March 2025 arXiv papers — page 114

Pranay Patil, Owen Benton

Rydberg atom arrays have recently been conjectured to host $Z_2$ quantum spin liquids (QSLs) in certain parameter regimes. Due to the strong interactions between these atoms, it is not possible to analytically study these systems, and one must resort to Monte Carlo sampling of the path integral to reach definite conclusions. We use a tailored update, specifi

Shoichi Koyama, Enzo De Sena, Prasanga Samarasinghe, Mark R. P. Thomas

The study of spatial audio and room acoustics aims to create immersive audio experiences by modeling the physics and psychoacoustics of how sound behaves in space. In the long history of this research area, various key technologies have been developed based both on theoretical advancements and practical innovations. We highlight historical achievements, init

Yunqi Shi, Chengrui Gao, Wanqi Ren, Peng Xie

This work introduces Open3DBench, an open-source 3D-IC backend implementation benchmark built upon the OpenROAD-flow-scripts framework, enabling comprehensive evaluation of power, performance, area, and thermal metrics. Our proposed flow supports modular integration of 3D partitioning, placement, 3D routing, RC extraction, and thermal simulation, aligning wi

Luis A. Escamilla, Özgür Akarsu, Eleonora Di Valentino, Emre Özülker

Recent observational analyses have revealed a significant tension in the growth index $\gamma$, which characterizes the growth rate of cosmic structures. Specifically, when treating $\gamma$ as a free parameter within $\Lambda$CDM framework, a combination of Planck and $ f\sigma_8 $ data yields $\gamma \approx 0.64$, in $\sim4\sigma$ tension with the theoret

Jianzheng Huang, Xianyu Mo, Ziling Liu, Jinyu Yang

Point tracking is becoming a powerful solver for motion estimation and video editing. Compared to classical feature matching, point tracking methods have the key advantage of robustly tracking points under complex camera motion trajectories and over extended periods. However, despite certain improvements in methodologies, current point tracking methods still

C. R. Muniz, M. B. Cruz, R. M. P. Neves, Mushayydha Farooq

In this work, we explore the thermal effects on Casimir wormholes in the context of higher-dimensional Einstein-Gauss-Bonnet gravity. Motivated by the fundamental role of EGB gravity in describing a wide range of gravitational phenomena, we investigate how thermal fluctuations affect the quantum vacuum energy density associated with the Casimir effect and it

Ramon Skane, Philip A. Schneider, William D. A. Rickard, Franca Jones

Geopolymers are an emerging class of binding materials used in sustainable cements, concretes, and composites. However, despite growing research, the lack of standardised processes and stability analyses for formulating activator solutions - a crucial component of geopolymer systems - remains a barrier to quality control and research advancement. This study

Haiyang Guo, Fanhu Zeng, Ziwei Xiang, Fei Zhu

Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of human instructions. However, it is infeasible to collect all possible instruction datasets simultaneously in real-world scenarios. Thus, enabling MLLM with continual instruction t

Max Arnott, Niels Jakob Laustsen

Every closed subspace of each of the Banach spaces $X = \ell_p(\Gamma)$ and $X=c_0(\Gamma)$, where $\Gamma$ is a set and $1<p<\infty$, is the kernel of a bounded operator $X\to X$. On the other hand, whenever $\Gamma$ is an uncountable set, $\ell_1(\Gamma)$ contains a closed subspace that is not the kernel of any bounded operator $\ell_1(\Gamma)\to\ell_1(\Ga

Nicolò De Ponti, Giacomo Enrico Sodini, Luca Tamanini

We show that the Hellinger-Kantorovich distance can be expressed as the metric infimal convolution of the Hellinger and the Wasserstein distances, as conjectured by Liero, Mielke, and Savar\'e. To prove it, we study with the tools of Unbalanced Optimal Transport the so called Marginal Entropy-Transport problem that arises as a single minimization step in the

Magnus Fries, Magnus Goffeng, Ada Masters

We introduce to spectral noncommutative geometry the notion of tangled spectral triple, which encompasses the anisotropies arising in parabolic geometry as well as the parabolic commutator bounds arising in so-called "bad Kasparov products". Tangled spectral triples incorporate anisotropy by replacing the unbounded operator in a spectral triple that mimics a

Jingyi Zhang, Jiaxing Huang, Huanjin Yao, Shunyu Liu

Recent studies generally enhance MLLMs' reasoning capabilities via supervised fine-tuning on high-quality chain-of-thought reasoning data, which often leads models to merely imitate successful reasoning paths without understanding what the wrong reasoning paths are. In this work, we aim to enhance the MLLMs' reasoning ability beyond passively imitating posit

