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2502.09303
|
Towards Seamless Hierarchical Federated Learning under Intermittent
Client Participation: A Stagewise Decision-Making Methodology
|
cs.LG cs.DC
|
Federated Learning (FL) offers a pioneering distributed learning paradigm
that enables devices/clients to build a shared global model. This global model
is obtained through frequent model transmissions between clients and a central
server, which may cause high latency, energy consumption, and congestion over
backhaul links. To overcome these drawbacks, Hierarchical Federated Learning
(HFL) has emerged, which organizes clients into multiple clusters and utilizes
edge nodes (e.g., edge servers) for intermediate model aggregations between
clients and the central server. Current research on HFL mainly focus on
enhancing model accuracy, latency, and energy consumption in scenarios with a
stable/fixed set of clients. However, addressing the dynamic availability of
clients -- a critical aspect of real-world scenarios -- remains underexplored.
This study delves into optimizing client selection and client-to-edge
associations in HFL under intermittent client participation so as to minimize
overall system costs (i.e., delay and energy), while achieving fast model
convergence. We unveil that achieving this goal involves solving a complex
NP-hard problem. To tackle this, we propose a stagewise methodology that splits
the solution into two stages, referred to as Plan A and Plan B. Plan A focuses
on identifying long-term clients with high chance of participation in
subsequent model training rounds. Plan B serves as a backup, selecting
alternative clients when long-term clients are unavailable during model
training rounds. This stagewise methodology offers a fresh perspective on
client selection that can enhance both HFL and conventional FL via enabling
low-overhead decision-making processes. Through evaluations on MNIST and
CIFAR-10 datasets, we show that our methodology outperforms existing benchmarks
in terms of model accuracy and system costs.
|
2502.09304
|
KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for
Graph-RAG
|
cs.IR
|
Graph-RAG constructs a knowledge graph from text chunks to improve retrieval
in Large Language Model (LLM)-based question answering. It is particularly
useful in domains such as biomedicine, law, and political science, where
retrieval often requires multi-hop reasoning over proprietary documents. Some
existing Graph-RAG systems construct KNN graphs based on text chunk relevance,
but this coarse-grained approach fails to capture entity relationships within
texts, leading to sub-par retrieval and generation quality. To address this,
recent solutions leverage LLMs to extract entities and relationships from text
chunks, constructing triplet-based knowledge graphs. However, this approach
incurs significant indexing costs, especially for large document collections.
To ensure a good result accuracy while reducing the indexing cost, we propose
KET-RAG, a multi-granular indexing framework. KET-RAG first identifies a small
set of key text chunks and leverages an LLM to construct a knowledge graph
skeleton. It then builds a text-keyword bipartite graph from all text chunks,
serving as a lightweight alternative to a full knowledge graph. During
retrieval, KET-RAG searches both structures: it follows the local search
strategy of existing Graph-RAG systems on the skeleton while mimicking this
search on the bipartite graph to improve retrieval quality. We evaluate eight
solutions on two real-world datasets, demonstrating that KET-RAG outperforms
all competitors in indexing cost, retrieval effectiveness, and generation
quality. Notably, it achieves comparable or superior retrieval quality to
Microsoft's Graph-RAG while reducing indexing costs by over an order of
magnitude. Additionally, it improves the generation quality by up to 32.4%
while lowering indexing costs by around 20%.
|
2502.09305
|
Predicting Drive Test Results in Mobile Networks Using Optimization
Techniques
|
cs.NI cs.AI cs.SE
|
Mobile network operators constantly optimize their networks to ensure
superior service quality and coverage. This optimization is crucial for
maintaining an optimal user experience and requires extensive data collection
and analysis. One of the primary methods for gathering this data is through
drive tests, where technical teams use specialized equipment to collect signal
information across various regions. However, drive tests are both costly and
time-consuming, and they face challenges such as traffic conditions,
environmental factors, and limited access to certain areas. These constraints
make it difficult to replicate drive tests under similar conditions. In this
study, we propose a method that enables operators to predict received signal
strength at specific locations using data from other drive test points. By
reducing the need for widespread drive tests, this approach allows operators to
save time and resources while still obtaining the necessary data to optimize
their networks and mitigate the challenges associated with traditional drive
tests.
|
2502.09306
|
Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for
Generative Modelling
|
stat.ML cs.LG math.PR stat.CO
|
We investigate the theoretical properties of general diffusion
(interpolation) paths and their Langevin Monte Carlo implementation, referred
to as diffusion annealed Langevin Monte Carlo (DALMC), under weak conditions on
the data distribution. Specifically, we analyse and provide non-asymptotic
error bounds for the annealed Langevin dynamics where the path of distributions
is defined as Gaussian convolutions of the data distribution as in diffusion
models. We then extend our results to recently proposed heavy-tailed (Student's
t) diffusion paths, demonstrating their theoretical properties for heavy-tailed
data distributions for the first time. Our analysis provides theoretical
guarantees for a class of score-based generative models that interpolate
between a simple distribution (Gaussian or Student's t) and the data
distribution in finite time. This approach offers a broader perspective
compared to standard score-based diffusion approaches, which are typically
based on a forward Ornstein-Uhlenbeck (OU) noising process.
|
2502.09307
|
When the LM misunderstood the human chuckled: Analyzing garden path
effects in humans and language models
|
cs.CL cs.AI
|
Modern Large Language Models (LLMs) have shown human-like abilities in many
language tasks, sparking interest in comparing LLMs' and humans' language
processing. In this paper, we conduct a detailed comparison of the two on a
sentence comprehension task using garden-path constructions, which are
notoriously challenging for humans. Based on psycholinguistic research, we
formulate hypotheses on why garden-path sentences are hard, and test these
hypotheses on human participants and a large suite of LLMs using comprehension
questions. Our findings reveal that both LLMs and humans struggle with specific
syntactic complexities, with some models showing high correlation with human
comprehension. To complement our findings, we test LLM comprehension of
garden-path constructions with paraphrasing and text-to-image generation tasks,
and find that the results mirror the sentence comprehension question results,
further validating our findings on LLM understanding of these constructions.
|
2502.09309
|
Frequency Domain Stability and Convergence Analysis for General Reset
Control Systems Architecture
|
eess.SY cs.SY
|
A key factor that generates significant interest in reset control systems,
especially within industrial contexts, is their potential to be designed using
a frequency-domain loop-shaping procedure. On the other hand, formulating and
assessing stability analysis for these nonlinear elements often depends on
access to parametric models and numerically solving linear matrix inequalities.
These specific factors could present challenges to the successful
implementation of reset control within industrial settings. Moreover, one of
the most effective structures for implementing reset elements is to use them in
parallel with a linear element. Therefore, this article presents the
development of the frequency domain-based $H_\beta$ stability method from a
series to a more general structure of reset control systems. Additionally, it
investigates the behavior of different reset elements in terms of the
feasibility of stability in the presence of time delay. To illustrate the
research findings, two examples are provided, including one from an industrial
application.
|
2502.09310
|
Global Stabilization of Chemostats with Nonzero Mortality and Substrate
Dynamics
|
math.OC cs.SY eess.SY q-bio.PE
|
In "chemostat"-type population models that incorporate substrate (nutrient)
dynamics, the dependence of the birth (or growth) rate on the substrate
concentration introduces nonlinear coupling that creates a challenge for
stabilization that is global, namely, for all positive concentrations of the
biomass and nutrients. This challenge for global stabilization has been
overcome in the literature using relatively simple feedback when natural
mortality of the biomass is absent. However, under natural mortality, it takes
fortified, more complex feedback, outside of the existing nonlinear control
design toolbox, to avoid biomass extinction from nutrient-depleted initial
conditions. Such fortified feedback, the associated control Laypunov function
design, and Lyapunov analysis of global stability are provided in this paper.
We achieve global stabilization for two different chemostat models: (i) a
lumped model, with two state variables, and (ii) a three-state model derived
from an age-structured infinite-dimensional model. The proposed feedback
stabilizers are explicit, applicable to both the lumped and the age-structured
models, and coincide with simple feedback laws proposed in the literature when
the mortality rate is zero. Global stabilization means subject to constraints:
all positive biomass and nutrient concentrations are within the region of
attraction of the desired equilibrium, and, additionally, this is achieved with
a dilution input that is guaranteed to remain positive. For the lumped case
with Haldane kinetics, we show that the reproduction rate dominating the
mortality (excluding the reproduction and mortality being in balance) is not
only sufficient but also necessary for global stabilization. The obtained
results are illustrated with simple examples.
|
2502.09311
|
Mitigating the Impact of Prominent Position Shift in Drone-based RGBT
Object Detection
|
cs.CV
|
Drone-based RGBT object detection plays a crucial role in many
around-the-clock applications. However, real-world drone-viewed RGBT data
suffers from the prominent position shift problem, i.e., the position of a tiny
object differs greatly in different modalities. For instance, a slight
deviation of a tiny object in the thermal modality will induce it to drift from
the main body of itself in the RGB modality. Considering RGBT data are usually
labeled on one modality (reference), this will cause the unlabeled modality
(sensed) to lack accurate supervision signals and prevent the detector from
learning a good representation. Moreover, the mismatch of the corresponding
feature point between the modalities will make the fused features confusing for
the detection head. In this paper, we propose to cast the cross-modality box
shift issue as the label noise problem and address it on the fly via a novel
Mean Teacher-based Cross-modality Box Correction head ensemble (CBC). In this
way, the network can learn more informative representations for both
modalities. Furthermore, to alleviate the feature map mismatch problem in RGBT
fusion, we devise a Shifted Window-Based Cascaded Alignment (SWCA) module. SWCA
mines long-range dependencies between the spatially unaligned features inside
shifted windows and cascaded aligns the sensed features with the reference
ones. Extensive experiments on two drone-based RGBT object detection datasets
demonstrate that the correction results are both visually and quantitatively
favorable, thereby improving the detection performance. In particular, our CBC
module boosts the precision of the sensed modality ground truth by 25.52 aSim
points. Overall, the proposed detector achieves an mAP_50 of 43.55 points on
RGBTDronePerson and surpasses a state-of-the-art method by 8.6 mAP50 on a shift
subset of DroneVehicle dataset. The code and data will be made publicly
available.
|
2502.09316
|
A Judge-free LLM Open-ended Generation Benchmark Based on the
Distributional Hypothesis
|
cs.CL
|
Evaluating the open-ended text generation of large language models (LLMs) is
challenging because of the lack of a clear ground truth and the high cost of
human or LLM-based assessments. We propose a novel benchmark that evaluates
LLMs using n-gram statistics and rules, without relying on human judgement or
LLM-as-a-judge approaches. Using 50 question and reference answer sets, we
introduce three new metrics based on n-grams and rules: Fluency, Truthfulness,
and Helpfulness. Our benchmark strongly correlates with GPT-4o-based
evaluations while requiring significantly fewer computational resources,
demonstrating its effectiveness as a scalable alternative for assessing LLMs'
open-ended generation capabilities.
|
2502.09318
|
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating
Mechanisms
|
cs.LG
|
In this paper, we propose a novel approach that enhances recurrent neural
networks (RNNs) by incorporating path signatures into their gating mechanisms.
Our method modifies both Long Short-Term Memory (LSTM) and Gated Recurrent Unit
(GRU) architectures by replacing their forget and reset gates, respectively,
with learnable path signatures. These signatures, which capture the geometric
features of the entire path history, provide a richer context for controlling
information flow through the network's memory. This modification allows the
networks to make memory decisions based on the full historical context rather
than just the current input and state. Through experimental studies, we
demonstrate that our Signature-LSTM (SigLSTM) and Signature-GRU (SigGRU) models
outperform their traditional counterparts across various sequential learning
tasks. By leveraging path signatures in recurrent architectures, this method
offers new opportunities to enhance performance in time series analysis and
forecasting applications.
|
2502.09319
|
Bridging Jensen Gap for Max-Min Group Fairness Optimization in
Recommendation
|
cs.IR cs.LG
|
Group max-min fairness (MMF) is commonly used in fairness-aware recommender
systems (RS) as an optimization objective, as it aims to protect marginalized
item groups and ensures a fair competition platform. However, our theoretical
analysis indicates that integrating MMF constraint violates the assumption of
sample independence during optimization, causing the loss function to deviate
from linear additivity. Such nonlinearity property introduces the Jensen gap
between the model's convergence point and the optimal point if mini-batch
sampling is applied. Both theoretical and empirical studies show that as the
mini-batch size decreases and the group size increases, the Jensen gap will
widen accordingly. Some methods using heuristic re-weighting or debiasing
strategies have the potential to bridge the Jensen gap. However, they either
lack theoretical guarantees or suffer from heavy computational costs. To
overcome these limitations, we first theoretically demonstrate that the
MMF-constrained objective can be essentially reformulated as a group-weighted
optimization objective. Then we present an efficient and effective algorithm
named FairDual, which utilizes a dual optimization technique to minimize the
Jensen gap. Our theoretical analysis demonstrates that FairDual can achieve a
sub-linear convergence rate to the globally optimal solution and the Jensen gap
can be well bounded under a mini-batch sampling strategy with random shuffle.
