id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2501.02721 | Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer
Operators | cs.LG | We consider an operator-based latent Markov representation of a stochastic
nonlinear dynamical system, where the stochastic evolution of the latent state
embedded in a reproducing kernel Hilbert space is described with the
corresponding transfer operator, and develop a spectral method to learn this
representation based on the theory of stochastic realization. The embedding may
be learned simultaneously using reproducing kernels, for example, constructed
with feed-forward neural networks. We also address the generalization of
sequential state-estimation (Kalman filtering) in stochastic nonlinear systems,
and of operator-based eigen-mode decomposition of dynamics, for the
representation. Several examples with synthetic and real-world data are shown
to illustrate the empirical characteristics of our methods, and to investigate
the performance of our model in sequential state-estimation and mode
decomposition.
|
2501.02725 | Artificial Intelligence in Creative Industries: Advances Prior to 2025 | cs.AI | The rapid advancements in artificial intelligence (AI), particularly in
generative AI and large language models (LLMs), have profoundly impacted the
creative industries by enabling innovative content creation, enhancing
workflows, and democratizing access to creative tools. This paper explores the
significant technological shifts since our previous review in 2022,
highlighting how these developments have expanded creative opportunities and
efficiency. These technological advancements have enhanced the capabilities of
text-to-image, text-to-video, and multimodal generation technologies. In
particular, key breakthroughs in LLMs have established new benchmarks in
conversational AI, while advancements in image generators have revolutionized
content creation. We also discuss AI integration into post-production
workflows, which has significantly accelerated and refined traditional
processes. Despite these innovations, challenges remain, particularly for the
media industry, due to the demands on communication traffic from creative
content. We therefore include data compression and quality assessment in this
paper. Furthermore, we highlight the trend toward unified AI frameworks capable
of addressing multiple creative tasks and underscore the importance of human
oversight to mitigate AI-generated inaccuracies. Finally, we explore AI's
future potential in the creative sector, stressing the need to navigate
emerging challenges to maximize its benefits while addressing associated risks.
|
2501.02727 | Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test
Data | cs.IR cs.AI | We present HiRMed (Hierarchical RAG-enhanced Medical Test Recommendation), a
novel tree-structured recommendation system that leverages Retrieval-Augmented
Generation (RAG) for intelligent medical test recommendations. Unlike
traditional vector similarity-based approaches, our system performs medical
reasoning at each tree node through a specialized RAG process. Starting from
the root node with initial symptoms, the system conducts step-wise medical
analysis to identify potential underlying conditions and their corresponding
diagnostic requirements. At each level, instead of simple matching, our
RAG-enhanced nodes analyze retrieved medical knowledge to understand
symptom-disease relationships and determine the most appropriate diagnostic
path. The system dynamically adjusts its recommendation strategy based on
medical reasoning results, considering factors such as urgency levels and
diagnostic uncertainty. Experimental results demonstrate that our approach
achieves superior performance in terms of coverage rate, accuracy, and miss
rate compared to conventional retrieval-based methods. This work represents a
significant advance in medical test recommendation by introducing medical
reasoning capabilities into the traditional tree-based retrieval structure.
|
2501.02728 | OpenGU: A Comprehensive Benchmark for Graph Unlearning | cs.LG cs.AI | Graph Machine Learning is essential for understanding and analyzing
relational data. However, privacy-sensitive applications demand the ability to
efficiently remove sensitive information from trained graph neural networks
(GNNs), avoiding the unnecessary time and space overhead caused by retraining
models from scratch. To address this issue, Graph Unlearning (GU) has emerged
as a critical solution, with the potential to support dynamic graph updates in
data management systems and enable scalable unlearning in distributed data
systems while ensuring privacy compliance. Unlike machine unlearning in
computer vision or other fields, GU faces unique difficulties due to the
non-Euclidean nature of graph data and the recursive message-passing mechanism
of GNNs. Additionally, the diversity of downstream tasks and the complexity of
unlearning requests further amplify these challenges. Despite the proliferation
of diverse GU strategies, the absence of a benchmark providing fair comparisons
for GU, and the limited flexibility in combining downstream tasks and
unlearning requests, have yielded inconsistencies in evaluations, hindering the
development of this domain. To fill this gap, we present OpenGU, the first GU
benchmark, where 16 SOTA GU algorithms and 37 multi-domain datasets are
integrated, enabling various downstream tasks with 13 GNN backbones when
responding to flexible unlearning requests. Based on this unified benchmark
framework, we are able to provide a comprehensive and fair evaluation for GU.
Through extensive experimentation, we have drawn $8$ crucial conclusions about
existing GU methods, while also gaining valuable insights into their
limitations, shedding light on potential avenues for future research.
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2501.02732 | AFed: Algorithmic Fair Federated Learning | cs.LG cs.AI | Federated Learning (FL) has gained significant attention as it facilitates
collaborative machine learning among multiple clients without centralizing
their data on a server. FL ensures the privacy of participating clients by
locally storing their data, which creates new challenges in fairness.
Traditional debiasing methods assume centralized access to sensitive
information, rendering them impractical for the FL setting. Additionally, FL is
more susceptible to fairness issues than centralized machine learning due to
the diverse client data sources that may be associated with group information.
Therefore, training a fair model in FL without access to client local data is
important and challenging. This paper presents AFed, a straightforward yet
effective framework for promoting group fairness in FL. The core idea is to
circumvent restricted data access by learning the global data distribution.
This paper proposes two approaches: AFed-G, which uses a conditional generator
trained on the server side, and AFed-GAN, which improves upon AFed-G by
training a conditional GAN on the client side. We augment the client data with
the generated samples to help remove bias. Our theoretical analysis justifies
the proposed methods, and empirical results on multiple real-world datasets
demonstrate a substantial improvement in AFed over several baselines.
|
2501.02735 | Sequence Complementor: Complementing Transformers For Time Series
Forecasting with Learnable Sequences | cs.LG | Since its introduction, the transformer has shifted the development
trajectory away from traditional models (e.g., RNN, MLP) in time series
forecasting, which is attributed to its ability to capture global dependencies
within temporal tokens. Follow-up studies have largely involved altering the
tokenization and self-attention modules to better adapt Transformers for
addressing special challenges like non-stationarity, channel-wise dependency,
and variable correlation in time series. However, we found that the expressive
capability of sequence representation is a key factor influencing Transformer
performance in time forecasting after investigating several representative
methods, where there is an almost linear relationship between sequence
representation entropy and mean square error, with more diverse representations
performing better. In this paper, we propose a novel attention mechanism with
Sequence Complementors and prove feasible from an information theory
perspective, where these learnable sequences are able to provide complementary
information beyond current input to feed attention. We further enhance the
Sequence Complementors via a diversification loss that is theoretically
covered. The empirical evaluation of both long-term and short-term forecasting
has confirmed its superiority over the recent state-of-the-art methods.
|
2501.02737 | Holistic Semantic Representation for Navigational Trajectory Generation | cs.CV cs.LG | Trajectory generation has garnered significant attention from researchers in
the field of spatio-temporal analysis, as it can generate substantial
synthesized human mobility trajectories that enhance user privacy and alleviate
data scarcity. However, existing trajectory generation methods often focus on
improving trajectory generation quality from a singular perspective, lacking a
comprehensive semantic understanding across various scales. Consequently, we
are inspired to develop a HOlistic SEmantic Representation (HOSER) framework
for navigational trajectory generation. Given an origin-and-destination (OD)
pair and the starting time point of a latent trajectory, we first propose a
Road Network Encoder to expand the receptive field of road- and zone-level
semantics. Second, we design a Multi-Granularity Trajectory Encoder to
integrate the spatio-temporal semantics of the generated trajectory at both the
point and trajectory levels. Finally, we employ a Destination-Oriented
Navigator to seamlessly integrate destination-oriented guidance. Extensive
experiments on three real-world datasets demonstrate that HOSER outperforms
state-of-the-art baselines by a significant margin. Moreover, the model's
performance in few-shot learning and zero-shot learning scenarios further
verifies the effectiveness of our holistic semantic representation.
|
2501.02738 | SCSC: A Novel Standards-Compatible Semantic Communication Framework for
Image Transmission | cs.IT math.IT | Joint source-channel coding (JSCC) is a promising paradigm for
next-generation communication systems, particularly in challenging transmission
environments. In this paper, we propose a novel standard-compatible JSCC
framework for the transmission of images over multiple-input multiple-output
(MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC
schemes, our framework consists of learnable modules that enable communication
using conventional separate source and channel codes (SSCC), which makes it
amenable for easy deployment on legacy systems. Specifically, the learnable
modules involve a preprocessing-empowered network (PPEN) for preserving
essential semantic information, and a precoder \& combiner-enhanced network
(PCEN) for efficient transmission over a resource-constrained MIMO channel. We
treat existing compression and channel coding modules as non-trainable blocks.
Since the parameters of these modules are non-differentiable, we employ a proxy
network that mimics their operations when training the learnable modules.
Numerical results demonstrate that our scheme can save more than 29\% of the
channel bandwidth, and requires lower complexity compared to the constrained
baselines. We also show its generalization capability to unseen datasets and
tasks through extensive experiments.
|
2501.02739 | TARDiS : Text Augmentation for Refining Diversity and Separability | cs.CL cs.AI cs.LG | Text augmentation (TA) is a critical technique for text classification,
especially in few-shot settings. This paper introduces a novel LLM-based TA
method, TARDiS, to address challenges inherent in the generation and alignment
stages of two-stage TA methods. For the generation stage, we propose two
generation processes, SEG and CEG, incorporating multiple class-specific
prompts to enhance diversity and separability. For the alignment stage, we
introduce a class adaptation (CA) method to ensure that generated examples
align with their target classes through verification and modification.
Experimental results demonstrate TARDiS's effectiveness, outperforming
state-of-the-art LLM-based TA methods in various few-shot text classification
tasks. An in-depth analysis confirms the detailed behaviors at each stage.
|
2501.02740 | Interpretable Recognition of Fused Magnesium Furnace Working Conditions
with Deep Convolutional Stochastic Configuration Networks | cs.CV cs.AI | To address the issues of a weak generalization capability and
interpretability in working condition recognition model of a fused magnesium
furnace, this paper proposes an interpretable working condition recognition
method based on deep convolutional stochastic configuration networks (DCSCNs).
Firstly, a supervised learning mechanism is employed to generate physically
meaningful Gaussian differential convolution kernels. An incremental method is
utilized to construct a DCSCNs model, ensuring the convergence of recognition
errors in a hierarchical manner and avoiding the iterative optimization process
of convolutional kernel parameters using the widely used backpropagation
algorithm. The independent coefficient of channel feature maps is defined to
obtain the visualization results of feature class activation maps for the fused
magnesium furnace. A joint reward function is constructed based on the
recognition accuracy, the interpretable trustworthiness evaluation metrics, and
the model parameter quantity. Reinforcement learning (RL) is applied to
adaptively prune the convolutional kernels of the DCSCNs model, aiming to build
a compact, highly performed and interpretable network. The experimental results
demonstrate that the proposed method outperforms the other deep learning
approaches in terms of recognition accuracy and interpretability.
|
2501.02741 | Brick-Diffusion: Generating Long Videos with Brick-to-Wall Denoising | cs.CV | Recent advances in diffusion models have greatly improved text-driven video
generation. However, training models for long video generation demands
significant computational power and extensive data, leading most video
diffusion models to be limited to a small number of frames. Existing
training-free methods that attempt to generate long videos using pre-trained
short video diffusion models often struggle with issues such as insufficient
motion dynamics and degraded video fidelity. In this paper, we present
Brick-Diffusion, a novel, training-free approach capable of generating long
videos of arbitrary length. Our method introduces a brick-to-wall denoising
strategy, where the latent is denoised in segments, with a stride applied in
subsequent iterations. This process mimics the construction of a staggered
brick wall, where each brick represents a denoised segment, enabling
communication between frames and improving overall video quality. Through
quantitative and qualitative evaluations, we demonstrate that Brick-Diffusion
outperforms existing baseline methods in generating high-fidelity videos.
|
2501.02749 | Enhancing Robot Route Optimization in Smart Logistics with Transformer
and GNN Integration | cs.RO cs.AI | This research delves into advanced route optimization for robots in smart
logistics, leveraging a fusion of Transformer architectures, Graph Neural
Networks (GNNs), and Generative Adversarial Networks (GANs). The approach
utilizes a graph-based representation encompassing geographical data, cargo
allocation, and robot dynamics, addressing both spatial and resource
limitations to refine route efficiency. Through extensive testing with
authentic logistics datasets, the proposed method achieves notable
improvements, including a 15% reduction in travel distance, a 20% boost in time
efficiency, and a 10% decrease in energy consumption. These findings highlight
the algorithm's effectiveness, promoting enhanced performance in intelligent
logistics operations.
|
2501.02750 | Spectrum Sharing in Satellite-Terrestrial Integrated Networks:
Frameworks, Approaches, and Opportunities | eess.SP cs.IT cs.SY eess.SY math.IT | To accommodate the increasing communication needs in non-terrestrial networks
(NTNs), wireless users in remote areas may require access to more spectrum than
is currently allocated. Terrestrial networks (TNs), such as cellular networks,
are deployed in specific areas, but many underused licensed spectrum bands
remain in remote areas. Therefore, bringing NTNs to a shared spectrum with TNs
can improve network capacity under reasonable interference management. However,
in satellite-terrestrial integrated networks (STINs), the comprehensive
coverage of a satellite and the unbalanced communication resources of STINs
make it challenging to effectively manage mutual interference between NTN and
TN. This article presents the fundamentals and prospects of spectrum sharing
(SS) in STINs by introducing four SS frameworks, their potential application
scenarios, and technical challenges. Furthermore, advanced SS approaches
related to interference management in STINs and performance metrics of SS in
STINs are introduced. Moreover, a preliminary performance evaluation showcases
the potential for sharing the spectrum between NTN and TN. Finally, future
research opportunities for SS in STINs are discussed.
|
2501.02751 | Ultrasound-QBench: Can LLMs Aid in Quality Assessment of Ultrasound
Imaging? | eess.IV cs.CV cs.MM | With the dramatic upsurge in the volume of ultrasound examinations,
low-quality ultrasound imaging has gradually increased due to variations in
operator proficiency and imaging circumstances, imposing a severe burden on
diagnosis accuracy and even entailing the risk of restarting the diagnosis in
critical cases. To assist clinicians in selecting high-quality ultrasound
images and ensuring accurate diagnoses, we introduce Ultrasound-QBench, a
comprehensive benchmark that systematically evaluates multimodal large language
models (MLLMs) on quality assessment tasks of ultrasound images.