Tong Lei, Qinwen Hu, Ziyao Lin, Andong Li

The prevailing method for neural speech enhancement predominantly utilizes fully-supervised deep learning with simulated pairs of far-field noisy-reverberant speech and clean speech. Nonetheless, these models frequently demonstrate restricted generalizability to mixtures recorded in real-world conditions. To address this issue, this study investigates traini

Guoliang Xu, Jianqin Yin, Ren Zhang, Yonghao Dang

Since COVID-19, crowd-counting tasks have gained wide applications. While supervised methods are reliable, annotation is more challenging in high-density scenes due to small head sizes and severe occlusion, whereas it's simpler in low-density scenes. Interestingly, can we train the model in low-density scenes and generalize it to high-density scenes? Therefo

Wan-ying Li, Nan-jing Huang

Distributed optimization problems have received much attention due to their privacy preservation, parallel computation, less communication, and strong robustness. This paper presents and studies the time-varying distributed optimization problem for a class of stochastic multi-agent systems for the first time. For this, we initially propose a protocol in the

Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra

With app-based interaction increasingly permeating all aspects of daily living, it is essential to ensure that apps are designed to be \emph{inclusive} and are usable by a wider audience such as the elderly, with various impairments (e.g., visual, audio and motor). We propose \names, a system that fosters empathetic design, by allowing app designers, \emph{i

Wei Hung, Shao-Hua Sun, Ping-Chun Hsieh

Action-constrained reinforcement learning (ACRL) is a generic framework for learning control policies with zero action constraint violation, which is required by various safety-critical and resource-constrained applications. The existing ACRL methods can typically achieve favorable constraint satisfaction but at the cost of either high computational burden i

Rui Pu, Chaozhuo Li, Rui Ha, Litian Zhang

Defending large language models (LLMs) against jailbreak attacks is crucial for ensuring their safe deployment. Existing defense strategies typically rely on predefined static criteria to differentiate between harmful and benign prompts. However, such rigid rules fail to accommodate the inherent complexity and dynamic nature of real-world jailbreak attacks.

Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon

Following the introduction of Dynamic Mode Decomposition and its numerous extensions, many neural autoencoder-based implementations of the Koopman operator have recently been proposed. This class of methods appears to be of interest for modeling dynamical systems, either through direct long-term prediction of the evolution of the state or as a powerful embed

Xuying Zhang, Yupeng Zhou, Kai Wang, Yikai Wang

Novel view synthesis (NVS) is a cornerstone for image-to-3d creation. However, existing works still struggle to maintain consistency between the generated views and the input views, especially when there is a significant camera pose difference, leading to poor-quality 3D geometries and textures. We attribute this issue to their treatment of all target views

Aaron R. Rowntree, Fiorenzo Vincenzo, Ankit Singh, Changbom Park

We study the stellar mass-gas metallicity relation (MZR) which shows a significant scatter for a fixed stellar mass. By defining global environments, nodes, filaments, and voids within the Horizon Run 5 cosmological hydrodynamical simulation, we explore when and where the enrichment of galaxies occurs, analysing key evolution parameters such as star-formatio

Huangwei Chen, Yifei Chen, Zhenyu Yan, Mingyang Ding

Neuroblastoma (NB), a leading cause of childhood cancer mortality, exhibits significant histopathological variability, necessitating precise subtyping for accurate prognosis and treatment. Traditional diagnostic methods rely on subjective evaluations that are time-consuming and inconsistent. To address these challenges, we introduce MMLNB, a multi-modal lear

Evangelos Georganas, Dhiraj Kalamkar, Alexander Kozlov, Alexander Heinecke

Speculative decoding (SD) has emerged as a method to accelerate LLM inference without sacrificing any accuracy over the 16-bit model inference. In a typical SD setup, the idea is to use a full-precision, small, fast model as "draft" to generate the next few tokens and use the "target" large model to verify the draft-generated tokens. The efficacy of this met

Cheng Yuan, Zhening Liu, Jiashu Lv, Jiawei Shao

With the rapid development of large multimodal models (LMMs), multimodal understanding applications are emerging. As most LMM inference requests originate from edge devices with limited computational capabilities, the predominant inference pipeline involves directly forwarding the input data to an edge server which handles all computations. However, this app

Daisuke Inoue, Youichi Yamakawa, Seiichiro Onari, Hiroshi Kontani

Unconventional charge and spin density-wave states are commonly observed in bilayer nickelates, drawing considerable attention due to their proximity to high-$T_c$ superconductivity in various phase diagrams. However, the nature and mechanisms of charge and spin density-waves (DWs) in nickelates remain poorly understood. Numerous experiments have reported th

