OpenRaiser/Intern-Atlas huggingface dataset:
AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures
Venue: arXiv cs.NE 2025
Year: 2025

Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.
Capsule Networks Do Not Need to Model Everything
Venue: arXiv cs.CV 2025
Year: 2025

Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly representing an object or one of its parts. The routing mechanism connects capsules in consecutive layers, forming a hierarchical structure between parts and objects, also known as a parse tree. Capsule networks often attempt to model all elements in an image, requiring large network sizes to handle complexities such as intricate backgrounds or irrelevant objects. However, this comprehensive modeling leads to increased parameter counts and computational inefficiencies. Our goal is to enable capsule networks to focus only on the object of interest, reducing the number of parse trees. We accomplish this with REM (Routing Entropy Minimization), a technique that minimizes the entropy of the parse tree-like structure. REM drives the model parameters distribution towards low entropy configurations through a pruning mechanism, significantly reducing the generation of intra-class parse trees. This empowers capsules to learn more stable and succinct representations with fewer parameters and negligible performance loss.
PP-MARL: Efficient Privacy-Preserving Multi-Agent Reinforcement Learning for Cooperative Intelligence in Communications
Venue: arXiv cs.MA 2025
Year: 2025

Cooperative intelligence (CI) is expected to become an integral element in next-generation networks because it can aggregate the capabilities and intelligence of multiple devices. Multi-agent reinforcement learning (MARL) is a popular approach for achieving CI in communication problems by enabling effective collaboration among agents to address sequential problems. However, ensuring privacy protection for MARL is a challenging task because of the presence of heterogeneous agents that learn interdependently via sharing information. Implementing privacy protection techniques such as data encryption and federated learning to MARL introduces the notable overheads (e.g., computation and bandwidth). To overcome these challenges, we propose PP-MARL, an efficient privacy-preserving learning scheme for MARL. PP-MARL leverages homomorphic encryption (HE) and differential privacy (DP) to protect privacy, while introducing split learning to decrease overheads via reducing the volume of shared messages, and then improve efficiency. We apply and evaluate PP-MARL in two communication-related use cases. Simulation results reveal that PP-MARL can achieve efficient and reliable collaboration with 1.1-6 times better privacy protection and lower overheads (e.g., 84-91% reduction in bandwidth) than state-of-the-art approaches.
Investigating writing style as a contributor to gender gaps in science and technology
Venue: arXiv cs.CY 2025
Year: 2025

A growing stream of research finds that scientific contributions are evaluated differently depending on the gender of the author. In this article, we consider whether gender differences in writing styles - how men and women communicate their work - may contribute to these observed gender gaps. We ground our investigation in a framework for characterizing the linguistic style of written text, with two sets of features - informational (i.e., features that emphasize facts) and involved (i.e., features that emphasize relationships). Using a large sample of academic papers and patents, we find significant differences in writing style by gender, with women using more involved features in their writing. Papers and patents with more involved features also tend to be cited more by women. Our findings suggest that scientific text is not devoid of personal character, which could contribute to bias in evaluation, thereby compromising the norm of universalism as a foundational principle of science.
Accelerating Verified-Compiler Development with a Verified Rewriting Engine
Venue: arXiv cs.PL 2025
Year: 2025

Compilers are a prime target for formal verification, since compiler bugs invalidate higher-level correctness guarantees, but compiler changes may become more labor-intensive to implement, if they must come with proof patches. One appealing approach is to present compilers as sets of algebraic rewrite rules, which a generic engine can apply efficiently. Now each rewrite rule can be proved separately, with no need to revisit past proofs for other parts of the compiler. We present the first realization of this idea, in the form of a framework for the Coq proof assistant. Our new Coq command takes normal proved theorems and combines them automatically into fast compilers with proofs. We applied our framework to improve the Fiat Cryptography toolchain for generating cryptographic arithmetic, producing an extracted command-line compiler that is about 1000$\times$ faster while actually featuring simpler compiler-specific proofs.
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Existing works either utilize self-supervised schemes to refine relational feature signals by iteratively leveraging adaptive clustering and classification that provoke gradual drift problems, or adopt instance-wise contrastive learning which unreasonably pushes apart those sentence pairs that are semantically similar. To overcome these defects, we propose a novel contrastive learning framework named HiURE, which has the capability to derive hierarchical signals from relational feature space using cross hierarchy attention and effectively optimize relation representation of sentences under exemplar-wise contrastive learning. Experimental results on two public datasets demonstrate the advanced effectiveness and robustness of HiURE on unsupervised relation extraction when compared with state-of-the-art models.
Consensus Capacity of Noisy Broadcast Channels
Venue: arXiv cs.IT 2025
Year: 2025