Extensive experiments conducted using six large-scale RS backbone models on
three publicly available datasets demonstrate that FairDual outperforms all
baselines in terms of both accuracy and fairness. Our data and codes are shared
at https://github.com/XuChen0427/FairDual.
|
2502.09324
|
Depth-Bounds for Neural Networks via the Braid Arrangement
|
cs.LG cs.DM cs.NE math.CO
|
We contribute towards resolving the open question of how many hidden layers
are required in ReLU networks for exactly representing all continuous and
piecewise linear functions on $\mathbb{R}^d$. While the question has been
resolved in special cases, the best known lower bound in general is still 2. We
focus on neural networks that are compatible with certain polyhedral complexes,
more precisely with the braid fan. For such neural networks, we prove a
non-constant lower bound of $\Omega(\log\log d)$ hidden layers required to
exactly represent the maximum of $d$ numbers. Additionally, under our
assumption, we provide a combinatorial proof that 3 hidden layers are necessary
to compute the maximum of 5 numbers; this had only been verified with an
excessive computation so far. Finally, we show that a natural generalization of
the best known upper bound to maxout networks is not tight, by demonstrating
that a rank-3 maxout layer followed by a rank-2 maxout layer is sufficient to
represent the maximum of 7 numbers.
|
2502.09325
|
A Benchmark for Crime Surveillance Video Analysis with Large Models
|
cs.CV
|
Anomaly analysis in surveillance videos is a crucial topic in computer
vision. In recent years, multimodal large language models (MLLMs) have
outperformed task-specific models in various domains. Although MLLMs are
particularly versatile, their abilities to understand anomalous concepts and
details are insufficiently studied because of the outdated benchmarks of this
field not providing MLLM-style QAs and efficient algorithms to assess the
model's open-ended text responses. To fill this gap, we propose a benchmark for
crime surveillance video analysis with large models denoted as UCVL, including
1,829 videos and reorganized annotations from the UCF-Crime and UCF-Crime
Annotation datasets. We design six types of questions and generate diverse QA
pairs. Then we develop detailed instructions and use OpenAI's GPT-4o for
accurate assessment. We benchmark eight prevailing MLLMs ranging from 0.5B to
40B parameters, and the results demonstrate the reliability of this bench.
Moreover, we finetune LLaVA-OneVision on UCVL's training set. The improvement
validates our data's high quality for video anomaly analysis.
|
2502.09329
|
Bayesian Optimization for Simultaneous Selection of Machine Learning
Algorithms and Hyperparameters on Shared Latent Space
|
cs.LG
|
Selecting the optimal combination of a machine learning (ML) algorithm and
its hyper-parameters is crucial for the development of high-performance ML
systems. However, since the combination of ML algorithms and hyper-parameters
is enormous, the exhaustive validation requires a significant amount of time.
Many existing studies use Bayesian optimization (BO) for accelerating the
search. On the other hand, a significant difficulty is that, in general, there
exists a different hyper-parameter space for each one of candidate ML
algorithms. BO-based approaches typically build a surrogate model independently
for each hyper-parameter space, by which sufficient observations are required
for all candidate ML algorithms. In this study, our proposed method embeds
different hyper-parameter spaces into a shared latent space, in which a
surrogate multi-task model for BO is estimated. This approach can share
information of observations from different ML algorithms by which efficient
optimization is expected with a smaller number of total observations. We
further propose the pre-training of the latent space embedding with an
adversarial regularization, and a ranking model for selecting an effective
pre-trained embedding for a given target dataset. Our empirical study
demonstrates effectiveness of the proposed method through datasets from OpenML.
|
2502.09331
|
Beyond English: The Impact of Prompt Translation Strategies across
Languages and Tasks in Multilingual LLMs
|
cs.CL
|
Despite advances in the multilingual capabilities of Large Language Models
(LLMs) across diverse tasks, English remains the dominant language for LLM
research and development. So, when working with a different language, this has
led to the widespread practice of pre-translation, i.e., translating the task
prompt into English before inference. Selective pre-translation, a more
surgical approach, focuses on translating specific prompt components. However,
its current use is sporagic and lacks a systematic research foundation.
Consequently, the optimal pre-translation strategy for various multilingual
settings and tasks remains unclear. In this work, we aim to uncover the optimal
setup for pre-translation by systematically assessing its use. Specifically, we
view the prompt as a modular entity, composed of four functional parts:
instruction, context, examples, and output, either of which could be translated
or not. We evaluate pre-translation strategies across 35 languages covering
both low and high-resource languages, on various tasks including Question
Answering (QA), Natural Language Inference (NLI), Named Entity Recognition
(NER), and Abstractive Summarization. Our experiments show the impact of
factors as similarity to English, translation quality and the size of
pre-trained data, on the model performance with pre-translation. We suggest
practical guidelines for choosing optimal strategies in various multilingual
settings.
|
2502.09332
|
Full Swap Regret and Discretized Calibration
|
cs.LG cs.GT
|
We study the problem of minimizing swap regret in structured normal-form
games. Players have a very large (potentially infinite) number of pure actions,
but each action has an embedding into $d$-dimensional space and payoffs are
given by bilinear functions of these embeddings. We provide an efficient
learning algorithm for this setting that incurs at most
$\tilde{O}(T^{(d+1)/(d+3)})$ swap regret after $T$ rounds.
To achieve this, we introduce a new online learning problem we call
\emph{full swap regret minimization}. In this problem, a learner repeatedly
takes a (randomized) action in a bounded convex $d$-dimensional action set
$\mathcal{K}$ and then receives a loss from the adversary, with the goal of
minimizing their regret with respect to the \emph{worst-case} swap function
mapping $\mathcal{K}$ to $\mathcal{K}$. For varied assumptions about the
convexity and smoothness of the loss functions, we design algorithms with full
swap regret bounds ranging from $O(T^{d/(d+2)})$ to $O(T^{(d+1)/(d+2)})$.
Finally, we apply these tools to the problem of online forecasting to
minimize calibration error, showing that several notions of calibration can be
viewed as specific instances of full swap regret. In particular, we design
efficient algorithms for online forecasting that guarantee at most $O(T^{1/3})$
$\ell_2$-calibration error and $O(\max(\sqrt{\epsilon T}, T^{1/3}))$
\emph{discretized-calibration} error (when the forecaster is restricted to
predicting multiples of $\epsilon$).
|
2502.09335
|
Graph Diffusion Network for Drug-Gene Prediction
|
cs.LG cs.AI
|
Predicting drug-gene associations is crucial for drug development and disease
treatment. While graph neural networks (GNN) have shown effectiveness in this
task, they face challenges with data sparsity and efficient contrastive
learning implementation. We introduce a graph diffusion network for drug-gene
prediction (GDNDGP), a framework that addresses these limitations through two
key innovations. First, it employs meta-path-based homogeneous graph learning
to capture drug-drug and gene-gene relationships, ensuring similar entities
share embedding spaces. Second, it incorporates a parallel diffusion network
that generates hard negative samples during training, eliminating the need for
exhaustive negative sample retrieval. Our model achieves superior performance
on the DGIdb 4.0 dataset and demonstrates strong generalization capability on
tripartite drug-gene-disease networks. Results show significant improvements
over existing methods in drug-gene prediction tasks, particularly in handling
complex heterogeneous relationships. The source code is publicly available at
https://github.com/csjywu1/GDNDGP.
|
2502.09340
|
This looks like what? Challenges and Future Research Directions for
Part-Prototype Models
|
cs.LG
|
The growing interest in eXplainable Artificial Intelligence (XAI) has
prompted research into models with built-in interpretability, the most
prominent of which are part-prototype models. Part-Prototype Models (PPMs) make
decisions by comparing an input image to a set of learned prototypes, providing
human-understandable explanations in the form of ``this looks like that''.
Despite their inherent interpretability, PPMS are not yet considered a valuable
alternative to post-hoc models. In this survey, we investigate the reasons for
this and provide directions for future research. We analyze papers from 2019 to
2024, and derive a taxonomy of the challenges that current PPMS face. Our
analysis shows that the open challenges are quite diverse. The main concern is
the quality and quantity of prototypes. Other concerns are the lack of
generalization to a variety of tasks and contexts, and general methodological
issues, including non-standardized evaluation. We provide ideas for future
research in five broad directions: improving predictive performance, developing
novel architectures grounded in theory, establishing frameworks for human-AI
collaboration, aligning models with humans, and establishing metrics and
benchmarks for evaluation. We hope that this survey will stimulate research and
promote intrinsically interpretable models for application domains. Our list of
surveyed papers is available at https://github.com/aix-group/ppm-survey.
|
2502.09341
|
Neural Spatiotemporal Point Processes: Trends and Challenges
|
cs.LG cs.AI
|
Spatiotemporal point processes (STPPs) are probabilistic models for events
occurring in continuous space and time. Real-world event data often exhibit
intricate dependencies and heterogeneous dynamics. By incorporating modern deep
learning techniques, STPPs can model these complexities more effectively than
traditional approaches. Consequently, the fusion of neural methods with STPPs
has become an active and rapidly evolving research area. In this review, we
categorize existing approaches, unify key design choices, and explain the
challenges of working with this data modality. We further highlight emerging
trends and diverse application domains. Finally, we identify open challenges
and gaps in the literature.
|
2502.09344
|
Revisiting Topological Interference Management: A Learning-to-Code on
Graphs Perspective
|
cs.IT math.IT
|
The advance of topological interference management (TIM) has been one of the
driving forces of recent developments in network information theory. However,
state-of-the-art coding schemes for TIM are usually handcrafted for specific
families of network topologies, relying critically on experts' domain knowledge
and sophisticated treatments. The lack of systematic and automatic generation
of solutions inevitably restricts their potential wider applications to
wireless communication systems, due to the limited generalizability of coding
schemes to wider network configurations. To address such an issue, this work
makes the first attempt to advocate revisiting topological interference
alignment (IA) from a novel learning-to-code perspective. Specifically, we
recast the one-to-one and subspace IA conditions as vector assignment policies
and propose a unifying learning-to-code on graphs (LCG) framework by leveraging
graph neural networks (GNNs) for capturing topological structures and
reinforcement learning (RL) for decision-making of IA beamforming vector
assignment. Interestingly, the proposed LCG framework is capable of recovering
known one-to-one scalar/vector IA solutions for a significantly wider range of
network topologies, and more remarkably of discovering new subspace IA coding
schemes for multiple-antenna cases that are challenging to be handcrafted. The
extensive experiments demonstrate that the LCG framework is an effective way to
automatically produce systematic coding solutions to the TIM instances with
arbitrary network topologies, and at the same time, the underlying learning
algorithm is efficient with respect to online inference time and possesses
excellent generalizability and transferability for practical deployment.
|
2502.09346
|
Machine learning for modelling unstructured grid data in computational
physics: a review
|
cs.LG cs.CE physics.data-an physics.flu-dyn
|
Unstructured grid data are essential for modelling complex geometries and
dynamics in computational physics. Yet, their inherent irregularity presents
significant challenges for conventional machine learning (ML) techniques. This
paper provides a comprehensive review of advanced ML methodologies designed to
handle unstructured grid data in high-dimensional dynamical systems. Key
approaches discussed include graph neural networks, transformer models with
spatial attention mechanisms, interpolation-integrated ML methods, and meshless
techniques such as physics-informed neural networks. These methodologies have
proven effective across diverse fields, including fluid dynamics and
environmental simulations. This review is intended as a guidebook for
computational scientists seeking to apply ML approaches to unstructured grid
data in their domains, as well as for ML researchers looking to address
challenges in computational physics. It places special focus on how ML methods
can overcome the inherent limitations of traditional numerical techniques and,
conversely, how insights from computational physics can inform ML development.
To support benchmarking, this review also provides a summary of open-access
datasets of unstructured grid data in computational physics. Finally, emerging
directions such as generative models with unstructured data, reinforcement
learning for mesh generation, and hybrid physics-data-driven paradigms are
discussed to inspire future advancements in this evolving field.
|
2502.09352
|
Wasserstein distributional adversarial training for deep neural networks
|
cs.LG cs.CV math.OC
|
Design of adversarial attacks for deep neural networks, as well as methods of
adversarial training against them, are subject of intense research. In this
paper, we propose methods to train against distributional attack threats,
extending the TRADES method used for pointwise attacks. Our approach leverages
recent contributions and relies on sensitivity analysis for Wasserstein
distributionally robust optimization problems. We introduce an efficient
fine-tuning method which can be deployed on a previously trained model. We test
our methods on a range of pre-trained models on RobustBench. These experimental
results demonstrate the additional training enhances Wasserstein distributional
robustness, while maintaining original levels of pointwise robustness, even for
already very successful networks. The improvements are less marked for models
pre-trained using huge synthetic datasets of 20-100M images. However,
remarkably, sometimes our methods are still able to improve their performance
even when trained using only the original training dataset (50k images).
|
2502.09355
|
Simultaneous solution of incompressible Navier-Stokes flows on multiple
surfaces
|
cs.CE
|
A mechanical model and finite element method for the simultaneous solution of
Stokes and incompressible Navier-Stokes flows on multiple curved surfaces over
a bulk domain are proposed. The two-dimensional surfaces are defined implicitly
by all level sets of a scalar function, bounded by the three-dimensional bulk
domain. This bulk domain is discretized with hexahedral finite elements which
do not necessarily conform with the level sets but with the boundary. The
resulting numerical method is a hybrid between conforming and non-conforming
finite element methods. Taylor-Hood elements or equal-order element pairs for
velocity and pressure, together with stabilization techniques, are applied to
fulfil the inf-sup conditions resulting from the mixed-type formulation of the
governing equations. Numerical studies confirm good agreement with
independently obtained solutions on selected, individual surfaces. Furthermore,
higher-order convergence rates are obtained for sufficiently smooth solutions.
|
2502.09356
|
Galileo: Learning Global and Local Features in Pretrained Remote Sensing
Models
|
cs.CV
|
From crop mapping to flood detection, machine learning in remote sensing has
a wide range of societally beneficial applications. The commonalities between
remote sensing data in these applications present an opportunity for pretrained
machine learning models tailored to remote sensing to reduce the labeled data
and effort required to solve individual tasks. However, such models must be:
(i) flexible enough to ingest input data of varying sensor modalities and
shapes (i.e., of varying spatial and temporal dimensions), and (ii) able to
model Earth surface phenomena of varying scales and types. To solve this gap,
we present Galileo, a family of pretrained remote sensing models designed to
flexibly process multimodal remote sensing data. We also introduce a novel and
highly effective self-supervised learning approach to learn both large- and
small-scale features, a challenge not addressed by previous models. Our Galileo
models obtain state-of-the-art results across diverse remote sensing tasks.
|
2502.09363
|
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment
Weak Labeling for Events in Time
|
cs.LG
|
Accurate labels are critical for deriving robust machine learning models.
