March 2025 arXiv papers — page 129

Jonna Koponen, Antoine Gerardin, Harvey B. Meyer, Konstantin Ottnad

We present the status of the Mainz group's lattice QCD calculation of the pion transition form factor $\mathcal{F}_{\pi^0\gamma^\ast\gamma^\ast}$, which describes the interaction of an on-shell pion with two off-shell photons. This form factor is the main ingredient in the calculation of the pion-pole contribution to hadronic light-by-light scattering in the

Naoufal El Bekri, Lucas Drumetz, Franck Vermet

Solving the Fokker-Planck equation for high-dimensional complex dynamical systems remains a pivotal yet challenging task due to the intractability of analytical solutions and the limitations of traditional numerical methods. In this work, we present FlowKac, a novel approach that reformulates the Fokker-Planck equation using the Feynman-Kac formula, allowing

David Gu, Peter Belcak, Roger Wattenhofer

We challenge the prevailing assumption that LLMs must rely fully on sub-word tokens for high-quality text generation. To this end, we propose the "Generative Pretrained Thoughtformer" (GPTHF), a hierarchical transformer language model capable of text generation by compressing text into sentence embeddings and employing a sentence attention mechanism. GPTHF r

Chen Xu, Andreas Haller, Suraj Hegde, Tobias Meng

We study the dynamics of electrons in crystalline solids in the presence of inhomogeneous external electric and magnetic fields. We present a manifestly gauge-invariant operator-based approach without relying on a semiclassical wavepacket construction, and derive the field-induced corrections to the equations of motion at the operator level. This includes th

Trung Chau, Kanoy Kumar Das, Aryaman Maithani

A monomial ideal $I$ is said to have homological linear quotients if for each $k\geq 0$, the homological shift ideal $\mathrm{HS}_k(I)$ has linear quotients. It is a well-known fact that if an edge ideal $I(G)$ has homological linear quotients, then $G$ is co-chordal. We construct a family of co-chordal graphs $\{\mathrm{H}_n^c\}_{n\geq 6}$ and propose a con

Hongxiang Zhao, Xingchen Liu, Mutian Xu, Yiming Hao

We address key limitations in existing datasets and models for task-oriented hand-object interaction video generation, a critical approach of generating video demonstrations for robotic imitation learning. Current datasets, such as Ego4D, often suffer from inconsistent view perspectives and misaligned interactions, leading to reduced video quality and limiti

Alois Schiessl

In our previous publication we have shown a method for calculating series of even powers of $\pi$ based on the product representation of the $sinc$ function. We refer the readers to [1] for more details. In this work we apply the method to the product representation of the $cosine$ function and and thereby derive nice series formulas for even powers of the n

Zhengguang Liu, Nan Zheng, Xiaoli Li

In this paper, we develop a novel staggered mesh (SM) approach for general nonlinear dissipative systems with arbitrary energy distributions (including cases with known or unknown energy lower bounds). Based on this framework, we propose several second-order semi-discrete schemes that maintain linearity, computational decoupling, and unconditional energy sta

Shida Xu, Kaicheng Zhang, Sen Wang

Underwater environments pose significant challenges for visual Simultaneous Localization and Mapping (SLAM) systems due to limited visibility, inadequate illumination, and sporadic loss of structural features in images. Addressing these challenges, this paper introduces a novel, tightly-coupled Acoustic-Visual-Inertial SLAM approach, termed AQUA-SLAM, to fus

Seyed Mahmoud Sajjadi Mohammadabadi

This paper explores the critical transition from Generative Artificial Intelligence (GenAI) to Innovative Artificial Intelligence (InAI). While recent advancements in GenAI have enabled systems to produce high-quality content across various domains, these models often lack the capacity for true innovation. In this context, innovation is defined as the abilit

Juan-David Lizarazo-Ferro, Tharnier O. Puel, Michael E. Flatté, Rashid Zia

The historical calculation of spectroscopic properties for trivalent lanthanide ions is a complex multistep process that has been prone to inaccuracies. In this work, we revise the parametric semi-empirical Hamiltonian and address long-standing discrepancies in the literature. We also resurface the distinctions between orthogonal and non-orthogonal operators

Onur Beker, Nico Gürtler, Ji Shi, A. René Geist

Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. A major contributor to the success of such methods is their robustness in the face of non-smooth and discontinuous optimization landscapes that are characteristic of contact interactions, yet zeroth-order methods remain computati

