March 2025 arXiv papers — page 197

Zhewen Xu, Marius L. Palm, William S. Huxter, Konstantin Herb

Scanning magnetometry with nitrogen-vacancy (NV) centers in diamond has led to significant advances in the sensitive imaging of magnetic systems. The spatial resolution of the technique, however, remains limited to tens to hundreds of nanometers, even for probes where NV centers are engineered within 10 nm from the tip apex. Here, we present a correlated inv

Jing Dou, Yingjie Peng, Qiusheng Gu, Luis C. Ho

Understanding the physical mechanisms that drive star formation is crucial for advancing our knowledge of galaxy evolution. We explore the interrelationships between key galaxy properties associated with star formation, with a particular focus on the impact of dark matter halos. Given the sensitivity of atomic hydrogen (HI) to external processes, we concentr

Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang

Offline optimization has recently emerged as an increasingly popular approach to mitigate the prohibitively expensive cost of online experimentation. The key idea is to learn a surrogate of the black-box function that underlines the target experiment using a static (offline) dataset of its previous input-output queries. Such an approach is, however, fraught

Julian Aron Prenner, Romain Robbes

Modern Large Language Models (LLMs) have shown astounding capabilities of code understanding and synthesis. In order to assess such capabilities, several benchmarks have been devised (e.g., HumanEval). However, most benchmarks focus on code synthesis from natural language instructions. Hence, such benchmarks do not test for other forms of code understanding.

Ruizhe Chen, Wenhao Chai, Zhifei Yang, Xiaotian Zhang

Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy-specific value functions and latency during the inference phase. In this paper, we propose a novel approach, Diffusion-styled Preference Optimization (\model), which provides an e

Christos Papalitsas, Yanfei Guan, Shreyas Waghe, Athanasios Liakos

Molecular docking is a critical process for drug discovery and challenging due to the complexity and size of biomolecular systems, where the optimal binding configuration of a drug to a target protein is determined. Hybrid classical-quantum computing techniques offer a novel approach to address these challenges. The Quantum Approximate Optimization Algorithm

Andreas Eberle, Arnaud Guillin, Leo Hahn, Francis Lörler

We propose a general approach for quantitative convergence analysis of non-reversible Markov processes, based on the concept of second-order lifts and a variational approach to hypocoercivity. To this end, we introduce the flow Poincar{\'e} inequality, a space-time Poincar{\'e} inequality along trajectories of the semigroup, and a general divergence lemma ba

Vladyslav M. Kuchkin, Unnar B. Arnalds, Hannes Jónsson, Pavel F. Bessarab

The switching mechanisms in artificial spin ice systems are investigated with focus on shakti and modified shakti lattices. Minimum energy paths are calculated using the geodesic nudged elastic band (GNEB) method implemented with a micromagnetic description of the system, including the internal magnetic structure of the islands and edge modulations. Two swit

Diego Chamorro, María Eugenia Martínez

We study in this article a variation of the Whitham equation which was introduced as an alternative to the KdV equation. We first prove the global existence of weak solutions, then we establish a regularity criterion from which we deduce the uniqueness of weak solutions. Local in time criterions for regularity and uniqueness are also given.

Hui Zhang, Zhiyang Wu, Qianqian Shangguan, Kang An

Monocular visual localization plays a pivotal role in advanced driver assistance systems and autonomous driving by estimating a vehicle's ego-motion from a single pinhole camera. Nevertheless, conventional monocular visual odometry encoun-ters challenges in scale estimation due to the absence of depth information during projection. Previous methodologies, wh

Zesong Zhang, Jianzhong Qi, Xin Cao, Christian S. Jensen

Geo-textual objects, i.e., objects with both spatial and textual attributes, such as points-of-interest or web documents with location tags, are prevalent and fuel a range of location-based services. Existing spatial keyword querying methods that target such data have focused primarily on efficiency and often involve proposals for index structures for effici

Shengjie Liu, Chenyang Yang, Shengqian Han

User scheduling and hybrid precoding in wideband multi-antenna systems have never been learned jointly due to the challenges arising from the massive user combinations on resource blocks (RBs) and the shared analog precoder among RBs. In this paper, we strive to jointly learn the scheduling and precoding policies with graph neural networks (GNNs), which have

