November 2024 arXiv papers — page 126

Lei Huang, Peng-fei Wang, Han-xiao Zhang, Yu Zhu

In the regime of Rydberg electromagnetically induced transparency, we study the correlated behaviors between the transmission spectra of a pair of probe fields passing through respective parallel one-dimensional cold Rydberg ensembles. Due to the van der Waals (vdW) interactions between Rydberg atoms, each ensemble exhibits a local optical nonlinearity, wher

Dubing Chen, Jin Fang, Wencheng Han, Xinjing Cheng

3D semantic occupancy and flow prediction are fundamental to spatiotemporal scene understanding. This paper proposes a vision-based framework with three targeted improvements. First, we introduce an occlusion-aware adaptive lifting mechanism incorporating depth denoising. This enhances the robustness of 2D-to-3D feature transformation while mitigating relian

Huynh Khanh, Bui Trong Kien, Arnd Rösch

A class of time-optimal control problems governed by semilinear parabolic equations with mixed pointwise constraints and final point constraints is considered. By introducing the so-called locally optimal solution to time-optimal control problems, we establish first and second-order necessary optimality conditions of KKT-type and second-order sufficient cond

Zirui Shao, Feiyu Gao, Zhaoqing Zhu, Chuwei Luo

Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, due to different types of annotation noise in training, current MLL

Jiri Jaros, Michal Majer, Jakub Horky, Jan Vavra

Mastering computational architectures is essential for developing fast and power-efficient programs. Our advanced simulator empowers both IT students and professionals to grasp the fundamentals of superscalar RISC-V processors, HW/SW co-design and HPC optimization techniques. With customizable processor and memory architecture, full C compiler support, and d

Yue Feng, Georg Maierhofer, Chushan Wang

The numerical approximation of low-regularity solutions to the nonlinear Schr\"odinger equation is notoriously difficult and even more so if structure-preserving schemes are sought. Recent works have been successful in establishing symmetric low-regularity integrators for this equation. However, so far, all prior symmetric low-regularity algorithms are fully

Jihan K. Zaki, Jakub Tomasik, Jade A. McCune, Sabine Bahn

Surface-enhanced Raman spectroscopy (SERS) is a potential fast and inexpensive method of analyte quantification, which can be combined with deep learning to discover biomarker-disease relationships. This study aims to address present challenges of SERS through a novel SERS bio-quantification framework, including spectral processing, analyte quantification, a

Niklas Hanselmann, Simon Doll, Marius Cordts, Hendrik P. A. Lensch

To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning needs to be robust to the long-tail of errors incurred by a no

Mojtaba Eshghie, Viktor Åryd, Cyrille Artho, Martin Monperrus

Structured code differencing is the act of comparing the hierarchical structure of code via its abstract syntax tree (AST) to capture modifications. AST-based source code differencing enables tasks such as vulnerability detection and automated repair where traditional line-based differencing falls short. We introduce SoliDiffy, the first AST differencing too

Samarth G. Kashyap, Shravan M. Hanasoge

Solar meridional circulation, which manifests as poleward flow near the surface, is a relatively weak flow. While meridional circulation has been measured through various local helioseismic techniques, there is a lack of consensus about the nature of the depth profile and location of return flow, owing to its small amplitude and poor signal-to-noise ratio in

Riya Gaba, Maitreyee Mukherjee, Vipin Bhatnagar

Supernovae represent some of the most energetically explosive events in the universe, with a substantial fraction of their released gravitational energy carried away by neutrinos. This study evaluates the sensitivity of three next-generation neutrino detectors which are Deep Underground Neutrino Experiment (DUNE), Hyper-Kamiokande (Hyper-K), and the Jiangmen

Yiming Ju, Huanhuan Ma

In 2022, with the release of ChatGPT, large-scale language models gained widespread attention. ChatGPT not only surpassed previous models in terms of parameters and the scale of its pretraining corpus but also achieved revolutionary performance improvements through fine-tuning on a vast amount of high-quality, human-annotated data. This progress has led ente