Artur Solomonik, Hendrik Heuer

As people engage with the social media landscape, popular platforms rise and fall. As current research uncovers the experiences people have on various platforms, rarely do we engage with the sociotechnical migration processes when joining and leaving them. In this paper, we asked 32 visitors of a science communication festival to draw out artifacts that we c

Zhiyi Huang, Xiaohan Shan, Jianmin Li

Lifelong Reinforcement Learning (LRL) holds significant potential for addressing sequential tasks, but it still faces considerable challenges. A key difficulty lies in effectively preventing catastrophic forgetting and facilitating knowledge transfer while maintaining reliable decision-making performance across subsequent tasks in dynamic environments. To ta

Midori Amano Patino, Stephane Raymond, Georg Knebel, Pierre Le Berre

We report on the synthesis of single crystals of the kagome compound, UV$_6$Sn$_6$, and present the results of magnetization, electrical resistivity, heat capacity, x-ray, and neutron diffraction experiments to characterize the structure and magnetic properties. UV$_6$Sn$_6$ crystallizes in a large supercell of the HfFe$_6$Ge$_6$ parent structure with an hex

André Markus, Astrid Carolus, Carolin Wienrich

As Artificial Intelligence (AI) becomes more pervasive in various aspects of life, AI literacy is becoming a fundamental competency that enables individuals to move safely and competently in an AI-pervaded world. There is a growing need to measure this competency, e.g., to develop targeted educational interventions. Although several measurement tools already

Yi-Fan Zhang, Xiao-Sheng Ni, Ke Chen, Kun Cao

Altermagnetism has been proposed as a new type of magnetism, simultaneously exhibiting compensated spin moments in real space and spin-split electronic bands in reciprocal space. Alternating chiral magnon splitting is considered a unique feature of altermagnets. In this work, utilizing linear spin wave theory (LSWT), which is based on a localized spin pictur

Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros, Jhony H. Giraldo

Simplicial complexes provide a powerful framework for modeling higher-order interactions in structured data, making them particularly suitable for applications such as trajectory prediction and mesh processing. However, existing simplicial neural networks (SNNs), whether convolutional or attention-based, rely primarily on discrete filtering techniques, which

Pengcheng Wen, Jiaming Ji, Chi-Min Chan, Juntao Dai

Large language models (LLMs) have demonstrated enhanced performance through the \textit{Thinking then Responding} paradigm, where models generate internal thoughts before final responses (aka, System 2 thinking). However, existing research lacks a systematic understanding of the mechanisms underlying how thinking patterns affect performance across model size

Lin-Han Jia, Wen-Chao Hu, Jie-Jing Shao, Lan-Zhe Guo

The current Neuro-Symbolic (NeSy) Learning paradigm suffers from an over-reliance on labeled data, so if we completely disregard labels, it leads to less symbol information, a larger solution space, and more shortcuts-issues that current Nesy systems cannot resolve. This paper introduces a novel learning paradigm, Verification Learning (VL), which addresses

Kaimin Li, Jiahuan Wang, Haixia Cui, Bingpeng Zhou

Low peak-to-average power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) waveform design is a crucial issue in integrated sensing and communications (ISAC). This paper introduces an OFDM-ISAC waveform design that utilizes the entire spectrum simultaneously for both communication and sensing by leveraging a novel degree of freedom (DoF): the f

Huang Chengzhi

For the composite multi-objective optimization problem composed of two nonsmooth terms, a smoothing method is used to overcome the nonsmoothness of the objective function, making the objective function contain at most one nonsmooth term. Then, inspired by the design idea of the aforementioned backtracking strategy, an update rule is proposed by constructing

Zhuoqun Su, Huimin Lu, Shuaifeng Jiao, Junhao Xiao

Multimodal 3D object detectors leverage the strengths of both geometry-aware LiDAR point clouds and semantically rich RGB images to enhance detection performance. However, the inherent heterogeneity between these modalities, including unbalanced convergence and modal misalignment, poses significant challenges. Meanwhile, the large size of the detection-orien

Jakob Möderl, Anders Malte Westerkam, Alexander Venus, Erik Leitinger

We propose an sparse Bayesian learning (SBL)-based method that leverages group sparsity and multiple parameterized dictionaries to detect the relevant dictionary entries and estimate their continuous parameters by combining data from multiple independent sensors. In a MIMO multi-radar setup, we demonstrate its effectiveness in jointly detecting and localizin