Ultrasound-QBench establishes two datasets collected from diverse sources:
IVUSQA, consisting of 7,709 images, and CardiacUltraQA, containing 3,863
images. These images encompassing common ultrasound imaging artifacts are
annotated by professional ultrasound experts and classified into three quality
levels: high, medium, and low. To better evaluate MLLMs, we decompose the
quality assessment task into three dimensionalities: qualitative
classification, quantitative scoring, and comparative assessment. The
evaluation of 7 open-source MLLMs as well as 1 proprietary MLLMs demonstrates
that MLLMs possess preliminary capabilities for low-level visual tasks in
ultrasound image quality classification. We hope this benchmark will inspire
the research community to delve deeper into uncovering and enhancing the
untapped potential of MLLMs for medical imaging tasks.
|
2501.02754 | MBTSAD: Mitigating Backdoors in Language Models Based on Token Splitting
and Attention Distillation | cs.CR cs.CL | In recent years, attention-based models have excelled across various domains
but remain vulnerable to backdoor attacks, often from downloading or
fine-tuning on poisoned datasets. Many current methods to mitigate backdoors in
NLP models rely on the pre-trained (unfine-tuned) weights, but these methods
fail in scenarios where the pre-trained weights are not available. In this
work, we propose MBTSAD, which can mitigate backdoors in the language model by
utilizing only a small subset of clean data and does not require pre-trained
weights. Specifically, MBTSAD retrains the backdoored model on a dataset
generated by token splitting. Then MBTSAD leverages attention distillation, the
retrained model is the teacher model, and the original backdoored model is the
student model. Experimental results demonstrate that MBTSAD achieves comparable
backdoor mitigation performance as the methods based on pre-trained weights
while maintaining the performance on clean data. MBTSAD does not rely on
pre-trained weights, enhancing its utility in scenarios where pre-trained
weights are inaccessible. In addition, we simplify the min-max problem of
adversarial training and visualize text representations to discover that the
token splitting method in MBTSAD's first step generates Out-of-Distribution
(OOD) data, leading the model to learn more generalized features and eliminate
backdoor patterns.
|
2501.02758 | Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction
and Calibration | eess.SP cs.IT math.IT | Effective channel estimation in sparse and high-dimensional environments is
essential for next-generation wireless systems, particularly in large-scale
MIMO deployments. This paper introduces a novel framework that leverages
digital twins (DTs) as priors to enable efficient zone-specific subspace-based
channel estimation (CE). Subspace-based CE significantly reduces feedback
overhead by focusing on the dominant channel components, exploiting sparsity in
the angular domain while preserving estimation accuracy. While DT channels may
exhibit inaccuracies, their coarse-grained subspaces provide a powerful
starting point, reducing the search space and accelerating convergence. The
framework employs a two-step clustering process on the Grassmann manifold,
combined with reinforcement learning (RL), to iteratively calibrate subspaces
and align them with real-world counterparts. Simulations show that digital
twins not only enable near-optimal performance but also enhance the accuracy of
subspace calibration through RL, highlighting their potential as a step towards
learnable digital twins.
|
2501.02760 | CHAT: Beyond Contrastive Graph Transformer for Link Prediction in
Heterogeneous Networks | cs.CE cs.LG | Link prediction in heterogeneous networks is crucial for understanding the
intricacies of network structures and forecasting their future developments.
Traditional methodologies often face significant obstacles, including
over-smoothing-wherein the excessive aggregation of node features leads to the
loss of critical structural details-and a dependency on human-defined
meta-paths, which necessitate extensive domain knowledge and can be inherently
restrictive. These limitations hinder the effective prediction and analysis of
complex heterogeneous networks. In response to these challenges, we propose the
Contrastive Heterogeneous grAph Transformer (CHAT). CHAT introduces a novel
sampling-based graph transformer technique that selectively retains nodes of
interest, thereby obviating the need for predefined meta-paths. The method
employs an innovative connection-aware transformer to encode node sequences and
their interconnections with high fidelity, guided by a dual-faceted loss
function specifically designed for heterogeneous network link prediction.
Additionally, CHAT incorporates an ensemble link predictor that synthesizes
multiple samplings to achieve enhanced prediction accuracy. We conducted
comprehensive evaluations of CHAT using three distinct drug-target interaction
(DTI) datasets. The empirical results underscore CHAT's superior performance,
outperforming both general-task approaches and models specialized in DTI
prediction. These findings substantiate the efficacy of CHAT in addressing the
complex problem of link prediction in heterogeneous networks.
|
2501.02761 | Beyond $\mathcal{O}(\sqrt{T})$ Regret: Decoupling Learning and
Decision-making in Online Linear Programming | stat.ML cs.LG math.OC | Online linear programming plays an important role in both revenue management
and resource allocation, and recent research has focused on developing
efficient first-order online learning algorithms. Despite the empirical success
of first-order methods, they typically achieve a regret no better than
$\mathcal{O} ( \sqrt{T} )$, which is suboptimal compared to the $\mathcal{O}
(\log T)$ bound guaranteed by the state-of-the-art linear programming
(LP)-based online algorithms. This paper establishes a general framework that
improves upon the $\mathcal{O} ( \sqrt{T} )$ result when the LP dual problem
exhibits certain error bound conditions. For the first time, we show that
first-order learning algorithms achieve $o( \sqrt{T} )$ regret in the
continuous support setting and $\mathcal{O} (\log T)$ regret in the finite
support setting beyond the non-degeneracy assumption. Our results significantly
improve the state-of-the-art regret results and provide new insights for
sequential decision-making.
|
2501.02763 | LDMapNet-U: An End-to-End System for City-Scale Lane-Level Map Updating | cs.CV | An up-to-date city-scale lane-level map is an indispensable infrastructure
and a key enabling technology for ensuring the safety and user experience of
autonomous driving systems. In industrial scenarios, reliance on manual
annotation for map updates creates a critical bottleneck. Lane-level updates
require precise change information and must ensure consistency with adjacent
data while adhering to strict standards. Traditional methods utilize a
three-stage approach-construction, change detection, and updating-which often
necessitates manual verification due to accuracy limitations. This results in
labor-intensive processes and hampers timely updates. To address these
challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm
for city-scale lane-level map updating. By reconceptualizing the update task as
an end-to-end map generation process grounded in historical map data, we
introduce a paradigm shift in map updating that simultaneously generates
vectorized maps and change information. To achieve this, a Prior-Map Encoding
(PME) module is introduced to effectively encode historical maps, serving as a
critical reference for detecting changes. Additionally, we incorporate a novel
Instance Change Prediction (ICP) module that learns to predict associations
with historical maps. Consequently, LDMapNet-U simultaneously achieves
vectorized map element generation and change detection. To demonstrate the
superiority and effectiveness of LDMapNet-U, extensive experiments are
conducted using large-scale real-world datasets. In addition, LDMapNet-U has
been successfully deployed in production at Baidu Maps since April 2024,
supporting map updating for over 360 cities and significantly shortening the
update cycle from quarterly to weekly. The updated maps serve hundreds of
millions of users and are integrated into the autonomous driving systems of
several leading vehicle companies.
|
2501.02765 | Visual Large Language Models for Generalized and Specialized
Applications | cs.CV cs.AI | Visual-language models (VLM) have emerged as a powerful tool for learning a
unified embedding space for vision and language. Inspired by large language
models, which have demonstrated strong reasoning and multi-task capabilities,
visual large language models (VLLMs) are gaining increasing attention for
building general-purpose VLMs. Despite the significant progress made in VLLMs,
the related literature remains limited, particularly from a comprehensive
application perspective, encompassing generalized and specialized applications
across vision (image, video, depth), action, and language modalities. In this
survey, we focus on the diverse applications of VLLMs, examining their using
scenarios, identifying ethics consideration and challenges, and discussing
future directions for their development. By synthesizing these contents, we aim
to provide a comprehensive guide that will pave the way for future innovations
and broader applications of VLLMs. The paper list repository is available:
https://github.com/JackYFL/awesome-VLLMs.
|
2501.02766 | Are GNNs Effective for Multimodal Fault Diagnosis in Microservice
Systems? | cs.SE cs.AI | Fault diagnosis in microservice systems has increasingly embraced multimodal
observation data for a holistic and multifaceted view of the system, with Graph
Neural Networks (GNNs) commonly employed to model complex service dependencies.
However, despite the intuitive appeal, there remains a lack of compelling
justification for the adoption of GNNs, as no direct evidence supports their
necessity or effectiveness. To critically evaluate the current use of GNNs, we
propose DiagMLP, a simple topology-agnostic baseline as a substitute for GNNs
in fault diagnosis frameworks. Through experiments on five public datasets, we
surprisingly find that DiagMLP performs competitively with and even outperforms
GNN-based methods in fault diagnosis tasks, indicating that the current
paradigm of using GNNs to model service dependencies has not yet demonstrated a
tangible contribution. We further discuss potential reasons for this
observation and advocate shifting the focus from solely pursuing novel model
designs to developing challenging datasets, standardizing preprocessing
protocols, and critically evaluating the utility of advanced deep learning
modules.
|
2501.02767 | Enhancing Trustworthiness of Graph Neural Networks with Rank-Based
Conformal Training | cs.LG cs.AI | Graph Neural Networks (GNNs) has been widely used in a variety of fields
because of their great potential in representing graph-structured data.
However, lacking of rigorous uncertainty estimations limits their application
in high-stakes. Conformal Prediction (CP) can produce statistically guaranteed
uncertainty estimates by using the classifier's probability estimates to obtain
prediction sets, which contains the true class with a user-specified
probability. In this paper, we propose a Rank-based CP during training
framework to GNNs (RCP-GNN) for reliable uncertainty estimates to enhance the
trustworthiness of GNNs in the node classification scenario. By exploiting rank
information of the classifier's outcome, prediction sets with desired coverage
rate can be efficiently constructed. The strategy of CP during training with
differentiable rank-based conformity loss function is further explored to adapt
prediction sets according to network topology information. In this way, the
composition of prediction sets can be guided by the goal of jointly reducing
inefficiency and probability estimation errors. Extensive experiments on
several real-world datasets show that our model achieves any pre-defined target
marginal coverage while significantly reducing the inefficiency compared with
state-of-the-art methods.
|
2501.02770 | Multi-Agent Path Finding under Limited Communication Range Constraint
via Dynamic Leading | cs.AI cs.MA cs.RO | This paper proposes a novel framework to handle a multi-agent path finding
problem under a limited communication range constraint, where all agents must
have a connected communication channel to the rest of the team. Many existing
approaches to multi-agent path finding (e.g., leader-follower platooning)
overcome computational challenges of planning in this domain by planning one
agent at a time in a fixed order. However, fixed leader-follower approaches can
become stuck during planning, limiting their practical utility in dense-clutter
environments. To overcome this limitation, we develop dynamic leading
multi-agent path finding, which allows for dynamic reselection of the leading
agent during path planning whenever progress cannot be made. The experiments
show the efficiency of our framework, which can handle up to 25 agents with
more than 90% success-rate across five environment types where baselines
routinely fail.
|
2501.02771 | WorldPose: A World Cup Dataset for Global 3D Human Pose Estimation | cs.CV | We present WorldPose, a novel dataset for advancing research in multi-person
global pose estimation in the wild, featuring footage from the 2022 FIFA World
Cup. While previous datasets have primarily focused on local poses, often
limited to a single person or in constrained, indoor settings, the
infrastructure deployed for this sporting event allows access to multiple fixed
and moving cameras in different stadiums. We exploit the static multi-view
setup of HD cameras to recover the 3D player poses and motions with
unprecedented accuracy given capture areas of more than 1.75 acres. We then
leverage the captured players' motions and field markings to calibrate a moving
broadcasting camera. The resulting dataset comprises more than 80 sequences
with approx 2.5 million 3D poses and a total traveling distance of over 120 km.
Subsequently, we conduct an in-depth analysis of the SOTA methods for global
pose estimation. Our experiments demonstrate that WorldPose challenges existing
multi-person techniques, supporting the potential for new research in this area
and others, such as sports analysis. All pose annotations (in SMPL format),
broadcasting camera parameters and footage will be released for academic
research purposes.
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2501.02772 | GeAR: Generation Augmented Retrieval | cs.IR cs.CL | Document retrieval techniques form the foundation for the development of
large-scale information systems. The prevailing methodology is to construct a
bi-encoder and compute the semantic similarity. However, such scalar similarity
is difficult to reflect enough information and impedes our comprehension of the
retrieval results. In addition, this computational process mainly emphasizes
the global semantics and ignores the fine-grained semantic relationship between
the query and the complex text in the document. In this paper, we propose a new
method called $\textbf{Ge}$neration $\textbf{A}$ugmented $\textbf{R}$etrieval
($\textbf{GeAR}$) that incorporates well-designed fusion and decoding modules.