Jie He, Bo Peng, Yi Liao, Qun Liu

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from whi

Maciej Krzysztof Zuziak, Roberto Pellungrini, Salvatore Rinzivillo

Federated Learning (FL) is a widespread and well adopted paradigm of decentralized learning that allows training one model from multiple sources without the need to directly transfer data between participating clients. Since its inception in 2015, it has been divided into numerous sub-fields that deal with application-specific issues, be it data heterogeneit

Yu-Kun Zhang, Tariq Aziz, Li-Juan Li, Xue-Ke Song

In this study, we investigate a pair of detectors operating in Minkowski space-time and analyze the characteristics of various quantum resources within this framework. Specifically, we focus on examining the properties of Bell nonlocality, quantum coherence, the nonlocal advantage of quantum coherence (NAQC), and measured uncertainty in relation to the energ

Bin Wu, Wuxuan Shi, Jinqiao Wang, Mang Ye

Pre-trained Vision-Language Models (VLMs) require Continual Learning (CL) to efficiently update their knowledge and adapt to various downstream tasks without retraining from scratch. However, for VLMs, in addition to the loss of knowledge previously learned from downstream tasks, pre-training knowledge is also corrupted during continual fine-tuning. This iss

Kevin Hendrey, David R. Wood

A graph $A$ is "apex" if $A-z$ is planar for some vertex $z\in V(A)$. Eppstein [Algorithmica, 2000] showed that for a minor-closed class $\mathcal{G}$, the graphs in $\mathcal{G}$ with bounded radius have bounded treewidth if and only if some apex graph is not in $\mathcal{G}$. In particular, for every apex graph $A$ and integer $r$, there is a minimum integ

Jean-Marie Malherbe

We present a dataset of high resolution spectra of the Sun and ten bright stars of the domains [6297-6303] and [6361-6367] Angtr{\"o}m. Solar spectra were obtained in the quiet Sun at various distances from disk centre with the ground based Meudon Solar Tower and Themis telescope (12 m{\AA} resolution) and with the Solar Optical Telescope (SOT) onboard the H

Michal Cuadrat-Grzybowski, Joao G. Teixeira da Encarnacao, Pieter N. A. M. Visser

We present the first extensive analysis of K/Ka-band ranging post-fit residuals of an official Level-2 product, characterised as Line-of-Sight Gravity Differences (LGD), which exhibit and showcase interesting sub-monthly geophysical signals. These residuals, provided by CSR, were derived from the difference between spherical harmonic coefficient least-square

Nguyen Dinh, Miguel A. Goberna, Michel Volle

This paper deals with the characterization, in terms of closedness of certain sets regarding other sets, of Farkas lemmas determining when the upperlevel set of a given convex function contains the intersection, say F, of a convex set of a locally convex space X with the inverse image by a continuous linear operator from X to another locally convex space Y o

Paulina Quintanilla, Francisco Fernández, Cristóbal Mancilla, Matías Rojas

This study presents the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin integrates three key components of the closed-loop operation: (1) fuzzy logic for expert control, (2) a state-space model for regulatory control, and (3) a recurrent neural network to simulate the SAG

Keisuke Korogi, Ryoji Tanabe

In evolutionary multi-objective optimization, the indicator-based subset selection problem involves finding a subset of points that maximizes a given quality indicator. Local search is an effective approach for obtaining a high-quality subset in this problem. However, local search requires high computational cost, especially as the size of the point set and

Yi Xiao, Qiangqiang Yuan, Kui Jiang, Wenke Huang

Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain underexplored in remote sensing super-resolution (SR) tasks. I

Ziyi Yang, Fanqi Wan, Longguang Zhong, Canbin Huang

We introduce FuseChat-3.0, a suite of large language models (LLMs) developed by integrating the strengths of heterogeneous source LLMs into more compact target LLMs. Our source models include the powerful Gemma-2-27B-it, Mistral-Large-Instruct-2407, Qwen-2.5-72B-Instruct, and Llama-3.1-70B-Instruct. For target models, we focus on three widely-used smaller va