We study communication with consensus over a broadcast channel - the receivers reliably decode the sender's message when the sender is honest, and their decoder outputs agree even if the sender acts maliciously. We characterize the broadcast channels which permit this byzantine consensus and determine their capacity. We show that communication with consensus is possible only when the broadcast channel has embedded in it a natural ''common channel'' whose output both receivers can unambiguously determine from their own channel outputs. Interestingly, in general, the consensus capacity may be larger than the point-to-point capacity of the common channel, i.e., while decoding, the receivers may make use of parts of their output signals on which they may not have consensus provided there are some parts (namely, the common channel output) on which they can agree.
Computing well-balanced spanning trees of unweighted networks
Venue: arXiv cs.SI 2025
Year: 2025

A spanning tree of a network or graph is a subgraph that connects all nodes with the least number or weight of edges. The spanning tree is one of the most straightforward techniques for network simplification and sampling, and for discovering its backbone or skeleton. Prim's algorithm and Kruskal's algorithm are well-known algorithms for computing a spanning tree of a weighted network, and are therefore also the default procedure for unweighted networks in the most popular network libraries. In this paper, we empirically study the performance of these algorithms on unweighted networks and compare them with different priority-first search algorithms. We show that the structure of a network, such as the distances between the nodes, is better preserved by a simpler algorithm based on breadth-first search. The spanning trees are also most compact and well-balanced as measured by classical graph indices. We support our findings with experiments on synthetic graphs and more than a thousand real networks, and demonstrate practical applications of the computed spanning trees. We conclude that if a spanning tree is to maintain the structure of an unweighted network, the breadth-first search algorithm should be the preferred choice, and it should be implemented as such in network libraries.
Narrowing the LOCAL$\unicode{x2013}$CONGEST Gaps in Sparse Networks via Expander Decompositions
Venue: arXiv cs.DS 2025
Year: 2025

Many combinatorial optimization problems can be approximated within $(1 \pm ε)$ factors in $\text{poly}(\log n, 1/ε)$ rounds in the LOCAL model via network decompositions [Ghaffari, Kuhn, and Maus, STOC 2018]. These approaches require sending messages of unlimited size, so they do not extend to the CONGEST model, which restricts the message size to be $O(\log n)$ bits. In this paper, we develop a generic framework for obtaining $\text{poly}(\log n, 1/ε)$-round $(1\pm ε)$-approximation algorithms for many combinatorial optimization problems, including maximum weighted matching, maximum independent set, and correlation clustering, in graphs excluding a fixed minor in the CONGEST model. This class of graphs covers many sparse network classes that have been studied in the literature, including planar graphs, bounded-genus graphs, and bounded-treewidth graphs. Furthermore, we show that our framework can be applied to give an efficient distributed property testing algorithm for an arbitrary minor-closed graph property that is closed under taking disjoint union, significantly generalizing the previous distributed property testing algorithm for planarity in [Levi, Medina, and Ron, PODC 2018 & Distributed Computing 2021]. Our framework uses distributed expander decomposition algorithms [Chang and Saranurak, FOCS 2020] to decompose the graph into clusters of high conductance. We show that any graph excluding a fixed minor admits small edge separators. Using this result, we show the existence of a high-degree vertex in each cluster in an expander decomposition, which allows the entire graph topology of the cluster to be routed to a vertex. Similar to the use of network decompositions in the LOCAL model, the vertex will be able to perform any local computation on the subgraph induced by the cluster and broadcast the result over the cluster.
Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoods
Venue: arXiv cs.LG 2025
Year: 2025

Gaussian Process (GP) models are a powerful tool in probabilistic machine learning with a solid theoretical foundation. Thanks to current advances, modeling complex data with GPs is becoming increasingly feasible, which makes them an interesting alternative to deep learning and related approaches. As the latter are getting more and more influential on society, the need for making a model's decision making process transparent and explainable is now a major focus of research. A major direction in interpretable machine learning is the use of gradient-based approaches, such as Integrated Gradients, to quantify feature attribution, locally for a given datapoint of interest. Since GPs and the behavior of their partial derivatives are well studied and straightforward to derive, studying gradient-based explainability for GPs is a promising direction of research. Unfortunately, partial derivatives for GPs become less trivial to handle when dealing with non-Gaussian target data as in classification or more sophisticated regression problems. This paper therefore proposes an approach for applying Integrated Gradient-based explainability to non-Gaussian GP models, offering both analytical and approximate solutions. This extends gradient-based explainability to probabilistic models with complex likelihoods to extend their practical applicability.
On the Placement and Sustainability of Drone FSO Backhaul Relays
Venue: arXiv cs.NI 2025
Year: 2025

