March 2025 arXiv papers — page 193

Iacopo Tirelli, Miguel Alfonso Mendez, Andrea Ianiro, Stefano Discetti

We propose a novel meshless method to achieve super resolution from scattered data obtained from sparse, randomly positioned sensors such as the particle tracers of particle tracking velocimetry. The method combines K Nearest Neighbor Particle Tracking Velocimetry (KNN PTV, Tirelli et al. 2023) with meshless Proper Orthogonal Decomposition (meshless POD, Tir

Xiangchao Yan, Shiyang Feng, Jiakang Yuan, Renqiu Xia

Survey paper plays a crucial role in scientific research, especially given the rapid growth of research publications. Recently, researchers have begun using LLMs to automate survey generation for better efficiency. However, the quality gap between LLM-generated surveys and those written by human remains significant, particularly in terms of outline quality a

Mahmoud Hamash, Md Raqib Khan, Peter Tiernan

STEAM education integrates Science, Technology, Engineering, Arts, and Mathematics to foster creativity and problem-solving. However, students with visual impairments (VI) encounter significant challenges in programming and robotics, particularly in tracking robot movements and developing spatial awareness. This paper presents a framework that leverages pre-

Alejandro González Nevado

We develop the tools to bound extreme roots of multivariate real zero polynomials globally. This is done through the use of a relaxation that approximates their rigidly convex sets. This relaxation can easily be constructed using the degree $3$ truncation of the polynomial and it produces in this way a spectrahedron whose computation is relatively easy and w

Yuping Shi, Partha Roy, Naoki Higashitarumizue, Tsung-Yen Lee

A combination of ultrafast, long-range and low-loss excitation energy transfer from the photo-receptor location to a functionally active site is essential for cost-effective polymeric semiconductors. Delocalized electronic wavefunctions along {\pi}-conjugated polymer backbone can enable efficient intrachain transport, while interchain transport is generally

Yu Pan, Chaozheng Wang, Zekai Wu, Qifan Wang

Deep neural networks have achieved remarkable accomplishments in practice. The success of these networks hinges on effective initialization methods, which are vital for ensuring stable and rapid convergence during training. Recently, initialization methods that maintain identity transition within layers have shown good efficiency in network training. These t

Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang

Large reasoning models (LRMs) like OpenAI-o1 and DeepSeek-R1 have demonstrated remarkable capabilities in complex reasoning tasks through the utilization of long Chain-of-thought (CoT). However, these models often suffer from hallucinations and inefficiencies due to their reliance solely on internal reasoning processes. In this paper, we introduce START (Sel

Silvia Gasparotto

Ultralight Dark Matter (ULDM) offers an alternative to cold dark matter, characterized by wavelike behavior on galactic scales. This contribution summarizes our work~\cite{Blas:2024duy} on how solitonic cores induced by ULDM affect gravitational waves (GWs), enabling their detection through next-generation observatories like LISA, Einstein Telescope and Cosm

Sakshi S. Naik, Lavinia M. Ghilardi, Robert B. Parker, Lorenz T. Biegler

Multistage model predictive control (MPC) provides a robust control strategy for dynamic systems with uncertainties and a setpoint tracking objective. Moreover, extending MPC to minimize an economic cost instead of tracking a pre-calculated optimal setpoint improves controller performance. In this paper, we develop a formulation for multistage economic MPC w

Yeryun Cheon, Mehrdad T. Kiani, Yi-Hsin Tu, Sushant Kumar

Ongoing demands for smaller and more energy efficient electronic devices necessitate alternative interconnect materials with lower electrical resistivity at reduced dimensions. Despite the emergence of many promising candidates, synthesizing high quality nanostructures remains a major bottleneck in evaluating their performance. Here, we report the successful

Zhijian Lai, Jiang Hu, Taehee Ko, Jiayuan Wu

Parameterized quantum circuits (PQCs) are ubiquitous in the design of hybrid quantum-classical algorithms. In this work, we propose an interpolation-based coordinate descent (ICD) method to address the parameter optimization problem in PQCs. The ICD method provides a unified framework for existing structure optimization techniques such as Rotosolve, sequenti

Xin Zhang, Qiyu Wei, Yingjie Zhu, Linhai Zhang

User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors. Traditional sentiment analysis methods focus on static reviews, failing to capture the evolving temporal relationship between user sentiment rating and textual content. Sentiment analysis on streaming reviews addresses this limitation by modelin

Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo

Process supervision, i.e., evaluating each step, is critical for complex large language model (LLM) reasoning and test-time searching with increased inference compute. Existing approaches, represented by process reward models (PRMs), primarily focus on rewarding signals up to the current step, exhibiting a one-directional nature and lacking a mechanism to mo

Rufus Lawrence, Aleš Wodecki, Johannes Aspman, Llorenç Balada Gaggioli

Understanding and mitigating noise in quantum systems is a fundamental challenge in achieving scalable and fault-tolerant quantum computation. Error modeling for quantum systems can be formulated in many ways, some of which are very fundamental, but hard to analyze (evolution by general dynamical map) and others perhaps too simplistic to represent physical r

Xuyang Sha, Xuejin Zhang, Hao Liu, Jin Cao

While the time-reversal-even (T-even) nonlinear Hall effect has been extensively discussed in nonmagnetic materials, the impact of magnetic phase transition on it remains largely overlooked. Here, we report an abrupt enhancement of the T-even nonlinear Hall effect in non-centrosymmetric SrRuO3(111) thin films during the paramagnetic-ferromagnetic transition.

Ashok Urlana, Gopichand Kanumolu, Charaka Vinayak Kumar, Bala Mallikarjunarao Garlapati

Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-box models typically rely on but are inaccessible in closed-source LLMs. However, their inability to capture query-response alignment patterns often results in lower detection accu

Conor J. McCluskey, James Dalzell, Amit Kumar, J. Marty Gregg

The electrical conductivity of parallel plate capacitors, with ferroelectric lithium niobate as the dielectric layer, can be extensively and progressively modified by the controlled injection of conducting domain walls. Domain wall-based memristor devices hence result. Microstructures, developed as a result of partial switching, are complex and so simple mod

John Z. Zhang, Taylor A. Howell, Zeji Yi, Chaoyi Pan

We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion a

Jairo Bochi, Pablo Lessa

We study the distribution of the angles between Oseledets subspaces and their log-integrability, focusing on dimension $2$. For random i.i.d. products of matrices, we construct examples of probability measures on $\mathrm{GL}_2(\mathbb{R})$ with finite first moment where the Oseledets angle is not log-integrable. We also show that for probability measures wi

Mohammad Amin Ghanizadeh, Mohammad Javad Dousti

In this work, we explain our approach employed in the BabyLM Challenge, which uses various methods of training language models (LMs) with significantly less data compared to traditional large language models (LLMs) and are inspired by how human children learn. While a human child is exposed to far less linguistic input than an LLM, they still achieve remarka

Christoph Hellings, Nathan Lacroix, Ants Remm, Richard Boell

Fast tuning of the transition frequency of superconducting qubits using magnetic flux is essential, for example, for realizing high-fidelity two-qubit gates with low leakage or for reducing errors in dispersive qubit readout. To apply accurately shaped flux pulses, signal distortions induced by the flux control lines need to be carefully compensated for. Thi

Letian Chen, Gunnar Pruessner

In active matter, such as the Vicsek Model of flocking, particles possesses an internal degree of freedom, such as their director, which is subject to interaction with other particles, provided they are within a certain range. In an effort to understand better the interplay between spatial and internal degrees of freedom, we study numerically a variation of

Philip J. Morrison

The algebra of invariants for both the relativistic and nonrelativistic multispecies Vlasov-Maxwell system is examined, including the case with a fixed ion background. Invariants and their associated fluxes are obtained directly from the Vlasov-Maxwell system. The invariants are shown to Poisson commute with the Hamiltonian and the rest of the Poisson bracke

Román Aranda, Alexander Zupan

Heegaard splittings stratify 3-manifolds by complexity; only $S^3$ admits a genus-zero splitting, and only $S^3$, $S^1 \times S^2$, and lens spaces $L(p,q)$ admit genus-one splittings. In dimension four, the second author and Jeffrey Meier proved that only a handful of simply-connected 4-manifolds have trisection genus two or less, while Meier conjectured th

Aoxiong Yin, Kai Shen, Yichong Leng, Xu Tan

Recent advancements in text-to-video (T2V) generation have been driven by two competing paradigms: autoregressive language models and diffusion models. However, each paradigm has intrinsic limitations: language models struggle with visual quality and error accumulation, while diffusion models lack semantic understanding and causal modeling. In this work, we

Hongshun Yao, Xin Wang

Retrieving classical information from quantum systems is central to quantum information processing. As a more general task than quantum state discrimination, which focuses on identifying the exact state, quantum state exclusion only requires ruling out options, revealing fundamental limits of information extraction from quantum systems. In this work, we stud

