
and resells data points on a large scale. We show that a deterrent can be
created by deliberately adding ML shortcuts. Such augmented datasets are then
unusable for ML use cases, which deters crawlers and the unauthorized use of
data from the internet. Using real-world data from three use cases, we show
that the proposed approach renders such collected data unusable, while the
shortcut is at the same time difficult to notice in human perception. Thus, our
proposed approach can serve as a proactive protection against illegitimate data
crawling.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images
Abstract: In the last years, the weakly supervised paradigm of multiple instance
learning (MIL) has become very popular in many different areas. A paradigmatic
example is computational pathology, where the lack of patch-level labels for
whole-slide images prevents the application of supervised models. Probabilistic
MIL methods based on Gaussian Processes (GPs) have obtained promising results
due to their excellent uncertainty estimation capabilities. However, these are
general-purpose MIL methods that do not take into account one important fact:
in (histopathological) images, the labels of neighboring patches are expected
to be correlated. In this work, we extend a state-of-the-art GP-based MIL
method, which is called VGPMIL-PR, to exploit such correlation. To do so, we
develop a novel coupling term inspired by the statistical physics Ising model.
We use variational inference to estimate all the model parameters.
Interestingly, the VGPMIL-PR formulation is recovered when the weight that
regulates the strength of the Ising term vanishes. The performance of the
proposed method is assessed in two real-world problems of prostate cancer
detection. We show that our model achieves better results than other
state-of-the-art probabilistic MIL methods. We also provide different
visualizations and analysis to gain insights into the influence of the novel
Ising term. These insights are expected to facilitate the application of the
proposed model to other research areas.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models
Abstract: Text-to-image diffusion models have been adopted into key commercial
workflows, such as art generation and image editing. Characterising the
implicit social biases they exhibit, such as gender and racial stereotypes, is
a necessary first step in avoiding discriminatory outcomes. While existing
studies on social bias focus on image generation, the biases exhibited in
alternate applications of diffusion-based foundation models remain
under-explored. We propose methods that use synthetic images to probe two
applications of diffusion models, image editing and classification, for social
bias. Using our methodology, we uncover meaningful and significant
inter-sectional social biases in \textit{Stable Diffusion}, a state-of-the-art
open-source text-to-image model. Our findings caution against the uninformed
adoption of text-to-image foundation models for downstream tasks and services.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: er.autopilot 1.0: The Full Autonomous Stack for Oval Racing at High Speeds
Abstract: The Indy Autonomous Challenge (IAC) brought together for the first time in
history nine autonomous racing teams competing at unprecedented speed and in
head-to-head scenario, using independently developed software on open-wheel
racecars. This paper presents the complete software architecture used by team
TII EuroRacing (TII-ER), covering all the modules needed to avoid static
obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h).
In addition to the most common modules related to perception, planning, and
control, we discuss the approaches used for vehicle dynamics modelling,
simulation, telemetry, and safety. Overall results and the performance of each
module are described, as well as the lessons learned during the first two
events of the competition on oval tracks, where the team placed respectively
second and third.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Alignment for Honesty
Abstract: Recent research has made significant strides in applying alignment techniques
to enhance the helpfulness and harmlessness of large language models (LLMs) in
accordance with human intentions. In this paper, we argue for the importance of
alignment for honesty, ensuring that LLMs proactively refuse to answer
questions when they lack knowledge, while still not being overly conservative.
However, a pivotal aspect of alignment for honesty involves discerning the
limits of an LLM's knowledge, which is far from straightforward. This challenge
demands comprehensive solutions in terms of metric development, benchmark
creation, and training methodologies. In this paper, we address these
challenges by first establishing a precise problem definition and defining
``honesty'' inspired by the Analects of Confucius. This serves as a cornerstone
for developing metrics that effectively measure an LLM's honesty by quantifying
its progress post-alignment. Furthermore, we introduce a flexible training
framework which is further instantiated by several efficient fine-tuning
techniques that emphasize honesty without sacrificing performance on other
tasks. Our extensive experiments reveal that these aligned models show a marked
increase in honesty, as indicated by our proposed metrics. We open-source a
wealth of resources to facilitate future research at
https://github.com/GAIR-NLP/alignment-for-honesty, including honesty-aligned
models, training and evaluation datasets for honesty alignment, concept
glossary, as well as all relevant source code.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models
Abstract: Denoising Diffusion models have exhibited remarkable capabilities in image
generation. However, generating high-quality samples requires a large number of
iterations. Knowledge distillation for diffusion models is an effective method
to address this limitation with a shortened sampling process but causes
degraded generative quality. Based on our analysis with bias-variance
decomposition and experimental observations, we attribute the degradation to
the spatial fitting error occurring in the training of both the teacher and
student model. Accordingly, we propose $\textbf{S}$patial
$\textbf{F}$itting-$\textbf{E}$rror $\textbf{R}$eduction
$\textbf{D}$istillation model ($\textbf{SFERD}$). SFERD utilizes attention
guidance from the teacher model and a designed semantic gradient predictor to
reduce the student's fitting error. Empirically, our proposed model facilitates
high-quality sample generation in a few function evaluations. We achieve an FID
of 5.31 on CIFAR-10 and 9.39 on ImageNet 64$\times$64 with only one step,
outperforming existing diffusion methods. Our study provides a new perspective
on diffusion distillation by highlighting the intrinsic denoising ability of
models.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Abstract: Over the past few years, Machine Learning-as-a-Service (MLaaS) has received a
surging demand for supporting Machine Learning-driven services to offer
revolutionized user experience across diverse application areas. MLaaS provides
inference service with low inference latency to application users based on an
ML model trained using a dataset collected from numerous individual data
owners. Recently, for the sake of data owners' privacy and to comply with the
"right to be forgotten (RTBF)" as enacted by data protection legislation, many
machine unlearning methods have been proposed to remove data owners' data from
trained models upon their unlearning requests. However, despite their promising
efficiency, almost all existing machine unlearning methods handle unlearning
requests in a manner that is independent of inference requests, which
unfortunately introduces new security and privacy vulnerabilities for machine
unlearning in MLaaS. In this paper, we propose the ERASER framework for machinE
unleaRning in MLaAS via an inferencE seRving-aware approach. ERASER proposes a
novel certified inference consistency mechanism that reduces inference latency
by selectively postponing unlearning execution incurred by unlearning requests
from data owners, while strictly adhering to the RTBF principle. ERASER offers
three groups of design choices to allow for tailor-made variants that best suit
the specific environments and preferences of different MLaaS systems. Extensive
empirical evaluations across various settings confirm ERASER's effectiveness,
e.g., it can effectively save up to 99% of inference latency and 31% of
computation overhead over the inference-oblivion baseline.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture
Abstract: Over the past decade, climate change has become an increasing problem with
one of the major contributing factors being carbon dioxide (CO2) emissions;
almost 51% of total US carbon emissions are from factories. Current materials
used in CO2 capture are lacking either in efficiency, sustainability, or cost.