We consider free-space optical (FSO) communication links for the backhaul connectivity of small cells (SCs) where a UAV with an FSO apparatus can serve as a backhaul relay node. We demonstrate how such drone relay stations (DRSs) can be deployed in a high-rise urban area in order to provide FSO line-of-sight (LOS) links that are unobstructed by buildings. Also, in our solution we consider the case where solar panels are mounted on DRSs such that placing the DRS in a sunny location is prioritized, and we show the gain in terms of number of required trips to recharge the UAV.
EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-Design
Venue: arXiv cs.HC 2025
Year: 2025

Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and enhanced visual privacy. However, existing eye tracking systems are still limited by their: (1) large form-factor largely due to the adopted bulky lens-based cameras; and (2) high communication cost required between the camera and backend processor, thus prohibiting their more extensive applications. To this end, we propose a lensless FlatCam-based eye tracking algorithm and accelerator co-design framework dubbed EyeCoD to enable eye tracking systems with a much reduced form-factor and boosted system efficiency without sacrificing the tracking accuracy, paving the way for next-generation eye tracking solutions. On the system level, we advocate the use of lensless FlatCams to facilitate the small form-factor need in mobile eye tracking systems. On the algorithm level, EyeCoD integrates a predict-then-focus pipeline that first predicts the region-of-interest (ROI) via segmentation and then only focuses on the ROI parts to estimate gaze directions, greatly reducing redundant computations and data movements. On the hardware level, we further develop a dedicated accelerator that (1) integrates a novel workload orchestration between the aforementioned segmentation and gaze estimation models, (2) leverages intra-channel reuse opportunities for depth-wise layers, and (3) utilizes input feature-wise partition to save activation memory size. On-silicon measurement validates that our EyeCoD consistently reduces both the communication and computation costs, leading to an overall system speedup of 10.95x, 3.21x, and 12.85x over CPUs, GPUs, and a prior-art eye tracking processor called CIS-GEP, respectively, while maintaining the tracking accuracy.
JigsawHSI: a network for Hyperspectral Image classification
Venue: arXiv cs.CV 2025
Year: 2025

This article describes Jigsaw, a convolutional neural network (CNN) used in geosciences and based on Inception but tailored for geoscientific analyses. Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover (LULC) classification problem with the Indian Pines, Pavia University and Salinas hyperspectral image data sets. The network is compared against HybridSN, a spectral-spatial 3D-CNN followed by 2D-CNN that achieves state-of-the-art results on the datasets. This short article proves that JigsawHSI is able to meet or exceed HybridSN's performance in all three cases. It also introduces a generalized Jigsaw architecture in d-dimensional space for any number of multimodal inputs. Additionally, the use of jigsaw in geosciences is highlighted, while the code and toolkit are made available.
Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer
Venue: arXiv cs.CV 2025
Year: 2025

Currently, transformer-based algorithms are making a splash in the domain of image deblurring. Their achievement depends on the self-attention mechanism with CNN stem to model long range dependencies between tokens. Unfortunately, this ear-pleasing pipeline introduces high computational complexity and makes it difficult to run an ultra-high-definition image on a single GPU in real time. To trade-off accuracy and efficiency, the input degraded image is computed cyclically over three dimensional ($C$, $W$, and $H$) signals without a self-attention mechanism. We term this deep network as Multi-scale Cubic-Mixer, which is acted on both the real and imaginary components after fast Fourier transform to estimate the Fourier coefficients and thus obtain a deblurred image. Furthermore, we combine the multi-scale cubic-mixer with a slicing strategy to generate high-quality results at a much lower computational cost. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring approaches on the several benchmarks and a new ultra-high-definition dataset in terms of accuracy and speed.
Extremal Fitting Problems for Conjunctive Queries
Venue: arXiv cs.DB 2025
Year: 2025

