November 2024 arXiv papers — page 11

Jonathan J. Thio, Wilfred Salmon, Crispin H. W. Barnes, Stephan De Bièvre

We uncover new features of generalized contextuality by connecting it to the Kirkwood-Dirac (KD) quasiprobability distribution. Quantum states can be represented by KD distributions, which take values in the complex unit disc. Only for ``KD-positive'' states are the KD distributions joint probability distributions. A KD distribution can be measured b

Igor Lugo, Martha G. Alatriste-Contreras

This study is a theoretical approach for exploring the applicability of a 2D cellular automaton based on melodic and harmonic intervals in random arrays of musical notes. The aim of this study was to explore alternatives uses for a cellular automaton in the musical context for better understanding the musical creativity. We used the complex systems and human

Gerhard Gompper, Howard A. Stone, Christina Kurzthaler, David Saintillan

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which p

Bhandaru Phani Parasar, Yuval Gefen, Vijay B. Shenoy

We develop a theory of edge excitations of fractonic systems in two dimensions, and elucidate their connections to bulk transport properties and quantum statistics of bulk excitations. The system we consider has immobile point charges, dipoles constrained to move only along lines perpendicular to their moment, and freely mobile quadrupoles and higher multipo

Xin Wang, Wendi Zhang, Hong Xie, Haibin Ai

True Digital Orthophoto Maps (TDOMs) are essential products for digital twins and Geographic Information Systems (GIS). Traditionally, TDOM generation involves a complex set of traditional photogrammetric process, which may deteriorate due to various challenges, including inaccurate Digital Surface Model (DSM), degenerated occlusion detections, and visual ar

Dániel Molnár, Tímea Nóra Török, János Volk, Roland Kövecs

Memristive devices are commonly benchmarked by the multi-level programmability of their resistance states. Neural networks utilizing memristor crossbar arrays as synaptic layers largely rely on this feature. However, the dynamical properties of memristors, such as the adaptive response times arising from the exponential voltage dependence of the resistive sw

Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris, Efstratios Gavves

Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we buil

Yuri K Shestopaloff

Production of energy (metabolism) and its distribution is vital for living organisms, both at individual level - between different functions of an organism, as well as between species of communities at different organizational levels, including food chains. Here, a new general method for finding distribution of metabolic energy between different organismal f

Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu

Achieving precise alignment between textual instructions and generated images in text-to-image generation is a significant challenge, particularly in rendering written text within images. Sate-of-the-art models like Stable Diffusion 3 (SD3), Flux, and AuraFlow still struggle with accurate text depiction, resulting in misspelled or inconsistent text. We intro

Martin Atzmueller, Tim Bohne, Patricia Windler

Knowledge-augmented learning enables the combination of knowledge-based and data-driven approaches. For anomaly detection and diagnosis, understandability is typically an important factor, especially in high-risk areas. Therefore, explainability and interpretability are also major criteria in such contexts. This chapter focuses on knowledge-augmented explain

Christopher Natale, Ethan Diak, Ray LaPierre, Ryan B. Lewis

Dense arrays of semiconductor quantum dots are currently employed in highly efficient quantum dot lasers for data communications and other applications. Traditionally, the electronic properties of such quantum nanostructures have been treated as isolated objects, with the degree of hybridization between neighboring quantum dots and the wetting layer left une

Viviana Carolina Guerrero Pantoja, John H. Castillo, Carlos Alberto Trujillo Solarte

Let $(G,+)$ be an Abelian group. Given $h\in \mathbb{Z}^+$, a non-empty subset $A$ of $G$ is called an $S_h$-set if all the sums of $h$ distinct elements of $A$ are different. We extend the concept of $S_h$-set to a more general context in the context of finite vectorial spaces over finite fields. More precisely, a $\emptyset \neq A\subseteq \mathbb{F}_q^r$

Anirban Dey, Sara Mouradian, Cosmo Lupo, Zixin Huang

Resolving frequencies in a time-dependent field is classically limited by the measurement bandwidth. Using tools from quantum metrology and quantum control may overcome this limit, yet the full advantage afforded by entanglement so far remains elusive. Here we map the problem of frequency measurement to that of estimating a global dephasing quantum channel.

