March 2025 arXiv papers — page 85

Yair Shpitzer, Gal Chechik, Idan Schwartz

Personalizing image generation and editing is particularly challenging when we only have a few images of the subject, or even a single image. A common approach to personalization is concept learning, which can integrate the subject into existing models relatively quickly, but produces images whose quality tends to deteriorate quickly when the number of subje

Ruihan Yang, Fanghua Ye, Jian Li, Siyu Yuan

Large language models (LLMs) have recently transformed from text-based assistants to autonomous agents capable of planning, reasoning, and iteratively improving their actions. While numerical reward signals and verifiers can effectively rank candidate actions, they often provide limited contextual guidance. In contrast, natural language feedback better align

Zenghui Yuan, Jiawen Shi, Pan Zhou, Neil Zhenqiang Gong

Multi-modal large language models (MLLMs) extend large language models (LLMs) to process multi-modal information, enabling them to generate responses to image-text inputs. MLLMs have been incorporated into diverse multi-modal applications, such as autonomous driving and medical diagnosis, via plug-and-play without fine-tuning. This deployment paradigm increa

Mario Sanz-Guerrero, Katharina von der Wense

In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose corrective in-context learning

Emil Bakkensen Johansen, Oliver Baumann

Recent developments in large language models (LLMs) have facilitated autonomous AI agents capable of imitating human-generated content, raising fundamental questions about how AI may reshape democratic information environments such as news. We develop a large-scale simulation framework to examine the system-level effects of AI-based imitation, using the full

James A. Sellers, Nicolas Allen Smoot

For the past several years, numerous authors have studied POD and PED partitions from a variety of perspectives. These are integer partitions wherein the odd parts must be distinct (in the case of POD partitions) or the even parts must be distinct (in the case of PED partitions). More recently, Ballantine and Welch were led to consider POND and PEND partitio

Nikola Koutná, Shuyao Lin, Lars Hultman, Davide G. Sangiovanni

Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs) are typically trained for ``simple'' materials and properties with minor size effects. Our study of MAB phases (MABs) - alternating transition metal boride (MB) and group A element

Tomás Pacheco

In this work, we study groupoids and their approximation properties, generalizing both the definitions and some known results for the group case. More precisely, we introduce weak amenability for groupoids using the definition of the Fourier algebra given by Renault. We prove that weakly amenable groupoids are inner exact. We also generalize its algebraic co

Piotr Chudzinski, Abyay Ghosh, Myrta Gruening

We study 2D spin and orbital systems, in a classical limit, in a regime where their coupling is so strong that orbital fluctuations are able to change sign of spin-exchange. Our aim is to understand how different phases in the orbital sector determine the ordering in the spin sector. The existence of intermediate vortex crystal (VC) phases, beside the canoni

Ivan N. Burenev, Daniël W. H. Cloete, Vansh Kharbanda, Hugo Touchette

These notes are based on the lectures that one of us (HT) gave at the Summer School on the "Theory of Large Deviations and Applications", held in July 2024 at Les Houches in France. They present the basic definitions and mathematical results that form the theory of large deviations, as well as many simple motivating examples of applications in statistical ph

Jouni Helske

Hidden Markov models are widely used for modeling sequential data but typically have limited applicability in observational causal inference due to their strong conditional independence assumptions. I introduce feedback-augmented non-homogeneous hidden Markov model (FAN-HMM), which incorporate time-varying covariates and feedback mechanisms from past observa

Xiaomeng Chu, Jiajun Deng, Guoliang You, Wei Liu

Flexible instruction-guided 6-DoF grasping is a significant yet challenging task for real-world robotic systems. Existing methods utilize the contextual understanding capabilities of the large language models (LLMs) to establish mappings between expressions and targets, allowing robots to comprehend users' intentions in the instructions. However, the LLM's k

Stijn Groenen, Marzieh Hassanshahi Varposhti, Mahyar Shahsavari

This work introduces GazeSCRNN, a novel spiking convolutional recurrent neural network designed for event-based near-eye gaze tracking. Leveraging the high temporal resolution, energy efficiency, and compatibility of Dynamic Vision Sensor (DVS) cameras with event-based systems, GazeSCRNN uses a spiking neural network (SNN) to address the limitations of tradi