Bas van den Heuvel, Daniele Nantes-Sobrinho, Joseph W. N. Paulus, Jorge A. Pérez

We consider the problem of designing typed concurrent calculi with non-deterministic choice in which types leverage linearity for controlling resources, thereby ensuring strong correctness properties for processes. This problem is constrained by the delicate tension between non-determinism and linearity. Prior work developed a session-typed {\pi}-calculus wi

Dhong Yeon Cheong, Koichi Hamaguchi, Yoshiki Kanazawa, Sung Mook Lee

Non-perturbative gravitational effects induce explicit global symmetry breaking terms within axion models. These exponentially suppressed terms in the potential give a mass contribution to the axion-like particles (ALPs). In this work we investigate this scenario with a scalar field charged under a global $U(1)$ symmetry and having a non-minimal coupling to

Thomas Christiansen, Katrin Grunert

We prove that $\alpha$-dissipative solutions to the Cauchy problem of the Hunter-Saxton equation, where $\alpha \in W^{1, \infty}(\mathbb{R}, [0, 1))$, can be computed numerically with order $\mathcal{O}(\Delta x^{{1}/{8}}+\Delta x^{{\beta}/{4}})$ in $L^{\infty}(\mathbb{R})$, provided there exist constants $C > 0$ and $\beta \in (0, 1]$ such that the initial

Jiaxi Li, Lu Yin, Xilu Wang

The integration of Large Language Models (LLMs) into autonomous driving systems offers promising enhancements in environmental understanding and decision-making. However, the substantial computational demands of deploying LLMs locally on vehicles render this approach unfeasible for real-world automotive applications. To address this challenge, we introduce O

Emilie Mai Elkiær

We show via an application of techniques from complex interpolation theory how the $L^p$-pseudofunction algebras of a locally compact group $G$ can be understood as sitting between $L^1(G)$ and $C^*(G)$. Motivated by this, we collect and review various characterizations of group amenability connected to the $p$-pseudofunction algebra of Herz and generalize t

Evelin Bakos, Henk Boterenbrood, Mark Dönszelmann, Florian Egli

The ATLAS Muon Drift Tube (MDT) ReadOut Drivers (MROD), 204 VME modules that are an essential part of the readout chain of the 1,150 MDT chambers, have been in operation for more than 15 years and are expected to remain in operation until about 2026. In the event of extensive failures the number of spare MROD modules may be insufficient. However, deployment

Sanchayan Vivekananthan

This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion recognition systems algorithms primarily train on adult faces, the model developed is achieved by using advanced concepts of

Bin Gui, Hao Zhang

Let $\mathbb V=\bigoplus_{n\in\mathbb N}\mathbb V(n)$ be a $C_2$-cofinite vertex operator algebra. We prove the convergence of Segal's sewing of conformal blocks associated to analytic families of pointed compact Riemann surfaces and grading-restricted generalized $\mathbb V^{\otimes N}$-modules (where $N=1,2,\dots$) that are not necessarily tensor produ

Peter O'Donovan, Philipp Strasberg, Kavan Modi, John Goold

We use the eigenstate thermalization hypothesis to derive a quantum master equation for a system weakly coupled to a chaotic finite-sized bath prepared in a pure state. We show that the emergence of Markovianity is controlled by the spectral function of the ETH and that local detailed balance emerges in the Markovian regime for a broad class of pure bath sta

David H. Lee, Aditya Prasad, Ramiro Deo-Campo Vuong, Tianyu Wang

Dynamic programming (DP) is a fundamental and powerful algorithmic paradigm taught in most undergraduate (and many graduate) algorithms classes. DP problems are challenging for many computer science students because they require identifying unique problem structures and a refined understanding of recursion. In this paper, we present dpvis, a Python library t

Hiro Wakimura, Takayuki Aoki, Feng Xiao

A low-dissipation numerical method for compressible gas-liquid two-phase flow with phase change on unstructured grids is proposed. The governing equations adopt the six-equation model. The non-conservative terms included in the volume fraction and total energy equations of the six-equation model are defined on cell boundaries using second-order accurate appr