Oliver Baker, Carl P. Dettmann

We use a multivariate central limit theorem (CLT) to study the distribution of random geometric graphs (RGGs) on the cube and torus in the high-dimensional limit with general node distributions. We find that the distribution of RGGs on the torus converges to the Erd\H os-R\'enyi (ER) ensemble when the nodes are uniformly distributed, but that the distributio

Sribalaji C. Anand, Kamil Hassan, Henrik Sandberg

This paper considers the problem of detector tuning against false data injection attacks. In particular, we consider an adversary injecting false sensor data to maximize the state deviation of the plant, referred to as impact, whilst being stealthy. To minimize the impact of stealthy attacks, inspired by moving target defense, the operator randomly switches

Quinlan Lee

A novel approach to Forecast Error Variance Decompositions (FEVD) in nonlinear Structural Vector Autoregressive models with Gaussian innovations is proposed, called the Hermite FEVD (HFEVD). This method employs a Hermite polynomial expansion to approximate the future trajectory of a nonlinear process. The orthogonality of Hermite polynomials under the Gaussi

Grażyna Stasińska

We explore the hypothesis that the weak emission lines observed in some early-type galaxies (ETGs) are due to ionization by hot low-mass evolved stars (HOLMES) and analyze the pros and cons.

Chen Shu, Mengke Li, Yiqun Zhang, Yang Lu

In real-world datasets, the challenges of long-tailed distributions and noisy labels often coexist, posing obstacles to the model training and performance. Existing studies on long-tailed noisy label learning (LTNLL) typically assume that the generation of noisy labels is independent of the long-tailed distribution, which may not be true from a practical per

Niels T. Pronk, Bowy M. La Rivière, Natalia Chepiga

We report a phase diagram of the antiferromagnetic spin-1 chain with nearest-neighbor Heisenberg and three-site interactions in the presence of single-ion anisotropy. We show that the Gaussian and Ising transitions that separate the topological Haldane phase from the two anisotropic phases eventually fuse into a higher symmetry point characterized by the Wes

Shiyuan Yang, Zheng Gu, Liang Hou, Xin Tao

Video inpainting involves modifying local regions within a video, ensuring spatial and temporal consistency. Most existing methods focus primarily on scene completion (i.e., filling missing regions) and lack the capability to insert new objects into a scene in a controllable manner. Fortunately, recent advancements in text-to-video (T2V) diffusion models pav

Xu Liu, Taha Aksu, Juncheng Liu, Qingsong Wen

Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs), enabling generalized learning and integrating contextual information. However, their success depends on large, diverse,

Zunbo Yu, Miaomiao Wei, Huatang Tan

In this paper, we propose a driven-dissipative scheme for generating non-Gaussian mechanical entangled states and remotely preparing mechanical Schr\"{o}dinger cat states via the entanglement. The system under study consists of a cavity optomechanical setup with two frequency-mismatched mechanical oscillators coupled to a cavity field driven by a bichromatic

Shuaifeng Jiao, Zhiwen Zeng, Zhuoqun Su, Xieyuanli Chen

As lunar exploration missions grow increasingly complex, ensuring safe and autonomous rover-based surface exploration has become one of the key challenges in lunar exploration tasks. In this work, we have developed a lunar surface simulation system called the Lunar Exploration Simulator System (LESS) and the LunarSeg dataset, which provides RGB-D data for lu

Xin Zhong, Weiwei Ling, Kejia Pan, Pinxia Wu

Traditional three-dimensional magnetotelluric (MT) numerical forward modeling methods, such as the finite element method (FEM) and finite volume method (FVM), suffer from high computational costs and low efficiency due to limitations in mesh refinement and computational resources. We propose a novel neural network architecture named MTAGU-Net, which integrat

Amol Aggarwal

In this paper we consider the Toda lattice $(\boldsymbol{p}(t); \boldsymbol{q}(t))$ at thermal equilibrium, meaning that its variables $(p_i)$ and $(e^{q_i-q_{i+1}})$ are independent Gaussian and Gamma random variables, respectively. This model can be thought of a dense collection of many ``quasiparticles'' that act as solitons. We establish a law of large n