This enables GeAR to generate the relevant text from documents based on the
fused representation of the query and the document, thus learning to "focus on"
the fine-grained information. Also when used as a retriever, GeAR does not add
any computational burden over bi-encoders. To support the training of the new
framework, we have introduced a pipeline to efficiently synthesize high-quality
data by utilizing large language models. GeAR exhibits competitive retrieval
and localization performance across diverse scenarios and datasets. Moreover,
the qualitative analysis and the results generated by GeAR provide novel
insights into the interpretation of retrieval results. The code, data, and
models will be released after completing technical review to facilitate future
research.
|
2501.02773 | Unsupervised Domain Adaptation for Occlusion Resilient Human Pose
Estimation | cs.CV | Occlusions are a significant challenge to human pose estimation algorithms,
often resulting in inaccurate and anatomically implausible poses. Although
current occlusion-robust human pose estimation algorithms exhibit impressive
performance on existing datasets, their success is largely attributed to
supervised training and the availability of additional information, such as
multiple views or temporal continuity. Furthermore, these algorithms typically
suffer from performance degradation under distribution shifts. While existing
domain adaptive human pose estimation algorithms address this bottleneck, they
tend to perform suboptimally when the target domain images are occluded, a
common occurrence in real-life scenarios. To address these challenges, we
propose OR-POSE: Unsupervised Domain Adaptation for Occlusion Resilient Human
POSE Estimation. OR-POSE is an innovative unsupervised domain adaptation
algorithm which effectively mitigates domain shifts and overcomes occlusion
challenges by employing the mean teacher framework for iterative pseudo-label
refinement. Additionally, OR-POSE reinforces realistic pose prediction by
leveraging a learned human pose prior which incorporates the anatomical
constraints of humans in the adaptation process. Lastly, OR-POSE avoids
overfitting to inaccurate pseudo labels generated from heavily occluded images
by employing a novel visibility-based curriculum learning approach. This
enables the model to gradually transition from training samples with relatively
less occlusion to more challenging, heavily occluded samples. Extensive
experiments show that OR-POSE outperforms existing analogous state-of-the-art
algorithms by $\sim$ 7% on challenging occluded human pose estimation datasets.
|
2501.02774 | Learn A Flexible Exploration Model for Parameterized Action Markov
Decision Processes | cs.LG | Hybrid action models are widely considered an effective approach to
reinforcement learning (RL) modeling. The current mainstream method is to train
agents under Parameterized Action Markov Decision Processes (PAMDPs), which
performs well in specific environments. Unfortunately, these models either
exhibit drastic low learning efficiency in complex PAMDPs or lose crucial
information in the conversion between raw space and latent space. To enhance
the learning efficiency and asymptotic performance of the agent, we propose a
model-based RL (MBRL) algorithm, FLEXplore. FLEXplore learns a
parameterized-action-conditioned dynamics model and employs a modified Model
Predictive Path Integral control. Unlike conventional MBRL algorithms, we
carefully design the dynamics loss function and reward smoothing process to
learn a loose yet flexible model. Additionally, we use the variational lower
bound to maximize the mutual information between the state and the hybrid
action, enhancing the exploration effectiveness of the agent. We theoretically
demonstrate that FLEXplore can reduce the regret of the rollout trajectory
through the Wasserstein Metric under given Lipschitz conditions. Our empirical
results on several standard benchmarks show that FLEXplore has outstanding
learning efficiency and asymptotic performance compared to other baselines.
|
2501.02778 | ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction | eess.IV cs.AI cs.CV | Survival prediction is a crucial task in the medical field and is essential
for optimizing treatment options and resource allocation. However, current
methods often rely on limited data modalities, resulting in suboptimal
performance. In this paper, we propose an Integrated Cross-modal Fusion Network
(ICFNet) that integrates histopathology whole slide images, genomic expression
profiles, patient demographics, and treatment protocols. Specifically, three
types of encoders, a residual orthogonal decomposition module and a unification
fusion module are employed to merge multi-modal features to enhance prediction
accuracy. Additionally, a balanced negative log-likelihood loss function is
designed to ensure fair training across different patients. Extensive
experiments demonstrate that our ICFNet outperforms state-of-the-art algorithms
on five public TCGA datasets, including BLCA, BRCA, GBMLGG, LUAD, and UCEC, and
shows its potential to support clinical decision-making and advance precision
medicine. The codes are available at: https://github.com/binging512/ICFNet.
|
2501.02781 | From Dense to Sparse: Event Response for Enhanced Residential Load
Forecasting | cs.LG | Residential load forecasting (RLF) is crucial for resource scheduling in
power systems. Most existing methods utilize all given load records (dense
data) to indiscriminately extract the dependencies between historical and
future time series. However, there exist important regular patterns residing in
the event-related associations among different appliances (sparse knowledge),
which have yet been ignored. In this paper, we propose an Event-Response
Knowledge Guided approach (ERKG) for RLF by incorporating the estimation of
electricity usage events for different appliances, mining event-related sparse
knowledge from the load series. With ERKG, the event-response estimation
enables portraying the electricity consumption behaviors of residents,
revealing regular variations in appliance operational states. To be specific,
ERKG consists of knowledge extraction and guidance: i) a forecasting model is
designed for the electricity usage events by estimating appliance operational
states, aiming to extract the event-related sparse knowledge; ii) a novel
knowledge-guided mechanism is established by fusing such state estimates of the
appliance events into the RLF model, which can give particular focuses on the
patterns of users' electricity consumption behaviors. Notably, ERKG can
flexibly serve as a plug-in module to boost the capability of existing
forecasting models by leveraging event response. In numerical experiments,
extensive comparisons and ablation studies have verified the effectiveness of
our ERKG, e.g., over 8% MAE can be reduced on the tested state-of-the-art
forecasting models.
|
2501.02785 | Hybrid deep convolution model for lung cancer detection with transfer
learning | cs.CV cs.AI cs.LG | Advances in healthcare research have significantly enhanced our understanding
of disease mechanisms, diagnostic precision, and therapeutic options. Yet, lung
cancer remains one of the leading causes of cancer-related mortality worldwide
due to challenges in early and accurate diagnosis. While current lung cancer
detection models show promise, there is considerable potential for further
improving the accuracy for timely intervention. To address this challenge, we
introduce a hybrid deep convolution model leveraging transfer learning, named
the Maximum Sensitivity Neural Network (MSNN). MSNN is designed to improve the
precision of lung cancer detection by refining sensitivity and specificity.
This model has surpassed existing deep learning approaches through experimental
validation, achieving an accuracy of 98% and a sensitivity of 97%. By
overlaying sensitivity maps onto lung Computed Tomography (CT) scans, it
enables the visualization of regions most indicative of malignant or benign
classifications. This innovative method demonstrates exceptional performance in
distinguishing lung cancer with minimal false positives, thereby enhancing the
accuracy of medical diagnoses.
|
2501.02786 | CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural
Audio Generation | cs.SD cs.CV eess.AS | Binaural audio generation (BAG) aims to convert monaural audio to stereo
audio using visual prompts, requiring a deep understanding of spatial and
semantic information. However, current models risk overfitting to room
environments and lose fine-grained spatial details. In this paper, we propose a
new audio-visual binaural generation model incorporating an audio-visual
conditional normalisation layer that dynamically aligns the mean and variance
of the target difference audio features using visual context, along with a new
contrastive learning method to enhance spatial sensitivity by mining negative
samples from shuffled visual features. We also introduce a cost-efficient way
to utilise test-time augmentation in video data to enhance performance. Our
approach achieves state-of-the-art generation accuracy on the FAIR-Play and
MUSIC-Stereo benchmarks.
|
2501.02788 | GLoG-CSUnet: Enhancing Vision Transformers with Adaptable Radiomic
Features for Medical Image Segmentation | cs.CV cs.AI cs.LG | Vision Transformers (ViTs) have shown promise in medical image semantic
segmentation (MISS) by capturing long-range correlations. However, ViTs often
struggle to model local spatial information effectively, which is essential for
accurately segmenting fine anatomical details, particularly when applied to
small datasets without extensive pre-training. We introduce Gabor and Laplacian
of Gaussian Convolutional Swin Network (GLoG-CSUnet), a novel architecture
enhancing Transformer-based models by incorporating learnable radiomic
features. This approach integrates dynamically adaptive Gabor and Laplacian of
Gaussian (LoG) filters to capture texture, edge, and boundary information,
enhancing the feature representation processed by the Transformer model. Our
method uniquely combines the long-range dependency modeling of Transformers
with the texture analysis capabilities of Gabor and LoG features. Evaluated on
the Synapse multi-organ and ACDC cardiac segmentation datasets, GLoG-CSUnet
demonstrates significant improvements over state-of-the-art models, achieving a
1.14% increase in Dice score for Synapse and 0.99% for ACDC, with minimal
computational overhead (only 15 and 30 additional parameters, respectively).
GLoG-CSUnet's flexible design allows integration with various base models,
offering a promising approach for incorporating radiomics-inspired feature
extraction in Transformer architectures for medical image analysis. The code
implementation is available on GitHub at: https://github.com/HAAIL/GLoG-CSUnet.
|
2501.02790 | Segmenting Text and Learning Their Rewards for Improved RLHF in Language
Model | cs.CL cs.AI | Reinforcement learning from human feedback (RLHF) has been widely adopted to
align language models (LMs) with human preference. Prior RLHF works typically
take a bandit formulation, which, though intuitive, ignores the sequential
nature of LM generation and can suffer from the sparse reward issue. While
recent works propose dense token-level RLHF, treating each token as an action
may be oversubtle to proper reward assignment. In this paper, we seek to get
the best of both by training and utilizing a segment-level reward model, which
assigns a reward to each semantically complete text segment that spans over a
short sequence of tokens. For reward learning, our method allows dynamic text
segmentation and compatibility with standard sequence-preference datasets. For
effective RL-based LM training against segment reward, we generalize the
classical scalar bandit reward normalizers into location-aware normalizer
functions and interpolate the segment reward for further densification. With
these designs, our method performs competitively on three popular RLHF
benchmarks for LM policy: AlpacaEval 2.0, Arena-Hard, and MT-Bench. Ablation
studies are conducted to further demonstrate our method.
|
2501.02791 | Orthogonal greedy algorithm for linear operator learning with shallow
neural network | math.NA cs.LG cs.NA | Greedy algorithms, particularly the orthogonal greedy algorithm (OGA), have
proven effective in training shallow neural networks for fitting functions and
solving partial differential equations (PDEs). In this paper, we extend the
application of OGA to the tasks of linear operator learning, which is
equivalent to learning the kernel function through integral transforms.
Firstly, a novel greedy algorithm is developed for kernel estimation rate in a
new semi-inner product, which can be utilized to approximate the Green's
function of linear PDEs from data. Secondly, we introduce the OGA for
point-wise kernel estimation to further improve the approximation rate,
achieving orders of accuracy improvement across various tasks and baseline
models. In addition, we provide a theoretical analysis on the kernel estimation
problem and the optimal approximation rates for both algorithms, establishing
their efficacy and potential for future applications in PDEs and operator
learning tasks.
|
2501.02792 | Gaming on Coincident Peak Shaving: Equilibrium and Strategic Behavior | eess.SY cs.SY | Coincident peak demand charges are imposed by power system operators or
electric utilities when the overall system demand, aggregated across multiple
consumers, reaches its peak. These charges incentivize consumers to reduce
their demand during peak periods, a practice known as coincident peak shaving.
In this paper, we analyze the coincident peak shaving problem through the lens
of game theory, developing a theoretical model to examine the impact of
strategic consumer behavior on system efficiency. We demonstrate that the game
structure exhibits varying characteristics - concave,
quasiconcave/discontinuous, or non-concave/discontinuous - depending on the
extent of consumers demand-shifting capabilities. For a two-agent, two-period
setting, we derive closed-form Nash equilibrium solutions under each condition
and generalize our findings to cases with multiple agents. We prove the
stability of the equilibrium points and present an algorithm for computing
equilibrium outcomes across all game scenarios. We also show that the
peak-shaving effectiveness of the game model matches that of the centralized
peak-shaving model but with increased levels of anarchy. In the cases of
quasiconcave and non-concave game conditions, we analytically demonstrate in
the two-agent setting that anarchy increases with consumers' flexibility and
inequity, as measured by their marginal shifting costs, and we also analyze the
influence of the number of agents on anarchy. Finally, we provide numerical
simulations to validate our theoretical results.
|
2501.02793 | Fairness Through Matching | cs.AI cs.LG stat.ML | Group fairness requires that different protected groups, characterized by a
given sensitive attribute, receive equal outcomes overall. Typically, the level
of group fairness is measured by the statistical gap between predictions from
different protected groups. In this study, we reveal an implicit property of
existing group fairness measures, which provides an insight into how the
group-fair models behave. Then, we develop a new group-fair constraint based on
this implicit property to learn group-fair models. To do so, we first introduce
a notable theoretical observation: every group-fair model has an implicitly
corresponding transport map between the input spaces of each protected group.
Based on this observation, we introduce a new group fairness measure termed
Matched Demographic Parity (MDP), which quantifies the averaged gap between
predictions of two individuals (from different protected groups) matched by a
given transport map. Then, we prove that any transport map can be used in MDP
to learn group-fair models, and develop a novel algorithm called Fairness
Through Matching (FTM), which learns a group-fair model using MDP constraint
with an user-specified transport map. We specifically propose two favorable
types of transport maps for MDP, based on the optimal transport theory, and
discuss their advantages. Experiments reveal that FTM successfully trains
group-fair models with certain desirable properties by choosing the transport
map accordingly.
|
2501.02795 | InfiFusion: A Unified Framework for Enhanced Cross-Model Reasoning via
LLM Fusion | cs.CL cs.CV | We introduce InfiFusion, an efficient training pipeline designed to integrate
multiple domain-specialized Large Language Models (LLMs) into a single pivot
model, effectively harnessing the strengths of each source model. Traditional
fusion methods either merge model parameters directly or rely on knowledge
distillation with rigid assumptions, limiting their flexibility and efficiency.
