March 2025 arXiv papers — page 103

Kathrin Marina Eckert, Jelisa Bonsen, Anja Hajnal, Johannes Gmeiner

Stimuli-responsive lyogels are known for their ability to undergo significant macroscopic changes when exposed to external stimuli. While thermo-responsive gels, such as poly N isopropylacrylamide (pNIPAM), have been extensively studied across various applications, solvent-induced swelling has predominantly been investigated in aqueous solutions. This study

Miquel Saucedo, Sergey Tikhonov

We extend the Kahane-Katznelson-de Leeuw theorem to smoothness spaces by showing that for any $g \in W^{l,2}(\mathbb{T}^d)$, there exists a function $f\in C^l(\mathbb{T}^d)$ satisfying $|\widehat{f}(n)|\geq |\widehat{g}(n)|$ and $$\omega_r(D^l f,t)_\infty \approx \omega_r(D^l g,t)_2, \quad t>0. $$ We apply this result to solve the Bernstein problem of findin

Enrico Pasqualetto, Tapio Rajala

In the setting of length PI spaces satisfying a suitable deformation property, it is known that each isoperimetric set has an open representative. In this paper, we construct an example of a length PI space (without the deformation property) where an isoperimetric set does not have any representative whose topological interior is non-empty. Moreover, we prov

Hiroki Nakai, Masafumi Udagawa, Chisa Hotta

We investigate the emergence and transformation of pinch-point singularities in the excitation spectrum of electronic flat band systems on kagome and pyrochlore lattices with spin-orbit coupling (SOC) and Coulomb interactions. While pinch points are widely recognized as signatures of classical spin liquids, they also appear in electronic flat-band systems wh

Paul Darm, James Xie, Annalisa Riccardi

Steering the behavior of Large Language Models (LLMs) remains a challenge, particularly in engineering applications where precision and reliability are critical. While fine-tuning and prompting methods can modify model behavior, they lack the dynamic and exact control necessary for engineering applications. Inference-time intervention techniques provide a pr

Nathan Blake, David A. Kelly, Akchunya Chanchal, Sarah Kapllani-Mucaj

Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of such models and the sensitivity of medical diagnosis together with regulatory requirements necessitate the need for explainable AI tools. We introduce SpecReX, specifically adapted

Subhadeep Koley, Tapas Kumar Dutta, Aneeshan Sain, Pinaki Nath Chowdhury

While foundation models have revolutionised computer vision, their effectiveness for sketch understanding remains limited by the unique challenges of abstract, sparse visual inputs. Through systematic analysis, we uncover two fundamental limitations: Stable Diffusion (SD) struggles to extract meaningful features from abstract sketches (unlike its success wit

Yantao Cao, Andi Liu, Bin Wang, Mingxin Zhang

The discovery of high-temperature superconductivity in layered nickelates under pressure has recently triggered enormous interest. Studies of these compounds have revealed a density-wave-like transition at ambient pressure, though its connection with superconductivity is still not well understood. Here, we report a detailed \msr\ study on single crystals of

Yuqi Hu, Yujie Peng, Jennifer Gohumpu, Caijun Zhuang

Autistic children often face challenges in social interaction and communication, impacting their social connectivity, especially with their parents. Despite the effectiveness of game-based interactive therapy in improving motor skills, research on enhancing parent-child relationships is lacking. We address this gap with Magicarpet, an interactive play carpet

Puspita Chatterjee, Nitu Saini, Ajay Kumar Srivastava

Radiation hard n-Fz Double Sided Silicon microstrip Detectors are used at the Silicon Tracker for the detection of two-dimensional position and energy measurement of the incident protons in the R3B experiment at FAIR, Darmstadt, Germany. For the development of the detectors for the R3B Silicon Tracker, the macroscopic analysis is conducted on the test struct

Wooseok Choi, Thomas van Bodegraven, Jelle Verest, Olivier Maher

We demonstrate the first hardware implementation of an oscillatory neural network (ONN) utilizing resistive memory (ReRAM) for coupling elements. A ReRAM crossbar array chip, integrated into the Back End of Line (BEOL) of CMOS technology, is leveraged to establish dense coupling elements between oscillator neurons, allowing phase-encoded analog information t

Defa Zhu, Hongzhi Huang, Jundong Zhou, Zihao Huang

Residual connections are central to modern deep learning architectures, enabling the training of very deep networks by mitigating gradient vanishing. Hyper-Connections recently generalized residual connections by introducing multiple connection strengths at different depths, thereby addressing the seesaw effect between gradient vanishing and representation c

