October 2025 arXiv papers — page 25

M. Bayer, M. Vieweg, K. P. Schmidt

We present a solvable one-dimensional spin-1/2 model on the diamond chain featuring three-spin interactions, which displays both, mobile excitations driving a second-order phase transition between an ordered and a $\mathbb{Z}_2$-symmetry broken phase, as well as non-trivial fully immobile excitations. The model is motivated by the physics of fracton excitati

Ayman Abaid, Gianpiero Guidone, Sara Alsubai, Foziyah Alquahtani

Coronary artery calcium (CAC) scoring plays a crucial role in the early detection and risk stratification of coronary artery disease (CAD). In this study, we focus on non-contrast coronary computed tomography angiography (CCTA) scans, which are commonly used for early calcification detection in clinical settings. To address the challenge of limited annotated

Chi Qiu, Wen Chen, Qingqing Wu, Fen Hou

Intelligent reflecting surface (IRS)-assisted full-duplex (FD) terahertz (THz) communication systems have emerged as a promising paradigm to satisfy the escalating demand for ultra-high data rates and spectral efficiency in future wireless networks. However, the practical deployment of such systems presents unique technical challenges, stemming from severe p

Zhigang Tu, Zhengbo Zhang, Jia Gong, Junsong Yuan

Skeleton-based human action recognition aims to classify human skeletal sequences, which are spatiotemporal representations of actions, into predefined categories. To reduce the reliance on costly annotations of skeletal sequences while maintaining competitive recognition accuracy, the task of 3D Action Recognition with Limited Training Samples, also known a

Arpad Hegedus

In this paper we propose two sets of nonlinear integral equations (NLIE) for describing the thermodynamics in the sine-Gordon model, when higher Lorentz spin conserved charges are also coupled to the Gibbs ensemble. We call them NLIE I and II. The derivation of the equations, is based on T-Q relations given by the equivalent thermodynamic Bethe ansatz (TBA)

Jinghong Tan, Zhichen Zhang, Kun Guo, Tsung-Hui Chang

Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterogeneity, limit its practicality in mobile edge computing. Existing compression methods like sparsification and pruning reduce per-round costs but may increase training rounds and th

Dinesh Udar, Shiksha Saini

In this paper, we study a new class of rings, called $\sqrt{J}$-clean rings. A ring in which every element can be expressed as the addition of an idempotent and an element from $\sqrt{J(R)}$ is called a $\sqrt{J}$-clean ring. Here, $\sqrt{J(R)}=\{ z\in R : z^n\in J(R) \ \mathrm{for \ some} \ n \geq 1 \}$ where, $J(R)$ is the Jacobson radical. We provide the

Akaash Chatterjee, Suman Kundu

Learning is most effective when it's connected to relevant, relatable examples that resonate with learners on a personal level. However, existing educational AI tools don't focus on generating examples or adapting to learners' changing understanding, struggles, or growing skills. We've developed ExaCraft, an AI system that generates personalized examples by

Beiwen Zhang, Yongheng Liang, Guowei Zou, Haitao Wang

Learning to collaborate with various unfamiliar teammates poses a great challenge in the domain of multi-agent systems. Existing ad hoc teamwork methods typically drive controlled agents to collaborate with a group of teammates exhibiting a single coordination phenotype shaped by the same reward function. However, in real-world applications, controlled agent

Frederik J. Zuiderveen Borgesius, Sanne Kruikemeier, Sophie C. Boerman, Natali Helberger

On the internet, we encounter take-it-or-leave-it choices regarding our privacy on a daily basis. In Europe, online tracking for targeted advertising generally requires the internet users' consent to be lawful. Some websites use a tracking wall, a barrier that visitors can only pass if they consent to tracking by third parties. When confronted with such a tr

Bernhard Rameder, Hubert Gattringer, Andreas Mueller

In this paper a new method for geometric robot calibration is introduced, which uses a calibration plate with precisely known distances between its measuring points. The relative measurement between two points on the calibration plate is used to determine predefined error parameters of the system. In comparison to conventional measurement methods, like laser

Sophie C. Boerman, Sanne Kruikemeier, Frederik J. Zuiderveen Borgesius

Advertisers are increasingly monitoring people's online behavior and using the information collected to show people individually targeted advertisements. This phenomenon is called online behavioral advertising (OBA). Although advertisers can benefit from OBA, the practice also raises concerns about privacy. Therefore, OBA has received much attention from adv

