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2501.09870
|
An LLM-Guided Tutoring System for Social Skills Training
|
cs.LG
|
Social skills training targets behaviors necessary for success in social
interactions. However, traditional classroom training for such skills is often
insufficient to teach effective communication -- one-to-one interaction in
real-world scenarios is preferred to lecture-style information delivery. This
paper introduces a framework that allows instructors to collaborate with large
language models to dynamically design realistic scenarios for students to
communicate. Our framework uses these scenarios to enable student rehearsal,
provide immediate feedback, and visualize performance for both students and
instructors. Unlike traditional intelligent tutoring systems, instructors can
easily co-create scenarios with a large language model without technical
skills. Additionally, the system generates new scenario branches in real time
when existing options do not fit the student's response.
|
2501.09876
|
Geometry-Preserving Encoder/Decoder in Latent Generative Models
|
math.NA cs.LG cs.NA
|
Generative modeling aims to generate new data samples that resemble a given
dataset, with diffusion models recently becoming the most popular generative
model. One of the main challenges of diffusion models is solving the problem in
the input space, which tends to be very high-dimensional. Recently, solving
diffusion models in the latent space through an encoder that maps from the data
space to a lower-dimensional latent space has been considered to make the
training process more efficient and has shown state-of-the-art results. The
variational autoencoder (VAE) is the most commonly used encoder/decoder
framework in this domain, known for its ability to learn latent representations
and generate data samples. In this paper, we introduce a novel encoder/decoder
framework with theoretical properties distinct from those of the VAE,
specifically designed to preserve the geometric structure of the data
distribution. We demonstrate the significant advantages of this
geometry-preserving encoder in the training process of both the encoder and
decoder. Additionally, we provide theoretical results proving convergence of
the training process, including convergence guarantees for encoder training,
and results showing faster convergence of decoder training when using the
geometry-preserving encoder.
|
2501.09877
|
CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic
Recognition
|
eess.AS cs.LG
|
Contrastive Language-Audio Pretraining (CLAP) models have demonstrated
unprecedented performance in various acoustic signal recognition tasks.
Fiber-optic-based acoustic recognition is one of the most important downstream
tasks and plays a significant role in environmental sensing. Adapting CLAP for
fiber-optic acoustic recognition has become an active research area. As a
non-conventional acoustic sensor, fiber-optic acoustic recognition presents a
challenging, domain-specific, low-shot deployment environment with significant
domain shifts due to unique frequency response and noise characteristics. To
address these challenges, we propose a support-based adaptation method, CLAP-S,
which linearly interpolates a CLAP Adapter with the Support Set, leveraging
both implicit knowledge through fine-tuning and explicit knowledge retrieved
from memory for cross-domain generalization. Experimental results show that our
method delivers competitive performance on both laboratory-recorded fiber-optic
ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our
research also provides valuable insights for other downstream acoustic
recognition tasks. The code and gunshot-firework dataset are available at
https://github.com/Jingchensun/clap-s.
|
2501.09878
|
ASTRA: A Scene-aware TRAnsformer-based model for trajectory prediction
|
cs.CV cs.AI
|
We present ASTRA (A} Scene-aware TRAnsformer-based model for trajectory
prediction), a light-weight pedestrian trajectory forecasting model that
integrates the scene context, spatial dynamics, social inter-agent interactions
and temporal progressions for precise forecasting. We utilised a U-Net-based
feature extractor, via its latent vector representation, to capture scene
representations and a graph-aware transformer encoder for capturing social
interactions. These components are integrated to learn an agent-scene aware
embedding, enabling the model to learn spatial dynamics and forecast the future
trajectory of pedestrians. The model is designed to produce both deterministic
and stochastic outcomes, with the stochastic predictions being generated by
incorporating a Conditional Variational Auto-Encoder (CVAE). ASTRA also
proposes a simple yet effective weighted penalty loss function, which helps to
yield predictions that outperform a wide array of state-of-the-art
deterministic and generative models. ASTRA demonstrates an average improvement
of 27%/10% in deterministic/stochastic settings on the ETH-UCY dataset, and 26%
improvement on the PIE dataset, respectively, along with seven times fewer
parameters than the existing state-of-the-art model (see Figure 1).
Additionally, the model's versatility allows it to generalize across different
perspectives, such as Bird's Eye View (BEV) and Ego-Vehicle View (EVV).
|
2501.09884
|
Semi-Supervised Image-Based Narrative Extraction: A Case Study with
Historical Photographic Records
|
cs.CV cs.IR
|
This paper presents a semi-supervised approach to extracting narratives from
historical photographic records using an adaptation of the narrative maps
algorithm. We extend the original unsupervised text-based method to work with
image data, leveraging deep learning techniques for visual feature extraction
and similarity computation. Our method is applied to the ROGER dataset, a
collection of photographs from the 1928 Sacambaya Expedition in Bolivia
captured by Robert Gerstmann. We compare our algorithmically extracted visual
narratives with expert-curated timelines of varying lengths (5 to 30 images) to
evaluate the effectiveness of our approach. In particular, we use the Dynamic
Time Warping (DTW) algorithm to match the extracted narratives with the
expert-curated baseline. In addition, we asked an expert on the topic to
qualitatively evaluate a representative example of the resulting narratives.
Our findings show that the narrative maps approach generally outperforms random
sampling for longer timelines (10+ images, p < 0.05), with expert evaluation
confirming the historical accuracy and coherence of the extracted narratives.
This research contributes to the field of computational analysis of visual
cultural heritage, offering new tools for historians, archivists, and digital
humanities scholars to explore and understand large-scale image collections.
The method's ability to generate meaningful narratives from visual data opens
up new possibilities for the study and interpretation of historical events
through photographic evidence.
|
2501.09887
|
FLORA: Formal Language Model Enables Robust Training-free Zero-shot
Object Referring Analysis
|
cs.CV
|
Object Referring Analysis (ORA), commonly known as referring expression
comprehension, requires the identification and localization of specific objects
in an image based on natural descriptions. Unlike generic object detection, ORA
requires both accurate language understanding and precise visual localization,
making it inherently more complex. Although recent pre-trained large visual
grounding detectors have achieved significant progress, they heavily rely on
extensively labeled data and time-consuming learning. To address these, we
introduce a novel, training-free framework for zero-shot ORA, termed FLORA
(Formal Language for Object Referring and Analysis). FLORA harnesses the
inherent reasoning capabilities of large language models (LLMs) and integrates
a formal language model - a logical framework that regulates language within
structured, rule-based descriptions - to provide effective zero-shot ORA. More
specifically, our formal language model (FLM) enables an effective,
logic-driven interpretation of object descriptions without necessitating any
training processes. Built upon FLM-regulated LLM outputs, we further devise a
Bayesian inference framework and employ appropriate off-the-shelf interpretive
models to finalize the reasoning, delivering favorable robustness against LLM
hallucinations and compelling ORA performance in a training-free manner. In
practice, our FLORA boosts the zero-shot performance of existing pretrained
grounding detectors by up to around 45%. Our comprehensive evaluation across
different challenging datasets also confirms that FLORA consistently surpasses
current state-of-the-art zero-shot methods in both detection and segmentation
tasks associated with zero-shot ORA. We believe our probabilistic parsing and
reasoning of the LLM outputs elevate the reliability and interpretability of
zero-shot ORA. We shall release codes upon publication.
|
2501.09889
|
Learning port maneuvers from data for automatic guidance of Unmanned
Surface Vehicles
|
eess.SY cs.SY
|
At shipping ports, some repetitive maneuvering tasks such as entering/leaving
port, transporting goods inside it or just making surveillance activities, can
be efficiently and quickly carried out by a domestic pilot according to his
experience. This know-how can be seized by Unmanned Surface Vehicles (USV) in
order to autonomously replicate the same tasks. However, the inherent
nonlinearity of ship trajectories and environmental perturbations as wind or
marine currents make it difficult to learn a model and its respective control.
We therefore present a data-driven learning and control methodology for USV,
which is based on Gaussian Mixture Model, Gaussian Mixture Regression and the
Sontag's universal formula. Our approach is capable to learn the nonlinear
dynamics as well as guarantee the convergence toward the target with a robust
controller. Real data have been collected through experiments with a vessel at
the port of Ceuta. The complex trajectories followed by an expert have been
learned including the robust controller. The effect of the controller over
noise/perturbations are presented, a measure of error is used to compare
estimates and real data trajectories, and finally, an analysis of computational
complexity is performed.
|
2501.09890
|
Exploring the Implementation of AI in Early Onset Interviews to Help
Mitigate Bias
|
cs.AI
|
This paper investigates the application of artificial intelligence (AI) in
early-stage recruitment interviews in order to reduce inherent bias,
specifically sentiment bias. Traditional interviewers are often subject to
several biases, including interviewer bias, social desirability effects, and
even confirmation bias. In turn, this leads to non-inclusive hiring practices,
and a less diverse workforce. This study further analyzes various AI
interventions that are present in the marketplace today such as multimodal
platforms and interactive candidate assessment tools in order to gauge the
current market usage of AI in early-stage recruitment. However, this paper aims
to use a unique AI system that was developed to transcribe and analyze
interview dynamics, which emphasize skill and knowledge over emotional
sentiments. Results indicate that AI effectively minimizes sentiment-driven
biases by 41.2%, suggesting its revolutionizing power in companies' recruitment
processes for improved equity and efficiency.
|
2501.09891
|
Evolving Deeper LLM Thinking
|
cs.AI
|
We explore an evolutionary search strategy for scaling inference time compute
in Large Language Models. The proposed approach, Mind Evolution, uses a
language model to generate, recombine and refine candidate responses. The
proposed approach avoids the need to formalize the underlying inference problem
whenever a solution evaluator is available. Controlling for inference cost, we
find that Mind Evolution significantly outperforms other inference strategies
such as Best-of-N and Sequential Revision in natural language planning tasks.
In the TravelPlanner and Natural Plan benchmarks, Mind Evolution solves more
than 98% of the problem instances using Gemini 1.5 Pro without the use of a
formal solver.
|
2501.09893
|
Sparse Binary Representation Learning for Knowledge Tracing
|
cs.LG
|
Knowledge tracing (KT) models aim to predict students' future performance
based on their historical interactions. Most existing KT models rely
exclusively on human-defined knowledge concepts (KCs) associated with
exercises. As a result, the effectiveness of these models is highly dependent
on the quality and completeness of the predefined KCs. Human errors in labeling
and the cost of covering all potential underlying KCs can limit model
performance.
In this paper, we propose a KT model, Sparse Binary Representation KT
(SBRKT), that generates new KC labels, referred to as auxiliary KCs, which can
augment the predefined KCs to address the limitations of relying solely on
human-defined KCs. These are learned through a binary vector representation,
where each bit indicates the presence (one) or absence (zero) of an auxiliary
KC. The resulting discrete representation allows these auxiliary KCs to be
utilized in training any KT model that incorporates KCs. Unlike pre-trained
dense embeddings, which are limited to models designed to accept such vectors,
our discrete representations are compatible with both classical models, such as
Bayesian Knowledge Tracing (BKT), and modern deep learning approaches.
To generate this discrete representation, SBRKT employs a binarization method
that learns a sparse representation, fully trainable via stochastic gradient
descent. Additionally, SBRKT incorporates a recurrent neural network (RNN) to
capture temporal dynamics and predict future student responses by effectively
combining the auxiliary and predefined KCs. Experimental results demonstrate
that SBRKT outperforms the tested baselines on several datasets and achieves
competitive performance on others. Furthermore, incorporating the learned
auxiliary KCs consistently enhances the performance of BKT across all tested
datasets.
|
2501.09898
|
FoundationStereo: Zero-Shot Stereo Matching
|
cs.CV cs.LG cs.RO
|
Tremendous progress has been made in deep stereo matching to excel on
benchmark datasets through per-domain fine-tuning. However, achieving strong
zero-shot generalization - a hallmark of foundation models in other computer
vision tasks - remains challenging for stereo matching. We introduce
FoundationStereo, a foundation model for stereo depth estimation designed to
achieve strong zero-shot generalization. To this end, we first construct a
large-scale (1M stereo pairs) synthetic training dataset featuring large
diversity and high photorealism, followed by an automatic self-curation
pipeline to remove ambiguous samples. We then design a number of network
architecture components to enhance scalability, including a side-tuning feature
backbone that adapts rich monocular priors from vision foundation models to
mitigate the sim-to-real gap, and long-range context reasoning for effective
cost volume filtering. Together, these components lead to strong robustness and
accuracy across domains, establishing a new standard in zero-shot stereo depth
estimation. Project page: https://nvlabs.github.io/FoundationStereo/
|
2501.09900
|
SBAMDT: Bayesian Additive Decision Trees with Adaptive Soft
Semi-multivariate Split Rules
|
stat.ML cs.LG math.ST stat.ME stat.TH
|
Bayesian Additive Regression Trees [BART, Chipman et al., 2010] have gained
significant popularity due to their remarkable predictive performance and
ability to quantify uncertainty. However, standard decision tree models rely on
recursive data splits at each decision node, using deterministic decision rules
based on a single univariate feature. This approach limits their ability to
effectively capture complex decision boundaries, particularly in scenarios
involving multiple features, such as spatial domains, or when transitions are
either sharp or smoothly varying. In this paper, we introduce a novel
probabilistic additive decision tree model that employs a soft split rule. This
method enables highly flexible splits that leverage both univariate and
multivariate features, while also respecting the geometric properties of the
feature domain. Notably, the probabilistic split rule adapts dynamically across
decision nodes, allowing the model to account for varying levels of smoothness
in the regression function. We demonstrate the utility of the proposed model
through comparisons with existing tree-based models on synthetic datasets and a
New York City education dataset.
|
2501.09905
|
SLIM: Sim-to-Real Legged Instructive Manipulation via Long-Horizon
Visuomotor Learning
|
cs.RO cs.AI cs.CV cs.LG
|
We present a low-cost legged mobile manipulation system that solves
long-horizon real-world tasks, trained by reinforcement learning purely in
simulation. This system is made possible by 1) a hierarchical design of a
high-level policy for visual-mobile manipulation following task instructions,
and a low-level quadruped locomotion policy, 2) a teacher and student training
pipeline for the high level, which trains a teacher to tackle long-horizon
tasks using privileged task decomposition and target object information, and
further trains a student for visual-mobile manipulation via RL guided by the
teacher's behavior, and 3) a suite of techniques for minimizing the sim-to-real
gap.