Lukas Tobias Hergt, Douglas Scott

The standard cosmological model is sufficiently well constrained that precise estimates can be provided for the redshift of various physically defined times in the chronology of the Universe. For example, it is well known that matter-radiation equality, recombination and reionisation happen at redshifts of around 3000, 1000 and 10, respectively, and these ca

Pouyan Fotouhi Tehrani, Raphael Hiesgen, Thomas C. Schmidt, Matthias Wählisch

Certification Authority Authentication (CAA) is a safeguard against illegitimate certificate issuance. We show how shortcomings in CAA concepts and operational aspects undermine its effectiveness in preventing certificate misissuance. Our discussion reveals pitfalls and highlights best practices when designing security protocols based on DNS.

Rodrigo Carmo Terin

We propose a detailed analysis of datasets generated from simulations of two-dimensional quantum spin systems using the quantum Ising model at absolute zero temperature. Our focus is on examining how fundamental physical properties, energy, magnetization, and entanglement entropy, evolve under varying external transverse magnetic fields and system sizes. Fro

Long He, Geng Sun, Zemin Sun, Jiacheng Wang

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the

Stefan Pranger, Hana Chockler, Martin Tappler, Bettina Könighofer

In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are very complex, testing efforts should concentrate on states in which the agent's decisions have the highest impact on the expected outcome. In this paper, we propose a novel model-ba

Shuocheng Yang, Yueming Cao, Shengbo Eben Li, Jianqiang Wang

Odometry in adverse weather conditions, such as fog, rain, and snow, presents significant challenges, as traditional vision and LiDAR-based methods often suffer from degraded performance. Radar-Inertial Odometry (RIO) has emerged as a promising solution due to its resilience in such environments. In this paper, we present RINO, a non-iterative RIO framework

Himanshu Gaur

We investigate mixed-state bipartite entanglement between multiple disjoint intervals using the computable cross-norm criterion (CCNR). We consider entanglement between a single interval and the union of remaining disjoint intervals, and compute $2$-R\'enyi CCNR negativity for $2$d massless compact boson. The expression for $2$-R\'enyi CCNR negativity is giv

Richard P. Anstee, Benjamin Kreiswirth, Bowen Li, Attila Sali

Stability is a well investigated concept in extremal combinatorics. The main idea is that if some object is close in size to an extremal object, then it retains the structure of the extremal construction. In the present paper we study stability in the context of forbidden configurations. $(0,1)$-matrix $F$ is a configuration in a $(0,1)$-matrix $A$ if $F$ is

Gabriel López Castro, Alejandro Miranda, Pablo Roig

We revisit the isospin-breaking corrections relating the $e^+e^-$ hadronic cross-section and the tau decay spectral function, focusing on the di-pion channel, that gives the dominant contribution to the hadronic vacuum polarization piece of the muon $g-2$. We test different types of electromagnetic and weak form factors and show that both, the Gounaris-Sakur

J. Scholtz, M. Curti, F. D'Eugenio, H. Übler

We present deep JWST/NIRSpec integral-field spectroscopy (IFS) and ALMA [CII]$\lambda$158$\mu$m observations of COS-3018, a star-forming galaxy at z$\sim$6.85, as part of the GA-NIFS programme. Both G395H (R$\sim$ 2700) and PRISM (R$\sim$ 100) NIRSpec observations revealed that COS-3018 is comprised of three separate components detected in [OIII]$\lambda$500

Angus Crookes, Ben Yuen, Angela Demetriadou

Plasmonic nanocavities enable access to the quantum properties of matter, but are often simplified to single mode models despite their complex multimode structure. Here, we show that off-resonant plasmonic modes in fact play a crucial role in strong coupling, and determine the onset of a novel collective interaction. Our analysis reveals that $n$ strongly co

Xing-Kun Zhang, Xin Zhao, Zhang-Yu Nie, Ya-Peng Hu

In this work, we investigate the interior structure of a holographic multi-band superconductor model. We focus on the holographic superconductor system with two scalar fields which correspond to two s-wave order parameters in the dual condensed matter system. We discover a new kind of transition rule among Kasner universe near the black hole singularity whic