B. Bodo, N. Helberger, K. Irion, F. Zuiderveen Borgesius

Algorithmic agents permeate every instant of our online existence. Based on our digital profiles built from the massive surveillance of our digital existence, algorithmic agents rank search results, filter our emails, hide and show news items on social networks feeds, try to guess what products we might buy next for ourselves and for others, what movies we w

Rony Keppens, Yuhao Zhou, Chun Xia

We review major achievements in our understanding of multiphase coronal plasma, where cool-dense and hot-tenuous matter coexists, brought about by advances in modeling and theory, inspired by observations. We give an overview of models that self-consistently form solar (or stellar) prominences and filaments, or (postflare) coronal rain, and clarify how these

Jakob Ziegler, Bernhard Rameder, Hubert Gattringer, Andreas Mueller

In this paper, an approach for gait assistance with a lower body exoskeleton is described. Two concepts, transparency and motion assistance, are combined. The transparent mode, where the system is following the user's free motion with a minimum of perceived interaction forces, is realized by exploiting the gear backlash of the actuation units. During walking

S. V. Jeffers, J. R. Barnes, P. Schöfer, S. Reffert

Context. Current exoplanet surveys are focused on detecting small exoplanets orbiting in the liquid-water habitable zones of their host stars. Despite the recent significant advancements in instrumental developments, the current limitation in detecting these exoplanets is the intrinsic variability of the host star itself. Aims. Our aim is to use the full CAR

Yuhang Hu, Zhenyu Yang, Shihan Wang, Shengsheng Qian

The rapid growth of streaming video applications demands multimodal models with enhanced capabilities for temporal dynamics understanding and complex reasoning. However, current Video Question Answering (VideoQA) datasets suffer from two critical limitations: 1) Static annotation mechanisms fail to capture the evolving nature of answers in temporal video str

Alex Elliott, Jacob Ngaha, Scott Parkins, Takao Aoki

Photon counting statistics are explored, theoretically, from a pair of cavity modes coupled to the fluorescent transitions in a strongly-driven two-level atom. We show that the cavity modes acquire nonclassical photon statistics that are representative of dressed-state picture atomic transitions. In particular, the modes are shown to be antibunched, while si

Adam K. Taras, Barnaby R. M. Norris, Christopher Betters, Andrew Ross-Adams

While photonic lanterns efficiently and uniquely map a set of input modes to single-mode outputs (or vice versa), the optical mode transfer matrix of any particular fabricated device cannot be constrained at the design stage due to manufacturing imperfections. Accurate knowledge of the mapping enables complex sensing or beam control applications that leverag

Quanliang Jing, Xinxin Fan, Yanyan Liu, Jingping Bi

To date, traffic obfuscation techniques have been widely adopted to protect network data privacy and security by obscuring the true patterns of traffic. Nevertheless, as the pre-trained models emerge, especially transformer-based classifiers, existing traffic obfuscation methods become increasingly vulnerable, as witnessed by current studies reporting the tr

Amaury H. M. J. Triaud, Thomas A. Baycroft, Neda Heidari, Alexandre Santerne

Circumbinary configurations offer a test of planet formation in an altered environment, where the inner binary has perturbed a protoplanetary disc. Comparisons of the physical and orbital parameters between the circumbinary planet population and the population of exoplanets orbiting single stars will reveal how these disc perturbations affect the assembly of

Julius Werner, Malte Schmidt, Francesco Pennazio, Jorge Roser

Particle therapy relies on up-to-date knowledge of the stopping power of the patient tissues to deliver the prescribed dose distribution. The stopping power describes the average particle motion, which is encoded in the distribution of prompt-gamma photon emissions in time and space. We reconstruct the spatiotemporal emission distribution from multi-detector

Runxi Huang, Mingxuan Yu, Mingyu Tsoi, Xiaomin Ouyang

Real-time multimodal inference on resource-constrained edge devices is essential for applications such as autonomous driving, human-computer interaction, and mobile health. However, prior work often overlooks the tight coupling between sensing dynamics and model execution, as well as the complex inter-modality dependencies. In this paper, we propose MMEdge,

Irfan Glogić, Martina Hofmanová, Eliseo Luongo

We consider stochastically perturbed wave maps from $\mathbb{R}^{1+d}$ into $\mathbb{S}^d$, in all energy-supercritical dimensions $d \geq 3$. We show that corotational non-degenerate Gaussian additive noise leads to self-similar blowup with positive probability for any corotational initial data. The same result without noise is conjectured, but unknown, for