Besjon Cifliku, Hendrik Heuer

As the capabilities of Large Language Models (LLMs) expand, more researchers are studying their adoption in newsrooms. However, much of the research focus remains broad and does not address the specific technical needs of investigative journalists. Therefore, this paper presents several applied use cases where automation and AI intersect with investigative j

Upasana Tiwari, Rupayan Chakraborty, Sunil Kumar Kopparapu

Computer interfaces are advancing towards using multi-modalities to enable better human-computer interactions. The use of automatic emotion recognition (AER) can make the interactions natural and meaningful thereby enhancing the user experience. Though speech is the most direct and intuitive modality for AER, it is not reliable because it can be intentionall

Claudio Fantasia, Luca Calatroni, Xavier Descombes, Rim Rekik

We consider a patch-based learning approach defined in terms of neural networks to estimate spatially adaptive regularisation parameter maps for image denoising with weighted Total Variation (TV) and test it to situations when the noise distribution is unknown. As an example, we consider situations where noise could be either Gaussian or Poisson and perform

Maximilian Stargardt, Justus Hugenberg, Christoph Winkler, Heidi Heinrichs

Due to climate change, natural hazards that affect energy infrastructure will become more frequent in the future. However, to incorporate natural hazard risk into infrastructure investment decisions, we develop an approach to translate this risk into discount rates. Thus, our newly developed discount rate approach incorporates both economic risk and natural

Valery E. Lyubovitskij, Alexey S. Zhevlakov, Iurii A. Anikin

We present comprehensive analysis of the angular structure of the Drell-Yan process at different rapidity and kinematical ranges using data of the ATLAS, LHCb, and CMS Collaborations at CERN LHC. From theory side we discuss next-to-leading order calculations in the framework of the collinear perturbative QCD and geometrical method.

Subhasish Basak, Anik Roy, Partha Sarathi Mukherjee

Decision trees are one of the most widely used nonparametric methods for regression and classification. In existing literature, decision tree-based methods have been used for estimating continuous functions or piecewise-constant functions. However, they are not flexible enough to estimate the complex shapes of jump location curves (JLCs) in two-dimensional r

S. Ayadh, S. Touili, Y. Hadouch, S. Elmouloua

This research study the sol/gel synthesis of lithium-doped barium stannate titanate BaTi0.89Sn0.11O3 (BTS11) and investigates how varying composition with lithium affect its structural, morphological, ferroelectric, optical, and electrical properties. The phase of the sol/gel prepared samples with different compositions BaLixTi0.89Sn0.11O3 (BLxTS11), where x

J. Cimprič, M. Schötz

We say that a ring is strongly (resp. weakly) left Jacobson if every semiprime (resp. prime) left ideal is an intersection of maximal left ideals. There exist Jacobson rings that are not weakly left Jacobson, e.g. the Weyl algebra. Our main result is the following one-sided noncommutative Nullstellensatz: For any finite-dimensional F-algebra A the ring A[$x_

Yu. M. Zinoviev

We elaborate on the partially massless spin 5/2 supermultiplet, which contains partially massless spin 5/2, massless and partially massless spin 2, as well as massless spin 3/2. We consider the global supertransformations connecting partially massless spin 5/2 to its two possible superpartners, massless and partially massless spin 2, and make them local by s

Zhejian Cao, Santosh Pandit, Francoise M. Amombo Noa, Jian Zhang

Mechano-bactericidal (MB) surfaces have been proposed as an emerging strategy for preventing biofilm formation. Unlike antibiotics and metal ions that chemically interfere with cellular processes, MB nanostructures cause physical damage to the bacteria. The antibacterial performance of artificial MB surfaces relies on rational control of surface features, wh

Hadi Hosseini, Šimon Schierreich

Distributing services, goods, and tasks in the gig economy heavily relies upon on-demand workers (aka agents), leading to new challenges varying from logistics optimization to the ethical treatment of gig workers. We focus on fair and efficient distribution of delivery tasks -- placed on the vertices of a graph -- among a fixed set of agents. We consider the

Niels Benedikter, Chiara Boccato, Domenico Monaco, Ngoc Nhi Nguyen

We study the quantum dynamics of a large number of interacting fermionic particles in a constant magnetic field. In a coupled mean-field and semiclassical scaling limit, we show that solutions of the many-body Schr\"odinger equation converge to solutions of a non-linear Hartree-Fock equation. The central ingredient of the proof are certain semiclassical trac