In contrast to many previous works that use high-end equipments, our system
demonstrates effective performance with more accessible hardware --
specifically, a Unitree Go1 quadruped, a WidowX-250S arm, and a single
wrist-mounted RGB camera -- despite the increased challenges of sim-to-real
transfer. Trained fully in simulation, a single policy autonomously solves
long-horizon tasks involving search, move to, grasp, transport, and drop into,
achieving nearly 80% real-world success. This performance is comparable to that
of expert human teleoperation on the same tasks while the robot is more
efficient, operating at about 1.5x the speed of the teleoperation. Finally, we
perform extensive ablations on key techniques for efficient RL training and
effective sim-to-real transfer, and demonstrate effective deployment across
diverse indoor and outdoor scenes under various lighting conditions.
|
2501.09909
|
Demo: Interactive Visualization of Semantic Relationships in a
Biomedical Project's Talent Knowledge Graph
|
cs.SI
|
We present an interactive visualization of the Cell Map for AI Talent
Knowledge Graph (CM4AI TKG), a detailed semantic space comprising approximately
28,000 experts and 1,000 datasets focused on the biomedical field. Our tool
leverages transformer-based embeddings, WebGL visualization techniques, and
generative AI, specifically Large Language Models (LLMs), to provide a
responsive and user-friendly interface. This visualization supports the
exploration of around 29,000 nodes, assisting users in identifying potential
collaborators and dataset users within the health and biomedical research
fields. Our solution transcends the limitations of conventional graph
visualization tools like Gephi, particularly in handling large-scale
interactive graphs. We utilize GPT-4o to furnish detailed justifications for
recommended collaborators and dataset users, promoting informed
decision-making. Key functionalities include responsive search and exploration,
as well as GenAI-driven recommendations, all contributing to a nuanced
representation of the convergence between biomedical and AI research
landscapes. In addition to benefiting the Bridge2AI and CM4AI communities, this
adaptable visualization framework can be extended to other biomedical knowledge
graphs, fostering advancements in medical AI and healthcare innovation through
improved user interaction and data exploration. The demonstration is available
at: https://jiawei-alpha.vercel.app/.
|
2501.09913
|
Towards A Litmus Test for Common Sense
|
cs.AI
|
This paper is the second in a planned series aimed at envisioning a path to
safe and beneficial artificial intelligence. Building on the conceptual
insights of "Common Sense Is All You Need," we propose a more formal litmus
test for common sense, adopting an axiomatic approach that combines minimal
prior knowledge (MPK) constraints with diagonal or Godel-style arguments to
create tasks beyond the agent's known concept set. We discuss how this approach
applies to the Abstraction and Reasoning Corpus (ARC), acknowledging
training/test data constraints, physical or virtual embodiment, and large
language models (LLMs). We also integrate observations regarding emergent
deceptive hallucinations, in which more capable AI systems may intentionally
fabricate plausible yet misleading outputs to disguise knowledge gaps. The
overarching theme is that scaling AI without ensuring common sense risks
intensifying such deceptive tendencies, thereby undermining safety and trust.
Aligning with the broader goal of developing beneficial AI without causing
harm, our axiomatic litmus test not only diagnoses whether an AI can handle
truly novel concepts but also provides a stepping stone toward an ethical,
reliable foundation for future safe, beneficial, and aligned artificial
intelligence.
|
2501.09918
|
GenSC-6G: A Prototype Testbed for Integrated Generative AI, Quantum, and
Semantic Communication
|
cs.AI eess.SP quant-ph
|
We introduce a prototyping testbed, GenSC-6G, developed to generate a
comprehensive dataset that supports the integration of generative artificial
intelligence (AI), quantum computing, and semantic communication for emerging
sixth-generation (6G) applications. The GenSC-6G dataset is designed with
noise-augmented synthetic data optimized for semantic decoding, classification,
and localization tasks, significantly enhancing flexibility for diverse
AI-driven communication applications. This adaptable prototype supports
seamless modifications across baseline models, communication modules, and
goal-oriented decoders. Case studies demonstrate its application in lightweight
classification, semantic upsampling, and edge-based language inference under
noise conditions. The GenSC-6G dataset serves as a scalable and robust resource
for developing goal-oriented communication systems tailored to the growing
demands of 6G networks.
|
2501.09921
|
TalkingEyes: Pluralistic Speech-Driven 3D Eye Gaze Animation
|
cs.CV
|
Although significant progress has been made in the field of speech-driven 3D
facial animation recently, the speech-driven animation of an indispensable
facial component, eye gaze, has been overlooked by recent research. This is
primarily due to the weak correlation between speech and eye gaze, as well as
the scarcity of audio-gaze data, making it very challenging to generate 3D eye
gaze motion from speech alone. In this paper, we propose a novel data-driven
method which can generate diverse 3D eye gaze motions in harmony with the
speech. To achieve this, we firstly construct an audio-gaze dataset that
contains about 14 hours of audio-mesh sequences featuring high-quality eye gaze
motion, head motion and facial motion simultaneously. The motion data is
acquired by performing lightweight eye gaze fitting and face reconstruction on
videos from existing audio-visual datasets. We then tailor a novel
speech-to-motion translation framework in which the head motions and eye gaze
motions are jointly generated from speech but are modeled in two separate
latent spaces. This design stems from the physiological knowledge that the
rotation range of eyeballs is less than that of head. Through mapping the
speech embedding into the two latent spaces, the difficulty in modeling the
weak correlation between speech and non-verbal motion is thus attenuated.
Finally, our TalkingEyes, integrated with a speech-driven 3D facial motion
generator, can synthesize eye gaze motion, eye blinks, head motion and facial
motion collectively from speech. Extensive quantitative and qualitative
evaluations demonstrate the superiority of the proposed method in generating
diverse and natural 3D eye gaze motions from speech. The project page of this
paper is: https://lkjkjoiuiu.github.io/TalkingEyes_Home/
|
2501.09923
|
Study on a Fast Solver for Combined Field Integral Equations of 3D
Conducting Bodies Based on Graph Neural Networks
|
cs.LG cs.AI cs.NA math.NA
|
In this paper, we present a graph neural networks (GNNs)-based fast solver
(GraphSolver) for solving combined field integral equations (CFIEs) of 3D
conducting bodies. Rao-Wilton-Glisson (RWG) basis functions are employed to
discretely and accurately represent the geometry of 3D conducting bodies. A
concise and informative graph representation is then constructed by treating
each RWG function as a node in the graph, enabling the flow of current between
nodes. With the transformed graphs, GraphSolver is developed to directly
predict real and imaginary parts of the x, y and z components of the surface
current densities at each node (RWG function). Numerical results demonstrate
the efficacy of GraphSolver in solving CFIEs for 3D conducting bodies with
varying levels of geometric complexity, including basic 3D targets,
missile-shaped targets, and airplane-shaped targets.
|
2501.09926
|
ForestProtector: An IoT Architecture Integrating Machine Vision and Deep
Reinforcement Learning for Efficient Wildfire Monitoring
|
cs.AI cs.CV
|
Early detection of forest fires is crucial to minimizing the environmental
and socioeconomic damage they cause. Indeed, a fire's duration directly
correlates with the difficulty and cost of extinguishing it. For instance, a
fire burning for 1 minute might require 1 liter of water to extinguish, while a
2-minute fire could demand 100 liters, and a 10-minute fire might necessitate
1,000 liters. On the other hand, existing fire detection systems based on novel
technologies (e.g., remote sensing, PTZ cameras, UAVs) are often expensive and
require human intervention, making continuous monitoring of large areas
impractical. To address this challenge, this work proposes a low-cost forest
fire detection system that utilizes a central gateway device with computer
vision capabilities to monitor a 360{\deg} field of view for smoke at long
distances. A deep reinforcement learning agent enhances surveillance by
dynamically controlling the camera's orientation, leveraging real-time sensor
data (smoke levels, ambient temperature, and humidity) from distributed IoT
devices. This approach enables automated wildfire monitoring across expansive
areas while reducing false positives.
|
2501.09927
|
IE-Bench: Advancing the Measurement of Text-Driven Image Editing for
Human Perception Alignment
|
cs.CV cs.AI
|
Recent advances in text-driven image editing have been significant, yet the
task of accurately evaluating these edited images continues to pose a
considerable challenge. Different from the assessment of text-driven image
generation, text-driven image editing is characterized by simultaneously
conditioning on both text and a source image. The edited images often retain an
intrinsic connection to the original image, which dynamically change with the
semantics of the text. However, previous methods tend to solely focus on
text-image alignment or have not aligned with human perception. In this work,
we introduce the Text-driven Image Editing Benchmark suite (IE-Bench) to
enhance the assessment of text-driven edited images. IE-Bench includes a
database contains diverse source images, various editing prompts and the
corresponding results different editing methods, and total 3,010 Mean Opinion
Scores (MOS) provided by 25 human subjects. Furthermore, we introduce IE-QA, a
multi-modality source-aware quality assessment method for text-driven image
editing. To the best of our knowledge, IE-Bench offers the first IQA dataset
and model tailored for text-driven image editing. Extensive experiments
demonstrate IE-QA's superior subjective-alignments on the text-driven image
editing task compared with previous metrics. We will make all related data and
code available to the public.
|
2501.09928
|
Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective
Retrieval-Augmented LLMs
|
cs.CL cs.AI
|
Dialogue benchmarks are crucial in training and evaluating chatbots engaging
in domain-specific conversations. Knowledge graphs (KGs) represent semantically
rich and well-organized data spanning various domains, such as DBLP, DBpedia,
and YAGO. Traditionally, dialogue benchmarks have been manually created from
documents, neglecting the potential of KGs in automating this process. Some
question-answering benchmarks are automatically generated using extensive
preprocessing from KGs, but they do not support dialogue generation. This paper
introduces Chatty-Gen, a novel multi-stage retrieval-augmented generation
platform for automatically generating high-quality dialogue benchmarks tailored
to a specific domain using a KG. Chatty-Gen decomposes the generation process
into manageable stages and uses assertion rules for automatic validation
between stages. Our approach enables control over intermediate results to
prevent time-consuming restarts due to hallucinations. It also reduces reliance
on costly and more powerful commercial LLMs. Chatty-Gen eliminates upfront
processing of the entire KG using efficient query-based retrieval to find
representative subgraphs based on the dialogue context. Our experiments with
several real and large KGs demonstrate that Chatty-Gen significantly
outperforms state-of-the-art systems and ensures consistent model and system
performance across multiple LLMs of diverse capabilities, such as GPT-4o,
Gemini 1.5, Llama 3, and Mistral.
|
2501.09929
|
Steering Large Language Models with Feature Guided Activation Additions
|
cs.LG cs.AI cs.CL
|
Effective and reliable control over large language model (LLM) behavior is a
significant challenge. While activation steering methods, which add steering
vectors to a model's hidden states, are a promising approach, existing
techniques often lack precision and interpretability in how they influence
model outputs. We introduce Feature Guided Activation Additions (FGAA), a novel
activation steering method that leverages insights from Contrastive Activation
Addition (CAA) and Sparse Autoencoder-Targeted Steering (SAE-TS). By operating
in the latent space of a Sparse Autoencoder (SAE) and employing optimization
techniques to select desired SAE features, FGAA constructs precise steering
vectors that provide better steering effects while maintaining coherence of
steered model outputs. In this regard, evaluations on Gemma-2-2B and Gemma-2-9B
models across various steering tasks demonstrate that FGAA outperforms existing
steering methods of CAA, SAE decoder steering, and SAE-TS. Our results also
highlight important trade-offs between steering scale and general model
capabilities that are consistent across all tested steering methods.
|
2501.09933
|
Statistical Inference for Sequential Feature Selection after Domain
Adaptation
|
stat.ML cs.LG
|
In high-dimensional regression, feature selection methods, such as sequential
feature selection (SeqFS), are commonly used to identify relevant features.
When data is limited, domain adaptation (DA) becomes crucial for transferring
knowledge from a related source domain to a target domain, improving
generalization performance. Although SeqFS after DA is an important task in
machine learning, none of the existing methods can guarantee the reliability of
its results. In this paper, we propose a novel method for testing the features
selected by SeqFS-DA. The main advantage of the proposed method is its
capability to control the false positive rate (FPR) below a significance level
$\alpha$ (e.g., 0.05). Additionally, a strategic approach is introduced to
enhance the statistical power of the test. Furthermore, we provide extensions
of the proposed method to SeqFS with model selection criteria including AIC,
BIC, and adjusted R-squared. Extensive experiments are conducted on both
synthetic and real-world datasets to validate the theoretical results and
demonstrate the proposed method's superior performance.
|
2501.09934
|
HEART: Achieving Timely Multi-Model Training for
Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning
|
cs.LG cs.AI
|
The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient
machine learning (ML) solutions that can handle high vehicular mobility and
decentralized data. This has motivated the emergence of Hierarchical Federated
Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one
aspect which is underexplored in the literature on VEC-HFL is that vehicles
often need to execute multiple ML tasks simultaneously, where this multi-model
training environment introduces crucial challenges. First, improper aggregation
rules can lead to model obsolescence and prolonged training times. Second,
vehicular mobility may result in inefficient data utilization by preventing the
vehicles from returning their models to the network edge. Third, achieving a
balanced resource allocation across diverse tasks becomes of paramount
importance as it majorly affects the effectiveness of collaborative training.