Bibhas Ghanta, Kuldeep Singh Chikara, Uttam Kumar Goutam, Anup Kumar Bera

In the quest of green energy, Solid Oxide Fuel Cells (SOFC) have drawn considerable attention for chemical-to-electric energy conversion. In the present paper, we report an enhancement of ionic conductivity in Cu2P2-xVxO7 by vanadium substitution. The electrical (dc and ac conductivity, diffusivity, hopping rate, electric modulus and dielectric properties) a

Erik Börve, Nikolce Murgovski, Leo Laine

Trajectory planning in dense, interactive traffic scenarios presents significant challenges for autonomous vehicles, primarily due to the uncertainty of human driver behavior and the non-convex nature of collision avoidance constraints. This paper introduces a stochastic optimal control framework to address these issues simultaneously, without excessively co

Xuchen Feng, Siyu Liao

Normalizing flows are deep generative models that enable efficient likelihood estimation and sampling through invertible transformations. A key challenge is to design linear layers that enhance expressiveness while maintaining efficient computation of the Jacobian determinant and inverse. We introduce a novel invertible linear layer based on the product of c

Andrea Medini, Juris Steprāns

We give a unified treatment of the countable dense homogeneity of products of Polish spaces, with a focus on uncountable products. Our main result states that a product of fewer than $\mathfrak{p}$ Polish spaces is countable dense homogeneous if the following conditions hold: (1) Each factor is strongly locally homogeneous, (2) Each factor is strongly $n$-ho

Corentin Correia, Juan Paucar

Given a measure equivalence coupling between two finitely generated groups, Delabie, Koivisto, Le Ma\^itre and Tessera have found explicit upper bounds on how integrable the associated cocycles can be. We extend these results to the broader framework of unimodular compactly generated locally compact groups. We also generalize a result by the first-named auth

Jiaqi Wu, Qinlao Zhao, Zefeng Chen, Kai Qin

Autonomous agents powered by large language models (LLMs) have shown impressive capabilities in tool manipulation for complex task-solving. However, existing paradigms such as ReAct rely on sequential reasoning and execution, failing to exploit the inherent parallelism among independent sub-tasks. This sequential bottleneck leads to inefficient tool utilizat

Hao Chen, Jiaqi Wang, Yonggang Qi, Ke Li

We present a novel task: text-to-3D sketch animation, which aims to bring freeform sketches to life in dynamic 3D space. Unlike prior works focused on photorealistic content generation, we target sparse, stylized, and view-consistent 3D vector sketches, a lightweight and interpretable medium well-suited for visual communication and prototyping. However, this

Apu Chakraborty, Anshul Kumar, Gagan Raj Gupta

Detecting anomalous nodes in attributed networks, where each node is associated with both structural connections and descriptive attributes, is essential for identifying fraud, misinformation, and suspicious behavior in domains such as social networks, academic citation graphs, and e-commerce platforms. We propose Flex-GAD, a novel unsupervised framework for

Yushen Huang, Zhiming Wang

Few-shot object detection (FSOD) often suffers from base-class bias and unstable calibration when only a few novel samples are available. We propose Prototype-Driven Alignment (PDA), a lightweight, plug-in metric head for DeFRCN that provides a prototype-based "second opinion" complementary to the linear classifier. PDA maintains support-only prototypes in a

Léon Monnier, Frédéric Faure

We study a family of pseudodifferential operators (quantum Hamiltonians) on $L^{2}(\mathbb{R}^{n};\mathbb{C}^{d})$ whose spectrum exhibits two energy bands exchanging a finite number of eigenvalues. We show that this number coincides with the Chern index of a vector bundle associated to the principal symbol (the classical Hamiltonian). This result provides a

Tianbo Chen, Xiaojun Song

This paper introduces a novel spectral M-estimator, called the asymmetric Huber periodogram (AHP), as a generalization of the ordinary periodogram (PG), the quantile periodogram (QP), and the Huber periodogram (HP). The AHP is constructed via trigonometric asymmetric Huber regression (AHR), in which a specially designed loss function replaces the squared $\e

Oleksandr Volkov, Nataliia Voinalovych

The article examines the distribution of the power series of the function $ w(y) = \left( 1 + \sqrt{1 - y} \right)^{-\frac{1}{2}}. $ The distribution of the considered function into a power series is obtained $ \left(1 + \sqrt{1 - y}\right)^{-\frac{1}{2}} = \sum_{m=0}^{\infty} \frac{(4m)! \, 16^{-m}}{(2m)! \, (2m+1)! \, \sqrt{2}} \, y^m. $ The dispersion fun