Michał Fiedorowicz

In this paper, the Painlev\'e property to fractional differential equations (FDEs) are extended and the existence and uniqueness theorems for both linear and nonlinear FDEs are established. The results contribute to the research of integrability and solvability in the context of fractional calculus, which has significant implications in various fields such a

Xintong Zhou, Zhenyang Xu, Mengxiao Zhang, Yongqiang Tian

Delta Debugging is a widely used family of algorithms (e.g., ddmin and ProbDD) to automatically minimize bug-triggering test inputs, thus to facilitate debugging. It takes a list of elements with each element representing a fragment of the test input, systematically partitions the list at different granularities, identifies and deletes bug-irrelevant partiti

Ahmed Ghatasheh

This article demonstrates that the recent proof of the invariant subspace problem, as presented by Khalil et al., is incorrect.

Benjamin G. Greenland, Josh Pinskier, Xing Wang, Daniel Nguyen

Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a novel protocol, the Soft Grasping Benchmarki

Gabriel Gendler

Arrow proved that for three or more candidates, the IIA condition is enough to forbid all non-dictatorial election rules (or Social Welfare Functions). Maskin introduced the weaker MIIA condition, which permits the ``Borda'' election rules where each voter assigns points linearly to each candidate according to their order of preference. However, in previous

Noy Soffer Aranov, Angelot Behajaina

Let $\mathcal{K}=\mathbb{F}_q((x^{-1}))$. Analogous to orthogonality in the Euclidean space $\mathbb{R}^n$, there exists a well-studied notion of ultrametric orthogonality in $\mathcal{K}^n$. In this paper, we extend the work of Soffer-Aranov and Behajaina on counting problems related to orthogonality in $\mathcal{K}^n$. For example, we resolve an open quest

Naoki Sato

We develop a Hamiltonian framework for general relativistic kinetic theory on the cotangent bundle $T^{\ast}M$ of a Lorentzian (pseudo-Riemannian) manifold. Starting from the geodesic Hamiltonian $H$, we derive a Landau-type collision operator for self-gravitating particles undergoing binary interactions mediated by an arbitrary potential energy $V$, and cou

The Anh Bui

Let $\nu=(\nu_1,\ldots,\nu_n)\in (-1,\infty)^n$, $n\ge 1$, and let $\mathcal{L}_\nu$ be a self-adjoint extension of the differential operator \[ L_\nu := \sum_{i=1}^n \left[-\frac{\partial^2}{\partial x_i^2} + x_i^2 + \frac{1}{x_i^2}(\nu_i^2 - \frac{1}{4})\right] \] on $C_c^\infty(\mathbb{R}_+^n)$ as the natural domain. In this paper, we investigate the weig

The Anh Bui

Let $\nu\in [-1/2,\infty)^n$, $n\ge 1$, and let $\mathcal{L}_\nu$ be a self-adjoint extension of the differential operator \[ L_\nu := \sum_{i=1}^n \left[-\frac{\partial^2}{\partial x_i^2} + x_i^2 + \frac{1}{x_i^2}(\nu_i^2 - \frac{1}{4})\right] \] on $C_c^\infty(\mathbb{R}_+^n)$ as the natural domain. In this paper, we first prove that the Riesz transform as

Giang Do, Kha Pham, Hung Le, Truyen Tran

Sparse mixture of experts (SMoE) is an effective solution for scaling up model capacity without increasing the computational costs. A crucial component of SMoE is the router, responsible for directing the input to relevant experts; however, it also presents a major weakness, leading to routing inconsistencies and representation collapse issues. Instead of fi

Mariana Lira, Laura O. Villegas, Javier M. Antelis, Víctor Jaramillo

In this work, we investigate head-on collisions of $\ell$-boson stars, potential candidates for dark matter compact objects. We begin with a review of the general properties and features of these stars, leveraging results from prior studies to analyze the gravitational wave signals generated by such collisions. Considering a maximum distance of 100 Mpc for p

Michael Davis, Kyle Hayden, Jingyin Huang, Daniel Ruberman

We construct closed, aspherical, smooth 4-manifolds that are homeomorphic but not diffeomorphic. These provide counterexamples to a smooth analog of the Borel conjecture in dimension four. Our technique is to apply the `reflection group trick' of the first author to pairs of exotic 4-manifolds with boundary constructed by the second author and Piccirillo.