Chun Ho Lau, Claudio Vasconcelos

In this paper, we explore the relationship between the operators mapping atoms to molecules in local Hardy spaces $h^p(\mathbb{R}^n)$ and the size conditions of its kernel. In particular, we show that if the kernel of a Calder\'on--Zygmund-type operator satisfies an integral-type size condition and a $T^*-$type cancellation, then the operator maps $h^p(\math

Kanghui Ning, Zijie Pan, Yu Liu, Yushan Jiang

Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they often struggle to generalize across diverse and unseen datasets. Moreover, existing Time Series Foundation Models (TSFMs) still face challenges in handling non-stationary dynamics an

Behnam Moradkhani, Pejman Kheradmand, Harshith Jella, Joseph Klein

Spinal cord stimulation (SCS) electrodes are traditionally placed in the dorsal epidural space to stimulate the dorsal column fibers for pain therapy. Recently, SCS has gained attention in restoring gait. However, the motor fibers triggering locomotion are located in the ventral and lateral spinal cord. Currently, SCS electrodes are steered manually, making

Zhiyu Lu, Théo Simon, Pierre Zhang

We analyse pre-DESI clustering data using a dark energy equation of state $w(z)$ parametrised by $(w_0, w_a)$, finding a $2.8-3.9σ$ preference for evolving dark energy over the cosmological constant $Λ$ when combined with cosmic microwave background data from \textit{Planck} and supernova data from Pantheon+, Union3, or DESY5. Our constraints, consistent wit

Yaiza Aragonés-Soria, Manuel Oriol

Large language model (LLM)-based tools such as ChatGPT seem useful for classical programming assignments. The more specialized the field, the more likely they lack reliability because of the lack of data to train them. In the case of quantum computing, the quality of answers of generic chatbots is low. C4Q is a chatbot focused on quantum programs that addres

Balazs Gyenis

I argue that John Norton's notions of empirical, hypothetical, and counterfactual possibility can be successfully used to analyze counterintuitive examples of physical possibility and align better with modal intuitions of practicing physicists. First, I clarify the relationship between Norton's possibility notions and the received view of logical and physica

Yiting Wei, Bingo Wing-Kuen Ling, Danni Chen, Yuheng Dai

Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. To address this issue, this paper proposes a

Aline Novais, Chloe Fisher, Luan Ghezzi, Daniel Kitzmann

The Wide Field Camera 3 (WFC3) instrument on the Hubble Space Telescope has provided an abundance of exoplanet spectra over the years. These spectra have enabled analysis studies using atmospheric retrievals to constrain the properties of these objects. However, follow-up observations from the James Webb Space Telescope have called into question some of the

Georgios Chrysanidis, Antonios Argyriou, Le-Nam Tran, Yanming Zhang

Eavesdroppers of wireless signals want to infer as much as possible regarding the transmitter (Tx). Popular methods to minimize information leakage to the eavesdropper include covert communication, directional modulation, and beamforming with nulling. In this paper we do not attempt to prevent information leakage to the eavesdropper like the previous methods

Zhijian Zhuo, Yutao Zeng, Ya Wang, Sijun Zhang

Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, many challenges remain in training deep transformer networks, especially regarding the position of the layer normalization. While Pre-Norm structures facilitate more stable training

Willem Lambrichts, Jules Mace, Drazen Dujic, Mario Paolone

This paper proposes an optimal, grid-aware control framework for the islanding, island-operation and resynchronisation of hybrid AC/DC microgrids. The optimal control framework is based on a formally derived linearized load-flow model for multiterminal hybrid AC/DC networks. The load flow model integrates the AC grid, DC grid, and interfacing converters (IC)

Joan C. Timoneda, Sebastián Vallejo Vera

Generative Large Language Models (LLMs) have shown promising results in text annotation using zero-shot and few-shot learning. Yet these approaches do not allow the model to retain information from previous annotations, making each response independent from the preceding ones. This raises the question of whether model memory -- the LLM having knowledge about

Xinyi Hou, Yanjie Zhao, Haoyu Wang

The development of large language models (LLMs) has given rise to four major application paradigms: LLM app stores, LLM agents, self-hosted LLM services, and LLM-powered devices. Each has its advantages but also shares common challenges. LLM app stores lower the barrier to development but lead to platform lock-in; LLM agents provide autonomy but lack a unifi