Nick Loris, Philip Rossetti, Chung-Yi See, Ashley Nunes

The United States has long pursued regulations that aim to reduce fossil fuel use. However, while potential emission reduction motivates the introduction and enforcement of these regulations, realization of this potential does not obfuscate the need for prudent economic policy. Scrutinizing nearly two decades of climate regulations in the transportation, ind

H. Babujian, M. Karowski, A. Sedrakyan

We investigate structure functions in deep inelastic scattering processes (DIS) at Bj\"{o}rken limit and found that they are factorized into the longitudinal and transversal parts. We see that the longitudinal part can be linked to exact form factors calculated earlier in 1+1 dimensional integrable quantum field theories, such as sine-Gordon model. We extrac

Zheng Ma, Karim-Alexandros Kantre, Huan Tan, Maciej O. Liedke

Voltage control of magnetism via magneto-ionics, where ion transport and/or redox processes drive magnetic modulation, holds great promise for next-generation memories and computing. This stems from its non-volatility and ability to precisely tune both the magnitude and speed of magnetic properties in an energy-efficient manner. However, expanding magneto-io

Jonas Thietke, Andreas Müller, Denis Lukovnikov, Asja Fischer

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One line of approaches building on Gaussian Shading relies on cryptographic primitives to steer the sampling process of the latent noise. However, we identify several issues in the usage

Cristina Improta, Rosalia Tufano, Pietro Liguori, Domenico Cotroneo

Deep Learning-based code generators have seen significant advancements in recent years. Tools such as GitHub Copilot are used by thousands of developers with the main promise of a boost in productivity. However, researchers have recently questioned their impact on code quality showing, for example, that code generated by DL-based tools may be affected by sec

Yasutaka Koga, Nobuyuki Asaka, Masashi Kimura, Kazumasa Okabayashi

A black hole illuminated by a background light source is observed as a black hole shadow. For a black hole formed by gravitational collapse of a transmissive object, redshift of light due to the spacetime dynamics is expected to play a crucial role in the shadow formation. In this paper, we investigate the redshift of light caused by the spacetime dynamics.

Tin Stribor Sohn, Philipp Reis, Maximilian Dillitzer, Johannes Bach

Multimodal large language models (MLLMs) hold the potential to enhance autonomous driving by combining domain-independent world knowledge with context-specific language guidance. Their integration into autonomous driving systems shows promising results in isolated proof-of-concept applications, while their performance is evaluated on selective singular aspec

Jin-Tao Pan, Yun-Kun Wu, Ling-Ling Ma, Ning Wang

Quantum nonreciprocity-a fundamental phenomenon enabling directional control of quantum states and photon correlations-has long been recognized as pivotal for quantum technologies. However, the experimental realization of nonreciprocal quantum photon-pair generation, as a critical prerequisite for advancing quantum systems, continues to be an outstanding cha

Jone Ugarte Valdivielso, Jose I. Aizpurua, Manex Barrenetxea, Brian G. Stewart

Transformers are essential components for the reliable operation of power grids. The transformer core is constituted by a ferromagnetic material, and accordingly, depending on the magnetization state, the energization of the transformer can lead to high magnetizing inrush currents. Such high amplitudes shorten the life expectancy of a transformer and cause p

Erik Burman, Alexandre Ern, Romain Mottier

In this work, we study the design and analysis of a novel hybrid high-order (HHO) method on unfitted meshes. HHO methods rely on a pair of unknowns, combining polynomials attached to the mesh faces and the mesh cells. In the unfitted framework, the interface can cut through the mesh cells in a very general fashion, and the polynomial unknowns are doubled in

Hossein Vahid, Jens-Uwe Sommer, Abhinav Sharma

Tangentially driven active polymers (TDAPs), model systems for motor-driven filaments, have been extensively studied in uniform activity fields. Here, we show that an activity gradient breaks fore-aft symmetry, generating net body forces that steer dimers, asters, and larger assemblies toward high-activity regions. Including temporal stochasticity softens th

Pavan, Olaf Kaczmarek, Guy D. Moore, Christian Schmidt

The shear viscosity of the quark-gluon plasma (QGP) plays a crucial role in interpreting current measurements from heavy-ion collisions and is a key input to hydro-dynamical models. The interest in shear viscosity also lies in the fact that QGP is the most ideal fluid ever observed and has the shear viscosity to entropy ratio ($\eta / s$) close to the theore