The Anh Bui, Xuan Thinh Duong

Consider the discrete Laplacian $\Delta_d$ defined on the set of integers $\mathbb Z$ by \[ \Delta_d f(n) = -f(n+1) + 2f(n) -f(n-1), \ \ \ \ n\in \mathbb Z, \] where $f$ is a function defined on $\mathbb Z$. In this paper, we define Hardy spaces, Besov spaces and Triebel--Lizorkin spaces associated with $\Delta_d$ and then show that these function spaces coi

Zhongyi Jiang, Mohammad H. Ansari

Introducing flexible native entanglement gates can significantly reduce circuit complexity. We propose a novel gate integrating iswap and cphase operations within a single gate cycle. We theoretically show one possible realization of this gate for superconducting qubits using bichromatic parametric drives at distinct frequencies. We show how various paramete

Clément Allain, Frédéric Bour, Basile Clément, François Pottier

Common functional languages incentivize tail-recursive functions, as opposed to general recursive functions that consume stack space and may not scale to large inputs. This distinction occasionally requires writing functions in a tail-recursive style that may be more complex and slower than the natural, non-tail-recursive definition. This work describes our

Gautam Rai, Arman Babakhani, Ying Wang, Stephan Haas

We present a theory of SNS junctions, a normal metal sandwiched between two superconductors, along the crossover from the BCS to the BEC regime. We calculate the Josephson current as a function of the chemical potential relative to the band edge in the superconducting region, $\mu_S$, where the BEC phase is indicated by $\mu_S <0$. The chemical potential rel

Ritam Majumdar, Jack Teversham, Sonali Parbhoo

Evaluating off-policy decisions using batch data poses significant challenges due to limited sample sizes leading to high variance. To improve Off-Policy Evaluation (OPE), we must identify and address the sources of this variance. Recent research on Concept Bottleneck Models (CBMs) shows that using human-explainable concepts can improve predictions and provi

Ana Kovacevic, Sonja D. Radenkovic, Dragana Nikolic

The rapid advancements in artificial intelligence (AI) have presented new opportunities for enhancing efficiency and economic competitiveness across various industries, espcially in banking. Machine learning (ML), as a subset of artificial intelligence, enables systems to adapt and learn from vast datasets, revolutionizing decision-making processes, fraud de

Anders Aamand, Ioana O. Bercea, Jakob Bæk Tejs Houen, Jonas Klausen

Hash-based sampling and estimation are common themes in computing. Using hashing for sampling gives us the coordination needed to compare samples from different sets. Hashing is also used when we want to count distinct elements. The quality of the estimator for, say, the Jaccard similarity between two sets, depends on the concentration of the number of sampl

An T. Le, Kay Hansel, João Carvalho, Joe Watson

Batch planning is increasingly necessary to quickly produce diverse and quality motion plans for downstream learning applications, such as distillation and imitation learning. This paper presents Global Tensor Motion Planning (GTMP) -- a sampling-based motion planning algorithm comprising only tensor operations. We introduce a novel discretization structure

Qin Jiang, Chengjia Wang, Michael Lones, Dongdong Chen

Most Graph Neural Networks (GNNs) operate at the first-order scale, even though multi-scale representations are known to be crucial in domains such as image classification. In this work, we investigate whether GNNs can similarly benefit from multi-scale learning, rather than being limited to a fixed depth of $k$-hop aggregation. We begin by formalizing scale

Alekzander Kosakowski, Matti Dorsch, Warren R. Brown, Thomas Kupfer

We present the discovery and analysis of a nearby eclipsing ultra-compact accreting binary at coordinates 11:38:10.91 $-$51:39:49.15 (SMSS J1138$-$5139), the first well-constrained LISA-detectable Type Ia supernova progenitor. Our time series optical spectroscopy identifies its orbital period through radial velocity monitoring at $P_{\rm orb,RV}=27.69\pm0.03

Shwetha Ram, Tal Neiman, Qianli Feng, Andrew Stuart

Given a small number of images of a subject, personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subject in novel contexts, conditioned on text prompts. In doing so, a trade-off is made between prompt fidelity, subject fidelity and diversity. As the pre-trained model is fine-tuned,

Umar Sohail Qureshi, Raghav Kunnawalkam Elayavalli

Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these modifications in the jet's hard substructure. In this Letter, we present a machine learning framework to identify quenched jets whil

Zoe Ruo-Yu Li, Caswell Barry, Mutlu Cukurova

The widespread use of generative AI has led to multiple applications of AI-generated text and media to potentially enhance learning outcomes. However, there are a limited number of well-designed experimental studies investigating the impact of learning gains and affective feedback from AI-generated media compared to traditional media (e.g., text from documen