Paul C. Bressloff

In this paper we consider a random search process with stochastic resetting and a partially accessible target $\calU$. That is, when the searcher finds the target by attaching to its surface $\partial \calU$ it does not have immediate access to the resources within the target interior. After a random waiting time, the searcher either gains access to the reso

Z. Haba

We consider an alternative quantization of the electromagnetic field around the Chern-Simons state $\psi_{CS}$ which is a zero energy solution of quantum electrodynamics. The solution determines a stochastic process which is a random perturbation of the self-duality equation for the electromagnetic potential ${\bf A}$. The stochastic process defines a soluti

Alireza Habibi, Saeed Ghoorchian, Setareh Maghsudi

The complexity of online decision-making under uncertainty stems from the requirement of finding a balance between exploiting known strategies and exploring new possibilities. Naturally, the uncertainty type plays a crucial role in developing decision-making strategies that manage complexity effectively. In this paper, we focus on a specific form of uncertai

Yiheng Ding, Gangnan Yuan, Dewei Zuo, Ting Gao

Derivatives, as a critical class of financial instruments, isolate and trade the price attributes of risk assets such as stocks, commodities, and indices, aiding risk management and enhancing market efficiency. However, traditional hedging models, constrained by assumptions such as continuous trading and zero transaction costs, fail to satisfy risk control r

Charlotte Croucher, Panagiotis Kourtesis, Georgios Papaioannou

Cybersickness remains a significant challenge in virtual reality (VR), limiting its usability across various applications. Existing mitigation strategies focus on optimising VR hardware and/or software and enhancing self-motion perception to minimise sensory conflict. However, anticipatory postural adaptation, a strategy widely studied with regards to motion

Tomas Palenik, Viktor Szitkey

In this paper we present an overview of various kinds of interference, that arise in the Orthogonal Frequency-Domain Multiplexing (OFDM)-based digital communications systems at the physical layer. Inter-symbol, inter-block, inter-carrier interference types are described in detail, valid for any OFDM transmission, along with Inter-cell interference specific t

Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen, Mohammad Abu Alsheikh

Full 3D human pose reconstruction is a critical enabler for extended reality (XR) applications in future sixth generation (6G) networks, supporting immersive interactions in gaming, virtual meetings, and remote collaboration. However, achieving accurate pose reconstruction over wireless networks remains challenging due to channel impairments, bit errors, and

Rui Jiang, Xinghe Fu, Guangcong Zheng, Teng Li

The rapid advancement of pretrained text-driven diffusion models has significantly enriched applications in image generation and editing. However, as the demand for personalized content editing increases, new challenges emerge especially when dealing with arbitrary objects and complex scenes. Existing methods usually mistakes mask as the object shape prior,

Stefano De Paoli, Walter Stan Mathis

This paper reflects on the process of performing Thematic Analysis with Large Language Models (LLMs). Specifically, the paper deals with the problem of analytical saturation of initial codes, as produced by LLMs. Thematic Analysis is a well-established qualitative analysis method composed of interlinked phases. A key phase is the initial coding, where the an

Julian Aron Prenner, Romain Robbes

Deep learning and language models are increasingly dominating automated program repair research. While previous generate-and-validate approaches were able to find and use fix ingredients on a file or even project level, neural language models are limited to the code that fits their input window. In this work we investigate how important identifier ingredient

Anzori Sh. Georgadze

Cherenkov detectors have been extensively developed and utilized in various scientific fields, including particle physics, astrophysics, and nuclear engineering. These detectors operate based on Cherenkov radiation, which is emitted when a charged particle traverses a dielectric medium at a velocity greater than the phase velocity of light in that medium. In

Hengzhi Li, Wanyue Xiao, Tong Fu, Zheng Yang

Optical force can enable precise manipulations of small particles for various applications. It is well known that an isotropic lossless dielectric sphere is only subject to forward optical force under the illumination of an electromagnetic plane wave. By using rigorous full-wave simulations, we show that such a sphere can experience a lateral optical force a

Shuhang Yang, Puguang An, Peng Yang, Xianbin Cao

To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various

Naotaka Kajino, Ryoichiro Noda

We establish a formula for moments of certain random variables involving positive continuous additive functionals (PCAFs) of standard processes which have absolutely continuous transition functions and are in duality with standard processes with absolutely continuous transition functions,generalizing the classical Kac's moment formula. In particular, all