  Electrocatalysis of CO2 is a new approach where CO2 can be reduced and the
components used industrially as fuel, saving transportation costs, creating
financial incentives. Metal Organic Frameworks (MOFs) are crystals made of
organo-metals that adsorb, filter, and electrocatalyze CO2. The current
available MOFs for capture & electrocatalysis are expensive to manufacture and
inefficient at capture. The goal therefore is to computationally design a MOF
that can adsorb CO2 and catalyze carbon monoxide & oxygen with low cost.
  A novel active transfer learning neural network was developed, utilizing
transfer learning due to limited available data on 15 MOFs. Using the Cambridge
Structural Database with 10,000 MOFs, the model used incremental mutations to
fit a trained fitness hyper-heuristic function. Eventually, a Selenium MOF
(C18MgO25Se11Sn20Zn5) was converged on. Through analysis of predictions &
literature, the converged MOF was shown to be more effective & more
synthetically accessible than existing MOFs, showing the model had an
understanding of effective electrocatalytic structures in the material space.
This novel network can be implemented for other gas separations and catalysis
applications that have limited training accessible datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Abstract: Neural language models (LMs) can be used to evaluate the truth of factual
statements in two ways: they can be either queried for statement probabilities,
or probed for internal representations of truthfulness. Past work has found
that these two procedures sometimes disagree, and that probes tend to be more
accurate than LM outputs. This has led some researchers to conclude that LMs
"lie" or otherwise encode non-cooperative communicative intents. Is this an
accurate description of today's LMs, or can query-probe disagreement arise in
other ways? We identify three different classes of disagreement, which we term
confabulation, deception, and heterogeneity. In many cases, the superiority of
probes is simply attributable to better calibration on uncertain answers rather
than a greater fraction of correct, high-confidence answers. In some cases,
queries and probes perform better on different subsets of inputs, and accuracy
can further be improved by ensembling the two. Code is available at
github.com/lingo-mit/lm-truthfulness.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Abstract: Recent studies on semi-supervised learning (SSL) have achieved great success.
Despite their promising performance, current state-of-the-art methods tend
toward increasingly complex designs at the cost of introducing more network
components and additional training procedures. In this paper, we propose a
simple method named Ensemble Projectors Aided for Semi-supervised Learning
(EPASS), which focuses mainly on improving the learned embeddings to boost the
performance of the existing contrastive joint-training semi-supervised learning
frameworks. Unlike standard methods, where the learned embeddings from one
projector are stored in memory banks to be used with contrastive learning,
EPASS stores the ensemble embeddings from multiple projectors in memory banks.
As a result, EPASS improves generalization, strengthens feature representation,
and boosts performance. For instance, EPASS improves strong baselines for
semi-supervised learning by 39.47\%/31.39\%/24.70\% top-1 error rate, while
using only 100k/1\%/10\% of labeled data for SimMatch, and achieves
40.24\%/32.64\%/25.90\% top-1 error rate for CoMatch on the ImageNet dataset.
These improvements are consistent across methods, network architectures, and
datasets, proving the general effectiveness of the proposed methods. Code is
available at https://github.com/beandkay/EPASS.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Meta learning with language models: Challenges and opportunities in the classification of imbalanced text
Abstract: Detecting out of policy speech (OOPS) content is important but difficult.
While machine learning is a powerful tool to tackle this challenging task, it
is hard to break the performance ceiling due to factors like quantity and
quality limitations on training data and inconsistencies in OOPS definition and
data labeling. To realize the full potential of available limited resources, we
propose a meta learning technique (MLT) that combines individual models built
with different text representations. We analytically show that the resulting
technique is numerically stable and produces reasonable combining weights. We
combine the MLT with a threshold-moving (TM) technique to further improve the
performance of the combined predictor on highly-imbalanced in-distribution and
out-of-distribution datasets. We also provide computational results to show the
statistically significant advantages of the proposed MLT approach.