InfiFusion overcomes these limitations by enhancing Universal Logit
Distillation (ULD) with Top-K selection and Logits Standardization. We propose
two fusion strategies: Pairwise Fusion (InfiFusion$_p$), where each source
model knowledge is distilled individually into the pivot model followed by
merging and Unified Fusion (InfiFusion$_u$), where knowledge from all source
models is distilled simultaneously into the pivot model. InfiFusion outperforms
the state-of-the-art models, such as Qwen-2.5-14B-Instruct and Phi-4, across 11
widely applied benchmarks covering reasoning, coding, mathematics, and
instruction-following tasks. Notably, InfiFusion achieves this superior
performance while significantly reduces computational costs, completing full
training with only 160 H800 GPU hours compared to the millions typically
required for traditional LLM training.
|
2501.02796 | GraphDART: Graph Distillation for Efficient Advanced Persistent Threat
Detection | cs.CR cs.LG | Cyber-physical-social systems (CPSSs) have emerged in many applications over
recent decades, requiring increased attention to security concerns. The rise of
sophisticated threats like Advanced Persistent Threats (APTs) makes ensuring
security in CPSSs particularly challenging. Provenance graph analysis has
proven effective for tracing and detecting anomalies within systems, but the
sheer size and complexity of these graphs hinder the efficiency of existing
methods, especially those relying on graph neural networks (GNNs). To address
these challenges, we present GraphDART, a modular framework designed to distill
provenance graphs into compact yet informative representations, enabling
scalable and effective anomaly detection. GraphDART can take advantage of
diverse graph distillation techniques, including classic and modern graph
distillation methods, to condense large provenance graphs while preserving
essential structural and contextual information. This approach significantly
reduces computational overhead, allowing GNNs to learn from distilled graphs
efficiently and enhance detection performance. Extensive evaluations on
benchmark datasets demonstrate the robustness of GraphDART in detecting
malicious activities across cyber-physical-social systems. By optimizing
computational efficiency, GraphDART provides a scalable and practical solution
to safeguard interconnected environments against APTs.
|
2501.02798 | Ray-Tracing Channel Modeling for LEO Satellite-to-Ground Communication
Systems | eess.SY cs.SY | Based on the vision of global coverage for sixth-generation (6G) wireless
communication systems, the low earth orbit (LEO) satellite-to-ground channel
model for urban scenarios has emerged as highly important for the system
design. In this paper, we propose an LEO satellite-to-ground channel model
through shooting and bouncing rays (SBR) algorithm to analyze the channel
characteristics. The orbit of LEO is modeled by the simplified general
perturbations 4 (SGP4), and an accurate celestial model is applied to calculate
the Doppler shift of multipath in a transmission time window of LEO
satellite-to-ground communications. Channel characteristics of LEO
satellite-to-ground communications such as the root-mean-square (RMS) delay
spread, the Doppler shift, and the received power at different times are
obtained. The simulation results show that the received power is only
significantly noticeable in the transmission time window when the satellite is
close to the receiver. Proposed model validates the effectiveness of
ray-tracing in actual LEO satellite-to-ground communication scenarios and
extends the calculation of the Doppler shift.
|
2501.02800 | COph100: A comprehensive fundus image registration dataset from infants
constituting the "RIDIRP" database | cs.CV cs.CE | Retinal image registration is vital for diagnostic therapeutic applications
within the field of ophthalmology. Existing public datasets, focusing on adult
retinal pathologies with high-quality images, have limited number of image
pairs and neglect clinical challenges. To address this gap, we introduce
COph100, a novel and challenging dataset known as the Comprehensive
Ophthalmology Retinal Image Registration dataset for infants with a wide range
of image quality issues constituting the public "RIDIRP" database. COph100
consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a
total of 491 image pairs carefully selected from the publicly available
dataset. We manually labeled the corresponding ground truth image points and
provided automatic vessel segmentation masks for each image. We have assessed
COph100 in terms of image quality and registration outcomes using
state-of-the-art algorithms. This resource enables a robust comparison of
retinal registration methodologies and aids in the analysis of disease
progression in infants, thereby deepening our understanding of pediatric
ophthalmic conditions.
|
2501.02803 | Enhancing Lifelong Multi-Agent Path Finding with Cache Mechanism | cs.RO cs.AI | Multi-Agent Path Finding (MAPF), which focuses on finding collision-free
paths for multiple robots, is crucial in autonomous warehouse operations.
Lifelong MAPF (L-MAPF), where agents are continuously reassigned new targets
upon completing their current tasks, offers a more realistic approximation of
real-world warehouse scenarios. While cache storage systems can enhance
efficiency and reduce operational costs, existing approaches primarily rely on
expectations and mathematical models, often without adequately addressing the
challenges of multi-robot planning and execution. In this paper, we introduce a
novel mechanism called Lifelong MAPF with Cache Mechanism (L-MAPF-CM), which
integrates high-level cache storage with low-level path planning. We have
involved a new type of map grid called cache for temporary item storage.
Additionally, we involved a task assigner (TA) with a locking mechanism to
bridge the gap between the new cache grid and L-MAPF algorithm. The TA
dynamically allocates target locations to agents based on their status in
various scenarios. We evaluated L-MAPF-CM using different cache replacement
policies and task distributions. L-MAPF-CM has demonstrated performance
improvements particularly with high cache hit rates and smooth traffic
conditions.
|
2501.02807 | AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal
Conditions and Larger Scene | cs.CV | Compared to frame-based methods, computational neuromorphic imaging using
event cameras offers significant advantages, such as minimal motion blur,
enhanced temporal resolution, and high dynamic range. The multi-view
consistency of Neural Radiance Fields combined with the unique benefits of
event cameras, has spurred recent research into reconstructing NeRF from data
captured by moving event cameras. While showing impressive performance,
existing methods rely on ideal conditions with the availability of uniform and
high-quality event sequences and accurate camera poses, and mainly focus on the
object level reconstruction, thus limiting their practical applications. In
this work, we propose AE-NeRF to address the challenges of learning event-based
NeRF from non-ideal conditions, including non-uniform event sequences, noisy
poses, and various scales of scenes. Our method exploits the density of event
streams and jointly learn a pose correction module with an event-based NeRF
(e-NeRF) framework for robust 3D reconstruction from inaccurate camera poses.
To generalize to larger scenes, we propose hierarchical event distillation with
a proposal e-NeRF network and a vanilla e-NeRF network to resample and refine
the reconstruction process. We further propose an event reconstruction loss and
a temporal loss to improve the view consistency of the reconstructed scene. We
established a comprehensive benchmark that includes large-scale scenes to
simulate practical non-ideal conditions, incorporating both synthetic and
challenging real-world event datasets. The experimental results show that our
method achieves a new state-of-the-art in event-based 3D reconstruction.
|
2501.02808 | DarkFarseer: Inductive Spatio-temporal Kriging via Hidden Style
Enhancement and Sparsity-Noise Mitigation | cs.LG | With the rapid growth of the Internet of Things and Cyber-Physical Systems,
widespread sensor deployment has become essential. However, the high costs of
building sensor networks limit their scale and coverage, making fine-grained
deployment challenging. Inductive Spatio-Temporal Kriging (ISK) addresses this
issue by introducing virtual sensors. Based on graph neural networks (GNNs)
extracting the relationships between physical and virtual sensors, ISK can
infer the measurements of virtual sensors from physical sensors. However,
current ISK methods rely on conventional message-passing mechanisms and network
architectures, without effectively extracting spatio-temporal features of
physical sensors and focusing on representing virtual sensors. Additionally,
existing graph construction methods face issues of sparse and noisy
connections, destroying ISK performance. To address these issues, we propose
DarkFarseer, a novel ISK framework with three key components. First, we propose
the Neighbor Hidden Style Enhancement module with a style transfer strategy to
enhance the representation of virtual nodes in a temporal-then-spatial manner
to better extract the spatial relationships between physical and virtual nodes.
Second, we propose Virtual-Component Contrastive Learning, which aims to enrich
the node representation by establishing the association between the patterns of
virtual nodes and the regional patterns within graph components. Lastly, we
design a Similarity-Based Graph Denoising Strategy, which reduces the
connectivity strength of noisy connections around virtual nodes and their
neighbors based on their temporal information and regional spatial patterns.
Extensive experiments demonstrate that DarkFarseer significantly outperforms
existing ISK methods.
|
2501.02809 | Theoretical Data-Driven MobilePosenet: Lightweight Neural Network for
Accurate Calibration-Free 5-DOF Magnet Localization | cs.RO | Permanent magnet tracking using the external sensor array is crucial for the
accurate localization of wireless capsule endoscope robots. Traditional
tracking algorithms, based on the magnetic dipole model and Levenberg-Marquardt
(LM) algorithm, face challenges related to computational delays and the need
for initial position estimation. More recently proposed neural network-based
approaches often require extensive hardware calibration and real-world data
collection, which are time-consuming and labor-intensive. To address these
challenges, we propose MobilePosenet, a lightweight neural network architecture
that leverages depthwise separable convolutions to minimize computational cost
and a channel attention mechanism to enhance localization accuracy. Besides,
the inputs to the network integrate the sensors' coordinate information and
random noise, compensating for the discrepancies between the theoretical model
and the actual magnetic fields and thus allowing MobilePosenet to be trained
entirely on theoretical data. Experimental evaluations conducted in a \(90
\times 90 \times 80\) mm workspace demonstrate that MobilePosenet exhibits
excellent 5-DOF localization accuracy ($1.54 \pm 1.03$ mm and $2.24 \pm
1.84^{\circ}$) and inference speed (0.9 ms) against state-of-the-art methods
trained on real-world data. Since network training relies solely on theoretical
data, MobilePosenet can eliminate the hardware calibration and real-world data
collection process, improving the generalizability of this permanent magnet
localization method and the potential for rapid adoption in different clinical
settings.
|
2501.02811 | First-place Solution for Streetscape Shop Sign Recognition Competition | cs.CV | Text recognition technology applied to street-view storefront signs is
increasingly utilized across various practical domains, including map
navigation, smart city planning analysis, and business value assessments in
commercial districts. This technology holds significant research and commercial
potential. Nevertheless, it faces numerous challenges. Street view images often
contain signboards with complex designs and diverse text styles, complicating
the text recognition process. A notable advancement in this field was
introduced by our team in a recent competition. We developed a novel multistage
approach that integrates multimodal feature fusion, extensive self-supervised
training, and a Transformer-based large model. Furthermore, innovative
techniques such as BoxDQN, which relies on reinforcement learning, and text
rectification methods were employed, leading to impressive outcomes.
Comprehensive experiments have validated the effectiveness of these methods,
showcasing our potential to enhance text recognition capabilities in complex
urban environments.
|
2501.02814 | Analogue Forecast System for Daily Precipitation Prediction Using
Autoencoder Feature Extraction: Application in Hong Kong | physics.ao-ph cs.LG | In the Hong Kong Observatory, the Analogue Forecast System (AFS) for
precipitation has been providing useful reference in predicting possible daily
rainfall scenarios for the next 9 days, by identifying historical cases with
similar weather patterns to the latest output from the deterministic model of
the European Centre for Medium-Range Weather Forecasts (ECMWF). Recent advances
in machine learning allow more sophisticated models to be trained using
historical data and the patterns of high-impact weather events to be
represented more effectively. As such, an enhanced AFS has been developed using
the deep learning technique autoencoder. The datasets of the fifth generation
of the ECMWF Reanalysis (ERA5) are utilised where more meteorological elements
in higher horizontal, vertical and temporal resolutions are available as
compared to the previous ECMWF reanalysis products used in the existing AFS.
The enhanced AFS features four major steps in generating the daily rain class
forecasts: (1) preprocessing of gridded ERA5 and ECMWF model forecast, (2)
feature extraction by the pretrained autoencoder, (3) application of optimised
feature weightings based on historical cases, and (4) calculation of the final
rain class from a weighted ensemble of top analogues. The enhanced AFS
demonstrates a consistent and superior performance over the existing AFS,
especially in capturing heavy rain cases, during the verification period from
2019 to 2022. This paper presents the detailed formulation of the enhanced AFS
and discusses its advantages and limitations in supporting precipitation
forecasting in Hong Kong.
|
2501.02815 | Local Reactive Control for Mobile Manipulators with Whole-Body Safety in
Complex Environments | cs.RO cs.SY eess.SY | Mobile manipulators typically encounter significant challenges in navigating
narrow, cluttered environments due to their high-dimensional state spaces and
complex kinematics. While reactive methods excel in dynamic settings, they
struggle to efficiently incorporate complex, coupled constraints across the
entire state space. In this work, we present a novel local reactive controller
that reformulates the time-domain single-step problem into a multi-step
optimization problem in the spatial domain, leveraging the propagation of a
serial kinematic chain. This transformation facilitates the formulation of
customized, decoupled link-specific constraints, which is further solved
efficiently with augmented Lagrangian differential dynamic programming
(AL-DDP). Our approach naturally absorbs spatial kinematic propagation in the
forward pass and processes all link-specific constraints simultaneously during
the backward pass, enhancing both constraint management and computational
efficiency. Notably, in this framework, we formulate collision avoidance
constraints for each link using accurate geometric models with extracted free
regions, and this improves the maneuverability of the mobile manipulator in
narrow, cluttered spaces. Experimental results showcase significant
improvements in safety, efficiency, and task completion rates. These findings
underscore the robustness of the proposed method, particularly in narrow,
cluttered environments where conventional approaches could falter. The
open-source project can be found at
https://github.com/Chunx1nZHENG/MM-with-Whole-Body-Safety-Release.git.
|
2501.02816 | InpDiffusion: Image Inpainting Localization via Conditional Diffusion
Models | cs.CV cs.AI | As artificial intelligence advances rapidly, particularly with the advent of
GANs and diffusion models, the accuracy of Image Inpainting Localization (IIL)
has become increasingly challenging. Current IIL methods face two main
challenges: a tendency towards overconfidence, leading to incorrect
predictions; and difficulty in detecting subtle tampering boundaries in
inpainted images. In response, we propose a new paradigm that treats IIL as a
conditional mask generation task utilizing diffusion models. Our method,
InpDiffusion, utilizes the denoising process enhanced by the integration of
image semantic conditions to progressively refine predictions. During
denoising, we employ edge conditions and introduce a novel edge supervision
strategy to enhance the model's perception of edge details in inpainted
objects. Balancing the diffusion model's stochastic sampling with edge
supervision of tampered image regions mitigates the risk of incorrect
predictions from overconfidence and prevents the loss of subtle boundaries that
can result from overly stochastic processes. Furthermore, we propose an
innovative Dual-stream Multi-scale Feature Extractor (DMFE) for extracting
multi-scale features, enhancing feature representation by considering both
semantic and edge conditions of the inpainted images. Extensive experiments
across challenging datasets demonstrate that the InpDiffusion significantly
outperforms existing state-of-the-art methods in IIL tasks, while also
showcasing excellent generalization capabilities and robustness.
|
2501.02820 | Rydberg Atomic Quantum Receivers for Multi-Target DOA Estimation | eess.SP cs.IT math.IT quant-ph | Quantum sensing technologies have experienced rapid progresses since entering
the `second quantum revolution'. Among various candidates, schemes relying on
Rydberg atoms exhibit compelling advantages for detecting radio frequency
signals. Based on this, Rydberg atomic quantum receivers (RAQRs) have emerged
as a promising solution to classical wireless communication and sensing. To
harness the advantages and exploit the potential of RAQRs in wireless sensing,
we investigate the realization of the direction of arrival (DOA) estimation by
RAQRs. Specifically, we first conceive a Rydberg atomic quantum uniform linear
array (RAQ-ULA) aided receiver for multi-target detection and propose the
corresponding signal model of this sensing system. Furthermore, we propose the
Rydberg atomic quantum estimation of signal parameters by designing a
rotational invariance based technique termed as RAQ-ESPRIT relying on our
model. The proposed algorithm solves the sensor gain mismatch problem, which is
due to the presence of the RF local oscillator in the RAQ-ULA and cannot be
well addressed by using the conventional ESPRIT. Lastly, we characterize our
scheme through numerical simulations.
|
2501.02821 | Targetless Intrinsics and Extrinsic Calibration of Multiple LiDARs and
Cameras with IMU using Continuous-Time Estimation | cs.RO | Accurate spatiotemporal calibration is a prerequisite for multisensor fusion.
However, sensors are typically asynchronous, and there is no overlap between
the fields of view of cameras and LiDARs, posing challenges for intrinsic and
extrinsic parameter calibration. To address this, we propose a calibration
pipeline based on continuous-time and bundle adjustment (BA) capable of
simultaneous intrinsic and extrinsic calibration (6 DOF transformation and time
offset). We do not require overlapping fields of view or any calibration board.