We take one of the first steps towards addressing these challenges via
proposing a framework for multi-model training in dynamic VEC-HFL with the goal
of minimizing global training latency while ensuring balanced training across
various tasks-a problem that turns out to be NP-hard. To facilitate timely
model training, we introduce a hybrid synchronous-asynchronous aggregation
rule. Building on this, we present a novel method called Hybrid Evolutionary
And gReedy allocaTion (HEART). The framework operates in two stages: first, it
achieves balanced task scheduling through a hybrid heuristic approach that
combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms
(GA); second, it employs a low-complexity greedy algorithm to determine the
training priority of assigned tasks on vehicles. Experiments on real-world
datasets demonstrate the superiority of HEART over existing methods.
|
2501.09935
|
Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked
Diffusion
|
eess.IV cs.CV physics.med-ph
|
Diffusion model shows remarkable potential on sparse-view computed tomography
(SVCT) reconstruction. However, when a network is trained on a limited sample
space, its generalization capability may be constrained, which degrades
performance on unfamiliar data. For image generation tasks, this can lead to
issues such as blurry details and inconsistencies between regions. To alleviate
this problem, we propose a Sinogram-based Wavelet random decomposition And
Random mask diffusion Model (SWARM) for SVCT reconstruction. Specifically,
introducing a random mask strategy in the sinogram effectively expands the
limited training sample space. This enables the model to learn a broader range
of data distributions, enhancing its understanding and generalization of data
uncertainty. In addition, applying a random training strategy to the
high-frequency components of the sinogram wavelet enhances feature
representation and improves the ability to capture details in different
frequency bands, thereby improving performance and robustness. Two-stage
iterative reconstruction method is adopted to ensure the global consistency of
the reconstructed image while refining its details. Experimental results
demonstrate that SWARM outperforms competing approaches in both quantitative
and qualitative performance across various datasets.
|
2501.09937
|
Adaptive Twisting Sliding Control for Integrated Attack UAV's Autopilot
and Guidance
|
cs.RO
|
This paper investigates an adaptive sliding-mode control for an integrated
UAV autopilot and guidance system. First, a two-dimensional mathematical model
of the system is derived by considering the incorporated lateral dynamics and
relative kinematics of the UAV and its potential target of attack. Then, a
sliding surface is derived utilizing the zero-effort miss distance. An adaptive
twisting sliding mode (ATSMC) algorithm is applied to the integrated system.
Simulation and comparisons have been accomplished. The results show our
proposed design performs well in interception precision, even with high
nonlinearity, uncertainties, disturbances, and abrupt changes in the target's
movement, thanks to the adaptation strategy.
|
2501.09938
|
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for
Classification of Wheat Diseases
|
cs.CV cs.LG
|
Wheat is an important source of dietary fiber and protein that is negatively
impacted by a number of risks to its growth. The difficulty of identifying and
classifying wheat diseases is discussed with an emphasis on wheat loose smut,
leaf rust, and crown and root rot. Addressing conditions like crown and root
rot, this study introduces an innovative approach that integrates multi-scale
feature extraction with advanced image segmentation techniques to enhance
classification accuracy. The proposed method uses neural network models
Xception, Inception V3, and ResNet 50 to train on a large wheat disease
classification dataset 2020 in conjunction with an ensemble of machine vision
classifiers, including voting and stacking. The study shows that the suggested
methodology has a superior accuracy of 99.75% in the classification of wheat
diseases when compared to current state-of-the-art approaches. A deep learning
ensemble model Xception showed the highest accuracy.
|
2501.09940
|
Passage Segmentation of Documents for Extractive Question Answering
|
cs.CL cs.IR
|
Retrieval-Augmented Generation (RAG) has proven effective in open-domain
question answering. However, the chunking process, which is essential to this
pipeline, often receives insufficient attention relative to retrieval and
synthesis components. This study emphasizes the critical role of chunking in
improving the performance of both dense passage retrieval and the end-to-end
RAG pipeline. We then introduce the Logits-Guided Multi-Granular Chunker
(LGMGC), a novel framework that splits long documents into contextualized,
self-contained chunks of varied granularity. Our experimental results,
evaluated on two benchmark datasets, demonstrate that LGMGC not only improves
the retrieval step but also outperforms existing chunking methods when
integrated into a RAG pipeline.
|
2501.09943
|
Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech
Resources
|
cs.CL
|
Argentina has a large yet little-known Indigenous linguistic diversity,
encompassing at least 40 different languages. The majority of these languages
are at risk of disappearing, resulting in a significant loss of world heritage
and cultural knowledge. Currently, unified information on speakers and
computational tools is lacking for these languages. In this work, we present a
systematization of the Indigenous languages spoken in Argentina, classifying
them into seven language families: Mapuche, Tup\'i-Guaran\'i, Guaycur\'u,
Quechua, Mataco-Mataguaya, Aymara, and Chon. For each one, we present an
estimation of the national Indigenous population size, based on the most recent
Argentinian census. We discuss potential reasons why the census questionnaire
design may underestimate the actual number of speakers. We also provide a
concise survey of computational resources available for these languages,
whether or not they were specifically developed for Argentinian varieties.
|
2501.09944
|
Minimum-Time Sequential Traversal by a Team of Small Unmanned Aerial
Vehicles in an Unknown Environment with Winds
|
eess.SY cs.SY
|
We consider the problem of transporting multiple packages from an initial
location to a destination location in a windy urban environment using a team of
SUAVs. Each SUAV carries one package. We assume that the wind field is unknown,
but wind speed can be measured by SUAVs during flight. The SUAVs fly
sequentially one after the other, measure wind speeds along their trajectories,
and report the measurements to a central computer. The overall objective is to
minimize the total travel time of all SUAVs, which is in turn related to the
number of SUAV traversals through the environment. For a discretized
environment modeled by a graph, we describe a method to estimate wind speeds
and the time of traversal for each SUAV path. Each SUAV traverses a
minimum-time path planned based on the current wind field estimate. We study
cases of static and time-varying wind fields with and without measurement
noise. For each case, we demonstrate via numerical simulation that the proposed
method finds the optimal path after a minimal number of traversals.
|
2501.09946
|
Client-Centric Federated Adaptive Optimization
|
cs.LG cs.AI math.OC
|
Federated Learning (FL) is a distributed learning paradigm where clients
collaboratively train a model while keeping their own data private. With an
increasing scale of clients and models, FL encounters two key challenges,
client drift due to a high degree of statistical/system heterogeneity, and lack
of adaptivity. However, most existing FL research is based on unrealistic
assumptions that virtually ignore system heterogeneity. In this paper, we
propose Client-Centric Federated Adaptive Optimization, which is a class of
novel federated adaptive optimization approaches. We enable several features in
this framework such as arbitrary client participation, asynchronous server
aggregation, and heterogeneous local computing, which are ubiquitous in
real-world FL systems but are missed in most existing works. We provide a
rigorous convergence analysis of our proposed framework for general nonconvex
objectives, which is shown to converge with the best-known rate. Extensive
experiments show that our approaches consistently outperform the baseline by a
large margin across benchmarks.
|
2501.09947
|
Surface-SOS: Self-Supervised Object Segmentation via Neural Surface
Representation
|
cs.CV
|
Self-supervised Object Segmentation (SOS) aims to segment objects without any
annotations. Under conditions of multi-camera inputs, the structural, textural
and geometrical consistency among each view can be leveraged to achieve
fine-grained object segmentation. To make better use of the above information,
we propose Surface representation based Self-supervised Object Segmentation
(Surface-SOS), a new framework to segment objects for each view by 3D surface
representation from multi-view images of a scene. To model high-quality
geometry surfaces for complex scenes, we design a novel scene representation
scheme, which decomposes the scene into two complementary neural representation
modules respectively with a Signed Distance Function (SDF). Moreover,
Surface-SOS is able to refine single-view segmentation with multi-view
unlabeled images, by introducing coarse segmentation masks as additional input.
To the best of our knowledge, Surface-SOS is the first self-supervised approach
that leverages neural surface representation to break the dependence on large
amounts of annotated data and strong constraints. These constraints typically
involve observing target objects against a static background or relying on
temporal supervision in videos. Extensive experiments on standard benchmarks
including LLFF, CO3D, BlendedMVS, TUM and several real-world scenes show that
Surface-SOS always yields finer object masks than its NeRF-based counterparts
and surpasses supervised single-view baselines remarkably. Code is available
at: https://github.com/zhengxyun/Surface-SOS.
|
2501.09948
|
AI Explainability for Power Electronics: From a Lipschitz Continuity
Perspective
|
eess.SY cs.AI cs.SY
|
Lifecycle management of power converters continues to thrive with emerging
artificial intelligence (AI) solutions, yet AI mathematical explainability
remains unexplored in power electronics (PE) community. The lack of theoretical
rigor challenges adoption in mission-critical applications. Therefore, this
letter proposes a generic framework to evaluate mathematical explainability,
highlighting inference stability and training convergence from a Lipschitz
continuity perspective. Inference stability governs consistent outputs under
input perturbations, essential for robust real-time control and fault
diagnosis. Training convergence guarantees stable learning dynamics,
facilitating accurate modeling in PE contexts. Additionally, a Lipschitz-aware
learning rate selection strategy is introduced to accelerate convergence while
mitigating overshoots and oscillations. The feasibility of the proposed
Lipschitz-oriented framework is demonstrated by validating the mathematical
explainability of a state-of-the-art physics-in-architecture neural network,
and substantiated through empirical case studies on dual-active-bridge
converters. This letter serves as a clarion call for the PE community to
embrace mathematical explainability, heralding a transformative era of
trustworthy and explainable AI solutions that potentially redefine the future
of power electronics.
|
2501.09949
|
MultiPruner: Balanced Structure Removal in Foundation Models
|
cs.LG cs.AI
|
Recently, state-of-the-art approaches for pruning large pre-trained models
(LPMs) have demonstrated that the training-free removal of non-critical
residual blocks in Transformers is viable for reducing model size, achieving
results that outperform previous training-free pruning approaches. Motivated by
these findings, we extend BlockPruner (Zhong et al., 2024) and propose
MultiPruner, a pruning approach that surpasses recent training-free pruning
methods by adopting a multidimensional, iterative, fine-grained pruning
strategy. In MultiPruner, multidimensional pruning reinstates the structural
balance in block-pruned models by sequentially compressing along three
dimensions: i) residual blocks, ii) channels of multilayer perceptrons (MLP),
and iii) attention heads. This solution enhances zero-shot accuracy on
downstream tasks compared to other techniques while improving model compression
ratios, producing compressed models with fewer computing and memory
requirements. Extensive experiments demonstrate the advantages of the proposed
method across various large pre-trained models. The code and pruning
configurations are available at
https://github.com/IntelLabs/Hardware-Aware-Automated-Machine-Learning.
|
2501.09950
|
Sympathy over Polarization: A Computational Discourse Analysis of Social
Media Posts about the July 2024 Trump Assassination Attempt
|
cs.SI cs.CL
|
On July 13, 2024, at the Trump rally in Pennsylvania, someone attempted to
assassinate Republican Presidential Candidate Donald Trump. This attempt
sparked a large-scale discussion on social media. We collected posts from X
(formerly known as Twitter) one week before and after the assassination attempt
and aimed to model the short-term effects of such a ``shock'' on public
opinions and discussion topics. Specifically, our study addresses three key
questions: first, we investigate how public sentiment toward Donald Trump
shifts over time and across regions (RQ1) and examine whether the assassination
attempt itself significantly affects public attitudes, independent of the
existing political alignments (RQ2). Finally, we explore the major themes in
online conversations before and after the crisis, illustrating how discussion
topics evolved in response to this politically charged event (RQ3). By
integrating large language model-based sentiment analysis,
difference-in-differences modeling, and topic modeling techniques, we find that
following the attempt the public response was broadly sympathetic to Trump
rather than polarizing, despite baseline ideological and regional disparities.
|
2501.09954
|
AIRCHITECT v2: Learning the Hardware Accelerator Design Space through
Unified Representations
|
cs.LG cs.AI cs.AR
|
Design space exploration (DSE) plays a crucial role in enabling custom
hardware architectures, particularly for emerging applications like AI, where
optimized and specialized designs are essential. With the growing complexity of
deep neural networks (DNNs) and the introduction of advanced foundational
models (FMs), the design space for DNN accelerators is expanding at an
exponential rate. Additionally, this space is highly non-uniform and
non-convex, making it increasingly difficult to navigate and optimize.
Traditional DSE techniques rely on search-based methods, which involve
iterative sampling of the design space to find the optimal solution. However,
this process is both time-consuming and often fails to converge to the global
optima for such design spaces. Recently, AIrchitect v1, the first attempt to
address the limitations of search-based techniques, transformed DSE into a
constant-time classification problem using recommendation networks. In this
work, we propose AIrchitect v2, a more accurate and generalizable
learning-based DSE technique applicable to large-scale design spaces that
overcomes the shortcomings of earlier approaches. Specifically, we devise an
encoder-decoder transformer model that (a) encodes the complex design space
into a uniform intermediate representation using contrastive learning and (b)
leverages a novel unified representation blending the advantages of
classification and regression to effectively explore the large DSE space
without sacrificing accuracy. Experimental results evaluated on 10^5 real DNN
workloads demonstrate that, on average, AIrchitect v2 outperforms existing
techniques by 15% in identifying optimal design points. Furthermore, to
demonstrate the generalizability of our method, we evaluate performance on
unseen model workloads (LLMs) and attain a 1.7x improvement in inference
latency on the identified hardware architecture.
|
2501.09957
|
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation
based on Knowledge Graphs
|
cs.CL
|
To mitigate the hallucination and knowledge deficiency in large language
models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG)
has shown promising potential by utilizing KGs as external resource to enhance
LLMs reasoning. However, existing KG-RAG approaches struggle with a trade-off
between flexibility and retrieval quality. Modular methods prioritize
flexibility by avoiding the use of KG-fine-tuned models during retrieval,
leading to fixed retrieval strategies and suboptimal retrieval quality.