Ragheed Alhyder, Mikhail Lemeshko, Alberto Cappellaro

We investigate quantum transport in a two-dimensional electron system coupled to a chiral molecular potential, demonstrating how molecular chirality and orientation affect charge and spin transport properties. We propose a minimal model for realizing true chiral symmetry breaking on a magnetized surface, with a crucial role played by the tilt angle of the mo

S. G. Scott

We prove a wave trace singularity formula for a family of generalised Laplacians defined by a Riemannian fibre bundle; for example, the superconnection curvature operator associated to the Bismut superconnection. It is explained how this generalises Poisson summation formulae for families.

Yannick Kibolwe Mulundule, Yao Cheng, Amir Ubed, Abdiaziz Omar Hassan

Empowerment in smart clothing, which incorporates advanced technologies, requires the integration of scientific and technological expertise with artistic and design principles. Little research has focused on this unique and innovative field of design until now, and that is about to change. The concept of 'wearables' cut across several fields. A global 'langu

Yizhou Xu, Antoine Maillard, Lenka Zdeborová, Florent Krzakala

In the matrix sensing problem, one wishes to reconstruct a matrix from (possibly noisy) observations of its linear projections along given directions. We consider this model in the high-dimensional limit: while previous works on this model primarily focused on the recovery of low-rank matrices, we consider in this work more general classes of structured sign

Paweł Moskal, Aleksander Bilewicz, Manish Das, Bangyan Huang

Positronium imaging was recently proposed to image the properties of positronium atoms in the patient body. Positronium properties depend on the size of intramolecular voids and oxygen concentration; therefore, they deliver information different and complementary to the anatomic, morphological, and metabolic images. Thus far, the mean ortho-positronium lifet

Beniamino Di Lorenzo, Gian Carlo Maffettone, Mario di Bernardo

This paper introduces a novel decentralized implementation of a continuification-based strategy to control the density of large-scale multi-agent systems on the unit circle. While continuification methods effectively address micro-to-macro control problems by reformulating ordinary/stochastic differential equations (ODEs/SDEs) agent-based models into more tr

Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo

Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent ''universal'' models deliver qualitative accuracy across the periodic table but are of

Bruno P. Cavalar, Igor C. Oliveira

We reduce the problem of proving deterministic and nondeterministic Boolean circuit size lower bounds to the analysis of certain two-dimensional combinatorial cover problems. This is obtained by combining results of Razborov (1989), Karchmer (1993), and Wigderson (1993) in the context of the fusion method for circuit lower bounds with the graph complexity fr

Ilja Gogić, Mateo Tomašević

Let $M_n$ denote the algebra of $n \times n$ complex matrices and let $\mathcal{A}\subseteq M_n$ be an arbitrary structural matrix algebra, i.e. a subalgebra of $M_n$ that contains all diagonal matrices. We consider injective maps $\phi : \mathcal{A}\to M_n$ that satisfy the condition $$ \phi(X \bullet Y) = \phi(X) \bullet \phi(Y), \quad \text{for all } X,Y

Ivor van der Hoog, Lara Ost, Eva Rotenberg, Daniel Rutschmann

We cluster a set of trajectories T using subtrajectories of T. Clustering quality may be measured by the number of clusters, the number of vertices of T that are absent from the clustering, and by the Fr\'{e}chet distance between subtrajectories in a cluster. A $\Delta$-cluster of T is a cluster ${\mathcal{P}}$ of subtrajectories of T with a centre $P \in {\

V. Anemogiannis, B. Andreou, K. Myrtollari, K. Panagidi

Kubernetes, in recent years, has become widely used for the deployment and management of software projects on cloud infrastructure. Due to the execution of these applications across numerous Nodes, each one with its unique specifications, it has become a challenge to identify problems and ensure the smooth operation of the application. Effective supervision

Melanie Harms, Michael Herty, Chiara Segala, Eva Zerz

This work investigates the decay properties of Lyapunov functions in leader-follower systems seen as a sparse control framework. Starting with a microscopic representation, we establish conditions under which the total Lyapunov function, encompassing both leaders and followers, exhibits exponential decay. The analysis is extended to a hybrid setting combinin