Labels are used to train supervised learning models and to evaluate most
machine learning paradigms. In this paper, we model the accuracy and cost of a
common weak labeling process where annotators assign presence or absence labels
to fixed-length data segments for a given event class. The annotator labels a
segment as "present" if it sufficiently covers an event from that class, e.g.,
a birdsong sound event in audio data. We analyze how the segment length affects
the label accuracy and the required number of annotations, and compare this
fixed-length labeling approach with an oracle method that uses the true event
activations to construct the segments. Furthermore, we quantify the gap between
these methods and verify that in most realistic scenarios the oracle method is
better than the fixed-length labeling method in both accuracy and cost. Our
findings provide a theoretical justification for adaptive weak labeling
strategies that mimic the oracle process, and a foundation for optimizing weak
labeling processes in sequence labeling tasks.
|
2502.09365
|
Simple Path Structural Encoding for Graph Transformers
|
cs.LG cs.AI
|
Graph transformers extend global self-attention to graph-structured data,
achieving notable success in graph learning. Recently, random walk structural
encoding (RWSE) has been found to further enhance their predictive power by
encoding both structural and positional information into the edge
representation. However, RWSE cannot always distinguish between edges that
belong to different local graph patterns, which reduces its ability to capture
the full structural complexity of graphs. This work introduces Simple Path
Structural Encoding (SPSE), a novel method that utilizes simple path counts for
edge encoding. We show theoretically and experimentally that SPSE overcomes the
limitations of RWSE, providing a richer representation of graph structures,
particularly for capturing local cyclic patterns. To make SPSE computationally
tractable, we propose an efficient approximate algorithm for simple path
counting. SPSE demonstrates significant performance improvements over RWSE on
various benchmarks, including molecular and long-range graph datasets,
achieving statistically significant gains in discriminative tasks. These
results pose SPSE as a powerful edge encoding alternative for enhancing the
expressivity of graph transformers.
|
2502.09368
|
Optimal Microcontroller Usage in Reconfigurable Intelligent Surface:
Batteryless IoT Systems Case Study
|
cs.IT math.IT
|
To enhance wireless communication in IoT systems using reconfigurable
intelligent surfaces (RISs), efficient control of programmable passive and
active elements is essential. However, increasing RIS elements requires more
microcontrollers, raising complexity and cost. This paper proposes a modular
approach ("Module"), where each microcontroller controls a module of optimal
active or passive elements. The module size is determined using a non-linear
energy harvesting model, where a batteryless IoT (b-IoT) sensor harvests energy
from base station (BS) RF signals. We optimize the number of modules
(microcontrollers) to minimize energy consumption while satisfying energy
harvesting and information causality constraints. Simulations show that RIS
module-assisted energy harvesting improves IoT system performance by ~100%
compared to models without RIS panels.
|
2502.09369
|
Language Agents as Digital Representatives in Collective Decision-Making
|
cs.LG cs.AI cs.CL cs.CY
|
Consider the process of collective decision-making, in which a group of
individuals interactively select a preferred outcome from among a universe of
alternatives. In this context, "representation" is the activity of making an
individual's preferences present in the process via participation by a proxy
agent -- i.e. their "representative". To this end, learned models of human
behavior have the potential to fill this role, with practical implications for
multi-agent scenario studies and mechanism design. In this work, we investigate
the possibility of training \textit{language agents} to behave in the capacity
of representatives of human agents, appropriately expressing the preferences of
those individuals whom they stand for. First, we formalize the setting of
\textit{collective decision-making} -- as the episodic process of interaction
between a group of agents and a decision mechanism. On this basis, we then
formalize the problem of \textit{digital representation} -- as the simulation
of an agent's behavior to yield equivalent outcomes from the mechanism.
Finally, we conduct an empirical case study in the setting of
\textit{consensus-finding} among diverse humans, and demonstrate the
feasibility of fine-tuning large language models to act as digital
representatives.
|
2502.09374
|
Mitigating multiple single-event upsets during deep neural network
inference using fault-aware training
|
cs.LG
|
Deep neural networks (DNNs) are increasingly used in safety-critical
applications. Reliable fault analysis and mitigation are essential to ensure
their functionality in harsh environments that contain high radiation levels.
This study analyses the impact of multiple single-bit single-event upsets in
DNNs by performing fault injection at the level of a DNN model. Additionally, a
fault aware training (FAT) methodology is proposed that improves the DNNs'
robustness to faults without any modification to the hardware. Experimental
results show that the FAT methodology improves the tolerance to faults up to a
factor 3.
|
2502.09375
|
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming
Recommendation
|
cs.IR
|
Live-streaming services have attracted widespread popularity due to their
real-time interactivity and entertainment value. Users can engage with
live-streaming authors by participating in live chats, posting likes, or
sending virtual gifts to convey their preferences and support. However, the
live-streaming services faces serious data-sparsity problem, which can be
attributed to the following two points: (1) User's valuable behaviors are
usually sparse, e.g., like, comment and gift, which are easily overlooked by
the model, making it difficult to describe user's personalized preference. (2)
The main exposure content on our platform is short-video, which is 9 times
higher than the exposed live-streaming, leading to the inability of
live-streaming content to fully model user preference. To this end, we propose
a Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation, termed
as FARM. Specifically, we first present the intra-domain frequency aware module
to enable our model to perceive user's sparse yet valuable behaviors, i.e.,
high-frequency information, supported by the Discrete Fourier Transform (DFT).
To transfer user preference across the short-video and live-streaming domains,
we propose a novel preference align before fuse strategy, which consists of two
parts: the cross-domain preference align module to align user preference in
both domains with contrastive learning, and the cross-domain preference fuse
module to further fuse user preference in both domains using a serious of
tailor-designed attention mechanisms. Extensive offline experiments and online
A/B testing on Kuaishou live-streaming services demonstrate the effectiveness
and superiority of FARM. Our FARM has been deployed in online live-streaming
services and currently serves hundreds of millions of users on Kuaishou.
|
2502.09376
|
LoRA Training Provably Converges to a Low-Rank Global Minimum or It
Fails Loudly (But it Probably Won't Fail)
|
cs.LG
|
Low-rank adaptation (LoRA) has become a standard approach for fine-tuning
large foundation models. However, our theoretical understanding of LoRA remains
limited as prior analyses of LoRA's training dynamics either rely on
linearization arguments or consider highly simplified setups. In this work, we
analyze the LoRA loss landscape without such restrictive assumptions. We define
two regimes: a ``special regime'', which includes idealized setups where
linearization arguments hold, and a ``generic regime'' representing more
realistic setups where linearization arguments do not hold. In the generic
regime, we show that LoRA training converges to a global minimizer with low
rank and small magnitude, or a qualitatively distinct solution with high rank
and large magnitude. Finally, we argue that the zero-initialization and weight
decay in LoRA training induce an implicit bias toward the low-rank,
small-magnitude region of the parameter space -- where global minima lie --
thus shedding light on why LoRA training usually succeeds in finding global
minima.
|
2502.09378
|
A Deep Inverse-Mapping Model for a Flapping Robotic Wing
|
cs.AI cs.RO
|
In systems control, the dynamics of a system are governed by modulating its
inputs to achieve a desired outcome. For example, to control the thrust of a
quad-copter propeller the controller modulates its rotation rate, relying on a
straightforward mapping between the input rotation rate and the resulting
thrust. This mapping can be inverted to determine the rotation rate needed to
generate a desired thrust. However, in complex systems, such as flapping-wing
robots where intricate fluid motions are involved, mapping inputs (wing
kinematics) to outcomes (aerodynamic forces) is nontrivial and inverting this
mapping for real-time control is computationally impractical. Here, we report a
machine-learning solution for the inverse mapping of a flapping-wing system
based on data from an experimental system we have developed. Our model learns
the input wing motion required to generate a desired aerodynamic force outcome.
We used a sequence-to-sequence model tailored for time-series data and
augmented it with a novel adaptive-spectrum layer that implements
representation learning in the frequency domain. To train our model, we
developed a flapping wing system that simultaneously measures the wing's
aerodynamic force and its 3D motion using high-speed cameras. We demonstrate
the performance of our system on an additional open-source dataset of a
flapping wing in a different flow regime. Results show superior performance
compared with more complex state-of-the-art transformer-based models, with 11%
improvement on the test datasets median loss. Moreover, our model shows
superior inference time, making it practical for onboard robotic control. Our
open-source data and framework may improve modeling and real-time control of
systems governed by complex dynamics, from biomimetic robots to biomedical
devices.
|
2502.09379
|
TRIFFID: Autonomous Robotic Aid For Increasing First Responders
Efficiency
|
cs.RO cs.AI
|
The increasing complexity of natural disaster incidents demands innovative
technological solutions to support first responders in their efforts. This
paper introduces the TRIFFID system, a comprehensive technical framework that
integrates unmanned ground and aerial vehicles with advanced artificial
intelligence functionalities to enhance disaster response capabilities across
wildfires, urban floods, and post-earthquake search and rescue missions. By
leveraging state-of-the-art autonomous navigation, semantic perception, and
human-robot interaction technologies, TRIFFID provides a sophisticated system
composed of the following key components: hybrid robotic platform, centralized
ground station, custom communication infrastructure, and smartphone
application. The defined research and development activities demonstrate how
deep neural networks, knowledge graphs, and multimodal information fusion can
enable robots to autonomously navigate and analyze disaster environments,
reducing personnel risks and accelerating response times. The proposed system
enhances emergency response teams by providing advanced mission planning,
safety monitoring, and adaptive task execution capabilities. Moreover, it
ensures real-time situational awareness and operational support in complex and
risky situations, facilitating rapid and precise information collection and
coordinated actions.
|
2502.09387
|
Truth Knows No Language: Evaluating Truthfulness Beyond English
|
cs.CL cs.AI cs.CY
|
We introduce a professionally translated extension of the TruthfulQA
benchmark designed to evaluate truthfulness in Basque, Catalan, Galician, and
Spanish. Truthfulness evaluations of large language models (LLMs) have
primarily been conducted in English. However, the ability of LLMs to maintain
truthfulness across languages remains under-explored. Our study evaluates 12
state-of-the-art open LLMs, comparing base and instruction-tuned models using
human evaluation, multiple-choice metrics, and LLM-as-a-Judge scoring. Our
findings reveal that, while LLMs perform best in English and worst in Basque
(the lowest-resourced language), overall truthfulness discrepancies across
languages are smaller than anticipated. Furthermore, we show that
LLM-as-a-Judge correlates more closely with human judgments than
multiple-choice metrics, and that informativeness plays a critical role in
truthfulness assessment. Our results also indicate that machine translation
provides a viable approach for extending truthfulness benchmarks to additional
languages, offering a scalable alternative to professional translation.
Finally, we observe that universal knowledge questions are better handled
across languages than context- and time-dependent ones, highlighting the need
for truthfulness evaluations that account for cultural and temporal
variability. Dataset and code are publicly available under open licenses.
|
2502.09389
|
S$^2$-Diffusion: Generalizing from Instance-level to Category-level
Skills in Robot Manipulation
|
cs.RO cs.AI
|
Recent advances in skill learning has propelled robot manipulation to new
heights by enabling it to learn complex manipulation tasks from a practical
number of demonstrations. However, these skills are often limited to the
particular action, object, and environment \textit{instances} that are shown in
the training data, and have trouble transferring to other instances of the same
category. In this work we present an open-vocabulary Spatial-Semantic Diffusion
policy (S$^2$-Diffusion) which enables generalization from instance-level
training data to category-level, enabling skills to be transferable between
instances of the same category. We show that functional aspects of skills can
be captured via a promptable semantic module combined with a spatial
representation. We further propose leveraging depth estimation networks to
allow the use of only a single RGB camera. Our approach is evaluated and
compared on a diverse number of robot manipulation tasks, both in simulation
and in the real world. Our results show that S$^2$-Diffusion is invariant to
changes in category-irrelevant factors as well as enables satisfying
performance on other instances within the same category, even if it was not
trained on that specific instance. Full videos of all real-world experiments
are available in the supplementary material.
|
2502.09390
|
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
Chain-of-Thought in Large Language Models
|
cs.CL cs.AI cs.LG
|
In the rapidly evolving field of Natural Language Processing, Large Language
Models (LLMs) are tasked with increasingly complex reasoning challenges.
Traditional methods like chain-of-thought prompting have shown promise but
often fall short in fully leveraging a model's reasoning capabilities. This
paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a
novel prompting technique designed to improve reasoning through a
self-interrogation paradigm. Building upon CoT frameworks, SQuARE prompts
models to generate and resolve multiple auxiliary questions before tackling the
main query, promoting a more thorough exploration of various aspects of a
topic. Our expansive evaluations, conducted with Llama 3 and GPT-4o models
across multiple question-answering datasets, demonstrate that SQuARE
significantly surpasses traditional CoT prompts and existing
rephrase-and-respond methods. By systematically decomposing queries, SQuARE
advances LLM capabilities in reasoning tasks. The code is publicly available at
https://github.com/IntelLabs/RAG-FiT/tree/square.
|
2502.09393
|
Generalizable Reinforcement Learning with Biologically Inspired
Hyperdimensional Occupancy Grid Maps for Exploration and Goal-Directed Path
Planning
|
cs.RO cs.NE
|
Real-time autonomous systems utilize multi-layer computational frameworks to
perform critical tasks such as perception, goal finding, and path planning.
Traditional methods implement perception using occupancy grid mapping (OGM),
segmenting the environment into discretized cells with probabilistic
information. This classical approach is well-established and provides a
structured input for downstream processes like goal finding and path planning
algorithms. Recent approaches leverage a biologically inspired mathematical
framework known as vector symbolic architectures (VSA), commonly known as
hyperdimensional computing, to perform probabilistic OGM in hyperdimensional
space. This approach, VSA-OGM, provides native compatibility with spiking
neural networks, positioning VSA-OGM as a potential neuromorphic alternative to
conventional OGM. However, for large-scale integration, it is essential to
assess the performance implications of VSA-OGM on downstream tasks compared to
established OGM methods. This study examines the efficacy of VSA-OGM against a
traditional OGM approach, Bayesian Hilbert Maps (BHM), within reinforcement
learning based goal finding and path planning frameworks, across a controlled
exploration environment and an autonomous driving scenario inspired by the
F1-Tenth challenge. Our results demonstrate that VSA-OGM maintains comparable
learning performance across single and multi-scenario training configurations
while improving performance on unseen environments by approximately 47%. These
findings highlight the increased generalizability of policy networks trained
with VSA-OGM over BHM, reinforcing its potential for real-world deployment in
diverse environments.
|
2502.09395
|
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative
Action Parameters Using Probabilistic Actual Causation
|
cs.RO cs.LG
|
In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a
large variety of objects and goals. When confronted with an unexpected or
unwanted outcome, we take corrective actions and try again until achieving the
desired result. The reasoning performed to identify a cause of the observed
outcome and to select an appropriate corrective action is a crucial aspect of
human reasoning for successful task execution. Central to this reasoning is the
assumption that a factor is responsible for producing the observed outcome. In
this paper, we investigate the use of probabilistic actual causation to
determine whether a factor is the cause of an observed undesired outcome.