Bin Tang, Keqi Pan, Miao Zheng, Ning Zhou

In recent years, predicting Big Five personality traits from multimodal data has received significant attention in artificial intelligence (AI). However, existing computational models often fail to achieve satisfactory performance. Psychological research has shown a strong correlation between pose and personality traits, yet previous research has largely ign

Siang-Yao Ciou, Tien Hsieh, Da-Shin Lee

We study the orbits of a spinning particle in the Reissner-Nordstr\"om black hole exterior through the spin-curvature coupling to leading order in its spin. The dynamics is governed by the Mathisson-Papapetrou equations in the pole-dipole approximation. The equations of motion can be derived and show in particular that in the polar coordinate, the orbits can

Jingyi Yuan, Chenqiang Gao, Pengyu Jie, Xuan Xia

Recently, zero-shot anomaly detection (ZSAD) has emerged as a pivotal paradigm for industrial inspection and medical diagnostics, detecting defects in novel objects without requiring any target-dataset samples during training. Existing CLIP-based ZSAD methods generate anomaly maps by measuring the cosine similarity between visual and textual features. Howeve

Jasem Hamoud, Duaa Abdullah

In this paper, we refer to a asymptotic degree sequence as $\mathscr{D}=(d_1,d_2,\dots,d_n)$. The examination of topological indices on trees gives us a general overview through bounds to find the maximum and minimum bounds which reflect the maximum and minimum number of edges incident to every vertex in the graph, Albertson index known as $\sum_{uv\in E(G)}

Xinyan Jiang, Hang Ye, Yongxin Zhu, Xiaoying Zheng

Large Language Models (LLMs) often generate hallucinations, producing outputs that are contextually inaccurate or factually incorrect. We introduce HICD, a novel method designed to induce hallucinations for contrastive decoding to mitigate hallucinations. Unlike existing contrastive decoding methods, HICD selects attention heads crucial to the model's predic

Taewoo Park, Eunhye Hong, Yo-Seb Jeon, Namyoon Lee

Semantic communications based on deep joint source-channel coding (JSCC) aim to improve communication efficiency by transmitting only task-relevant information. However, ensuring robustness to the stochasticity of communication channels remains a key challenge in learning-based JSCC. In this paper, we propose a novel regularization technique for learning-bas

Christian Puntini

Starting from the governing equations for geophysical flows, by means of a thin-shell approximation and a tangent plane approximation, we derive the equations describing, at leading order, the nonlinear ice-drift flow for regions centered around the North Pole. An exact solution is derived in the material/Lagrangian formalism, describing a superposition of o

Cheng-Gang Qin, Yu-Jie Tan, Xiao-Yu Lu, Tong Liu

Lorentz symmetry is a cornerstone of both the General relativity and Standard Model and its experimental verification deepens our understanding of nature. This paper focuses on the investigation of Lorentz violations with the context of clock comparison experiments in the framework of Standard Model Extension (SME). Considering matter-gravity coupling sector

Yuanbin Qian, Shuhan Ye, Chong Wang, Xiaojie Cai

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high d

Erik Engwall, Victor Mikhalev, Johan Sundström, Otte Marthin

Background: Upright proton therapy with compact delivery systems has the potential to reduce costs for treatments but could also lead to broadening of the beam penumbra due to energy selection close to the patient. Purpose: This study aims at combining upright static proton arcs with additional layers of shoot-through (ST) protons to sharpen the beam penumbr

Pingyu Wu, Daiheng Gao, Jing Tang, Huimin Chen

Retrieval-Augmented Generation (RAG) improves Large Language Models (LLMs) by using external knowledge, but it struggles with precise entity information retrieval. In this paper, we proposed MES-RAG framework, which enhances entity-specific query handling and provides accurate, secure, and consistent responses. MES-RAG introduces proactive security measures

Y. Z. Zhou, X. Han, J. Luo, D. T. Wu

Motivated by recent studies of the cluster Mott insulator candidate compound Nb3Cl8, this study performs 93Nb and 35Cl nuclear magnetic resonance (NMR) measurements to investigate the electron correlations. Below the structural transition temperature Ts ~ 97 K, all satellites of the 93Nb NMR spectra split into three distinct peaks, which suggests symmetry lo

Levin Maier

In this paper, we introduce a nonlinear system of partial differential equations, the magnetic two-component Hunter-Saxton system (M2HS). This system is formulated as a magnetic geodesic equation on an infinite-dimensional Lie group equipped with a right-invariant metric, the $\dot{H}^1$ -metric, which is closely related to the infinite-dimensional Fisher-Ra