Louis Sharrock, Christopher Nemeth

We introduce adaptive, tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of Wasserstein gradient flows. The result is a suite of tuning-free sampling algorithms, including tuning-free variants of the unadjusted Langevin algorithm (ULA), stochastic gradient Langevin dynamics (SGLD), mean-field Langevin dyn

Zongxi Yu, Xiaolong Qian, Shaohua Gao, Qi Jiang

Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-

Jingyan Liu, Yue Sun, Jianwei Xu, Ming-Jing Zhao

Complex numbers are indispensable in quantum mechanics and the resource theory of imaginarity has been developed recently. In this paper, we propose a method to construct imaginary measures by real part states. Specifically, we propose an imaginarity measure in terms of fidelity and explore its properties. The analytical expression of the imaginarity measure

Rolf Andreasson, Ludvig Svensson

We consider the canonical ensemble of a system of point particles on the sphere interacting via a logarithmic pair potential. In this setting, we study the associated Gibbs measure and partition function, and we derive explicit formulas relating the critical temperature, at which the partition function diverges, to a certain discrete optimization problem. We

Sagalpreet Singh, Rishi Saket, Aravindan Raghuveer

Reinforcement Learning algorithms are primarily focused on learning a policy that maximizes expected return. As a result, the learned policy can exploit one or few reward sources. However, in many natural situations, it is desirable to learn a policy that induces a dispersed marginal state distribution over rewarding states, while maximizing the expected ret

Senjie Jin, Lu Chen, Zhiheng Xi, Yuhui Wang

Natural language chain-of-thought (N-CoT) and Program chain-of-thought (P-CoT) have emerged as two primary paradigms for large language models (LLMs) to solve mathematical reasoning problems. Current research typically endeavors to achieve unidirectional enhancement: P-CoT enhanced N-CoT or N-CoT enhanced P-CoT. In this paper, we seek to fully unleash the tw

Seokbong Seol

Behrend, Liao, and Xu showed that differential graded (DG) manifolds of positive amplitude forms a category of fibrant objects. In particular, this ensures that notion of derived intersection -- more generally, homotopy fibre product -- is well-defined up to weak equivalences. We prove that the Atiyah and Todd classes of DG manifolds of positive amplitude ar

Frédéric Faure

Anosov geodesic flows are among the simplest mathematical models of deterministic chaos. In this survey we explain how, quite unexpectedly, quantum dynamics emerges from purely classical correlation functions. The underlying mechanism is the discrete Pollicott Ruelle spectrum of the geodesic flow, revealed through microlocal analysis. This spectrum naturally

Yitzchak Grunbaum, Eitan Yaakobi

We study coverage processes in which each draw reveals a subset of $[n]$, and the goal is to determine the expected number of draws until all items are seen at least once. A classical example is the Coupon Collector's Problem, where each draw reveals exactly one item. Motivated by shotgun DNA sequencing, we introduce a model where each draw is a contiguous w

Savitri Gallego, Uwe Oberlack, Jan Lommler, Christopher M. Karwin

The Compton Spectrometer and Imager (COSI) is a Compton telescope designed to survey the 0.2 - 5 MeV sky, consisting of a compact array of cross-strip germanium detectors. It is planned to be launched in 2027 into an equatorial low-Earth (530 km) orbit with a prime mission duration of 2 years. The observation of MeV gamma rays is dominated by background, mos

Soumyadeep Jana, Sanasam Ranbir Singh

Multimodal sarcasm detection is challenging, especially in low-resource settings where subtle image-text contradictions are hard to learn due to scarce annotated data, which hinders the model's performance. Parameter-efficient fine-tuning (PEFT) methods like adapters, LoRA, and prompt tuning reduce overfitting but struggle to reach optimal performance due to

Ryuki Hyodo, Shigeru Ida

Planetary rings are not only ubiquitous around the giant planets in the outer Solar System, but have also been discovered around several small distant bodies. In contrast, no rings have been observed around any inner Solar System objects. To constrain the dynamical origin of this ringed-versus-ringless dichotomy, we employ a numerically cross-checked analyti

Bharathi Kannan Nithyanantham, Tobias Sesterhenn, Ashwin Nedungadi, Sergio Peral Garijo