Thorge Müller, Ajainderpal Singh, Frank K. Wilhelm, Tim Bode

The ability of the Quantum Approximate Optimization Algorithm (QAOA) to deliver a quantum advantage on combinatorial optimization problems is still unclear. Recently, a scaling advantage over a classical solver was postulated to exist for random 8-SAT at the satisfiability threshold. At the same time, the viability of quantum error mitigation for deep circui

Cristian Gomez, Kimberly Solon, Pieter-Jan Haest, Mark Morley

The importance of drinking water distribution networks (DWDNs) as critical urban infrastructures has led to the development and utilization of models for the analysis, design, operation, and management of DWDNs, to ensure optimal efficiency and water quality. In order to provide models that accurately represent real-world behavior and characteristics of an a

Merlin Carl

This paper is an extended version of our work in \cite{Ca2025}. We extend the concept of effective reducibility between statements of set theory with ordinal Turing machines (OTMs) explored in \cite{Ca2018} for $\Pi_{2}$-statements to statements of arbitrary quantifier complexity in prenex normal form and use this to compare various fundamental set-theoretic

Jingzhi Hu, Geoffrey Ye Li

Future communication networks are expected to connect massive distributed artificial intelligence (AI). Exploiting aligned priori knowledge of AI pairs, it is promising to convert high-dimensional data transmission into highly-compressed semantic communications (SC). However, to accommodate the local data distribution and user preferences, AIs generally adap

Quan Vu, Francis K. C. Hui, Samuel Muller, A. H. Welsh

When fitting generalized linear mixed models (GLMMs), one important decision to make relates to the choice of the random effects distribution. As the random effects are unobserved, misspecification of this distribution is a real possibility. In this article, we investigate the consequences of random effects misspecification for point prediction and predictio

Vitali Vougalter, Vitaly Volpert

The paper is devoted to the investigation of the solvability of an integro-differential equation in the case of the double scale anomalous diffusion with a sum of two negative Laplacians in different fractional powers in R^3. The proof of the existence of solutions relies on a fixed point technique. Solvability conditions for the elliptic operators without t

Shu-Rui Zhang, Jorge A. Rueda, Rodrigo Negreiros

The exceptionally low mass of $0.77_{-0.17}^{+0.2} M_{\odot}$ for the central compact object (CCO) XMMU J173203.3 -- 344518 (XMMU J1732) in the supernova remnant (SNR) HESS J1731 -- 347 challenges standard neutron star (NS) formation models. The nearby post-AGB star IRAS 17287 -- 3443 ($\approx 0.6 M_\odot$), also within the SNR, enriches the scenario. To ad

Emily Liu, Michael Noseworthy, Nicholas Roy

The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation. It is expensive and often infeasible for a robot to interact with many objects. Conversely, visual data of objects, without interaction, is abundantly available and can be leveraged for pretraining and feature extract

Gaurav Rai, Ojaswa Sharma

Animating hand-drawn sketches using traditional tools is challenging and complex. Sketches provide a visual basis for explanations, and animating these sketches offers an experience of real-time scenarios. We propose an approach for animating a given input sketch based on a descriptive text prompt. Our method utilizes a parametric representation of the sketc

Céline Fietz

Let $X$ be a projective variety with an isolated $A_2$ singularity. We study its bounded derived category and prove that there exists a crepant categorical resolution $\pi_*\colon \widetilde{\mathcal{D}} \to D^b(X)$, which is a Verdier localization. More importantly, we give an explicit description of a generating set for its kernel. In the case of an even d

Rui Pan, Zhuang Wang, Zhen Jia, Can Karakus

Hybrid models that combine the language modeling capabilities of Attention layers with the efficiency of Recurrent layers (e.g., State Space Models) have gained traction in practically supporting long contexts in Large Language Model serving. Yet, the unique properties of these models complicate the usage of complementary efficiency optimizations such as pre

Xi Zhang, Zaiqiao Meng, Jake Lever, Edmond S. L. Ho

Radiology report generation (RRG) requires advanced medical image analysis, effective temporal reasoning, and accurate text generation. While multimodal large language models (MLLMs) align with pre-trained vision encoders to enhance visual-language understanding, most existing methods rely on single-image analysis or rule-based heuristics to process multiple

