Instruction: What field is the article from?

Input:
Title: Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization
Abstract: The rise of renewables coincides with the shift towards Electrical Vehicles
(EVs) posing technical and operational challenges for the energy balance of the
local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage
leading to a need for more coordinated and grid aware EVs charging and
discharging strategies. However, coordinating power flow from multiple EVs into
the grid requires sophisticated algorithms and load-balancing strategies as the
complexity increases with more control variables and EVs, necessitating large
optimization and decision search spaces. In this paper, we propose an EVs fleet
coordination model for the day ahead aiming to ensure a reliable energy supply
and maintain a stable local grid, by utilizing EVs to store surplus energy and
discharge it during periods of energy deficit. The optimization problem is
addressed using Harris Hawks Optimization (HHO) considering criteria related to
energy grid balancing, time usage preference, and the location of EV drivers.
The EVs schedules, associated with the position of individuals from the
population, are adjusted through exploration and exploitation operations, and
their technical and operational feasibility is ensured, while the rabbit
individual is updated with a non-dominated EV schedule selected per iteration
using a roulette wheel algorithm. The solution is evaluated within the
framework of an e-mobility service in Terni city. The results indicate that
coordinated charging and discharging of EVs not only meet balancing service
requirements but also align with user preferences with minimal deviations.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: KnowSafe: Combined Knowledge and Data Driven Hazard Mitigation in Artificial Pancreas Systems
Abstract: Significant progress has been made in anomaly detection and run-time
monitoring to improve the safety and security of cyber-physical systems (CPS).
However, less attention has been paid to hazard mitigation. This paper proposes
a combined knowledge and data driven approach, KnowSafe, for the design of
safety engines that can predict and mitigate safety hazards resulting from
safety-critical malicious attacks or accidental faults targeting a CPS
controller. We integrate domain-specific knowledge of safety constraints and
context-specific mitigation actions with machine learning (ML) techniques to
estimate system trajectories in the far and near future, infer potential
hazards, and generate optimal corrective actions to keep the system safe.
Experimental evaluation on two realistic closed-loop testbeds for artificial
pancreas systems (APS) and a real-world clinical trial dataset for diabetes
treatment demonstrates that KnowSafe outperforms the state-of-the-art by
achieving higher accuracy in predicting system state trajectories and potential
hazards, a low false positive rate, and no false negatives. It also maintains
the safe operation of the simulated APS despite faults or attacks without
introducing any new hazards, with a hazard mitigation success rate of 92.8%,
which is at least 76% higher than solely rule-based (50.9%) and data-driven
(52.7%) methods.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Aria-NeRF: Multimodal Egocentric View Synthesis
Abstract: We seek to accelerate research in developing rich, multimodal scene models
trained from egocentric data, based on differentiable volumetric ray-tracing
inspired by Neural Radiance Fields (NeRFs). The construction of a NeRF-like
model from an egocentric image sequence plays a pivotal role in understanding
human behavior and holds diverse applications within the realms of VR/AR. Such
egocentric NeRF-like models may be used as realistic simulations, contributing
significantly to the advancement of intelligent agents capable of executing
tasks in the real-world. The future of egocentric view synthesis may lead to
novel environment representations going beyond today's NeRFs by augmenting
visual data with multimodal sensors such as IMU for egomotion tracking, audio
sensors to capture surface texture and human language context, and eye-gaze
trackers to infer human attention patterns in the scene. To support and
facilitate the development and evaluation of egocentric multimodal scene
modeling, we present a comprehensive multimodal egocentric video dataset. This
dataset offers a comprehensive collection of sensory data, featuring RGB
images, eye-tracking camera footage, audio recordings from a microphone,
atmospheric pressure readings from a barometer, positional coordinates from
GPS, connectivity details from Wi-Fi and Bluetooth, and information from
dual-frequency IMU datasets (1kHz and 800Hz) paired with a magnetometer. The
dataset was collected with the Meta Aria Glasses wearable device platform. The
diverse data modalities and the real-world context captured within this dataset
serve as a robust foundation for furthering our understanding of human behavior
and enabling more immersive and intelligent experiences in the realms of VR,
AR, and robotics.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays
Abstract: Accurate teeth segmentation and orientation are fundamental in modern oral
healthcare, enabling precise diagnosis, treatment planning, and dental implant
design. In this study, we present a comprehensive approach to teeth
segmentation and orientation from panoramic X-ray images, leveraging deep
learning techniques. We build our model based on FUSegNet, a popular model
originally developed for wound segmentation, and introduce modifications by
incorporating grid-based attention gates into the skip connections. We
introduce oriented bounding box (OBB) generation through principal component
analysis (PCA) for precise tooth orientation estimation. Evaluating our
approach on the publicly available DNS dataset, comprising 543 panoramic X-ray
images, we achieve the highest Intersection-over-Union (IoU) score of 82.43%
and Dice Similarity Coefficient (DSC) score of 90.37% among compared models in
teeth instance segmentation. In OBB analysis, we obtain the Rotated IoU (RIoU)
score of 82.82%. We also conduct detailed analyses of individual tooth labels
and categorical performance, shedding light on strengths and weaknesses. The
proposed model's accuracy and versatility offer promising prospects for
improving dental diagnoses, treatment planning, and personalized healthcare in
the oral domain. Our generated OBB coordinates and codes are available at
https://github.com/mrinal054/Instance_teeth_segmentation.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: DREAM: Diffusion Rectification and Estimation-Adaptive Models
Abstract: We present DREAM, a novel training framework representing Diffusion
Rectification and Estimation-Adaptive Models, requiring minimal code changes
(just three lines) yet significantly enhancing the alignment of training with
sampling in diffusion models. DREAM features two components: diffusion
rectification, which adjusts training to reflect the sampling process, and
estimation adaptation, which balances perception against distortion. When
applied to image super-resolution (SR), DREAM adeptly navigates the tradeoff
between minimizing distortion and preserving high image quality. Experiments
demonstrate DREAM's superiority over standard diffusion-based SR methods,
showing a $2$ to $3\times $ faster training convergence and a $10$ to
$20\times$ reduction in necessary sampling steps to achieve comparable or
superior results. We hope DREAM will inspire a rethinking of diffusion model
training paradigms.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: I Know You Did Not Write That! A Sampling Based Watermarking Method for Identifying Machine Generated Text
Abstract: Potential harms of Large Language Models such as mass misinformation and
plagiarism can be partially mitigated if there exists a reliable way to detect
machine generated text. In this paper, we propose a new watermarking method to
detect machine-generated texts. Our method embeds a unique pattern within the
generated text, ensuring that while the content remains coherent and natural to
human readers, it carries distinct markers that can be identified
algorithmically. Specifically, we intervene with the token sampling process in
a way which enables us to trace back our token choices during the detection
phase. We show how watermarking affects textual quality and compare our
proposed method with a state-of-the-art watermarking method in terms of
robustness and detectability. Through extensive experiments, we demonstrate the
effectiveness of our watermarking scheme in distinguishing between watermarked
and non-watermarked text, achieving high detection rates while maintaining
textual quality.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Cognitively Inspired Components for Social Conversational Agents
Abstract: Current conversational agents (CA) have seen improvement in conversational
quality in recent years due to the influence of large language models (LLMs)
like GPT3. However, two key categories of problem remain. Firstly there are the
unique technical problems resulting from the approach taken in creating the CA,
such as scope with retrieval agents and the often nonsensical answers of former
generative agents. Secondly, humans perceive CAs as social actors, and as a
result expect the CA to adhere to social convention. Failure on the part of the
CA in this respect can lead to a poor interaction and even the perception of
threat by the user. As such, this paper presents a survey highlighting a
potential solution to both categories of problem through the introduction of
cognitively inspired additions to the CA. Through computational facsimiles of
semantic and episodic memory, emotion, working memory, and the ability to
learn, it is possible to address both the technical and social problems
encountered by CAs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Loss Modeling for Multi-Annotator Datasets
Abstract: Accounting for the opinions of all annotators of a dataset is critical for
fairness. However, when annotating large datasets, individual annotators will
frequently provide thousands of ratings which can lead to fatigue.
Additionally, these annotation processes can occur over multiple days which can
lead to an inaccurate representation of an annotator's opinion over time. To
combat this, we propose to learn a more accurate representation of diverse
opinions by utilizing multitask learning in conjunction with loss-based label
correction. We show that using our novel formulation, we can cleanly separate
agreeing and disagreeing annotations. Furthermore, we demonstrate that this
modification can improve prediction performance in a single or multi-annotator
setting. Lastly, we show that this method remains robust to additional label
noise that is applied to subjective data.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose Estimation
Abstract: Monocular 3D human pose estimation (3D-HPE) is an inherently ambiguous task,
as a 2D pose in an image might originate from different possible 3D poses. Yet,
most 3D-HPE methods rely on regression models, which assume a one-to-one
mapping between inputs and outputs. In this work, we provide theoretical and
empirical evidence that, because of this ambiguity, common regression models
are bound to predict topologically inconsistent poses, and that traditional
evaluation metrics, such as the MPJPE, P-MPJPE and PCK, are insufficient to
assess this aspect. As a solution, we propose ManiPose, a novel
manifold-constrained multi-hypothesis model capable of proposing multiple
candidate 3D poses for each 2D input, together with their corresponding
plausibility. Unlike previous multi-hypothesis approaches, our solution is
completely supervised and does not rely on complex generative models, thus
greatly facilitating its training and usage. Furthermore, by constraining our
model to lie within the human pose manifold, we can guarantee the consistency
of all hypothetical poses predicted with our approach, which was not possible
in previous works. We illustrate the usefulness of ManiPose in a synthetic
1D-to-2D lifting setting and demonstrate on real-world datasets that it
outperforms state-of-the-art models in pose consistency by a large margin,
while still reaching competitive MPJPE performance.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Combining Shape Completion and Grasp Prediction for Fast and Versatile Grasping with a Multi-Fingered Hand
Abstract: Grasping objects with limited or no prior knowledge about them is a highly
relevant skill in assistive robotics. Still, in this general setting, it has
remained an open problem, especially when it comes to only partial
observability and versatile grasping with multi-fingered hands. We present a
novel, fast, and high fidelity deep learning pipeline consisting of a shape
completion module that is based on a single depth image, and followed by a
grasp predictor that is based on the predicted object shape. The shape
completion network is based on VQDIF and predicts spatial occupancy values at
arbitrary query points. As grasp predictor, we use our two-stage architecture
that first generates hand poses using an autoregressive model and then
regresses finger joint configurations per pose. Critical factors turn out to be
sufficient data realism and augmentation, as well as special attention to
difficult cases during training. Experiments on a physical robot platform
demonstrate successful grasping of a wide range of household objects based on a
depth image from a single viewpoint. The whole pipeline is fast, taking only
about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps
(0.3 s).