Lander Bogers, Faezeh Khodabandehlou, Christian Maes

Steady nonequilibria dissipate energy and, when changing external parameters, an extra or excess heat accompanies the relaxation to the new nonequilibrium condition. For nonequilibrium systems in contact with a thermal bath, the heat capacity is defined as that excess heat per degree temperature for a quasistatic change of the bath temperature. It is fairly

Davide Torielli, Leonardo Franco, Maria Pozzi, Luca Muratore

The teleoperation of complex, kinematically redundant robots with loco-manipulation capabilities represents a challenge for human operators, who have to learn how to operate the many degrees of freedom of the robot to accomplish a desired task. In this context, developing an easy-to-learn and easy-to-use human-robot interface is paramount. Recent works intro

P. Schulz, T. Hempel, A. Al-Hamadi

3D detection is a critical task to understand spatial characteristics of the environment and is used in a variety of applications including robotics, augmented reality, and image retrieval. Training performant detection models require diverse, precisely annotated, and large scale datasets that involve complex and expensive creation processes. Hence, there ar

Marc Benedí San Millán, Angela Dai, Matthias Nießner

Animation of humanoid characters is essential in various graphics applications, but requires significant time and cost to create realistic animations. We propose an approach to synthesize 4D animated sequences of input static 3D humanoid meshes, leveraging strong generalized motion priors from generative video models -- as such video models contain powerful

Bilal Ahmad, Jinfu Chen, Haibao Chen

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of effective diagnostic tools to enable early diagnosis and clinical decision-making. This study evaluates the impact of feature selection techniques Mutual Information (MI), Analysis of Variance (ANOVA), and Chi-Square on the predictive perfor

Håkan Andréasson, Joakim Blomqvist

Static spherically symmetric solutions to the Einstein-Dirac system were constructed numerically for the first time in 1999 by Finster, Smoller and Yau \cite{FSY1} in the case of two fermions. In 2020 this result was generalized by Leith, Hooley, Horne and Dritschel \cite{LHHD} to a system consisting of an even number $\kappa$ of fermions. They constructed s

Nicholas Mueller, Santiago Badia

In this paper, we introduce GridapROMs, a Julia-based library for the numerical approximation of parameterized partial differential equations (PDEs) using a comprehensive suite of linear reduced order models (ROMs). The library is designed to be extendable and productive, leveraging an expressive high-level API built on the Gridap PDE solver backend, while a

T. F. Kamalov, A. V. Kondakova

Aim. Implement a stochastic representation of the wave function for a pair of entangled soliton functions in a liquid crystal. Show the applicability of a special soliton representation of quantum mechanics for modeling real entangled systems. Methodology. The central place in the study is occupied by the method of mathematical modeling. As part of the calcu

Angelo Martella, Cristian Martella, Antonella Longo

The emerging paradigm of data economy can constitute an unmissable and attractive opportunity for companies that aim to consider their data as valuable assets. To fully leverage this opportunity, data owners need to have specific and precise guarantees regarding the protection of data they share from unauthorized access, but also from their misuse. Thus, it

Alessandro Pesci

A recently developed tool allows for a description of spacetime as a manifold with a Lorentz-invariant (lower) limit length built-in. This is accomplished in terms of geometric quantities depending on two spacetime events (bitensors) and looking at the 2-point function of fields on it, all this being well suited to embody nonlocality at the small scale. What

Xiaoning Kang, Zhenguo Gao, Xi Liang, Xinwei Deng

The modified Cholesky decomposition (MCD) is an efficient technique for estimating a covariance matrix. However, it is known that the MCD technique often requires a pre-specified variable ordering in the estimation procedure. In this work, we propose a weighted average ensemble covariance estimation for high-dimensional data based on the MCD technique. It ca

Giovanni Abramo, Tindaro Cicero, Ciriaco Andrea D'Angelo

This study aims to improve the accuracy of long-term citation impact prediction by integrating early citation counts, Mendeley readership, and various non-scientific factors, such as journal impact factor, authorship and reference list characteristics, funding and open-access status. Traditional citation-based models often fall short by relying solely on ear