Benita Nortmann, Mario Sassano, Thulasi Mylvaganam

Considering infinite-horizon, discrete-time, linear quadratic, N-player dynamic games with scalar dynamics, a graphical representation of feedback Nash equilibrium solutions is provided. This representation is utilised to derive conditions for the number and properties of different feedback Nash equilibria a game may admit. The results are illustrated via a

Philipp Braun, Timothy L. Molloy, Iman Shames

A new surveillance-evasion differential game is posed and solved in which an agile pursuer (the prying pedestrian) seeks to remain within a given surveillance range of a less agile evader that aims to escape. In contrast to previous surveillance-evasion games, the pursuer is agile in the sense of being able to instantaneously change the direction of its velo

Mehran Noori, Nahid Azimi-Tafreshi, Mohammad Salahshour

People's cooperation in adopting protective measures is effective in epidemic control and creates herd immunity as a public good. Similarly, the presence of an epidemic is a driving factor for the formation and improvement of cooperation. Here, we study the coevolution of epidemic dynamics and the public good game as a paradigm of cooperation dynamics. Using

Colby Fronk, Linda Petzold

Stiff systems of ordinary differential equations (ODEs) arise in a wide range of scientific and engineering disciplines and are traditionally solved using implicit integration methods due to their stability and efficiency. However, these methods are computationally expensive, particularly for applications requiring repeated integration, such as parameter est

Ethan J. Saunders, Peter Selinger

The game of Paintbucket was recently introduced by Amundsen and Erickson. It is played on a rectangular grid of black and white pixels. The players alternately fill in one of their opponent's connected components with their own color, until the entire board is just a single color. The player who makes the last move wins. It is not currently known whether the

Nadia Guiñazú, Pablo Neme, Jorge Oviedo

This paper examines equilibria in dynamic two-sided matching games, extending Gale and Shapley's foundational model to a non-cooperative, decentralized, and dynamic framework. We focus on markets where agents have utility functions and commitments vary. Specifically, we analyze a dynamic matching game in which firms make offers to workers in each period, con

Yiwei Ding, Alexander Lerch

More music foundation models are recently being released, promising a general, mostly task independent encoding of musical information. Common ways of adapting music foundation models to downstream tasks are probing and fine-tuning. These common transfer learning approaches, however, face challenges. Probing might lead to suboptimal performance because the p

Difei Zhang, Frank Schäfer, Julian Arnold

The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a discriminative or a generative method. In general, it is unclear which of these two approaches is most suitable

Yifan Chen, Chieh Tsao, Hendrik Utzat

The inherent non-linearity of intensity correlation functions can be used to spatially distinguish identical emitters beyond the diffraction limit, as achieved, for example, in Super-Resolution Optical Fluctuation Imaging (SOFI). Here, we propose a complementary concept based on spectral correlation functions, termed Spectral Fluctuation Super-Resolution (SF

Luben M. C. Cabezas, Guilherme P. Soares, Thiago R. Ramos, Rafael B. Stern

Constructing valid confidence sets is a crucial task in statistical inference, yet traditional methods often face challenges when dealing with complex models or limited observed sample sizes. These challenges are frequently encountered in modern applications, such as Likelihood-Free Inference (LFI). In these settings, confidence sets may fail to maintain a c

Boyan Sirakov, Philippe Souplet

We revisit the classical theory of linear second-order uniformly elliptic equations in divergence form whose solutions have H\"older continuous gradients, and prove versions of the generalized maximum principle, the $C^{1,\alpha}$-estimate, the Hopf-Oleinik lemma, the boundary weak Harnack inequality and the differential Harnack inequality, in which the cons

Neta Singer, Theophile Thiery

We consider the problem of finding an independent set of maximum weight simultaneously contained in $k$ matroids over a common ground set. This $k$-matroid intersection problem appears naturally in many contexts, for example in generalizing graph and hypergraph matching problems. In this paper, we provide a $(k+1)/(2 \ln 2)$-approximation algorithm for the w

Faith Ellen, Gal Sela

Because strongly-linearizable objects provide stronger guarantees than linearizability, they serve as valuable building blocks for the design of concurrent data structures. Yet, many objects that have linearizable implementations from base objects weaker than compare&swap objects do not have strongly-linearizable implementations from the same base objects. W

Pengcheng Zhang

The question of integer complexity asks about the minimal number of $1$'s that are needed to express a positive integer using only addition and multiplication (and parentheses). In this paper, we propose the notion of $l$-complexity of multiples of $l$, which specializes to integer complexity when $l=1$, prove several elementary results on $2$-complexity of