Guodong Ding, Rongyu Chen, Angela Yao

This work presents the first condensation approach for procedural video datasets used in temporal action segmentation. We propose a condensation framework that leverages generative prior learned from the dataset and network inversion to condense data into compact latent codes with significant storage reduced across temporal and channel aspects. Orthogonally,

Egor Kuznetsov, Kirill Aistov, Maxim Koroteev

Using stochastic gradient approach we study the properties of adversarial perturbations resulting in noticeable growth of VMAF image quality metric. The structure of the perturbations is investigated depending on the acceptable PSNR values and based on the Fourier power spectrum computations for the perturbations. It is demonstrated that moderate variation o

Hao Zhang, Mingyue Cheng, Qi Liu, Junzhe Jiang

Recommender systems (RS) have become crucial tools for information filtering in various real world scenarios. And cross domain recommendation (CDR) has been widely explored in recent years in order to provide better recommendation results in the target domain with the help of other domains. The CDR technology has developed rapidly, yet there is a lack of a c

Nicolas Gonthier

Rapid evolution of territories due to climate change and human impact requires prompt and effective updates to geospatial databases maintained by the National Mapping Agency. This paper presents a comprehensive overview of change detection methods tailored for the operational updating of large-scale geographic databases. This review first outlines the fundam

Ji Li, Chong-Wei Liang, Chun-Yen Shen

The Falconer distance problem for Cartesian product sets was introduced and studied by Iosevich and Liu (\cite{MR3525385}). In this paper, by implementing a new observation on Cartesian product sets associated with a particular parabolic structure, we study the pinned version of Falconer distance problem for Cartesian product sets, and improve the threshold

David Meier, Thomas Zeschke, Peter Feuer-Forson, Bernhard Sick

Beamline alignment is challenging as the beamline components must be set up ideally so that the rays follow the desired optical path. Automated methods using a digital twin allow for faster diagnostics and improved beam properties compared to manual tuning. We introduce an automated method of finding the offsets to improve this digital twin model. These offs

Jef Jonkers, Frank Coopman, Luc Duchateau, Glenn Van Wallendael

Automatic anatomical landmark localization in medical imaging requires not just accurate predictions but reliable uncertainty quantification for effective clinical decision support. Current uncertainty quantification approaches often fall short, particularly when combined with normality assumptions, systematically underestimating total predictive uncertainty

M. Jarzyna, M. Jachura, K. Banaszek

Intensity modulation/direct detection (IM/DD) optical key distribution (OKD) is a method to generate a secret key whose security against passive eavesdropping is guaranteed by the shot noise inherent to the photodetection process. Here the effects of intensity-dependent symbol shape distortion on the IM/DD OKD security are investigated assuming that the eave

Anatoly A. Krasnovsky

Distributed systems often exhibit emergent behaviors that impact their resilience (Franz-Kaiser et al., 2020; Adilson E. Motter, 2002; Jianxi Gao, 2016). This paper presents a theoretical framework combining attributed graph models, flow-on-graph simulation, and sheaf-theoretic causal emergence analysis to evaluate system resilience. We model a distributed s

Jonas Oppenlaender

Planning a trip into a potentially unsafe area is a difficult task. We conducted a formative study on travelers' information needs, finding that most of them turn to search engines for trip planning. Search engines, however, fail to provide easily interpretable results adapted to the context and personal information needs of a traveler. Large language models

Youssef Abdalla, Elia Gatti, Mine Orlu, Marianna Obrist

The five senses are gateways to our wellbeing and their decline is considered a significant public health challenge which is linked to multiple conditions that contribute significantly to morbidity and mortality. Modern technology, with its ubiquitous nature and fast data processing has the ability to leverage the power of the senses to transform our approac

Qiang Tan, Hongyu Wang, Ken Wang, Zuyi Zhang

In this paper, we introduce $\mathcal{D}^+_J$, a generalization of $\partial\bar{\partial}$ operator on higher dimensional almost K\"{a}hler manifolds. Using the $\mathcal{D}^+_J$ operator, we investigate the $\bar{\partial}$-problem in almost K\"{a}hler geometry and explore the generalized Monge-Amp\`{e}re equation on almost K\"{a}hler manifolds. We establi

Xiangjun Ma, Ali Arshad Nasir, Daniel Benevides da Costa

This paper investigates the downlink (DL) physical layer security (PLS) in a near-field (NF) extra-large multiple-input multiple-output MIMO (XL-MIMO) system. To enhance the secrecy rate (SR), null-space artificial noise (AN) is transmitted alongside the confidential message, ensuring orthogonality with legitimate user equipment (LUE) channels. The objective