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision
Abstract: With the rapidly increasing demand for oriented object detection (OOD),
recent research involving weakly-supervised detectors for learning rotated box
(RBox) from the horizontal box (HBox) has attracted more and more attention. In
this paper, we explore a more challenging yet label-efficient setting, namely
single point-supervised OOD, and present our approach called Point2RBox.
Specifically, we propose to leverage two principles: 1) Synthetic pattern
knowledge combination: By sampling around each labelled point on the image, we
transfer the object feature to synthetic visual patterns with the known
bounding box to provide the knowledge for box regression. 2) Transform
self-supervision: With a transformed input image (e.g. scaled/rotated), the
output RBoxes are trained to follow the same transformation so that the network
can perceive the relative size/rotation between objects. The detector is
further enhanced by a few devised techniques to cope with peripheral issues,
e.g. the anchor/layer assignment as the size of the object is not available in
our point supervision setting. To our best knowledge, Point2RBox is the first
end-to-end solution for point-supervised OOD. In particular, our method uses a
lightweight paradigm, yet it achieves a competitive performance among
point-supervised alternatives, 41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: InstructBooth: Instruction-following Personalized Text-to-Image Generation
Abstract: Personalizing text-to-image models using a limited set of images for a
specific object has been explored in subject-specific image generation.
However, existing methods often encounter challenges in aligning with text
prompts due to overfitting to the limited training images. In this work, we
introduce InstructBooth, a novel method designed to enhance image-text
alignment in personalized text-to-image models. Our approach first personalizes
text-to-image models with a small number of subject-specific images using a
unique identifier. After personalization, we fine-tune personalized
text-to-image models using reinforcement learning to maximize a reward that
quantifies image-text alignment. Additionally, we propose complementary
techniques to increase the synergy between these two processes. Our method
demonstrates superior image-text alignment compared to baselines while
maintaining personalization ability. In human evaluations, InstructBooth
outperforms DreamBooth when considering all comprehensive factors.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AI-driven E-Liability Knowledge Graphs: A Comprehensive Framework for Supply Chain Carbon Accounting and Emissions Liability Management
Abstract: While carbon accounting plays a fundamental role in our fight against climate
change, it is not without its challenges. We begin the paper with a critique of
the conventional carbon accounting practices, after which we proceed to
introduce the E-liability carbon accounting methodology and Emissions Liability
Management (ELM) originally proposed by Kaplan and Ramanna, highlighting their
strengths. Recognizing the immense value of this novel approach for real-world
carbon accounting improvement, we introduce a novel data-driven integrative
framework that leverages AI and computation - the E-Liability Knowledge Graph
framework - to achieve real-world implementation of the E-liability carbon
accounting methodology. In addition to providing a path-to-implementation, our
proposed framework brings clarity to the complex environmental interactions
within supply chains, thus enabling better informed and more responsible
decision-making. We analyze the implementation aspects of this framework and
conclude with a discourse on the role of this AI-aided knowledge graph in
ensuring the transparency and decarbonization of global supply chains.

Output: Computers and Society


Instruction: What field is the article from?