Kaloian D. Lozanov, Misao Sasaki

We consider a scalar field theory with a Minkowski false vacuum and an unbounded (or very deep) true vacuum. We show compelling evidence that an AdS bubble of vanishing total energy, embedded in asymptotically flat spacetime, generically undergoes a spherical collapse which leads to a space-like curvature singularity after the formation of trapped surfaces a

Meng Yang, Tianqing Zhu, Chi Liu, WanLei Zhou

Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on various tasks, such as neural language processing, computer vision, and more. Despite their powerful capabilities, pre-trained models have also sparked attention to the emerging security

Zifan Zeng, Chongzhe Zhang, Feng Liu, Joseph Sifakis

With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially in the context of AI agents. It is arguably evolving into an essential foundation for building AI agent systems. A WM is intended to help the agent predict the future evolution of

Corentin Correia

Given a measure equivalence coupling between two finitely generated groups, Delabie, Koivisto, Le Ma\^itre and Tessera have found explicit upper bounds on how integrable the associated cocycles can be. These bounds are optimal in many cases but the integrability of the cocycles with respect to these critical thresholds remained unclear. For instance, a cocyc

Zilun Zhang, Haozhan Shen, Tiancheng Zhao, Zian Guan

Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 $\times$ 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size, the extensive spatial and contextual information that UHR images contain will be neglected.

Roberto Sala, Bruno Guindani, Enrico Galimberti, Federica Filippini

This paper proposes an automated framework for efficient application profiling and training of Machine Learning (ML) performance models, composed of two parts: OSCAR-P and aMLLibrary. OSCAR-P is an auto-profiling tool designed to automatically test serverless application workflows running on multiple hardware and node combinations in cloud and edge environme

Suman Rath, Subham Sahoo

Distributed microgrids are conventionally dependent on communication networks to achieve secondary control objectives. This dependence makes them vulnerable to stealth data integrity attacks (DIAs) where adversaries may perform manipulations via infected transmitters and repeaters to jeopardize stability. This paper presents a physics-guided, supervised Arti

Deng Xu, Chao Zhang, Zechao Li, Chunlin Chen

Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some

Yudara Kularathne, Janitha Prathapa, Prarththanan Sothyrajah, Salomi Arasaratnam

This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point detection on both images and videos to measure penile curvature angles, utilizing advanced computer vision techniques. This tool has demonstrated high accuracy in identifying anat

Yejian Lyu, Zeyu Huang, Stefan Schwarz, Chong Han

THz ISAC aims to integrate novel functionalities, such as positioning and environmental sensing, into communication systems. Accurate channel modeling is crucial for the design and performance evaluation of future ISAC systems. In this paper, a THz measurement campaign for monostatic sensing is presented. VNA-based channel measurements are conducted in a lab

Francesco Calogero

The evolution, as functions of the "ticking time" $\ell =0,1,2,...$, of the solutions of the system of $N$ quadratic recursions \begin{eqnarray*} x_{n}\left( \ell +1\right) =c_{n}+\sum_{m=1}^{N}\left[ C_{nm}x_{m}\left( \ell \right) \right] +\sum_{m=1}^{N}\left\{ d_{nm}\left[ x_{m}\left( \ell \right) \right] ^{2}\right\} +\sum_{m_{1}>m_{2}=1}^{N}\left[ D_{nm_

Katie Kang, Amrith Setlur, Dibya Ghosh, Jacob Steinhardt

Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream generalization. Our analysis focuses on reasoning tasks, whose problem structure allows us to distinguish betw

Lukas Gräfner, Nicolas Perkowski, Shyam Popat

In this paper we extend the theory of energy solutions for singular SPDEs, focusing on equations driven by highly irregular noise with bilinear nonlinearities, including scaling critical examples. By introducing Gelfand triples and leveraging infinite-dimensional analysis in Hilbert spaces together with an integration by parts formula under the invariant mea

Tongxin Li, Tinashe Handina, Shaolei Ren, Adam Wierman

The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors given a set of types, each specifying a possible behavior for the other agents. The idea is to plan an agent's own actions with respect to those types which it believes are most likel