Furthermore, we show how the actual causation probabilities can be used to find
alternative actions to change the outcome. We apply the probabilistic actual
causation analysis to a robot pouring task. When spillage occurs, the analysis
indicates whether a task parameter is the cause and how it should be changed to
avoid spillage. The analysis requires a causal graph of the task and the
corresponding conditional probability distributions. To fulfill these
requirements, we perform a complete causal modeling procedure (i.e., task
analysis, definition of variables, determination of the causal graph structure,
and estimation of conditional probability distributions) using data from a
realistic simulation of the robot pouring task, covering a large combinatorial
space of task parameters. Based on the results, we discuss the implications of
the variables' representation and how the alternative actions suggested by the
actual causation analysis would compare to the alternative solutions proposed
by a human observer. The practical use of the analysis of probabilistic actual
causation to select alternative action parameters is demonstrated.
|
2502.09396
|
A hierarchical approach for assessing the vulnerability of tree-based
classification models to membership inference attack
|
cs.LG cs.CR
|
Machine learning models can inadvertently expose confidential properties of
their training data, making them vulnerable to membership inference attacks
(MIA). While numerous evaluation methods exist, many require computationally
expensive processes, such as training multiple shadow models. This article
presents two new complementary approaches for efficiently identifying
vulnerable tree-based models: an ante-hoc analysis of hyperparameter choices
and a post-hoc examination of trained model structure. While these new methods
cannot certify whether a model is safe from MIA, they provide practitioners
with a means to significantly reduce the number of models that need to undergo
expensive MIA assessment through a hierarchical filtering approach.
More specifically, it is shown that the rank order of disclosure risk for
different hyperparameter combinations remains consistent across datasets,
enabling the development of simple, human-interpretable rules for identifying
relatively high-risk models before training. While this ante-hoc analysis
cannot determine absolute safety since this also depends on the specific
dataset, it allows the elimination of unnecessarily risky configurations during
hyperparameter tuning. Additionally, computationally inexpensive structural
metrics serve as indicators of MIA vulnerability, providing a second filtering
stage to identify risky models after training but before conducting expensive
attacks. Empirical results show that hyperparameter-based risk prediction rules
can achieve high accuracy in predicting the most at risk combinations of
hyperparameters across different tree-based model types, while requiring no
model training. Moreover, target model accuracy is not seen to correlate with
privacy risk, suggesting opportunities to optimise model configurations for
both performance and privacy.
|
2502.09411
|
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation
|
cs.CV cs.GR
|
Diffusion models enable high-quality and diverse visual content synthesis.
However, they struggle to generate rare or unseen concepts. To address this
challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with
image generation models. We propose ImageRAG, a method that dynamically
retrieves relevant images based on a given text prompt, and uses them as
context to guide the generation process. Prior approaches that used retrieved
images to improve generation, trained models specifically for retrieval-based
generation. In contrast, ImageRAG leverages the capabilities of existing image
conditioning models, and does not require RAG-specific training. Our approach
is highly adaptable and can be applied across different model types, showing
significant improvement in generating rare and fine-grained concepts using
different base models.
Our project page is available at: https://rotem-shalev.github.io/ImageRAG
|
2502.09416
|
Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use
a Different Evaluation Process than Human?
|
cs.CL
|
One of the goals of automatic evaluation metrics in grammatical error
correction (GEC) is to rank GEC systems such that it matches human preferences.
However, current automatic evaluations are based on procedures that diverge
from human evaluation. Specifically, human evaluation derives rankings by
aggregating sentence-level relative evaluation results, e.g., pairwise
comparisons, using a rating algorithm, whereas automatic evaluation averages
sentence-level absolute scores to obtain corpus-level scores, which are then
sorted to determine rankings. In this study, we propose an aggregation method
for existing automatic evaluation metrics which aligns with human evaluation
methods to bridge this gap. We conducted experiments using various metrics,
including edit-based metrics, $n$-gram based metrics, and sentence-level
metrics, and show that resolving the gap improves results for the most of
metrics on the SEEDA benchmark. We also found that even BERT-based metrics
sometimes outperform the metrics of GPT-4. We publish our unified
implementation of the metrics and meta-evaluations.
|
2502.09417
|
A Survey of Reinforcement Learning for Optimization in Automation
|
cs.LG cs.AI cs.NE cs.RO cs.SY eess.SY
|
Reinforcement Learning (RL) has become a critical tool for optimization
challenges within automation, leading to significant advancements in several
areas. This review article examines the current landscape of RL within
automation, with a particular focus on its roles in manufacturing, energy
systems, and robotics. It discusses state-of-the-art methods, major challenges,
and upcoming avenues of research within each sector, highlighting RL's capacity
to solve intricate optimization challenges. The paper reviews the advantages
and constraints of RL-driven optimization methods in automation. It points out
prevalent challenges encountered in RL optimization, including issues related
to sample efficiency and scalability; safety and robustness; interpretability
and trustworthiness; transfer learning and meta-learning; and real-world
deployment and integration. It further explores prospective strategies and
future research pathways to navigate these challenges. Additionally, the survey
includes a comprehensive list of relevant research papers, making it an
indispensable guide for scholars and practitioners keen on exploring this
domain.
|
2502.09419
|
On multi-token prediction for efficient LLM inference
|
cs.CL cs.LG
|
We systematically investigate multi-token prediction (MTP) capabilities
within LLMs pre-trained for next-token prediction (NTP). We first show that
such models inherently possess MTP capabilities via numerical marginalization
over intermediate token probabilities, though performance is data-dependent and
improves with model scale. Furthermore, we explore the challenges of
integrating MTP heads into frozen LLMs and find that their hidden layers are
strongly specialized for NTP, making adaptation non-trivial. Finally, we show
that while joint training of MTP heads with the backbone improves performance,
it cannot fully overcome this barrier, prompting further research in this
direction. Our findings provide a deeper understanding of MTP applied to
pretrained LLMs, informing strategies for accelerating inference through
parallel token prediction.
|
2502.09423
|
Transformer-Enhanced Variational Autoencoder for Crystal Structure
Prediction
|
cond-mat.mtrl-sci cs.AI
|
Crystal structure forms the foundation for understanding the physical and
chemical properties of materials. Generative models have emerged as a new
paradigm in crystal structure prediction(CSP), however, accurately capturing
key characteristics of crystal structures, such as periodicity and symmetry,
remains a significant challenge. In this paper, we propose a
Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction
(TransVAE-CSP), who learns the characteristic distribution space of stable
materials, enabling both the reconstruction and generation of crystal
structures. TransVAE-CSP integrates adaptive distance expansion with
irreducible representation to effectively capture the periodicity and symmetry
of crystal structures, and the encoder is a transformer network based on an
equivariant dot product attention mechanism. Experimental results on the
carbon_24, perov_5, and mp_20 datasets demonstrate that TransVAE-CSP
outperforms existing methods in structure reconstruction and generation tasks
under various modeling metrics, offering a powerful tool for crystal structure
design and optimization.
|
2502.09425
|
A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone
Scans with Deep Learning Based Methods Using Geometry and Morphometry
Criteria
|
cs.CV
|
Three-dimensional (3D) facial shape analysis has gained interest due to its
potential clinical applications. However, the high cost of advanced 3D facial
acquisition systems limits their widespread use, driving the development of
low-cost acquisition and reconstruction methods. This study introduces a novel
evaluation methodology that goes beyond traditional geometry-based benchmarks
by integrating morphometric shape analysis techniques, providing a statistical
framework for assessing facial morphology preservation. As a case study, we
compare smartphone-based 3D scans with state-of-the-art deep learning
reconstruction methods from 2D images, using high-end stereophotogrammetry
models as ground truth. This methodology enables a quantitative assessment of
global and local shape differences, offering a biologically meaningful
validation approach for low-cost 3D facial acquisition and reconstruction
techniques.
|
2502.09431
|
On Usage of Non-Volatile Memory as Primary Storage for Database
Management Systems
|
cs.DB
|
This paper explores the implications of employing non-volatile memory (NVM)
as primary storage for a data base management system (DBMS). We investigate the
modifications necessary to be applied on top of a traditional relational DBMS
to take advantage of NVM features. As a case study, we modify the storage
engine (SE) of PostgreSQL enabling efficient use of NVM hardware. We detail the
necessary changes and challenges such modifications entail and evaluate them
using a comprehensive emulation platform. Results indicate that our modified SE
reduces query execution time by up to 45% and 13% when compared to disk and NVM
storage, with average reductions of 19% and 4%, respectively. Detailed analysis
of these results shows that while our modified SE is able to access data more
efficiently, data is not close to the processing units when needed for
processing, incurring long latency misses that hinder the performance. To solve
this, we develop a general purpose library that employs helper threads to
prefetch data from NVM hardware via a simple API. Our library further improves
query execution time for our modified SE when compared to disk and NVM storage
by up to 54% and 17%, with average reductions of 23% and 8%, respectively.
|
2502.09432
|
Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes
|
cs.AI cs.LG
|
We study robust Markov decision processes (RMDPs) with non-rectangular
uncertainty sets, which capture interdependencies across states unlike
traditional rectangular models. While non-rectangular robust policy evaluation
is generally NP-hard, even in approximation, we identify a powerful class of
$L_p$-bounded uncertainty sets that avoid these complexity barriers due to
their structural simplicity. We further show that this class can be decomposed
into infinitely many \texttt{sa}-rectangular $L_p$-bounded sets and leverage
its structural properties to derive a novel dual formulation for $L_p$ RMDPs.
This formulation provides key insights into the adversary's strategy and
enables the development of the first robust policy evaluation algorithms for
non-rectangular RMDPs. Empirical results demonstrate that our approach
significantly outperforms brute-force methods, establishing a promising
foundation for future investigation into non-rectangular robust MDPs.
|
2502.09434
|
Redistribute Ensemble Training for Mitigating Memorization in Diffusion
Models
|
cs.CV
|
Diffusion models, known for their tremendous ability to generate high-quality
samples, have recently raised concerns due to their data memorization behavior,
which poses privacy risks. Recent methods for memory mitigation have primarily
addressed the issue within the context of the text modality in cross-modal
generation tasks, restricting their applicability to specific conditions. In
this paper, we propose a novel method for diffusion models from the perspective
of visual modality, which is more generic and fundamental for mitigating
memorization. Directly exposing visual data to the model increases memorization
risk, so we design a framework where models learn through proxy model
parameters instead. Specially, the training dataset is divided into multiple
shards, with each shard training a proxy model, then aggregated to form the
final model. Additionally, practical analysis of training losses illustrates
that the losses for easily memorable images tend to be obviously lower. Thus,
we skip the samples with abnormally low loss values from the current mini-batch
to avoid memorizing. However, balancing the need to skip memorization-prone
samples while maintaining sufficient training data for high-quality image
generation presents a key challenge. Thus, we propose IET-AGC+, which
redistributes highly memorizable samples between shards, to mitigate these
samples from over-skipping. Furthermore, we dynamically augment samples based
on their loss values to further reduce memorization. Extensive experiments and
analysis on four datasets show that our method successfully reduces memory
capacity while maintaining performance. Moreover, we fine-tune the pre-trained
diffusion models, e.g., Stable Diffusion, and decrease the memorization score
by 46.7\%, demonstrating the effectiveness of our method. Code is available in:
https://github.com/liuxiao-guan/IET_AGC.
|
2502.09436
|
Variable Stiffness for Robust Locomotion through Reinforcement Learning
|
cs.RO cs.AI
|
Reinforcement-learned locomotion enables legged robots to perform highly
dynamic motions but often accompanies time-consuming manual tuning of joint
stiffness. This paper introduces a novel control paradigm that integrates
variable stiffness into the action space alongside joint positions, enabling
grouped stiffness control such as per-joint stiffness (PJS), per-leg stiffness
(PLS) and hybrid joint-leg stiffness (HJLS). We show that variable stiffness
policies, with grouping in per-leg stiffness (PLS), outperform position-based
control in velocity tracking and push recovery. In contrast, HJLS excels in
energy efficiency. Furthermore, our method showcases robust walking behaviour
on diverse outdoor terrains by sim-to-real transfer, although the policy is
sorely trained on a flat floor. Our approach simplifies design by eliminating
per-joint stiffness tuning while keeping competitive results with various
metrics.
|
2502.09443
|
Relational Conformal Prediction for Correlated Time Series
|
cs.LG cs.AI
|
We address the problem of uncertainty quantification in time series
forecasting by exploiting observations at correlated sequences. Relational deep
learning methods leveraging graph representations are among the most effective
tools for obtaining point estimates from spatiotemporal data and correlated
time series. However, the problem of exploiting relational structures to
estimate the uncertainty of such predictions has been largely overlooked in the
same context. To this end, we propose a novel distribution-free approach based
on the conformal prediction framework and quantile regression. Despite the
recent applications of conformal prediction to sequential data, existing
methods operate independently on each target time series and do not account for
relationships among them when constructing the prediction interval. We fill
this void by introducing a novel conformal prediction method based on graph
deep learning operators. Our method, named Conformal Relational Prediction
(CoRel), does not require the relational structure (graph) to be known as a
prior and can be applied on top of any pre-trained time series predictor.