Firstly, we establish data associations between cameras using Structure from
Motion (SFM) and perform self-calibration of camera intrinsics. Then, we
establish data associations between LiDARs through adaptive voxel map
construction, optimizing for extrinsic calibration within the map. Finally, by
matching features between the intensity projection of LiDAR maps and camera
images, we conduct joint optimization for intrinsic and extrinsic parameters.
This pipeline functions in texture-rich structured environments, allowing
simultaneous calibration of any number of cameras and LiDARs without the need
for intricate sensor synchronization triggers. Experimental results demonstrate
our method's ability to fulfill co-visibility and motion constraints between
sensors without accumulating errors.
|
2501.02822 | RDD4D: 4D Attention-Guided Road Damage Detection And Classification | cs.CV cs.AI cs.RO | Road damage detection and assessment are crucial components of infrastructure
maintenance. However, current methods often struggle with detecting multiple
types of road damage in a single image, particularly at varying scales. This is
due to the lack of road datasets with various damage types having varying
scales. To overcome this deficiency, first, we present a novel dataset called
Diverse Road Damage Dataset (DRDD) for road damage detection that captures the
diverse road damage types in individual images, addressing a crucial gap in
existing datasets. Then, we provide our model, RDD4D, that exploits Attention4D
blocks, enabling better feature refinement across multiple scales. The
Attention4D module processes feature maps through an attention mechanism
combining positional encoding and "Talking Head" components to capture local
and global contextual information. In our comprehensive experimental analysis
comparing various state-of-the-art models on our proposed, our enhanced model
demonstrated superior performance in detecting large-sized road cracks with an
Average Precision (AP) of 0.458 and maintained competitive performance with an
overall AP of 0.445. Moreover, we also provide results on the CrackTinyNet
dataset; our model achieved around a 0.21 increase in performance. The code,
model weights, dataset, and our results are available on
\href{https://github.com/msaqib17/Road_Damage_Detection}{https://github.com/msaqib17/Road\_Damage\_Detection}.
|
2501.02824 | Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated
Anesthesia | q-bio.BM cs.LG | Anesthetics are crucial in surgical procedures and therapeutic interventions,
but they come with side effects and varying levels of effectiveness, calling
for novel anesthetic agents that offer more precise and controllable effects.
Targeting Gamma-aminobutyric acid (GABA) receptors, the primary inhibitory
receptors in the central nervous system, could enhance their inhibitory action,
potentially reducing side effects while improving the potency of anesthetics.
In this study, we introduce a proteomic learning of GABA receptor-mediated
anesthesia based on 24 GABA receptor subtypes by considering over 4000 proteins
in protein-protein interaction (PPI) networks and over 1.5 millions known
binding compounds. We develop a corresponding drug-target interaction network
to identify potential lead compounds for novel anesthetic design. To ensure
robust proteomic learning predictions, we curated a dataset comprising 136
targets from a pool of 980 targets within the PPI networks. We employed three
machine learning algorithms, integrating advanced natural language processing
(NLP) models such as pretrained transformer and autoencoder embeddings. Through
a comprehensive screening process, we evaluated the side effects and
repurposing potential of over 180,000 drug candidates targeting the GABRA5
receptor. Additionally, we assessed the ADMET (absorption, distribution,
metabolism, excretion, and toxicity) properties of these candidates to identify
those with near-optimal characteristics. This approach also involved optimizing
the structures of existing anesthetics. Our work presents an innovative
strategy for the development of new anesthetic drugs, optimization of
anesthetic use, and deeper understanding of potential anesthesia-related side
effects.
|
2501.02825 | Randomly Sampled Language Reasoning Problems Reveal Limits of LLMs | cs.LG | Can LLMs pick up language structure from examples? Evidence in prior work
seems to indicate yes, as pretrained models repeatedly demonstrate the ability
to adapt to new language structures and vocabularies. However, this line of
research typically considers languages that are present within common
pretraining datasets, or otherwise share notable similarities with these seen
languages. In contrast, in this work we attempt to measure models' language
understanding capacity while circumventing the risk of dataset recall. We
parameterize large families of language tasks recognized by deterministic
finite automata (DFAs), and can thus sample novel language reasoning problems
to fairly evaulate LLMs regardless of training data. We find that, even in the
strikingly simple setting of 3-state DFAs, LLMs underperform unparameterized
ngram models on both language recognition and synthesis tasks. These results
suggest that LLMs struggle to match the ability of basic language models in
recognizing and reasoning over languages that are sufficiently distinct from
the ones they see at training time, underscoring the distinction between
learning individual languages and possessing a general theory of language.
|
2501.02831 | Universal Features Guided Zero-Shot Category-Level Object Pose
Estimation | cs.CV | Object pose estimation, crucial in computer vision and robotics applications,
faces challenges with the diversity of unseen categories. We propose a
zero-shot method to achieve category-level 6-DOF object pose estimation, which
exploits both 2D and 3D universal features of input RGB-D image to establish
semantic similarity-based correspondences and can be extended to unseen
categories without additional model fine-tuning. Our method begins with
combining efficient 2D universal features to find sparse correspondences
between intra-category objects and gets initial coarse pose. To handle the
correspondence degradation of 2D universal features if the pose deviates much
from the target pose, we use an iterative strategy to optimize the pose.
Subsequently, to resolve pose ambiguities due to shape differences between
intra-category objects, the coarse pose is refined by optimizing with dense
alignment constraint of 3D universal features. Our method outperforms previous
methods on the REAL275 and Wild6D benchmarks for unseen categories.
|
2501.02832 | Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured
State-Space Models | cs.CL cs.AI cs.SD eess.AS | We propose Samba ASR,the first state of the art Automatic Speech
Recognition(ASR)model leveraging the novel Mamba architecture as both encoder
and decoder,built on the foundation of state space models(SSMs).Unlike
transformerbased ASR models,which rely on self-attention mechanisms to capture
dependencies,Samba ASR effectively models both local and global temporal
dependencies using efficient statespace dynamics,achieving remarkable
performance gains.By addressing the limitations of transformers,such as
quadratic scaling with input length and difficulty in handling longrange
dependencies,Samba ASR achieves superior accuracy and efficiency.Experimental
results demonstrate that Samba ASR surpasses existing opensource
transformerbased ASR models across various standard benchmarks,establishing it
as the new state of theart in ASR.Extensive evaluations on the benchmark
dataset show significant improvements in Word Error Rate(WER),with competitive
performance even in lowresource scenarios.Furthermore,the inherent
computational efficiency and parameter optimization of the Mamba architecture
make Samba ASR a scalable and robust solution for diverse ASR tasks.Our
contributions include the development of a new Samba ASR architecture for
automatic speech recognition(ASR),demonstrating the superiority of structured
statespace models(SSMs)over transformer based models for speech sequence
processing.We provide a comprehensive evaluation on public
benchmarks,showcasing stateoftheart(SOTA)performance,and present an indepth
analysis of computational efficiency,robustness to noise,and sequence
generalization.This work highlights the viability of Mamba SSMs as a
transformerfree alternative for efficient and accurate ASR.By leveraging the
advancements of statespace modeling,Samba ASR redefines ASR performance
standards and sets a new benchmark for future research in this field.
|
2501.02837 | Forward Once for All: Structural Parameterized Adaptation for Efficient
Cloud-coordinated On-device Recommendation | cs.DC cs.AI cs.IR | In cloud-centric recommender system, regular data exchanges between user
devices and cloud could potentially elevate bandwidth demands and privacy
risks. On-device recommendation emerges as a viable solution by performing
reranking locally to alleviate these concerns. Existing methods primarily focus
on developing local adaptive parameters, while potentially neglecting the
critical role of tailor-made model architecture. Insights from broader research
domains suggest that varying data distributions might favor distinct
architectures for better fitting. In addition, imposing a uniform model
structure across heterogeneous devices may result in risking inefficacy on less
capable devices or sub-optimal performance on those with sufficient
capabilities. In response to these gaps, our paper introduces Forward-OFA, a
novel approach for the dynamic construction of device-specific networks (both
structure and parameters). Forward-OFA employs a structure controller to
selectively determine whether each block needs to be assembled for a given
device. However, during the training of the structure controller, these
assembled heterogeneous structures are jointly optimized, where the co-adaption
among blocks might encounter gradient conflicts. To mitigate this, Forward-OFA
is designed to establish a structure-guided mapping of real-time behaviors to
the parameters of assembled networks. Structure-related parameters and parallel
components within the mapper prevent each part from receiving heterogeneous
gradients from others, thus bypassing the gradient conflicts for coupled
optimization. Besides, direct mapping enables Forward-OFA to achieve adaptation
through only one forward pass, allowing for swift adaptation to changing
interests and eliminating the requirement for on-device backpropagation.
Experiments on real-world datasets demonstrate the effectiveness and efficiency
of Forward-OFA.
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2501.02838 | Improving GenIR Systems Based on User Feedback | cs.IR | In this chapter, we discuss how to improve the GenIR systems based on user
feedback. Before describing the approaches, it is necessary to be aware that
the concept of "user" has been extended in the interactions with the GenIR
systems. Different types of feedback information and strategies are also
provided. Then the alignment techniques are highlighted in terms of objectives
and methods. Following this, various ways of learning from user feedback in
GenIR are presented, including continual learning, learning and ranking in the
conversational context, and prompt learning. Through this comprehensive
exploration, it becomes evident that innovative techniques are being proposed
beyond traditional methods of utilizing user feedback, and contribute
significantly to the evolution of GenIR in the new era. We also summarize some
challenging topics and future directions that require further investigation.
|
2501.02840 | Enhanced Rooftop Solar Panel Detection by Efficiently Aggregating Local
Features | cs.CV cs.AI cs.LG | In this paper, we present an enhanced Convolutional Neural Network
(CNN)-based rooftop solar photovoltaic (PV) panel detection approach using
satellite images. We propose to use pre-trained CNN-based model to extract the
local convolutional features of rooftops. These local features are then
combined using the Vectors of Locally Aggregated Descriptors (VLAD) technique
to obtain rooftop-level global features, which are then used to train
traditional Machine Learning (ML) models to identify rooftop images that do and
do not contain PV panels. On the dataset used in this study, the proposed
approach achieved rooftop-PV classification scores exceeding the predefined
threshold of 0.9 across all three cities for each of the feature extractor
networks evaluated. Moreover, we propose a 3-phase approach to enable efficient
utilization of the previously trained models on a new city or region with
limited labelled data. We illustrate the effectiveness of this 3-phase approach
for multi-city rooftop-PV detection task.
|
2501.02841 | Integrating Language-Image Prior into EEG Decoding for Cross-Task
Zero-Calibration RSVP-BCI | cs.HC cs.IR | Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI)
is an effective technology used for information detection by detecting
Event-Related Potentials (ERPs). The current RSVP decoding methods can perform
well in decoding EEG signals within a single RSVP task, but their decoding
performance significantly decreases when directly applied to different RSVP
tasks without calibration data from the new tasks. This limits the rapid and
efficient deployment of RSVP-BCI systems for detecting different categories of
targets in various scenarios. To overcome this limitation, this study aims to
enhance the cross-task zero-calibration RSVP decoding performance. First, we
design three distinct RSVP tasks for target image retrieval and build an
open-source dataset containing EEG signals and corresponding stimulus images.
Then we propose an EEG with Language-Image Prior fusion Transformer
(ELIPformer) for cross-task zero-calibration RSVP decoding. Specifically, we
propose a prompt encoder based on the language-image pre-trained model to
extract language-image features from task-specific prompts and stimulus images
as prior knowledge for enhancing EEG decoding. A cross bidirectional attention
mechanism is also adopted to facilitate the effective feature fusion and
alignment between the EEG and language-image features. Extensive experiments
demonstrate that the proposed model achieves superior performance in cross-task
zero-calibration RSVP decoding, which promotes the RSVP-BCI system from
research to practical application.
|
2501.02842 | Foundations of GenIR | cs.IR cs.LG | The chapter discusses the foundational impact of modern generative AI models
on information access (IA) systems. In contrast to traditional AI, the
large-scale training and superior data modeling of generative AI models enable
them to produce high-quality, human-like responses, which brings brand new
opportunities for the development of IA paradigms. In this chapter, we identify
and introduce two of them in details, i.e., information generation and
information synthesis. Information generation allows AI to create tailored
content addressing user needs directly, enhancing user experience with
immediate, relevant outputs. Information synthesis leverages the ability of
generative AI to integrate and reorganize existing information, providing
grounded responses and mitigating issues like model hallucination, which is
particularly valuable in scenarios requiring precision and external knowledge.
This chapter delves into the foundational aspects of generative models,
including architecture, scaling, and training, and discusses their applications
in multi-modal scenarios. Additionally, it examines the retrieval-augmented
generation paradigm and other methods for corpus modeling and understanding,
demonstrating how generative AI can enhance information access systems. It also
summarizes potential challenges and fruitful directions for future studies.
|
2501.02843 | RAHN: A Reputation Based Hourglass Network for Web Service QoS
Prediction | cs.SE cs.LG | As the homogenization of Web services becomes more and more common, the
difficulty of service recommendation is gradually increasing. How to predict
Quality of Service (QoS) more efficiently and accurately becomes an important
challenge for service recommendation. Considering the excellent role of
reputation and deep learning (DL) techniques in the field of QoS prediction, we
propose a reputation and DL based QoS prediction network, RAHN, which contains
the Reputation Calculation Module (RCM), the Latent Feature Extraction Module
(LFEM), and the QoS Prediction Hourglass Network (QPHN). RCM obtains the user
reputation and the service reputation by using a clustering algorithm and a
Logit model. LFEM extracts latent features from known information to form an
initial latent feature vector. QPHN aggregates latent feature vectors with
different scales by using Attention Mechanism, and can be stacked multiple
times to obtain the final latent feature vector for prediction. We evaluate
RAHN on a real QoS dataset. The experimental results show that the Mean
Absolute Error (MAE) and Root Mean Square Error (RMSE) of RAHN are smaller than
the six baseline methods.
|
2501.02844 | Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text
Classification | cs.CL cs.IR cs.LG | Text classification is a fundamental task in data mining, pivotal to various
applications such as tabular understanding and recommendation. Although neural
network-based models, such as CNN and BERT, have demonstrated remarkable
performance in text classification, their effectiveness heavily relies on
abundant labeled training data. This dependency makes these models less
effective in dynamic few-shot text classification, where labeled data is
scarce, and new target labels frequently appear based on application needs.