Florian Linß, Mike Hewitt, Janis S. Neufeld, Udo Buscher

We consider a capacitated job shop problem with order acceptance. This research is motivated by the management of a research and development project pipeline for a company in the agricultural industry whose success depends on regularly releasing new and innovative products. The setting requires the consideration of multiple problem characteristics not common

Ramy Fikri, Esraa Elkhateeb, E. I. Lashin, Waleed El Hanafy

Baryon Acoustic Oscillation (BAO) provides a powerful tool to measure cosmic expansion and consequently the nature of the Dark Energy (DE). Recent precise BAO measurements by Dark Energy Spectroscopic Instrument data release 1 (DESI DR1), when combined with Cosmic Microwave Background (CMB) data from Planck and Supernovae of Type Ia (SN Ia), favor evolving d

Maria Gabriella Cavalcante Basílio, Daniel Ratton Figueiredo

Coral reefs are crucial to marine biodiversity and rely on a delicate symbiotic relationship between corals and zooxanthellae algae. Water temperature variations, however, disrupt this association, leading to coral bleaching events that severely affect marine ecosystems. This study presents a mathematical model for the population dynamics of coral and symbio

Hui Dai, Dan Pechi, Xinyi Yang, Garvit Banga

The Needle-in-a-haystack (NIAH) test is a general task used to assess language models' (LMs') abilities to recall particular information from long input context. This framework however does not provide a means of analyzing what factors, beyond context length, contribute to LMs' abilities or inabilities to separate and recall needles from their haystacks. To

Dickness Kakitahi Kwesiga, Suyash Chandra Vishnoi, Angshuman Guin, Michael Hunter

This study integrates Transit Signal Priority (TSP) into multi-agent reinforcement learning (MARL) based traffic signal control. The first part of the study develops adaptive signal control based on MARL for a pair of coordinated intersections in a microscopic simulation environment. The two agents, one for each intersection, are centrally trained using a va

Vikas Kambhampati, Nehaz Hussain Mohammed, Amin Milani Fard

JavaScript has been consistently among the most popular programming languages in the past decade. However, its dynamic, weakly-typed, and asynchronous nature can make it challenging to write maintainable code for developers without in-depth knowledge of the language. Consequently, many JavaScript applications tend to contain code smells that adversely influe

Diego Lauer, Jhon W. González, Eric Suárez Morell, Andrés Ayuela

The magnetic properties of CrI$_3$ monolayers, which were recently measured, have been investigated considering electronic repulsion and localization effects in Cr 3d orbitals. In this study, we propose a DFT approach using Hubbard U corrections to improve accuracy. We compare the valence density-of-states using the HSE06 hybrid functional and the DFT+U appr

Philipp Brauner, Felix Glawe, Gian Luca Liehner, Luisa Vervier

Understanding public perception of artificial intelligence (AI) and the tradeoffs between potential risks and benefits is crucial, as these perceptions might shape policy decisions, influence innovation trajectories for successful market strategies, and determine individual and societal acceptance of AI technologies. Using a representative sample of 1100 par

Roberto Bruschini, Pedro González, Tatiana Tarutina

We revisit the phenomenological ${^{3}\!}P_{0}$ model for the decay of quarkonium $\left( Q\bar{Q}\right) $ into two open flavor mesons ($\bar{\mathfrak{M}}\mathfrak{M}% $). We take the heavy-quark limit and derive a transition rate between $Q\bar{Q}$ and $\bar{\mathfrak{M}}\mathfrak{M}$ to be compared with the one calculated in studies of string breaking us

José António Filipe, Manuel Alberto M. Ferreira, Manuel Coelho, Maria Isabel C. Pedro

The problems raised by anti-commons and bureaucracy have been linked since the study of Buchanan and Yoon (2000). Bureaucracy involves a multitude of agents that have deciding power. At the view of conflicting interests, the decision makers inertia or the inertia of the system itself, excessive administrative procedures or too many administrative circuits pu

Manoj RameshChandra Thakur

Dynamic taint tracking is the process of assigning label to variables in a program and then tracking the flow of the labels as the program executes. Dynamic taint tracking for java applications is achieved by instrumenting the application ie. adding parallel variable for each actual variable of the program and inserting additional bytecode instructions to tr