Marc Abboud

We define a local intersection number for metrised line bundles over quasiprojective varieties with compact support and show the local arithmetic Hodge index theorem for this intersection number. As a consequence we obtain a uniqueness result for the Monge-Amp\`ere equation over quasiprojective varieties within a certain class of solutions both in the archim

Johanne Haugland, Mads Hustad Sandøy

Determining when a finite dimensional algebra satisfies the finiteness property known as the $(\textbf{Fg})$-condition is of fundamental importance in the celebrated and influential theory of support varieties. We give an answer to this question for higher Koszul algebras, generalizing a result by Erdmann and Solberg. This allows us to establish a strong con

Weihong Chen, Xuemiao Xu, Haoxin Yang, Yi Xie

Existing 3D Human Pose Estimation (HPE) methods achieve high accuracy but suffer from computational overhead and slow inference, while knowledge distillation methods fail to address spatial relationships between joints and temporal correlations in multi-frame inputs. In this paper, we propose Sparse Correlation and Joint Distillation (SCJD), a novel framewor

Jiin Choi, Seung Won Lee, Kyung Hoon Hyun

In 3D design, specifying design objectives and visualizing complex shapes through text alone proves to be a significant challenge. Although advancements in 3D GenAI have significantly enhanced part assembly and the creation of high-quality 3D designs, many systems still to dynamically generate and edit design elements based on the shape parameters. To bridge

Bipin Kumar, Bhvisy Kumar Yadav, Soumypdeep Mukhopadhyay, Rakshit Rohan

Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation,

Roman Denkin, Orcun Goksel

Accurate speed-of-sound (SoS) estimation is crucial for ultrasound image formation, yet conventional systems often rely on an assumed value for imaging. We propose to leverage conventional image analysis techniques and metrics as a novel and simple approach to estimate tissue SoS. We study eleven metrics in three categories for assessing image quality, image

Melanie Habermann, Ashkaan K. Fahimipour, Justin D. Yeakel, Thilo Gross

When studying a complex system it is often useful to think of the system as a network of interacting units. One can then ask if some properties of the entire network are already explained by a small part of the network - a network motif. A famous example of an ecological motif is competitive exclusion in foodwebs, where the presence of two species competing

Ngo T. Que, Anh D. Phan, Truyen Tran, Pham T. Huy

Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (T_m) and glass transition temperature (T_g) remains a significant challenge. In this study, we present a comprehensive approach that integrates machine learning (ML)

Jan Göpfert, Patrick Kuckertz, Jann M. Weinand, Detlef Stolten

To cope with the large number of publications, more and more researchers are automatically extracting data of interest using natural language processing methods based on supervised learning. Much data, especially in the natural and engineering sciences, is quantitative, but there is a lack of datasets for identifying quantities and their context in text. To

S. P. Katoorani, C. Kohlfürst, F. Queisser, G. Schaller

We discuss the limit cycle regime of a finite-time quantum Otto cycle with a frictionless two-dimensional anisotropic Ising model as the working fluid. From Onsagers exact equilibrium solution, we first find optimal parameters for the operational modes of work extraction and cooling for infinitely slow cycles. The equilibrium points in these optimal cycles c

Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin K. Leung

In this work, we introduce a Distributed Quantum Long Short-Term Memory (QLSTM) framework that leverages modular quantum computing to address scalability challenges on Noisy Intermediate-Scale Quantum (NISQ) devices. By embedding variational quantum circuits into LSTM cells, the QLSTM captures long-range temporal dependencies, while a distributed architectur

Kristóf Sárosi, Thomas Kummer, Thomas Rösgen, Stijn Vandenberghe

Resuscitative thoracotomy, a high-risk procedure involving open heart massage, serves as a last resort for life-threatening conditions like penetrating chest wounds, severe blunt trauma, or surgery-related cardiac arrest. However, its success rate remains low, even with highly trained specialists. This research investigates the potential of an external biven

Georgios P. Georgiou

While research on using Artificial Intelligence (AI) through various applications to enhance foreign language pronunciation is expanding, it has primarily focused on aspects such as comprehensibility and intelligibility, largely neglecting the improvement of individual speech sounds in both perception and production. This study seeks to address this gap by e