Xinyu Wei, Luojia Liu

Recently, large language models (LLMs) with hundreds of billions of parameters have demonstrated the emergent ability, surpassing traditional methods in various domains even without fine-tuning over domain-specific data. However, when it comes to financial sentiment analysis (FSA)$\unicode{x2013}$a fundamental task in financial AI$\unicode{x2013}$these model

Xiaodong Qi, Xinran Chen, Asiy, Neil Han

The increasing adoption of blockchain technology has led to a growing demand for higher transaction throughput. Traditional blockchain platforms, such as Ethereum, execute transactions sequentially within each block, limiting scalability. Parallel execution has been proposed to enhance performance, but existing approaches either impose strict dependency anno

Jazmín Ordóñez-Toro, Sergio A. Dzib, Laurent Loinard, Gisela Ortiz-León

Oph-S1 is the most luminous and massive stellar member of the nearby Ophiuchus star-forming region. Previous Very Long Baseline Array (VLBA) observations have shown it to be an intermediate-mass binary system ($\sim 5\,{\rm M}_\odot$) with an orbital period of about 21 months, but a paucity of radio detections of the secondary near periastron could potential

L. H. Vanegas, S. A. Calderón, L. M. Rondón

A threshold autoregressive (TAR) model is a powerful tool for analyzing nonlinear multivariate time series, which includes special cases like self-exciting threshold autoregressive (SETAR) models and vector autoregressive (VAR) models. In this paper, estimation, inference, and forecasting using the Bayesian approach are developed for multivariate TAR (MTAR)

Qing Zhou, Tao Yang, Junyu Gao, Weiping Ni

Remote Sensing Image Captioning (RSIC) is a cross-modal field bridging vision and language, aimed at automatically generating natural language descriptions of features and scenes in remote sensing imagery. Despite significant advances in developing sophisticated methods and large-scale datasets for training vision-language models (VLMs), two critical challen

Kévin Perrot, Sylvain Sené, Léah Tapin

We settle the theoretical ground for the study of automata networks under block-parallel update schedules, which are somehow dual to the block-sequential ones, but allow for repetitions of automaton updates. This gain in expressivity brings new challenges, and we analyse natural equivalence classes of update schedules: those leading to the same dynamics, and

Nam Van Tran, Le Thi Thanh Hai

In this paper, we propose two projection dynamical systems for solving inverse quasi-variational inequality problems in finite-dimensional Hilbert spaces-one ensuring finite-time stability and the other guaranteeing fixed-time stability. We first establish the connection between these dynamical systems and the solutions of inverse quasi-variational problems.

Andrea Manini, Matteo Rossi, Pierluigi San Pietro

A key challenge in formal verification, particularly in Model Checking, is ensuring the correctness of the verification tools. Erroneous results on complex models can be difficult to detect, yet a high level of confidence in the outcome is expected. Indeed, these tools are frequently novel and may not have been thoroughly tested. When standard benchmarks may

Soumya Sahu, Thomas Mathew, Robert Gibbons, Dulal K. Bhaumik

This article addresses calibration challenges in analytical chemistry by employing a random-effects calibration curve model and its generalizations to capture variability in analyte concentrations. The model is motivated by specific issues in analytical chemistry, where measurement errors remain constant at low concentrations but increase proportionally as c

Lin Sun, Guangxiang Zhao, Xiaoqi Jian, Yuhan Wu

The challenge of reducing the size of Large Language Models (LLMs) while maintaining their performance has gained significant attention. However, existing methods, such as model distillation and transfer learning, often fail to achieve high accuracy. To address this limitation, we introduce the Branch-Merge distillation approach, which enhances model compres

Ken Kikuchi

We prove conformal and global dimensions monotonically decrease under the infinitely many Tanaka-Nakayama renormalization group flows between Virasoro minimal models. The flows also satisfy the half-integer condition.