Davide Cipollini, Hugh Greatorex, Michele Mastella, Elisabetta Chicca

In an era characterized by the rapid growth of data processing, developing new and efficient data processing technologies has become a priority. We address this by proposing a novel type of neuromorphic technology we call Fused-MemBrain. Our proposal is inspired by Golgi's theory modeling the brain as a syncytial continuum, in contrast to Cajal's theory of n

Se-eun Yoon, Xiaokai Wei, Yexi Jiang, Rachit Pareek

In this paper, we present a systematic effort to design, evaluate, and implement a realistic conversational recommender system (CRS). The objective of our system is to allow users to input free-form text to request recommendations, and then receive a list of relevant and diverse items. While previous work on synthetic queries augments large language models (

Aleksey Bolotnikov, Anwar Irmatov

This article presents examples of an application of the finite field method for the computation of the characteristic polynomial of the matching arrangement of a graph. Weight functions on edges of a graph with weights from a finite field are divided into proper and improper functions in connection with proper colorings of vertices of the matching polytope o

Aleksander Głódkowski, Paweł Matus, Francisco Peña-Benítez, Lazaros Tsaloukidis

Motivated by the duality between elasticity and fracton gauge theory, we study an extension of the gauge group that includes the quadrupole moment. Remarkably, we find that spontaneous breaking of the quadrupole symmetry increases the number of massless excitations. This result appears to challenge the well-established paradigm, according to which gauge fiel

Andrei D. Polyanin, Nikolay A. Kudryashov

For the first time, Schr\"odinger equations with cubic and more complex nonlinearities containing the unknown function with constant delay are analyzed. The physical considerations that can lead to the appearance of a delay in such nonlinear equations and mathematical models are expressed. One-dimensional non-symmetry reductions are described, which lead the

S. Yu. Shugarov, P. Yu. Golysheva, S. Dallaporta, U. Munari

We present a status report of our intensive and long-term UBV RI photometric monitoring of nova KT Eri since its outbust in 2009. The old-nova in quiescence is characterized by very high excitation conditions (HeII 4686 being constantly the strongest emission line in optical spectra) and a complex-pattern photometric variability of one mag amplitude in which

Ivan Chajda, Helmut Länger

The Sasaki projection and its dual were introduced as a mapping from the lattice of closed subspaces of a Hilbert space onto one of its segments. In a previous paper the authors showed that the Sasaki operations induced by the Sasaki projection and its dual form an adjoint pair in every orthomodular lattice. Later on the authors described large classes of al

Mohamed Fazli Imam, Rufael Fedaku Marew, Jameel Hassan, Mustansar Fiaz

In the era of foundation models, CLIP has emerged as a powerful tool for aligning text & visual modalities into a common embedding space. However, the alignment objective used to train CLIP often results in subpar visual features for fine-grained tasks. In contrast, SSL-pretrained models like DINO excel at extracting rich visual features due to their special

Sima Soltanpour, Arnold Chang, Dan Madularu, Praveen Kulkarni

Functional magnetic resonance imaging (fMRI) is extensively used in clinical and preclinical settings to study brain function, however, fMRI data is inherently noisy due to physiological processes, hardware, and external noise. Denoising is one of the main preprocessing steps in any fMRI analysis pipeline. This process is challenging in preclinical data in c

Amir M. Hajisadeghi, Hamid R. Zarandi, Mahmoud Momtazpour

In-memory computing (IMC) offloads parts of the computations to memory to fulfill the performance and energy demands of applications such as neuromorphic computing, machine learning, and image processing. Fortunately, the main features that stochastic computing (SC) and IMC share, which are low computation complexity and high bit-parallel computation capabil

Simone Prili, Valeria Bragaglia, Vara Prasad Jonnalagadda, Jesse Luchtenveld

Highly textured chalcogenide films have recently gained significant interest for phase-change memory applications. Several reports have highlighted that programming efficiency improves in devices featuring superlattice stacks, such as Ge2Sb2Te5/Sb2Te3. However, to be technologically relevant, these films must be deposited on foundry-scale wafers using proces

Saki Koizumi

In the path integral formulation of the superstring, the chiral measure acquires a phase under the modular transformation of a Riemann surface. This motivated the use of anomaly inflow to define the superstring chiral measure by a path integral formalism of a modular invariant $3$-dimensional theory. A Gelca-Hamilton topological field theory (TQFT) is one of