  All authors contributed equally to this work.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Saturn: Efficient Multi-Large-Model Deep Learning
Abstract: In this paper, we propose Saturn, a new data system to improve the efficiency
of multi-large-model training (e.g., during model selection/hyperparameter
optimization). We first identify three key interconnected systems challenges
for users building large models in this setting -- parallelism technique
selection, distribution of GPUs over jobs, and scheduling. We then formalize
these as a joint problem, and build a new system architecture to tackle these
challenges simultaneously. Our evaluations show that our joint-optimization
approach yields 39-49% lower model selection runtimes than typical current DL
practice.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Hessian Aware Low-Rank Weight Perturbation for Continual Learning
Abstract: Continual learning aims to learn a series of tasks sequentially without
forgetting the knowledge acquired from the previous ones. In this work, we
propose the Hessian Aware Low-Rank Perturbation algorithm for continual
learning. By modeling the parameter transitions along the sequential tasks with
the weight matrix transformation, we propose to apply the low-rank
approximation on the task-adaptive parameters in each layer of the neural
networks. Specifically, we theoretically demonstrate the quantitative
relationship between the Hessian and the proposed low-rank approximation. The
approximation ranks are then globally determined according to the marginal
increment of the empirical loss estimated by the layer-specific gradient and
low-rank approximation error. Furthermore, we control the model capacity by
pruning less important parameters to diminish the parameter growth. We conduct
extensive experiments on various benchmarks, including a dataset with
large-scale tasks, and compare our method against some recent state-of-the-art
methods to demonstrate the effectiveness and scalability of our proposed
method. Empirical results show that our method performs better on different
benchmarks, especially in achieving task order robustness and handling the
forgetting issue. A demo code can be found at https://github.com/lijiaqi/HALRP.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Content-based Controls For Music Large Language Modeling
Abstract: Recent years have witnessed a rapid growth of large-scale language models in
the domain of music audio. Such models enable end-to-end generation of
higher-quality music, and some allow conditioned generation using text
descriptions. However, the control power of text controls on music is
intrinsically limited, as they can only describe music indirectly through
meta-data (such as singers and instruments) or high-level representations (such
as genre and emotion). We aim to further equip the models with direct and
content-based controls on innate music languages such as pitch, chords and drum
track. To this end, we contribute Coco-Mulla, a content-based control method
for music large language modeling. It uses a parameter-efficient fine-tuning
(PEFT) method tailored for Transformer-based audio models. Experiments show
that our approach achieved high-quality music generation with low-resource
semi-supervised learning, tuning with less than 4% parameters compared to the
original model and training on a small dataset with fewer than 300 songs.
Moreover, our approach enables effective content-based controls, and we
illustrate the control power via chords and rhythms, two of the most salient
features of music audio. Furthermore, we show that by combining content-based
controls and text descriptions, our system achieves flexible music variation
generation and style transfer. Our source codes and demos are available online.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Data-driven project planning: An integrated network learning and constraint relaxation approach in favor of scheduling
Abstract: Our focus is on projects, i.e., business processes, which are emerging as the
economic drivers of our times. Differently from day-to-day operational
processes that do not require detailed planning, a project requires planning
and resource-constrained scheduling for coordinating resources across sub- or
related projects and organizations. A planner in charge of project planning has
to select a set of activities to perform, determine their precedence
constraints, and schedule them according to temporal project constraints. We
suggest a data-driven project planning approach for classes of projects such as
infrastructure building and information systems development projects. A project
network is first learned from historical records. The discovered network
relaxes temporal constraints embedded in individual projects, thus uncovering
where planning and scheduling flexibility can be exploited for greater benefit.
Then, the network, which contains multiple project plan variations, from which
one has to be selected, is enriched by identifying decision rules and frequent
paths. The planner can rely on the project network for: 1) decoding a project
variation such that it forms a new project plan, and 2) applying
resource-constrained project scheduling procedures to determine the project's
schedule and resource allocation. Using two real-world project datasets, we
show that the suggested approach may provide the planner with significant
flexibility (up to a 26% reduction of the critical path of a real project) to
adjust the project plan and schedule. We believe that the proposed approach can
play an important part in supporting decision making towards automated
data-driven project planning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching
Abstract: In real-world scenarios, arbitrary interactions with the environment can
often be costly, and actions of expert demonstrations are not always available.
To reduce the need for both, Offline Learning from Observations (LfO) is
extensively studied, where the agent learns to solve a task with only expert
states and \textit{task-agnostic} non-expert state-action pairs. The
state-of-the-art DIstribution Correction Estimation (DICE) methods minimize the
state occupancy divergence between the learner and expert policies. However,
they are limited to either $f$-divergences (KL and $\chi^2$) or Wasserstein
distance with Rubinstein duality, the latter of which constrains the underlying
distance metric crucial to the performance of Wasserstein-based solutions. To
address this problem, we propose Primal Wasserstein DICE (PW-DICE), which
minimizes the primal Wasserstein distance between the expert and learner state
occupancies with a pessimistic regularizer and leverages a contrastively
learned distance as the underlying metric for the Wasserstein distance.