Yue Tan, Zi-Xuan Ma, Xiaoyun Chen, Xiaohuang Hu

In this work, we systematically study $N(1440)$, $N(1535)$, and $\Lambda(1405)$ in both the quenched three-quark and five-quark frameworks using the Gaussian Expansion Method (GEM) within the chiral quark model. Our calculations show that $N(1535)$ can be reproduced as a three-quark state ($N(1P)$), while $N(1440)$ and $\Lambda(1405)$ cannot be accommodated

Manuel Santos Pereira, Luís Tripa, Nélson Lima, Francisco Caldas

First formulated by Sir Isaac Newton in his work "Philosophiae Naturalis Principia Mathematica", the concept of the Three-Body Problem was put forth as a study of the motion of the three celestial bodies within the Earth-Sun-Moon system. In a generalized definition, it seeks to predict the motion for an isolated system composed of three point masses freely i

A. D. Levin, G. M. Gusev, V. A. Chitta, Z. D. Kvon

The viscous flow of electrons in a narrow channel requires both strong electron-electron interactions and no-slip boundary conditions. However, introducing obstacles within the liquid can significantly increase flow resistance and, as a result, amplify the effects of viscosity. Even in samples with smooth walls, the presence of an obstacle can strongly alter

Alexey Buzovkin, Evgeny Shilov

We investigate methods to reduce inference time and memory footprint in stable diffusion models by introducing lightweight decoders for both image and video synthesis. Traditional latent diffusion pipelines rely on large Variational Autoencoder decoders that can slow down generation and consume considerable GPU memory. We propose custom-trained decoders usin

Uri Mendlovic, Yossi Matias

Given a network of routing nodes, represented as a directed graph, we prove the following necessary and sufficient condition for the existence of deadlock-free message routing: The directed graph must contain two edge-disjoint directed trees rooted at the same node, one tree directed into the root node and the other directed away from the root node. While th

Théo Gnassounou, Antoine Collas, Rémi Flamary, Alexandre Gramfort

Distribution shift poses a significant challenge in machine learning, particularly in biomedical applications using data collected across different subjects, institutions, and recording devices, such as sleep data. While existing normalization layers, BatchNorm, LayerNorm and InstanceNorm, help mitigate distribution shifts, when applied over the time dimensi

Mattia Sinigaglia, Amirhossein Kiamarzi, Marco Bertuletti, Luigi Ghionda

Most Wearable Ultrasound (WUS) devices lack the computational power to process signals at the edge, instead relying on remote offload, which introduces latency, high power consumption, and privacy concerns. We present Maestro, a RISC-V SoC with unified Vector-Tensor Unit (VTU) and memory-coupled Fast Fourier Transform (FFT) accelerators targeting edge proces

Yibin Wu, Jian Kuang, Shahram Khorshidi, Xiaoji Niu

Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDARs and cameras, may become unreliable. In this paper, we propose DogLegs, a state estimation system for legged robots that fuses the measurements from a body-mounted inertial measu

Rafael I. Cabral Muchacho, Florian T. Pokorny

Geodesic distances play a fundamental role in robotics, as they efficiently encode global geometric information of the domain. Recent methods use neural networks to approximate geodesic distances by solving the Eikonal equation through physics-informed approaches. While effective, these approaches often suffer from unstable convergence during training in com

Jintao Deng, Ryo Toyota

In this paper, we study the geometric property (T) for discretized warped cones of an action on a compact Lie group $M$ by its finitely generated subgroup. We show that if a subgroup $G$ is dense in $M$, then the associated discretized warped cone $\bigsqcup_n M\times \{t(n)\}$ does not have geometric property (T) for any sequence of positive numbers $\{t(n)

Alireza Kabgani, Masoud Ahookhosh

This paper is devoted to investigating the fundamental properties of the high-order proximal operator (HOPE) and the high-order Moreau envelope (HOME) in the nonconvex setting, where the quadratic regularization ($p=2$) is replaced by a $p$-order regularizer with $p > 1$. After establishing several basic properties of HOPE and HOME, we study the differentiab

Emily Vorderwülbeke, Isabella Graßl

Queer students often encounter discrimination and a lack of belonging in their academic environments. This may be especially true in heteronormative male-dominated fields like software engineering, which already faces a diversity crisis. In contrast, disciplines like humanities have a higher proportion of queer students, suggesting a more diverse academic cu

Konstantin A. Rybakov

This paper considers the orthogonal expansion of the fractional Brownian motion relative to the Legendre polynomials. Such an expansion has not only theoretical but also practical interest, since it can be applied to approximate and simulate the fractional Brownian motion in continuous time. The relations for the mean square approximation error are presented

Danai Roumelioti, Stelios Stefas, George Zoupanos

We present a unification scenario of the conformal and fuzzy gravities with the internal interactions, based on the observation that the tangent space of a curved space and the space itself do not necessarily have the same dimensions. Accordingly, the construction is based on enlarging the tangent space, while it is formulated in a gauge-theoretical way.