A. R. Wildes, B. Fåk, U. B. Hansen, A. Ivanov

Neutron three-axis spectrometry has been used to determine the interplanar magnetic exchange parameter in the magnetic van der Waals compound CoPS$_3$. The exchange is found to be small and antiferromagnetic, estimated to be 0.020 $\pm$ 0.001 meV, which is surprising considering that the magnetic structure is correlated ferromagnetically between the ab plane

Jian Gu, Aldeida Aleti, Chunyang Chen, Hongyu Zhang

Language Models (LMs) are widely used in software engineering for code generation, but they may produce erroneous code. Rather than repairing outputs, a more thorough remedy is to address underlying model failures. LM repair offers a lightweight solution: it requires minimal data, lowers computational cost, and limits side effects. Unlike full retraining, LM

Wen-Juan Liu, Luis C. Ho, Xiao-Bo Dong, Su Yao

We present a sample of 930 intermediate-mass black hole (IMBH) active galactic nuclei (AGNs) with black hole masses of $M_\mathrm{BH} \leqslant 2 \times 10^{6}$ M$_{\odot}$, uniformly selected from the Seventeenth Data Release of the Sloan Digital Sky Survey, based on the detection of broad H$\alpha$ or H$\beta$ emission lines. Taking advantage of the wide w

Haiyang Guo, Fanhu Zeng, Fei Zhu, Wenzhuo Liu

A vast amount of instruction tuning data is crucial for the impressive performance of Large Multimodal Models (LMMs), but the associated computational costs and data collection demands during supervised fine-tuning make it impractical for most researchers. Federated learning (FL) has the potential to leverage all distributed data and training resources to re

Shuaifan Jin, Xiaoyi Pang, Zhibo Wang, He Wang

Recent studies improve on-device language model (LM) inference through end-cloud collaboration, where the end device retrieves useful information from cloud databases to enhance local processing, known as Retrieval-Augmented Generation (RAG). Typically, to retrieve information from the cloud while safeguarding privacy, the end device transforms original data

Martin Hauden, Jacques Millo, Martina Matusko, Francisco S Ponciano Ojeda

We present absolute frequency measurements of a laser stabilized using an offset sideband locking technique on the P(49)24-1 rovibrational transition of $^{127}\mathrm{I}_2$ near 556 nm. The P(49)24-1 transition is offset by 4.8 GHz from the intercombination transition of ${}^{171}\mathrm{Yb}$. A dual-tone electro-optical modulator is employed to bridge this

Anamika Singh, Abhay Kumar Singh, Eitan Yaakobi

Function-correcting codes are an innovative class of codes that are designed to protect a function evaluation of the data against errors or corruptions. Due to its usefulness in machine learning applications and archival data storage, where preserving the integrity of computation is crucial, Lenz et al. recently introduced function-correcting codes for binar

Masanari Kimura

We develop a higher-order asymptotic analysis for the semi-hard triplet loss using the Edgeworth expansion. It is known that this loss function enforces that embeddings of similar samples are close while those of dissimilar samples are separated by a specified margin. By refining the classical central limit theorem, our approach quantifies the impact of the

Pengcheng Zhou, Yinglun Feng, Zhongliang Yang

The widespread adoption of Retrieval-Augmented Generation (RAG) systems in real-world applications has heightened concerns about the confidentiality and integrity of their proprietary knowledge bases. These knowledge bases, which play a critical role in enhancing the generative capabilities of Large Language Models (LLMs), are increasingly vulnerable to brea

Kimikazu Taniguchi

Using the norm operator method, which extends and corrects the conventional boson expansion theories, we investigate two boson mappings of the boson expansion theory, the so-called mapping after truncation and the mapping before truncation. The difference between them stems from the treatment of the phonon excitation modes; those not adopted as boson excitat

Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Si-Ye Han

In real-world applications, it is highly challenging to detect anomalous samples with extremely sparse anomalies, as they are highly similar to and thus easily confused with normal samples. Moreover, the number of anomalous samples is inherently scarce. This results in a dual imbalance Multi-Instance Learning (MIL) problem, manifesting at both the macro and

Hansol Lim, Jee Won Lee, Seung-Bok Choi, Jongseong Brad Choi

This paper presents a Virtual Inertia Skyhook (VISKY) controller for magnetorheological (MR) dampers in semi-active suspensions. The proposed law is derived from a continuous sky-ground damping baseline augmented with acceleration feedback on the sprung and unsprung masses. In the closed-loop equations, these acceleration terms appear as a mass-like virtual