Theoretically, we prove that our framework is a generalization of the
state-of-the-art, SMODICE, and unifies $f$-divergence and Wasserstein
minimization. Empirically, we find that PW-DICE improves upon several
state-of-the-art methods on multiple testbeds.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
Abstract: The quest for accurate prediction of drug molecule properties poses a
fundamental challenge in the realm of Artificial Intelligence Drug Discovery
(AIDD). An effective representation of drug molecules emerges as a pivotal
component in this pursuit. Contemporary leading-edge research predominantly
resorts to self-supervised learning (SSL) techniques to extract meaningful
structural representations from large-scale, unlabeled molecular data,
subsequently fine-tuning these representations for an array of downstream
tasks. However, an inherent shortcoming of these studies lies in their singular
reliance on one modality of molecular information, such as molecule image or
SMILES representations, thus neglecting the potential complementarity of
various molecular modalities. In response to this limitation, we propose MolIG,
a novel MultiModaL molecular pre-training framework for predicting molecular
properties based on Image and Graph structures. MolIG model innovatively
leverages the coherence and correlation between molecule graph and molecule
image to execute self-supervised tasks, effectively amalgamating the strengths
of both molecular representation forms. This holistic approach allows for the
capture of pivotal molecular structural characteristics and high-level semantic
information. Upon completion of pre-training, Graph Neural Network (GNN)
Encoder is used for the prediction of downstream tasks. In comparison to
advanced baseline models, MolIG exhibits enhanced performance in downstream
tasks pertaining to molecular property prediction within benchmark groups such
as MoleculeNet Benchmark Group and ADMET Benchmark Group.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models
Abstract: Document-level Relation Extraction (DocRE), which aims to extract relations
from a long context, is a critical challenge in achieving fine-grained
structural comprehension and generating interpretable document representations.
Inspired by recent advances in in-context learning capabilities emergent from
large language models (LLMs), such as ChatGPT, we aim to design an automated
annotation method for DocRE with minimum human effort. Unfortunately, vanilla
in-context learning is infeasible for document-level relation extraction due to
the plenty of predefined fine-grained relation types and the uncontrolled
generations of LLMs. To tackle this issue, we propose a method integrating a
large language model (LLM) and a natural language inference (NLI) module to
generate relation triples, thereby augmenting document-level relation datasets.
We demonstrate the effectiveness of our approach by introducing an enhanced
dataset known as DocGNRE, which excels in re-annotating numerous long-tail
relation types. We are confident that our method holds the potential for
broader applications in domain-specific relation type definitions and offers
tangible benefits in advancing generalized language semantic comprehension.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods
Abstract: In this work, we systematically study the problem of personalized
text-to-image generation, where the output image is expected to portray
information about specific human subjects. E.g., generating images of oneself
appearing at imaginative places, interacting with various items, or engaging in
fictional activities. To this end, we focus on text-to-image systems that input
a single image of an individual to ground the generation process along with
text describing the desired visual context. Our first contribution is to fill
the literature gap by curating high-quality, appropriate data for this task.
Namely, we introduce a standardized dataset (Stellar) that contains
personalized prompts coupled with images of individuals that is an order of
magnitude larger than existing relevant datasets and where rich semantic
ground-truth annotations are readily available. Having established Stellar to
promote cross-systems fine-grained comparisons further, we introduce a rigorous
ensemble of specialized metrics that highlight and disentangle fundamental
properties such systems should obey. Besides being intuitive, our new metrics
correlate significantly more strongly with human judgment than currently used
metrics on this task. Last but not least, drawing inspiration from the recent
works of ELITE and SDXL, we derive a simple yet efficient, personalized
text-to-image baseline that does not require test-time fine-tuning for each
subject and which sets quantitatively and in human trials a new SoTA. For more
information, please visit our project's website:
https://stellar-gen-ai.github.io/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Temporal Shift -- Multi-Objective Loss Function for Improved Anomaly Fall Detection
Abstract: Falls are a major cause of injuries and deaths among older adults worldwide.
Accurate fall detection can help reduce potential injuries and additional
health complications. Different types of video modalities can be used in a home
setting to detect falls, including RGB, Infrared, and Thermal cameras. Anomaly
detection frameworks using autoencoders and their variants can be used for fall
detection due to the data imbalance that arises from the rarity and diversity
of falls. However, the use of reconstruction error in autoencoders can limit
the application of networks' structures that propagate information. In this
paper, we propose a new multi-objective loss function called Temporal Shift,
which aims to predict both future and reconstructed frames within a window of
sequential frames. The proposed loss function is evaluated on a
semi-naturalistic fall detection dataset containing multiple camera modalities.
The autoencoders were trained on normal activities of daily living (ADL)
performed by older adults and tested on ADLs and falls performed by young
adults. Temporal shift shows significant improvement to a baseline 3D
Convolutional autoencoder, an attention U-Net CAE, and a multi-modal neural
network. The greatest improvement was observed in an attention U-Net model
improving by 0.20 AUC ROC for a single camera when compared to reconstruction
alone. With significant improvement across different models, this approach has
the potential to be widely adopted and improve anomaly detection capabilities
in other settings besides fall detection.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
Abstract: Recent advances in Competitive Self-Play (CSP) have achieved, or even
surpassed, human level performance in complex game environments such as Dota 2
and StarCraft II using Distributed Multi-Agent Reinforcement Learning (MARL).
One core component of these methods relies on creating a pool of learning
agents -- consisting of the Main Agent, past versions of this agent, and
Exploiter Agents -- where Exploiter Agents learn counter-strategies to the Main
Agents. A key drawback of these approaches is the large computational cost and
physical time that is required to train the system, making them impractical to
deploy in highly iterative real-life settings such as video game productions.