Zhe-hui Ma, Hang-ye Zhang, Chuang Liu, Ming Zhang

Superheated steam is widely employed in various energy systems, particularly in power plants, chemical industries, and other applications where high-temperature and high-pressure steam is essential for efficient energy conversion and process control. In these systems, regulation valves are crucial components that control the flow of steam, adjusting its pres

Guan-Ru Qiao, Bing Bai, Zi-Xuan Weng, Jia-Ying Wu

White Gaussian noise (WGN) is widely used in communication system testing, physical modeling, Monte Carlo simulations, and electronic countermeasures. WGN generation relies heavily on random numbers. In this work, we present an implementation of WGN generation utilizing a quantum entropy source chip for the first time. A photonic integrated chip based on the

Kejia Chen, Jiawen Zhang, Jiacong Hu, Jiazhen Yang

Large Visual Language Models (LVLMs) increasingly rely on preference alignment to ensure reliability, which steers the model behavior via preference fine-tuning on preference data structured as ``image - winner text - loser text'' triplets. However, existing approaches often suffer from limited diversity and high costs associated with human-annotated prefere

Haitao Wu, Qing Li, Changqing Zhang, Zhen He

Can our brain signals faithfully reflect the original visual stimuli, even including high-frequency details? Although human perceptual and cognitive capacities enable us to process and remember visual information, these abilities are constrained by several factors, such as limited attentional resources and the finite capacity of visual memory. When visual st

R. Dengler

The refraction of light by dispersion-free dielectric media can be modeled using well-localized macroscopic wave packets, enabling a description in terms of pseudo-particles. This approach is often used in thought experiments to illustrate aspects of the Abraham-Minkowski debate. This work uses the particle picture to show at an elementary level how differen

Changgang Wang, Wei Liu, Yu Cao, Dong Liang

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-

Xingcan Hu, Wei Wang, Li Xiao

In MRI-based mental disorder diagnosis, most previous studies focus on functional connectivity network (FCN) derived from functional MRI (fMRI). However, the small size of annotated fMRI datasets restricts its wide application. Meanwhile, structural MRIs (sMRIs), such as 3D T1-weighted (T1w) MRI, which are commonly used and readily accessible in clinical set

Zhanhong Jiang, Md Zahid Hasan, Aditya Balu, Joshua R. Waite

Stochastic optimization methods have actively been playing a critical role in modern machine learning algorithms to deliver decent performance. While numerous works have proposed and developed diverse approaches, first-order and second-order methods are in entirely different situations. The former is significantly pivotal and dominating in emerging deep lear

Cyril Voyant, Milan Despotovic, Gilles Notton, Yves-Marie Saint-Drenan

Clearsky models are widely used in solar energy for many applications such as quality control, resource assessment, satellite-base irradiance estimation and forecasting. However, their use in forecasting and nowcasting is associated with a number of challenges. Synchronization errors, reliance on the Clearsky index (ratio of the global horizontal irradiance

Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Ch. Paschalidis

We build on a recently introduced geometric interpretation of Markov Decision Processes (MDPs) to analyze classical MDP-solving algorithms: Value Iteration (VI) and Policy Iteration (PI). First, we develop a geometry-based analytical apparatus, including a transformation that modifies the discount factor $\gamma$, to improve convergence guarantees for these

Angelos Assos, Yuval Dagan, Nived Rajaraman

Online learning algorithms are widely used in strategic multi-agent settings, including repeated auctions, contract design, and pricing competitions, where agents adapt their strategies over time. A key question in such environments is how an optimizing agent can best respond to a learning agent to improve its own long-term outcomes. While prior work has dev

Bin Chen, Yu Zhang, Hongfei Ye, Ziyi Huang

Few-shot multimodal dialogue intention recognition is a critical challenge in the e-commerce domainn. Previous methods have primarily enhanced model classification capabilities through post-training techniques. However, our analysis reveals that training for few-shot multimodal dialogue intention recognition involves two interconnected tasks, leading to a se