Alessandro Doldi, Marco Frittelli, Marco Maggis

This paper builds on "Collective Arbitrage and the Value of Cooperation" by Biagini et al. (2025, forthcoming in "Finance and Stochastics"), which introduced in discrete time the notions of collective arbitrage and super-replication in a multi-agent market framework, where agents may operate in several submarkets and collaborate through risk exchange mechani

Matthew Cordes, Ivan Levcovitz

We study the connectivity of Morse boundaries of Coxeter groups. We define two conditions on the defining graph of a Coxeter group: wide-avoidant and wide-spherical-avoidant. We show that wide-spherical-avoidant, one-ended, affine-free Coxeter groups have connected and locally connected Morse boundaries. On the other hand, one-ended Coxeter groups that are n

Xingrun Yan, Shiyuan Zuo, Yifeng Lyu, Rongfei Fan

Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex network topologies with multiple users, the enormous combinations of client data and channel state information (CSI) pose significant challenges for existing semantic communication architectures. To improve the general

Oksana Moryakova, Thomas Eriksson, Håkan Johansson

This paper deals with modeling, analysis, and optimization of power amplifiers (PAs) placed in a cascaded structure, particularly the effect of cascaded nonlinearities is studied by showing potential ways to minimize the total nonlinearities. The nonlinear least-squares algorithm is proposed to optimize the PA parameters along with the input power level, and

B. M. Rebeiro, J. -C. Thomas, B. Blank

Corrected transition rates ($\mathcal{F}t^{0^+ \rightarrow 0^+}$) of $0^+ \rightarrow 0^+$ superallowed $\beta$ decays currently give the most precise value of $V_{ud}$, the dominant term of the Cabibbo-Kobayashi-Maskawa (CKM) quark mixing matrix. By setting stringent constrains on the CKM unitarity, these decays allow probing physics beyond the Standard Mod

Manan Tayal, Aditya Singh, Pushpak Jagtap, Shishir Kolathaya

Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a challenge. Recent efforts have explored learning-based methods, such as neural CBFs (NCBFs), to address this issue. However, ensuring the validity of NCBFs is difficult due to potential

Lei Cai, Yingying Jiang, Wenjuan Chen

Quasi-MV* algebras were introduced as generalizations of MV*-algebras and quasi-MV algebras. The recent investigation into quasi-MV* algebras shows that they are closely related to quantum computational logic and complex fuzzy logic. In this paper, we aim to study the logical system associated with quasi-MV* algebras in detail. First, we introduce quasi-Wajs

Hidemasa Suzuki

Fukaya and Oh studied the correspondence between pseudoholomorphic disks in $T^{*}M$ which are bounded by Lagrangian sections $\{L_{i}^{\epsilon}\}$ and gradient trees in $M$ which consist of gradient curves of $\{f_{i}-f_{j}\}$. Here, $L_{i}^{\epsilon}$ is defined by $L_{i}^{\epsilon}=$\,graph$(\epsilon df_{i})$. They constructed approximate pseudoholomorph

Olivier Zeyen, Maxime Cordy, Martin Gubri, Gilles Perrouin

Boolean formulae compactly encode huge, constrained search spaces. Thus, variability-intensive systems are often encoded with Boolean formulae. The search space of a variability-intensive system is usually too large to explore without statistical inference (e.g. testing). Testing every valid configuration is computationally expensive (if not impossible) for

Alexis Anagnostakis, David Criens, Mikhail Urusov

We establish deterministic necessary and sufficient conditions for the no-arbitrage notions "no increasing profit" (NIP), "no strong arbitrage" (NSA) and "no unbounded profit with bounded risk" (NUPBR) in one-dimensional general diffusion markets. These are markets with one risky asset, which is modeled as a regular continuous strong Markov process that is a

Sevan Gharabeiki, Francesco Lodola, Tilly Schaaf, Taowen Wang

The radiative open circuit voltage loss in a solar cell occurs because the absorptance spectrum near the band gap shows gradual increase rather than sharp step function like transition. This broadening effect has been attributed to band gap fluctuations and or to Urbach tails. In this report, we use modelling based on Planck s generalized law to distinguish

Jiangxuan Long, Zhao Song, Chiwun Yang

Recent studies suggest utilizing generative models instead of traditional auto-regressive algorithms for time series forecasting (TSF) tasks. These non-auto-regressive approaches involving different generative methods, including GAN, Diffusion, and Flow Matching for time series, have empirically demonstrated high-quality generation capability and accuracy. H