Christoph A. Ternes, Giulia Pagliaroli, Francesco L. Villante

We obtain stringent bounds on neutrino quantum decoherence from the analysis of SN1987A data. We show that for the decoherence model considered here, which allows for neutrino-loss along the trajectory, the bounds are many orders of magnitude stronger than the ones that can be obtained from the analysis of data from reactor neutrino oscillation experiments o

Feng Fu, Ran Zhuo, Xingru Chen

Declines in vaccination coverage for vaccine-preventable diseases, such as measles and chickenpox, have enabled their surprising comebacks and pose significant public health challenges in the wake of growing vaccine hesitancy. Vaccine opt-outs and refusals are often fueled by beliefs concerning perceptions of vaccine effectiveness and exaggerated risks. Here

Toshiyuki Kodama, Nobuaki Kikuchi, Takahiro Chiba, Seigo Ohno

We demonstrate magnetic permeability time-varying metamaterials at GHz frequencies using ferromagnetic permalloy (Ni80Fe20; Py). We observe frequency up and down conversion of 4 GHz microwaves through the metamaterials, which is caused by the temporal modulation of permeability in the Py layer. Moreover, the efficiency of the up-conversion to a higher freque

Louis McConnell

In settings where both spurious and causal predictors are available, standard neural networks trained under the objective of empirical risk minimization (ERM) with no additional inductive biases tend to have a dependence on a spurious feature. As a result, it is necessary to integrate additional inductive biases in order to guide the network toward generaliz

Devi Dutta Biswajeet, Sara Kadkhodaei

Machine learning in materials science faces challenges due to limited experimental data, as generating synthesis data is costly and time-consuming, especially with in-house experiments. Mining data from existing literature introduces issues like mixed data quality, inconsistent formats, and variations in reporting experimental parameters, complicating the cr

Yitong Luo, Hou Hei Lam, Ziang Chen, Zhenliang Zhang

Despite recent advances in artificial intelligence (AI), it poses challenges to ensure personalized decision-making in tasks that are not considered in training datasets. To address this issue, we propose ValuePilot, a two-phase value-driven decision-making framework comprising a dataset generation toolkit DGT and a decision-making module DMM trained on the

Jens Robben, Karim Barigou, Torsten Kleinow

This paper develops a granular regime-switching framework to model mortality deviations from seasonal baseline trends driven by temperature and epidemic shocks. The framework features three states: (1) a baseline state that captures observed seasonal mortality patterns, (2) an environmental shock state for heat waves, and (3) a respiratory shock state that a

Joan Gimeno, Rafael de la Llave, Jiaqi Yang

We rigorously construct a variety of orbits for certain delay differential equations, including the electrodynamic equations formulated by Wheeler and Feynman in 1949. These equations involve delays and advances that depend on the trajectory itself, making it unclear how to formulate them as evolution equations in a conventional phase space. Despite their fu

Fuchuan Wei, Zi-Wen Liu

As a necessary resource for quantum computational advantage, quantum magic (nonstabilizerness) is of fundamental importance in the study of quantum computation and physics. We develop a systematic theory of \emph{long-range magic (LRM)} -- nonstabilizerness that cannot be erased by shallow unitary circuits -- and demonstrate its broad relevance. By bridging

Kai Luo, Hao Shi, Sheng Wu, Fei Teng

Panoramic imagery, with its 360{\deg} field of view, offers comprehensive information to support Multi-Object Tracking (MOT) in capturing spatial and temporal relationships of surrounding objects. However, most MOT algorithms are tailored for pinhole images with limited views, impairing their effectiveness in panoramic settings. Additionally, panoramic image

Xiang Zhang, Zhou Li, Kai Wan, Hua Sun

Secure aggregation is motivated by federated learning (FL) where a cloud server aims to compute an {aggregated} model (i.e., weights of deep neural networks) of the locally-trained models of numerous clients {through an iterative communication process}, while adhering to data security requirements. Hierarchical secure aggregation (HSA) extends this concept t

Minzhe Zheng, Lei Zheng, Lei Zhu, Jun Ma

Ensuring safety and motion consistency for robot navigation in occluded, obstacle-dense environments is a critical challenge. In this context, this study presents an occlusion-aware Consistent Model Predictive Control (CMPC) strategy. To account for the occluded obstacles, it incorporates adjustable risk regions that represent their potential future location

Elio Godoy-Lorite, Laureano Moreno-Pozas, José Manuel Luque-González, Ana Sánchez-Ramírez