Langming Liu, Haibin Chen, Yuhao Wang, Yujin Yuan

Large language models (LLMs) have demonstrated their capabilities across various NLP tasks. Their potential in e-commerce is also substantial, evidenced by practical implementations such as platform search, personalized recommendations, and customer service. One primary concern associated with LLMs is their factuality (e.g., hallucination), which is urgent i

Linying Yang, Robin J. Evans

Reliable causal effect estimation from observational data requires adjustment for confounding and sufficient overlap in covariate distributions between treatment groups. However, in high-dimensional settings, lack of overlap often inflates the variance and weakens the robustness of inverse propensity score weighting (IPW) based estimators. Although many appr

Miroslava Kassayová, Miguel Jiménez-Redondo, János Sarka, Petr Dohnal

Spectra of vibrational overtone and combination bands from vibrational ground state of HCNH+ were measured using an action spectroscopy technique with active background suppression in a cryogenic 22 pole radio frequency ion trap apparatus. Spectroscopic constants for the upper vibrational levels of the transitions were determined with vibrational band origin

Bo Hu, Han Yuan, Vlad Pandelea, Wuqiong Luo

The rapid advancement of large language models (LLMs) has sparked widespread adoption across diverse applications, making robust evaluation frameworks crucial for assessing their performance. While conventional evaluation metrics remain applicable for shorter texts, their efficacy diminishes when evaluating the quality of long-form answers. This limitation i

Davide Torielli, Liana Bertoni, Luca Muratore, Nikos Tsagarakis

Robotics has shown significant potential in assisting people with disabilities to enhance their independence and involvement in daily activities. Indeed, a societal long-term impact is expected in home-care assistance with the deployment of intelligent robotic interfaces. This work presents a human-robot interface developed to help people with upper limbs im

Zeqi Zheng, Yanchen Huang, Yingchao Yu, Zizheng Zhu

Spiking Neural Networks (SNNs) based on Transformers have garnered significant attention due to their superior performance and high energy efficiency. However, the spiking attention modules of most existing Transformer-based SNNs are adapted from those of analog Transformers, failing to fully address the issue of over-allocating attention to irrelevant conte

Lohit Bibar, Samali bose, Tribeni Prasad Banerjee

A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially powerline interference at 50 Hz or 60 Hz. This interference overlaps with vital ECG signal features, affecting the accuracy of downstream classification tasks (e.g., arrhythmia detection). A properly designed notch filter enhances signal quality, preserves essentia

Julien Guillot, Brigitte Roussiere, Sandrine Tusseau-Nenez, Isabelle Deloncle

The release properties of 4 targets (UC2, UC, UBC, UB2) were measured for 11 elements (Kr, Sr, Ru, Sn, Sb, Te, I, Cs, Ba, La, and Ce) using an off-line technique. The crystal packing fraction and the size of the studied element play a key role in the release process. However, physicochemical properties are also involved, notably melting and boiling points in

Han Yuan, Li Zhang, Zheng Ma

Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accura

Yair Stolero, Itzik Klein

Autonomous underwater vehicles (AUVs) are essential for various applications, including oceanographic surveys, underwater mapping, and infrastructure inspections. Accurate and robust navigation are critical to completing these tasks. To this end, a Doppler velocity log (DVL) and inertial sensors are fused together. Recently, a model-based approach demonstrat

Suraj Singh, Anastasia Batsheva, Oleg Y. Rogov, Ahmed Bouridane

Modern image restoration and super-resolution methods utilize deep learning due to its superior performance compared to traditional algorithms. However, deep learning typically requires large labeled training datasets, which are rarely available in astrophotography. Deep Image Prior (DIP) bypasses this constraint by performing unsupervised optimization on a

Tony Zhang, Rickard Brännvall

This work explores optimizing transformer-based language models by integrating model compression techniques with inhibitor attention, a novel alternative attention mechanism. Inhibitor attention employs Manhattan distances and ReLU activations instead of the matrix multiplications and softmax activation of the conventional scaled dot-product attention. This

Roberto Vergallo, Luís Cruz, Alessio Errico, Luca Mainetti

'Follow-the-Sun' (FtS) is a theoretical computational model aimed at minimizing the carbon footprint of computer workloads. It involves dynamically moving workloads to regions with cleaner energy sources as demand increases and energy production relies more on fossil fuels. With the significant power consumption of Artificial Intelligence (AI) being a subjec