Bringing generative AI into the architecture, engineering and construction (AEC) field requires systems that can translate natural language instructions into actions on standardized data models. We present MCP4IFC, a comprehensive open-source framework that enables Large Language Models (LLMs) to directly manipulate Industry Foundation Classes (IFC) data thr

Yang Jin, Guangyu Guo, Binglu Wang

Gaze object detection and gaze following are fundamental tasks for interpreting human gaze behavior or intent. However, most previous methods usually solve these two tasks separately, and their prediction of gaze objects and gaze following typically depend on head-related prior knowledge during both the training phase and real-world deployment. This dependen

Kentaro Kasai, Naoya Kitajima

We show that the merger of oscillons results in a broad spectrum of the oscillon mass. A huge number of oscillon samples obtained from numerical lattice simulations reveal that the oscillon mass distribution has an exponential tail in a heavy-mass region. This enables us to infer the fractional abundance of heavy oscillons. Using the criterion for the primor

Kenneth R. Davidson, Vern I. Paulsen, Mizanur Rahaman

In this paper, we employ operator system techniques to investigate structural properties of C*-algebras. In particular, we provide more direct proofs of results concerning exactness and the local lifting property (LLP) of group C$^*$-algebras that avoid relying on the traditional heavy machinery of C$^*$-algebra theory. Briefly, these methods allow us to ded

Rufan Zhang, Lin Zhang, Xianghang Mi

The proliferation of harmful online content--e.g., toxicity, spam, and negative sentiment--demands robust and adaptable moderation systems. However, prevailing moderation systems are centralized and task-specific, offering limited transparency and neglecting diverse user preferences--an approach ill-suited for privacy-sensitive or decentralized environments.

Jaewon Chu, Seunghun Lee, Hyunwoo J. Kim

Large language models (LLMs) have achieved remarkable success across diverse domains, due to their strong instruction-following capabilities. This has led to increasing interest in optimizing instructions for black-box LLMs, whose internal parameters are inaccessible but widely used due to their strong performance. To optimize instructions for black-box LLMs

Hidetake Tanaka, Haruto Tanaka, Kazumasa Shimari, Kenichi Matsumoto

As Large Language Models (LLMs) increasingly generate code in software development, ensuring the quality of LLM-generated code has become important. Traditional testing approaches using Example-based Testing (EBT) often miss edge cases -- defects that occur at boundary values, special input patterns, or extreme conditions. This research investigates the char

Rachel Axelrod, Uri Obolski, Daniel Nevo

Vaccine randomized trials are typically designed to be blinded, ensuring that the estimated vaccine efficacy (VE) reflects the immunological effect of the vaccine. When blinding is broken, however, the estimated VE reflects not only the immunological effect but also behavioral effects stemming from participants' awareness of their treatment status. Recent wo

Haode Yan, Maosheng Xiong

We employ analytic number theoretic techniques, specifically character sums and Weil type estimates, to study the covering radius of the generalized Zetterberg codes over all finite fields. Although the even and odd field cases require distinct technical treatment, the proofs follow a unified analytic framework that is substantially simpler and more transpar

Wei-Bin Chen, Ding Fang, Cheng-Hao Zhang, Jin-Ming Cui

Trapped ions in micro-cavities constitute a key platform for advancing quantum information processing and quantum networking. By providing an efficient light-matter interface within a compact architecture, they serve as highly efficient quantum nodes with strong potential for scalable quantum network. However, in such systems, ion trapping stability is often

Marina Murakami, Ryoko Iwase, Chiemi Iba, Daisuke Ogura

This study demonstrates the feasibility of radar-based non-contact respiratory monitoring for wombats. Two measurement experiments were conducted in June and December 2024 using 79-GHz millimeter-wave radar systems to monitor the respiration of two wombats. To estimate the respiratory interval, we used a method based on summing harmonic components in the aut

Charlotte Claye, Pierre Marschall, Wassila Ouerdane, Céline Hudelot

Single-cell RNA-seq foundation models achieve strong performance on downstream tasks but remain black boxes, limiting their utility for biological discovery. Recent work has shown that sparse dictionary learning can extract concepts from deep learning models, with promising applications in biomedical imaging and protein models. However, interpreting biologic

Yannis Voet, Leonardo De Novellis

The Kronecker product is an invaluable tool for data-sparse representations of large networks and matrices with countless applications in machine learning, graph theory and numerical linear algebra. In some instances, the sparsity pattern of large matrices may already hide a Kronecker product. Similarly, a large network, represented by its adjacency matrix,