Francesco d'Amore

Quantum advantage is well-established in centralized computing, where quantum algorithms can solve certain problems exponentially faster than classical ones. In the distributed setting, significant progress has been made in bandwidth-limited networks, where quantum distributed networks have shown computational advantages over classical counterparts. However,

Brian Kintu

In this follow-up paper, we again inspect a surprising connection between the set of fixed points of a polynomial map $\varphi_{d,c}$ defined by $\varphi_{d,c}(z) = z^d + c$ for all $c, z \in \mathcal{O}_{K}$ and the coefficient $c$, where $K$ is any number field of degree $n > 1$ and $d > 2$ is an integer. As before, we wish to study counting problems which

David Gastager, Ghazal Ghazaei, Constantin Patsch

Automated surgical workflow analysis is crucial for education, research, and clinical decision-making, but the lack of annotated datasets hinders the development of accurate and comprehensive workflow analysis solutions. We introduce a novel approach for addressing the sparsity and heterogeneity of annotated training data inspired by the human learning proce

Antoine de Saint Germain, Jiang-Hua Lu

Inspired by a recent work of Y. Mizuno, we show that the DT transformations on a cluster ensemble of finite type admit unique totally positive fixed points, and that the exponents of the linearizations of the DT transformations at the fixed points are precisely the degrees of the Weyl group of the corresponding finite root system.

Jonathan Ansari, Sebastian Fuchs

While measures of concordance -- such as Spearman's rho, Kendall's tau, and Blomqvist's beta -- are continuous with respect to weak convergence, Chatterjee's rank correlation xi recently introduced in Azadkia and Chatterjee (2021) does not share this property, causing drawbacks in statistical inference as pointed out in B\"ucher and Dette (2025). As we study

Lukas Kroiß, Johannes Reschke

Fake News and especially deepfakes (generated, non-real image or video content) have become a serious topic over the last years. With the emergence of machine learning algorithms it is now easier than ever before to generate such fake content, even for private persons. This issue of generated fake images is especially critical in the context of politics and

Marco Zamponi, Emilio Incerto, Daniele Masti, Mirco Tribastone

In fields such as autonomous and safety-critical systems, online optimization plays a crucial role in control and decision-making processes, often requiring the integration of continuous and discrete variables. These tasks are frequently modeled as mixed-integer programming (MIP) problems, where feedback data are incorporated as parameters. However, solving

Wenbo Yan, Shurui Wang, Ying Tan

Mamba has demonstrated excellent performance in various time series forecasting tasks due to its superior selection mechanism. Nevertheless, conventional Mamba-based models encounter significant challenges in accurately predicting stock time series, as they fail to adequately capture both the overarching market dynamics and the intricate interdependencies am

Jenny Power, Tristan Pryer

PDE-constrained optimal control problems require regularisation to ensure well-posedness, introducing small perturbations that make the solutions challenging to approximate accurately. We propose a finite element approach that couples both regularisation and discretisation adaptivity, varying both the regularisation parameter and mesh-size locally based on r

João Vitor M. Muniz, Néstor Ortiz, Raissa F. P. Mendes

In certain modified gravity theories that include additional scalar degrees of freedom, compact objects such as black holes and neutron stars may undergo a process known as spontaneous scalarization, in which the scalar field is suddenly activated beyond a certain critical point. Since its discovery, it has been clear that this effect can be understood in ma

Jiyeong Kim, Stephen P. Ma, Michael L. Chen, Isaac R. Galatzer-Levy

Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance of large language models (LLMs) in detecting these symptoms from secure patient messages. We applied multiple approaches, including engineered prompts, systemic persona, temperature adjustments, and zero-shot and

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $e^+e^-$ annihilation data corresponding to a total integrated luminosity of 7.33 $\rm fb^{-1}$ collected at center-of-mass energies between 4.128 and 4.226~GeV with the BESIII detector, we provide the first amplitude analysis and absolute branching fraction measurement of the hadronic decay $D_{s}^{+} \to K_{S}^{0}K_{L}^{0}\pi^{+}$. The branching frac

Steven Khang Truong, Fan Yang, Jun Yin

We study a general class of random block Schr\"odinger operators (RBSOs) in dimensions 1 and 2, which naturally extend the Anderson model by replacing the random potential with a random block potential. Specifically, we focus on two RBSOs -- the block Anderson and Wegner orbital models -- defined on the $d$-dimensional torus $(\mathbb Z/L\mathbb Z)^d$. They