Nuwan Rupasinghe, Julian Frederic Gerken, Andreas Baumann, Peter Eberhard

Ejector deep hole drilling is advantageous due to its high material removal rate and bore quality without requiring a complex sealing system for drilling applications with large length to diameter ratios. Sufficient supply of metal working fluid and efficient removal of the swarf is crucial, which would otherwise lead to poor bore quality, increased friction

Dimpi KM, Geetha Thangavelu

We derive a new expression for the diagonal matrix elements of irreducible representations of the hyperoctahedral group. This expression is obtained using Grime's hook fusion procedure for symmetric groups, which minimizes the number of auxiliary parameters required in the fusion process.

Giuseppe Galante, Christiancarmine Esposito, Pietro Catalano, Salvatore Moscariello

The financial sustainability of a generic supply chain is a complex problem, which can be addressed through detailed monitoring of financial operations deriving from stakeholder interrelationships and consequent analysis of these financial data to compute the relative economic indicators. This allows the identification of specific fintech tools that can be s

Navneet Agarwal, Kairit Sirts

In recent years, there has been growing interest in studying cognitive distortions and emotional appraisals from both computational and psychological perspectives. Despite considerable similarities between emotional reappraisal and cognitive reframing as emotion regulation techniques, these concepts have largely been examined in isolation. This research expl

Pengyu Liu, Guohua Dong, Dan Guo, Kun Li

In daily life, we encounter diverse external stimuli, such as images, sounds, and videos. As research in multimodal stimuli and neuroscience advances, fMRI-based brain decoding has become a key tool for understanding brain perception and its complex cognitive processes. Decoding brain signals to reconstruct stimuli not only reveals intricate neural mechanism

Fabio Ragosta, Giulia Illiano, Andrea Simongini, Matteo Imbrogno

Stripped-envelope supernovae (SESNe) display a wide range of photometric and spectroscopic behaviours, often reflecting complex progenitor evolution. SN~2015ap is a type Ib event located in the nearby galaxy IC~1776, previously modelled as powered by radioactive decay and possibly a magnetar engine. In this work, we revisit its multi-band photometry and spec

E. Dondoglio, A. F. Marino, A. P. Milone, S. Jang

Our understanding of multiple populations in globular clusters (GCs) largely comes from photometry and spectroscopy: appropriate photometric diagrams can disentangle first and second populations (1P and 2P)-1P having chemical signatures similar to field stars, and 2P stars showing unique light-element variations-while spectroscopy enables detailed chemical a

Kendong Liu, Zhiyu Zhu, Hui Liu, Junhui Hou

We present Acc3D to tackle the challenge of accelerating the diffusion process to generate 3D models from single images. To derive high-quality reconstructions through few-step inferences, we emphasize the critical issue of regularizing the learning of score function in states of random noise. To this end, we propose edge consistency, i.e., consistent predic

Jinlian Hu, Yuechun Jiao, Yuwen Yin, Cheng Lu

We report the enhanced optical transmission in the coherent, off-resonant excitation of Rydberg atom gases at room temperature via a two-photon process. Here thermal resonance-enhanced transparency (TRET) is induced when the detuning of the two lasers is adjusted to compensate the atomic thermal-motion-induced energy shifts, i.e. single and two-photon Dopple

Zichen Liu, Kunlun Xu, Bing Su, Xu Zou

Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging due to limited labeled video data and high training costs. Recent video prompting methods attempt to adapt CLIP for video

Elisabeth Griesbauer, Claudia Czado, Arnoldo Frigessi, Ingrid Hobæk Haff

We propose TVineSynth, a vine copula based synthetic tabular data generator, which is designed to balance privacy and utility, using the vine tree structure and its truncation to do the trade-off. Contrary to synthetic data generators that achieve DP by globally adding noise, TVineSynth performs a controlled approximation of the estimated data generating dis

Yanhong Chen, Anshui Li, Biao Wu, Huajun Zhang

Two families $\mathcal A\subseteq\binom{[n]}{k}$ and $\mathcal B\subseteq\binom{[n]}{\ell}$ are called cross-$t$-intersecting if $|A\cap B|\geq t$ for all $A\in\mathcal A$, $B\in\mathcal B$. Let $n$, $k$ and $\ell$ be positive integers such that $n\geq 3.38\ell$ and $\ell\geq k\geq 2$. In this paper, we will determine the upper bound of $|\mathcal A||\mathca