Sten Madec, Erida Gjini

The interplay between local and regional processes in the dynamics of ecological communities remains a challenge to model, analyze and predict. This is especially notable in infectious diseases with multiple strains, where several layers of heterogeneity can interact, including strain biological traits and environmental heterogeneity among locations where di

Yuan Guo, Wen Chen, Qingqing Wu, Zhendong Li

This paper explores a multi-cell multiple-input single-output (MISO) downlink communication system enabled by a unique transmissive reconfigurable intelligent surface (TRIS) transceiver configuration. Within this system framework, we formulate an optimization problem for the purpose of maximizing the minimum rate of users for each cell via designing the tran

Dong Huang, Pengkun Yang

We investigate the problem of detecting correlation between two Erd\H{o}s-R\'enyi graphs $G(n,p)$, formulated as a hypothesis testing problem: under the null hypothesis, the two graphs are independent, while under the alternative hypothesis, they are correlated through a latent bijective mapping between their vertex sets. We develop a polynomial-time test by

Denis S. Grebenkov, Raphael Maurette

We investigate the capacity of a flat partially reactive patch of arbitrary shape to trap independent particles that undergo steady-state diffusion in the three-dimensional space. We focus on the total flux of particles onto the patch that determines its reactive capacitance. To disentangle the respective roles of the reactivity and the shape of the patch, w

Ferdinand Genans, Antoine Godichon-Baggioni, François-Xavier Vialard, Olivier Wintenberger

We investigate the semi-discrete Optimal Transport (OT) problem, where a continuous source measure $\mu$ is transported to a discrete target measure $\nu$, with particular attention to the OT map approximation. In this setting, Stochastic Gradient Descent (SGD) based solvers have demonstrated strong empirical performance in recent machine learning applicatio

Antonin Louiset, Daniel Förster, Vincent Jourdain, Saïd Tahir

Single wall carbon nanotubes (SWCNT) exhibit remarkable optical and electrical properties making them one of the most promising materials for next generation electronic and optoelectronic devices. Their electronic properties strongly depend on their chirality, i.e., their structural configuration, as well as on the presence and nature of atomic defects. Curr

Qiushi Pan, Hao Wang, Guoyuan An, Luankang Zhang

In recommendation systems, how to effectively scale up recommendation models has been an essential research topic. While significant progress has been made in developing advanced and scalable architectures for sequential recommendation(SR) models, there are still challenges due to items' multi-faceted characteristics and dynamic item relevance in the user co

Jiamin Wu, Chenguang Zhao, Huan Yu

Lane changes are common yet challenging driving maneuvers that require continuous decision-making and dynamic interaction with surrounding vehicles. Relying solely on human drivers for lane-changing can lead to traffic disturbances due to the stochastic nature of human behavior and its variability under different task demands. Such uncertainties may signific

Muneer Ahmad, Undie Felicia Nkatv

This study employs scientometric methods to assess the research output and performance of the University of Ibadan from 2014 to 2023. By analyzing publication trends, citation patterns, and collaboration networks, the research aims to comprehensively evaluate the university's research productivity, impact, and disciplinary focus. This article's endeavors are

Blaise Delattre

Deep learning has achieved remarkable success across a wide range of tasks, but its models often suffer from instability and vulnerability: small changes to the input may drastically affect predictions, while optimization can be hindered by sharp loss landscapes. This thesis addresses these issues through the unifying perspective of sensitivity analysis, whi

Lukas Prause, Mark Akselrod

The behavior of loss-based TCP congestion control algorithms like TCP CUBIC continues to be a challenge in modern cellular networks. Due to the large RLC layer buffers required to deal with short-term changes in channel capacity, the behavior of both the Slow Start and congestion avoidance phases may be heavily impacted by the lack of packet losses and the r

Yuyang Huang, Yabo Chen, Junyu Zhou, Wenrui Dai

Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data. Existing SFDA methods are restricted by the fundamental limitation of source-target domain discrepancy. Non-generation SFDA methods suffer from unreliable pseudo-labels in challenging scenarios with large doma

Kunming Shao, Zhipeng Liao, Jiangnan Yu, Liang Zhao

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieval but faces challenges on edge devices due to high storage, energy, and latency demands. Computing-in-Memory (CIM) offers a promising solution by storing document embeddings in CIM macros and enabling in-situ parallel retrievals but is constra

Simon Süwer, Mai Khanh Mai, Christoph Klein, Nicola Götzenberger

The integration of clinical data offers significant potential for the development of personalized medicine. However, its use is severely restricted by the General Data Protection Regulation (GDPR), especially for small cohorts with rare diseases. High-quality, structured data is essential for the development of predictive medical AI. In this case study, we p