Conversely, coupled methods embed KG information within models to improve
retrieval quality, but at the expense of flexibility. In this paper, we propose
a novel flexible modular KG-RAG framework, termed FRAG, which synergizes the
advantages of both approaches. FRAG estimates the hop range of reasoning paths
based solely on the query and classify it as either simple or complex. To match
the complexity of the query, tailored pipelines are applied to ensure efficient
and accurate reasoning path retrieval, thus fostering the final reasoning
process. By using the query text instead of the KG to infer the structural
information of reasoning paths and employing adaptable retrieval strategies,
FRAG improves retrieval quality while maintaining flexibility. Moreover, FRAG
does not require extra LLMs fine-tuning or calls, significantly boosting
efficiency and conserving resources. Extensive experiments show that FRAG
achieves state-of-the-art performance with high efficiency and low resource
consumption.
|
2501.09958
|
Evaluation and Efficiency Comparison of Evolutionary Algorithms for
Service Placement Optimization in Fog Architectures
|
cs.NE cs.DC
|
This study compares three evolutionary algorithms for the problem of fog
service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting
genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based
on decomposition (MOEA/D). A model for the problem domain (fog architecture and
fog applications) and for the optimization (objective functions and solutions)
is presented. Our main concerns are related to optimize the network latency,
the service spread and the use of the resources. The algorithms are evaluated
with a random Barabasi-Albert network topology with 100 devices and with two
experiment sizes of 100 and 200 application services. The results showed that
NSGA-II obtained the highest optimizations of the objectives and the highest
diversity of the solution space. On the contrary, MOEA/D was better to reduce
the execution times. The WSGA algorithm did not show any benefit with regard to
the other two algorithms.
|
2501.09959
|
A Survey on Multi-Turn Interaction Capabilities of Large Language Models
|
cs.CL
|
Multi-turn interaction in the dialogue system research refers to a system's
ability to maintain context across multiple dialogue turns, enabling it to
generate coherent and contextually relevant responses. Recent advancements in
large language models (LLMs) have significantly expanded the scope of
multi-turn interaction, moving beyond chatbots to enable more dynamic agentic
interactions with users or environments. In this paper, we provide a focused
review of the multi-turn capabilities of LLMs, which are critical for a wide
range of downstream applications, including conversational search and
recommendation, consultation services, and interactive tutoring. This survey
explores four key aspects: (1) the core model capabilities that contribute to
effective multi-turn interaction, (2) how multi-turn interaction is evaluated
in current practice, (3) the general algorithms used to enhance multi-turn
interaction, and (4) potential future directions for research in this field.
|
2501.09960
|
Discrete Prior-based Temporal-coherent Content Prediction for Blind Face
Video Restoration
|
cs.CV
|
Blind face video restoration aims to restore high-fidelity details from
videos subjected to complex and unknown degradations. This task poses a
significant challenge of managing temporal heterogeneity while at the same time
maintaining stable face attributes. In this paper, we introduce a Discrete
Prior-based Temporal-Coherent content prediction transformer to address the
challenge, and our model is referred to as DP-TempCoh. Specifically, we
incorporate a spatial-temporal-aware content prediction module to synthesize
high-quality content from discrete visual priors, conditioned on degraded video
tokens. To further enhance the temporal coherence of the predicted content, a
motion statistics modulation module is designed to adjust the content, based on
discrete motion priors in terms of cross-frame mean and variance. As a result,
the statistics of the predicted content can match with that of real videos over
time. By performing extensive experiments, we verify the effectiveness of the
design elements and demonstrate the superior performance of our DP-TempCoh in
both synthetically and naturally degraded video restoration.
|
2501.09961
|
A High-Resolution Analysis of Receiver Quantization in Communication
|
cs.IT math.IT
|
We investigate performance limits and design of communication in the presence
of uniform output quantization with moderate to high resolution. Under
independent and identically distributed (i.i.d.) complex Gaussian codebook and
nearest neighbor decoding rule, an achievable rate is derived in an analytical
form by the generalized mutual information (GMI). The gain control before
quantization is shown to be increasingly important as the resolution decreases,
due to the fact that the loading factor (normalized one-sided quantization
range) has increasing impact on performance. The impact of imperfect gain
control in the high-resolution regime is characterized by two asymptotic
results: 1) the rate loss due to overload distortion decays exponentially as
the loading factor increases, and 2) the rate loss due to granular distortion
decays quadratically as the step size vanishes. For a $2K$-level uniform
quantizer, we prove that the optimal loading factor that maximizes the
achievable rate scales like $2\sqrt{\ln 2K}$ as the resolution increases. An
asymptotically tight estimate of the optimal loading factor is further given,
which is also highly accurate for finite resolutions.
|
2501.09967
|
Explainable artificial intelligence (XAI): from inherent explainability
to large language models
|
cs.LG cs.AI cs.CV
|
Artificial Intelligence (AI) has continued to achieve tremendous success in
recent times. However, the decision logic of these frameworks is often not
transparent, making it difficult for stakeholders to understand, interpret or
explain their behavior. This limitation hinders trust in machine learning
systems and causes a general reluctance towards their adoption in practical
applications, particularly in mission-critical domains like healthcare and
autonomous driving. Explainable AI (XAI) techniques facilitate the
explainability or interpretability of machine learning models, enabling users
to discern the basis of the decision and possibly avert undesirable behavior.
This comprehensive survey details the advancements of explainable AI methods,
from inherently interpretable models to modern approaches for achieving
interpretability of various black box models, including large language models
(LLMs). Additionally, we review explainable AI techniques that leverage LLM and
vision-language model (VLM) frameworks to automate or improve the
explainability of other machine learning models. The use of LLM and VLM as
interpretability methods particularly enables high-level, semantically
meaningful explanations of model decisions and behavior. Throughout the paper,
we highlight the scientific principles, strengths and weaknesses of
state-of-the-art methods and outline different areas of improvement. Where
appropriate, we also present qualitative and quantitative comparison results of
various methods to show how they compare. Finally, we discuss the key
challenges of XAI and directions for future research.
|
2501.09972
|
GVMGen: A General Video-to-Music Generation Model with Hierarchical
Attentions
|
cs.SD cs.AI cs.MM eess.AS
|
Composing music for video is essential yet challenging, leading to a growing
interest in automating music generation for video applications. Existing
approaches often struggle to achieve robust music-video correspondence and
generative diversity, primarily due to inadequate feature alignment methods and
insufficient datasets. In this study, we present General Video-to-Music
Generation model (GVMGen), designed for generating high-related music to the
video input. Our model employs hierarchical attentions to extract and align
video features with music in both spatial and temporal dimensions, ensuring the
preservation of pertinent features while minimizing redundancy. Remarkably, our
method is versatile, capable of generating multi-style music from different
video inputs, even in zero-shot scenarios. We also propose an evaluation model
along with two novel objective metrics for assessing video-music alignment.
Additionally, we have compiled a large-scale dataset comprising diverse types
of video-music pairs. Experimental results demonstrate that GVMGen surpasses
previous models in terms of music-video correspondence, generative diversity,
and application universality.
|
2501.09976
|
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
|
cs.NE
|
Backpropagation is the foundational algorithm for training neural networks
and a key driver of deep learning's success. However, its biological
plausibility has been challenged due to three primary limitations: weight
symmetry, reliance on global error signals, and the dual-phase nature of
training, as highlighted by the existing literature. Although various
alternative learning approaches have been proposed to address these issues,
most either fail to satisfy all three criteria simultaneously or yield
suboptimal results. Inspired by the dynamics and plasticity of pyramidal
neurons, we propose Dendritic Localized Learning (DLL), a novel learning
algorithm designed to overcome these challenges. Extensive empirical
experiments demonstrate that DLL satisfies all three criteria of biological
plausibility while achieving state-of-the-art performance among algorithms that
meet these requirements. Furthermore, DLL exhibits strong generalization across
a range of architectures, including MLPs, CNNs, and RNNs. These results,
benchmarked against existing biologically plausible learning algorithms, offer
valuable empirical insights for future research. We hope this study can inspire
the development of new biologically plausible algorithms for training
multilayer networks and advancing progress in both neuroscience and machine
learning.
|
2501.09978
|
GaussianAvatar-Editor: Photorealistic Animatable Gaussian Head Avatar
Editor
|
cs.CV
|
We introduce GaussianAvatar-Editor, an innovative framework for text-driven
editing of animatable Gaussian head avatars that can be fully controlled in
expression, pose, and viewpoint. Unlike static 3D Gaussian editing, editing
animatable 4D Gaussian avatars presents challenges related to motion occlusion
and spatial-temporal inconsistency. To address these issues, we propose the
Weighted Alpha Blending Equation (WABE). This function enhances the blending
weight of visible Gaussians while suppressing the influence on non-visible
Gaussians, effectively handling motion occlusion during editing. Furthermore,
to improve editing quality and ensure 4D consistency, we incorporate
conditional adversarial learning into the editing process. This strategy helps
to refine the edited results and maintain consistency throughout the animation.
By integrating these methods, our GaussianAvatar-Editor achieves photorealistic
and consistent results in animatable 4D Gaussian editing. We conduct
comprehensive experiments across various subjects to validate the effectiveness
of our proposed techniques, which demonstrates the superiority of our approach
over existing methods. More results and code are available at: [Project
Link](https://xiangyueliu.github.io/GaussianAvatar-Editor/).
|
2501.09980
|
Aneumo: A Large-Scale Comprehensive Synthetic Dataset of Aneurysm
Hemodynamics
|
cs.CV cs.AI cs.LG
|
Intracranial aneurysm (IA) is a common cerebrovascular disease that is
usually asymptomatic but may cause severe subarachnoid hemorrhage (SAH) if
ruptured. Although clinical practice is usually based on individual factors and
morphological features of the aneurysm, its pathophysiology and hemodynamic
mechanisms remain controversial. To address the limitations of current
research, this study constructed a comprehensive hemodynamic dataset of
intracranial aneurysms. The dataset is based on 466 real aneurysm models, and
10,000 synthetic models were generated by resection and deformation operations,
including 466 aneurysm-free models and 9,534 deformed aneurysm models. The
dataset also provides medical image-like segmentation mask files to support
insightful analysis. In addition, the dataset contains hemodynamic data
measured at eight steady-state flow rates (0.001 to 0.004 kg/s), including
critical parameters such as flow velocity, pressure, and wall shear stress,
providing a valuable resource for investigating aneurysm pathogenesis and
clinical prediction. This dataset will help advance the understanding of the
pathologic features and hemodynamic mechanisms of intracranial aneurysms and
support in-depth research in related fields. Dataset hosted at
https://github.com/Xigui-Li/Aneumo.
|
2501.09982
|
RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding
Interpolation
|
cs.CV cs.AI cs.CL cs.LG
|
Text-to-video generation models have made impressive progress, but they still
struggle with generating videos with complex features. This limitation often
arises from the inability of the text encoder to produce accurate embeddings,
which hinders the video generation model. In this work, we propose a novel
approach to overcome this challenge by selecting the optimal text embedding
through interpolation in the embedding space. We demonstrate that this method
enables the video generation model to produce the desired videos. Additionally,
we introduce a simple algorithm using perpendicular foot embeddings and cosine
similarity to identify the optimal interpolation embedding. Our findings
highlight the importance of accurate text embeddings and offer a pathway for
improving text-to-video generation performance.
|
2501.09993
|
Agent-as-Judge for Factual Summarization of Long Narratives
|
cs.CL
|
Large Language Models (LLMs) have demonstrated near-human performance in
summarization tasks based on traditional metrics such as ROUGE and BERTScore.
However, these metrics do not adequately capture critical aspects of
summarization quality, such as factual accuracy, particularly for long
narratives (>100K tokens). Recent advances, such as LLM-as-a-Judge, address the
limitations of metrics based on lexical similarity but still exhibit factual
inconsistencies, especially in understanding character relationships and
states. In this work, we introduce NarrativeFactScore, a novel
"Agent-as-a-Judge" framework for evaluating and refining summaries. By
leveraging a Character Knowledge Graph (CKG) extracted from input and generated
summaries, NarrativeFactScore assesses the factual consistency and provides
actionable guidance for refinement, such as identifying missing or erroneous
facts. We demonstrate the effectiveness of NarrativeFactScore through a
detailed workflow illustration and extensive validation on widely adopted
benchmarks, achieving superior performance compared to competitive methods. Our
results highlight the potential of agent-driven evaluation systems to improve
the factual reliability of LLM-generated summaries.
|
2501.09994
|
Multi-Modal Attention Networks for Enhanced Segmentation and Depth
Estimation of Subsurface Defects in Pulse Thermography
|
cs.CV cs.AI eess.IV
|
AI-driven pulse thermography (PT) has become a crucial tool in
non-destructive testing (NDT), enabling automatic detection of hidden anomalies
in various industrial components. Current state-of-the-art techniques feed
segmentation and depth estimation networks compressed PT sequences using either
Principal Component Analysis (PCA) or Thermographic Signal Reconstruction
(TSR). However, treating these two modalities independently constrains the
performance of PT inspection models as these representations possess
complementary semantic features. To address this limitation, this work proposes
PT-Fusion, a multi-modal attention-based fusion network that fuses both PCA and
TSR modalities for defect segmentation and depth estimation of subsurface
defects in PT setups. PT-Fusion introduces novel feature fusion modules,
Encoder Attention Fusion Gate (EAFG) and Attention Enhanced Decoding Block
(AEDB), to fuse PCA and TSR features for enhanced segmentation and depth
estimation of subsurface defects. In addition, a novel data augmentation
technique is proposed based on random data sampling from thermographic
sequences to alleviate the scarcity of PT datasets. The proposed method is
benchmarked against state-of-the-art PT inspection models, including U-Net,
attention U-Net, and 3D-CNN on the Universit\'e Laval IRT-PVC dataset. The
results demonstrate that PT-Fusion outperforms the aforementioned models in
defect segmentation and depth estimation accuracies with a margin of 10%.
|
2501.09996
|
Fast energy-aware OLSR routing in VANETs by means of a parallel
evolutionary algorithm
|
cs.NE cs.AI cs.NI
|
This work tackles the problem of reducing the power consumption of the OLSR
routing protocol in vehicular networks. Nowadays, energy-aware and green
communication protocols are important research topics, specially when deploying
wireless mobile networks. This article introduces a fast automatic methodology
to search for energy-efficient OLSR configurations by using a parallel
evolutionary algorithm. The experimental analysis demonstrates that significant
improvements over the standard configuration can be attained in terms of power
consumption, with no noteworthy loss in the QoS.
|
2501.09997
|
Attention-guided Self-reflection for Zero-shot Hallucination Detection
in Large Language Models
|
cs.CL cs.AI
|
Hallucination has emerged as a significant barrier to the effective
application of Large Language Models (LLMs). In this work, we introduce a novel
Attention-Guided SElf-Reflection (AGSER) approach for zero-shot hallucination
detection in LLMs. The AGSER method utilizes attention contributions to
categorize the input query into attentive and non-attentive queries. Each query
is then processed separately through the LLMs, allowing us to compute
consistency scores between the generated responses and the original answer. The
difference between the two consistency scores serves as a hallucination
estimator. In addition to its efficacy in detecting hallucinations, AGSER
notably reduces computational overhead, requiring only three passes through the
LLM and utilizing two sets of tokens. We have conducted extensive experiments
with four widely-used LLMs across three different hallucination benchmarks,
demonstrating that our approach significantly outperforms existing methods in
zero-shot hallucination detection.
|
2501.09999
|
Deep Learning for Early Alzheimer Disease Detection with MRI Scans
|
cs.CV cs.AI eess.IV
|
Alzheimer's Disease is a neurodegenerative condition characterized by
dementia and impairment in neurological function. The study primarily focuses
on the individuals above age 40, affecting their memory, behavior, and
cognitive processes of the brain. Alzheimer's disease requires diagnosis by a
detailed assessment of MRI scans and neuropsychological tests of the patients.