JunGyu Lee, Kunyoung Lee, Haesol Park, Ig-Jae Kim

Facial Expression Recognition (FER) plays a crucial role in human affective analysis and has been widely applied in computer vision tasks such as human-computer interaction and psychological assessment. The 8th Affective Behavior Analysis in-the-Wild (ABAW) Challenge aims to assess human emotions using the video-based Aff-Wild2 dataset. This challenge includ

Clive Tinashe Marimo, Benedikt Blumenstiel, Maximilian Nitsche, Johannes Jakubik

Vision-language models for Earth observation (EO) typically rely on the visual spectrum of data as the only model input, thus failing to leverage the rich spectral information available in the multispectral channels recorded by satellites. Therefore, we introduce Llama3-MS-CLIP, the first vision-language model pre-trained with contrastive learning on a large

Raul Cristian Bag

In the rapidly evolving landscape of digital assets and blockchain technologies, the necessity for robust, scalable, and secure data management platforms has never been more critical. This paper introduces a novel software architecture designed to meet these demands by leveraging the inherent strengths of cloud-native technologies and modular micro-service b

Xin Ye, Shu Yang, Xiaofei Wang, Yanyan Liu

In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors.

Burak Dindar, Can Berk Saner, Hüseyin K. Çakmak, Veit Hagenmeyer

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concern

Hang Kin Poon

Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an innovative approach to portfolio optimization tha

Daria Schumm, Katharina O. E. Müller, Burkhard Stiller

The development of Decentralized Identities (DI) and Self-Sovereign Identities (SSI) has seen significant growth in recent years. This is accompanied by a numerous academic and commercial contributions to the development of principles, standards, and systems. While several comprehensive reviews have been produced, they predominantly focus on academic literat

Shu Yang, Chengting Yu, Lei Liu, Hanzhi Ma

Spiking Neural Networks (SNNs) have garnered considerable attention as a potential alternative to Artificial Neural Networks (ANNs). Recent studies have highlighted SNNs' potential on large-scale datasets. For SNN training, two main approaches exist: direct training and ANN-to-SNN (ANN2SNN) conversion. To fully leverage existing ANN models in guiding SNN lea

Hans-Peter Piepho, Ingrid Claß-Mahler, Beate Zimmermann, Wilfried Hermann

The project Agriculture 4.0 without chemical synthetical plant protection (NOcsPS) tests a number of cropping systems that avoid the use of chemical synthetical pesticides while at the same time using mineral fertilizers. The experiment started in 2020 (sowing fall 2019). In 2024 (sowing fall 2023), some of the cropping systems were modified. Analysis of thi

Pierre Del Moral

We develop a quantitative contraction framework for Schrodinger and Sinkhorn bridges based on transportation-cost inequalities and Riccati matrix difference equations. Our approach combines logarithmic Sobolev and Talagrand-type inequalities to obtain explicit entropy and Wasserstein contraction bounds for Sinkhorn bridge measures, entropic optimal transport

Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson

Information and free-energy maximization are physics principles that provide general rules for an agent to optimize actions in line with specific goals and policies. These principles are the building blocks for designing decision-making policies capable of efficient performance with only partial information. Notably, the information maximization principle ha

Rahul Kesarwani, Vaibhav Varade, Artur Slobodeniuk, Martin Kalbac

In the quest to optimize the optoelectronic and valleytronic properties of 2D materials, various strategies such as strain engineering, doping, and heterostructuring have been explored. In this direction, isotope engineering also offers a potential avenue to alter electron-phonon interaction and impact quasiparticle scattering processes. In this study, we in

Jean-François Delmas, Dylan Dronnier, Pierre-André Zitt

We study in a general mathematical framework the optimal allocation of vaccine in an heterogeneous population. We cast the problem of optimal vaccination as a bi-objective minimization problem min(C($\eta$),L($\eta$)), where C and L stand respectively for the cost and the loss incurred when following the vaccination strategy $\eta$, where the function $\eta$