Input:
Title: Ego-Network Transformer for Subsequence Classification in Time Series Data
Abstract: Time series classification is a widely studied problem in the field of time
series data mining. Previous research has predominantly focused on scenarios
where relevant or foreground subsequences have already been extracted, with
each subsequence corresponding to a single label. However, real-world time
series data often contain foreground subsequences that are intertwined with
background subsequences. Successfully classifying these relevant subsequences
requires not only distinguishing between different classes but also accurately
identifying the foreground subsequences amidst the background. To address this
challenge, we propose a novel subsequence classification method that represents
each subsequence as an ego-network, providing crucial nearest neighbor
information to the model. The ego-networks of all subsequences collectively
form a time series subsequence graph, and we introduce an algorithm to
efficiently construct this graph. Furthermore, we have demonstrated the
significance of enforcing temporal consistency in the prediction of adjacent
subsequences for the subsequence classification problem. To evaluate the
effectiveness of our approach, we conducted experiments using 128 univariate
and 30 multivariate time series datasets. The experimental results demonstrate
the superior performance of our method compared to alternative approaches.
Specifically, our method outperforms the baseline on 104 out of 158 datasets.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape
Abstract: This comprehensive survey explored the evolving landscape of generative
Artificial Intelligence (AI), with a specific focus on the transformative
impacts of Mixture of Experts (MoE), multimodal learning, and the speculated
advancements towards Artificial General Intelligence (AGI). It critically
examined the current state and future trajectory of generative Artificial
Intelligence (AI), exploring how innovations like Google's Gemini and the
anticipated OpenAI Q* project are reshaping research priorities and
applications across various domains, including an impact analysis on the
generative AI research taxonomy. It assessed the computational challenges,
scalability, and real-world implications of these technologies while
highlighting their potential in driving significant progress in fields like
healthcare, finance, and education. It also addressed the emerging academic
challenges posed by the proliferation of both AI-themed and AI-generated
preprints, examining their impact on the peer-review process and scholarly
communication. The study highlighted the importance of incorporating ethical
and human-centric methods in AI development, ensuring alignment with societal
norms and welfare, and outlined a strategy for future AI research that focuses
on a balanced and conscientious use of MoE, multimodality, and AGI in
generative AI.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FLORIDA: Fake-looking Real Images Dataset
Abstract: Although extensive research has been carried out to evaluate the
effectiveness of AI tools and models in detecting deep fakes, the question
remains unanswered regarding whether these models can accurately identify
genuine images that appear artificial. In this study, as an initial step
towards addressing this issue, we have curated a dataset of 510 genuine images
that exhibit a fake appearance and conducted an assessment using two AI models.
We show that two models exhibited subpar performance when applied to our
dataset. Additionally, our dataset can serve as a valuable tool for assessing
the ability of deep learning models to comprehend complex visual stimuli. We
anticipate that this research will stimulate further discussions and
investigations in this area. Our dataset is accessible at
https://github.com/aliborji/FLORIDA.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification
Abstract: Building comprehensive brain connectomes has proved of fundamental importance
in resting-state fMRI (rs-fMRI) analysis. Based on the foundation of brain
network, spatial-temporal-based graph convolutional networks have dramatically
improved the performance of deep learning methods in rs-fMRI time series
classification. However, existing works either pre-define the brain network as
the correlation matrix derived from the raw time series or jointly learn the
connectome and model parameters without any topology constraint. These methods
could suffer from degraded classification performance caused by the deviation
from the intrinsic brain connectivity and lack biological interpretability of
demonstrating the causal structure (i.e., effective connectivity) among brain
regions. Moreover, most existing methods for effective connectivity learning
are unaware of the downstream classification task and cannot sufficiently
exploit useful rs-fMRI label information. To address these issues in an
end-to-end manner, we model the brain network as a directed acyclic graph (DAG)
to discover direct causal connections between brain regions and propose
Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer
effective connectivity and classify rs-fMRI time series by learning brain
representations based on nonlinear structural equation model. The optimization
problem is formulated into a continuous program and solved with score-based
learning method via gradient descent. We evaluate ST-DAGCN on two public
rs-fMRI databases. Experiments show that ST-DAGCN outperforms existing models
by evident margins in rs-fMRI classification and simultaneously learns
meaningful edges of effective connectivity that help understand brain activity
patterns and pathological mechanisms in brain disease.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer
Abstract: Model-based RL is a promising approach for real-world robotics due to its
improved sample efficiency and generalization capabilities compared to
model-free RL. However, effective model-based RL solutions for vision-based
real-world applications require bridging the sim-to-real gap for any world
model learnt. Due to its significant computational cost, standard domain
randomisation does not provide an effective solution to this problem. This
paper proposes TWIST (Teacher-Student World Model Distillation for Sim-to-Real
Transfer) to achieve efficient sim-to-real transfer of vision-based model-based
RL using distillation. Specifically, TWIST leverages state observations as
readily accessible, privileged information commonly garnered from a simulator
to significantly accelerate sim-to-real transfer. Specifically, a teacher world
model is trained efficiently on state information. At the same time, a matching
dataset is collected of domain-randomised image observations. The teacher world
model then supervises a student world model that takes the domain-randomised
image observations as input. By distilling the learned latent dynamics model
from the teacher to the student model, TWIST achieves efficient and effective
sim-to-real transfer for vision-based model-based RL tasks. Experiments in
simulated and real robotics tasks demonstrate that our approach outperforms
naive domain randomisation and model-free methods in terms of sample efficiency
and task performance of sim-to-real transfer.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Abstract: Large Language Models (LLMs) stand out for their impressive performance in
intricate language modeling tasks. However, their demanding computational and
memory needs pose obstacles for broad use on edge devices. Quantization is then
introduced to boost LLMs' on-device efficiency. Recent works show that 8-bit or
lower weight quantization is feasible with minimal impact on end-to-end task
performance, while the activation is still not quantized. On the other hand,
mainstream commodity edge devices still struggle to execute these sub-8-bit
quantized networks effectively. In this paper, we propose Agile-Quant, an
activation-guided quantization framework for popular Large Language Models
(LLMs), and implement an end-to-end accelerator on multiple edge devices for
faster inference. Considering the hardware profiling and activation analysis,
we first introduce a basic activation quantization strategy to balance the
trade-off of task performance and real inference speed. Then we leverage the
activation-aware token pruning technique to reduce the outliers and the adverse
impact on attentivity. Ultimately, we utilize the SIMD-based 4-bit multiplier
and our efficient TRIP matrix multiplication to implement the accelerator for
LLMs on the edge. We apply our framework on different scales of LLMs including
LLaMA, OPT, and BLOOM with 4-bit or 8-bit for the activation and 4-bit for the
weight quantization. Experiments show that Agile-Quant achieves simultaneous
quantization of model weights and activations while maintaining task
performance comparable to existing weight-only quantization methods. Moreover,
in the 8- and 4-bit scenario, Agile-Quant achieves an on-device speedup of up
to 2.55x compared to its FP16 counterparts across multiple edge devices,
marking a pioneering advancement in this domain.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
Abstract: Generalized quantifiers (e.g., few, most) are used to indicate the
proportions predicates are satisfied (for example, some apples are red). One
way to interpret quantifier semantics is to explicitly bind these satisfactions
with percentage scopes (e.g., 30%-40% of apples are red). This approach can be
helpful for tasks like logic formalization and surface-form quantitative
reasoning (Gordon and Schubert, 2010; Roy et al., 2015). However, it remains
unclear if recent foundation models possess this ability, as they lack direct
training signals. To explore this, we introduce QuRe, a crowd-sourced dataset
of human-annotated generalized quantifiers in Wikipedia sentences featuring
percentage-equipped predicates. We explore quantifier comprehension in language
models using PRESQUE, a framework that combines natural language inference and
the Rational Speech Acts framework. Experimental results on the HVD dataset and
QuRe illustrate that PRESQUE, employing pragmatic reasoning, performs 20%
better than a literal reasoning baseline when predicting quantifier percentage
scopes, with no additional training required.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: FrameFinder: Explorative Multi-Perspective Framing Extraction from News Headlines
Abstract: Revealing the framing of news articles is an important yet neglected task in
information seeking and retrieval. In the present work, we present FrameFinder,
an open tool for extracting and analyzing frames in textual data. FrameFinder
visually represents the frames of text from three perspectives, i.e., (i) frame
labels, (ii) frame dimensions, and (iii) frame structure. By analyzing the
well-established gun violence frame corpus, we demonstrate the merits of our
proposed solution to support social science research and call for subsequent
integration into information interactions.

Output: Information Retrieval


Instruction: What field is the article from?