Wei Peng, Kang Liu, Jiaxi Shi, Jianchen Hu

The electroencephalography (EEG)-based motor imagery (MI) classification is a critical and challenging task in brain-computer interface (BCI) technology, which plays a significant role in assisting patients with functional impairments to regain mobility. We present a novel multi-scale atrous convolutional neural network (CNN) model called EEG-dilated convolu

Cosimo Mandriota, Nicola Menga, Giuseppe Carbone

We present our recent study on rough adhesive contacts of viscoelastic materials in steady-state sliding, focusing on the interplay between adhesion and viscoelasticity by means of a novel energy approach. We investigate tribological features over a wide range of velocity values, exploring the effect of small- and large- scale viscoelasticity on the overall

Jasper Roe, Mike Perkins

This scoping review examines the current body of knowledge at the intersection of Generative Artificial Intelligence (GenAI) and Self-Directed Learning (SDL). By synthesising the findings from 18 studies published from 2020 to 2024 and following the PRISMA-SCR guidelines for scoping reviews, we developed four key themes. This includes GenAI as a Potential En

Stefano Rinaldi, María Claudia Ramírez-Tannus

Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepen our understanding of these processes. We propose a method to investigate the observed radial velocity distribution of massive stars in young clusters with the goal of identifying binary systems. We reconstruct the radial velocity distr

Daniel Barlet

We study the action of symmetric PDO on the module of anti-symmetric functions in $z_1, \dots, z_k$. We show that over the Weyl algebra of the elementary symmetric functions, this module is simple.

Ngoc-Sang Pham

We prove that a two-cycle equilibrium in a general equilibrium model with infinitely-lived agents (GEILA) constitutes an equilibrium in an overlapping generations (OLG) model. Conversely, an equilibrium in an OLG model that satisfies additional conditions is part of an equilibrium in a GEILA model. Our framework, which includes three assets (physical capital

Maxime Chatal, Claire Chavaudret, L. Hakan Eliasson

We prove that infinitely differentiable almost reducible quasi-periodic cocycles, under a Diophantine condition on the frequency vector, are almost reducible to a sequence of real constant cocycles with a sequence of real conjugations, up to a period doubling.

Yilun Zheng, Zhuofan Zhang, Ziming Wang, Xiang Li

To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing issues like heterophily, over-squashing, and noisy structures. While GSL is generally thought to improve GNN performance, it often leads to longer training times and

Celal Umut Yaran, Mine Çağlar

We study general Markov additive processes when the state space of the modulator is a Polish space. Under some regularity assumptions, our main result is the characterization of the long-time behavior of the ordinate in terms of the associated ladder time process and the excursion measure. An important application of Markov additive processes is the Lamperti

Victor Levrague, R. Delorme, Mathieu Roccia, Ngoc Hoang Tran

This work aims at investigating the impact of DNA geometry, compaction and calculation chain on DNA break and chromosome aberration predictions for high charge and energy (HZE) ions, using the Monte Carlo codes Geant4-DNA, RITRACKS and RITCARD. To ensure consistency of ion transport of both codes, we first compared microdosimetry and nanodosimetry spectra fo

Julien Birgi

This paper revisits the well-known wood-furniture cluster of Jepara (Central Java, Indonesia) with new parameters inspired by the theories of industrial districts and clusters. So far, literature on this production centre, mostly focused on value chain analysis, has failed to explain how it could survive external shocks and rising pressure of global competit

Marina Garcia-Berenguer, Lucas Gasparino, Oriol Lehmkuhl, Ivette Rodriguez

This study analyzes the behavior of a differentially heated channel flow by means of a direct numerical simulations (DNS) with variable thermophysical properties under low-speed conditions focusing on the impact of the temperature gradient on the turbulence structures near the channel walls. The simulations were conducted at a mean friction Reynolds number o

Stefan Wagner, Marvin Muñoz Barón, Davide Falessi, Sebastian Baltes

In the short period since the release of ChatGPT, large language models (LLMs) have changed the software engineering research landscape. While there are numerous opportunities to use LLMs for supporting research or software engineering tasks, solid science needs rigorous empirical evaluations. However, so far, there are no specific guidelines for conducting