Zhenwei Shao, Mingyang Wang, Weijun Zhang, Zhou Yu

Large vision-language models (VLMs) have demonstrated remarkable capabilities in open-world multimodal understanding, yet their high computational overheads pose great challenges for practical deployment. Some recent works have proposed methods to accelerate VLMs by pruning redundant visual tokens guided by the attention maps of VLM's early layers. Despite t

Shengping Zhang, Xiaoyu Han, Weigang Zhang, Xiangyuan Lan

Image-based virtual try-on aims to transfer an in-shop clothing image to a person image. Most existing methods adopt a single global deformation to perform clothing warping directly, which lacks fine-grained modeling of in-shop clothing and leads to distorted clothing appearance. In addition, existing methods usually fail to generate limb details well becaus

Walaa Asakly, Noor Kezil

We define two new statistics on words: the k-connector and the gk-connector. For a word $\pi = \pi_1\pi_2\cdots\pi_n$ of length $n$ over the alphabet $[k]$, a k-connector is defined as an ordered pair $(\pi_j, \pi_{j+1})$ where $1 \leq j \leq n-1$ and $\pi_j + \pi_{j+1} = k$. Conversely, a gk-connector is defined as an ordered pair $(\pi_j, \pi_{j+1})$ where

Harry L. F. Ip, Charlie Mathey, Laurent Mertz, Jonathan J. Wylie

We propose a hybrid method combining partial differential equation (PDE) and Monte Carlo (MC) techniques to obtain efficient estimates of statistics for plastic deformation related to kinematic hardening models driven by transient coloured noise. Our approach employs a control variate strategy inspired by [CPAM, 75 (3), 455-492, 2022] and relies on a class o

Rossana Mastrandrea, Fabio Montobbio, Gabriele Pellegrino, Massimo Riccaboni

This study examines the roles of public and private sector actors in the development of mRNA vaccines, a breakthrough innovation in modern medicine. Using a dataset of 151 core patent families and 2,416 antecedent (cited) patents, we analyze the structure and dynamics of the mRNA vaccine knowledge network through network theory. Our findings highlight the ce

Colin Snodgrass, Carrie E. Holt, Michael S. P. Kelley, Cyrielle Opitom

We observed the new Long Period Comet C/2024 E1 (Wierzchos), inbound at 7 au from the Sun, using the NIRSpec integral field unit on JWST. The spectrum shows absorption features due to water ice in the coma and evidence for CO$_2$ driven activity, with a production rate of $Q(CO_2) = 2.546 \pm 0.019 \times 10^{25}$ molecules s$^{-1}$, and no emission features

Yang Ye, Junliang Guo, Haoyu Wu, Tianyu He

Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, particularly for long videos represented by tens of thousands of tokens. In this paper, we propose Diagonal Decoding (DiagD), a training-free inference acceleration algorithm for autore

D. Sanchez-Fuentes, R. Desgarceaux, A. Rahal, L. Garcia

To sustainably support the ongoing energetic transition, we need functional metal oxides capable of converting energy, and produce storage, and sensing devices. However, these materials suffer from a high economic cost of manufacturing, and their production in a sustainable way is, to date, a milestone. Additionally, the technical challenges, such as scalabi

Elena P. Ushakova

Criteria for the fulfillment of inequalities in weighted smoothness function spaces of Besov type with Riemann-Liouville operators of natural orders on the real axis and semi-axes are found. The obtained estimates are refined under additional conditions on weights.

Laslo Hunhold

Modern microprocessors extend their instruction set architecture (ISA) with Single Instruction, Multiple Data (SIMD) operations to improve performance. The Intel Advanced Vector Extensions (AVX) enhance the x86 ISA and are widely supported in Intel and AMD processors. The latest version, AVX10.2, places a strong emphasis on low-precision, non-standard floati

Georgios Kokkinis, Alexandros Iosifidis, Qi Zhang

Enabling video-haptic radio resource slicing in the Tactile Internet requires a sophisticated strategy to meet the distinct requirements of video and haptic data, ensure their synchronized transmission, and address the stringent latency demands of haptic feedback. This paper introduces a Deep Reinforcement Learning-based radio resource slicing framework that

Tomas Fullana, Stéphane Zaleski, Gustav Amberg

Dynamic wetting poses a well-known challenge in classical sharp-interface formulation as the no-slip wall condition leads to a contact line singularity that is typically regularized with a Navier boundary condition, often requiring empirical fitting for the slip length. On the other hand, this paradox does not appear in phase-field models as the contact line