Input:
Title: Hierarchical Randomized Smoothing
Abstract: Real-world data is complex and often consists of objects that can be
decomposed into multiple entities (e.g. images into pixels, graphs into
interconnected nodes). Randomized smoothing is a powerful framework for making
models provably robust against small changes to their inputs - by guaranteeing
robustness of the majority vote when randomly adding noise before
classification. Yet, certifying robustness on such complex data via randomized
smoothing is challenging when adversaries do not arbitrarily perturb entire
objects (e.g. images) but only a subset of their entities (e.g. pixels). As a
solution, we introduce hierarchical randomized smoothing: We partially smooth
objects by adding random noise only on a randomly selected subset of their
entities. By adding noise in a more targeted manner than existing methods we
obtain stronger robustness guarantees while maintaining high accuracy. We
initialize hierarchical smoothing using different noising distributions,
yielding novel robustness certificates for discrete and continuous domains. We
experimentally demonstrate the importance of hierarchical smoothing in image
and node classification, where it yields superior robustness-accuracy
trade-offs. Overall, hierarchical smoothing is an important contribution
towards models that are both - certifiably robust to perturbations and
accurate.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: ChessVision -- A Dataset for Logically Coherent Multi-label Classification
Abstract: Starting with early successes in computer vision tasks, deep learning based
techniques have since overtaken state of the art approaches in a multitude of
domains. However, it has been demonstrated time and again that these techniques
fail to capture semantic context and logical constraints, instead often relying
on spurious correlations to arrive at the answer. Since application of deep
learning techniques to critical scenarios are dependent on adherence to domain
specific constraints, several attempts have been made to address this issue.
One limitation holding back a thorough exploration of this area, is a lack of
suitable datasets which feature a rich set of rules. In order to address this,
we present the ChessVision Dataset, consisting of 200,000+ images of annotated
chess games in progress, requiring recreation of the game state from its
corresponding image. This is accompanied by a curated set of rules which
constrains the set of predictions to "reasonable" game states, and are designed
to probe key semantic abilities like localization and enumeration. Alongside
standard metrics, additional metrics to measure performance with regards to
logical consistency is presented. We analyze several popular and state of the
art vision models on this task, and show that, although their performance on
standard metrics are laudable, they produce a plethora of incoherent results,
indicating that this dataset presents a significant challenge for future works.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Exploring the hierarchical structure of human plans via program generation
Abstract: Human behavior is inherently hierarchical, resulting from the decomposition
of a task into subtasks or an abstract action into concrete actions. However,
behavior is typically measured as a sequence of actions, which makes it
difficult to infer its hierarchical structure. In this paper, we explore how
people form hierarchically-structured plans, using an experimental paradigm
that makes hierarchical representations observable: participants create
programs that produce sequences of actions in a language with explicit
hierarchical structure. This task lets us test two well-established principles
of human behavior: utility maximization (i.e. using fewer actions) and minimum
description length (MDL; i.e. having a shorter program). We find that humans
are sensitive to both metrics, but that both accounts fail to predict a
qualitative feature of human-created programs, namely that people prefer
programs with reuse over and above the predictions of MDL. We formalize this
preference for reuse by extending the MDL account into a generative model over
programs, modeling hierarchy choice as the induction of a grammar over actions.
Our account can explain the preference for reuse and provides the best
prediction of human behavior, going beyond simple accounts of compressibility
to highlight a principle that guides hierarchical planning.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Multiple Disciplinary Data Work Practices in Artificial Intelligence Research: a Healthcare Case Study in the UK
Abstract: Developing artificial intelligence (AI) tools for healthcare is a multiple
disciplinary effort, bringing data scientists, clinicians, patients and other
disciplines together. In this paper, we explore the AI development workflow and
how participants navigate the challenges and tensions of sharing and generating
knowledge across disciplines. Through an inductive thematic analysis of 13
semi-structured interviews with participants in a large research consortia, our
findings suggest that multiple disciplinarity heavily impacts work practices.
Participants faced challenges to learn the languages of other disciplines and
needed to adapt the tools used for sharing and communicating with their
audience, particularly those from a clinical or patient perspective. Large
health datasets also posed certain restrictions on work practices. We
identified meetings as a key platform for facilitating exchanges between
disciplines and allowing for the blending and creation of knowledge. Finally,
we discuss design implications for data science and collaborative tools, and
recommendations for future research.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status
Abstract: Intensive Care Units (ICU) provide close supervision and continuous care to
patients with life-threatening conditions. However, continuous patient
assessment in the ICU is still limited due to time constraints and the workload
on healthcare providers. Existing patient assessments in the ICU such as pain
or mobility assessment are mostly sporadic and administered manually, thus
introducing the potential for human errors. Developing Artificial intelligence
(AI) tools that can augment human assessments in the ICU can be beneficial for
providing more objective and granular monitoring capabilities. For example,
capturing the variations in a patient's facial cues related to pain or
agitation can help in adjusting pain-related medications or detecting
agitation-inducing conditions such as delirium. Additionally, subtle changes in
visual cues during or prior to adverse clinical events could potentially aid in
continuous patient monitoring when combined with high-resolution physiological
signals and Electronic Health Record (EHR) data. In this paper, we examined the
association between visual cues and patient condition including acuity status,
acute brain dysfunction, and pain. We leveraged our AU-ICU dataset with 107,064
frames collected in the ICU annotated with facial action units (AUs) labels by
trained annotators. We developed a new "masked loss computation" technique that
addresses the data imbalance problem by maximizing data resource utilization.
We trained the model using our AU-ICU dataset in conjunction with three
external datasets to detect 18 AUs. The SWIN Transformer model achieved 0.57
mean F1-score and 0.89 mean accuracy on the test set. Additionally, we
performed AU inference on 634,054 frames to evaluate the association between
facial AUs and clinically important patient conditions such as acuity status,
acute brain dysfunction, and pain.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: SMILE: Multimodal Dataset for Understanding Laughter in Video with Language Models
Abstract: Despite the recent advances of the artificial intelligence, building social
intelligence remains a challenge. Among social signals, laughter is one of the
distinctive expressions that occurs during social interactions between humans.
In this work, we tackle a new challenge for machines to understand the
rationale behind laughter in video, Video Laugh Reasoning. We introduce this
new task to explain why people laugh in a particular video and a dataset for
this task. Our proposed dataset, SMILE, comprises video clips and language
descriptions of why people laugh. We propose a baseline by leveraging the
reasoning capacity of large language models (LLMs) with textual video
representation. Experiments show that our baseline can generate plausible
explanations for laughter. We further investigate the scalability of our
baseline by probing other video understanding tasks and in-the-wild videos. We
release our dataset, code, and model checkpoints on
https://github.com/SMILE-data/SMILE.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Hijacking Context in Large Multi-modal Models
Abstract: Recently, Large Multi-modal Models (LMMs) have demonstrated their ability to
understand the visual contents of images given the instructions regarding the
images. Built upon the Large Language Models (LLMs), LMMs also inherit their
abilities and characteristics such as in-context learning where a coherent
sequence of images and texts are given as the input prompt. However, we
identify a new limitation of off-the-shelf LMMs where a small fraction of
incoherent images or text descriptions mislead LMMs to only generate biased
output about the hijacked context, not the originally intended context. To
address this, we propose a pre-filtering method that removes irrelevant
contexts via GPT-4V, based on its robustness towards distribution shift within
the contexts. We further investigate whether replacing the hijacked visual and
textual contexts with the correlated ones via GPT-4V and text-to-image models
can help yield coherent responses.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts
Abstract: This paper focuses on the problem of detecting and reacting to changes in the
distribution of a sensorimotor controller's observables. The key idea is the
design of switching policies that can take conformal quantiles as input, which
we define as conformal policy learning, that allows robots to detect
distribution shifts with formal statistical guarantees. We show how to design
such policies by using conformal quantiles to switch between base policies with
different characteristics, e.g. safety or speed, or directly augmenting a
policy observation with a quantile and training it with reinforcement learning.
Theoretically, we show that such policies achieve the formal convergence
guarantees in finite time. In addition, we thoroughly evaluate their advantages
and limitations on two compelling use cases: simulated autonomous driving and
active perception with a physical quadruped. Empirical results demonstrate that
our approach outperforms five baselines. It is also the simplest of the
baseline strategies besides one ablation. Being easy to use, flexible, and with
formal guarantees, our work demonstrates how conformal prediction can be an
effective tool for sensorimotor learning under uncertainty.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Spot: A Natural Language Interface for Geospatial Searches in OSM
Abstract: Investigative journalists and fact-checkers have found OpenStreetMap (OSM) to
be an invaluable resource for their work due to its extensive coverage and
intricate details of various locations, which play a crucial role in
investigating news scenes. Despite its value, OSM's complexity presents
considerable accessibility and usability challenges, especially for those
without a technical background. To address this, we introduce 'Spot', a
user-friendly natural language interface for querying OSM data. Spot utilizes a
semantic mapping from natural language to OSM tags, leveraging artificially
generated sentence queries and a T5 transformer. This approach enables Spot to
extract relevant information from user-input sentences and display candidate
locations matching the descriptions on a map. To foster collaboration and
future advancement, all code and generated data is available as an open-source