Joseph Tooby-Smith

The physics community relies on index notation to effectively manipulate types of tensors. This paper introduces the first formally verified implementation of index notation in the interactive theorem prover Lean 4. By integrating index notation into Lean, we bridge the gap between traditional physics notation and formal verification tools, making it more ac

Huy Q. Nguyen, Ivan Derkach, Adnan A. E. Hajomer, Hou-Man Chin

Squeezed light plays a vital role in quantum information processing. By nature, it is highly sensitive, which presents significant practical challenges, particularly in remote detection, traditionally requiring complex systems such as active phase locking, clock synchronization, and polarization control. Here, we propose and demonstrate an asynchronous detec

Hichem Rami Ait El Hara, François Bobot, Guillaume Bury

The SMT (Satisfiability Modulo Theories) theory of arrays is well-established and widely used, with variousdecision procedures and extensions developed for it. However, recent works suggest that developing tailoredreasoning for some theories, such as sequences and strings, is more efficient than reasoning over them throughaxiomatization over the theory of ar

Shang Hong Sim, Clarence Lee, Alvin Tan, Cheston Tan

Understanding spatial relations is a crucial cognitive ability for both humans and AI. While current research has predominantly focused on the benchmarking of text-to-image (T2I) models, we propose a more comprehensive evaluation that includes \textit{both} T2I and Large Language Models (LLMs). As spatial relations are naturally understood in a visuo-spatial

Yilun Zheng, Xiang Li, Sitao Luan, Xiaojiang Peng

Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph convolution, we raise an important question: Is the graph convolution operation equally beneficial for each feature? If not, the convolution operation on certain feature dimension

Mathilde Bouvel, Eric S. Egge, Rebecca N. Smith, Jessica Striker

We completely classify the asymptotic behavior of the number of alternating sign matrices classically avoiding a single permutation pattern, in the sense of [Johansson and Linusson 2007]. In particular, we give a uniform proof of an exponential upper bound for the number of alternating sign matrices classically avoiding one of eleven particular patterns, and

Xinhua Shen, Zaijiu Shang, Hongpeng Sun

This paper introduces a second-order convex splitting scheme for gradient flows arising in phase-field models, based on the backward differentiation formula (BDF2) for the implicit part and the Adams-Bashforth method for the nonlinear and explicit component. The method is formulated and analyzed in finite-dimensional spaces, where energy stability plays a ce

Cheng Jin, Luyang Luo, Huangjing Lin, Jun Hou

Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which presents a significant challenge. While the mul

V. I. Bakhtin, N. A. Tsarev

In the paper we prove criteria for convexity and concavity of $f$-potentials ($f$-means, Kolmogorov means, weighted quasi-arithmetic means), which particular cases are the arithmetic, geometric, harmonic means, the thermodynamic potential (exponential mean), and the $L^{p}$-norm. Then we compute in quadratures all functions $f$ satisfying these criteria.

Xin Zhou, Lei Zhang, Honglei Zhang, Yixin Zhang

Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts. Recommender systems, which generate personalized suggestions ba

Hong Wang, Ming Lu, Jie Liu

We investigate the interplay between non-Hermitian skin effect (NHSE) and topological properties in two-dimensional topological superconductor. Two kinds of non-Hermiticity are considered. The first is the spin-independent non-reciprocal hopping, which respects particle-hole symmetry (PHS) and dictates the bulk as well as topological edge modes localize at o

Tianyi Huang, Arya Somasundaram

Large Language Models (LLMs) often perpetuate biases in pronoun usage, leading to misrepresentation or exclusion of queer individuals. This paper addresses the specific problem of biased pronoun usage in LLM outputs, particularly the inappropriate use of traditionally gendered pronouns ("he," "she") when inclusive language is needed to accurately represent a