Additionally, CoRel includes an adaptive component to handle non-exchangeable
data and changes in the input time series. Our approach provides accurate
coverage and archives state-of-the-art uncertainty quantification in relevant
benchmarks.
|
2502.09445
|
A Differentiable Rank-Based Objective For Better Feature Learning
|
stat.ML cs.LG
|
In this paper, we leverage existing statistical methods to better understand
feature learning from data. We tackle this by modifying the model-free variable
selection method, Feature Ordering by Conditional Independence (FOCI), which is
introduced in \cite{azadkia2021simple}. While FOCI is based on a non-parametric
coefficient of conditional dependence, we introduce its parametric,
differentiable approximation. With this approximate coefficient of correlation,
we present a new algorithm called difFOCI, which is applicable to a wider range
of machine learning problems thanks to its differentiable nature and learnable
parameters. We present difFOCI in three contexts: (1) as a variable selection
method with baseline comparisons to FOCI, (2) as a trainable model parametrized
with a neural network, and (3) as a generic, widely applicable neural network
regularizer, one that improves feature learning with better management of
spurious correlations. We evaluate difFOCI on increasingly complex problems
ranging from basic variable selection in toy examples to saliency map
comparisons in convolutional networks. We then show how difFOCI can be
incorporated in the context of fairness to facilitate classifications without
relying on sensitive data.
|
2502.09446
|
Drivers of cooperation in social dilemmas on higher-order networks
|
physics.soc-ph cs.GT cs.SI q-bio.PE
|
Understanding cooperation in social dilemmas requires models that capture the
complexity of real-world interactions. While network frameworks have provided
valuable insights to model the evolution of cooperation, they are unable to
encode group interactions properly. Here, we introduce a general higher-order
network framework for multi-player games on structured populations. Our model
considers multi-dimensional strategies, based on the observation that social
behaviours are affected by the size of the group interaction. We investigate
dynamical and structural coupling between different orders of interactions,
revealing the crucial role of nested multilevel interactions, and showing how
such features can enhance cooperation beyond the limit of traditional models
with uni-dimensional strategies. Our work identifies the key drivers promoting
cooperative behaviour commonly observed in real-world group social dilemmas.
|
2502.09447
|
Pixel-Level Reasoning Segmentation via Multi-turn Conversations
|
cs.CV cs.CL
|
Existing visual perception systems focus on region-level segmentation in
single-turn dialogues, relying on complex and explicit query instructions. Such
systems cannot reason at the pixel level and comprehend dynamic user intent
that changes over interaction. Our work tackles this issue by introducing a
novel task, Pixel-level Reasoning Segmentation (Pixel-level RS) based on
multi-turn conversations, tracking evolving user intent via multi-turn
interactions for fine-grained segmentation. To establish a benchmark for this
novel task, we build a Pixel-level ReasonIng Segmentation Dataset Based on
Multi-Turn Conversations (PRIST), comprising 24k utterances from 8.3k
multi-turn conversational scenarios with segmentation targets. Building on
PRIST, we further propose MIRAS, a Multi-turn Interactive ReAsoning
Segmentation framework, integrates pixel-level segmentation with robust
multi-turn conversation understanding, generating pixel-grounded explanations
aligned with user intent. The PRIST dataset and MIRSA framework fill the gap in
pixel-level reasoning segmentation. Experimental results on the PRIST dataset
demonstrate that our method outperforms current segmentation-specific baselines
in terms of segmentation and LLM-based reasoning metrics. The code and data are
available at: https://github.com/ccccai239/PixelRIST.
|
2502.09449
|
Spiking Neural Networks for Temporal Processing: Status Quo and Future
Prospects
|
cs.NE
|
Temporal processing is fundamental for both biological and artificial
intelligence systems, as it enables the comprehension of dynamic environments
and facilitates timely responses. Spiking Neural Networks (SNNs) excel in
handling such data with high efficiency, owing to their rich neuronal dynamics
and sparse activity patterns. Given the recent surge in the development of
SNNs, there is an urgent need for a comprehensive evaluation of their temporal
processing capabilities. In this paper, we first conduct an in-depth assessment
of commonly used neuromorphic benchmarks, revealing critical limitations in
their ability to evaluate the temporal processing capabilities of SNNs. To
bridge this gap, we further introduce a benchmark suite consisting of three
temporal processing tasks characterized by rich temporal dynamics across
multiple timescales. Utilizing this benchmark suite, we perform a thorough
evaluation of recently introduced SNN approaches to elucidate the current
status of SNNs in temporal processing. Our findings indicate significant
advancements in recently developed spiking neuron models and neural
architectures regarding their temporal processing capabilities, while also
highlighting a performance gap in handling long-range dependencies when
compared to state-of-the-art non-spiking models. Finally, we discuss the key
challenges and outline potential avenues for future research.
|
2502.09457
|
The Multilingual Mind : A Survey of Multilingual Reasoning in Language
Models
|
cs.CL
|
While reasoning and multilingual capabilities in Language Models (LMs) have
achieved remarkable progress in recent years, their integration into a unified
paradigm, multilingual reasoning, is at a nascent stage. Multilingual reasoning
requires language models to handle logical reasoning across languages while
addressing misalignment, biases, and challenges in low-resource settings. This
survey provides the first in-depth review of multilingual reasoning in LMs. In
this survey, we provide a systematic overview of existing methods that leverage
LMs for multilingual reasoning, specifically outlining the challenges,
motivations, and foundational aspects of applying language models to reason
across diverse languages. We provide an overview of the standard data resources
used for training multilingual reasoning in LMs and the evaluation benchmarks
employed to assess their multilingual capabilities. Next, we analyze various
state-of-the-art methods and their performance on these benchmarks. Finally, we
explore future research opportunities to improve multilingual reasoning in LMs,
focusing on enhancing their ability to handle diverse languages and complex
reasoning tasks.
|
2502.09460
|
Metamorphic Testing for Pose Estimation Systems
|
cs.SE cs.AI cs.CV
|
Pose estimation systems are used in a variety of fields, from sports
analytics to livestock care. Given their potential impact, it is paramount to
systematically test their behaviour and potential for failure. This is a
complex task due to the oracle problem and the high cost of manual labelling
necessary to build ground truth keypoints. This problem is exacerbated by the
fact that different applications require systems to focus on different subjects
(e.g., human versus animal) or landmarks (e.g., only extremities versus whole
body and face), which makes labelled test data rarely reusable. To combat these
problems we propose MET-POSE, a metamorphic testing framework for pose
estimation systems that bypasses the need for manual annotation while assessing
the performance of these systems under different circumstances. MET-POSE thus
allows users of pose estimation systems to assess the systems in conditions
that more closely relate to their application without having to label an ad-hoc
test dataset or rely only on available datasets, which may not be adapted to
their application domain. While we define MET-POSE in general terms, we also
present a non-exhaustive list of metamorphic rules that represent common
challenges in computer vision applications, as well as a specific way to
evaluate these rules. We then experimentally show the effectiveness of MET-POSE
by applying it to Mediapipe Holistic, a state of the art human pose estimation
system, with the FLIC and PHOENIX datasets. With these experiments, we outline
numerous ways in which the outputs of MET-POSE can uncover faults in pose
estimation systems at a similar or higher rate than classic testing using hand
labelled data, and show that users can tailor the rule set they use to the
faults and level of accuracy relevant to their application.
|
2502.09471
|
Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for
Weakly-supervised Oriented Object Detection
|
cs.CV cs.AI
|
Accurately estimating the orientation of visual objects with compact rotated
bounding boxes (RBoxes) has become a prominent demand, which challenges
existing object detection paradigms that only use horizontal bounding boxes
(HBoxes). To equip the detectors with orientation awareness, supervised
regression/classification modules have been introduced at the high cost of
rotation annotation. Meanwhile, some existing datasets with oriented objects
are already annotated with horizontal boxes or even single points. It becomes
attractive yet remains open for effectively utilizing weaker single point and
horizontal annotations to train an oriented object detector (OOD). We develop
Wholly-WOOD, a weakly-supervised OOD framework, capable of wholly leveraging
various labeling forms (Points, HBoxes, RBoxes, and their combination) in a
unified fashion. By only using HBox for training, our Wholly-WOOD achieves
performance very close to that of the RBox-trained counterpart on remote
sensing and other areas, significantly reducing the tedious efforts on
labor-intensive annotation for oriented objects. The source codes are available
at https://github.com/VisionXLab/whollywood (PyTorch-based) and
https://github.com/VisionXLab/whollywood-jittor (Jittor-based).
|
2502.09473
|
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse
Measurements
|
cs.LG eess.SP
|
Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all
treatment with limited success in persistent AF. This may be due to our
inability to map the dynamics of AF with the limited resolution and coverage
provided by sequential contact mapping catheters, preventing effective patient
phenotyping for personalised, targeted ablation. Here we introduce FibMap, a
graph recurrent neural network model that reconstructs global AF dynamics from
sparse measurements. Trained and validated on 51 non-contact whole atria
recordings, FibMap reconstructs whole atria dynamics from 10% surface coverage,
achieving a 210% lower mean absolute error and an order of magnitude higher
performance in tracking phase singularities compared to baseline methods.
Clinical utility of FibMap is demonstrated on real-world contact mapping
recordings, achieving reconstruction fidelity comparable to non-contact
mapping. FibMap's state-spaces and patient-specific parameters offer insights
for electrophenotyping AF. Integrating FibMap into clinical practice could
enable personalised AF care and improve outcomes.
|
2502.09477
|
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation
Networks for Quantitative Nanomaterial Analysis through Differentiable
Rendering and Generative Modelling
|
cond-mat.mtrl-sci cs.CV cs.LG
|
Nanomaterials exhibit distinctive properties governed by parameters such as
size, shape, and surface characteristics, which critically influence their
applications and interactions across technological, biological, and
environmental contexts. Accurate quantification and understanding of these
materials are essential for advancing research and innovation. In this regard,
deep learning segmentation networks have emerged as powerful tools that enable
automated insights and replace subjective methods with precise quantitative
analysis. However, their efficacy depends on representative annotated datasets,
which are challenging to obtain due to the costly imaging of nanoparticles and
the labor-intensive nature of manual annotations. To overcome these
limitations, we introduce DiffRenderGAN, a novel generative model designed to
produce annotated synthetic data. By integrating a differentiable renderer into
a Generative Adversarial Network (GAN) framework, DiffRenderGAN optimizes
textural rendering parameters to generate realistic, annotated nanoparticle
images from non-annotated real microscopy images. This approach reduces the
need for manual intervention and enhances segmentation performance compared to
existing synthetic data methods by generating diverse and realistic data.
Tested on multiple ion and electron microscopy cases, including titanium
dioxide (TiO$_2$), silicon dioxide (SiO$_2$)), and silver nanowires (AgNW),
DiffRenderGAN bridges the gap between synthetic and real data, advancing the
quantification and understanding of complex nanomaterial systems.
|
2502.09479
|
Assessing Generative AI value in a public sector context: evidence from
a field experiment
|
q-fin.GN cs.LG econ.GN q-fin.EC
|
The emergence of Generative AI (Gen AI) has motivated an interest in
understanding how it could be used to enhance productivity across various
tasks. We add to research results for the performance impact of Gen AI on
complex knowledge-based tasks in a public sector setting. In a pre-registered
experiment, after establishing a baseline level of performance, we find mixed
evidence for two types of composite tasks related to document understanding and
data analysis. For the Documents task, the treatment group using Gen AI had a
17% improvement in answer quality scores (as judged by human evaluators) and a
34% improvement in task completion time compared to a control group. For the
Data task, we find the Gen AI treatment group experienced a 12% reduction in
quality scores and no significant difference in mean completion time compared
to the control group. These results suggest that the benefits of Gen AI may be
task and potentially respondent dependent. We also discuss field notes and
lessons learned, as well as supplementary insights from a post-trial survey and
feedback workshop with participants.
|
2502.09482
|
Standardisation of Convex Ultrasound Data Through Geometric Analysis and
Augmentation
|
cs.CV
|
The application of ultrasound in healthcare has seen increased diversity and
importance. Unlike other medical imaging modalities, ultrasound research and
development has historically lagged, particularly in the case of applications
with data-driven algorithms. A significant issue with ultrasound is the extreme
variability of the images, due to the number of different machines available
and the possible combination of parameter settings. One outcome of this is the
lack of standardised and benchmarking ultrasound datasets. The method proposed
in this article is an approach to alleviating this issue of disorganisation.
For this purpose, the issue of ultrasound data sparsity is examined and a novel
perspective, approach, and solution is proposed; involving the extraction of
the underlying ultrasound plane within the image and representing it using
annulus sector geometry. An application of this methodology is proposed, which
is the extraction of scan lines and the linearisation of convex planes.
Validation of the robustness of the proposed method is performed on both
private and public data. The impact of deformation and the invertibility of
augmentation using the estimated annulus sector parameters is also studied.
Keywords: Ultrasound, Annulus Sector, Augmentation, Linearisation.
|
2502.09484
|
PenTest++: Elevating Ethical Hacking with AI and Automation
|
cs.CR cs.AI
|
Traditional ethical hacking relies on skilled professionals and
time-intensive command management, which limits its scalability and efficiency.
To address these challenges, we introduce PenTest++, an AI-augmented system
that integrates automation with generative AI (GenAI) to optimise ethical
hacking workflows. Developed in a controlled virtual environment, PenTest++
streamlines critical penetration testing tasks, including reconnaissance,
scanning, enumeration, exploitation, and documentation, while maintaining a
modular and adaptable design. The system balances automation with human
oversight, ensuring informed decision-making at key stages, and offers
significant benefits such as enhanced efficiency, scalability, and
adaptability. However, it also raises ethical considerations, including privacy
concerns and the risks of AI-generated inaccuracies (hallucinations). This
research underscores the potential of AI-driven systems like PenTest++ to
complement human expertise in cybersecurity by automating routine tasks,
enabling professionals to focus on strategic decision-making. By incorporating
robust ethical safeguards and promoting ongoing refinement, PenTest++
demonstrates how AI can be responsibly harnessed to address operational and
ethical challenges in the evolving cybersecurity landscape.
|
2502.09487
|
Objective quantification of mood states using large language models
|
cs.CL cs.AI cs.LG
|
Emotional states influence human behaviour and cognition, leading to diverse
thought trajectories. Similarly, Large Language Models (LLMs) showcase an
excellent level of response consistency across wide-ranging contexts (prompts).