In this paper, we propose the Minimax Exploiter, a game theoretic approach to
exploiting Main Agents that leverages knowledge of its opponents, leading to
significant increases in data efficiency. We validate our approach in a
diversity of settings, including simple turn based games, the arcade learning
environment, and For Honor, a modern video game. The Minimax Exploiter
consistently outperforms strong baselines, demonstrating improved stability and
data efficiency, leading to a robust CSP-MARL method that is both flexible and
easy to deploy.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Automated Material Properties Extraction For Enhanced Beauty Product Discovery and Makeup Virtual Try-on
Abstract: The multitude of makeup products available can make it challenging to find
the ideal match for desired attributes. An intelligent approach for product
discovery is required to enhance the makeup shopping experience to make it more
convenient and satisfying. However, enabling accurate and efficient product
discovery requires extracting detailed attributes like color and finish type.
Our work introduces an automated pipeline that utilizes multiple customized
machine learning models to extract essential material attributes from makeup
product images. Our pipeline is versatile and capable of handling various
makeup products. To showcase the efficacy of our pipeline, we conduct extensive
experiments on eyeshadow products (both single and multi-shade ones), a
challenging makeup product known for its diverse range of shapes, colors, and
finish types. Furthermore, we demonstrate the applicability of our approach by
successfully extending it to other makeup categories like lipstick and
foundation, showcasing its adaptability and effectiveness across different
beauty products. Additionally, we conduct ablation experiments to demonstrate
the superiority of our machine learning pipeline over human labeling methods in
terms of reliability. Our proposed method showcases its effectiveness in
cross-category product discovery, specifically in recommending makeup products
that perfectly match a specified outfit. Lastly, we also demonstrate the
application of these material attributes in enabling virtual-try-on experiences
which makes makeup shopping experience significantly more engaging.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Investigating Data Contamination in Modern Benchmarks for Large Language Models
Abstract: Recent observations have underscored a disparity between the inflated
benchmark scores and the actual performance of LLMs, raising concerns about
potential contamination of evaluation benchmarks. This issue is especially
critical for closed-source models and certain open-source models where training
data transparency is lacking. In this paper we study data contamination by
proposing two methods tailored for both open-source and proprietary LLMs. We
first introduce a retrieval-based system to explore potential overlaps between
evaluation benchmarks and pretraining corpora. We further present a novel
investigation protocol named \textbf{T}estset \textbf{S}lot Guessing
(\textit{TS-Guessing}), applicable to both open and proprietary models. This
approach entails masking a wrong answer in a multiple-choice question and
prompting the model to fill in the gap. Additionally, it involves obscuring an
unlikely word in an evaluation example and asking the model to produce it. We
find that certain commercial LLMs could surprisingly guess the missing option
in various test sets. Specifically, in the TruthfulQA benchmark, we find that
LLMs exhibit notable performance improvement when provided with additional
metadata in the benchmark. Further, in the MMLU benchmark, ChatGPT and GPT-4
demonstrated an exact match rate of 52\% and 57\%, respectively, in guessing
the missing options in benchmark test data. We hope these results underscore
the need for more robust evaluation methodologies and benchmarks in the field.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Modular Neural Networks for Time Series Forecasting: Interpretability and Feature Selection using Attention
Abstract: Multivariate time series have many applications, from healthcare and
meteorology to life science. Although deep learning models have shown excellent
predictive performance for time series, they have been criticised for being
"black-boxes" or non-interpretable. This paper proposes a novel modular neural
network model for multivariate time series prediction that is interpretable by
construction. A recurrent neural network learns the temporal dependencies in
the data while an attention-based feature selection component selects the most
relevant features and suppresses redundant features used in the learning of the
temporal dependencies. A modular deep network is trained from the selected
features independently to show the users how features influence outcomes,
making the model interpretable. Experimental results show that this approach
can outperform state-of-the-art interpretable Neural Additive Models (NAM) and
variations thereof in both regression and classification of time series tasks,
achieving a predictive performance that is comparable to the top
non-interpretable methods for time series, LSTM and XGBoost.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
Abstract: Autonomous shipping has recently gained much interest in the research
community. However, little research focuses on inland - and port navigation,
even though this is identified by countries such as Belgium and the Netherlands
as an essential step towards a sustainable future. These environments pose
unique challenges, since they can contain dynamic obstacles that do not
broadcast their location, such as small vessels, kayaks or buoys. Therefore,
this research proposes a navigational algorithm which can navigate an inland
vessel in a wide variety of complex port scenarios using ranging sensors to
observe the environment. The proposed methodology is based on a machine
learning approach that has recently set benchmark results in various domains:
model-based reinforcement learning. By randomizing the port environments during
training, the trained model can navigate in scenarios that it never encountered
during training. Furthermore, results show that our approach outperforms the
commonly used dynamic window approach and a benchmark model-free reinforcement
learning algorithm. This work is therefore a significant step towards vessels
that can navigate autonomously in complex port scenarios.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems
Abstract: Making big purchases requires consumers to research or consult a salesperson
to gain domain expertise. However, existing conversational recommender systems
(CRS) often overlook users' lack of background knowledge, focusing solely on
gathering preferences. In this work, we define a new problem space for
conversational agents that aim to provide both product recommendations and