Wangdan Liao, Weidong Wang

Background: Small interfering RNA (siRNA) is a promising therapeutic agent due to its ability to silence disease-related genes via RNA interference. While traditional machine learning and early deep learning methods have made progress in predicting siRNA efficacy, there remains significant room for improvement. Advanced deep learning techniques can enhance p

Fuyang Liu, Shun Lu, Jilin Mei, Yu Hu

RGB-Thermal fusion is a potential solution for various weather and light conditions in challenging scenarios. However, plenty of studies focus on designing complex modules to fuse different modalities. With the widespread application of large language models (LLMs), valuable information can be more effectively extracted from natural language. Therefore, we a

Cong Li

We present a quantum-field-theoretical study of bremsstrahlung from an electron propagating in a linearly polarized background photon field. Starting from the photon two-point correlator, the background-modified photon propagator is parameterized via transverse-momentum-dependent photon distributions. Coulomb correction is incorporated through a gauge-link f

Atsushi Simojo, Harumi Haraguchi

SEVENTEEN is a K-pop group with a large number of members 13 in total and the significant physical disparity between the tallest and shortest members among K-pop groups. However, despite their large numbers and physical differences, their dance performances exhibit unparalleled unity in the K-pop industry. According to one theory, their dance synchronization

Umair Saeed, Felip Sandiumenge, Kumara Cordero-Edwards, Jessica Padilla-Pantoja

The fabrication of perovskite oxide free-standing films (membranes) by lift-off methods using water-soluble sacrificial layers is appealing because of the new mechanical degrees of freedom that these membranes present over conventional epitaxial films. However, little is known about how their fabrication process, and in particular the exposure to water durin

Bo Peng, Zhihao Gavin Tang

We revisit the celebrated Ranking algorithm by Karp, Vazirani, and Vazirani (STOC 1990) for online bipartite matching under the random arrival model, that is shown to be $0.696$-competitive for unweighted graphs by Mahdian and Yan (STOC 2011) and $0.662$-competitive for vertex-weighted graphs by Jin and Williamson (WINE 2021). In this work, we explore the li

Quanhao Niu, Bei Yan, Lei Shen, Hao Lin

Photonic crystal fibers (PCFs) that trap and guide light using photonic bandgaps have revolutionized modern optics with enormous scientific innovations and technological applications spanning many disciplines. Recently, inspired by the discovery of topological phases of matter, Dirac-vortex topological PCFs have been theoretically proposed with intriguing to

Boris Sedlak, Andrea Morichetta, Philipp Raith, Víctor Casamayor Pujol

This paper proposes a hierarchical solution to scale streaming services across quality and resource dimensions. Modern scenarios, like smart cities, heavily rely on the continuous processing of IoT data to provide real-time services and meet application targets (Service Level Objectives -- SLOs). While the tendency is to process data at nearby Edge devices,

Logan McDonald

Cardinal characteristics of the continuum represent the boundaries in size between the countable and the continuum with respect to certain properties of sets. They are often defined as the minimum sizes of families of reals that meet some criteria. Taking these families and considering their analogues in the setting of computability theory provides a rich hi

Abhisek Ganguly, Alessandro Gabbana, Vybhav Rao, Sauro Succi

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy bet

Sara Sangalli, Gary Sarwin, Ertunc Erdil, Alessandro Carretta

Forecasting surgical instrument trajectories and predicting the next surgical action recently started to attract attention from the research community. Both these tasks are crucial for automation and assistance in endoscopy surgery. Given the safety-critical nature of these tasks, reliable uncertainty quantification is essential. Conformal prediction is a fa

Yuyan Wu, Yiwen Dong, Sumer Vaid, Gabriella M. Harari

Emotion recognition is critical for various applications such as early detection of mental health disorders and emotion based smart home systems. Previous studies used various sensing methods for emotion recognition, such as wearable sensors, cameras, and microphones. However, these methods have limitations in long term domestic, including intrusiveness and

Benjamin Colmey, Richard Lascar

We state and prove here semiclassical results about the construction of asymptotic solutions by the WKB method for pseudo-differential equations of real principal type. It is a Gevrey version; the smooth $C^\infty$ and the analytic ones may be found in H\"ormander (Hormander1985) and Sj\"ostrand (Sj\"ostrand1982). We present here three versions depending on