The fitting problem for conjunctive queries (CQs) is the problem to construct a CQ that fits a given set of labeled data examples. When a fitting CQ exists, it is in general not unique. This leads us to proposing natural refinements of the notion of a fitting CQ, such as most-general fitting CQ, most-specific fitting CQ, and unique fitting CQ. We give structural characterizations of these notions in terms of (suitable refinements of) homomorphism dualities, frontiers, and direct products, which enable the construction of the refined fitting CQs when they exist. We also pinpoint the complexity of the associated existence and verification problems, and determine the size of fitting CQs. We study the same problems for UCQs and for the more restricted class of tree CQs.
A Live Extensible Ontology of Quality Factors for Textual Requirements
Venue: arXiv cs.SE 2025
Year: 2025

Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defects in requirements artifacts that potentially propagate to later stages in the development life cycle. However, as a research community, we still lack a holistic perspective on quality factors. This inhibits not only a comprehensive understanding of the existing body of knowledge but also the effective use and evolution of these factors. To this end, we propose an ontology of quality factors for textual requirements, which includes (1) a structure framing quality factors and related elements and (2) a central repository and web interface making these factors publicly accessible and usable. We contribute the first version of both by applying a rigorous ontology development method to 105 eligible primary studies and construct a first version of the repository and interface. We illustrate the usability of the ontology and invite fellow researchers to a joint community effort to complete and maintain this knowledge repository. We envision our ontology to reflect the community's harmonized perception of requirements quality factors, guide reporting of new quality factors, and provide central access to the current body of knowledge.
The Complexity of Contracting Bipartite Graphs into Small Cycles
Venue: arXiv cs.CC 2025
Year: 2025

For a positive integer $\ell \geq 3$, the $C_\ell$-Contractibility problem takes as input an undirected simple graph $G$ and determines whether $G$ can be transformed into a graph isomorphic to $C_\ell$ (the induced cycle on $\ell$ vertices) using only edge contractions. Brouwer and Veldman [JGT 1987] showed that $C_4$-Contractibility is NP-complete in general graphs. It is easy to verify that $C_3$-Contractibility is polynomial-time solvable. Dabrowski and Paulusma [IPL 2017] showed that $C_{\ell}$-Contractibility is \NP-complete\ on bipartite graphs for $\ell = 6$ and posed as open problems the status of the problem when $\ell$ is 4 or 5. In this paper, we show that both $C_5$-Contractibility and $C_4$-Contractibility are NP-complete on bipartite graphs.
On the fast convergence of minibatch heavy ball momentum
Venue: arXiv cs.LG 2025
Year: 2025

Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the gap between theory and practice by showing that stochastic heavy ball momentum retains the fast linear rate of (deterministic) heavy ball momentum on quadratic optimization problems, at least when minibatching with a sufficiently large batch size. The algorithm we study can be interpreted as an accelerated randomized Kaczmarz algorithm with minibatching and heavy ball momentum. The analysis relies on carefully decomposing the momentum transition matrix, and using new spectral norm concentration bounds for products of independent random matrices. We provide numerical illustrations demonstrating that our bounds are reasonably sharp.
Characterizing Human Actions in the Digital Platform by Temporal Context
Venue: arXiv cs.HC 2025
Year: 2025

Recent advances in digital platforms generate rich, high-dimensional logs of human behavior, and machine learning models have helped social scientists explain knowledge accumulation, communication, and information diffusion. Such models, however, almost always treat behavior as sequences of actions, abstracting the inter-temporal information among actions. To close this gap, we introduce a two-scale Action-Timing Context(ATC) framework that jointly embeds each action and its time interval. ATC obtains low-dimensional representations of actions and characterizes them with inter-temporal information. We provide three applications of ATC to real-world datasets and demonstrate that the method offers a unified view of human behavior. The presented qualitative findings demonstrate that explicitly modeling inter-temporal context is essential for a comprehensive, interpretable understanding of human activity on digital platforms.
Traffic Congestion Prediction Using Machine Learning Techniques
Venue: arXiv cs.LG 2025
Year: 2025

The prediction of traffic congestion can serve a crucial role in making future decisions. Although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). We proposed a prediction model for traffic congestion that can predict congestion based on day, time and several weather data (e.g., temperature, humidity). To evaluate our model, it has been tested against the traffic data of New Delhi. With this model, congestion of a road can be predicted one week ahead with an average RMSE of 1.12. Therefore, this model can be used to take preventive measure beforehand.