This project compares existing deep learning models in the pursuit of enhancing
the accuracy and efficiency of AD diagnosis, specifically focusing on the
Convolutional Neural Network, Bayesian Convolutional Neural Network, and the
U-net model with the Open Access Series of Imaging Studies brain MRI dataset.
Besides, to ensure robustness and reliability in the model evaluations, we
address the challenge of imbalance in data. We then perform rigorous evaluation
to determine strengths and weaknesses for each model by considering
sensitivity, specificity, and computational efficiency. This comparative
analysis would shed light on the future role of AI in revolutionizing AD
diagnostics but also paved ways for future innovation in medical imaging and
the management of neurodegenerative diseases.
|
2501.10007
|
A swarm algorithm for collaborative traffic in vehicular networks
|
cs.NE cs.NI
|
Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning
messages with each other. These specific kinds of networks help reduce
hazardous traffic situations and improve safety, which are two of the main
objectives in developing Intelligent Transportation Systems (ITS). For this,
the performance of VANETs should guarantee the delivery of messages in a
required time. An obstacle to this is that the data traffic generated may cause
network congestion. Data congestion control is used to enhance network
capabilities, increasing the reliability of the VANET by decreasing packet
losses and communication delays. In this study, we propose a swarm intelligence
based distributed congestion control strategy to maintain the channel usage
level under the threshold of network malfunction, while keeping the
quality-of-service of the VANET high. An exhaustive experimentation shows that
the proposed strategy improves the throughput of the network, the channel
usage, and the stability of the communications in comparison with other
competing congestion control strategies.
|
2501.10010
|
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph
Learning
|
cs.LG cs.AI
|
Dynamic graph augmentation is used to improve the performance of dynamic
GNNs. Most methods assume temporal locality, meaning that recent edges are more
influential than earlier edges. However, for temporal changes in edges caused
by random noise, overemphasizing recent edges while neglecting earlier ones may
lead to the model capturing noise. To address this issue, we propose STAA
(SpatioTemporal Activity-Aware Random Walk Diffusion). STAA identifies nodes
likely to have noisy edges in spatiotemporal dimensions. Spatially, it analyzes
critical topological positions through graph wavelet coefficients. Temporally,
it analyzes edge evolution through graph wavelet coefficient change rates.
Then, random walks are used to reduce the weights of noisy edges, deriving a
diffusion matrix containing spatiotemporal information as an augmented
adjacency matrix for dynamic GNN learning. Experiments on multiple datasets
show that STAA outperforms other dynamic graph augmentation methods in node
classification and link prediction tasks.
|
2501.10011
|
Mitigating Hallucinations on Object Attributes using Multiview Images
and Negative Instructions
|
cs.CV cs.AI
|
Current popular Large Vision-Language Models (LVLMs) are suffering from
Hallucinations on Object Attributes (HoOA), leading to incorrect determination
of fine-grained attributes in the input images. Leveraging significant
advancements in 3D generation from a single image, this paper proposes a novel
method to mitigate HoOA in LVLMs. This method utilizes multiview images sampled
from generated 3D representations as visual prompts for LVLMs, thereby
providing more visual information from other viewpoints. Furthermore, we
observe the input order of multiple multiview images significantly affects the
performance of LVLMs. Consequently, we have devised Multiview Image Augmented
VLM (MIAVLM), incorporating a Multiview Attributes Perceiver (MAP) submodule
capable of simultaneously eliminating the influence of input image order and
aligning visual information from multiview images with Large Language Models
(LLMs). Besides, we designed and employed negative instructions to mitigate
LVLMs' bias towards ``Yes" responses. Comprehensive experiments demonstrate the
effectiveness of our method.
|
2501.10016
|
Infrastructure Deployment in Vehicular Communication Networks Using a
Parallel Multiobjective Evolutionary Algorithm
|
cs.NE cs.NI
|
This article describes the application of a multiobjective evolutionary
algorithm for locating roadside infrastructure for vehicular communication
networks over realistic urban areas. A multiobjective formulation of the
problem is introduced, considering quality-of-service and cost objectives. The
experimental analysis is performed over a real map of M\'alaga, using real
traffic information and antennas, and scenarios that model different
combinations of traffic patterns and applications (text/audio/video) in the
communications. The proposed multiobjective evolutionary algorithm computes
accurate trade-off solutions, significantly improving over state-of-the-art
algorithms previously applied to the problem.
|
2501.10017
|
Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by
Hybrid VAE-Diffusion-Based Generative Neural Networks
|
cs.AI cs.DB
|
Crash frequency modelling analyzes the impact of factors like traffic volume,
road geometry, and environmental conditions on crash occurrences. Inaccurate
predictions can distort our understanding of these factors, leading to
misguided policies and wasted resources, which jeopardize traffic safety. A key
challenge in crash frequency modelling is the prevalence of excessive zero
observations, caused by underreporting, the low probability of crashes, and
high data collection costs. These zero observations often reduce model accuracy
and introduce bias, complicating safety decision making. While existing
approaches, such as statistical methods, data aggregation, and resampling,
attempt to address this issue, they either rely on restrictive assumptions or
result in significant information loss, distorting crash data. To overcome
these limitations, we propose a hybrid VAE-Diffusion neural network, designed
to reduce zero observations and handle the complexities of multi-type tabular
crash data (count, ordinal, nominal, and real-valued variables). We assess the
synthetic data quality generated by this model through metrics like similarity,
accuracy, diversity, and structural consistency, and compare its predictive
performance against traditional statistical models. Our findings demonstrate
that the hybrid VAE-Diffusion model outperforms baseline models across all
metrics, offering a more effective approach to augmenting crash data and
improving the accuracy of crash frequency predictions. This study highlights
the potential of synthetic data to enhance traffic safety by improving crash
frequency modelling and informing better policy decisions.
|
2501.10018
|
DiffuEraser: A Diffusion Model for Video Inpainting
|
cs.CV
|
Recent video inpainting algorithms integrate flow-based pixel propagation
with transformer-based generation to leverage optical flow for restoring
textures and objects using information from neighboring frames, while
completing masked regions through visual Transformers. However, these
approaches often encounter blurring and temporal inconsistencies when dealing
with large masks, highlighting the need for models with enhanced generative
capabilities. Recently, diffusion models have emerged as a prominent technique
in image and video generation due to their impressive performance. In this
paper, we introduce DiffuEraser, a video inpainting model based on stable
diffusion, designed to fill masked regions with greater details and more
coherent structures. We incorporate prior information to provide initialization
and weak conditioning,which helps mitigate noisy artifacts and suppress
hallucinations. Additionally, to improve temporal consistency during
long-sequence inference, we expand the temporal receptive fields of both the
prior model and DiffuEraser, and further enhance consistency by leveraging the
temporal smoothing property of Video Diffusion Models. Experimental results
demonstrate that our proposed method outperforms state-of-the-art techniques in
both content completeness and temporal consistency while maintaining acceptable
efficiency.
|
2501.10020
|
Textoon: Generating Vivid 2D Cartoon Characters from Text Descriptions
|
cs.CV
|
The 2D cartoon style is a prominent art form in digital character creation,
particularly popular among younger audiences. While advancements in digital
human technology have spurred extensive research into photorealistic digital
humans and 3D characters, interactive 2D cartoon characters have received
comparatively less attention. Unlike 3D counterparts, which require
sophisticated construction and resource-intensive rendering, Live2D, a
widely-used format for 2D cartoon characters, offers a more efficient
alternative, which allows to animate 2D characters in a manner that simulates
3D movement without the necessity of building a complete 3D model. Furthermore,
Live2D employs lightweight HTML5 (H5) rendering, improving both accessibility
and efficiency. In this technical report, we introduce Textoon, an innovative
method for generating diverse 2D cartoon characters in the Live2D format based
on text descriptions. The Textoon leverages cutting-edge language and vision
models to comprehend textual intentions and generate 2D appearance, capable of
creating a wide variety of stunning and interactive 2D characters within one
minute. The project homepage is https://human3daigc.github.io/Textoon_webpage/.
|
2501.10021
|
X-Dyna: Expressive Dynamic Human Image Animation
|
cs.CV
|
We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for
animating a single human image using facial expressions and body movements
derived from a driving video, that generates realistic, context-aware dynamics
for both the subject and the surrounding environment. Building on prior
approaches centered on human pose control, X-Dyna addresses key shortcomings
causing the loss of dynamic details, enhancing the lifelike qualities of human
video animations. At the core of our approach is the Dynamics-Adapter, a
lightweight module that effectively integrates reference appearance context
into the spatial attentions of the diffusion backbone while preserving the
capacity of motion modules in synthesizing fluid and intricate dynamic details.
Beyond body pose control, we connect a local control module with our model to
capture identity-disentangled facial expressions, facilitating accurate
expression transfer for enhanced realism in animated scenes. Together, these
components form a unified framework capable of learning physical human motion
and natural scene dynamics from a diverse blend of human and scene videos.
Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna
outperforms state-of-the-art methods, creating highly lifelike and expressive
animations. The code is available at https://github.com/bytedance/X-Dyna.
|
2501.10024
|
Automatic Speech Recognition for Sanskrit with Transfer Learning
|
cs.CL cs.AI
|
Sanskrit, one of humanity's most ancient languages, has a vast collection of
books and manuscripts on diverse topics that have been accumulated over
millennia. However, its digital content (audio and text), which is vital for
the training of AI systems, is profoundly limited. Furthermore, its intricate
linguistics make it hard to develop robust NLP tools for wider accessibility.
Given these constraints, we have developed an automatic speech recognition
model for Sanskrit by employing transfer learning mechanism on OpenAI's Whisper
model. After carefully optimising the hyper-parameters, we obtained promising
results with our transfer-learned model achieving a word error rate of 15.42%
on Vaksancayah dataset. An online demo of our model is made available for the
use of public and to evaluate its performance firsthand thereby paving the way
for improved accessibility and technological support for Sanskrit learning in
the modern era.
|
2501.10030
|
Informativity Conditions for Multiple Signals: Properties, Experimental
Design, and Applications
|
eess.SY cs.IT cs.SY math.IT
|
Recent studies highlight the importance of persistently exciting condition in
single signal sequence for model identification and data-driven control
methodologies. However, maintaining prolonged excitation in control signals
introduces significant challenges, as continuous excitation can reduce the
lifetime of mechanical devices. In this paper, we introduce three informativity
conditions for various types of multi-signal data, each augmented by weight
factors. We explore the interrelations between these conditions and their rank
properties in linear time-invariant systems. Furthermore, we introduce
open-loop experimental design methods tailored to each of the three conditions,
which can synthesize the required excitation conditions either offline or
online, even in the presence of limited information within each signal segment.
We demonstrate the effectiveness of these informativity conditions in
least-squares identification. Additionally, all three conditions can extend
Willems' fundamental lemma and are utilized to assess the properties of the
system. Illustrative examples confirm that these conditions yield satisfactory
outcomes in both least-squares identification and the construction of
data-driven controllers.
|
2501.10040
|
LWGANet: A Lightweight Group Attention Backbone for Remote Sensing
Visual Tasks
|
cs.CV
|
Remote sensing (RS) visual tasks have gained significant academic and
practical importance. However, they encounter numerous challenges that hinder
effective feature extraction, including the detection and recognition of
multiple objects exhibiting substantial variations in scale within a single
image. While prior dual-branch or multi-branch architectural strategies have
been effective in managing these object variances, they have concurrently
resulted in considerable increases in computational demands and parameter
counts. Consequently, these architectures are rendered less viable for
deployment on resource-constrained devices. Contemporary lightweight backbone
networks, designed primarily for natural images, frequently encounter
difficulties in effectively extracting features from multi-scale objects, which
compromises their efficacy in RS visual tasks. This article introduces LWGANet,
a specialized lightweight backbone network tailored for RS visual tasks,
incorporating a novel lightweight group attention (LWGA) module designed to
address these specific challenges. LWGA module, tailored for RS imagery,
adeptly harnesses redundant features to extract a wide range of spatial
information, from local to global scales, without introducing additional
complexity or computational overhead. This facilitates precise feature
extraction across multiple scales within an efficient framework.LWGANet was
rigorously evaluated across twelve datasets, which span four crucial RS visual
tasks: scene classification, oriented object detection, semantic segmentation,
and change detection. The results confirm LWGANet's widespread applicability
and its ability to maintain an optimal balance between high performance and low
complexity, achieving SOTA results across diverse datasets. LWGANet emerged as
a novel solution for resource-limited scenarios requiring robust RS image
processing capabilities.
|
2501.10041
|
Spatiotemporal Prediction of Secondary Crashes by Rebalancing Dynamic
and Static Data with Generative Adversarial Networks
|
cs.AI
|
Data imbalance is a common issue in analyzing and predicting sudden traffic
events. Secondary crashes constitute only a small proportion of all crashes.
These secondary crashes, triggered by primary crashes, significantly exacerbate
traffic congestion and increase the severity of incidents. However, the severe
imbalance of secondary crash data poses significant challenges for prediction
models, affecting their generalization ability and prediction accuracy.
Existing methods fail to fully address the complexity of traffic crash data,
particularly the coexistence of dynamic and static features, and often struggle
to effectively handle data samples of varying lengths. Furthermore, most
current studies predict the occurrence probability and spatiotemporal
distribution of secondary crashes separately, lacking an integrated solution.