R. Patrick Xian, Qiming Cui, Stefan Bauer, Reza Abbasi-Asl

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as grounding for agent knowledge, yet an improper configuration affects the quality of the agent's responses. Here, we assess the agent behavior using distinct date-controlled tools (DCTs) as stress test to measure the knowledge variability of larg

Simone Noja, Steffen Schmidt, Raphael Senghaas

We study the Dirac cohomology of supermodules over basic classical Lie superalgebras, formulated in terms of cubic Dirac operators associated with parabolic subalgebras. Specifically, we establish a super-analog of the Casselman-Osborne theorem for supermodules with an infinitesimal character and use it to show that the Dirac cohomology of highest-weight sup

Sergey Smirnov

Nonequilibrium states produced by electric and thermal voltages ($V$, $V_T$) provide a straightforward insight into underlying degrees of freedom of composite nanostructures and are of particular interest to probe Majorana bound states. Here we explore fluctuations of thermoelectric currents at finite frequencies $\omega$ in a quantum dot coupled to two inte

Sonia Kaushik, Manisha Priyadarsini, Avinash G. Khanderao, Dileep Kumar

Organic spintronics has drawn the interest of the science community due to various applications in spin-valve devices. But to date, an efficient room-temperature Organic Spin Valve device has not been experimentally realized due to the complicated spin transport at the metal-organic interfaces. These studies are always challenging due to the complicated spin

Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi, Ahmed Elbakary

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed t

Sicong Liu, Bin Guo, Shiyan Luo, Yuzhan Wang

There are many deep learning (DL) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.To enable robust and private mobile sensing, DL models are often deployed locally on resource-constrained mobile devices using techniques such as model compression or offloading

Piero Giacomelli

In this paper, we mutuate the concept of Ducci matrices to the $p$-adic setting, generalizing the classical Ducci sequences to the framework of $p$-adic numbers. The classical Ducci operator, which iteratively computes the absolute differences of neighboring elements in a sequence or matrix, is redefined using the $p$-adic absolute value $| \cdot |_p$. We in

Manh Cuong Dao, Phi Le Nguyen, Thao Nguyen Truong, Trong Nghia Hoang

Offline optimization is an important task in numerous material engineering domains where online experimentation to collect data is too expensive and needs to be replaced by an in silico maximization of a surrogate of the black-box function. Although such a surrogate can be learned from offline data, its prediction might not be reliable outside the offline da

Wenhai Lei, Yuankai Yang, Shuo Yang, Ge Zhang

The transition to a sustainable, low-carbon energy future requires transformative advancements in energy and environmental technologies. Carbon capture and sequestration, underground hydrogen storage, and nuclear waste geological disposal will be central aspects of a sustainable energy future, both for mitigating CO2 emissions and providing green energy. A c

Taira Kawamura, Yoji Ohashi

We theoretically investigate nonequilibrium properties of a normal metal-superconductor-normal metal (NSN) junction under an external magnetic field. When a bias voltage is applied between the normal-metal leads, the confined superconductor is driven out of equilibrium, resulting in a nonequilibrium quasiparticle distribution function having a two-step struc

Evgeniy Eremin

In modern world the importance of cybersecurity of various systems is increasing from year to year. The number of information security events generated by information security tools grows up with the development of the IT infrastructure. At the same time, the cyber threat landscape does not remain constant, and monitoring should take into account both alread

Yuri Prokhorov

This paper is a sequel to [arXiv:2403.18389]. We investigate the rationality problem for $\mathbf{Q}$-Fano threefolds of Fano index $\ge 3$.