Recently, large language models (LLMs) have shown promise due to their
extensive pretraining and contextual understanding ability. Current approaches
provide LLMs with text inputs, candidate labels, and additional side
information (e.g., descriptions) to classify texts. However, their
effectiveness is hindered by the increased input size and the noise introduced
through side information processing. To address these limitations, we propose a
graph-based online retrieval-augmented generation framework, namely GORAG, for
dynamic few-shot text classification. Rather than treating each input
independently, GORAG constructs and maintains a weighted graph by extracting
side information across all target texts. In this graph, text keywords and
labels are represented as nodes, with edges indicating the correlations between
them. To model these correlations, GORAG employs an edge weighting mechanism to
prioritize the importance and reliability of extracted information and
dynamically retrieves relevant context using a minimum-cost spanning tree
tailored for each text input. Empirical evaluations demonstrate that GORAG
outperforms existing approaches by providing more comprehensive and precise
contextual information.
|
2501.02845 | HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian
Splatting Based Data Augmentation | cs.CV | Understanding of bimanual hand-object interaction plays an important role in
robotics and virtual reality. However, due to significant occlusions between
hands and object as well as the high degree-of-freedom motions, it is
challenging to collect and annotate a high-quality, large-scale dataset, which
prevents further improvement of bimanual hand-object interaction-related
baselines. In this work, we propose a new 3D Gaussian Splatting based data
augmentation framework for bimanual hand-object interaction, which is capable
of augmenting existing dataset to large-scale photorealistic data with various
hand-object pose and viewpoints. First, we use mesh-based 3DGS to model objects
and hands, and to deal with the rendering blur problem due to multi-resolution
input images used, we design a super-resolution module. Second, we extend the
single hand grasping pose optimization module for the bimanual hand object to
generate various poses of bimanual hand-object interaction, which can
significantly expand the pose distribution of the dataset. Third, we conduct an
analysis for the impact of different aspects of the proposed data augmentation
on the understanding of the bimanual hand-object interaction. We perform our
data augmentation on two benchmarks, H2O and Arctic, and verify that our method
can improve the performance of the baselines.
|
2501.02850 | Large Language Models for Video Surveillance Applications | cs.CV | The rapid increase in video content production has resulted in enormous data
volumes, creating significant challenges for efficient analysis and resource
management. To address this, robust video analysis tools are essential. This
paper presents an innovative proof of concept using Generative Artificial
Intelligence (GenAI) in the form of Vision Language Models to enhance the
downstream video analysis process. Our tool generates customized textual
summaries based on user-defined queries, providing focused insights within
extensive video datasets. Unlike traditional methods that offer generic
summaries or limited action recognition, our approach utilizes Vision Language
Models to extract relevant information, improving analysis precision and
efficiency. The proposed method produces textual summaries from extensive CCTV
footage, which can then be stored for an indefinite time in a very small
storage space compared to videos, allowing users to quickly navigate and verify
significant events without exhaustive manual review. Qualitative evaluations
result in 80% and 70% accuracy in temporal and spatial quality and consistency
of the pipeline respectively.
|
2501.02851 | Exact Matching in Correlated Networks with Node Attributes for Improved
Community Recovery | cs.SI cs.IT math.IT stat.ML | We study community detection in multiple networks whose nodes and edges are
jointly correlated. This setting arises naturally in applications such as
social platforms, where a shared set of users may exhibit both correlated
friendship patterns and correlated attributes across different platforms.
Extending the classical Stochastic Block Model (SBM) and its contextual
counterpart (CSBM), we introduce the correlated CSBM, which incorporates
structural and attribute correlations across graphs. To build intuition, we
first analyze correlated Gaussian Mixture Models, wherein only correlated node
attributes are available without edges, and identify the conditions under which
an estimator minimizing the distance between attributes achieves exact matching
of nodes across the two databases. For correlated CSBMs, we develop a two-step
procedure that first applies $k$-core matching to most nodes using edge
information, then refines the matching for the remaining unmatched nodes by
leveraging their attributes with a distance-based estimator. We identify the
conditions under which the algorithm recovers the exact node correspondence,
enabling us to merge the correlated edges and average the correlated attributes
for enhanced community detection. Crucially, by aligning and combining graphs,
we identify regimes in which community detection is impossible in a single
graph but becomes feasible when side information from correlated graphs is
incorporated. Our results illustrate how the interplay between graph matching
and community recovery can boost performance, broadening the scope of
multi-graph, attribute-based community detection.
|
2501.02855 | Synthetic Fungi Datasets: A Time-Aligned Approach | cs.CV | Fungi undergo dynamic morphological transformations throughout their
lifecycle, forming intricate networks as they transition from spores to mature
mycelium structures. To support the study of these time-dependent processes, we
present a synthetic, time-aligned image dataset that models key stages of
fungal growth. This dataset systematically captures phenomena such as spore
size reduction, branching dynamics, and the emergence of complex mycelium
networks. The controlled generation process ensures temporal consistency,
scalability, and structural alignment, addressing the limitations of real-world
fungal datasets. Optimized for deep learning (DL) applications, this dataset
facilitates the development of models for classifying growth stages, predicting
fungal development, and analyzing morphological patterns over time. With
applications spanning agriculture, medicine, and industrial mycology, this
resource provides a robust foundation for automating fungal analysis, enhancing
disease monitoring, and advancing fungal biology research through artificial
intelligence.
|
2501.02857 | ParetoLens: A Visual Analytics Framework for Exploring Solution Sets of
Multi-objective Evolutionary Algorithms | cs.NE cs.HC cs.LG | In the domain of multi-objective optimization, evolutionary algorithms are
distinguished by their capability to generate a diverse population of solutions
that navigate the trade-offs inherent among competing objectives. This has
catalyzed the ascension of evolutionary multi-objective optimization (EMO) as a
prevalent approach. Despite the effectiveness of the EMO paradigm, the analysis
of resultant solution sets presents considerable challenges. This is primarily
attributed to the high-dimensional nature of the data and the constraints
imposed by static visualization methods, which frequently culminate in visual
clutter and impede interactive exploratory analysis. To address these
challenges, this paper introduces ParetoLens, a visual analytics framework
specifically tailored to enhance the inspection and exploration of solution
sets derived from the multi-objective evolutionary algorithms. Utilizing a
modularized, algorithm-agnostic design, ParetoLens enables a detailed
inspection of solution distributions in both decision and objective spaces
through a suite of interactive visual representations. This approach not only
mitigates the issues associated with static visualizations but also supports a
more nuanced and flexible analysis process. The usability of the framework is
evaluated through case studies and expert interviews, demonstrating its
potential to uncover complex patterns and facilitate a deeper understanding of
multi-objective optimization solution sets. A demo website of ParetoLens is
available at https://dva-lab.org/paretolens/.
|
2501.02858 | A Novel Vision Transformer for Camera-LiDAR Fusion based Traffic Object
Segmentation | cs.CV | This paper presents Camera-LiDAR Fusion Transformer (CLFT) models for traffic
object segmentation, which leverage the fusion of camera and LiDAR data using
vision transformers. Building on the methodology of visual transformers that
exploit the self-attention mechanism, we extend segmentation capabilities with
additional classification options to a diverse class of objects including
cyclists, traffic signs, and pedestrians across diverse weather conditions.
Despite good performance, the models face challenges under adverse conditions
which underscores the need for further optimization to enhance performance in
darkness and rain. In summary, the CLFT models offer a compelling solution for
autonomous driving perception, advancing the state-of-the-art in multimodal
fusion and object segmentation, with ongoing efforts required to address
existing limitations and fully harness their potential in practical
deployments.
|
2501.02860 | Seeing the Whole in the Parts in Self-Supervised Representation Learning | cs.LG cs.CV | Recent successes in self-supervised learning (SSL) model spatial
co-occurrences of visual features either by masking portions of an image or by
aggressively cropping it. Here, we propose a new way to model spatial
co-occurrences by aligning local representations (before pooling) with a global
image representation. We present CO-SSL, a family of instance discrimination
methods and show that it outperforms previous methods on several datasets,
including ImageNet-1K where it achieves 71.5% of Top-1 accuracy with 100
pre-training epochs. CO-SSL is also more robust to noise corruption, internal
corruption, small adversarial attacks, and large training crop sizes. Our
analysis further indicates that CO-SSL learns highly redundant local
representations, which offers an explanation for its robustness. Overall, our
work suggests that aligning local and global representations may be a powerful
principle of unsupervised category learning.
|
2501.02866 | Constrained Multi-Modal Density Control of Linear Systems via Covariance
Steering Theory | math.OC cs.SY eess.SY | In this paper, we investigate finite-horizon optimal density steering
problems for discrete-time stochastic linear dynamical systems whose state
probability densities can be represented as Gaussian Mixture Models (GMMs). Our
goal is to compute optimal controllers that can ensure that the terminal state
distribution will match the desired distribution exactly (hard-constrained
version) or closely (soft-constrained version) where in the latter case we
employ a Wasserstein like metric that can measure the distance between
different GMMs. Our approach relies on a class of randomized control policies
which allow us to reformulate the proposed density steering problems as
finite-dimensional optimization problems, and in particular, linear and
bilinear programs. Additionally, we explore more general density steering
problems based on the approximation of general distributions by GMMs and
characterize bounds for the error between the terminal distribution under our
policy and the approximated GMM terminal state distribution. Finally, we
demonstrate the effectiveness of our approach through non-trivial numerical
experiments.
|
2501.02867 | Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological
Tissue Segmentation in Lung CT Scans | eess.IV cs.CV | Accurate quantification of the extent of lung pathological patterns
(fibrosis, ground-glass opacity, emphysema, consolidation) is prerequisite for
diagnosis and follow-up of interstitial lung diseases. However, segmentation is
challenging due to the significant class imbalance between healthy and
pathological tissues. This paper addresses this issue by leveraging a diffusion
model for data augmentation applied during training an AI model. Our approach
generates synthetic pathological tissue patches while preserving essential
shape characteristics and intricate details specific to each tissue type. This
method enhances the segmentation process by increasing the occurence of
underrepresented classes in the training data. We demonstrate that our
diffusion-based augmentation technique improves segmentation accuracy across
all pathological tissue types, particularly for the less common patterns. This
advancement contributes to more reliable automated analysis of lung CT scans,
potentially improving clinical decision-making and patient outcomes
|
2501.02869 | IIMedGPT: Promoting Large Language Model Capabilities of Medical Tasks
by Efficient Human Preference Alignment | cs.CL cs.AI | Recent researches of large language models(LLM), which is pre-trained on
massive general-purpose corpora, have achieved breakthroughs in responding
human queries. However, these methods face challenges including limited data
insufficiency to support extensive pre-training and can not align responses
with users' instructions. To address these issues, we introduce a medical
instruction dataset, CMedINS, containing six medical instructions derived from
actual medical tasks, which effectively fine-tunes LLM in conjunction with
other data. Subsequently, We launch our medical model, IIMedGPT, employing an
efficient preference alignment method, Direct preference Optimization(DPO). The
results show that our final model outperforms existing medical models in
medical dialogue.Datsets, Code and model checkpoints will be released upon
acceptance.
|
2501.02870 | Spectrum Sharing in 6G Space-Ground Integrated Networks: A Ground
Protection Zone-Based Design | cs.IT cs.SY eess.SP eess.SY math.IT | Space-ground integrated network (SGIN) has been envisioned as a competitive
solution for large scale and wide coverage of future wireless networks. By
integrating both the non-terrestrial network (NTN) and the terrestrial network
(TN), SGIN can provide high speed and omnipresent wireless network access for
the users using the predefined licensed spectrums. Considering the scarcity of
the spectrum resource and the low spectrum efficiency of the SGIN, we enable
the NTN and TN to share the spectrum to improve overall system performance,
i.e., weighted-sum area data rate (WS-ADR). However, mutual interference
between NTN and TN is often inevitable and thus causes SGIN performance
degradation. In this work, we consider a ground protection zone for the TN base
stations, in which the NTN users are only allowed to use the NTN reserved
spectrum to mitigate the NTN and TN mutual interference. We analytically derive
the coverage probability and area data rate (ADR) of the typical users and
study the performance under various protection zone sizes and spectrum
allocation parameter settings. Simulation and numerical results demonstrate
that the WS-ADR could be maximized by selecting the appropriate radius of
protection zone and bandwidth allocation factor in the SGIN.
|
2501.02872 | Two-Dimensional Unknown View Tomography from Unknown Angle Distributions | cs.CV | This study presents a technique for 2D tomography under unknown viewing
angles when the distribution of the viewing angles is also unknown. Unknown
view tomography (UVT) is a problem encountered in cryo-electron microscopy and
in the geometric calibration of CT systems. There exists a moderate-sized
literature on the 2D UVT problem, but most existing 2D UVT algorithms assume
knowledge of the angle distribution which is not available usually. Our
proposed methodology formulates the problem as an optimization task based on
cross-validation error, to estimate the angle distribution jointly with the
underlying 2D structure in an alternating fashion. We explore the algorithm's
capabilities for the case of two probability distribution models: a
semi-parametric mixture of von Mises densities and a probability mass function
model. We evaluate our algorithm's performance under noisy projections using a
PCA-based denoising technique and Graph Laplacian Tomography (GLT) driven by
order statistics of the estimated distribution, to ensure near-perfect
ordering, and compare our algorithm to intuitive baselines.
|
2501.02874 | Steering Flexible Linear Objects in Planar Environments by Two Robot
Hands Using Euler's Elastica Solutions | cs.RO | The manipulation of flexible objects such as cables, wires and fresh food
items by robot hands forms a special challenge in robot grasp mechanics. This
paper considers the steering of flexible linear objects in planar environments
by two robot hands. The flexible linear object, modeled as an elastic
non-stretchable rod, is manipulated by varying the gripping endpoint positions
while keeping equal endpoint tangents. The flexible linear object shape has a
closed form solution in terms of the grasp endpoint positions and tangents,
called Euler's elastica. This paper obtains the elastica solutions under the
optimal control framework, then uses the elastica solutions to obtain
closed-form criteria for non self-intersection, stability and obstacle
avoidance of the flexible linear object. The new tools are incorporated into a
planning scheme for steering flexible linear objects in planar environments
populated by sparsely spaced obstacles. The scheme is fully implemented and
demonstrated with detailed examples.
|
2501.02880 | Conditional Mutual Information Based Diffusion Posterior Sampling for
Solving Inverse Problems | cs.LG stat.ML | Inverse problems are prevalent across various disciplines in science and
engineering. In the field of computer vision, tasks such as inpainting,
deblurring, and super-resolution are commonly formulated as inverse problems.