Hao Zheng, Zhiguang Xiao, Ze Pan, Guowei Yang

Rare earth permanent magnets (REPMs) with both localized moments and itinerant conduction bands are not only important for fundamental research but also have significant technological applications. In particular, Sm$_{\rm 2}$Co$_{\rm 17}$ is a prototypical high-temperture REPM, where the Co atoms form a kagome-honeycomb stacked lattice. Here we report synthe

D. P. Lozano, M. Mongillo, B. Raes, Y. Canvel

$\alpha$-Tantalum ($\alpha$-Ta) is an emerging material for superconducting qubit fabrication due to the low microwave loss of its stable native oxide. However, hydrogen absorption during fabrication, particularly when removing the native oxide, can degrade performance by increasing microwave loss. In this work, we demonstrate that hydrogen can enter $\alpha

Siyuan Yao, Yang Guo, Yanyang Yan, Wenqi Ren

Transformer-based trackers have achieved promising success and become the dominant tracking paradigm due to their accuracy and efficiency. Despite the substantial progress, most of the existing approaches tackle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been greatly overlooked, which hampe

K. P. Wang, W. J. Shi, W. Z. Cao, X. T. Yang

PrAlSi, a non-centrosymmetric ferromagnetic Weyl semimetal candidate with a Curie temperature of 17.8K, offers a unique platform for exploring the interplay of symmetry breaking and topological electronic structures. Up to now, the Weyl fermion distribution as well as their evolution across the ferromagnetic to paramagnetic phase transition in PrAlSi has not

Linzhou Li, Yumeng Li, Yanlin Weng, Youyi Zheng

We present Reduced Gaussian Blendshapes Avatar (RGBAvatar), a method for reconstructing photorealistic, animatable head avatars at speeds sufficient for on-the-fly reconstruction. Unlike prior approaches that utilize linear bases from 3D morphable models (3DMM) to model Gaussian blendshapes, our method maps tracked 3DMM parameters into reduced blendshape wei

Dewei Zhou, Mingwei Li, Zongxin Yang, Yi Yang

Image-conditioned generation methods, such as depth- and canny-conditioned approaches, have demonstrated remarkable abilities for precise image synthesis. However, existing models still struggle to accurately control the content of multiple instances (or regions). Even state-of-the-art models like FLUX and 3DIS face challenges, such as attribute leakage betw

Kento Ueda, Atsushi Matsuo

We present a novel approach for improving the design of ansatzes in Quantum Generative Adversarial Networks (qGANs) by leveraging Large Language Models (LLMs). By combining the strengths of LLMs with qGANs, our approach iteratively refines ansatz structures to improve accuracy while reducing circuit depth and the number of parameters. This study paves the wa

Maximilian Kirsch, Jakob Wernicke, Pawan Datta, Christine Preisach

Climate change has increased the vulnerability of forests to insect-related damage, resulting in widespread forest loss in Central Europe and highlighting the need for effective, continuous monitoring systems. Remote sensing based forest health monitoring, oftentimes, relies on supervised machine learning algorithms that require labeled training data. Monito

Cho Hyeonsu, Dooyoung Kim, Youngjoong Ko

There have been attempts to utilize linear probe for detoxification, with existing studies relying on a single toxicity probe vector to reduce toxicity. However, toxicity can be fine-grained into various subcategories, making it difficult to remove certain types of toxicity by using a single toxicity probe vector. To address this limitation, we propose a cat

Nobuhiro Maekawa, Taiju Tanii

In this paper, we explore the natural grand unified theory (GUT) with spontaneous supersymmetry (SUSY) breaking, focusing on the contribution to sfermion and Higgs masses. Natural GUTs solve various problems in SUSY GUTs with the natural assumption that all terms allowed by $SO(10)\times U(1)_A$ symmetry are introduced with $O(1)$ coefficients. %, solve vari

Tianqi Luo, Chuhan Huang, Leixian Shen, Boyan Li

Text-to-Visualization (Text2VIS) enables users to create visualizations from natural language queries, making data insights more accessible. However, Text2VIS faces challenges in interpreting ambiguous queries, as users often express their visualization needs in imprecise language. To address this challenge, we introduce nBench 2.0, a new benchmark designed

Ferenc Orosi, Ferenc Fejes

Application deployment in cloud environment is dominated by Kubernetes-orchestrated microservices. Provides a secure environment, networking, storage, isolation, scheduling, and many other abstractions that can be easily extended to meet our needs. Time-Sensitive Applications (TSAs) have special requirements for compute and network. Deploying TSAs in Kuberne