Hejie Cui, Alyssa Unell, Bowen Chen, Jason Alan Fries

Large language models (LLMs) have emerged as promising tools for assisting in medical tasks, yet processing Electronic Health Records (EHRs) presents unique challenges due to their longitudinal nature. While LLMs' capabilities to perform medical tasks continue to improve, their ability to reason over temporal dependencies across multiple patient visits and t

S. A. Hosseini, I. V. Karlin

Detailed study of spectral properties and of linear stability is presented for a class of lattice Boltzmann models with a non-ideal equation of state. Examples include the van der Waals and the shallow water models. Both analytical and numerical approaches demonstrate that linear stability requires boundedness of propagation speeds of normal eigen-modes. The

Kingman Cheung, C. J. Ouseph, Po-Yan Tseng, Sin Kyu Kang

The detection of gamma-ray signals from primordial black holes (PBHs) could provide compelling evidence for their role as a dark matter candidate, particularly through the observation of their Hawking radiation. Future gamma-ray observatories, such as e-ASTROGAM, and the next-generation telescopes, are poised to explore this possibility by measuring both Sta

Binghui Wu, Dinil Mon Divakaran, Mohan Gurusamy

As modern networks grow increasingly complex--driven by diverse devices, encrypted protocols, and evolving threats--network traffic analysis has become critically important. Existing machine learning models often rely only on a single representation of packets or flows, limiting their ability to capture the contextual relationships essential for robust analy

Qi Xu, Chao Yu, Dajian Cui, Xuan-Yi Zhang

High-frequency sine wave gating (SWG) InGaAs/InP single-photon detectors (SPDs) are widely used for synchronous near-infrared single-photon detection. For practical use, the size of SPD is one of the most concerning features for system integration. Here we present, to the best of our knowledge, the most compact and fully functional high-frequency SWG InGaAs/

Izidor Benedičič, Yuri Tanuma, Žiga Gosar, Bastien Anézo

The nitrogen-vacancy (NV) centers in nanodiamonds can be utilized as low-cost, highly versatile quantum sensors for studying surface properties in condensed matter physics through the application of relaxometry protocols. For such applications, a detailed knowledge of the intrinsic relaxation processes of NV centers in nanodiamonds is necessary. Here, we stu

Zhiqiang Yan, Zhengxue Wang, Haoye Dong, Jun Li

We introduce DuCos, a novel depth super-resolution framework grounded in Lagrangian duality theory, offering a flexible integration of multiple constraints and reconstruction objectives to enhance accuracy and robustness. Our DuCos is the first to significantly improve generalization across diverse scenarios with foundation models as prompts. The prompt desi

Xiaolong Li, Jianhao Wei, Haidong Wang, Li Dong

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between p

Junwoo Ha, Hyunjun Kim, Sangyoon Yu, Haon Park

We introduce a novel framework for consolidating multi-turn adversarial ``jailbreak'' prompts into single-turn queries, significantly reducing the manual overhead required for adversarial testing of large language models (LLMs). While multi-turn human jailbreaks have been shown to yield high attack success rates, they demand considerable human effort and tim

Mara Sherlin D. Talento, Jordan Richards, Marco Pinto-Orellana, Raphael Huser

Coherence analysis plays a vital role in the study of functional brain connectivity. However, coherence captures only linear spectral associations, and thus can produce misleading findings when ignoring variations of connectivity in the tails of the distribution. This limitation becomes important when investigating extreme neural events that are characterize

Michael Klein, Yonatan Gershuni, Alisa Perutski, Jean-Paul Hugonin

Surface-polaritons play a pivotal role in strong light-matter interactions at the nanoscale due to their ability to confine light to deep subwavelength dimensions. A promising class of materials exhibiting such polaritonic response are polar dielectrics, which support surface phonon polaritons (SPhPs). While SPhPs offer significantly lower losses compared to

Yufang Liu, Yao Du, Tao Ji, Jianing Wang

Recent research has increasingly focused on multimodal mathematical reasoning, particularly emphasizing the creation of relevant datasets and benchmarks. Despite this, the role of visual information in reasoning has been underexplored. Our findings show that existing multimodal mathematical models minimally leverage visual information, and model performance

Pradeep Vishwakarma, Manisha Dhillon, Kuldeep Kumar Kataria

We study the composition of bivariate L\'evy process with bivariate inverse subordinator. The explicit expressions for its dispersion and auto correlation matrices are obtained. Also, the time-changed two parameter L\'evy processes with rectangular increments are studied. We introduce some time-changed variants of the Poisson random field in plane with and w