Yuxiang Zhou, Hainiu Xu, Desmond C. Ong, Maria Liakata

As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tasks, models are now expected to perform well on user-centric measurements involving confidence and human (dis)agreement-factors that reflect subjective preferences. While modeling s

Z. F. Li, J. H. Guo, X. Cheng, M. D. Ding

Spiral jets are impulsive plasma ejections that typically show an apparent rotation motion. Their generation, however, is still nont understood thoroughly. Based on a high-resolution vector magnetogram form the Polarimetric and Helioseismic Imager onboard Solar Orbiter, we constrcut a data-constrained three-dimensional (3D) MHD model, aiming to disclose the

A. Kobak, A. Bartkiewicz, K. L. J. Rygl, A. M. S. Richards

Astrophysical masers are widely used in star formation studies. In particular, they are valuable in investigations of high-mass star-forming regions that are difficult to observe at optical frequencies. We used multi-transition data to derive physical conditions in the immediate environment of forming high-mass stars. Simultaneous observations of two maser t

Pierre Gaspard

The stress tensor is calculated for dilute active suspensions composed of colloidal Janus particles propelled by self-diffusiophoresis and powered by a chemical reaction. The Janus particles are assumed to be spherical and made of catalytic and non-catalytic hemispheres. The chemical reaction taking place on the catalytic part of each Janus particle generate

Annika Tjuka, Robert Forkel, Christoph Rzymski, Johann-Mattis List

Lexical resources are crucial for cross-linguistic analysis and can provide new insights into computational models for natural language learning. Here, we present an advanced database for comparative studies of words with multiple meanings, a phenomenon known as colexification. The new version includes improvements in the handling, selection and presentation

Panggih Kusuma Ningrum, Philipp Mayr, Nina Smirnova, Iana Atanassova

UnScientify, a system designed to detect scientific uncertainty in scholarly full text. The system utilizes a weakly supervised technique to identify verbally expressed uncertainty in scientific texts and their authorial references. The core methodology of UnScientify is based on a multi-faceted pipeline that integrates span pattern matching, complex sentenc

Yixiao Sun, Haitian Xie, Yuhang Zhang

Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment

Harry W. T. Morgan, James E. S. Terhune, Albert Bao, Ricky Elwell

The $^{229}Th$ isotope has a laser-accessible nuclear transition allowing the construction of a solid-state nuclear clock. Solid-state $^{229}Th$ nuclear clock development has focused on $^{229}Th$-doped $CaF_2$ and other metal fluorides as the solid material. However, the accuracy of any fluoride-based clock will be reduced by nuclear magnetic dipole coupli

Tobias Morocutti, Florian Schmid, Jonathan Greif, Francesco Foscarin

We target the problem of developing new low-complexity networks for the sound event detection task. Our goal is to meticulously analyze the performance-complexity trade-off, aiming to be competitive with the large state-of-the-art models, at a fraction of the computational requirements. We find that low-complexity convolutional models previously proposed for

Ziyue Wang, Chenghao Shi, Neng Wang, Qinghua Yu

Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achieving remarkable success. Recently, the emergence of end-to-end localization approaches has offered distinct advantages, including a streamlined system architecture and the elimination of the need to store extensive

Zengyu Wan, Wei Zhai, Yang Cao, Zhengjun Zha

Visual 3D motion estimation aims to infer the motion of 2D pixels in 3D space based on visual cues. The key challenge arises from depth variation induced spatio-temporal motion inconsistencies, disrupting the assumptions of local spatial or temporal motion smoothness in previous motion estimation frameworks. In contrast, event cameras offer new possibilities

Dario Dennstädt

We address the problem of output reference tracking for unknown non-linear multi-input, multi-output systems described by functional differential equations. This class of systems includes those with a strict relative degree, and bounded-input bounded-output (BIBO) stable internal dynamics. The objective is to ensure that the tracking error evolves within a p

Liangliang Deng, Arnaud Ducrot, Quentin Griette

In this paper we study the invasion fronts of spatially periodic monotone reaction-diffusion systems in a multi-dimensional setting. We study the pulsating traveling waves that connect the trivial equilibrium, for which all components of the state variable are identically equal to zero, to a uniformly persistent stationary state, for which all components are