Shunsuke Hirota

We show that if a module M over a basic classical Lie superalgebra of type type I is simultaneously a Verma module with respect to some Borel \(\mathfrak b_1\) and a dual Verma module with respect to Borel \(\mathfrak b_2\), then M is isomorphic to a Verma module with respect to either distinguished or an anti-distinguished Borel. Our method proceeds by anal

Gustavo Bergantiños, Juan D. Moreno-Ternero

We study the problem of measuring the popularity of artists in music streaming platforms and the ensuing methods to compensate them (from the revenues platforms raise by charging users). We uncover the space of popularity indices upon exploring the implications of several axioms capturing principles with normative appeal. As a result, we characterize several

Akira Ito, Ryuichiro Akaho, Hiroki Nagakura, Shoichi Yamada

We propose a dual-resolution prescription meant for the Boltzmann neutrino transport, in which the advection and collision terms are calculated with different angular resolutions in momentum space. The purpose is to address the issue of the low resolution that afflicts the $S_N$ method in the multi-dimensional neutrino transport simulations for core-collapse

Sandipan Majhi, Paheli Bhattacharya

Domain-specific question answering in low-resource languages faces two key challenges: scarcity of annotated datasets and limited domain knowledge in general-purpose language models. In this work, we present a multi-stage finetuning strategy to adapt lightweight language models to the Hindi tourism domain by leveraging both original and synthetic training da

D. L. Helis, A. Melchiorre, S. Nagorny, M. Noia

A wide range of scintillating bolometers is under investigation for applications in the search for rare events and processes beyond the Standard Model. In this work, we report the first measurement of a natural, non-molybdenum-doped, lithium tungstate (LWO) crystal operated underground as a scintillating cryogenic calorimeter. The detector achieved a baselin

Sabrine Aroua, Christos Anastasios Bovolis, Bo Göransson, Anastasios Giovanidis

Initial access (IA) is the process by which user equipment (UE) establishes its first connection with a base station. In 5G systems, particularly at millimeter-wave frequencies, IA integrates beam management to support highly directional transmissions. The base station employs a codebook of beams for the transmission of Synchronization Signal Blocks (SSBs),

Nao Yoshimura, Hiroshi Oyama, Takuya Azumi

As embedded systems grow in complexity and scale due to increased functional diversity, component-based development (CBD) emerges as a solution to streamline their architecture and enhance functionality reuse. CBD typically utilizes the C programming language for its direct hardware access and low-level operations, despite its susceptibility to memory-relate

Benjamin A. Antunes

Pseudorandom number generators (PRNGs) are ubiquitous in stochastic simulations and machine learning (ML), where they drive sampling, parameter initialization, regularization, and data shuffling. While widely used, the potential impact of PRNG statistical quality on computational results remains underexplored. In this study, we investigate whether difference

Wang zhi, Yuyan Liu, Liu Liu, Li Zhang

Generating hand grasps with language instructions is a widely studied topic that benefits from embodied AI and VR/AR applications. While transferring into hand articulatied object interaction (HAOI), the hand grasps synthesis requires not only object functionality but also long-term manipulation sequence along the object deformation. This paper proposes a no

Luca Assogna, Giovanna Salvitti, Matteo Silvestri, Federico Perrella

Self diffraction is a four-wave mixing process proportional to the square modulus of third-order nonlinearity susceptibility $\chi^{(3)}$, which is related to the material's electronic and thermal properties. In this study, we investigate the wavelength dependence of the self-diffracted signal generated by a femtosecond pulsed laser in a dye solution to dire

Ziwei Liu, Wen Chen, Zhendong Li, Qiong Wu

In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-driven cooperative integrated sensing, computing, and communication (ISCC) network to meet the requirement for a diverse network with low energy consumption. The cooperative base stations (BSs) are equipped with TRIS transceivers to accomplish sensing data ac

Rafael Luque Merino, Felix Carrascoso, Eudomar Henríquez-Guerra, M. Reyes Calvo

Strain engineering is a powerful strategy for controlling the structural and electronic properties of two-dimensional materials, particularly in systems hosting charge density wave (CDW) order. In this work, we apply uniaxial tensile and compressive strain to thin 1T-TaS2 flakes using a flexible, device-compatible platform, and systematically investigate the