repository.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments
Abstract: Automatic summarization of legal case judgments is a practically important
problem that has attracted substantial research efforts in many countries. In
the context of the Indian judiciary, there is an additional complexity --
Indian legal case judgments are mostly written in complex English, but a
significant portion of India's population lacks command of the English
language. Hence, it is crucial to summarize the legal documents in Indian
languages to ensure equitable access to justice. While prior research primarily
focuses on summarizing legal case judgments in their source languages, this
study presents a pioneering effort toward cross-lingual summarization of
English legal documents into Hindi, the most frequently spoken Indian language.
We construct the first high-quality legal corpus comprising of 3,122 case
judgments from prominent Indian courts in English, along with their summaries
in both English and Hindi, drafted by legal practitioners. We benchmark the
performance of several diverse summarization approaches on our corpus and
demonstrate the need for further research in cross-lingual summarization in the
legal domain.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Grid Frequency Forecasting in University Campuses using Convolutional LSTM
Abstract: The modern power grid is facing increasing complexities, primarily stemming
from the integration of renewable energy sources and evolving consumption
patterns. This paper introduces an innovative methodology that harnesses
Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks
to establish robust time series forecasting models for grid frequency. These
models effectively capture the spatiotemporal intricacies inherent in grid
frequency data, significantly enhancing prediction accuracy and bolstering
power grid reliability. The research explores the potential and development of
individualized Convolutional LSTM (ConvLSTM) models for buildings within a
university campus, enabling them to be independently trained and evaluated for
each building. Individual ConvLSTM models are trained on power consumption data
for each campus building and forecast the grid frequency based on historical
trends. The results convincingly demonstrate the superiority of the proposed
models over traditional forecasting techniques, as evidenced by performance
metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Mean
Absolute Percentage Error (MAPE). Additionally, an Ensemble Model is formulated
to aggregate insights from the building-specific models, delivering
comprehensive forecasts for the entire campus. This approach ensures the
privacy and security of power consumption data specific to each building.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: High-Resolution Maps of Left Atrial Displacements and Strains Estimated with 3D CINE MRI and Unsupervised Neural Networks
Abstract: The functional analysis of the left atrium (LA) is important for evaluating
cardiac health and understanding diseases like atrial fibrillation. Cine MRI is
ideally placed for the detailed 3D characterisation of LA motion and
deformation, but it is lacking appropriate acquisition and analysis tools. In
this paper, we present Analysis for Left Atrial Displacements and Deformations
using unsupervIsed neural Networks, \textit{Aladdin}, to automatically and
reliably characterise regional LA deformations from high-resolution 3D Cine
MRI. The tool includes: an online few-shot segmentation network (Aladdin-S), an
online unsupervised image registration network (Aladdin-R), and a strain
calculations pipeline tailored to the LA. We create maps of LA Displacement
Vector Field (DVF) magnitude and LA principal strain values from images of 10
healthy volunteers and 8 patients with cardiovascular disease (CVD). We
additionally create an atlas of these biomarkers using the data from the
healthy volunteers. Aladdin is able to accurately track the LA wall across the
cardiac cycle and characterize its motion and deformation. The overall DVF
magnitude and principal strain values are significantly higher in the healthy
group vs CVD patients: $2.85 \pm 1.59~mm$ and $0.09 \pm 0.05$ vs $1.96 \pm
0.74~mm$ and $0.03 \pm 0.04$, respectively. The time course of these metrics is
also different in the two groups, with a more marked active contraction phase
observed in the healthy cohort. Finally, utilizing the LA atlas allows us to
identify regional deviations from the population distribution that may indicate
focal tissue abnormalities. The proposed tool for the quantification of novel
regional LA deformation biomarkers should have important clinical applications.
The source code, anonymized images, generated maps and atlas are publicly
available: https://github.com/cgalaz01/aladdin_cmr_la.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
Abstract: Neural style transfer (NST) has evolved significantly in recent years. Yet,
despite its rapid progress and advancement, existing NST methods either
struggle to transfer aesthetic information from a style effectively or suffer
from high computational costs and inefficiencies in feature disentanglement due
to using pre-trained models. This work proposes a lightweight but effective
model, AesFA -- Aesthetic Feature-Aware NST. The primary idea is to decompose
the image via its frequencies to better disentangle aesthetic styles from the
reference image while training the entire model in an end-to-end manner to
exclude pre-trained models at inference completely. To improve the network's
ability to extract more distinct representations and further enhance the
stylization quality, this work introduces a new aesthetic feature: contrastive
loss. Extensive experiments and ablations show the approach not only
outperforms recent NST methods in terms of stylization quality, but it also
achieves faster inference. Codes are available at
https://github.com/Sooyyoungg/AesFA.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: On Diverse Preferences for Large Language Model Alignment
Abstract: The alignment of large language models (LLMs) with human values is crucial
for the development of artificial general intelligence (AGI). One promising
approach to achieve this alignment is reinforcement learning from human
feedback, which employs a reward model (RM) learned from human preference
datasets to guide LLMs in generating text that aligns with human preferences.
Through intensive experiments and analysis of reward distribution, this paper
finds that preference datasets are diverse from each other, even though they
are all proposed to align human preference. Hence, mixing diverse human
preference datasets to increase data size for enhancing reward modeling could
fail. To address the issue and capture the shared human values from diverse
preferences, a new training policy called MORE is introduced, which minimizes
preference bias by adaptively adjusting the preference objective across diverse
preferences. Experiments with the Pythia-1.4B model and five mixed preference
datasets show that MORE achieves superior reward accuracy and lower calibration
error, highlighting its ability to leverage diverse human preference data.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist
Abstract: The widespread use of ChatGPT and other emerging technology powered by
generative artificial intelligence (AI) has drawn much attention to potential
ethical issues, especially in high-stakes applications such as healthcare.
However, less clear is how to resolve such issues beyond following guidelines
and regulations that are still under discussion and development. On the other
hand, other types of generative AI have been used to synthesize images and
other types of data for research and practical purposes, which have resolved
some ethical issues and exposed other ethical issues, but such technology is
less often the focus of ongoing ethical discussions. Here we highlight gaps in
current ethical discussions of generative AI via a systematic scoping review of
relevant existing research in healthcare, and reduce the gaps by proposing an
ethics checklist for comprehensive assessment and transparent documentation of
ethical discussions in generative AI development. While the checklist can be
readily integrated into the current peer review and publication system to
enhance generative AI research, it may also be used in broader settings to
disclose ethics-related considerations in generative AI-powered products (or
real-life applications of such products) to help users establish reasonable
trust in their capabilities.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Exploring Links between Conversational Agent Design Challenges and Interdisciplinary Collaboration
Abstract: Recent years have seen a steady rise in the popularity and use of
Conversational Agents (CA) for different applications, well before the more
immediate impact of large language models. This rise has been accompanied by an
extensive exploration and documentation of the challenges of designing and
creating conversational agents. Focusing on a recent scoping review of the
socio-technical challenges of CA creation, this opinion paper calls for an
examination of the extent to which interdisciplinary collaboration (IDC)
challenges might contribute towards socio-technical CA design challenges. The
paper proposes a taxonomy of CA design challenges using IDC as a lens, and
proposes practical strategies to overcome them which complement existing design
principles. The paper invites future work to empirically verify suggested
conceptual links and apply the proposed strategies within the space of CA
design to evaluate their effectiveness.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy
Abstract: Colonoscopy screening is the gold standard procedure for assessing
abnormalities in the colon and rectum, such as ulcers and cancerous polyps.
Measuring the abnormal mucosal area and its 3D reconstruction can help quantify
the surveyed area and objectively evaluate disease burden. However, due to the
complex topology of these organs and variable physical conditions, for example,
lighting, large homogeneous texture, and image modality estimating distance
from the camera aka depth) is highly challenging. Moreover, most colonoscopic
video acquisition is monocular, making the depth estimation a non-trivial
problem. While methods in computer vision for depth estimation have been
proposed and advanced on natural scene datasets, the efficacy of these
techniques has not been widely quantified on colonoscopy datasets. As the
colonic mucosa has several low-texture regions that are not well pronounced,
learning representations from an auxiliary task can improve salient feature
extraction, allowing estimation of accurate camera depths. In this work, we
propose to develop a novel multi-task learning (MTL) approach with a shared
encoder and two decoders, namely a surface normal decoder and a depth estimator
decoder. Our depth estimator incorporates attention mechanisms to enhance
global context awareness. We leverage the surface normal prediction to improve
geometric feature extraction. Also, we apply a cross-task consistency loss
among the two geometrically related tasks, surface normal and camera depth. We
demonstrate an improvement of 14.17% on relative error and 10.4% improvement on
$\delta_{1}$ accuracy over the most accurate baseline state-of-the-art BTS
approach. All experiments are conducted on a recently released C3VD dataset;
thus, we provide a first benchmark of state-of-the-art methods.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Text-Driven Image Editing via Learnable Regions
Abstract: Language has emerged as a natural interface for image editing. In this paper,
we introduce a method for region-based image editing driven by textual prompts,
without the need for user-provided masks or sketches. Specifically, our
approach leverages an existing pretrained text-to-image model and introduces a
bounding box generator to find the edit regions that are aligned with the
textual prompts. We show that this simple approach enables flexible editing
that is compatible with current image generation models, and is able to handle