Recently, diffusion models (DMs) have emerged as a promising approach for
addressing noisy linear inverse problems, offering effective solutions without
requiring additional task-specific training. Specifically, with the prior
provided by DMs, one can sample from the posterior by finding the likelihood.
Since the likelihood is intractable, it is often approximated in the
literature. However, this approximation compromises the quality of the
generated images. To overcome this limitation and improve the effectiveness of
DMs in solving inverse problems, we propose an information-theoretic approach.
Specifically, we maximize the conditional mutual information
$\mathrm{I}(\boldsymbol{x}_0; \boldsymbol{y} | \boldsymbol{x}_t)$, where
$\boldsymbol{x}_0$ represents the reconstructed signal, $\boldsymbol{y}$ is the
measurement, and $\boldsymbol{x}_t$ is the intermediate signal at stage $t$.
This ensures that the intermediate signals $\boldsymbol{x}_t$ are generated in
a way that the final reconstructed signal $\boldsymbol{x}_0$ retains as much
information as possible about the measurement $\boldsymbol{y}$. We demonstrate
that this method can be seamlessly integrated with recent approaches and, once
incorporated, enhances their performance both qualitatively and quantitatively.
|
2501.02882 | PARF-Net: integrating pixel-wise adaptive receptive fields into hybrid
Transformer-CNN network for medical image segmentation | cs.CV | Convolutional neural networks (CNNs) excel in local feature extraction while
Transformers are superior in processing global semantic information. By
leveraging the strengths of both, hybrid Transformer-CNN networks have become
the major architectures in medical image segmentation tasks. However, existing
hybrid methods still suffer deficient learning of local semantic features due
to the fixed receptive fields of convolutions, and also fall short in
effectively integrating local and long-range dependencies. To address these
issues, we develop a new method PARF-Net to integrate convolutions of
Pixel-wise Adaptive Receptive Fields (Conv-PARF) into hybrid Network for
medical image segmentation. The Conv-PARF is introduced to cope with
inter-pixel semantic differences and dynamically adjust convolutional receptive
fields for each pixel, thus providing distinguishable features to disentangle
the lesions with varying shapes and scales from the background. The features
derived from the Conv-PARF layers are further processed using hybrid
Transformer-CNN blocks under a lightweight manner, to effectively capture local
and long-range dependencies, thus boosting the segmentation performance. By
assessing PARF-Net on four widely used medical image datasets including
MoNuSeg, GlaS, DSB2018 and multi-organ Synapse, we showcase the advantages of
our method over the state-of-the-arts. For instance, PARF-Net achieves 84.27%
mean Dice on the Synapse dataset, surpassing existing methods by a large
margin.
|
2501.02885 | MDP3: A Training-free Approach for List-wise Frame Selection in
Video-LLMs | cs.CV cs.LG | Video large language models (Video-LLMs) have made significant progress in
understanding videos. However, processing multiple frames leads to lengthy
visual token sequences, presenting challenges such as the limited context
length cannot accommodate the entire video, and the inclusion of irrelevant
frames hinders visual perception. Hence, effective frame selection is crucial.
This paper emphasizes that frame selection should follow three key principles:
query relevance, list-wise diversity, and sequentiality. Existing methods, such
as uniform frame sampling and query-frame matching, do not capture all of these
principles. Thus, we propose Markov decision determinantal point process with
dynamic programming (MDP3) for frame selection, a training-free and
model-agnostic method that can be seamlessly integrated into existing
Video-LLMs. Our method first estimates frame similarities conditioned on the
query using a conditional Gaussian kernel within the reproducing kernel Hilbert
space~(RKHS). We then apply the determinantal point process~(DPP) to the
similarity matrix to capture both query relevance and list-wise diversity. To
incorporate sequentiality, we segment the video and apply DPP within each
segment, conditioned on the preceding segment selection, modeled as a Markov
decision process~(MDP) for allocating selection sizes across segments.
Theoretically, MDP3 provides a \((1 - 1/e)\)-approximate solution to the
NP-hard list-wise frame selection problem with pseudo-polynomial time
complexity, demonstrating its efficiency. Empirically, MDP3 significantly
outperforms existing methods, verifying its effectiveness and robustness.
|
2501.02888 | Revisiting Communication Efficiency in Multi-Agent Reinforcement
Learning from the Dimensional Analysis Perspective | cs.MA | In this work, we introduce a novel perspective, i.e., dimensional analysis,
to address the challenge of communication efficiency in Multi-Agent
Reinforcement Learning (MARL). Our findings reveal that simply optimizing the
content and timing of communication at sending end is insufficient to fully
resolve communication efficiency issues. Even after applying optimized and
gated messages, dimensional redundancy and confounders still persist in the
integrated message embeddings at receiving end, which negatively impact
communication quality and decision-making. To address these challenges, we
propose Dimensional Rational Multi-Agent Communication (DRMAC), designed to
mitigate both dimensional redundancy and confounders in MARL. DRMAC
incorporates a redundancy-reduction regularization term to encourage the
decoupling of information across dimensions within the learned representations
of integrated messages. Additionally, we introduce a dimensional mask that
dynamically adjusts gradient weights during training to eliminate the influence
of decision-irrelevant dimensions. We evaluate DRMAC across a diverse set of
multi-agent tasks, demonstrating its superior performance over existing
state-of-the-art methods in complex scenarios. Furthermore, the plug-and-play
nature of DRMAC's key modules highlights its generalizable performance, serving
as a valuable complement rather than a replacement for existing multi-agent
communication strategies.
|
2501.02891 | Explaining Humour Style Classifications: An XAI Approach to
Understanding Computational Humour Analysis | cs.CL cs.AI | Humour styles can have either a negative or a positive impact on well-being.
Given the importance of these styles to mental health, significant research has
been conducted on their automatic identification. However, the automated
machine learning models used for this purpose are black boxes, making their
prediction decisions opaque. Clarity and transparency are vital in the field of
mental health. This paper presents an explainable AI (XAI) framework for
understanding humour style classification, building upon previous work in
computational humour analysis. Using the best-performing single model
(ALI+XGBoost) from prior research, we apply comprehensive XAI techniques to
analyse how linguistic, emotional, and semantic features contribute to humour
style classification decisions. Our analysis reveals distinct patterns in how
different humour styles are characterised and misclassified, with particular
emphasis on the challenges in distinguishing affiliative humour from other
styles. Through detailed examination of feature importance, error patterns, and
misclassification cases, we identify key factors influencing model decisions,
including emotional ambiguity, context misinterpretation, and target
identification. The framework demonstrates significant utility in understanding
model behaviour, achieving interpretable insights into the complex interplay of
features that define different humour styles. Our findings contribute to both
the theoretical understanding of computational humour analysis and practical
applications in mental health, content moderation, and digital humanities
research.
|
2501.02892 | FoundPAD: Foundation Models Reloaded for Face Presentation Attack
Detection | cs.CV | Although face recognition systems have seen a massive performance enhancement
in recent years, they are still targeted by threats such as presentation
attacks, leading to the need for generalizable presentation attack detection
(PAD) algorithms. Current PAD solutions suffer from two main problems: low
generalization to unknown cenarios and large training data requirements.
Foundation models (FM) are pre-trained on extensive datasets, achieving
remarkable results when generalizing to unseen domains and allowing for
efficient task-specific adaption even when little training data are available.
In this work, we recognize the potential of FMs to address common PAD problems
and tackle the PAD task with an adapted FM for the first time. The FM under
consideration is adapted with LoRA weights while simultaneously training a
classification header. The resultant architecture, FoundPAD, is highly
generalizable to unseen domains, achieving competitive results in several
settings under different data availability scenarios and even when using
synthetic training data. To encourage reproducibility and facilitate further
research in PAD, we publicly release the implementation of FoundPAD at
https://github.com/gurayozgur/FoundPAD .
|
2501.02893 | A Volumetric Approach to Privacy of Dynamical Systems | eess.SY cs.SY | Information-theoretic metrics, such as mutual information, have been widely
used to evaluate privacy leakage in dynamic systems. However, these approaches
are typically limited to stochastic systems and face computational challenges.
In this paper, we introduce a novel volumetric framework for analyzing privacy
in systems affected by unknown but bounded noise. Our model considers a dynamic
system comprising public and private states, where an observation set of the
public state is released. An adversary utilizes the observed public state to
infer an uncertainty set of the private state, referred to as the inference
attack. We define the evolution dynamics of these inference attacks and
quantify the privacy level of the private state using the volume of its
uncertainty sets. For linear scalar systems, we derive an explicit formulation
of the uncertainty set. For multi-dimensional linear systems, we develop an
approximate computation method leveraging interval analysis. We investigate the
properties of the proposed volumetric privacy measure and demonstrate that it
is bounded by the information gain derived from the observation set.
Furthermore, we propose an optimization approach to designing privacy filter
using randomization and linear programming based on the proposed privacy
measure. The effectiveness of the optimal privacy filter design is evaluated
through a production-inventory case study, illustrating its robustness against
the inference attack.
|
2501.02894 | On Counting H-Intersecting Families and Graph Homomorphisms | math.CO cs.IT math.IT | This work leverages Shearer's inequalities to derive a new upper bound on the
maximum cardinality of a family of graphs on a fixed number of vertices, in
which every pair of graphs shares a fixed common subgraph. The derived bound is
expressed in terms of the chromatic number of the shared subgraph.
Additionally, Shearer's inequalities, in conjunction with properties of the
Shannon entropy, are employed to establish bounds related to the enumeration of
graph homomorphisms, providing further insights into the interplay between
combinatorial structures and information-theoretic principles.
|
2501.02895 | Region of Interest based Medical Image Compression | eess.IV cs.CV | The vast volume of medical image data necessitates efficient compression
techniques to support remote healthcare services. This paper explores Region of
Interest (ROI) coding to address the balance between compression rate and image
quality. By leveraging UNET segmentation on the Brats 2020 dataset, we
accurately identify tumor regions, which are critical for diagnosis. These
regions are then subjected to High Efficiency Video Coding (HEVC) for
compression, enhancing compression rates while preserving essential diagnostic
information. This approach ensures that critical image regions maintain their
quality, while non-essential areas are compressed more. Our method optimizes
storage space and transmission bandwidth, meeting the demands of telemedicine
and large-scale medical imaging. Through this technique, we provide a robust
solution that maintains the integrity of vital data and improves the efficiency
of medical image handling.
|
2501.02899 | Finite-Sample Learning Control for LQR Over Unknown Lossy Channels | eess.SY cs.SY | This paper investigates the Linear Quadratic Regulator (LQR) problem over an
unknown Bernoulli packet drop channel. The unknown packet drop probability is
estimated using finite samples, then the estimated probability is used to
design a formally equivalent optimal controller. If the estimation error is too
large, the estimated controller cannot mean-square stabilize the system. For
n-dimensional systems, the upper bound on the estimation error is provided to
guarantee the stability of the closed-loop system. And we present an analytical
expression for the gap between the performance of the estimated controller and
the optimal performance. Next, we derive the upper bound on sample complexity
for the stabilizability of the estimated controller. The tailored results with
less conservatism are delivered for scalar systems and n-dimensional systems
with invertible input matrix. Furthermore, a sufficient condition that does not
depend on unknown channel information is provided to determine whether the
estimated controller stabilizes the system with a certain probability. Finally,
Numerical examples are used to demonstrate our results.
|
2501.02902 | Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from
NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots | cs.RO cs.LG | Unprecedented agility and dexterous manipulation have been demonstrated with
controllers based on deep reinforcement learning (RL), with a significant
impact on legged and humanoid robots. Modern tooling and simulation platforms,
such as NVIDIA Isaac Sim, have been enabling such advances. This article
focuses on demonstrating the applications of Isaac in local planning and
obstacle avoidance as one of the most fundamental ways in which a mobile robot
interacts with its environments. Although there is extensive research on
proprioception-based RL policies, the article highlights less standardized and
reproducible approaches to exteroception. At the same time, the article aims to
provide a base framework for end-to-end local navigation policies and how a
custom robot can be trained in such simulation environment. We benchmark
end-to-end policies with the state-of-the-art Nav2, navigation stack in Robot
Operating System (ROS). We also cover the sim-to-real transfer process by
demonstrating zero-shot transferability of policies trained in the Isaac
simulator to real-world robots. This is further evidenced by the tests with
different simulated robots, which show the generalization of the learned
policy. Finally, the benchmarks demonstrate comparable performance to Nav2,
opening the door to quick deployment of state-of-the-art end-to-end local
planners for custom robot platforms, but importantly furthering the
possibilities by expanding the state and action spaces or task definitions for
more complex missions. Overall, with this article we introduce the most
important steps, and aspects to consider, in deploying RL policies for local
path planning and obstacle avoidance with Isaac Sim training, Gazebo testing,
and ROS 2 for real-time inference in real robots. The code is available at
https://github.com/sahars93/RL-Navigation.
|
2501.02905 | Skillful High-Resolution Ensemble Precipitation Forecasting with an
Integrated Deep Learning Framework | cs.LG cs.AI | High-resolution precipitation forecasts are crucial for providing accurate
weather prediction and supporting effective responses to extreme weather
events. Traditional numerical models struggle with stochastic subgrid-scale
processes, while recent deep learning models often produce blurry results. To
address these challenges, we propose a physics-inspired deep learning framework
for high-resolution (0.05\textdegree{} $\times$ 0.05\textdegree{}) ensemble
precipitation forecasting. Trained on ERA5 and CMPA high-resolution
precipitation datasets, the framework integrates deterministic and
probabilistic components. The deterministic model, based on a 3D
SwinTransformer, captures average precipitation at mesoscale resolution and
incorporates strategies to enhance performance, particularly for moderate to
heavy rainfall. The probabilistic model employs conditional diffusion in latent
space to account for uncertainties in residual precipitation at convective
scales. During inference, ensemble members are generated by repeatedly sampling
latent variables, enabling the model to represent precipitation uncertainty.
Our model significantly enhances spatial resolution and forecast accuracy. Rank
histogram shows that the ensemble system is reliable and unbiased. In a case
study of heavy precipitation in southern China, the model outputs align more
closely with observed precipitation distributions than ERA5, demonstrating
superior capability in capturing extreme precipitation events. Additionally,
5-day real-time forecasts show good performance in terms of CSI scores.
|
2501.02906 | Domain-Agnostic Co-Evolution of Generalizable Parallel Algorithm
Portfolios | cs.NE | Generalization is the core objective when training optimizers from data.