Rocco Manz Maruzzelli, Basile Lewandowski, Lydia Y. Chen

Diffusion models (DMs) generate remarkable high quality images via the stochastic denoising process, which unfortunately incurs high sampling time. Post-quantizing the trained diffusion models in fixed bit-widths, e.g., 4 bits on weights and 8 bits on activation, is shown effective in accelerating sampling time while maintaining the image quality. Motivated

Saptarshi Sengupta, Harsh Vashistha, Kristal Curtis, Akshay Mallipeddi

Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training an LLM is not always feasible because of the scarcity of domain data, legal holds on proprietary customer data, rapidly changing business requirements, and the need to prototype n

Ahmed Jaafar, Shreyas Sundara Raman, Sudarshan Harithas, Yichen Wei

Learning to execute long-horizon mobile manipulation tasks is crucial for advancing robotics in household and workplace settings. However, current approaches are typically data-inefficient, underscoring the need for improved models that require realistically sized benchmarks to evaluate their efficiency. To address this, we introduce the LAMBDA ({\lambda}) b

Heejeong Nam, Jihyun Kim, Jimin Yeom

Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in f

L. Linan, T. Baratashvili, A. Lani, B. Schmieder

This paper aims to present the time-dependent coupling between the coronal model COolfluid COroNal UnsTructured (COCONUT) and the heliospheric forecasting tool EUHFORIA. We perform six COCONUT simulations where a flux rope is implemented at the solar surface using either the Titov-D\'emoulin CME model or the Regularized Biot-Savart Laws (RBSL) CME model. At

Matthew Niedoba, Berend Zwartsenberg, Kevin Murphy, Frank Wood

We propose a simple, training-free mechanism which explains the generalization behaviour of diffusion models. By comparing pre-trained diffusion models to their theoretically optimal empirical counterparts, we identify a shared local inductive bias across a variety of network architectures. From this observation, we hypothesize that network denoisers general

Luca Chirolli, Angelo Greco, Alessandro Crippa, Elia Strambini

The Josephson diode effect describes the property of a Josephson junction to have different values of the critical current for different direction of applied bias current and it is the focus of intense research thanks to the possible applications. The ubiquity of the effect experimentally reported calls for a study of the impact that disorder can have in the

Dylan Bansard-Tresse

We study the process of suitably normalized successive return times to rare events in the setting of infinite-measure preserving dynamical systems. Specifically, we consider small neighborhoods of points whose measure tends to zero. We obtain two types of results. First, we conduct a detailed study of a class of interval maps with a neutral fixed point and w

Ali BenAmor

We investigate some analytic properties of traces of Dirichlet forms with respect to measures satisfying Hardy-type inequality. Among other results we prove convergence of spectra, ordered eigenvalues, eigenfunctions as well as convergence of resolvents on appropriate spaces, for traces of Dirichlet forms when the speed measure is the monotone weak limit of

Shenghui Li, Edith C. -H. Ngai, Fanghua Ye, Thiemo Voigt

Federated Parameter-Efficient Fine-Tuning (FedPEFT) has emerged as a promising paradigm for privacy-preserving and efficient adaptation of Pre-trained Language Models (PLMs) in Federated Learning (FL) settings. It preserves data privacy by keeping the data decentralized and training the model on local devices, ensuring that raw data never leaves the user's d

Boya Di, Hongliang Zhang, Rui Zhang, Zhu Han

Evolving from massive multiple-input multiple-output (MIMO) in current 5G communications, ultra-massive MIMO emerges as a seminal technology for fulfilling more stringent requirements of future 6G communications. However, widely-utilized phased arrays relying on active components make the implementation of ultra-massive MIMO in practice increasingly prohibit

Sunder S. K. Singh-Bal, George A. Blaylock-Squibbs, Richard J. Parker, Simon P. Goodwin

The stellar mass distribution in star-forming regions, stellar clusters and associations, the Initial Mass Function (IMF), appears to be invariant across different star-forming environments, and is consistent with the IMF observed in the Galactic field. Deviations from the field, or standard, IMF, if genuine, would be considered strong evidence for a differe

Aklant K Bhowmick, Laura Blecha, Paul Torrey, Rachel S Somerville

The first "seeds" of supermassive black holes (BHs) continue to be an outstanding puzzle, and it is currently unclear whether the imprints of early seed formation survive today. Here we examine the signatures of seeding in the local Universe using five $[18~\mathrm{Mpc}]^3$ BRAHMA simulation boxes run to $z=0$. They initialize $1.5\times10^5~M_{\odot}$ BHs u