Xiaokang Wei, Bowen Zhang, Xianghui Yang, Yuxuan Wang

Generating high-quality physically based rendering (PBR) materials is important to achieve realistic rendering in the downstream tasks, yet it remains challenging due to the intertwined effects of materials and lighting. While existing methods have made breakthroughs by incorporating material decomposition in the 3D generation pipeline, they tend to bake hig

Dimitrios Efstratiou, Leandros Perivolaropoulos

The observed cosmological constant may originate as the minimum value $U_{min}$ of a scalar field potential, where the scalar field is frozen due to a large mass. If this vacuum is metastable, it may decay to a true vacuum either at present or in the future. Assuming its decay rate $\Gamma$ is comparable to the Hubble expansion rate $H_0$, we estimate the sc

Renaud Baillou, Marta Pedrosa Garcia-Moreno, Quentin Guigue, Solene Meinier

Navigation of microorganisms is controlled by internal processes ultimately sensitive to mechanical or chemical signaling encountered along the path. In many natural environments, such as porous soils or physiological ducts, motile species alternate between bulk and surface motion displaying in each case, distinct kinematics. This inherent complexity is key

Tobias Morocutti, Florian Schmid, Khaled Koutini, Gerhard Widmer

Knowledge Distillation (KD) is a widespread technique for compressing the knowledge of large models into more compact and efficient models. KD has proved to be highly effective in building well-performing low-complexity Acoustic Scene Classification (ASC) systems and was used in all the top-ranked submissions to this task of the annual DCASE challenge in the

Zheyu Wu, Hanyi Chen, Mengmeng Long, Gangjian Jin

The heavy fermion metamagnet uranium ditelluride possesses two distinct magnetic field--induced superconducting states. One of these superconductive phases resides at magnetic fields immediately below a first-order metamagnetic transition to a field--polarized paramagnetic state at a field strength $H_m$, while the other exists predominantly above $H_m$. How

Swetha Nair, Guillaume Jeanmairet, Benjamin Rotenberg

We investigate the interplay between electronic screening inside a metal and screening by a polar molecular solvent, focusing on their impact on the charge induced by an ion and the solvent structure at the interface. To that end, we consider atomistically resolved electrodes within the Thomas-Fermi model of screening and describe the molecular solvent eithe

Mayank Nautiyal, Stela Arranz Gheorghe, Kristiana Stefa, Li Ju

Language-guided attention frameworks have significantly enhanced both interpretability and performance in image classification; however, the reliance on deterministic embeddings from pre-trained vision-language foundation models to generate reference attention maps frequently overlooks the intrinsic multivaluedness and ill-posed characteristics of cross-moda

Samuel Mallick, Gianpietro Battocletti, Qizhang Dong, Azita Dabiri

Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear positions may be too computationally intensive for a real-time implementation. This work proposes a learning-based MPC s

A. O. Mihalchenko

We have presented a new approach to separate small spectral $\mu$ and $y$ distortions of the CMB from foreground components with poorly defined spectral shapes. Our linear method, called the Least Response Method (LRM), is based on the idea of simultaneously minimizing the response to all possible foregrounds and photon noise while maintaining a constant res

Arpit Bhatia, Moaaz Hudhud Mughrabi, Diar Abdlkarim, Massimiliano Di Luca

Text entry for extended reality (XR) is far from perfect, and a variety of text entry techniques (TETs) have been proposed to fit various contexts of use. However, comparing between TETs remains challenging due to the lack of a consolidated collection of techniques, and limited understanding of how interaction attributes of a technique (e.g., presence of vis

Yi Feng, Kaiming Shen

Large-scale multiple-input multiple-output (MIMO) is an emerging wireless technology that deploys thousands of transmit antennas at the base-station to boost spectral efficiency. The classic weighted minimum mean-square-error (WMMSE) algorithm for beamforming is no suited for the large-scale MIMO because each iteration of the algorithm then requires invertin

Henk Bruin, Robbert Fokkink

We settle two questions on sequence A120243 in the OEIS that were raised by Clark Kimberling and partly solve a conjecture of Van de Lune and Arias de Reyna. We extend Kimberling's questions to the framework of deterministic random walks, automatic sequences, and linear recurrences. Our results indicate that there may be a deeper connection between these str