Sangita Majumdar, Zekun Shi, Giovanni Vignale

The practical success of density functional theory (DFT) is largely credited to the Kohn-Sham approach, which enables the exact calculation of the non-interacting electron kinetic energy via an auxiliary noninteracting system. Yet, the realization of DFT's full potential awaits the discovery of a direct link between the electron density, $n$, and the non-int

Caroline Hillairet, Thomas Peyrat, Anthony Réveillac

This paper introduces the class of multidimensional self-exciting processes with dependencies (MSPD), which is a unifying writing for a large class of processes: counting, loss, intensity, and also shifted processes. The framework takes into account dynamic dependencies between the frequency and the severity components of the risk, and therefore induces theo

Duboux Thibaut, Lucas Gerin, Yoann Offret

The Maximal Entropy Random Walk (MERW) is a natural process on a finite graph, introduced a few years ago with motivations from theoretical physics. The construction of this process relies on Perron-Frobenius theory for adjacency matrices. Generalizing to infinite graphs is rather delicate, and in this article, we treat in a fairly exhaustive manner the case

Talia Baravi, David A. Kessler, Eli Barkai

We study the first-passage time (FPT) problem for widespread recurrent processes in confined though large systems and present a comprehensive framework for characterizing the FPT distribution over many time scales. We find that the FPT statistics can be described by two scaling functions: one corresponds to the solution for an infinite system, and the other

Barreto Joaquim Reizi

This paper develops a systematic framework for integrating local categories that model logical connectives using higher category theory. By extending these local categories into a unified two-category enriched with natural isomorphisms, the universal properties of logical operations such as negation, conjunction, disjunction, and implication are rigorously c

Ting Wang, Zhuangzhuang Qiu, Xiaodong Lu, Yanlong Zhao

This paper investigates the problem of consensus tracking control of discrete time multi-agent systems under binary-valued communication. Different from most existing studies on consensus tracking, the transmitted information between agents is the binary-valued. Parameter identification with binary-valued observations is applied to the estimation of neighbor

Alfred Bovon, Henning Samtleben, Dimitrios Tsimpis

We study maximal supergravity in two dimensions, obtained from reduction of IIA supergravity on an $S^8$ sphere. The theory captures the low-lying fluctuations around the non-conformal D0-brane near-horizon geometry, dual to operators in the BFSS matrix model. Upon exciting some of the supergravity scalars, we construct half-supersymmetric domain wall soluti

Mohammed Attaoui, Fabrizio Pastore

The generation of synthetic inputs via simulators driven by search algorithms is essential for cost-effective testing of Deep Neural Network (DNN) components for safety-critical systems. However, in many applications, simulators are unable to produce the ground-truth data needed for automated test oracles and to guide the search process. To tackle this issue

Chen Li, Nazhou Liu, Kai Yang

Since DeepSeek-R1 popularized, Group Relative Policy Optimization (GRPO) has become the core part of training Reasoning LLMs. However, we find some deficiency that influences RL stability and inference efficiency, like zero-variance in advantage estimation. Thus, we propose Adaptive Group Policy Optimization (AGPO) which uses a simple but effective method, a

Claudia Diamantini, Alessandro Mele, Domenico Potena, Cristina Rossetti

The Big Data landscape poses challenges in managing diverse data formats, requiring efficient storage and processing for high-quality analysis. Effective metadata management is crucial for organizing, accessing, and reusing data within these data ecosystems. Existing metadata vocabularies and standard, however, do not adequately accommodate aggregated or sum

Xinmin Hou, Zhi Yin

The cycle space $\mathcal{C}(G)$ of a graph $G$ is defined as the linear space spanned by all cycles in $G$. For an integer $k\ge 3$, let $\mathcal{C}_k (G)$ denote the subspace of $\mathcal{C}(G)$ generated by the cycles of length exactly $k$. A graph $G$ on $n$ vertices is called Hamilton-generated if $\mathcal{C}_n (G) = \mathcal{C}(G)$, meaning every cyc

Yaxiong Chen, Minghong Wei, Zixuan Zheng, Jingliang Hu

Referring medical image segmentation targets delineating lesions indicated by textual descriptions. Aligning visual and textual cues is challenging due to their distinct data properties. Inspired by large-scale pre-trained vision-language models, we propose CausalCLIPSeg, an end-to-end framework for referring medical image segmentation that leverages CLIP. D

Elisei Rykov, Kseniia Petrushina, Kseniia Titova, Alexander Panchenko

Quantifying the realism of images remains a challenging problem in the field of artificial intelligence. For example, an image of Albert Einstein holding a smartphone violates common-sense because modern smartphone were invented after Einstein's death. We introduce a novel method for assessing image realism using Large Vision-Language Models (LVLMs) and Natu

Huang Dahu, Shan Tiecheng, Wang Cheng

Urban rural common prosperity is the ultimate goal of narrowing the gap between urban and rural areas and promoting urban rural integration development, and it is an indispensable and important element in the common wealth goal of Chinese style modernization.