We leverage these parallels to establish a framework for quantifying mental
states. Our approach utilises self-report questionnaires that reliably assess
these states due to their inherent sensitivity to patterns of co-occurring
responses. Specifically, we recruited a large sample of participants (N=422) to
investigate how well an LLM (Mistral-7B-OpenOrca) quantifies a heterogenous set
of depressive mood states measured with participants' open-ended responses to a
depression questionnaire. We show LLM responses to held-out multiple-choice
questions, given participants' open-ended answers, correlate strongly (r:
0.52-0.84) with true questionnaire scores, demonstrating LLM's generalisation
from mood representations. We explore a link between these representations and
factor analysis. Using ridge regression, we find depression-related subspaces
within LLM hidden states. We show these subspaces to be predictive of
participants' "Depression" and "Somatic & Emotional Distress" factor scores, as
well as suicidality severity. Overall, LLMs can provide quantitative measures
of mental states. The reliability of these hinges upon how informative the
questions we ask participants are. Used correctly, this approach could
supplement mental state assessment in a variety of settings.
|
2502.09490
|
Inverse Design with Dynamic Mode Decomposition
|
cs.LG cs.SY eess.SY math.DS math.OC physics.flu-dyn
|
We introduce a computationally efficient method for the automation of inverse
design in science and engineering. Based on simple least-square regression, the
underlying dynamic mode decomposition algorithm can be used to construct a
low-rank subspace spanning multiple experiments in parameter space. The
proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages
the computed low-dimensional subspace to enable fast digital design and
optimization on laptop-level computing, including the potential to prescribe
the dynamics themselves. Moreover, the method is robust to noise, physically
interpretable, and can provide uncertainty quantification metrics. The
architecture can also efficiently scale to large-scale design problems using
randomized algorithms in the ID-DMD. The simplicity of the method and its
implementation are highly attractive in practice, and the ID-DMD has been
demonstrated to be an order of magnitude more accurate than competing methods
while simultaneously being 3-5 orders faster on challenging engineering design
problems ranging from structural vibrations to fluid dynamics. Due to its
speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with
other leading machine learning methods represents a significant advancement in
data-driven methods for inverse design and optimization, promising a paradigm
shift in how to approach inverse design in practice.
|
2502.09494
|
Communicating Likelihoods with Normalising Flows
|
hep-ph cs.LG hep-ex physics.data-an
|
We present a machine-learning-based workflow to model an unbinned likelihood
from its samples. A key advancement over existing approaches is the validation
of the learned likelihood using rigorous statistical tests of the joint
distribution, such as the Kolmogorov-Smirnov test of the joint distribution.
Our method enables the reliable communication of experimental and
phenomenological likelihoods for subsequent analyses. We demonstrate its
effectiveness through three case studies in high-energy physics. To support
broader adoption, we provide an open-source reference implementation, nabu.
|
2502.09495
|
Cracking the Code: Enhancing Development finance understanding with
artificial intelligence
|
econ.GN cs.AI cs.LG q-fin.EC
|
Analyzing development projects is crucial for understanding donors aid
strategies, recipients priorities, and to assess development finance capacity
to adress development issues by on-the-ground actions. In this area, the
Organisation for Economic Co-operation and Developments (OECD) Creditor
Reporting System (CRS) dataset is a reference data source. This dataset
provides a vast collection of project narratives from various sectors
(approximately 5 million projects). While the OECD CRS provides a rich source
of information on development strategies, it falls short in informing project
purposes due to its reporting process based on donors self-declared main
objectives and pre-defined industrial sectors. This research employs a novel
approach that combines Machine Learning (ML) techniques, specifically Natural
Language Processing (NLP), an innovative Python topic modeling technique called
BERTopic, to categorise (cluster) and label development projects based on their
narrative descriptions. By revealing existing yet hidden topics of development
finance, this application of artificial intelligence enables a better
understanding of donor priorities and overall development funding and provides
methods to analyse public and private projects narratives.
|
2502.09496
|
On Agnostic PAC Learning in the Small Error Regime
|
cs.LG stat.ML
|
Binary classification in the classic PAC model exhibits a curious phenomenon:
Empirical Risk Minimization (ERM) learners are suboptimal in the realizable
case yet optimal in the agnostic case. Roughly speaking, this owes itself to
the fact that non-realizable distributions $\mathcal{D}$ are simply more
difficult to learn than realizable distributions -- even when one discounts a
learner's error by $\mathrm{err}(h^*_{\mathcal{D}})$, the error of the best
hypothesis in $\mathcal{H}$ for $\mathcal{D}$. Thus, optimal agnostic learners
are permitted to incur excess error on (easier-to-learn) distributions
$\mathcal{D}$ for which $\tau = \mathrm{err}(h^*_{\mathcal{D}})$ is small.
Recent work of Hanneke, Larsen, and Zhivotovskiy (FOCS `24) addresses this
shortcoming by including $\tau$ itself as a parameter in the agnostic error
term. In this more fine-grained model, they demonstrate tightness of the error
lower bound $\tau + \Omega \left(\sqrt{\frac{\tau (d + \log(1 / \delta))}{m}} +
\frac{d + \log(1 / \delta)}{m} \right)$ in a regime where $\tau > d/m$, and
leave open the question of whether there may be a higher lower bound when $\tau
\approx d/m$, with $d$ denoting $\mathrm{VC}(\mathcal{H})$. In this work, we
resolve this question by exhibiting a learner which achieves error $c \cdot
\tau + O \left(\sqrt{\frac{\tau (d + \log(1 / \delta))}{m}} + \frac{d + \log(1
/ \delta)}{m} \right)$ for a constant $c \leq 2.1$, thus matching the lower
bound when $\tau \approx d/m$. Further, our learner is computationally
efficient and is based upon careful aggregations of ERM classifiers, making
progress on two other questions of Hanneke, Larsen, and Zhivotovskiy (FOCS
`24). We leave open the interesting question of whether our approach can be
refined to lower the constant from 2.1 to 1, which would completely settle the
complexity of agnostic learning.
|
2502.09497
|
Improve LLM-based Automatic Essay Scoring with Linguistic Features
|
cs.CL cs.AI
|
Automatic Essay Scoring (AES) assigns scores to student essays, reducing the
grading workload for instructors. Developing a scoring system capable of
handling essays across diverse prompts is challenging due to the flexibility
and diverse nature of the writing task. Existing methods typically fall into
two categories: supervised feature-based approaches and large language model
(LLM)-based methods. Supervised feature-based approaches often achieve higher
performance but require resource-intensive training. In contrast, LLM-based
methods are computationally efficient during inference but tend to suffer from
lower performance. This paper combines these approaches by incorporating
linguistic features into LLM-based scoring. Experimental results show that this
hybrid method outperforms baseline models for both in-domain and out-of-domain
writing prompts.
|
2502.09500
|
Eidetic Learning: an Efficient and Provable Solution to Catastrophic
Forgetting
|
cs.LG
|
Catastrophic forgetting -- the phenomenon of a neural network learning a task
t1 and losing the ability to perform it after being trained on some other task
t2 -- is a long-standing problem for neural networks [McCloskey and Cohen,
1989]. We present a method, Eidetic Learning, that provably solves catastrophic
forgetting. A network trained with Eidetic Learning -- here, an EideticNet --
requires no rehearsal or replay. We consider successive discrete tasks and show
how at inference time an EideticNet automatically routes new instances without
auxiliary task information. An EideticNet bears a family resemblance to the
sparsely-gated Mixture-of-Experts layer Shazeer et al. [2016] in that network
capacity is partitioned across tasks and the network itself performs
data-conditional routing. An EideticNet is easy to implement and train, is
efficient, and has time and space complexity linear in the number of
parameters. The guarantee of our method holds for normalization layers of
modern neural networks during both pre-training and fine-tuning. We show with a
variety of network architectures and sets of tasks that EideticNets are immune
to forgetting. While the practical benefits of EideticNets are substantial, we
believe they can be benefit practitioners and theorists alike. The code for
training EideticNets is available at
https://github.com/amazon-science/eideticnet-training.
|
2502.09501
|
Prior-Constrained Association Learning for Fine-Grained Generalized
Category Discovery
|
cs.CV
|
This paper addresses generalized category discovery (GCD), the task of
clustering unlabeled data from potentially known or unknown categories with the
help of labeled instances from each known category. Compared to traditional
semi-supervised learning, GCD is more challenging because unlabeled data could
be from novel categories not appearing in labeled data. Current
state-of-the-art methods typically learn a parametric classifier assisted by
self-distillation. While being effective, these methods do not make use of
cross-instance similarity to discover class-specific semantics which are
essential for representation learning and category discovery. In this paper, we
revisit the association-based paradigm and propose a Prior-constrained
Association Learning method to capture and learn the semantic relations within
data. In particular, the labeled data from known categories provides a unique
prior for the association of unlabeled data. Unlike previous methods that only
adopts the prior as a pre or post-clustering refinement, we fully incorporate
the prior into the association process, and let it constrain the association
towards a reliable grouping outcome. The estimated semantic groups are utilized
through non-parametric prototypical contrast to enhance the representation
learning. A further combination of both parametric and non-parametric
classification complements each other and leads to a model that outperforms
existing methods by a significant margin. On multiple GCD benchmarks, we
perform extensive experiments and validate the effectiveness of our proposed
method.
|
2502.09502
|
Scalable First-order Method for Certifying Optimal k-Sparse GLMs
|
cs.LG math.OC
|
This paper investigates the problem of certifying optimality for sparse
generalized linear models (GLMs), where sparsity is enforced through an
$\ell_0$ cardinality constraint. While branch-and-bound (BnB) frameworks can
certify optimality by pruning nodes using dual bounds, existing methods for
computing these bounds are either computationally intensive or exhibit slow
convergence, limiting their scalability to large-scale problems. To address
this challenge, we propose a first-order proximal gradient algorithm designed
to solve the perspective relaxation of the problem within a BnB framework.
Specifically, we formulate the relaxed problem as a composite optimization
problem and demonstrate that the proximal operator of the non-smooth component
can be computed exactly in log-linear time complexity, eliminating the need to
solve a computationally expensive second-order cone program. Furthermore, we
introduce a simple restart strategy that enhances convergence speed while
maintaining low per-iteration complexity. Extensive experiments on synthetic
and real-world datasets show that our approach significantly accelerates dual
bound computations and is highly effective in providing optimality certificates
for large-scale problems.
|
2502.09503
|
AttentionSmithy: A Modular Framework for Rapid Transformer Development
and Customization
|
cs.LG cs.AI
|
Transformer architectures have transformed AI applications but remain complex
to customize for domain experts lacking low-level implementation expertise. We
introduce AttentionSmithy, a modular software package that simplifies
transformer innovation by breaking down key components into reusable building
blocks: attention modules, feed-forward networks, normalization layers, and
positional encodings. Users can rapidly prototype and evaluate transformer
variants without extensive coding. Our framework supports four positional
encoding strategies and integrates with neural architecture search for
automated design. We validate AttentionSmithy by replicating the original
transformer under resource constraints and optimizing translation performance
by combining positional encodings. Additionally, we demonstrate its
adaptability in gene-specific modeling, achieving over 95% accuracy in cell
type classification. These case studies highlight AttentionSmithy's potential
to accelerate research across diverse fields by removing framework
implementation barriers.
|
2502.09507
|
When and How Does CLIP Enable Domain and Compositional Generalization?
|
cs.LG cs.CV
|
The remarkable generalization performance of contrastive vision-language
models like CLIP is often attributed to the diversity of their training
distributions. However, key questions remain unanswered: Can CLIP generalize to
an entirely unseen domain when trained on a diverse mixture of domains (domain
generalization)? Can it generalize to unseen classes within partially seen
domains (compositional generalization)? What factors affect such
generalization? To answer these questions, we trained CLIP models on
systematically constructed training distributions with controlled domain
diversity and object class exposure. Our experiments show that domain diversity
is essential for both domain and compositional generalization, yet
compositional generalization can be surprisingly weaker than domain
generalization when the training distribution contains a suboptimal subset of
the test domain. Through data-centric and mechanistic analyses, we find that
successful generalization requires learning of shared representations already
in intermediate layers and shared circuitry.
|
2502.09509
|
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative
Image Modeling
|
cs.LG
|
Latent generative models have emerged as a leading approach for high-quality
image synthesis. These models rely on an autoencoder to compress images into a
latent space, followed by a generative model to learn the latent distribution.
We identify that existing autoencoders lack equivariance to semantic-preserving
transformations like scaling and rotation, resulting in complex latent spaces
that hinder generative performance. To address this, we propose EQ-VAE, a
simple regularization approach that enforces equivariance in the latent space,
reducing its complexity without degrading reconstruction quality. By finetuning
pre-trained autoencoders with EQ-VAE, we enhance the performance of several
state-of-the-art generative models, including DiT, SiT, REPA and MaskGIT,
achieving a 7 speedup on DiT-XL/2 with only five epochs of SD-VAE fine-tuning.
EQ-VAE is compatible with both continuous and discrete autoencoders, thus
offering a versatile enhancement for a wide range of latent generative models.
Project page and code: https://eq-vae.github.io/.
|
2502.09511
|
Diffusion Models for Molecules: A Survey of Methods and Tasks
|
cs.LG cs.AI cs.CE
|
Generative tasks about molecules, including but not limited to molecule
generation, are crucial for drug discovery and material design, and have
consistently attracted significant attention. In recent years, diffusion models
have emerged as an impressive class of deep generative models, sparking
extensive research and leading to numerous studies on their application to
molecular generative tasks. Despite the proliferation of related work, there
remains a notable lack of up-to-date and systematic surveys in this area.
Particularly, due to the diversity of diffusion model formulations, molecular
data modalities, and generative task types, the research landscape is
challenging to navigate, hindering understanding and limiting the area's
growth. To address this, this paper conducts a comprehensive survey of
diffusion model-based molecular generative methods. We systematically review
the research from the perspectives of methodological formulations, data
modalities, and task types, offering a novel taxonomy. This survey aims to
facilitate understanding and further flourishing development in this area. The
relevant papers are summarized at:
https://github.com/AzureLeon1/awesome-molecular-diffusion-models.
|
2502.09517
|
Coupled Rendezvous and Docking Maneuver control of satellite using
Reinforcement learning-based Adaptive Fixed-Time Sliding Mode Controller
|
eess.SY cs.SY
|
Satellite dynamics in unknown environments are inherently uncertain due to
factors such as varying gravitational fields, atmospheric drag, and
unpredictable interactions with space debris or other celestial bodies.