Xinhao Xiang, Xiao Liu, Zizhong Li, Zhuosheng Liu

The rapid advancement in AI-generated video synthesis has led to a growth demand for standardized and effective evaluation metrics. Existing metrics lack a unified framework for systematically categorizing methodologies, limiting a holistic understanding of the evaluation landscape. Additionally, fragmented implementations and the absence of standardized int

Paresh Upadhyay, Yatindra Nath Singh

In a circuit-switched network, traffic can be characterized by several factors that define how communication resources are allocated and utilized during a connection. The amount of traffic basically determines how frequently connection requests arrive, how long the setup connection remains active, and the bandwidth used. The Poisson Arrival Process models tr

Hillol Biswas

If quantum machine learning emulates the ways of classical machine learning, data encoding in a quantum neural network is imperative for many reasons. One of the key ones is the complexity attributed to the data size depending upon the features and types, which is the essence of machine learning. While the standard various encoding techniques exist for quant

Hans U. Simon, Jan Arne Telle

In this paper, we study three matching problems all of which came up quite recently in the field of machine teaching. The cost of a matching is defined in such a way that, for some formal model of teaching, it equals (or bounds) the number of labeled examples needed to solve a given teaching task. We show how the cost parameters associated with these problem

Sadaf F, V. Subrahmanyam

An exactly solvable cluster spin model with three-spin interaction couplings J_x (for XZX spin components) and J_y (for YZY spin components) in the presence of a transverse magnetic field $h$ for a spin chain is investigated. For $h=0$, and with only one nonzero interaction strength, the ground state is the cluster state. Through the Jordan-Wigner fermion ma

Zhonghao Tu, Ang Li

The thermal relaxation time of neutron stars, typically defined by a sudden drop in surface temperature, is usually on the order of 10 to 100 years. In this study, we investigate neutron star thermal relaxation by incorporating nucleon superfluidity and non-nucleonic particles, specifically considering hyperons as a representative case. We find that rapidly

Lijun Ma, Changli Ma, Zihong Tian

In this paper, we introduce strongly regular generalized partial geometries of grade $r$, which generalise partial geometries and strongly regular $(\alpha,\beta)$-geometries. By the properties of quadrics in PG$(2,q)$ and PG$(3,q)$, we construct two classes of strongly regular generalized partial geometries of grade $3$. Besides, we define low-density parit

Mohamed El Khatib, Arnaud Legout

Bitcoin burn addresses are addresses where bitcoins can be sent but never retrieved, resulting in the permanent loss of those coins. Given Bitcoin's fixed supply of 21 million coins, understanding the usage and the amount of bitcoins lost in burn addresses is crucial for evaluating their economic impact. However, identifying burn addresses is challenging due

Shanzhong Xie, Kan-Hao Xue, Shaojie Yuan, Shengxin Yang

Theoretical simulation to phase change materials such as Ge-Sb-Te has suffered from two methodology issues. On the one hand, there is a lack of efficient band gap correction method for density functional theory, which is suitable for these materials in both crystalline and amorphous phases, though the computational complexity should be kept at the local dens

Chengfeng Dou, Ying Zhang, Zhi Jin, Wenpin Jiao

Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making processes. Although it benefits from current retrieval-augmented generation~(RAG) technologies, it still faces two significant challenges: the collection of dispersed evidence and the

Guido Carnevale, Nicola Bastianello

In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-based approach, benefiting from the robustness of the former and the computational efficiency of the latter. Additionally, we integrate a stochastic integral action (error feedback) e

Abolfazl Zakeri, Nhan Thanh Nguyen, Ahmed Alkhateeb, Markku Juntti

This letter proposes a dynamic joint communications and sensing (JCAS) framework to adaptively design dedicated sensing and communications precoders. We first formulate a stochastic control problem to maximize the long-term average signal-to-noise ratio for sensing, subject to a minimum average communications signal-to-interference-plus-noise ratio requireme

Jake Rap, Amritam Das

Accurate traffic flow estimation and prediction are critical for the efficient management of transportation systems, particularly under increasing urbanization. Traditional methods relying on static sensors often suffer from limited spatial coverage, while probe vehicles provide richer, albeit sparse and irregular data. This work introduces ON-Traffic, a nov