Vasilios Cocotos, Light Mkhumbuza, Kayn A. Forbes, Robert de Mello Koch

Laguerre-Gaussian (LG) modes are solutions of the paraxial Helmholtz equation in cylindrical coordinates and are associated with light fields carrying orbital angular momentum (OAM). It is customary to modulate such beams using phase-only vortex profiles, for example, when increasing (laddering up) or decreasing (laddering down) the OAM content of some given

Yubo Song, Subham Sahoo

Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without the need of communication at all. This p

Rina Mishima, Takuro Nagai, Hiroyo Segawa, Masahiro Ehara

Moire superlattices (MSLs) are an emerging class of two-dimensional functional materials whose electronic states can be tuned by the twist angle between two van der Waals layers and/or the relative placement of the layers. The intriguing properties of MSLs are closely correlated to the moir\'e potential, which is the electrostatic potential induced by interl

Ted Dobson, Gregory Robson

Let $G$ be a group. The BCI problem asks whether two Haar graphs of $G$ are isomorphic if and only if they are isomorphic by an element of an explicit list of isomorphisms. We first generalize this problem in a natural way and give a theoretical way to solve the isomorphism problem for the natural generalization. We then restrict our attention to abelian gro

Ammarah Hashmi, Sahibzada Adil Shahzad, Chia-Wen Lin, Yu Tsao

Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deepfake detection, identifying complex deep

Juan P. Martinez-Esteso, Francisco J. Castellanos, Jorge Calvo-Zaragoza, Antonio Javier Gallego

The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with cameras and other integrated sensors. O

Masaaki Shimada, Albert Escrivá, Daiki Saito, Koichiro Uehara

This study investigates the formation of primordial black holes (PBHs) resulting from the collapse of adiabatic fluctuations with large amplitudes and non-Gaussianity. Ref. \cite{Uehara:2024yyp} showed that fluctuations with large amplitudes lead to the formation of type B PBHs, characterized by the existence of the bifurcating trapping horizons, distinct fr

Ryoto Inui, Cristian Joana, Hayato Motohashi, Shi Pi

We investigate the formation of primordial black hole (PBH) based on numerical relativity simulations and peak theory as well as the corresponding scalar induced gravitational wave (SIGW) signals in the presence of \emph{logarithmic non-Gaussianities} which has recently been confirmed in a wide class of inflation models. Through numerical calculations, we fi

Tim Bode, Krish Ramesh, Tobias Stollenwerk

Advances in quantum algorithms suggest a tentative scaling advantage on certain combinatorial optimization problems. Recent work, however, has also reinforced the idea that barren plateaus render variational algorithms ineffective on large Hilbert spaces. Hence, finding annealing protocols by variation ultimately appears to be difficult. Similarly, the adiab

Daomin Cao, Shuanglong Li, Guodong Wang

In this paper, we construct a family of global solutions to the incompressible Euler equation on a standard 2-sphere. These solutions are odd-symmetric with respect to the equatorial plane and rotate with a constant angular speed around the polar axis. More importantly, these solutions ``converges" to a pair of point vortices with equal strength and opposite

Rotem Atari, Eran Bamani, Avishai Sintov

Accurate human pose estimation is essential for effective Human-Robot Interaction (HRI). By observing a user's arm movements, robots can respond appropriately, whether it's providing assistance or avoiding collisions. While visual perception offers potential for human pose estimation, it can be hindered by factors like poor lighting or occlusions. Additional

Marvin Sextro, Gabriel Dernbach, Kai Standvoss, Simon Schallenberg

Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent advances in the spatial modeling of the tumor microenvironment using graph neural networks, we present an explain

Naeim Ebrahimi Toulkani, Reza Ghabcheloo

Articulated Frame Steering (AFS) vehicles are widely used in heavy-duty industries, where they often operate near operators and laborers. Therefore, designing safe controllers for AFS vehicles is essential. In this paper, we develop a Quadratic Program (QP)-based safety filter that ensures feasibility for AFS vehicles with affine actuator dynamics. To achiev

Chenxia Tang, Jianchun Liu, Hongli Xu, Liusheng Huang

Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-$n\sigma$, a novel sampling method that operates directly on pre-softmax logits by leveraging a statistical threshold. Our key insight i