Traditional sliding mode controllers with fixed parameters often struggle to
maintain optimal performance under these fluctuating conditions. Therefore, an
adaptive controller is essential to address these challenges by continuously
tuning its gains in real-time. In this paper, we have tuned the slopes of the
Fixed-time Sliding surface adaptively using reinforcement learning for coupled
rendezvous and docking maneuver of chaser satellite with the target satellite
in an unknown space environment. The neural network model is used to determine
the optimal gains of reaching law of the fixed-time sliding surface. We have
assumed that we don't have an accurate model of the system so we have added
noise in the tangent space instead of directly on the manifold to preserve the
geometric structure of the system while ensuring mathematically consistent
uncertainty propagation. The reinforcement learning is used as an approximator
to represent the value function of the agent to estimate the dynamical model of
the system using the Actor-Critic method. The proposed control algorithm
integrates a neural network and a sliding mode controller in a cascade loop
architecture, where the tracking error dynamically tunes the sliding surface
gains. Global fixed-time stability of the closed-loop feedback system is proved
within the Lyapunov framework. This comprehensive approach of fixed-time
sliding mode controller using a Reinforcement Learning based ensures the
completion of the mission efficiently while addressing the critical challenges
posed by the uncertain environment. The simulation results presented support
the claims made.
|
2502.09520
|
SQ-GAN: Semantic Image Communications Using Masked Vector Quantization
|
cs.CV eess.IV
|
This work introduces Semantically Masked VQ-GAN (SQ-GAN), a novel approach
integrating generative models to optimize image compression for
semantic/task-oriented communications. SQ-GAN employs off-the-shelf semantic
semantic segmentation and a new specifically developed semantic-conditioned
adaptive mask module (SAMM) to selectively encode semantically significant
features of the images. SQ-GAN outperforms state-of-the-art image compression
schemes such as JPEG2000 and BPG across multiple metrics, including perceptual
quality and semantic segmentation accuracy on the post-decoding reconstructed
image, at extreme low compression rates expressed in bits per pixel.
|
2502.09525
|
Robust Learning of Multi-index Models via Iterative Subspace
Approximation
|
cs.LG cs.DS math.ST stat.ML stat.TH
|
We study the task of learning Multi-Index Models (MIMs) with label noise
under the Gaussian distribution. A $K$-MIM is any function $f$ that only
depends on a $K$-dimensional subspace. We focus on well-behaved MIMs with
finite ranges that satisfy certain regularity properties. Our main contribution
is a general robust learner that is qualitatively optimal in the Statistical
Query (SQ) model. Our algorithm iteratively constructs better approximations to
the defining subspace by computing low-degree moments conditional on the
projection to the subspace computed thus far, and adding directions with
relatively large empirical moments. This procedure efficiently finds a subspace
$V$ so that $f(\mathbf{x})$ is close to a function of the projection of
$\mathbf{x}$ onto $V$. Conversely, for functions for which these conditional
moments do not help, we prove an SQ lower bound suggesting that no efficient
learner exists.
As applications, we provide faster robust learners for the following concept
classes:
* {\bf Multiclass Linear Classifiers} We give a constant-factor approximate
agnostic learner with sample complexity $N = O(d)
2^{\mathrm{poly}(K/\epsilon)}$ and computational complexity $\mathrm{poly}(N
,d)$. This is the first constant-factor agnostic learner for this class whose
complexity is a fixed-degree polynomial in $d$.
* {\bf Intersections of Halfspaces} We give an approximate agnostic learner
for this class achieving 0-1 error $K \tilde{O}(\mathrm{OPT}) + \epsilon$ with
sample complexity $N=O(d^2) 2^{\mathrm{poly}(K/\epsilon)}$ and computational
complexity $\mathrm{poly}(N ,d)$. This is the first agnostic learner for this
class with near-linear error dependence and complexity a fixed-degree
polynomial in $d$.
Furthermore, we show that in the presence of random classification noise, the
complexity of our algorithm scales polynomially with $1/\epsilon$.
|
2502.09528
|
SteROI-D: System Design and Mapping for Stereo Depth Inference on
Regions of Interest
|
cs.CV cs.AR
|
Machine learning algorithms have enabled high quality stereo depth estimation
to run on Augmented and Virtual Reality (AR/VR) devices. However, high energy
consumption across the full image processing stack prevents stereo depth
algorithms from running effectively on battery-limited devices. This paper
introduces SteROI-D, a full stereo depth system paired with a mapping
methodology. SteROI-D exploits Region-of-Interest (ROI) and temporal sparsity
at the system level to save energy. SteROI-D's flexible and heterogeneous
compute fabric supports diverse ROIs. Importantly, we introduce a systematic
mapping methodology to effectively handle dynamic ROIs, thereby maximizing
energy savings. Using these techniques, our 28nm prototype SteROI-D design
achieves up to 4.35x reduction in total system energy compared to a baseline
ASIC.
|
2502.09529
|
Exact Leader Estimation: A New Approach for Distributed Differentiation
|
eess.SY cs.MA cs.SY math.OC
|
A novel strategy aimed at cooperatively differentiating a signal among
multiple interacting agents is introduced, where none of the agents needs to
know which agent is the leader, i.e. the one producing the signal to be
differentiated. Every agent communicates only a scalar variable to its
neighbors; except for the leader, all agents execute the same algorithm. The
proposed strategy can effectively obtain derivatives up to arbitrary $m$-th
order in a finite time under the assumption that the $(m+1)$-th derivative is
bounded. The strategy borrows some of its structure from the celebrated
homogeneous robust exact differentiator by A. Levant, inheriting its exact
differentiation capability and robustness to measurement noise. Hence, the
proposed strategy can be said to perform robust exact distributed
differentiation. In addition, and for the first time in the distributed
leader-observer literature, sampled-data communication and bounded measurement
noise are considered, and corresponding steady-state worst-case accuracy bounds
are derived. The effectiveness of the proposed strategy is verified numerically
for second- and fourth-order systems, i.e., for estimating derivatives of up to
first and third order, respectively.
|
2502.09531
|
Data-Enabled Predictive Control for Flexible Spacecraft
|
eess.SY cs.SY
|
Spacecraft are vital to space exploration and are often equipped with
lightweight, flexible appendages to meet strict weight constraints. These
appendages pose significant challenges for modeling and control due to their
inherent nonlinearity. Data-driven control methods have gained traction to
address such challenges. This paper introduces, to the best of the authors'
knowledge, the first application of the data-enabled predictive control (DeePC)
framework to boundary control for flexible spacecraft. Leveraging the
fundamental lemma, DeePC constructs a non-parametric model by utilizing
recorded past trajectories, eliminating the need for explicit model
development. The developed method also incorporates dimension reduction
techniques to enhance computational efficiency. Through comprehensive numerical
simulations, this study compares the proposed method with Lyapunov-based
control, demonstrating superior performance and offering a thorough evaluation
of data-driven control for flexible spacecraft.
|
2502.09532
|
Mind the Gap! Choice Independence in Using Multilingual LLMs for
Persuasive Co-Writing Tasks in Different Languages
|
cs.CL cs.AI cs.HC
|
Recent advances in generative AI have precipitated a proliferation of novel
writing assistants. These systems typically rely on multilingual large language
models (LLMs), providing globalized workers the ability to revise or create
diverse forms of content in different languages. However, there is substantial
evidence indicating that the performance of multilingual LLMs varies between
languages. Users who employ writing assistance for multiple languages are
therefore susceptible to disparate output quality. Importantly, recent research
has shown that people tend to generalize algorithmic errors across independent
tasks, violating the behavioral axiom of choice independence. In this paper, we
analyze whether user utilization of novel writing assistants in a charity
advertisement writing task is affected by the AI's performance in a second
language. Furthermore, we quantify the extent to which these patterns translate
into the persuasiveness of generated charity advertisements, as well as the
role of peoples' beliefs about LLM utilization in their donation choices. Our
results provide evidence that writers who engage with an LLM-based writing
assistant violate choice independence, as prior exposure to a Spanish LLM
reduces subsequent utilization of an English LLM. While these patterns do not
affect the aggregate persuasiveness of the generated advertisements, people's
beliefs about the source of an advertisement (human versus AI) do. In
particular, Spanish-speaking female participants who believed that they read an
AI-generated advertisement strongly adjusted their donation behavior downwards.
Furthermore, people are generally not able to adequately differentiate between
human-generated and LLM-generated ads. Our work has important implications for
the design, development, integration, and adoption of multilingual LLMs as
assistive agents -- particularly in writing tasks.
|
2502.09533
|
Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion
Model
|
cs.CV
|
Recent advances in conditional diffusion models have shown promise for
generating realistic TalkingFace videos, yet challenges persist in achieving
consistent head movement, synchronized facial expressions, and accurate lip
synchronization over extended generations. To address these, we introduce the
\textbf{M}otion-priors \textbf{C}onditional \textbf{D}iffusion \textbf{M}odel
(\textbf{MCDM}), which utilizes both archived and current clip motion priors to
enhance motion prediction and ensure temporal consistency. The model consists
of three key elements: (1) an archived-clip motion-prior that incorporates
historical frames and a reference frame to preserve identity and context; (2) a
present-clip motion-prior diffusion model that captures multimodal causality
for accurate predictions of head movements, lip sync, and expressions; and (3)
a memory-efficient temporal attention mechanism that mitigates error
accumulation by dynamically storing and updating motion features. We also
release the \textbf{TalkingFace-Wild} dataset, a multilingual collection of
over 200 hours of footage across 10 languages. Experimental results demonstrate
the effectiveness of MCDM in maintaining identity and motion continuity for
long-term TalkingFace generation. Code, models, and datasets will be publicly
available.
|
2502.09534
|
Fast Tensor Completion via Approximate Richardson Iteration
|
cs.DS cs.LG math.ST stat.TH
|
We study tensor completion (TC) through the lens of low-rank tensor
decomposition (TD). Many TD algorithms use fast alternating minimization
methods, which solve highly structured linear regression problems at each step
(e.g., for CP, Tucker, and tensor-train decompositions). However, such
algebraic structure is lost in TC regression problems, making direct extensions
unclear. To address this, we propose a lifting approach that approximately
solves TC regression problems using structured TD regression algorithms as
blackbox subroutines, enabling sublinear-time methods. We theoretically analyze
the convergence rate of our approximate Richardson iteration based algorithm,
and we demonstrate on real-world tensors that its running time can be 100x
faster than direct methods for CP completion.
|
2502.09541
|
Vortex: Overcoming Memory Capacity Limitations in GPU-Accelerated
Large-Scale Data Analytics
|
cs.DB cs.DC
|
Despite the high computational throughput of GPUs, limited memory capacity
and bandwidth-limited CPU-GPU communication via PCIe links remain significant
bottlenecks for accelerating large-scale data analytics workloads. This paper
introduces Vortex, a GPU-accelerated framework designed for data analytics
workloads that exceed GPU memory capacity. A key aspect of our framework is an
optimized IO primitive that leverages all available PCIe links in multi-GPU
systems for the IO demand of a single target GPU. It routes data through other
GPUs to such target GPU that handles IO-intensive analytics tasks. This
approach is advantageous when other GPUs are occupied with compute-bound
workloads, such as popular AI applications that typically underutilize IO
resources. We also introduce a novel programming model that separates GPU
kernel development from IO scheduling, reducing programmer burden and enabling
GPU code reuse. Additionally, we present the design of certain important query
operators and discuss a late materialization technique based on GPU's zero-copy
memory access. Without caching any data in GPU memory, Vortex improves the
performance of the state-of-the-art GPU baseline, Proteus, by 5.7$\times$ on
average and enhances price performance by 2.5$\times$ compared to a CPU-based
DuckDB baseline.
|
2502.09553
|
SyntheticPop: Attacking Speaker Verification Systems With Synthetic
VoicePops
|
cs.CR cs.LG
|
Voice Authentication (VA), also known as Automatic Speaker Verification
(ASV), is a widely adopted authentication method, particularly in automated
systems like banking services, where it serves as a secondary layer of user
authentication. Despite its popularity, VA systems are vulnerable to various
attacks, including replay, impersonation, and the emerging threat of deepfake
audio that mimics the voice of legitimate users. To mitigate these risks,
several defense mechanisms have been proposed. One such solution, Voice Pops,
aims to distinguish an individual's unique phoneme pronunciations during the
enrollment process. While promising, the effectiveness of VA+VoicePop against a
broader range of attacks, particularly logical or adversarial attacks, remains
insufficiently explored. We propose a novel attack method, which we refer to as
SyntheticPop, designed to target the phoneme recognition capabilities of the
VA+VoicePop system. The SyntheticPop attack involves embedding synthetic "pop"
noises into spoofed audio samples, significantly degrading the model's
performance. We achieve an attack success rate of over 95% while poisoning 20%
of the training dataset. Our experiments demonstrate that VA+VoicePop achieves
69% accuracy under normal conditions, 37% accuracy when subjected to a baseline
label flipping attack, and just 14% accuracy under our proposed SyntheticPop
attack, emphasizing the effectiveness of our method.
|
2502.09556
|
Real-Time Fast Marching Tree for Mobile Robot Motion Planning in Dynamic
Environments
|
cs.RO cs.SY eess.SY
|
This paper proposes the Real-Time Fast Marching Tree (RT-FMT), a real-time
planning algorithm that features local and global path generation,
multiple-query planning, and dynamic obstacle avoidance. During the search,
RT-FMT quickly looks for the global solution and, in the meantime, generates
local paths that can be used by the robot to start execution faster. In
addition, our algorithm constantly rewires the tree to keep branches from
forming inside the dynamic obstacles and to maintain the tree root near the
robot, which allows the tree to be reused multiple times for different goals.
Our algorithm is based on the planners Fast Marching Tree (FMT*) and Real-time
Rapidly-Exploring Random Tree (RT-RRT*). We show via simulations that RT-FMT
outperforms RT- RRT* in both execution cost and arrival time, in most cases.