However, limited training instances often constrain the generalization
capability of the trained optimizers. Co-evolutionary approaches address this
challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and
an instance population to eventually obtain PAPs with good generalization. Yet,
when applied to a specific problem class, these approaches have a major
limitation. They require practitioners to provide instance generators specially
tailored to the problem class, which is often non-trivial to design. This work
proposes a general-purpose, off-the-shelf PAP construction approach, named
domain-agnostic co-evolution of parameterized search (DACE), for binary
optimization problems where decision variables take values of 0 or 1. The key
innovation of DACE lies in its neural network-based domain-agnostic instance
representation and generation mechanism that delimitates the need for
domain-specific instance generators. The strong generality of DACE is validated
across three real-world binary optimization problems: the complementary
influence maximization problem (CIMP), the compiler arguments optimization
problem (CAOP), and the contamination control problem (CCP). Given only a small
set of training instances from these classes, DACE, without requiring any
domain knowledge, constructs PAPs with better generalization performance than
existing approaches on all three classes, despite their use of domain-specific
instance generators.
|
2501.02909 | Comprehensive Pathological Image Segmentation via Teacher Aggregation
for Tumor Microenvironment Analysis | cs.CV | The tumor microenvironment (TME) plays a crucial role in cancer progression
and treatment response, yet current methods for its comprehensive analysis in
H&E-stained tissue slides face significant limitations in the diversity of
tissue cell types and accuracy. Here, we present PAGET (Pathological image
segmentation via AGgrEgated Teachers), a new knowledge distillation approach
that integrates multiple segmentation models while considering the hierarchical
nature of cell types in the TME. By leveraging a unique dataset created through
immunohistochemical restaining techniques and existing segmentation models,
PAGET enables simultaneous identification and classification of 14 key TME
components. We demonstrate PAGET's ability to perform rapid, comprehensive TME
segmentation across various tissue types and medical institutions, advancing
the quantitative analysis of tumor microenvironments. This method represents a
significant step forward in enhancing our understanding of cancer biology and
supporting precise clinical decision-making from large-scale histopathology
images.
|
2501.02913 | Pointmap-Conditioned Diffusion for Consistent Novel View Synthesis | cs.CV | In this paper, we present PointmapDiffusion, a novel framework for
single-image novel view synthesis (NVS) that utilizes pre-trained 2D diffusion
models. Our method is the first to leverage pointmaps (i.e. rasterized 3D scene
coordinates) as a conditioning signal, capturing geometric prior from the
reference images to guide the diffusion process. By embedding reference
attention blocks and a ControlNet for pointmap features, our model balances
between generative capability and geometric consistency, enabling accurate view
synthesis across varying viewpoints. Extensive experiments on diverse
real-world datasets demonstrate that PointmapDiffusion achieves high-quality,
multi-view consistent results with significantly fewer trainable parameters
compared to other baselines for single-image NVS tasks.
|
2501.02916 | Spiking monocular event based 6D pose estimation for space application | cs.CV cs.LG | With the growing interest in on On-orbit servicing (OOS) and Active Debris
Removal (ADR) missions, spacecraft poses estimation algorithms are being
developed using deep learning to improve the precision of this complex task and
find the most efficient solution. With the advances of bio-inspired low-power
solutions, such a spiking neural networks and event-based processing and
cameras, and their recent work for space applications, we propose to
investigate the feasibility of a fully event-based solution to improve
event-based pose estimation for spacecraft. In this paper, we address the first
event-based dataset SEENIC with real event frames captured by an event-based
camera on a testbed. We show the methods and results of the first event-based
solution for this use case, where our small spiking end-to-end network (S2E2)
solution achieves interesting results over 21cm position error and 14degree
rotation error, which is the first step towards fully event-based processing
for embedded spacecraft pose estimation.
|
2501.02917 | On Achievable Rates Over Noisy Nanopore Channels | cs.IT math.IT | In this paper, we consider a recent channel model of a nanopore sequencer
proposed by McBain, Viterbo, and Saunderson (2024), termed the noisy nanopore
channel (NNC). In essence, an NNC is a noisy duplication channel, whose input
source has a specific Markov structure. We present bounds on the channel
capacity of selected NNCs, via simple information-theoretic inequalities. In
particular, we provide a (tight) lower bound on the capacity of the noiseless
NCC and demonstrate that for an NNC with erasure noise, the capacity approaches
$1$ for nanopore memories that scale roughly logarithmically in the length of
the input sequence.
|
2501.02921 | Unsupervised Tomato Split Anomaly Detection using Hyperspectral Imaging
and Variational Autoencoders | cs.CV cs.AI | Tomato anomalies/damages pose a significant challenge in greenhouse farming.
While this method of cultivation benefits from efficient resource utilization,
anomalies can significantly degrade the quality of farm produce. A common
anomaly associated with tomatoes is splitting, characterized by the development
of cracks on the tomato skin, which degrades its quality. Detecting this type
of anomaly is challenging due to dynamic variations in appearance and sizes,
compounded by dataset scarcity. We address this problem in an unsupervised
manner by utilizing a tailored variational autoencoder (VAE) with hyperspectral
input. Preliminary analysis of the dataset enabled us to select the optimal
range of wavelengths for detecting this anomaly. Our findings indicate that the
530nm - 550nm range is suitable for identifying tomato dry splits. The analysis
on reconstruction loss allow us to not only detect the anomalies but also to
some degree estimate the anomalous regions.
|
2501.02922 | Label-free Concept Based Multiple Instance Learning for Gigapixel
Histopathology | cs.CV cs.AI | Multiple Instance Learning (MIL) methods allow for gigapixel Whole-Slide
Image (WSI) analysis with only slide-level annotations. Interpretability is
crucial for safely deploying such algorithms in high-stakes medical domains.
Traditional MIL methods offer explanations by highlighting salient regions.
However, such spatial heatmaps provide limited insights for end users. To
address this, we propose a novel inherently interpretable WSI-classification
approach that uses human-understandable pathology concepts to generate
explanations. Our proposed Concept MIL model leverages recent advances in
vision-language models to directly predict pathology concepts based on image
features. The model's predictions are obtained through a linear combination of
the concepts identified on the top-K patches of a WSI, enabling inherent
explanations by tracing each concept's influence on the prediction. In contrast
to traditional concept-based interpretable models, our approach eliminates the
need for costly human annotations by leveraging the vision-language model. We
validate our method on two widely used pathology datasets: Camelyon16 and
PANDA. On both datasets, Concept MIL achieves AUC and accuracy scores over 0.9,
putting it on par with state-of-the-art models. We further find that 87.1\%
(Camelyon16) and 85.3\% (PANDA) of the top 20 patches fall within the tumor
region. A user study shows that the concepts identified by our model align with
the concepts used by pathologists, making it a promising strategy for
human-interpretable WSI classification.
|
2501.02926 | Offline-to-online hyperparameter transfer for stochastic bandits | cs.LG | Classic algorithms for stochastic bandits typically use hyperparameters that
govern their critical properties such as the trade-off between exploration and
exploitation. Tuning these hyperparameters is a problem of great practical
significance. However, this is a challenging problem and in certain cases is
information theoretically impossible. To address this challenge, we consider a
practically relevant transfer learning setting where one has access to offline
data collected from several bandit problems (tasks) coming from an unknown
distribution over the tasks. Our aim is to use this offline data to set the
hyperparameters for a new task drawn from the unknown distribution. We provide
bounds on the inter-task (number of tasks) and intra-task (number of arm pulls
for each task) sample complexity for learning near-optimal hyperparameters on
unseen tasks drawn from the distribution. Our results apply to several classic
algorithms, including tuning the exploration parameters in UCB and LinUCB and
the noise parameter in GP-UCB. Our experiments indicate the significance and
effectiveness of the transfer of hyperparameters from offline problems in
online learning with stochastic bandit feedback.
|
2501.02928 | Deep Generative Model-Aided Power System Dynamic State Estimation and
Reconstruction with Unknown Control Inputs or Data Distributions | eess.SY cs.SY | Fast and robust dynamic state estimation (DSE) is essential for accurately
capturing the internal dynamic processes of power systems, and it serves as the
foundation for reliably implementing real-time dynamic modeling, monitoring,
and control applications. Nonetheless, on one hand, traditional DSE methods
based on Kalman filtering or particle filtering have high accuracy requirements
for system parameters, control inputs, phasor measurement unit (PMU) data, and
centralized DSE communication. Consequently, these methods often face accuracy
bottlenecks when dealing with structural or system process errors, unknown
control vectors, PMU anomalies, and communication contingencies. On the other
hand, deep learning-aided DSE, while parameter-free, often suffers from
generalization issues under unforeseen operating conditions. To address these
challenges, this paper proposes an effective approach that leverages deep
generative models from AI-generated content (AIGC) to assist DSE. The proposed
approach employs an encoder-decoder architecture to estimate unknown control
input variables, a robust encoder to mitigate the impact of bad PMU data, and
latent diffusion model to address communication issues in centralized DSE.
Additionally, a lightweight adaptor is designed to quickly adjust the latent
vector distribution. Extensive experimental results on the IEEE 39-bus system
and the NPCC 140-bus system demonstrate the effectiveness and superiority of
the proposed method in addressing DSE modeling imperfection, measurement
uncertainties, communication contingencies, and unknown distribution
challenges, while also proving its ability to reduce data storage and
communication resource requirements.
|
2501.02931 | Self-Attention as a Parametric Endofunctor: A Categorical Framework for
Transformer Architectures | cs.LG | Self-attention mechanisms have revolutionised deep learning architectures,
yet their core mathematical structures remain incompletely understood. In this
work, we develop a category-theoretic framework focusing on the linear
components of self-attention. Specifically, we show that the query, key, and
value maps naturally define a parametric 1-morphism in the 2-category
$\mathbf{Para(Vect)}$. On the underlying 1-category $\mathbf{Vect}$, these maps
induce an endofunctor whose iterated composition precisely models multi-layer
attention. We further prove that stacking multiple self-attention layers
corresponds to constructing the free monad on this endofunctor. For positional
encodings, we demonstrate that strictly additive embeddings correspond to
monoid actions in an affine sense, while standard sinusoidal encodings, though
not additive, retain a universal property among injective (faithful)
position-preserving maps. We also establish that the linear portions of
self-attention exhibit natural equivariance to permutations of input tokens,
and show how the "circuits" identified in mechanistic interpretability can be
interpreted as compositions of parametric 1-morphisms. This categorical
perspective unifies geometric, algebraic, and interpretability-based approaches
to transformer analysis, making explicit the underlying structures of
attention. We restrict to linear maps throughout, deferring the treatment of
nonlinearities such as softmax and layer normalisation, which require more
advanced categorical constructions. Our results build on and extend recent work
on category-theoretic foundations for deep learning, offering deeper insights
into the algebraic structure of attention mechanisms.
|
2501.02932 | Predicting band gap from chemical composition: A simple learned model
for a material property with atypical statistics | cond-mat.mtrl-sci cs.LG physics.chem-ph | In solid-state materials science, substantial efforts have been devoted to
the calculation and modeling of the electronic band gap. While a wide range of
ab initio methods and machine learning algorithms have been created that can
predict this quantity, the development of new computational approaches for
studying the band gap remains an active area of research. Here we introduce a
simple machine learning model for predicting the band gap using only the
chemical composition of the crystalline material. To motivate the form of the
model, we first analyze the empirical distribution of the band gap, which sheds
new light on its atypical statistics. Specifically, our analysis enables us to
frame band gap prediction as a task of modeling a mixed random variable, and we
design our model accordingly. Our model formulation incorporates thematic ideas
from chemical heuristic models for other material properties in a manner that
is suited towards the band gap modeling task. The model has exactly one
parameter corresponding to each element, which is fit using data. To predict
the band gap for a given material, the model computes a weighted average of the
parameters associated with its constituent elements and then takes the maximum
of this quantity and zero. The model provides heuristic chemical
interpretability by intuitively capturing the associations between the band gap
and individual chemical elements.
|
2501.02934 | A Bayesian Approach for Discovering Time- Delayed Differential Equation
from Data | stat.ML cs.LG physics.comp-ph | Time-delayed differential equations (TDDEs) are widely used to model complex
dynamic systems where future states depend on past states with a delay.
However, inferring the underlying TDDEs from observed data remains a
challenging problem due to the inherent nonlinearity, uncertainty, and noise in
real-world systems. Conventional equation discovery methods often exhibit
limitations when dealing with large time delays, relying on deterministic
techniques or optimization-based approaches that may struggle with scalability
and robustness. In this paper, we present BayTiDe - Bayesian Approach for
Discovering Time-Delayed Differential Equations from Data, that is capable of
identifying arbitrarily large values of time delay to an accuracy that is
directly proportional to the resolution of the data input to it. BayTiDe
leverages Bayesian inference combined with a sparsity-promoting discontinuous
spike-and-slab prior to accurately identify time-delayed differential
equations. The approach accommodates arbitrarily large time delays with
accuracy proportional to the input data resolution, while efficiently narrowing
the search space to achieve significant computational savings. We demonstrate
the efficiency and robustness of BayTiDe through a range of numerical examples,
validating its ability to recover delayed differential equations from noisy
data.
|
2501.02937 | 4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic
Segmentation | cs.CV | Semantic segmentation of LiDAR points has significant value for autonomous
driving and mobile robot systems. Most approaches explore spatio-temporal
information of multi-scan to identify the semantic classes and motion states
for each point. However, these methods often overlook the segmentation
consistency in space and time, which may result in point clouds within the same
object being predicted as different categories. To handle this issue, our core
idea is to generate cluster labels across multiple frames that can reflect the
complete spatial structure and temporal information of objects. These labels
serve as explicit guidance for our dual-branch network, 4D-CS, which integrates
point-based and cluster-based branches to enable more consistent segmentation.
Specifically, in the point-based branch, we leverage historical knowledge to
enrich the current feature through temporal fusion on multiple views. In the
cluster-based branch, we propose a new strategy to produce cluster labels of
foreground objects and apply them to gather point-wise information to derive
cluster features. We then merge neighboring clusters across multiple scans to
restore missing features due to occlusion. Finally, in the point-cluster fusion
stage, we adaptively fuse the information from the two branches to optimize
segmentation results. Extensive experiments confirm the effectiveness of the
proposed method, and we achieve state-of-the-art results on the multi-scan
semantic and moving object segmentation on SemanticKITTI and nuScenes datasets.
The code will be available at https://github.com/NEU-REAL/4D-CS.git.
|
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