Peijin Yu, Shin'ichi Konomi

In the field of group recommendation systems (GRS), effectively addressing the diverse preferences of group members poses a significant challenge. Traditional GRS approaches often aggregate individual preferences into a collective group preference to generate recommendations, which may overlook the intricate interactions between group members. We introduce a

Di Meng, Tianhao Zhao, Chaoyu Xue, Jun Wu

Multi-robot autonomous exploration in an unknown environment is an important application in robotics.Traditional exploration methods only use information around frontier points or viewpoints, ignoring spatial information of unknown areas. Moreover, finding the exact optimal solution for multi-robot task allocation is NP-hard, resulting in significant computa

Evelyn J. Mannix, Bartholomew A. Woodham

Biofouling$\unicode{x2013}$communities of organisms that grow on hard surfaces immersed in water$\unicode{x2013}$provides a pathway for the spread of invasive marine species and diseases. To address this risk, international vessels are increasingly being obligated to provide evidence of their biofouling management practices. Verification that these activitie

Xiaojun Jia, Sensen Gao, Simeng Qin, Ke Ma

Large pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive generalization but remain highly vulnerable to adversarial examples (AEs). Previous work has explored robust text prompts through adversarial training, achieving some improvement in both robustness and generalization. However, they primarily rely on singlegradient direction

Dehai Zhao, Zhenchang Xing, Qinghua Lu, Xiwei Xu

UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering use

Yunhui Qin, Yaru Fu, Haijun Zhang

This letter explores how pinching antennas, an advanced flexible-antenna system, can enhance the performance of integrated sensing and communication (ISAC) systems by leveraging their adaptability, cost-effectiveness, and ability to facilitate line-of-sight transmission. To achieve this, a joint antenna positioning and transmit power optimization problem is

Joseph Sifakis, Dongming Li, Hairong Huang, Yong Zhang

The vision of autonomous systems is becoming increasingly important in many application areas, where the aim is to replace humans with agents. These include autonomous vehicles and other agents' applications in business processes and problem-solving. For networks, the increasing scale and operation and management (O&M) complexity drive the need for autonomou

Guedong Park, Jinzhao Sun, Hyunseok Jeong

Graph and hypergraph states are important resource states for realizing universal quantum computation and diverse non-local physical phenomena. However, noise learning in such states is challenging due to their large entanglement and magic. This work establishes a low Clifford hierarchy circuit-based scheme for tailoring and learning noise in (hyper)graph st

Florian Vigneau, Sourav Majumder, Aniket Rath, Pedro Parrado-Rodríguez

Achieving industrial quantum advantage is unlikely without the use of quantum error correction (QEC). Other QEC codes beyond surface code are being experimentally studied, such as color codes and quantum Low-Density Parity Check (qLDPC) codes, that could benefit from new quantum processing unit (QPU) architectures. We introduce the six-qubit star lattice arc

Wenfeng Liu, Tomer A. Sigalov, Corentin Coulais, Yair Shokef

Metamaterials are a promising platform for a range of applications, from shock absorption to mechanical computing. These functionalities typically rely on floppy modes or mechanically frustrated loops, both of which are difficult to design. In particular, how to design multiple modes or loops with target deformations remains an open problem. We introduce a c

Chenyu Zhang, Kunlun Xu, Zichen Liu, Yuxin Peng

Vision-language models (VLMs) encounter considerable challenges when adapting to domain shifts stemming from changes in data distribution. Test-time adaptation (TTA) has emerged as a promising approach to enhance VLM performance under such conditions. In practice, test data often arrives in batches, leading to increasing interest in the transductive TTA sett

Yi-Fang Ren, Yusuf Turek

We investigated precision measurements in a two-level system coupled to a single-photon-added coherent state (SPACS) under postselection measurement. We analyzed strategies for improving measurement precision, including parameter estimation and the signal-to-noise ratio (SNR) in postselected weak measurements using the photon statistics of SPACS as the meter

Donglai Ma, Xiaoyu Cao, Bo Zeng, Chen Chen

This paper studies the robust co-planning of proactive network hardening and mobile hydrogen energy resources (MHERs) scheduling, which is to enhance the resilience of power distribution network (PDN) against the disastrous events. A decision-dependent robust optimization model is formulated with min-max resilience constraint and discrete recourse structure,

B. L. S. Prakasa Rao

We study the properties of a stochastic heat equation with a generalized mixed fractional Brownian noise. We obtain the covariance structure, stationarity and obtain bounds for the asymptotic behaviour of the solution. We suggest estimators for the unknown parameters based on discrete time observations and study their asymptotic properties.