educational value through mixed-type mixed-initiative dialog. We introduce
SalesOps, a framework that facilitates the simulation and evaluation of such
systems by leveraging recent advancements in large language models (LLMs). We
build SalesBot and ShopperBot, a pair of LLM-powered agents that can simulate
either side of the framework. A comprehensive human study compares SalesBot
against professional salespeople, revealing that although SalesBot approaches
professional performance in terms of fluency and informativeness, it lags
behind in recommendation quality. We emphasize the distinct limitations both
face in providing truthful information, highlighting the challenges of ensuring
faithfulness in the CRS context. We release our code and make all data
available.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks
Abstract: Contrastive representation learning has emerged as a promising technique for
continual learning as it can learn representations that are robust to
catastrophic forgetting and generalize well to unseen future tasks. Previous
work in continual learning has addressed forgetting by using previous task data
and trained models. Inspired by event models created and updated in the brain,
we propose a new mechanism that takes place during task boundaries, i.e., when
one task finishes and another starts. By observing the redundancy-inducing
ability of contrastive loss on the output of a neural network, our method
leverages the first few samples of the new task to identify and retain
parameters contributing most to the transfer ability of the neural network,
freeing up the remaining parts of the network to learn new features. We
evaluate the proposed methods on benchmark computer vision datasets including
CIFAR10 and TinyImagenet and demonstrate state-of-the-art performance in the
task-incremental, class-incremental, and domain-incremental continual learning
scenarios.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Towards Efficient 3D Object Detection in Bird's-Eye-View Space for Autonomous Driving: A Convolutional-Only Approach
Abstract: 3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a
prevalent approach in the field of autonomous driving. Despite the demonstrated
improvements in accuracy and velocity estimation compared to perspective view
methods, the deployment of BEV-based techniques in real-world autonomous
vehicles remains challenging. This is primarily due to their reliance on
vision-transformer (ViT) based architectures, which introduce quadratic
complexity with respect to the input resolution. To address this issue, we
propose an efficient BEV-based 3D detection framework called BEVENet, which
leverages a convolutional-only architectural design to circumvent the
limitations of ViT models while maintaining the effectiveness of BEV-based
methods. Our experiments show that BEVENet is 3$\times$ faster than
contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge,
achieving a mean average precision (mAP) of 0.456 and a nuScenes detection
score (NDS) of 0.555 on the NuScenes validation dataset, with an inference
speed of 47.6 frames per second. To the best of our knowledge, this study
stands as the first to achieve such significant efficiency improvements for
BEV-based methods, highlighting their enhanced feasibility for real-world
autonomous driving applications.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Can ChatGPT advance software testing intelligence? An experience report on metamorphic testing
Abstract: While ChatGPT is a well-known artificial intelligence chatbot being used to
answer human's questions, one may want to discover its potential in advancing
software testing. We examine the capability of ChatGPT in advancing the
intelligence of software testing through a case study on metamorphic testing
(MT), a state-of-the-art software testing technique. We ask ChatGPT to generate
candidates of metamorphic relations (MRs), which are basically necessary
properties of the object program and which traditionally require human
intelligence to identify. These MR candidates are then evaluated in terms of
correctness by domain experts. We show that ChatGPT can be used to generate new
correct MRs to test several software systems. Having said that, the majority of
MR candidates are either defined vaguely or incorrect, especially for systems
that have never been tested with MT. ChatGPT can be used to advance software
testing intelligence by proposing MR candidates that can be later adopted for
implementing tests; but human intelligence should still inevitably be involved
to justify and rectify their correctness.

Output: Software Engineering


Instruction: What field is the article from?

Input:
Title: An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback
Abstract: While search is the predominant method of accessing information, formulating
effective queries remains a challenging task, especially for situations where
the users are not familiar with a domain, or searching for documents in other
languages, or looking for complex information such as events, which are not
easily expressible as queries. Providing example documents or passages of
interest, might be easier for a user, however, such query-by-example scenarios
are prone to concept drift, and are highly sensitive to the query generation
method. This demo illustrates complementary approaches of using LLMs
interactively, assisting and enabling the user to provide edits and feedback at
all stages of the query formulation process. The proposed Query Generation
Assistant is a novel search interface which supports automatic and interactive
query generation over a mono-linguial or multi-lingual document collection.
Specifically, the proposed assistive interface enables the users to refine the
queries generated by different LLMs, to provide feedback on the retrieved
documents or passages, and is able to incorporate the users' feedback as
prompts to generate more effective queries. The proposed interface is a
valuable experimental tool for exploring fine-tuning and prompting of LLMs for
query generation to qualitatively evaluate the effectiveness of retrieval and
ranking models, and for conducting Human-in-the-Loop (HITL) experiments for
complex search tasks where users struggle to formulate queries without such
assistance.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: SAT-Based Algorithms for Regular Graph Pattern Matching
Abstract: Graph matching is a fundamental problem in pattern recognition, with many
applications such as software analysis and computational biology. One
well-known type of graph matching problem is graph isomorphism, which consists
of deciding if two graphs are identical. Despite its usefulness, the properties
that one may check using graph isomorphism are rather limited, since it only
allows strict equality checks between two graphs. For example, it does not
allow one to check complex structural properties such as if the target graph is
an arbitrary length sequence followed by an arbitrary size loop.