complex prompts featuring multiple objects, complex sentences or long
paragraphs. We conduct an extensive user study to compare our method against
state-of-the-art methods. Experiments demonstrate the competitive performance
of our method in manipulating images with high fidelity and realism that align
with the language descriptions provided. Our project webpage:
https://yuanze-lin.me/LearnableRegions_page.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
Abstract: With the recent surge in the use of touchscreen devices, free-hand sketching
has emerged as a promising modality for human-computer interaction. While
previous research has focused on tasks such as recognition, retrieval, and
generation of familiar everyday objects, this study aims to create a Sketch
Input Method Editor (SketchIME) specifically designed for a professional C4I
system. Within this system, sketches are utilized as low-fidelity prototypes
for recommending standardized symbols in the creation of comprehensive
situation maps. This paper also presents a systematic dataset comprising 374
specialized sketch types, and proposes a simultaneous recognition and
segmentation architecture with multilevel supervision between recognition and
segmentation to improve performance and enhance interpretability. By
incorporating few-shot domain adaptation and class-incremental learning, the
network's ability to adapt to new users and extend to new task-specific classes
is significantly enhanced. Results from experiments conducted on both the
proposed dataset and the SPG dataset illustrate the superior performance of the
proposed architecture. Our dataset and code are publicly available at
https://github.com/Anony517/SketchIME.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities
Abstract: Deep learning has taken by storm all fields involved in data analysis,
including remote sensing for Earth observation. However, despite significant
advances in terms of performance, its lack of explainability and
interpretability, inherent to neural networks in general since their inception,
remains a major source of criticism. Hence it comes as no surprise that the
expansion of deep learning methods in remote sensing is being accompanied by
increasingly intensive efforts oriented towards addressing this drawback
through the exploration of a wide spectrum of Explainable Artificial
Intelligence techniques. This chapter, organized according to prominent Earth
observation application fields, presents a panorama of the state-of-the-art in
explainable remote sensing image analysis.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation
Abstract: Improving the performance of semantic segmentation models using multispectral
information is crucial, especially for environments with low-light and adverse
conditions. Multi-modal fusion techniques pursue either the learning of
cross-modality features to generate a fused image or engage in knowledge
distillation but address multimodal and missing modality scenarios as distinct
issues, which is not an optimal approach for multi-sensor models. To address
this, a novel multi-modal fusion approach called CSK-Net is proposed, which
uses a contrastive learning-based spectral knowledge distillation technique
along with an automatic mixed feature exchange mechanism for semantic
segmentation in optical (EO) and infrared (IR) images. The distillation scheme
extracts detailed textures from the optical images and distills them into the
optical branch of CSK-Net. The model encoder consists of shared convolution
weights with separate batch norm (BN) layers for both modalities, to capture
the multi-spectral information from different modalities of the same objects. A
Novel Gated Spectral Unit (GSU) and mixed feature exchange strategy are
proposed to increase the correlation of modality-shared information and
decrease the modality-specific information during the distillation process.
Comprehensive experiments show that CSK-Net surpasses state-of-the-art models
in multi-modal tasks and for missing modalities when exclusively utilizing IR
data for inference across three public benchmarking datasets. For missing
modality scenarios, the performance increase is achieved without additional
computational costs compared to the baseline segmentation models.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Assessing LLMs for Moral Value Pluralism
Abstract: The fields of AI current lacks methods to quantitatively assess and
potentially alter the moral values inherent in the output of large language
models (LLMs). However, decades of social science research has developed and
refined widely-accepted moral value surveys, such as the World Values Survey
(WVS), eliciting value judgments from direct questions in various geographies.
We have turned those questions into value statements and use NLP to compute to
how well popular LLMs are aligned with moral values for various demographics
and cultures. While the WVS is accepted as an explicit assessment of values, we
lack methods for assessing implicit moral and cultural values in media, e.g.,
encountered in social media, political rhetoric, narratives, and generated by
AI systems such as LLMs that are increasingly present in our daily lives. As we
consume online content and utilize LLM outputs, we might ask, which moral
values are being implicitly promoted or undercut, or -- in the case of LLMs --
if they are intending to represent a cultural identity, are they doing so
consistently? In this paper we utilize a Recognizing Value Resonance (RVR) NLP
model to identify WVS values that resonate and conflict with a given passage of
output text. We apply RVR to the text generated by LLMs to characterize
implicit moral values, allowing us to quantify the moral/cultural distance
between LLMs and various demographics that have been surveyed using the WVS. In
line with other work we find that LLMs exhibit several Western-centric value
biases; they overestimate how conservative people in non-Western countries are,
they are less accurate in representing gender for non-Western countries, and
portray older populations as having more traditional values. Our results
highlight value misalignment and age groups, and a need for social science
informed technological solutions addressing value plurality in LLMs.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Fractal Landscapes in Policy Optimization
Abstract: Policy gradient lies at the core of deep reinforcement learning (RL) in
continuous domains. Despite much success, it is often observed in practice that
RL training with policy gradient can fail for many reasons, even on standard
control problems with known solutions. We propose a framework for understanding
one inherent limitation of the policy gradient approach: the optimization
landscape in the policy space can be extremely non-smooth or fractal for
certain classes of MDPs, such that there does not exist gradient to be
estimated in the first place. We draw on techniques from chaos theory and
non-smooth analysis, and analyze the maximal Lyapunov exponents and H\"older
exponents of the policy optimization objectives. Moreover, we develop a
practical method that can estimate the local smoothness of objective function
from samples to identify when the training process has encountered fractal
landscapes. We show experiments to illustrate how some failure cases of policy
optimization can be explained by such fractal landscapes.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Fusion-Eval: Integrating Evaluators with LLMs
Abstract: Evaluating Large Language Models (LLMs) is a complex task, especially
considering the intricacies of natural language understanding and the
expectations for high-level reasoning. Traditional evaluations typically lean
on human-based, model-based, or automatic-metrics-based paradigms, each with
its own advantages and shortcomings. We introduce "Fusion-Eval", a system that
employs LLMs not solely for direct evaluations, but to skillfully integrate
insights from diverse evaluators. This gives Fusion-Eval flexibility, enabling
it to work effectively across diverse tasks and make optimal use of multiple
references. In testing on the SummEval dataset, Fusion-Eval achieved a Spearman
correlation of 0.96, outperforming other evaluators. The success of Fusion-Eval
underscores the potential of LLMs to produce evaluations that closely align
human perspectives, setting a new standard in the field of LLM evaluation.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Artificial Neural Nets and the Representation of Human Concepts
Abstract: What do artificial neural networks (ANNs) learn? The machine learning (ML)
community shares the narrative that ANNs must develop abstract human concepts
to perform complex tasks. Some go even further and believe that these concepts
are stored in individual units of the network. Based on current research, I
systematically investigate the assumptions underlying this narrative. I
conclude that ANNs are indeed capable of performing complex prediction tasks,
and that they may learn human and non-human concepts to do so. However,
evidence indicates that ANNs do not represent these concepts in individual
units.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Learning Saliency From Fixations
Abstract: We present a novel approach for saliency prediction in images, leveraging
parallel decoding in transformers to learn saliency solely from fixation maps.
Models typically rely on continuous saliency maps, to overcome the difficulty
of optimizing for the discrete fixation map. We attempt to replicate the
experimental setup that generates saliency datasets. Our approach treats
saliency prediction as a direct set prediction problem, via a global loss that
enforces unique fixations prediction through bipartite matching and a
transformer encoder-decoder architecture. By utilizing a fixed set of learned
fixation queries, the cross-attention reasons over the image features to
directly output the fixation points, distinguishing it from other modern
saliency predictors. Our approach, named Saliency TRansformer (SalTR), achieves
metric scores on par with state-of-the-art approaches on the Salicon and MIT300
benchmarks.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications
Abstract: Large language models (LLMs) are increasingly deployed as the service backend
for LLM-integrated applications such as code completion and AI-powered search.
LLM-integrated applications serve as middleware to refine users' queries with
domain-specific knowledge to better inform LLMs and enhance the responses.
Despite numerous opportunities and benefits, LLM-integrated applications also
introduce new attack surfaces. Understanding, minimizing, and eliminating these
emerging attack surfaces is a new area of research. In this work, we consider a
setup where the user and LLM interact via an LLM-integrated application in the
middle. We focus on the communication rounds that begin with user's queries and
end with LLM-integrated application returning responses to the queries, powered
by LLMs at the service backend. For this query-response protocol, we identify
potential vulnerabilities that can originate from the malicious application
developer or from an outsider threat initiator that is able to control the
database access, manipulate and poison data that are high-risk for the user.
Successful exploits of the identified vulnerabilities result in the users
receiving responses tailored to the intent of a threat initiator. We assess
such threats against LLM-integrated applications empowered by OpenAI GPT-3.5
and GPT-4. Our empirical results show that the threats can effectively bypass
the restrictions and moderation policies of OpenAI, resulting in users
receiving responses that contain bias, toxic content, privacy risk, and
disinformation. To mitigate those threats, we identify and define four key
properties, namely integrity, source identification, attack detectability, and
utility preservation, that need to be satisfied by a safe LLM-integrated
application. Based on these properties, we develop a lightweight,
threat-agnostic defense that mitigates both insider and outsider threats.