Yu-Ting Zhan, He-bi Yang, Cheng-Yuan Ho, Jui-Chiu Chiang

3D Gaussian Splatting (3DGS) has shown immense potential for novel view synthesis. However, achieving rate-distortion-optimized compression of 3DGS representations for transmission and/or storage applications remains a challenge. CAT-3DGS introduces a context-adaptive triplane hyperprior for end-to-end optimized compression, delivering state-of-the-art codin

Zhi-Hong Sun

Let $p>3$ be a prime, $a_1,a_2,a_3\in\Bbb Z$ and let $N_p(x^3+a_1x^2+a_2x+a_3)$ denote the number of solutions to the congruence $x^3+a_1x^2+a_2x+a_3\equiv 0\pmod p$. In this paper, we give an explicit criterion for $N_p(x^3+a_1x^2+a_2x+a_3)=3$ via binary quadratic forms.

Feng Liu

Given a graph $H$, a graph $G$ is $H$-free if $G$ does not contain $H$ as an induced subgraph. Shi and Shan conjectured that every $1$-tough $2k$-connected $(P_2 \cup kP_1)$-free graph is hamiltonian for $k \geq 4$. This conjecture has been independently confirmed by Xu, Li, and Zhou, as well as by Ota and Sanka. Inspired by this, we prove that every $2k$-co

Duke Nguyen, Aditya Joshi, Flora Salim

Test-time domain adaptation (TTDA) is an excellent method which helps generalize models across domains, tasks, and distributions without the use of labeled datasets. Thus, TTDA is very useful in natural language processing (NLP) in the dialectal setting, since oftentimes, models are trained on Standard American English (SAE), evaluated on Indian English (Ind

Hongwei Xi, Lister Staveley-Smith, Bo Peng, Bi-Qing For

We present the ultraviolet, optical and infrared counterparts of 128 galaxies detected in neutral hydrogen (HI) in the FAST Ultra-Deep Survey (FUDS) field 0 (FUDS0). HI mass upper limits are also calculated for 134 non-detections in the field. Stellar masses ($M_*$), star formation rates (SFRs) and star formation histories are computed by fitting spectral en

Xian-Rong Zhang, Yue-Jiao Gong, Zhiguang Cao, Jun Zhang

In recent years, there has been a growing interest in data-driven evolutionary algorithms (DDEAs) employing surrogate models to approximate the objective functions with limited data. However, current DDEAs are primarily designed for lower-dimensional problems and their performance drops significantly when applied to large-scale optimization problems (LSOPs).

Zhaodong Wu, Qiaochu Zhao, Ming Hu, Yulong Li

With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and treatment of patients. Although deep-learning methods have notably been developed to assist radiologists in diagnosing abdominal diseases, existing models have the restricted ability

Yujie Lu, Yale Song, William Wang, Lorenzo Torresani

We investigate complex video question answering via chain-of-evidence reasoning -- identifying sequences of temporal spans from multiple relevant parts of the video, together with visual evidence within them. Existing models struggle with multi-step reasoning as they uniformly sample a fixed number of frames, which can miss critical evidence distributed nonu

Songjun Tu, Jiahao Lin, Xiangyu Tian, Qichao Zhang

Recent advancements in post-training methodologies for large language models (LLMs) have highlighted reinforcement learning (RL) as a critical component for enhancing reasoning. However, the substantial computational costs associated with RL-based approaches have led to growing interest in alternative paradigms, such as Direct Preference Optimization (DPO).

Yanlin Xiang, Qingyuan He, Ting Xu, Ran Hao

This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness. Aiming at the complex anatomical structure of spinal images, this paper introduces a multi-scale fusion mechanism to enhance the feature extraction capability by using information of different scales, thereby im

Rohit Kumar Srivastav, Mrityunjay Kundu

We propose a method for the laser-excitation of terahertz surface magnetoplasmons via the linear mode conversion of terahertz radiation on a graphene sheet deposited on an n-type semiconductor in presence of an external magnetic field parallel to the semiconductor surface. An obliquely incident p-polarized laser beam interacting with the graphene n-InSb semi

Aditi Tiwari, Klara Nahrstedt

Effective training and debriefing are critical in high-stakes, mission-critical environments such as disaster response, military simulations, and industrial safety, where precision and minimizing errors are paramount. The traditional post-training analysis relies on manually reviewing 2D videos, a time-consuming process that lacks comprehensive situational a