Moreover, we also demonstrate via simulation that it is worthwhile taking the
local path before the global path is available in order to reduce arrival time,
even though there is a small possibility of taking an inferior path.
|
2502.09560
|
EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language
Models for Vision-Driven Embodied Agents
|
cs.AI cs.CL cs.CV
|
Leveraging Multi-modal Large Language Models (MLLMs) to create embodied
agents offers a promising avenue for tackling real-world tasks. While
language-centric embodied agents have garnered substantial attention,
MLLM-based embodied agents remain underexplored due to the lack of
comprehensive evaluation frameworks. To bridge this gap, we introduce
EmbodiedBench, an extensive benchmark designed to evaluate vision-driven
embodied agents. EmbodiedBench features: (1) a diverse set of 1,128 testing
tasks across four environments, ranging from high-level semantic tasks (e.g.,
household) to low-level tasks involving atomic actions (e.g., navigation and
manipulation); and (2) six meticulously curated subsets evaluating essential
agent capabilities like commonsense reasoning, complex instruction
understanding, spatial awareness, visual perception, and long-term planning.
Through extensive experiments, we evaluated 13 leading proprietary and
open-source MLLMs within EmbodiedBench. Our findings reveal that: MLLMs excel
at high-level tasks but struggle with low-level manipulation, with the best
model, GPT-4o, scoring only 28.9% on average. EmbodiedBench provides a
multifaceted standardized evaluation platform that not only highlights existing
challenges but also offers valuable insights to advance MLLM-based embodied
agents. Our code is available at https://embodiedbench.github.io.
|
2502.09561
|
Enhancing Traffic Safety Analysis with Digital Twin Technology:
Integrating Vehicle Dynamics and Environmental Factors into Microscopic
Traffic Simulation
|
eess.SY cs.SY math.OC
|
Traffic safety is a critical concern in transportation engineering and urban
planning. Traditional traffic safety analysis requires trained observers to
collect data in the field, which is time-consuming, labor-intensive, and
sometimes inaccurate. In recent years, microscopic traffic simulation, which
simulates individual vehicles' movements within a transportation network, have
been utilized to study traffic safety. However, microscopic traffic simulation
only focuses on traffic-related factors, such as traffic volume, traffic
signals, and lane configurations, neglecting vehicle dynamics and
environment-related factors like weather and lighting conditions, which can
significantly impact traffic safety. In light of this, this paper explores the
application of digital twin technology in traffic safety analysis, integrating
vehicle simulators, which consider vehicle dynamics and environmental factors,
and microscopic traffic simulators, which simulate the operations of traffic
flow, for enhanced safety evaluations. Various scenarios, including different
weather conditions and visibility levels, are simulated using a digital twin of
a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety
Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a
Crash (DRAC) to assess safety under varying conditions. The results demonstrate
that traffic digital twin can identify potential safety issues that traditional
microscopic simulation cannot, providing insights for improving traffic control
strategies and transportation infrastructure to enhance traffic safety.
|
2502.09563
|
Self-Calibrating Gaussian Splatting for Large Field of View
Reconstruction
|
cs.CV cs.GR
|
In this paper, we present a self-calibrating framework that jointly optimizes
camera parameters, lens distortion and 3D Gaussian representations, enabling
accurate and efficient scene reconstruction. In particular, our technique
enables high-quality scene reconstruction from Large field-of-view (FOV)
imagery taken with wide-angle lenses, allowing the scene to be modeled from a
smaller number of images. Our approach introduces a novel method for modeling
complex lens distortions using a hybrid network that combines invertible
residual networks with explicit grids. This design effectively regularizes the
optimization process, achieving greater accuracy than conventional camera
models. Additionally, we propose a cubemap-based resampling strategy to support
large FOV images without sacrificing resolution or introducing distortion
artifacts. Our method is compatible with the fast rasterization of Gaussian
Splatting, adaptable to a wide variety of camera lens distortion, and
demonstrates state-of-the-art performance on both synthetic and real-world
datasets.
|
2502.09564
|
Diffusing DeBias: a Recipe for Turning a Bug into a Feature
|
cs.LG cs.CV
|
Deep learning model effectiveness in classification tasks is often challenged
by the quality and quantity of training data which, whenever containing strong
spurious correlations between specific attributes and target labels, can result
in unrecoverable biases in model predictions. Tackling these biases is crucial
in improving model generalization and trust, especially in real-world
scenarios. This paper presents Diffusing DeBias (DDB), a novel approach acting
as a plug-in for common methods in model debiasing while exploiting the
inherent bias-learning tendency of diffusion models. Our approach leverages
conditional diffusion models to generate synthetic bias-aligned images, used to
train a bias amplifier model, to be further employed as an auxiliary method in
different unsupervised debiasing approaches. Our proposed method, which also
tackles the common issue of training set memorization typical of this type of
techniques, beats current state-of-the-art in multiple benchmark datasets by
significant margins, demonstrating its potential as a versatile and effective
tool for tackling dataset bias in deep learning applications.
|
2502.09565
|
MDCrow: Automating Molecular Dynamics Workflows with Large Language
Models
|
cs.AI physics.chem-ph
|
Molecular dynamics (MD) simulations are essential for understanding
biomolecular systems but remain challenging to automate. Recent advances in
large language models (LLM) have demonstrated success in automating complex
scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an
agentic LLM assistant capable of automating MD workflows. MDCrow uses
chain-of-thought over 40 expert-designed tools for handling and processing
files, setting up simulations, analyzing the simulation outputs, and retrieving
relevant information from literature and databases. We assess MDCrow's
performance across 25 tasks of varying required subtasks and difficulty, and we
evaluate the agent's robustness to both difficulty and prompt style.
\texttt{gpt-4o} is able to complete complex tasks with low variance, followed
closely by \texttt{llama3-405b}, a compelling open-source model. While prompt
style does not influence the best models' performance, it has significant
effects on smaller models.
|
2502.09566
|
Zero-shot generation of synthetic neurosurgical data with large language
models
|
cs.CL cs.LG
|
Clinical data is fundamental to advance neurosurgical research, but access is
often constrained by data availability, small sample sizes, privacy
regulations, and resource-intensive preprocessing and de-identification
procedures. Synthetic data offers a potential solution to challenges associated
with accessing and using real-world data (RWD). This study aims to evaluate the
capability of zero-shot generation of synthetic neurosurgical data with a large
language model (LLM), GPT-4o, by benchmarking with the conditional tabular
generative adversarial network (CTGAN). Synthetic datasets were compared to
real-world neurosurgical data to assess fidelity (means, proportions,
distributions, and bivariate correlations), utility (ML classifier performance
on RWD), and privacy (duplication of records from RWD). The GPT-4o-generated
datasets matched or exceeded CTGAN performance, despite no fine-tuning or
access to RWD for pre-training. Datasets demonstrated high univariate and
bivariate fidelity to RWD without directly exposing any real patient records,
even at amplified sample size. Training an ML classifier on GPT-4o-generated
data and testing on RWD for a binary prediction task showed an F1 score (0.706)
with comparable performance to training on the CTGAN data (0.705) for
predicting postoperative functional status deterioration. GPT-4o demonstrated a
promising ability to generate high-fidelity synthetic neurosurgical data. These
findings also indicate that data synthesized with GPT-4o can effectively
augment clinical data with small sample sizes, and train ML models for
prediction of neurosurgical outcomes. Further investigation is necessary to
improve the preservation of distributional characteristics and boost classifier
performance.
|
2502.09567
|
MorphNLI: A Stepwise Approach to Natural Language Inference Using Text
Morphing
|
cs.CL cs.AI
|
We introduce MorphNLI, a modular step-by-step approach to natural language
inference (NLI). When classifying the premise-hypothesis pairs into
{entailment, contradiction, neutral}, we use a language model to generate the
necessary edits to incrementally transform (i.e., morph) the premise into the
hypothesis. Then, using an off-the-shelf NLI model we track how the entailment
progresses with these atomic changes, aggregating these intermediate labels
into a final output. We demonstrate the advantages of our proposed method
particularly in realistic cross-domain settings, where our method always
outperforms strong baselines with improvements up to 12.6% (relative). Further,
our proposed approach is explainable as the atomic edits can be used to
understand the overall NLI label.
|
2502.09570
|
Enhancing the Utility of Higher-Order Information in Relational Learning
|
cs.LG stat.ML
|
Higher-order information is crucial for relational learning in many domains
where relationships extend beyond pairwise interactions. Hypergraphs provide a
natural framework for modeling such relationships, which has motivated recent
extensions of graph neural network architectures to hypergraphs. However,
comparisons between hypergraph architectures and standard graph-level models
remain limited. In this work, we systematically evaluate a selection of
hypergraph-level and graph-level architectures, to determine their
effectiveness in leveraging higher-order information in relational learning.
Our results show that graph-level architectures applied to hypergraph
expansions often outperform hypergraph-level ones, even on inputs that are
naturally parametrized as hypergraphs. As an alternative approach for
leveraging higher-order information, we propose hypergraph-level encodings
based on classical hypergraph characteristics. While these encodings do not
significantly improve hypergraph architectures, they yield substantial
performance gains when combined with graph-level models. Our theoretical
analysis shows that hypergraph-level encodings provably increase the
representational power of message-passing graph neural networks beyond that of
their graph-level counterparts.
|
2502.09571
|
DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra
|
cs.LG q-bio.QM
|
Mass spectrometry plays a fundamental role in elucidating the structures of
unknown molecules and subsequent scientific discoveries. One formulation of the
structure elucidation task is the conditional $\textit{de novo}$ generation of
molecular structure given a mass spectrum. Toward a more accurate and efficient
scientific discovery pipeline for small molecules, we present DiffMS, a
formula-restricted encoder-decoder generative network that achieves
state-of-the-art performance on this task. The encoder utilizes a transformer
architecture and models mass spectra domain knowledge such as peak formulae and
neutral losses, and the decoder is a discrete graph diffusion model restricted
by the heavy-atom composition of a known chemical formula. To develop a robust
decoder that bridges latent embeddings and molecular structures, we pretrain
the diffusion decoder with fingerprint-structure pairs, which are available in
virtually infinite quantities, compared to structure-spectrum pairs that number
in the tens of thousands. Extensive experiments on established benchmarks show
that DiffMS outperforms existing models on $\textit{de novo}$ molecule
generation. We provide several ablations to demonstrate the effectiveness of
our diffusion and pretraining approaches and show consistent performance
scaling with increasing pretraining dataset size. DiffMS code is publicly
available at https://github.com/coleygroup/DiffMS.
|
2502.09573
|
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt
Engineering
|
cs.CV cs.CL cs.LG
|
In this study, we tackle industry challenges in video content classification
by exploring and optimizing GPT-based models for zero-shot classification
across seven critical categories of video quality. We contribute a novel
approach to improving GPT's performance through prompt optimization and policy
refinement, demonstrating that simplifying complex policies significantly
reduces false negatives. Additionally, we introduce a new
decomposition-aggregation-based prompt engineering technique, which outperforms
traditional single-prompt methods. These experiments, conducted on real
industry problems, show that thoughtful prompt design can substantially enhance
GPT's performance without additional finetuning, offering an effective and
scalable solution for improving video classification systems across various
domains in industry.
|
2502.09583
|
Learning to Coordinate with Experts
|
cs.LG stat.ML
|
When deployed in dynamic environments, AI agents will inevitably encounter
challenges that exceed their individual capabilities. Leveraging assistance
from expert agents-whether human or AI-can significantly enhance safety and
performance in such situations. However, querying experts is often costly,
necessitating the development of agents that can efficiently request and
utilize expert guidance. In this paper, we introduce a fundamental coordination
problem called Learning to Yield and Request Control (YRC), where the objective
is to learn a strategy that determines when to act autonomously and when to
seek expert assistance. We consider a challenging practical setting in which an
agent does not interact with experts during training but must adapt to novel
environmental changes and expert interventions at test time. To facilitate
empirical research, we introduce YRC-Bench, an open-source benchmark featuring
diverse domains. YRC-Bench provides a standardized Gym-like API, simulated
experts, evaluation pipeline, and implementation of competitive baselines.
Towards tackling the YRC problem, we propose a novel validation approach and
investigate the performance of various learning methods across diverse
environments, yielding insights that can guide future research.
|
2502.09587
|
Rolling Ahead Diffusion for Traffic Scene Simulation
|
cs.LG cs.RO
|
Realistic driving simulation requires that NPCs not only mimic natural
driving behaviors but also react to the behavior of other simulated agents.
Recent developments in diffusion-based scenario generation focus on creating
diverse and realistic traffic scenarios by jointly modelling the motion of all
the agents in the scene. However, these traffic scenarios do not react when the
motion of agents deviates from their modelled trajectories. For example, the
ego-agent can be controlled by a stand along motion planner. To produce
reactive scenarios with joint scenario models, the model must regenerate the
scenario at each timestep based on new observations in a Model Predictive
Control (MPC) fashion. Although reactive, this method is time-consuming, as one
complete possible future for all NPCs is generated per simulation step.
Alternatively, one can utilize an autoregressive model (AR) to predict only the
immediate next-step future for all NPCs. Although faster, this method lacks the
capability for advanced planning. We present a rolling diffusion based traffic
scene generation model which mixes the benefits of both methods by predicting
the next step future and simultaneously predicting partially noised further
future steps at the same time. We show that such model is efficient compared to
diffusion model based AR, achieving a beneficial compromise between reactivity
and computational efficiency.
|
2502.09589
|
Logical forms complement probability in understanding language model
(and human) performance
|
cs.CL cs.LO
|
With the increasing interest in using large language models (LLMs) for
planning in natural language, understanding their behaviors becomes an
important research question. This work conducts a systematic investigation of
LLMs' ability to perform logical reasoning in natural language. We introduce a
controlled dataset of hypothetical and disjunctive syllogisms in propositional
and modal logic and use it as the testbed for understanding LLM performance.
Our results lead to novel insights in predicting LLM behaviors: in addition to
the probability of input (Gonen et al., 2023; McCoy et al., 2024), logical
forms should be considered as important factors. In addition, we show
similarities and discrepancies between the logical reasoning performances of
humans and LLMs by collecting and comparing behavioral data from both.
|
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