Bodong Zhang, Hamid Manoochehri, Xiwen Li, Beatrice S. Knudsen

Weakly supervised multiple instance learning (MIL) is a challenging task given that only bag-level labels are provided, while each bag typically contains multiple instances. This topic has been extensively studied in histopathological image analysis, where labels are usually available only at the whole slide image (WSI) level, while each WSI could be divided

Yuki Tanaka, Ryuji Hashimoto, Takehiro Takayanagi, Zhe Piao

The generation of synthetic financial data is a critical technology in the financial domain, addressing challenges posed by limited data availability. Traditionally, statistical models have been employed to generate synthetic data. However, these models fail to capture the stylized facts commonly observed in financial data, limiting their practical applicabi

Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui

The emergence of vision-language-action (VLA) models has given rise to foundation models for robot manipulation. Although these models have achieved significant improvements, their generalization in multi-task manipulation remains limited. This study proposes a VLA model-expert collaboration framework that leverages a limited number of expert actions to enha

Ziqiang Cui, Yunpeng Weng, Xing Tang, Xiaokun Zhang

Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive pairs: they either rely on random perturbations that corrupt user preference patterns or depend on sparse collaborative data that generates unreliable contrastive pairs. Furthermore

Li-Xiang Chen, Dong-Hao Guan, Lu Qi, Xiuyun Zhang

Staggered magnetic fluxes (SMF) play a crucial role in achieving Chern insulators (CIs), by which a series of CI models have been established on various lattices. In addition, SMF induced higher-order topological insulator (HOTI) in a lattice model has been reported. In this work, we propose a square lattice model with SMF. We find intracellular SMF can indu

Shuzhi Gong, Richard Sinnott, Jianzhong Qi, Cecile Paris

The widespread dissemination of fake news on social media poses significant risks, necessitating timely and accurate detection. However, existing methods struggle with unseen news due to their reliance on training data from past events and domains, leaving the challenge of detecting novel fake news largely unresolved. To address this, we identify biases in t

Milin Patel, Marzana Khatun, Rolf Jung, Michael Glaß

One of the fundamental tasks of autonomous driving is safe trajectory planning, the task of deciding where the vehicle needs to drive, while avoiding obstacles, obeying safety rules, and respecting the fundamental limits of road. Real-world application of such a method involves consideration of surrounding environment conditions and movements such as Lane Ch

Dariusz Chruściński, Anindita Bera, Joonwoo Bae, Beatrix C. Hiesmayr

Two approaches can be utilized to handle the separability problem, finding out whether a given bipartite qudit state is separable or not: a direct procedure on the state space or the effective tool of entanglement witnesses (EWs). This contribution studies the structure of EWs. Exploiting the very concept of mirrored EWs, increasing the detection power, we s

Yiran Guo, Wei Chen, Bo Ai, Lun Li

As the number of antennas in frequency-division duplex (FDD) multiple-input multiple-output (MIMO) systems increases, acquiring channel state information (CSI) becomes increasingly challenging due to limited spectral resources and feedback overhead. In this paper, we investigate the impact of the feedback channel on CSI feedback in a multi-user MIMO orthogon

Yuan Liao, Yuhong Zhang, Qiushi Han, Yuhang Yang

Humans exhibit a remarkable ability to focus auditory attention in complex acoustic environments, such as cocktail parties. Auditory attention detection (AAD) aims to identify the attended speaker by analyzing brain signals, such as electroencephalography (EEG) data. Existing AAD algorithms often leverage deep learning's powerful nonlinear modeling capabilit

Chi Hang, Ruiqi Deng, Lavender Yao Jiang, Zihao Yang

Clinical measurements such as blood pressures and respiration rates are critical in diagnosing and monitoring patient outcomes. It is an important component of biomedical data, which can be used to train transformer-based language models (LMs) for improving healthcare delivery. It is, however, unclear whether LMs can effectively interpret and use clinical me

Chupeng Liu, Runkai Zhao, Weidong Cai

Weakly supervised monocular 3D detection, while less annotation-intensive, often struggles to capture the global context required for reliable 3D reasoning. Conventional label-efficient methods focus on object-centric features, neglecting contextual semantic relationships that are critical in complex scenes. In this work, we propose a Context-Aware Weak Supe