To address these challenges, this study proposes a hybrid model named
VarFusiGAN-Transformer, aimed at improving the fidelity of secondary crash data
generation and jointly predicting the occurrence and spatiotemporal
distribution of secondary crashes. The VarFusiGAN-Transformer model employs
Long Short-Term Memory (LSTM) networks to enhance the generation of
multivariate long-time series data, incorporating a static data generator and
an auxiliary discriminator to model the joint distribution of dynamic and
static features. In addition, the model's prediction module achieves
simultaneous prediction of both the occurrence and spatiotemporal distribution
of secondary crashes. Compared to existing methods, the proposed model
demonstrates superior performance in generating high-fidelity data and
improving prediction accuracy.
|
2501.10048
|
Virtual Nodes Improve Long-term Traffic Prediction
|
cs.LG cs.AI
|
Effective traffic prediction is a cornerstone of intelligent transportation
systems, enabling precise forecasts of traffic flow, speed, and congestion.
While traditional spatio-temporal graph neural networks (ST-GNNs) have achieved
notable success in short-term traffic forecasting, their performance in
long-term predictions remains limited. This challenge arises from
over-squashing problem, where bottlenecks and limited receptive fields restrict
information flow and hinder the modeling of global dependencies. To address
these challenges, this study introduces a novel framework that incorporates
virtual nodes, which are additional nodes added to the graph and connected to
existing nodes, in order to aggregate information across the entire graph
within a single GNN layer. Our proposed model incorporates virtual nodes by
constructing a semi-adaptive adjacency matrix. This matrix integrates
distance-based and adaptive adjacency matrices, allowing the model to leverage
geographical information while also learning task-specific features from data.
Experimental results demonstrate that the inclusion of virtual nodes
significantly enhances long-term prediction accuracy while also improving
layer-wise sensitivity to mitigate the over-squashing problem. Virtual nodes
also offer enhanced explainability by focusing on key intersections and
high-traffic areas, as shown by the visualization of their adjacency matrix
weights on road network heat maps. Our advanced approach enhances the
understanding and management of urban traffic systems, making it particularly
well-suited for real-world applications.
|
2501.10049
|
PandaSkill - Player Performance and Skill Rating in Esports: Application
to League of Legends
|
cs.LG
|
To take the esports scene to the next level, we introduce PandaSkill, a
framework for assessing player performance and skill rating. Traditional rating
systems like Elo and TrueSkill often overlook individual contributions and face
challenges in professional esports due to limited game data and fragmented
competitive scenes. PandaSkill leverages machine learning to estimate in-game
player performance from individual player statistics. Each in-game role is
modeled independently, ensuring a fair comparison between them. Then, using
these performance scores, PandaSkill updates the player skill ratings using the
Bayesian framework OpenSkill in a free-for-all setting. In this setting, skill
ratings are updated solely based on performance scores rather than game
outcomes, hightlighting individual contributions. To address the challenge of
isolated rating pools that hinder cross-regional comparisons, PandaSkill
introduces a dual-rating system that combines players' regional ratings with a
meta-rating representing each region's overall skill level. Applying PandaSkill
to five years of professional League of Legends matches worldwide, we show that
our method produces skill ratings that better predict game outcomes and align
more closely with expert opinions compared to existing methods.
|
2501.10050
|
Tracking student skills real-time through a continuous-variable dynamic
Bayesian network
|
stat.ML cs.LG
|
The field of Knowledge Tracing is focused on predicting the success rate of a
student for a given skill. Modern methods like Deep Knowledge Tracing provide
accurate estimates given enough data, but being based on neural networks they
struggle to explain how these estimates are formed. More classical methods like
Dynamic Bayesian Networks can do this, but they cannot give data on the
accuracy of their estimates and often struggle to incorporate new observations
in real-time due to their high computational load.
This paper presents a novel method, Performance Distribution Tracing (PDT),
in which the distribution of the success rate is traced live. It uses a Dynamic
Bayesian Network with continuous random variables as nodes. By tracing the
success rate distribution, there is always data available on the accuracy of
any success rate estimation. In addition, it makes it possible to combine data
from similar/related skills to come up with a more informed estimate of success
rates. This makes it possible to predict exercise success rates, providing both
explainability and an accuracy indication, even when an exercise requires a
combination of different skills to solve. And through the use of the beta
distribution functions as conjugate priors, all distributions are available in
analytical form, allowing efficient online updates upon new observations.
Experiments have shown that the resulting estimates generally feel sufficiently
accurate to end-users such that they accept recommendations based on them.
|
2501.10053
|
AirRAG: Activating Intrinsic Reasoning for Retrieval Augmented
Generation using Tree-based Search
|
cs.AI
|
Leveraging the autonomous decision-making capabilities of large language
models (LLMs) has demonstrated superior performance in reasoning tasks.
However, despite the success of iterative or recursive retrieval-augmented
generation (RAG) techniques, these methods are often constrained to a single
solution space when confronted with complex problems. In this paper, we propose
a novel thinking pattern in RAG that integrates system analysis with efficient
reasoning actions, significantly activating intrinsic reasoning capabilities
and expanding the solution space of specific tasks via Monte Carlo Tree Search
(MCTS), which we refer to as AirRAG. Specifically, our approach designs five
fundamental reasoning actions, which are expanded to a broad tree-based
reasoning space using MCTS. The approach also incorporates self-consistency
verification to explore potential reasoning paths and inference scaling law.
Additionally, computationally optimal strategies are employed to allocate more
inference resources to key actions, thereby enhancing overall performance.
Experimental results demonstrate the effectiveness of AirRAG, showing
significant performance gains on complex question-answering datasets.
Furthermore, AirRAG is flexible and lightweight, making it easy to integrate
with other advanced technologies.
|
2501.10054
|
Accelerating Large Language Models through Partially Linear Feed-Forward
Network
|
cs.LG cs.AI
|
Large language models (LLMs) demonstrate remarkable capabilities but face
deployment challenges due to their massive parameter counts. While existing
compression techniques like pruning can reduce model size, it leads to
significant accuracy degradation under high compression ratios. We present a
novel perspective inspired by constant folding in compiler optimization. Our
approach enables parameter reduction by treating activation functions in LLMs
as linear functions.
However, recent LLMs use complex non-linear activations like GELU that
prevent direct application of this technique. We propose TARDIS, which enables
optimization of LLMs with non-linear activations by partially approximating
them with linear functions in frequently occurring input ranges. For outlier
inputs, TARDIS employs an online predictor to dynamically fall back to original
computations.
Our experiments demonstrate that TARDIS achieves 80% parameter reduction in
feed-forward networks, while significantly outperforming state-of-the-art
pruning methods Wanda and RIA with up to 65% higher accuracy. In practical
deployments for a 7B model, TARDIS achieves 1.6x end-to-end inference speedup
when integrated with the vLLM serving system, and 1.4x speedup with the widely
adopted HuggingFace implementation, while incurring only a 10.9% accuracy
trade-off.
|
2501.10057
|
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
|
cs.CL
|
Vision-language models (VLMs), which process image and text inputs, are
increasingly integrated into chat assistants and other consumer AI
applications. Without proper safeguards, however, VLMs may give harmful advice
(e.g. how to self-harm) or encourage unsafe behaviours (e.g. to consume drugs).
Despite these clear hazards, little work so far has evaluated VLM safety and
the novel risks created by multimodal inputs. To address this gap, we introduce
MSTS, a Multimodal Safety Test Suite for VLMs. MSTS comprises 400 test prompts
across 40 fine-grained hazard categories. Each test prompt consists of a text
and an image that only in combination reveal their full unsafe meaning. With
MSTS, we find clear safety issues in several open VLMs. We also find some VLMs
to be safe by accident, meaning that they are safe because they fail to
understand even simple test prompts. We translate MSTS into ten languages,
showing non-English prompts to increase the rate of unsafe model responses. We
also show models to be safer when tested with text only rather than multimodal
prompts. Finally, we explore the automation of VLM safety assessments, finding
even the best safety classifiers to be lacking.
|
2501.10062
|
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal
Finetuning
|
cs.LG cs.CL
|
Building mixture-of-experts (MoE) architecture for Low-rank adaptation (LoRA)
is emerging as a potential direction in parameter-efficient fine-tuning (PEFT)
for its modular design and remarkable performance. However, simply stacking the
number of experts cannot guarantee significant improvement. In this work, we
first conduct qualitative analysis to indicate that experts collapse to similar
representations in vanilla MoE, limiting the capacity of modular design and
computational efficiency. Ulteriorly, Our analysis reveals that the performance
of previous MoE variants maybe limited by a lack of diversity among experts.
Motivated by these findings, we propose Orthogonal Mixture-of-Experts (OMoE), a
resource-efficient MoE variant that trains experts in an orthogonal manner to
promote diversity. In OMoE, a Gram-Schmidt process is leveraged to enforce that
the experts' representations lie within the Stiefel manifold. By applying
orthogonal constraints directly to the architecture, OMoE keeps the learning
objective unchanged, without compromising optimality. Our method is simple and
alleviates memory bottlenecks, as it incurs minimal experts compared to vanilla
MoE models. Experiments on diverse commonsense reasoning benchmarks demonstrate
that OMoE can consistently achieve stable and efficient performance improvement
when compared with the state-of-the-art methods while significantly reducing
the number of required experts.
|
2501.10063
|
Hybrid Parallel Collaborative Simulation Framework Integrating Device
Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment
|
eess.SY cs.SY
|
Optimizing high-performance power electronic equipment, such as power
converters, requires multiscale simulations that incorporate the physics of
power semiconductor devices and the dynamics of other circuit components,
especially in conducting Design of Experiments (DoEs), defining the safe
operating area of devices, and analyzing failures related to semiconductor
devices. However, current methodologies either overlook the intricacies of
device physics or do not achieve satisfactory computational speeds. To bridge
this gap, this paper proposes a Hybrid-Parallel Collaborative (HPC) framework
specifically designed to analyze the Partial Differential Algebraic Equation
(PDAE) modeled power electronic equipment, integrating the device physics and
circuit dynamics. The HPC framework employs a dynamic iteration to tackle the
challenges inherent in solving the coupled nonlinear PDAE system, and utilizes
a hybrid-parallel computing strategy to reduce computing time. Physics-based
system partitioning along with hybrid-process-thread parallelization on shared
and distributed memory are employed, facilitating the simulation of hundreds of
partial differential equations (PDEs)-modeled devices simultaneously without
compromising speed. Experiments based on the hybrid line commutated converter
and reverse-blocking integrated gate-commutated thyristors are conducted under
3 typical real-world scenarios: semiconductor device optimization for the
converter; converter design optimization; and device failure analysis. The HPC
framework delivers simulation speed up to 60 times faster than the leading
commercial software, while maintaining carrier-level accuracy in the
experiments. This shows great potential for comprehensive analysis and
collaborative optimization of devices and electronic power equipment,
particularly in extreme conditions and failure scenarios.
|
2501.10064
|
One-D-Piece: Image Tokenizer Meets Quality-Controllable Compression
|
cs.CV cs.LG
|
Current image tokenization methods require a large number of tokens to
capture the information contained within images. Although the amount of
information varies across images, most image tokenizers only support
fixed-length tokenization, leading to inefficiency in token allocation. In this
study, we introduce One-D-Piece, a discrete image tokenizer designed for
variable-length tokenization, achieving quality-controllable mechanism. To
enable variable compression rate, we introduce a simple but effective
regularization mechanism named "Tail Token Drop" into discrete one-dimensional
image tokenizers. This method encourages critical information to concentrate at
the head of the token sequence, enabling support of variadic tokenization,
while preserving state-of-the-art reconstruction quality. We evaluate our
tokenizer across multiple reconstruction quality metrics and find that it
delivers significantly better perceptual quality than existing
quality-controllable compression methods, including JPEG and WebP, at smaller
byte sizes. Furthermore, we assess our tokenizer on various downstream computer
vision tasks, including image classification, object detection, semantic
segmentation, and depth estimation, confirming its adaptability to numerous
applications compared to other variable-rate methods. Our approach demonstrates
the versatility of variable-length discrete image tokenization, establishing a
new paradigm in both compression efficiency and reconstruction performance.
Finally, we validate the effectiveness of tail token drop via detailed analysis
of tokenizers.
|
2501.10066
|
A Comprehensive Insights into Drones: History, Classification,
Architecture, Navigation, Applications, Challenges, and Future Trends
|
cs.RO cs.IT math.IT
|
Unmanned Aerial Vehicles (UAVs), commonly known as Drones, are one of 21st
century most transformative technologies. Emerging first for military use,
advancements in materials, electronics, and software have catapulted drones
into multipurpose tools for a wide range of industries. In this paper, we have
covered the history, taxonomy, architecture, navigation systems and branched
activities for the same. It explores important future trends like autonomous
navigation, AI integration, and obstacle avoidance systems, emphasizing how
they contribute to improving the efficiency and versatility of drones. It also
looks at the major challenges like technical, environmental, economic,
regulatory and ethical, that limit the actual take-up of drones, as well as
trends that are likely to mitigate these obstacles in the future. This work
offers a structured synthesis of existing studies and perspectives that enable
insights about how drones will transform agriculture, logistics, healthcare,
disaster management, and other areas, while also identifying new opportunities
for innovation and development.
|
2501.10067
|
FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained
Descriptions and Deformable Localization
|
cs.CV
|
Anomaly detection methods typically require extensive normal samples from the
target class for training, limiting their applicability in scenarios that
require rapid adaptation, such as cold start. Zero-shot and few-shot anomaly
detection do not require labeled samples from the target class in advance,
making them a promising research direction. Existing zero-shot and few-shot
approaches often leverage powerful multimodal models to detect and localize
anomalies by comparing image-text similarity. However, their handcrafted
generic descriptions fail to capture the diverse range of anomalies that may
emerge in different objects, and simple patch-level image-text matching often
struggles to localize anomalous regions of varying shapes and sizes. To address
these issues, this paper proposes the FiLo++ method, which consists of two key
components. The first component, Fused Fine-Grained Descriptions (FusDes),
utilizes large language models to generate anomaly descriptions for each object
category, combines both fixed and learnable prompt templates and applies a
runtime prompt filtering method, producing more accurate and task-specific
textual descriptions. The second component, Deformable Localization (DefLoc),
integrates the vision foundation model Grounding DINO with position-enhanced
text descriptions and a Multi-scale Deformable Cross-modal Interaction (MDCI)
module, enabling accurate localization of anomalies with various shapes and
sizes. In addition, we design a position-enhanced patch matching approach to
improve few-shot anomaly detection performance. Experiments on multiple
datasets demonstrate that FiLo++ achieves significant performance improvements
compared with existing methods. Code will be available at
https://github.com/CASIA-IVA-Lab/FiLo.
|
2501.10069
|
A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling,
Search Algorithms, and Relevant Frameworks
|
cs.AI
|
LLM test-time compute (or LLM inference) via search has emerged as a
promising research area with rapid developments. However, current frameworks
often adopt distinct perspectives on three key aspects (task definition, LLM
profiling, and search procedures), making direct comparisons challenging.