Andrea Conti, Lea Terracini

An algebraic extension of the rational numbers is said to have the $\textit{Bogomolov property}$ (B) if the absolute logarithmic Weil height of its non-torsion elements is uniformly bounded from below. Given a continuous representation $\rho$ of the absolute Galois group $G_{\mathbb{K}}$ of a number field ${\mathbb{K}}$, one says that $\rho$ has (B) if the s

Tianshu Wu, Jiyao Zhang, Shiqian Liang, Zhengxiao Han

Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration require offline image collection, limiting their suitability for online self-calibration. Recent learning-based robot pose estimation methods, while advancing online calibration, struggle with cross-robot generaliza

Laxman Nagireddy, Saleem Ayaz Khan, Maria Christine Richter, Olivier Heckmann

We present the first electronic structure measurements of the Hf(0001) single-crystal surface using angle-resolved photoemission spectroscopy (ARPES). The ARPES results are supported by theoretical calculations performed using the full-potential linearized augmented plane wave (FLAPW) method and the Korringa-Kohn-Rostoker (KKR) Green function method. In addi

Vincent Schorp, Frédéric Giraud, Gianluca Pargätzi, Michael Wäspe

Future surgical care demands real-time, integrated data to drive informed decision-making and improve patient outcomes. The pressing need for seamless and efficient data capture in the OR motivates our development of a modular solution that bridges the gap between emerging machine learning techniques and interventional medicine. We introduce a network of edg

Ginestra Bianconi

Recently, thanks to the development of artificial intelligence (AI) there is increasing scientific attention in establishing the connections between theoretical physics and AI. Traditionally, these connections have been focusing mostly on the relation between string theory and image processing and involve important theoretical paradigms such as holography. R

Basheer Joudeh, Boris Škorić

We develop an approximation method for the differential entropy $h(\mathbf{X})$ of a $q$-component Gaussian mixture in $\mathbb{R}^n$. We provide two examples of approximations using our method denoted by $\bar{h}^{\mathrm{Taylor}}_{C,m}(\mathbf{X})$ and $\bar{h}^{\mathrm{Polyfit}}_{C,m}(\mathbf{X})$. We show that $\bar{h}^{\mathrm{Taylor}}_{C,m}(\mathbf{X})

Paolo Acquistapace, Francesca Bucci

A study of the linear quadratic (LQ) control problem on a finite time interval for a model equation in Hilbert spaces which comprehends the memory of the inputs was performed recently by the authors. The outcome included a closed-loop representation of the unique optimal control, along with the derivation of a related coupled system of three quadratic (opera

Christophe Denis, Eddy Ella Mintsa

We address the multiclass classification problem for stochastic diffusion paths, assuming that the classes are distinguished by their drift functions, while the diffusion coefficient remains common across all classes. In this setting, we propose a classification algorithm that relies on the minimization of the L 2 risk. We establish rates of convergence for

Mao Hoshino

We show that finite index quantum subgroups of a discrete quantum group are induced from finite index quantum subgroups of the unimodularization. As an application, we classify all finite index quantum subgroups of free products of the duals of connected simply-connected compact Lie groups. We also put proofs for some fundamental facts on finite index right

Guy Bar-Shalom, Fabrizio Frasca, Derek Lim, Yoav Gelberg

The automated detection of hallucinations and training data contamination is pivotal to the safe deployment of Large Language Models (LLMs). These tasks are particularly challenging in settings where no access to model internals is available. Current approaches in this setup typically leverage only the probabilities of actual tokens in the text, relying on s

Gino Isidori

These lectures provide a concise introduction to flavor physics, within and beyond the Standard Model, with main focus on B-physics phenomenology and some recent developments. The first lecture is an introduction to the flavor sector of the Standard Model. The second lecture is devoted to B-meson mixing and rare B decays. The last lecture contains a general

Sally Gilles

We state a conjecture relating de Rham cohomology of a smooth rigid analytic variety to its compactly supported pro-\'etale cohomology. We prove the conjecture in the cases where the variety is a Stein curve of dimension one or a Stein space of higher dimension with low Frobenius slopes. The proof uses computation of the Galois cohomology of the almost de Rh

Binjie Liu, Lina Liu, Sanyi Zhang, Songen Gu

This work focuses on full-body co-speech gesture generation. Existing methods typically employ an autoregressive model accompanied by vector-quantized tokens for gesture generation, which results in information loss and compromises the realism of the generated gestures. To address this, inspired by the natural continuity of real-world human motion, we propos