Ryutaro Nishimura, Yoshio Suzuki, Hiroshi Sugiyama, Daisuke Wakabayashi

In this paper, a new phase-retrieval algorithm from an X-ray schlieren image is proposed. The schlieren method allows phase-contrast imaging with an objective lens and a knife-edge filter placed at the back focal plane of the objective. This method finds a wide range of applications in the visible-light region for transparent specimen visualization. The schl

Yang Miao, Jan-Nico Zaech, Xi Wang, Fabien Despinoy

We propose LangHOPS, the first Multimodal Large Language Model (MLLM) based framework for open-vocabulary object-part instance segmentation. Given an image, LangHOPS can jointly detect and segment hierarchical object and part instances from open-vocabulary candidate categories. Unlike prior approaches that rely on heuristic or learnable visual grouping, our

Xiandong Zou, Jia Li, Xiaotong Yuan, Pan Zhou

Normalization is fundamental to deep learning, but existing approaches such as BatchNorm, LayerNorm, and RMSNorm are variance-centric by enforcing zero mean and unit variance, stabilizing training without controlling how representations capture task-relevant information. We propose IB-Inspired Normalization (IBNorm), a simple yet powerful family of methods g

Lei Yang, Jiayi Zhu, Ling Liang, Kim-Chuan Toh

This article investigates the convergence properties of a relative-type inexact preconditioned proximal augmented Lagrangian method (rip$^2$ALM) for convex nonlinear programming, a fundamental class of optimization problems with broad applications in science and engineering. Inexact proximal augmented Lagrangian methods have proven to be highly effective for

Mustafa F. Abdelwahed, Ahmed Shafee, Joan Espasa

Cyber attacks threaten economic interests, critical infrastructure, and public health and safety. To counter this, entities adopt cyber threat hunting, a proactive approach that involves formulating hypotheses and searching for attack patterns within organisational networks. Automating cyber threat hunting presents challenges, particularly in generating hypo

Frank Drewes, Berthold Hoffmann, Mark Minas

Graph-based modeling plays a fundamental role in many areas of computer science. In this paper, we introduce systems of graph formulas with variables for specifying graph properties; this notion generalizes the graph formulas introduced in earlier work by incorporating recursion. We show that these formula systems have the same expressive power as alternatin

Yehjin Shin, Jeongwhan Choi, Seojin Kim, Noseong Park

Recently, convolutional filters have been increasingly adopted in sequential recommendation for their ability to capture local sequential patterns. However, most of these models complement convolutional filters with self-attention. This is because convolutional filters alone, generally fixed filters, struggle to capture global interactions necessary for accu

Xinru Tang, Jingxiang Hou, Dingcheng Jiang, Taiquan Wei

As large language models (LLMs) continue to scale up, mixture-of-experts (MoE) has become a common technology in SOTA models. MoE models rely on expert parallelism (EP) to alleviate memory bottleneck, which introduces all-to-all communication to dispatch and combine tokens across devices. However, in widely-adopted GPU clusters, high-overhead cross-node comm

Zijun Liao, Yian Zhao, Xin Shan, Yu Yan

Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the pursuit of high-speed inference via lightweight network designs often leads to degraded feature representation, which hinders further performance improvements and practical on-device deployment. In this paper, we

Fangbing Liu, Pengfei Duan, Wen Li, Yi He

Recent advancements have demonstrated the great potential of flow matching-based Multimodal Large Language Models (MLLMs) in image editing. However, state-of-the-art works like BAGEL face limitations, including detail degradation, content inconsistency, and inefficiency due to their reliance on random noise initialization. To address these issues, we propose

Talia Xu, Caitlin Smith, Charles Lo, Jami Shepherd

Several centimeters below the skin lie multiple biomarkers, such as glucose, oxygenation, and blood flow. Monitoring these biomarkers regularly and in a non-invasive manner would enable early insight into metabolic status and vascular health. Currently, there are only a handful of non-invasive monitoring systems. Optical methods offer molecular specificity (

Klaus Zauner, Hubert Gattringer, Andreas Mueller

Handling loosely placed objects with robotic manipulators is a difficult task from the point of view of trajectory planning and control. This becomes even more challenging when the object to be handled is a container filled with liquid. This paper addresses the task of transporting a liquid-filled cup placed on a tray along a prescribed path in shortest time

Zhixing Li, Arsham Gholamzadeh Khoee, Yinan Yu

The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applications, yet most existing methods rely on domain labels that may not be available and often ambiguous. We instead study the DG setting where models must generalize well without access