Anzhi Zhang, Massimiliano Fasi

TypedMatrices.jl is a Julia package to organize test matrices. By default, the package comes with a number of built-in matrices and interfaces to help users select test cases based on their properties. The package is designed to be extensible, allowing users to define their own matrix types. We discuss the design and implementation of the package and demonst

Heinz-Juergen Flad, Michael Griebel

Within the framework of many-particle perturbation theory, we develop an analytical approach that allows us to determine the small distance behavior of Green's functions and related quantities in electronic structure theory. As a case study, we consider the one-particle Green's function up to 2nd order in the perturbation approach. We derive explicit express

Taeuk Moon, Tongnyeol Rhee, Jae-Min Kwon, Young-Mu Jeon

We simulate ion loss induced by neutral beam injection (NBI) in three-dimensional (3D) space with high fidelity on the plasma-facing components (PFCs) of the Korea Superconducting Tokamak Advanced Research (KSTAR) device. Utilizing a 3D collision detection routine added to the NuBDeC code and computer-aided design data reflecting the recent upgrade to a tung

Arno Verduyn, Maxim Vochten, Joris De Schutter

The ability of robots to recognize human gestures facilitates a natural and accessible human-robot collaboration. However, most work in gesture recognition remains rooted in reference frame-dependent representations. This poses a challenge when reference frames vary due to different work cell layouts, imprecise frame calibrations, or other environmental chan

Zihan Wang, Yo Sato, Akimasa Ishikawa, Yutaka Ushiroda

Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II. In this paper, we first discuss the possible room for $\mu/\p

Maximilian Fischer, Peter Neher, Peter Schüffler, Shuhan Xiao

Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. This paper investigates the limitations of the widely-used JPEG algorithm, the current clinical standard, and reveals severe image artifacts i

Marinela Adam

Few-Shot Class-Incremental Learning (FSCIL) represents a cutting-edge paradigm within the broader scope of machine learning, designed to empower models with the ability to assimilate new classes of data with limited examples while safeguarding existing knowledge. The paper will present different solutions which contain extensive experiments across large-scal

Aissatou Diallo, Antonis Bikakis, Luke Dickens, Anthony Hunter

An interesting class of commonsense reasoning problems arises when people are faced with natural disasters. To investigate this topic, we present \textsf{RESPONSE}, a human-curated dataset containing 1789 annotated instances featuring 6037 sets of questions designed to assess LLMs' commonsense reasoning in disaster situations across different time frames. Th

Zhenyi Zhang, Yuhao Sun, Qiangwei Peng, Tiejun Li

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved sc

Fengyu Li, Yilin Li, Junhao Zhu, Lu Chen

Huawei has always been committed to exploring the AI application in historical research. Biography generation, as a specialized form of abstractive summarization, plays a crucial role in historical research but faces unique challenges that existing large language models (LLMs) struggle to address. These challenges include maintaining stylistic adherence to h

Di Li, Jie Feng, Jiahao Chen, Weisheng Dong

Egocentric scenes exhibit frequent occlusions, varied viewpoints, and dynamic interactions compared to typical scene understanding tasks. Occlusions and varied viewpoints can lead to multi-view semantic inconsistencies, while dynamic objects may act as transient distractors, introducing artifacts into semantic feature modeling. To address these challenges, w

Jonatan Langlet, Peiqing Chen, Michael Mitzenmacher, Ran Ben Basat

Streaming analytics are essential in a large range of applications, including databases, networking, and machine learning. To optimize performance, practitioners are increasingly offloading such analytics to network nodes such as switches. However, resources such as fast SRAM memory available at switches are limited, not uniform, and may serve other function

Dylan M. Paré, David T. Chuss, Kaitlyn Karpovich, Natalie Butterfield

The Central Molecular Zone (CMZ) of the Galactic Center (GC) region of the Milky Way contains a substantial fraction of the molecular mass of the Galaxy >10e7 solar masses yet exhibits an order of magnitude lower star formation efficiency (SFE) than expected given the high densities found in this region. There are multiple possible explanations for the depre

M. Akın Yılmaz, Ahmet Bilican, A. Murat Tekalp

Video frame prediction remains a fundamental challenge in computer vision with direct implications for autonomous systems, video compression, and media synthesis. We present FG-DFPN, a novel architecture that harnesses the synergy between optical flow estimation and deformable convolutions to model complex spatio-temporal dynamics. By guiding deformable samp