Output: Cryptography and Security


Instruction: What field is the article from?

Input:
Title: Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types
Abstract: In recent years, the field of single-cell data analysis has seen a marked
advancement in the development of clustering methods. Despite advancements,
most of these algorithms still concentrate on analyzing the provided
single-cell matrix data. However, in medical applications, single-cell data
often involves a wealth of exogenous information, including gene networks.
Overlooking this aspect could lead to information loss and clustering results
devoid of significant clinical relevance. An innovative single-cell deep
clustering method, incorporating exogenous gene information, has been proposed
to overcome this limitation. This model leverages exogenous gene network
information to facilitate the clustering process, generating discriminative
representations. Specifically, we have developed an attention-enhanced graph
autoencoder, which is designed to efficiently capture the topological features
between cells. Concurrently, we conducted a random walk on an exogenous
Protein-Protein Interaction (PPI) network, thereby acquiring the gene's
topological features. Ultimately, during the clustering process, we integrated
both sets of information and reconstructed the features of both cells and genes
to generate a discriminative representation. Extensive experiments have
validated the effectiveness of our proposed method. This research offers
enhanced insights into the characteristics and distribution of cells, thereby
laying the groundwork for early diagnosis and treatment of diseases.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning
Abstract: Large language models (LLMs) have shown remarkable capabilities in various
natural language understanding tasks. With only a few demonstration examples,
these LLMs can quickly adapt to target tasks without expensive gradient
updates. Common strategies to boost such 'in-context' learning ability are to
ensemble multiple model decoded results and require the model to generate an
explanation along with the prediction. However, these models often treat
different class predictions equally and neglect the potential discrepancy
between the explanations and predictions. To fully unleash the power of
explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to
empower in-context learning with LLMs. We design two techniques,
explanation-guided ensemble, and soft probability aggregation, to mitigate the
effect of unreliable explanations and improve the consistency between
explanations and final predictions. Experiments on seven natural language
understanding tasks and four varying-size LLMs demonstrate the effectiveness of
our proposed framework.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: On the Limitation of Diffusion Models for Synthesizing Training Datasets
Abstract: Synthetic samples from diffusion models are promising for leveraging in
training discriminative models as replications of real training datasets.
However, we found that the synthetic datasets degrade classification
performance over real datasets even when using state-of-the-art diffusion
models. This means that modern diffusion models do not perfectly represent the
data distribution for the purpose of replicating datasets for training
discriminative tasks. This paper investigates the gap between synthetic and
real samples by analyzing the synthetic samples reconstructed from real samples
through the diffusion and reverse process. By varying the time steps starting
the reverse process in the reconstruction, we can control the trade-off between
the information in the original real data and the information added by
diffusion models. Through assessing the reconstructed samples and trained
models, we found that the synthetic data are concentrated in modes of the
training data distribution as the reverse step increases, and thus, they are
difficult to cover the outer edges of the distribution. Our findings imply that
modern diffusion models are insufficient to replicate training data
distribution perfectly, and there is room for the improvement of generative
modeling in the replication of training datasets.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Automatic Time Signature Determination for New Scores Using Lyrics for Latent Rhythmic Structure
Abstract: There has recently been a sharp increase in interest in Artificial
Intelligence-Generated Content (AIGC). Despite this, musical components such as
time signatures have not been studied sufficiently to form an algorithmic
determination approach for new compositions, especially lyrical songs. This is
likely because of the neglect of musical details, which is critical for
constructing a robust framework. Specifically, time signatures establish the
fundamental rhythmic structure for almost all aspects of a song, including the
phrases and notes. In this paper, we propose a novel approach that only uses
lyrics as input to automatically generate a fitting time signature for lyrical
songs and uncover the latent rhythmic structure utilizing explainable machine
learning models. In particular, we devise multiple methods that are associated
with discovering lyrical patterns and creating new features that simultaneously
contain lyrical, rhythmic, and statistical information. In this approach, the
best of our experimental results reveal a 97.6% F1 score and a 0.996 Area Under
the Curve (AUC) of the Receiver Operating Characteristic (ROC) score. In
conclusion, our research directly generates time signatures from lyrics
automatically for new scores utilizing machine learning, which is an innovative
idea that approaches an understudied component of musicology and therefore
contributes significantly to the future of Artificial Intelligence (AI) music
generation.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery
Abstract: Monitoring wildfires is an essential step in minimizing their impact on the
planet, understanding the many negative environmental, economic, and social
consequences. Recent advances in remote sensing technology combined with the
increasing application of artificial intelligence methods have improved
real-time, high-resolution fire monitoring. This study explores two proposed
approaches based on the U-Net model for automating and optimizing the
burned-area mapping process. Denoted 128 and AllSizes (AS), they are trained on
datasets with a different class balance by cropping input images to different
sizes. They are then applied to Landsat imagery and time-series data from two
fire-prone regions in Chile. The results obtained after enhancement of model
performance by hyperparameter optimization demonstrate the effectiveness of
both approaches. Tests based on 195 representative images of the study area
show that increasing dataset balance using the AS model yields better
performance. More specifically, AS exhibited a Dice Coefficient (DC) of 0.93,
an Omission Error (OE) of 0.086, and a Commission Error (CE) of 0.045, while
the 128 model achieved a DC of 0.86, an OE of 0.12, and a CE of 0.12. These
findings should provide a basis for further development of scalable automatic
burned-area mapping tools.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: A Comprehensive Study of GPT-4V's Multimodal Capabilities in Medical Imaging
Abstract: This paper presents a comprehensive evaluation of GPT-4V's capabilities
across diverse medical imaging tasks, including Radiology Report Generation,
Medical Visual Question Answering (VQA), and Visual Grounding. While prior
efforts have explored GPT-4V's performance in medical image analysis, to the
best of our knowledge, our study represents the first quantitative evaluation
on publicly available benchmarks. Our findings highlight GPT-4V's potential in
generating descriptive reports for chest X-ray images, particularly when guided
by well-structured prompts. Meanwhile, its performance on the MIMIC-CXR dataset
benchmark reveals areas for improvement in certain evaluation metrics, such as
CIDEr. In the domain of Medical VQA, GPT-4V demonstrates proficiency in
distinguishing between question types but falls short of the VQA-RAD benchmark
in terms of accuracy. Furthermore, our analysis finds the limitations of
conventional evaluation metrics like the BLEU scores, advocating for the
development of more semantically robust assessment methods. In the field of
Visual Grounding, GPT-4V exhibits preliminary promise in recognizing bounding
boxes, but its precision is lacking, especially in identifying specific medical
organs and signs. Our evaluation underscores the significant potential of
GPT-4V in the medical imaging domain, while also emphasizing the need for
targeted refinements to fully unlock its capabilities.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
Abstract: Evaluating retrieval-augmented generation (RAG) systems traditionally relies
on hand annotations for input queries, passages to retrieve, and responses to
generate. We introduce ARES, an Automated RAG Evaluation System, for evaluating
RAG systems along the dimensions of context relevance, answer faithfulness, and
answer relevance. Using synthetic training data, ARES finetunes lightweight LM
judges to assess the quality of individual RAG components. To mitigate
potential prediction errors, ARES utilizes a small set of human-annotated
datapoints for prediction-powered inference (PPI). Across six different
knowledge-intensive tasks in KILT and SuperGLUE, ARES accurately evaluates RAG
systems while using a few hundred human annotations during evaluation.
Furthermore, ARES judges remain effective across domain shifts, proving
accurate even after changing the type of queries and/or documents used in the
evaluated RAG systems. We make our datasets and code for replication and
deployment available at https://github.com/stanford-futuredata/ARES.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Investigating the Encoding of Words in BERT's Neurons using Feature Textualization
Abstract: Pretrained language models (PLMs) form the basis of most state-of-the-art NLP
technologies. Nevertheless, they are essentially black boxes: Humans do not
have a clear understanding of what knowledge is encoded in different parts of
the models, especially in individual neurons. The situation is different in
computer vision, where feature visualization provides a decompositional
interpretability technique for neurons of vision models. Activation
maximization is used to synthesize inherently interpretable visual
representations of the information encoded in individual neurons. Our work is
inspired by this but presents a cautionary tale on the interpretability of
single neurons, based on the first large-scale attempt to adapt activation
maximization to NLP, and, more specifically, large PLMs. We propose feature
textualization, a technique to produce dense representations of neurons in the
PLM word embedding space. We apply feature textualization to the BERT model
(Devlin et al., 2019) to investigate whether the knowledge encoded in
individual neurons can be interpreted and symbolized. We find that the produced
representations can provide insights about the knowledge encoded in individual
neurons, but that individual neurons do not represent clearcut symbolic units
of language such as words. Additionally, we use feature textualization to
investigate how many neurons are needed to encode words in BERT.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language
Abstract: Aspect-based sentiment analysis is a method in natural language processing
aimed at identifying and understanding sentiments related to specific aspects
of an entity. Aspects are words or phrases that represent an aspect or
attribute of a particular entity. Previous research has utilized generative
pre-trained language models to perform aspect-based sentiment analysis.
LEGO-ABSA is one framework that has successfully employed generative
pre-trained language models in aspect-based sentiment analysis, particularly in
English. LEGO-ABSA uses a multitask learning and prompting approach to enhance
model performance. However, the application of this approach has not been done
in the context of Bahasa Indonesia. Therefore, this research aims to implement
the multitask learning and prompting approach in aspect-based sentiment
analysis for Bahasa Indonesia using generative pre-trained language models. In
this study, the Indo LEGO-ABSA model is developed, which is an aspect-based
sentiment analysis model utilizing generative pre-trained language models and
trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a
hotel domain dataset in the Indonesian language. The obtained results include
an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction task, 86.09%
for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair
Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term
Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5
model, specifically mT5, by applying multitask learning to train all tasks
within aspect-based sentiment analysis.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Enhancing Novel Object Detection via Cooperative Foundational Models
Abstract: In this work, we address the challenging and emergent problem of novel object
detection (NOD), focusing on the accurate detection of both known and novel
object categories during inference. Traditional object detection algorithms are
inherently closed-set, limiting their capability to handle NOD. We present a
novel approach to transform existing closed-set detectors into open-set
detectors. This transformation is achieved by leveraging the complementary
strengths of pre-trained foundational models, specifically CLIP and SAM,
through our cooperative mechanism. Furthermore, by integrating this mechanism
with state-of-the-art open-set detectors such as GDINO, we establish new
benchmarks in object detection performance. Our method achieves 17.42 mAP in
novel object detection and 42.08 mAP for known objects on the challenging LVIS
dataset. Adapting our approach to the COCO OVD split, we surpass the current
state-of-the-art by a margin of 7.2 $ \text{AP}_{50} $ for novel classes. Our
code is available at
https://github.com/rohit901/cooperative-foundational-models .

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
Abstract: The integration of AI into radiology introduces opportunities for improved
clinical care provision and efficiency but it demands a meticulous approach to
mitigate potential risks as with any other new technology. Beginning with
rigorous pre-deployment evaluation and validation, the focus should be on
ensuring models meet the highest standards of safety, effectiveness and
efficacy for their intended applications. Input and output guardrails
implemented during production usage act as an additional layer of protection,
identifying and addressing individual failures as they occur. Continuous
post-deployment monitoring allows for tracking population-level performance
(data drift), fairness, and value delivery over time. Scheduling reviews of
post-deployment model performance and educating radiologists about new
algorithmic-driven findings is critical for AI to be effective in clinical
practice. Recognizing that no single AI solution can provide absolute assurance
even when limited to its intended use, the synergistic application of quality
assurance at multiple levels - regulatory, clinical, technical, and ethical -
is emphasized. Collaborative efforts between stakeholders spanning healthcare
systems, industry, academia, and government are imperative to address the
multifaceted challenges involved. Trust in AI is an earned privilege,
contingent on a broad set of goals, among them transparently demonstrating that
the AI adheres to the same rigorous safety, effectiveness and efficacy
standards as other established medical technologies. By doing so, developers
can instil confidence among providers and patients alike, enabling the
responsible scaling of AI and the realization of its potential benefits. The
roadmap presented herein aims to expedite the achievement of deployable,
reliable, and safe AI in radiology.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy
Abstract: The recent development of connected and automated vehicle (CAV) technologies
has spurred investigations to optimize dense urban traffic. This paper
considers advisory autonomy, in which real-time driving advisories are issued
to drivers, thus blending the CAV and the human driver. Due to the complexity
of traffic systems, recent studies of coordinating CAVs have resorted to
leveraging deep reinforcement learning (RL). Advisory autonomy is formalized as
zero-order holds, and we consider a range of hold duration from 0.1 to 40
seconds. However, despite the similarity of the higher frequency tasks on CAVs,
a direct application of deep RL fails to be generalized to advisory autonomy
tasks. We introduce Temporal Transfer Learning (TTL) algorithms to select
source tasks, systematically leveraging the temporal structure to solve the
full range of tasks. TTL selects the most suitable source tasks to maximize the
performance of the range of tasks. We validate our algorithms on diverse
mixed-traffic scenarios, demonstrating that TTL more reliably solves the tasks
than baselines. This paper underscores the potential of coarse-grained advisory
autonomy with TTL in traffic flow optimization.