Moreover, the search algorithms employed often diverge from standard
implementations, and their specific characteristics are not thoroughly
specified. In this survey, we provide a comprehensive technical review that
unifies task definitions and provides modular definitions of LLM profiling and
search procedures. The definitions enable precise comparisons of various LLM
inference frameworks while highlighting their departures from conventional
search algorithms. We also discuss the applicability, performance, and
efficiency of these methods. For further details and ongoing updates, please
refer to our GitHub repository:
https://github.com/xinzhel/LLM-Agent-Survey/blob/main/search.md
|
2501.10071
|
CLIP-PCQA: Exploring Subjective-Aligned Vision-Language Modeling for
Point Cloud Quality Assessment
|
cs.CV cs.MM
|
In recent years, No-Reference Point Cloud Quality Assessment (NR-PCQA)
research has achieved significant progress. However, existing methods mostly
seek a direct mapping function from visual data to the Mean Opinion Score
(MOS), which is contradictory to the mechanism of practical subjective
evaluation. To address this, we propose a novel language-driven PCQA method
named CLIP-PCQA. Considering that human beings prefer to describe visual
quality using discrete quality descriptions (e.g., "excellent" and "poor")
rather than specific scores, we adopt a retrieval-based mapping strategy to
simulate the process of subjective assessment. More specifically, based on the
philosophy of CLIP, we calculate the cosine similarity between the visual
features and multiple textual features corresponding to different quality
descriptions, in which process an effective contrastive loss and learnable
prompts are introduced to enhance the feature extraction. Meanwhile, given the
personal limitations and bias in subjective experiments, we further covert the
feature similarities into probabilities and consider the Opinion Score
Distribution (OSD) rather than a single MOS as the final target. Experimental
results show that our CLIP-PCQA outperforms other State-Of-The-Art (SOTA)
approaches.
|
2501.10072
|
Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on
Word Class Distributions
|
cs.CL
|
Deep learning methods have been increasingly applied to computational
linguistics to uncover patterns in text data. This study investigates
author-specific word class distributions using part-of-speech (POS) tagging and
bigram analysis. By leveraging deep neural networks, we classify literary
authors based on POS tag vectors and bigram frequency matrices derived from
their works. We employ fully connected and convolutional neural network
architectures to explore the efficacy of unigram and bigram-based
representations. Our results demonstrate that while unigram features achieve
moderate classification accuracy, bigram-based models significantly improve
performance, suggesting that sequential word class patterns are more
distinctive of authorial style. Multi-dimensional scaling (MDS) visualizations
reveal meaningful clustering of authors' works, supporting the hypothesis that
stylistic nuances can be captured through computational methods. These findings
highlight the potential of deep learning and linguistic feature analysis for
author profiling and literary studies.
|
2501.10074
|
SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and
Chain-of-Thought for Embodied Task Planning
|
cs.RO cs.AI cs.CV
|
Spatial reasoning is an essential problem in embodied AI research. Efforts to
enhance spatial reasoning abilities through supplementary spatial data and
fine-tuning have proven limited and ineffective when addressing complex
embodied tasks, largely due to their dependence on language-based outputs.
While some approaches have introduced a point-based action space to mitigate
this issue, they fall short in managing more intricate tasks within complex
environments. This deficiency arises from their failure to fully exploit the
inherent thinking and reasoning capabilities that are fundamental strengths of
Vision-Language Models (VLMs). To address these limitations, we propose a novel
approach named SpatialCoT, specifically designed to bolster the spatial
reasoning capabilities of VLMs. Our approach comprises two stages: spatial
coordinate bi-directional alignment, which aligns vision-language inputs with
spatial coordinates, and chain-of-thought spatial grounding, which harnesses
the reasoning capabilities of language models for advanced spatial reasoning.
We evaluate SpatialCoT on challenging navigation and manipulation tasks, both
in simulation and real-world settings. Experimental results demonstrate that
our method significantly outperforms previous state-of-the-art approaches in
both tasks.
|
2501.10075
|
Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and
MModalCC Framework
|
cs.CV cs.AI cs.LG cs.MM
|
Remote sensing change captioning (RSICC) aims to describe changes between
bitemporal images in natural language. Existing methods often fail under
challenges like illumination differences, viewpoint changes, blur effects,
leading to inaccuracies, especially in no-change regions. Moreover, the images
acquired at different spatial resolutions and have registration errors tend to
affect the captions. To address these issues, we introduce SECOND-CC, a novel
RSICC dataset featuring high-resolution RGB image pairs, semantic segmentation
maps, and diverse real-world scenarios. SECOND-CC which contains 6,041 pairs of
bitemporal RS images and 30,205 sentences describing the differences between
images. Additionally, we propose MModalCC, a multimodal framework that
integrates semantic and visual data using advanced attention mechanisms,
including Cross-Modal Cross Attention (CMCA) and Multimodal Gated Cross
Attention (MGCA). Detailed ablation studies and attention visualizations
further demonstrate its effectiveness and ability to address RSICC challenges.
Comprehensive experiments show that MModalCC outperforms state-of-the-art RSICC
methods, including RSICCformer, Chg2Cap, and PSNet with +4.6% improvement on
BLEU4 score and +9.6% improvement on CIDEr score. We will make our dataset and
codebase publicly available to facilitate future research at
https://github.com/ChangeCapsInRS/SecondCC
|
2501.10077
|
Double descent in quantum machine learning
|
quant-ph cs.LG stat.ML
|
The double descent phenomenon challenges traditional statistical learning
theory by revealing scenarios where larger models do not necessarily lead to
reduced performance on unseen data. While this counterintuitive behavior has
been observed in a variety of classical machine learning models, particularly
modern neural network architectures, it remains elusive within the context of
quantum machine learning. In this work, we analytically demonstrate that
quantum learning models can exhibit double descent behavior by drawing on
insights from linear regression and random matrix theory. Additionally, our
numerical experiments on quantum kernel methods across different real-world
datasets and system sizes further confirm the existence of a test error peak, a
characteristic feature of double descent. Our findings provide evidence that
quantum models can operate in the modern, overparameterized regime without
experiencing overfitting, thereby opening pathways to improved learning
performance beyond traditional statistical learning theory.
|
2501.10080
|
Few-shot Structure-Informed Machinery Part Segmentation with Foundation
Models and Graph Neural Networks
|
cs.CV
|
This paper proposes a novel approach to few-shot semantic segmentation for
machinery with multiple parts that exhibit spatial and hierarchical
relationships. Our method integrates the foundation models CLIPSeg and Segment
Anything Model (SAM) with the interest point detector SuperPoint and a graph
convolutional network (GCN) to accurately segment machinery parts. By providing
1 to 25 annotated samples, our model, evaluated on a purely synthetic dataset
depicting a truck-mounted loading crane, achieves effective segmentation across
various levels of detail. Training times are kept under five minutes on
consumer GPUs. The model demonstrates robust generalization to real data,
achieving a qualitative synthetic-to-real generalization with a $J\&F$ score of
92.2 on real data using 10 synthetic support samples. When benchmarked on the
DAVIS 2017 dataset, it achieves a $J\&F$ score of 71.5 in semi-supervised video
segmentation with three support samples. This method's fast training times and
effective generalization to real data make it a valuable tool for autonomous
systems interacting with machinery and infrastructure, and illustrate the
potential of combined and orchestrated foundation models for few-shot
segmentation tasks.
|
2501.10081
|
Leveraging Confident Image Regions for Source-Free Domain-Adaptive
Object Detection
|
cs.CV
|
Source-free domain-adaptive object detection is an interesting but scarcely
addressed topic. It aims at adapting a source-pretrained detector to a distinct
target domain without resorting to source data during adaptation. So far, there
is no data augmentation scheme tailored to source-free domain-adaptive object
detection. To this end, this paper presents a novel data augmentation approach
that cuts out target image regions where the detector is confident, augments
them along with their respective pseudo-labels, and joins them into a
challenging target image to adapt the detector. As the source data is out of
reach during adaptation, we implement our approach within a teacher-student
learning paradigm to ensure that the model does not collapse during the
adaptation procedure. We evaluated our approach on three adaptation benchmarks
of traffic scenes, scoring new state-of-the-art on two of them.
|
2501.10084
|
Two-level Solar Irradiance Clustering with Season Identification: A
Comparative Analysis
|
cs.LG
|
Solar irradiance clustering can enhance solar power capacity planning and
help improve forecasting models by identifying similar irradiance patterns
influenced by seasonal and weather changes. In this study, we adopt an
efficient two-level clustering approach to automatically identify seasons using
the clear sky irradiance in first level and subsequently to identify daily
cloud level as clear, cloudy and partly cloudy within each season in second
level. In the second level of clustering, three methods are compared, namely,
Daily Irradiance Index (DII or $\beta$), Euclidean Distance (ED), and Dynamic
Time Warping (DTW) distance. The DII is computed as the ratio of time integral
of measured irradiance to time integral of the clear sky irradiance. The
identified clusters were compared quantitatively using established clustering
metrics and qualitatively by comparing the mean irradiance profiles. The
results clearly establish the superiority of the $\beta$-based clustering
approach as the leader, setting a new benchmark for solar irradiance clustering
studies. Moreover, $\beta$-based clustering remains effective even for annual
data unlike the time-series methods which suffer significant performance
degradation. Interestingly, contrary to expectations, ED-based clustering
outperforms the more compute-intensive DTW distance-based clustering. The
method has been rigorously validated using data from two distinct US locations,
demonstrating robust scalability for larger datasets and potential
applicability for other locations.
|
2501.10088
|
A recursive Bayesian neural network for constitutive modeling of sands
under monotonic loading
|
cs.LG
|
In geotechnical engineering, constitutive models play a crucial role in
describing soil behavior under varying loading conditions. Data-driven deep
learning (DL) models offer a promising alternative for developing predictive
constitutive models. When prediction is the primary focus, quantifying the
predictive uncertainty of a trained DL model and communicating this uncertainty
to end users is crucial for informed decision-making.
This study proposes a recursive Bayesian neural network (rBNN) framework,
which builds upon recursive feedforward neural networks (rFFNNs) by introducing
generalized Bayesian inference for uncertainty quantification. A significant
contribution of this work is the incorporation of a sliding window approach in
rFFNNs, allowing the models to effectively capture temporal dependencies across
load steps. The rBNN extends this framework by treating model parameters as
random variables, with their posterior distributions inferred using generalized
variational inference.
The proposed framework is validated on two datasets: (i) a numerically
simulated consolidated drained (CD) triaxial dataset employing a hardening soil
model and (ii) an experimental dataset comprising 28 CD triaxial tests on
Baskarp sand. Comparative analyses with LSTM, Bi-LSTM, and GRU models
demonstrate that the deterministic rFFNN achieves superior predictive accuracy,
attributed to its transparent structure and sliding window design. While the
rBNN marginally trails in accuracy for the experimental case, it provides
robust confidence intervals, addressing data sparsity and measurement noise in
experimental conditions. The study underscores the trade-offs between
deterministic and probabilistic approaches and the potential of rBNNs for
uncertainty-aware constitutive modeling.
|
2501.10089
|
Classifier Ensemble for Efficient Uncertainty Calibration of Deep Neural
Networks for Image Classification
|
cs.CV
|
This paper investigates novel classifier ensemble techniques for uncertainty
calibration applied to various deep neural networks for image classification.
We evaluate both accuracy and calibration metrics, focusing on Expected
Calibration Error (ECE) and Maximum Calibration Error (MCE). Our work compares
different methods for building simple yet efficient classifier ensembles,
including majority voting and several metamodel-based approaches. Our
evaluation reveals that while state-of-the-art deep neural networks for image
classification achieve high accuracy on standard datasets, they frequently
suffer from significant calibration errors. Basic ensemble techniques like
majority voting provide modest improvements, while metamodel-based ensembles
consistently reduce ECE and MCE across all architectures. Notably, the largest
of our compared metamodels demonstrate the most substantial calibration
improvements, with minimal impact on accuracy. Moreover, classifier ensembles
with metamodels outperform traditional model ensembles in calibration
performance, while requiring significantly fewer parameters. In comparison to
traditional post-hoc calibration methods, our approach removes the need for a
separate calibration dataset. These findings underscore the potential of our
proposed metamodel-based classifier ensembles as an efficient and effective
approach to improving model calibration, thereby contributing to more reliable
deep learning systems.
|
2501.10091
|
How Do Programming Students Use Generative AI?
|
cs.HC cs.AI cs.CY
|
Programming students have a widespread access to powerful Generative AI tools
like ChatGPT. While this can help understand the learning material and assist
with exercises, educators are voicing more and more concerns about an
over-reliance on generated outputs and lack of critical thinking skills. It is
thus important to understand how students actually use generative AI and what
impact this could have on their learning behavior. To this end, we conducted a
study including an exploratory experiment with 37 programming students, giving
them monitored access to ChatGPT while solving a code understanding and
improving exercise. While only 23 of the students actually opted to use the
chatbot, the majority of those eventually prompted it to simply generate a full
solution. We observed two prevalent usage strategies: to seek knowledge about
general concepts and to directly generate solutions. Instead of using the bot
to comprehend the code and their own mistakes, students often got trapped in a
vicious cycle of submitting wrong generated code and then asking the bot for a
fix. Those who self-reported using generative AI regularly were more likely to
prompt the bot to generate a solution. Our findings indicate that concerns
about potential decrease in programmers' agency and productivity with
Generative AI are justified. We discuss how researchers and educators can
respond to the potential risk of students uncritically over-relying on
generative AI. We also discuss potential modifications to our study design for
large-scale replications.
|
2501.10093
|
An Energy-Aware RIoT System: Analysis, Modeling and Prediction in the
SUPERIOT Framework
|
cs.ET cs.AR cs.NI cs.PF cs.SY eess.SY
|
This paper presents a comprehensive analysis of the energy consumption
characteristics of a Silicon (Si)-based Reconfigurable IoT (RIoT) node
developed in the initial phase of the SUPERIOT project, focusing on key
operating states, including Bluetooth Low Energy (BLE) communication,
Narrow-Band Visible Light Communication (NBVLC), sensing, and E-ink display.