  We propose a generalization of graph isomorphism that allows one to check
such properties through a declarative specification. This specification is
given in the form of a Regular Graph Pattern (ReGaP), a special type of graph,
inspired by regular expressions, that may contain wildcard nodes that represent
arbitrary structures such as variable-sized sequences or subgraphs. We propose
a SAT-based algorithm for checking if a target graph matches a given ReGaP. We
also propose a preprocessing technique for improving the performance of the
algorithm and evaluate it through an extensive experimental evaluation on
benchmarks from the CodeSearchNet dataset.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Abstract: Recently, large language models (LLMs) have exhibited significant progress in
language understanding and generation. By leveraging textual features,
customized LLMs are also applied for recommendation and demonstrate
improvements across diverse recommendation scenarios. Yet the majority of
existing methods perform training-free recommendation that heavily relies on
pretrained knowledge (e.g., movie recommendation). In addition, inference on
LLMs is slow due to autoregressive generation, rendering existing methods less
effective for real-time recommendation. As such, we propose a two-stage
framework using large language models for ranking-based recommendation
(LlamaRec). In particular, we use small-scale sequential recommenders to
retrieve candidates based on the user interaction history. Then, both history
and retrieved items are fed to the LLM in text via a carefully designed prompt
template. Instead of generating next-item titles, we adopt a verbalizer-based
approach that transforms output logits into probability distributions over the
candidate items. Therefore, the proposed LlamaRec can efficiently rank items
without generating long text. To validate the effectiveness of the proposed
framework, we compare against state-of-the-art baseline methods on benchmark
datasets. Our experimental results demonstrate the performance of LlamaRec,
which consistently achieves superior performance in both recommendation
performance and efficiency.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer
Abstract: Optical Intraoral Scanners (IOS) are widely used in digital dentistry to
provide detailed 3D information of dental crowns and the gingiva. Accurate 3D
tooth segmentation in IOSs is critical for various dental applications, while
previous methods are error-prone at complicated boundaries and exhibit
unsatisfactory results across patients. In this paper, we propose TSegFormer
which captures both local and global dependencies among different teeth and the
gingiva in the IOS point clouds with a multi-task 3D transformer architecture.
Moreover, we design a geometry-guided loss based on a novel point curvature to
refine boundaries in an end-to-end manner, avoiding time-consuming
post-processing to reach clinically applicable segmentation. In addition, we
create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of
our knowledge. The experimental results demonstrate that our TSegFormer
consistently surpasses existing state-of-the-art baselines. The superiority of
TSegFormer is corroborated by extensive analysis, visualizations and real-world
clinical applicability tests. Our code is available at
https://github.com/huiminxiong/TSegFormer.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
Abstract: The ability to perceive how objects change over time is a crucial ingredient
in human intelligence. However, current benchmarks cannot faithfully reflect
the temporal understanding abilities of video-language models (VidLMs) due to
the existence of static visual shortcuts. To remedy this issue, we present
VITATECS, a diagnostic VIdeo-Text dAtaset for the evaluation of TEmporal
Concept underStanding. Specifically, we first introduce a fine-grained taxonomy
of temporal concepts in natural language in order to diagnose the capability of
VidLMs to comprehend different temporal aspects. Furthermore, to disentangle
the correlation between static and temporal information, we generate
counterfactual video descriptions that differ from the original one only in the
specified temporal aspect. We employ a semi-automatic data collection framework
using large language models and human-in-the-loop annotation to obtain
high-quality counterfactual descriptions efficiently. Evaluation of
representative video-language understanding models confirms their deficiency in
temporal understanding, revealing the need for greater emphasis on the temporal
elements in video-language research.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Minimizing Factual Inconsistency and Hallucination in Large Language Models
Abstract: Large Language Models (LLMs) are widely used in critical fields such as
healthcare, education, and finance due to their remarkable proficiency in
various language-related tasks. However, LLMs are prone to generating factually
incorrect responses or "hallucinations," which can lead to a loss of
credibility and trust among users. To address this issue, we propose a
multi-stage framework that generates the rationale first, verifies and refines
incorrect ones, and uses them as supporting references to generate the answer.
The generated rationale enhances the transparency of the answer and our
framework provides insights into how the model arrived at this answer, by using
this rationale and the references to the context. In this paper, we demonstrate
its effectiveness in improving the quality of responses to drug-related
inquiries in the life sciences industry. Our framework improves traditional
Retrieval Augmented Generation (RAG) by enabling OpenAI GPT-3.5-turbo to be
14-25% more faithful and 16-22% more accurate on two datasets. Furthermore,
fine-tuning samples based on our framework improves the accuracy of smaller
open-access LLMs by 33-42% and competes with RAG on commercial models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes
Abstract: LLMs such as ChatGPT and PaLM can be utilized to train on a new language and
revitalize low-resource languages. However, it is evidently very costly to
pretrain pr fine-tune LLMs to adopt new languages. Another challenge is the
limitation of benchmark datasets and the metrics used to measure the
performance of models in multilingual settings. This paper proposes
cost-effective solutions to both of the aforementioned challenges. We introduce
the Multilingual Instruction-Tuning Dataset (MITS), which is comprised of the
translation of Alpaca-52K, Dolly-15K, and Vicuna Benchmark in 132 languages.