Output: Robotics


Instruction: What field is the article from?

Input:
Title: Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
Abstract: Precise and rapid delineation of sharp boundaries and robust semantics is
essential for numerous downstream robotic tasks, such as robot grasping and
manipulation, real-time semantic mapping, and online sensor calibration
performed on edge computing units. Although boundary detection and semantic
segmentation are complementary tasks, most studies focus on lightweight models
for semantic segmentation but overlook the critical role of boundary detection.
In this work, we introduce Mobile-Seed, a lightweight, dual-task framework
tailored for simultaneous semantic segmentation and boundary detection. Our
framework features a two-stream encoder, an active fusion decoder (AFD) and a
dual-task regularization approach. The encoder is divided into two pathways:
one captures category-aware semantic information, while the other discerns
boundaries from multi-scale features. The AFD module dynamically adapts the
fusion of semantic and boundary information by learning channel-wise
relationships, allowing for precise weight assignment of each channel.
Furthermore, we introduce a regularization loss to mitigate the conflicts in
dual-task learning and deep diversity supervision. Compared to existing
methods, the proposed Mobile-Seed offers a lightweight framework to
simultaneously improve semantic segmentation performance and accurately locate
object boundaries. Experiments on the Cityscapes dataset have shown that
Mobile-Seed achieves notable improvement over the state-of-the-art (SOTA)
baseline by 2.2 percentage points (pp) in mIoU and 4.2 pp in mF-score, while
maintaining an online inference speed of 23.9 frames-per-second (FPS) with
1024x2048 resolution input on an RTX 2080 Ti GPU. Additional experiments on
CamVid and PASCAL Context datasets confirm our method's generalizability. Code
and additional results are publicly available at
https://whu-usi3dv.github.io/Mobile-Seed/.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Relevance Feedback with Brain Signals
Abstract: The Relevance Feedback (RF) process relies on accurate and real-time
relevance estimation of feedback documents to improve retrieval performance.
Since collecting explicit relevance annotations imposes an extra burden on the
user, extensive studies have explored using pseudo-relevance signals and
implicit feedback signals as substitutes. However, such signals are indirect
indicators of relevance and suffer from complex search scenarios where user
interactions are absent or biased.
  Recently, the advances in portable and high-precision brain-computer
interface (BCI) devices have shown the possibility to monitor user's brain
activities during search process. Brain signals can directly reflect user's
psychological responses to search results and thus it can act as additional and
unbiased RF signals. To explore the effectiveness of brain signals in the
context of RF, we propose a novel RF framework that combines BCI-based
relevance feedback with pseudo-relevance signals and implicit signals to
improve the performance of document re-ranking. The experimental results on the
user study dataset show that incorporating brain signals leads to significant
performance improvement in our RF framework. Besides, we observe that brain
signals perform particularly well in several hard search scenarios, especially
when implicit signals as feedback are missing or noisy. This reveals when and
how to exploit brain signals in the context of RF.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: The NeurIPS 2022 Neural MMO Challenge: A Massively Multiagent Competition with Specialization and Trade
Abstract: In this paper, we present the results of the NeurIPS-2022 Neural MMO
Challenge, which attracted 500 participants and received over 1,600
submissions. Like the previous IJCAI-2022 Neural MMO Challenge, it involved
agents from 16 populations surviving in procedurally generated worlds by
collecting resources and defeating opponents. This year's competition runs on
the latest v1.6 Neural MMO, which introduces new equipment, combat, trading,
and a better scoring system. These elements combine to pose additional
robustness and generalization challenges not present in previous competitions.
This paper summarizes the design and results of the challenge, explores the
potential of this environment as a benchmark for learning methods, and presents
some practical reinforcement learning training approaches for complex tasks
with sparse rewards. Additionally, we have open-sourced our baselines,
including environment wrappers, benchmarks, and visualization tools for future
research.

Output: Artificial Intelligence


Instruction: What field is the article from?

Input:
Title: Speakerly: A Voice-based Writing Assistant for Text Composition
Abstract: We present Speakerly, a new real-time voice-based writing assistance system
that helps users with text composition across various use cases such as emails,
instant messages, and notes. The user can interact with the system through
instructions or dictation, and the system generates a well-formatted and
coherent document. We describe the system architecture and detail how we
address the various challenges while building and deploying such a system at
scale. More specifically, our system uses a combination of small, task-specific
models as well as pre-trained language models for fast and effective text
composition while supporting a variety of input modes for better usability.

Output: Computational Linguistics


Instruction: What field is the article from?

Input:
Title: Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone
Abstract: Parameter-efficient tuning has become a trend in transferring large-scale
foundation models to downstream applications. Existing methods typically embed
some light-weight tuners into the backbone, where both the design and the
learning of the tuners are highly dependent on the base model. This work offers
a new tuning paradigm, dubbed Res-Tuning, which intentionally unbinds tuners
from the backbone. With both theoretical and empirical evidence, we show that
popular tuning approaches have their equivalent counterparts under our
unbinding formulation, and hence can be integrated into our framework
effortlessly. Thanks to the structural disentanglement, we manage to free the
design of tuners from the network architecture, facilitating flexible
combination of various tuning strategies. We further propose a memory-efficient
variant of Res-Tuning, where the bypass i.e., formed by a sequence of tuners)
is effectively detached from the main branch, such that the gradients are
back-propagated only to the tuners but not to the backbone. Such a detachment
also allows one-time backbone forward for multi-task inference. Extensive
experiments on both discriminative and generative tasks demonstrate the
superiority of our method over existing alternatives from the perspectives of
efficacy and efficiency. Project page:
$\href{https://res-tuning.github.io/}{\textit{https://res-tuning.github.io/}}$.

Output: Computer Vision


Instruction: What field is the article from?

Input:
Title: Conversational Data Exploration: A Game-Changer for Designing Data Science Pipelines
Abstract: This paper proposes a conversational approach implemented by the system
Chatin for driving an intuitive data exploration experience. Our work aims to
unlock the full potential of data analytics and artificial intelligence with a
new generation of data science solutions. Chatin is a cutting-edge tool that
democratises access to AI-driven solutions, empowering non-technical users from
various disciplines to explore data and extract knowledge from it.

Output: Human-Computer Interaction


Instruction: What field is the article from?

Input:
Title: Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder
Abstract: Airfoil shape optimization plays a critical role in the design of
high-performance aircraft. However, the high-dimensional nature of airfoil
representation causes the challenging problem known as the "curse of
dimensionality". To overcome this problem, numerous airfoil parameterization
methods have been developed, which can be broadly classified as
polynomial-based and data-driven approaches. Each of these methods has
desirable characteristics such as flexibility, parsimony, feasibility, and
intuitiveness, but a single approach that encompasses all of these attributes
has yet to be found. For example, polynomial-based methods struggle to balance
parsimony and flexibility, while data-driven methods lack in feasibility and
intuitiveness. In recent years, generative models, such as generative
adversarial networks and variational autoencoders, have shown promising
potential in airfoil parameterization. However, these models still face
challenges related to intuitiveness due to their black-box nature. To address
this issue, we developed a novel airfoil parameterization method using
physics-aware variational autoencoder. The proposed method not only explicitly
separates the generation of thickness and camber distributions to produce
smooth and non-intersecting airfoils, thereby improving feasibility, but it
also directly aligns its latent dimensions with geometric features of the
airfoil, significantly enhancing intuitiveness. Finally, extensive comparative
studies were performed to demonstrate the effectiveness of our approach.

Output: Machine Learning


Instruction: What field is the article from?

Input:
Title: TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
Abstract: This work proposes TimeChat, a time-sensitive multimodal large language model
specifically designed for long video understanding. Our model incorporates two
key architectural contributions: (1) a timestamp-aware frame encoder that binds
visual content with the timestamp of each frame, and (2) a sliding video
Q-Former that produces a video token sequence of varying lengths to accommodate
videos of various durations. Additionally, we construct an instruction-tuning
dataset, encompassing 6 tasks and a total of 125K instances, to further enhance
TimeChat's instruction-following performance. Experiment results across various
video understanding tasks, such as dense captioning, temporal grounding, and
highlight detection, demonstrate TimeChat's strong zero-shot temporal
localization and reasoning capabilities. For example, it achieves +9.2 F1 score
and +2.8 CIDEr on YouCook2, +5.8 HIT@1 on QVHighlights, and +27.5 R@1 (IoU=0.5)
on Charades-STA, compared to state-of-the-art video large language models,
holding the potential to serve as a versatile video assistant for long-form
video comprehension tasks and satisfy realistic user requirements.

Output: Computer Vision