Tomas Brauner

Lecture notes for a one-semester master-level course on analytical mechanics and classical field theory, covering: 0 Mathematical Introduction, 1 Lagrangian Mechanics, 2 Application: Motion in Central Fields, 3 Hamiltonian Mechanics, 4 Application: Oscillations of Mechanical Systems, 5 Application: Relativistic Mechanics, 6 Geometry of Classical Mechanics, 7

Tianyi Hu, Qingxu Fu, Zhiqiang Pu, Yuan Wang

In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in te

Lorenzo Colombi, Michela Vespa, Nicolas Belletti, Matteo Brina

Industry5.0 environments present a critical need for effective anomaly detection methods that can indicate equipment malfunctions, process inefficiencies, or potential safety hazards. The ever-increasing sensorization of manufacturing lines makes processes more observable, but also poses the challenge of continuously analyzing vast amounts of multivariate ti

Andreas Stöckl, Joel Nitu

As AI-driven agents become increasingly integrated into the digital ecosystem, they reshape how online advertising is perceived and processed. Particularly in the travel and hotel booking sector, these autonomous systems influence the effectiveness of traditional advertising formats. While visual cues and emotional appeals sway human users, AI agents priorit

Aline Chu, Ludvig Doeser, Simon Ding, Jens Jasche

The future Rubin Legacy Survey of Space and Time (LSST) is expected to deliver its first data release in the current of 2025. The upcoming survey will provide us with images of galaxy clusters in the optical to the near-infrared, with unrivalled coverage, depth and uniformity. The study of galaxy clusters informs us on the effect of environmental processes o

Jinyi Liu, Yan Zheng, Rong Cheng, Qiyu Wu

Recent advances in large language models (LLMs) have shown remarkable progress, yet their capacity for logical ``slow-thinking'' reasoning persists as a critical research frontier. Current inference scaling paradigms suffer from two fundamental constraints: fragmented thought flows compromising logical coherence, and intensively computational complexity that

Shengqing Gao, Qing Gao, Yungui Gong, Xuchen Lu

We investigate the null tests of spatial flatness and the flat $\Lambda$CDM model using the Baryon Acoustic Oscillation (BAO) data measured by the Dark Energy Spectroscopic Instrument (DESI), the cosmic chronometers (CCH) $H(z)$ data, and the Union3 and Pantheon Plus type Ia supernovae (SNe Ia) datasets. We propose a novel non-parametric reconstruction of $F

Anna Ricarda Luther, Hendrik Heuer, Stephanie Geise, Sebastian Haunss

Social media is central to activists, who use it internally for coordination and externally to reach supporters and the public. To date, the HCI community has not explored activists' perspectives on future social media platforms. In interviews with 14 activists from an environmental and a queer-feminist movement in Germany, we identify activists' needs and f

Mohammad Ayyash

We present a general framework for generating single- and multimode qubit-conditional operations by extending cross-resonant driving to a generalized multimode scheme. This includes single-mode conditional displacements and squeezing induced by one- and two-photon cross-resonant drives in the presence of one- and two-photon qubit-oscillator interactions, res

Yaxiong Chen, Chuang Du, Chunlei Li, Jingliang Hu

Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists. While recent works have made progress, learning to align medical images and textual findings remains challenging due to the relative scarcity of labeled medical data. For example, datasets for this task are much smaller than those used for i

Dexie Lin, Hengyu Zhou

In almost K\"ahler manifolds, one of the challenges is to construct an elliptic operator on functions that plays a role analogous to the $\partial\bar{\partial}$ operator in complex or K\"ahler manifolds. One of the aims of this paper is to revisit the $\tmd$-operator introduced in \cite{TWZZ}. We will provide some local analysis estimates and highlight seve