Also, we propose a new method called \emph{TaCo: Translation-Assisted
Cross-Linguality}, which make uses of translation in a chain-of-thought process
to instruction-tune LLMs on a new languages through a curriculum learning
process. As a proof of concept, we experimented with the instruction-tuned
Guanaco-33B model and performed further instruction tuning using the TaCo
method in three low-resource languages and one high-resource language. Our
results show that the TaCo method impresses the GPT-4 with 82% for a
low-resource language in the Vicuna Benchmark dataset, and boosts performance
by double in contrast to the performance of instruction tuning only. Our
results show that TaCo is a promising method for creating multilingual LLMs,
even for low-resource languages. We have released our datasets and the model
adapters, and encourage the research community to make use of these resources
towards advancing work on multilingual LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Navigating the generative AI era: Introducing the AI assessment scale for ethical GenAI assessment
Abstract: Recent developments in Generative Artificial Intelligence (GenAI) have
created a paradigm shift in multiple areas of society, and the use of these
technologies is likely to become a defining feature of education in coming
decades. GenAI offers transformative pedagogical opportunities, while
simultaneously posing ethical and academic challenges. Against this backdrop,
we outline a practical, simple, and sufficiently comprehensive tool to allow
for the integration of GenAI tools into educational assessment: the AI
Assessment Scale (AIAS). The AIAS empowers educators to select the appropriate
level of GenAI usage in assessments based on the learning outcomes they seek to
address. The AIAS offers greater clarity and transparency for students and
educators, provides a fair and equitable policy tool for institutions to work
with, and offers a nuanced approach which embraces the opportunities of GenAI
while recognising that there are instances where such tools may not be
pedagogically appropriate or necessary. By adopting a practical, flexible
approach that can be implemented quickly, the AIAS can form a much-needed
starting point to address the current uncertainty and anxiety regarding GenAI
in education. As a secondary objective, we engage with the current literature
and advocate for a refocused discourse on GenAI tools in education, one which
foregrounds how technologies can help support and enhance teaching and
learning, which contrasts with the current focus on GenAI as a facilitator of
academic misconduct.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Gaussian Grouping: Segment and Edit Anything in 3D Scenes
Abstract: The recent Gaussian Splatting achieves high-quality and real-time novel-view
synthesis of the 3D scenes. However, it is solely concentrated on the
appearance and geometry modeling, while lacking in fine-grained object-level
scene understanding. To address this issue, we propose Gaussian Grouping, which
extends Gaussian Splatting to jointly reconstruct and segment anything in
open-world 3D scenes. We augment each Gaussian with a compact Identity
Encoding, allowing the Gaussians to be grouped according to their object
instance or stuff membership in the 3D scene. Instead of resorting to expensive
3D labels, we supervise the Identity Encodings during the differentiable
rendering by leveraging the 2D mask predictions by SAM, along with introduced
3D spatial consistency regularization. Comparing to the implicit NeRF
representation, we show that the discrete and grouped 3D Gaussians can
reconstruct, segment and edit anything in 3D with high visual quality, fine
granularity and efficiency. Based on Gaussian Grouping, we further propose a
local Gaussian Editing scheme, which shows efficacy in versatile scene editing
applications, including 3D object removal, inpainting, colorization and scene
recomposition. Our code and models will be at
https://github.com/lkeab/gaussian-grouping.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: TempTabQA: Temporal Question Answering for Semi-Structured Tables
Abstract: Semi-structured data, such as Infobox tables, often include temporal
information about entities, either implicitly or explicitly. Can current NLP
systems reason about such information in semi-structured tables? To tackle this
question, we introduce the task of temporal question answering on
semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454
question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning
more than 90 distinct domains. Using this dataset, we evaluate several
state-of-the-art models for temporal reasoning. We observe that even the
top-performing LLMs lag behind human performance by more than 13.5 F1 points.
Given these results, our dataset has the potential to serve as a challenging
benchmark to improve the temporal reasoning capabilities of NLP models.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: The Significance of Machine Learning in Clinical Disease Diagnosis: A Review
Abstract: The global need for effective disease diagnosis remains substantial, given
the complexities of various disease mechanisms and diverse patient symptoms. To
tackle these challenges, researchers, physicians, and patients are turning to
machine learning (ML), an artificial intelligence (AI) discipline, to develop
solutions. By leveraging sophisticated ML and AI methods, healthcare
stakeholders gain enhanced diagnostic and treatment capabilities. However,
there is a scarcity of research focused on ML algorithms for enhancing the
accuracy and computational efficiency. This research investigates the capacity
of machine learning algorithms to improve the transmission of heart rate data
in time series healthcare metrics, concentrating particularly on optimizing
accuracy and efficiency. By exploring various ML algorithms used in healthcare
applications, the review presents the latest trends and approaches in ML-based
disease diagnosis (MLBDD). The factors under consideration include the
algorithm utilized, the types of diseases targeted, the data types employed,
the applications, and the evaluation metrics. This review aims to shed light on
the prospects of ML in healthcare, particularly in disease diagnosis. By
analyzing the current literature, the study provides insights into
state-of-the-art methodologies and their performance metrics.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop
Abstract: To facilitate the advancement of research in healthcare robots without human
intervention or commands, we introduce the Autonomous Helping Challenge, along
with a crowd-sourcing large-scale dataset. The goal is to create healthcare
robots that possess the ability to determine when assistance is necessary,
generate useful sub-tasks to aid in planning, carry out these plans through a
physical robot, and receive feedback from the environment in order to generate
new tasks and continue the process. Besides the general challenge in open-ended
scenarios, Autonomous Helping focuses on three specific challenges: autonomous
task generation, the gap between the current scene and static commonsense, and
the gap between language instruction and the real world. Additionally, we
propose Helpy, a potential approach to close the healthcare loop in the
learning-free setting.

Output: Robotics