Yibing Weng, Yu Gu, Fuji Ren

Road rage, triggered by driving-related stimuli such as traffic congestion and aggressive driving, poses a significant threat to road safety. Previous research on road rage regulation has primarily focused on response suppression, lacking proactive prevention capabilities. With the advent of Vision-Language Models (VLMs), it has become possible to reason abo

Joona Kareinen, Tuomas Eerola, Kaisa Kraft, Lasse Lensu

Plankton recognition is an important computer vision problem due to plankton's essential role in ocean food webs and carbon capture, highlighting the need for species-level monitoring. However, this task is challenging due to its fine-grained nature and dataset shifts caused by different imaging instruments and varying species distributions. As new plankton

Samuel B. Walker, Suzanne M. Fielding

Double network hydrogels show remarkable mechanical performance, combining high strength and fracture toughness with sufficient stiffness to bear load, despite containing only a low density of cross-linked polymer molecules in water. We introduce a simple mesoscale model of a double network material, detailed enough to resolve the salient microphysics of loc

Moritz A. Zanger, Pascal R. Van der Vaart, Wendelin Böhmer, Matthijs T. J. Spaan

Uncertainty quantification is a critical aspect of reinforcement learning and deep learning, with numerous applications ranging from efficient exploration and stable offline reinforcement learning to outlier detection in medical diagnostics. The scale of modern neural networks, however, complicates the use of many theoretically well-motivated approaches such

Maxime Ramzi, Vladimir Sosnilo, Christoph Winges

We define a class of motivic equivalences of small stable $\infty$-categories $W_{\mathrm{mot}}$ and show that the Dwyer--Kan localization functor $\mathrm{Cat}^{\mathrm{perf}}_\infty \to \mathrm{Cat}^{\mathrm{perf}}_\infty[W_{\mathrm{mot}}^{-1}]$ is the universal localizing invariant in the sense of Blumberg--Gepner--Tabuada. In particular, we show that eve

Zhengwei Tan, Zheng Ma, Simone Privitera, Maciej Oskar Liedke

Control of magnetism through voltage-driven ionic processes (i.e., magneto-ionics) holds potential for next-generation memories and computing. This stems from its non-volatility, flexibility in adjusting the magnitude and speed of magnetic modulation, and energy efficiency. Since magneto-ionics depends on factors like ionic radius and electronegativity, iden

Aissatou Diallo, Antonis Bikakis, Luke Dickens, Anthony Hunter

In this paper, we introduce Rule-Guided Feedback (RGF), a framework designed to enhance Large Language Model (LLM) performance through structured rule adherence and strategic information seeking. RGF implements a teacher-student paradigm where rule-following is forced through established guidelines. Our framework employs a Teacher model that rigorously evalu

Moein Sorkhei, Emir Konuk, Kevin Smith, Christos Matsoukas

Existing adaptation techniques typically require architectural modifications or added parameters, leading to high computational costs and complexity. We introduce Attention Projection Layer Adaptation (APLA), a simple approach to adapt vision transformers (ViTs) without altering the architecture or adding parameters. Through a systematic analysis, we find th

Mingnan Ding, Michael E. Cates

The Hamiltonian evolution of an isolated classical system is reversible, yet the second law of thermodynamics states that its entropy can only increase. This has confounded attempts to identify a `Microscopic Dynamical Entropy' (MDE), by which we mean an entropy computable from the system's evolving phase-space density $\rho(t)$, that equates {\em quantitati

Clara J. Lapp, Julia M. Link, Carsten Timm

Nodal noncentrosymmetric superconductors can host zero-energy flat bands of Majorana surface states within the projection of the nodal lines onto the surface Brillouin zone. Thus, these systems can have stationary, localized Majorana wave packets on certain surfaces, which may be a promising platform for quantum computation. Such applications require protoco

Leila Bagheriye, Johan Kwisthout

Real-time computer-aided diagnosis using artificial intelligence (AI), with images, can help oncologists diagnose cancer with high accuracy and in an early phase. We reviewed real-time AI-based analyzed images for decision-making in different cancer types. This paper provides insights into the present and future potential of real-time imaging and image fusio

Masaya Mori, Yuto Omae, Yutaka Koyama, Kazuyuki Hara

As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, endomyocardial biopsy specimens are often small sample size and require techniques such as feature extraction and dimensionality reduction. This study aims to determine whether text