The significant growth of free-space optic communications and Light Detection and Ranging (LiDAR) is demanding gratings that emit highly collimated beams, i.e. with Rayleigh ranges of millimeters or even centimeters. Hence, weak-strength gratings, which radiate little amount of power per unit length, are needed. The main purpose of this work is to propose an

Pankaj Patel, Debopam Chakraborty, Jaitra Chattopadhyay

We consider the parametric family of elliptic curves over $\mathbb{Q}$ of the form $E_{m} : y^{2} = x(x - n_{1})(x - n_{2}) + t^{2}$, where $n_{1}$, $n_{2}$ and $t$ are particular polynomial expressions in an integral variable $m$. In this paper, we investigate the torsion group $E_{m}(\mathbb{Q})_{\rm{tors}}$, a lower bound for the Mordell-Weil rank $r({E_{

Ingvar Zappacosta, Matthew Houtput, Jacques Tempere

Superconducting systems based on attractive electron-phonon interactions are the ones which are best understood at a fundamental level. They are well described using Eliashberg theory, which, unlike BCS theory, explicitly takes into account phonon dynamics. It is most often assumed that only linear electron-phonon interactions are relevant. However, for some

Maxim Grigoriev, Alexander Mamekin

We elaborate on the presymplectic BV-AKSZ approach to supersymmetric systems. In particular, we construct such a formulation for the $N=1$, $D=4$ supergravity by taking as a target space the Chevalley-Eilenberg complex of the super-Poincar\'e algebra which, as we demonstrate, admits an invariant presymplectic structure of degree $3$. This data encodes a full

Alexander Y. Chen, Martin Luepker, Yajie Yuan

Low-luminosity Active Galactic Nuclei (AGN) are believed to be surrounded by a collisionless, highly magnetized accretion flow. As a result, Particle-in-Cell simulations are the best tools to study the immediate vicinity of the event horizons of these supermassive black holes. We present a GPU-based general relativistic particle-in-cell (GRPIC) code framewor

Hanyi Zhao, Jinxuan Zhu, Zihao Yan, Yichen Li

Multi-step cloth manipulation is a challenging problem for robots due to the high-dimensional state spaces and the dynamics of cloth. Despite recent significant advances in end-to-end imitation learning for multi-step cloth manipulation skills, these methods fail to generalize to unseen tasks. Our insight in tackling the challenge of generalizable multi-step

Jacqueline R. M. A. Maasch, Alihan Hüyük, Xinnuo Xu, Aditya V. Nori

Causal reasoning and compositional reasoning are two core aspirations in AI. Measuring the extent of these behaviors requires principled evaluation methods. We explore a unified perspective that considers both behaviors simultaneously, termed compositional causal reasoning (CCR): the ability to infer how causal measures compose and, equivalently, how causal

Alvaro Otero Sanchez

This article analyzes a key exchange protocol based on the triad tropical semiring, recently proposed by Jackson, J. and Perumal, R. We demonstrate that the triad tropical semiring is isomorphic to a circulant matrix over tropical numbers. Consequently, matrices in this semiring can be represented as tropical matrices. As a result, we conduct a cryptanalysis

Armel Zebaze, Benoît Sagot, Rachel Bawden

The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. Machine Translation (MT) has been shown to benefit from in-context examples, in particular when they are semantically similar to the sentence to translat

Tomasz Wasak, Gerard Pascual, Gregory E. Astrakharchik, Jordi Boronat

Quantum impurities interacting with quantum environments offer unique insights into many-body systems. Here, we explore the thermometric potential of a neutral impurity immersed in a harmonically trapped bosonic quantum gas below the Bose-Einstein condensation critical temperature $T_c$. Using ab-initio Path Integral Monte Carlo simulations at finite tempera

Xu Xia, Weihao Huang, Ke Huang, Xiaolong Deng

We introduce a one-dimensional quasiperiodic mosaic model with analytically solvable mobility edges that exhibit different phase transitions depending on the system parameters. Specifically, by combining mosaic quasiperiodic next-nearest-neighbor hoppings and quasiperiodic on-site potentials, we rigorously demonstrate the existence of two distinct types of m

Dalia Saha, Abhik Kumar Sanyal

The `Generalized Symmetric Teleparallel Gravity' (GSTG) does not admit diffeomorphic invariance, since the auxiliary field as well as the shift vector act as non-propagating dynamical variables carrying 1/2 degrees of freedom each. We show that in a minisuperspace model, which is devoid of the shift vector, the problem is alleviated for locally Lorentz invar