Extensive measurements were conducted to establish a detailed energy profile,
which serves as a benchmark for evaluating the effectiveness of subsequent
optimizations and future node iterations. To minimize the energy consumption,
multiple optimizations were implemented at both the software and hardware
levels, achieving a reduction of over 60% in total energy usage through
software modifications alone. Further improvements were realized by optimizing
the E-ink display driving waveform and implementing a very low-power mode for
non-communication activities. Based on the measured data, three
measurement-based energy consumption models were developed to characterize the
energy behavior of the node under: (i) normal, unoptimized operation, (ii)
low-power, software-optimized operation, and (iii) very low-power,
hardware-optimized operation. These models, validated with new measurement
data, achieved an accuracy exceeding 97%, confirming their reliability for
predicting energy consumption in diverse configurations.
|
2501.10097
|
Decomposition and Quantification of SOTIF Requirements for Perception
Systems of Autonomous Vehicles
|
eess.SY cs.SY
|
Ensuring the safety of autonomous vehicles (AVs) is paramount before they can
be introduced to the market.
More specifically, securing the Safety of the Intended Functionality (SOTIF)
poses a notable challenge; while ISO 21448 outlines numerous activities to
refine the performance of AVs, it offers minimal quantitative guidance. This
paper endeavors to decompose the acceptance criterion into quantitative
perception requirements, aiming to furnish developers with requirements that
are not only understandable but also actionable. This paper introduces a risk
decomposition methodology to derive SOTIF requirements for perception. More
explicitly, for subsystemlevel safety requirements, we define a collision
severity model to establish requirements for state uncertainty and present a
Bayesian model to discern requirements for existence uncertainty.
For component-level safety requirements, we proposed a decomposition method
based on the Shapley value. Our findings indicate that these methods can
effectively decompose the system-level safety requirements into quantitative
perception requirements, potentially facilitating the safety verification of
various AV components.
|
2501.10098
|
landmarker: a Toolkit for Anatomical Landmark Localization in 2D/3D
Images
|
cs.CV cs.AI cs.LG
|
Anatomical landmark localization in 2D/3D images is a critical task in
medical imaging. Although many general-purpose tools exist for landmark
localization in classical computer vision tasks, such as pose estimation, they
lack the specialized features and modularity necessary for anatomical landmark
localization applications in the medical domain. Therefore, we introduce
landmarker, a Python package built on PyTorch. The package provides a
comprehensive, flexible toolkit for developing and evaluating landmark
localization algorithms, supporting a range of methodologies, including static
and adaptive heatmap regression. landmarker enhances the accuracy of landmark
identification, streamlines research and development processes, and supports
various image formats and preprocessing pipelines. Its modular design allows
users to customize and extend the toolkit for specific datasets and
applications, accelerating innovation in medical imaging. landmarker addresses
a critical need for precision and customization in landmark localization tasks
not adequately met by existing general-purpose pose estimation tools.
|
2501.10099
|
Several Representations of $\alpha$-Mutual Information and
Interpretations as Privacy Leakage Measures
|
cs.IT math.IT
|
In this paper, we present several novel representations of $\alpha$-mutual
information ($\alpha$-MI) in terms of R{\' e}nyi divergence and conditional
R{\' e}nyi entropy. The representations are based on the variational
characterizations of $\alpha$-MI using a reverse channel. Based on these
representations, we provide several interpretations of the $\alpha$-MI as
privacy leakage measures using generalized mean and gain functions. Further, as
byproducts of the representations, we propose novel conditional R{\' e}nyi
entropies that satisfy the property that conditioning reduces entropy and
data-processing inequality.
|
2501.10100
|
Robotic World Model: A Neural Network Simulator for Robust Policy
Optimization in Robotics
|
cs.RO cs.AI cs.LG
|
Learning robust and generalizable world models is crucial for enabling
efficient and scalable robotic control in real-world environments. In this
work, we introduce a novel framework for learning world models that accurately
capture complex, partially observable, and stochastic dynamics. The proposed
method employs a dual-autoregressive mechanism and self-supervised training to
achieve reliable long-horizon predictions without relying on domain-specific
inductive biases, ensuring adaptability across diverse robotic tasks. We
further propose a policy optimization framework that leverages world models for
efficient training in imagined environments and seamless deployment in
real-world systems. Through extensive experiments, our approach consistently
outperforms state-of-the-art methods, demonstrating superior autoregressive
prediction accuracy, robustness to noise, and generalization across
manipulation and locomotion tasks. Notably, policies trained with our method
are successfully deployed on ANYmal D hardware in a zero-shot transfer,
achieving robust performance with minimal sim-to-real performance loss. This
work advances model-based reinforcement learning by addressing the challenges
of long-horizon prediction, error accumulation, and sim-to-real transfer. By
providing a scalable and robust framework, the introduced methods pave the way
for adaptive and efficient robotic systems in real-world applications.
|
2501.10103
|
Lossless data compression at pragmatic rates
|
cs.IT math.IT
|
The problem of variable-rate lossless data compression is considered, for
codes with and without prefix constraints. Sharp bounds are derived for the
best achievable compression rate of memoryless sources, when the excess-rate
probability is required to be exponentially small in the blocklength. Accurate
nonasymptotic expansions with explicit constants are obtained for the optimal
rate, using tools from large deviations and Gaussian approximation. Examples
are shown indicating that, in the small excess-rate-probability regime, the
approximation to the fundamental limit of the compression rate suggested by
these bounds is significantly more accurate than the approximations provided by
either normal approximation or error exponents. The new bounds reinforce the
crucial operational conclusion that, in applications where the blocklength is
relatively short and where stringent guarantees are required on the rate, the
best achievable rate is no longer close to the entropy. Rather, it is an
appropriate, more pragmatic rate, determined via the inverse error exponent
function and the blocklength.
|
2501.10105
|
Universal Actions for Enhanced Embodied Foundation Models
|
cs.RO cs.AI cs.CV
|
Training on diverse, internet-scale data is a key factor in the success of
recent large foundation models. Yet, using the same recipe for building
embodied agents has faced noticeable difficulties. Despite the availability of
many crowd-sourced embodied datasets, their action spaces often exhibit
significant heterogeneity due to distinct physical embodiment and control
interfaces for different robots, causing substantial challenges in developing
embodied foundation models using cross-domain data. In this paper, we introduce
UniAct, a new embodied foundation modeling framework operating in a tokenized
Universal Action Space. Our learned universal actions capture the generic
atomic behaviors across diverse robots by exploiting their shared structural
features, and enable enhanced cross-domain data utilization and
cross-embodiment generalizations by eliminating the notorious heterogeneity.
The universal actions can be efficiently translated back to heterogeneous
actionable commands by simply adding embodiment-specific details, from which
fast adaptation to new robots becomes simple and straightforward. Our 0.5B
instantiation of UniAct outperforms 14X larger SOTA embodied foundation models
in extensive evaluations on various real-world and simulation robots,
showcasing exceptional cross-embodiment control and adaptation capability,
highlighting the crucial benefit of adopting universal actions. Project page:
https://github.com/2toinf/UniAct
|
2501.10106
|
LLM Reasoner and Automated Planner: A new NPC approach
|
cs.AI
|
In domains requiring intelligent agents to emulate plausible human-like
behaviour, such as formative simulations, traditional techniques like behaviour
trees encounter significant challenges. Large Language Models (LLMs), despite
not always yielding optimal solutions, usually offer plausible and human-like
responses to a given problem. In this paper, we exploit this capability and
propose a novel architecture that integrates an LLM for decision-making with a
classical automated planner that can generate sound plans for that decision.
The combination aims to equip an agent with the ability to make decisions in
various situations, even if they were not anticipated during the design phase.
|
2501.10107
|
BBPOS: BERT-based Part-of-Speech Tagging for Uzbek
|
cs.CL cs.AI
|
This paper advances NLP research for the low-resource Uzbek language by
evaluating two previously untested monolingual Uzbek BERT models on the
part-of-speech (POS) tagging task and introducing the first publicly available
UPOS-tagged benchmark dataset for Uzbek. Our fine-tuned models achieve 91%
average accuracy, outperforming the baseline multi-lingual BERT as well as the
rule-based tagger. Notably, these models capture intermediate POS changes
through affixes and demonstrate context sensitivity, unlike existing rule-based
taggers.
|
2501.10110
|
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion
Models for Advanced Visual Quality and Temporal Consistency
|
cs.CV
|
Diffusion models have demonstrated exceptional capabilities in image
generation and restoration, yet their application to video super-resolution
faces significant challenges in maintaining both high fidelity and temporal
consistency. We present DiffVSR, a diffusion-based framework for real-world
video super-resolution that effectively addresses these challenges through key
innovations. For intra-sequence coherence, we develop a multi-scale temporal
attention module and temporal-enhanced VAE decoder that capture fine-grained
motion details. To ensure inter-sequence stability, we introduce a noise
rescheduling mechanism with an interweaved latent transition approach, which
enhances temporal consistency without additional training overhead. We propose
a progressive learning strategy that transitions from simple to complex
degradations, enabling robust optimization despite limited high-quality video
data. Extensive experiments demonstrate that DiffVSR delivers superior results
in both visual quality and temporal consistency, setting a new performance
standard in real-world video super-resolution.
|
2501.10114
|
Infrastructure for AI Agents
|
cs.AI
|
Increasingly many AI systems can plan and execute interactions in open-ended
environments, such as making phone calls or buying online goods. As developers
grow the space of tasks that such AI agents can accomplish, we will need tools
both to unlock their benefits and manage their risks. Current tools are largely
insufficient because they are not designed to shape how agents interact with
existing institutions (e.g., legal and economic systems) or actors (e.g.,
digital service providers, humans, other AI agents). For example, alignment
techniques by nature do not assure counterparties that some human will be held
accountable when a user instructs an agent to perform an illegal action. To
fill this gap, we propose the concept of agent infrastructure: technical
systems and shared protocols external to agents that are designed to mediate
and influence their interactions with and impacts on their environments. Agent
infrastructure comprises both new tools and reconfigurations or extensions of
existing tools. For example, to facilitate accountability, protocols that tie
users to agents could build upon existing systems for user authentication, such
as OpenID. Just as the Internet relies on infrastructure like HTTPS, we argue
that agent infrastructure will be similarly indispensable to ecosystems of
agents. We identify three functions for agent infrastructure: 1) attributing
actions, properties, and other information to specific agents, their users, or
other actors; 2) shaping agents' interactions; and 3) detecting and remedying
harmful actions from agents. We propose infrastructure that could help achieve
each function, explaining use cases, adoption, limitations, and open questions.
Making progress on agent infrastructure can prepare society for the adoption of
more advanced agents.
|
2501.10116
|
GAWM: Global-Aware World Model for Multi-Agent Reinforcement Learning
|
cs.MA
|
In recent years, Model-based Multi-Agent Reinforcement Learning (MARL) has
demonstrated significant advantages over model-free methods in terms of sample
efficiency by using independent environment dynamics world models for data
sample augmentation. However, without considering the limited sample size,
these methods still lag behind model-free methods in terms of final convergence
performance and stability. This is primarily due to the world model's
insufficient and unstable representation of global states in partially
observable environments. This limitation hampers the ability to ensure global
consistency in the data samples and results in a time-varying and unstable
distribution mismatch between the pseudo data samples generated by the world
model and the real samples. This issue becomes particularly pronounced in more
complex multi-agent environments. To address this challenge, we propose a
model-based MARL method called GAWM, which enhances the centralized world
model's ability to achieve globally unified and accurate representation of
state information while adhering to the CTDE paradigm. GAWM uniquely leverages
an additional Transformer architecture to fuse local observation information
from different agents, thereby improving its ability to extract and represent
global state information. This enhancement not only improves sample efficiency
but also enhances training stability, leading to superior convergence
performance, particularly in complex and challenging multi-agent environments.
This advancement enables model-based methods to be effectively applied to more
complex multi-agent environments. Experimental results demonstrate that GAWM
outperforms various model-free and model-based approaches, achieving
exceptional performance in the challenging domains of SMAC.
|
2501.10120
|
PaSa: An LLM Agent for Comprehensive Academic Paper Search
|
cs.IR cs.LG
|
We introduce PaSa, an advanced Paper Search agent powered by large language
models. PaSa can autonomously make a series of decisions, including invoking
search tools, reading papers, and selecting relevant references, to ultimately
obtain comprehensive and accurate results for complex scholarly queries. We
optimize PaSa using reinforcement learning with a synthetic dataset,
AutoScholarQuery, which includes 35k fine-grained academic queries and
corresponding papers sourced from top-tier AI conference publications.
Additionally, we develop RealScholarQuery, a benchmark collecting real-world
academic queries to assess PaSa performance in more realistic scenarios.
Despite being trained on synthetic data, PaSa significantly outperforms
existing baselines on RealScholarQuery, including Google, Google Scholar,
Google with GPT-4 for paraphrased queries, chatGPT (search-enabled GPT-4o),
GPT-o1, and PaSa-GPT-4o (PaSa implemented by prompting GPT-4o). Notably,
PaSa-7B surpasses the best Google-based baseline, Google with GPT-4o, by 37.78%
in recall@20 and 39.90% in recall@50. It also exceeds PaSa-GPT-4o by 30.36% in
recall and 4.25% in precision. Model, datasets, and code are available at
https://github.com/bytedance/pasa.
|
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