Pavan Pranjivan Mehta, Gianluigi Rozza

Recently, general fractional calculus was introduced by Kochubei (2011) and Luchko (2021) as a further generalisation of fractional calculus, where the derivative and integral operator admits arbitrary kernel. Such a formalism will have many applications in physics and engineering, since the kernel is no longer restricted. We first extend the work of Al-Refa

Naeim Ebrahimi Toulkani, Reza Ghabcheloo

Finding a controlled-invariant set for a system with state and control constraints is crucial for safety-critical applications. However, existing methods often produce overly conservative solutions. This paper presents a method for generating controlled-invariant (safe) sets for nonlinear polynomial control-affine systems using Control Barrier Certificates (

Kana Minami, Taku Yanagisawa

We construct a long-time asymptotic profile to the initial value problem of the n-dimensional heat equation. Specifically, we present a modified heat kernel as a long-time asymptotic profile which changes the mass, the center of mass and the variance of the n-dimensional heat kernel in accordance with the moments of the initial data.

Ciro Ciliberto, Rick Miranda

In this paper we present a variety of statements that are in the spirit of the famous theorem of Pascal, often referred to as the Mystic Hexagon. We give explicit equations describing the conditions for $d+4$ points to lie on rational normal curves. A collection of problems of Pascal type are considered for quadric surfaces in $\bbP^3$. Finally we reprove, u

A. Santiago, F. Mireles, E. Serrano-Ensástiga

We study the spin entanglement in a spin-1 Bose-Einstein condensate with antiferromagnetic atomic interactions using the Hartree-Fock approach. Based on the isomorphism between symmetric $N$-qubit states and spin-$j=N/2$ states, we analyze the negativity of the spin-1 ground state of the condensate viewed as a two spins $1/2$, and explore its dependence with

Xinhan Liu, Robert Kooij, Piet Van Mieghem

This paper discusses the reliability of a graph in which the links are perfectly reliable but the nodes may fail with certain probability p. Calculating graph node reliability is an NP-Hard problem. We introduce an efficient and accurate Monte Carlo method and a stochastic approximation for the node reliability polynomial based solely on the degree distribut

Qihang Fan, Huaibo Huang, Ran He

The Softmax attention mechanism in Transformer models is notoriously computationally expensive, particularly due to its quadratic complexity, posing significant challenges in vision applications. In contrast, linear attention provides a far more efficient solution by reducing the complexity to linear levels. However, compared to Softmax attention, linear att

Maria Zampella, Urtzi Otamendi, Xabier Belaunzaran, Arkaitz Artetxe

Scheduling problems pose significant challenges in resource, industry, and operational management. This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent Reinforcement Learning (MARL) approach. The study introduces the Reinforcement Learning environment and conducts empirical analyses,

Zbigniew Kozioł

The model of a chain of springs and masses (CSM), originating from the works of Schr\"odinger (1914), and Pater (1974), is found suitable as an analytical description of the dynamics of layers in orientated FCC crystals. An analytical extension of that model has been developed for the case of linear-in-time ramp pressure applied to a sample surface. Examples

Jie Zhang, Hongjing Huang, Xuzheng Xu, Xiang Li

The emerging microservice/serverless-based cloud programming paradigm and the rising networking speeds leave the RPC stack as the predominant data center tax. Domain-specific hardware acceleration holds the potential to disentangle the overhead and save host CPU cycles. However, state-of-the-art RPC accelerators integrate RPC logic into the CPU or use specia

Akbar Fahmi

One of fascinating phenomena of nature is quantum nonlocality, which is observed upon measurements on spacelike entangled systems. However, there are sets of post-quantum models which have stronger correlations than quantum mechanics, wherein instantaneous communication remains impossible. The set of almost quantum correlations is one of post-quantum models

Seokhyun Choi, Beomho Kim, Hansol Kim, Hojin Kim

We establish an unconditional result concerning the asymptotic formula for the moment of derivatives of $L$-functions $L(s, f \otimes \chi_{8d})L(s, g \otimes \chi_{8d})$ over quadratic twists, where $f$ and $g$ are distinct cuspidal newforms.