Tong Yu, Yongcheng Jing, Xikun Zhang, Wentao Jiang

Despite the recent success of large language models (LLMs) in reasoning such as DeepSeek, we for the first time identify a key dilemma in reasoning robustness and generalization: significant performance degradation on novel or incomplete data, suggesting a reliance on memorized patterns rather than systematic reasoning. Our closer examination reveals four ke

Lucien K. L. Ng, Pedro Moreno-Sanchez, Mohsen Minaei, Panagiotis Chatzigiannis

Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) schemes have gained significant adoption in privacy-preserving applications, decentralized systems (e.g., blockchain), and verifiable computation due to their efficiency. However, the most efficient zk-SNARKs often rely on a one-time trusted setup to generate a public parameter, often k

Zhipeng Chen, Yingqian Min, Beichen Zhang, Jie Chen

In this report, we present the third technical report on the development of slow-thinking models as part of the STILL project. As the technical pathway becomes clearer, scaling RL training has become a central technique for implementing such reasoning models. We systematically experiment with and document the effects of various factors influencing RL trainin

Charles F. Dunkl

A Young subgroup of the symmetric group $\mathcal{S}_{N}$, the permutation group of $\{ 1,2,\dots,N\} $, is generated by a subset of the adjacenttranspositions $\{ ( i,i+1) \mid 1\leq i < N\}$. Such a group is realized as the stabilizer $G_{n}$ of a monomial $x^{\lambda}$ $\big({=}\,x_{1}^{\lambda_{1}}x_{2}^{\lambda_{2}}\cdots x_{N}^{\lambda_{N}}\big)$ with

Qiang Jia, Ran Luo, Jiahua Tian, Yi-Nan Wang

In this Letter, we demonstrate that the Symmetry Topological Field Theory (SymTFT) associated to a Quantum Field Theory (QFT) with continuous non-abelian $G$-flavor symmetry is a $BF$-theory with gauge group $G$. We show that gauging $G$-symmetry with a flat connection yields a theory with global symmetry characterized by exchanging the conjugate variables i

Alessandro Scherl, Stefan Thalhammer, Bernhard Neuberger, Wilfried Wöber

Visual servoing enables robots to precisely position their end-effector relative to a target object. While classical methods rely on hand-crafted features and thus are universally applicable without task-specific training, they often struggle with occlusions and environmental variations, whereas learning-based approaches improve robustness but typically requ

Wenke Huang, Jian Liang, Xianda Guo, Yiyang Fang

Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. While MLLMs demonstrate remarkable versatility, MLLMs appears limited performance on special applications. But tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert Special

Cynthia Dwork, Chris Hays, Lunjia Hu, Nicole Immorlica

Professional networks are a key determinant of individuals' labor market outcomes. They may also play a role in either exacerbating or ameliorating inequality of opportunity across demographic groups. In a theoretical model of professional network formation, we show that inequality can increase even without exogenous in-group preferences, confirming and comp

Boyi Dai

We study the irreducibility of 6-dimensional strictly compatible systems of Q with distinct Hodge-Tate weights. We prove that if one of the representations $\rho$ in such a system is irreducible and satisfies a self-dual condition $\rho^{\vee}\otimes\chi\cong\rho$ for some character $\chi$, then all but finitely many of them are irreducible.

Ming-Yu Guo, Yun-Fan Yan, Pin Chen, Wei-Xiong Zhang

The precise regulation of chemical decompositions in energetic materials, whether towards rapid ignition or stable endurance, requires atomic-scale principles governing reactivity, which remain elusive yet. Herein, we resolve this challenge through deep potential molecular dynamics (DPMD) simulations, uncovering a universal collision-control principle in ene

Rahul Dhyani, Arnab Paul, Arindam Chatterjee

In this article, we consider Dark Matter (DM) interactions and study the same in the light of the Cosmic Microwave Background Radiation (CMBR) data. In particular, we focus on the DM-electron interactions. Assuming that such interactions are mediated by rather heavy mediators, we consider effective operators describing the relevant interaction terms in the l

Yijie Guo, Bingjie Tang, Iretiayo Akinola, Dieter Fox

Enabling robots to learn novel tasks in a data-efficient manner is a long-standing challenge. Common strategies involve carefully leveraging prior experiences, especially transition data collected on related tasks. Although much progress has been made for general pick-and-place manipulation, far fewer studies have investigated contact-rich assembly tasks, wh