December 2024 arXiv papers — page 14

Zoe Zhiqiu Jiang

In this paper, we explore the paradox of trust and vulnerability in human-machine interactions, inspired by Alexander Reben's BlabDroid project. This project used small, unassuming robots that actively engaged with people, successfully eliciting personal thoughts or secrets from individuals, often more effectively than human counterparts. This phenomenon rai

V. Gorbounov, A. Kazakov

A classic problem in data analysis is studying the systems of subsets defined by either a similarity or a dissimilarity function on $X$ which is either observed directly or derived from a data set. For an electrical network there are two functions on the set of the nodes defined by the resistance matrix and the response matrix either of which defines the net

Chong Liu, Zaiwen Feng, Lin Liu, Zhenyun Deng

Plausibility Estimation (PE) plays a crucial role for enabling language models to objectively comprehend the real world. While large language models (LLMs) demonstrate remarkable capabilities in PE tasks but sometimes produce trivial commonsense errors due to the complexity of commonsense knowledge. They lack two key traits of an ideal PE model: a) Language-

M. A. Seoud, A. Elsonbaty, A. Nasr, M. Anwar

A linear Diophantine equation $ax + by = n$ is solvable if and only if gcd$(a; b)$ divides $n$. A graph $G$ of order $n$ is called Diophantine if there exists a labeling function $f$ of vertices such that gcd$(f(u); f(v))$ divides $n$ for every two adjacent vertices $u; v$ in $G$. In this work, maximal Diophantine graphs on $n$ vertices, $D_n$, are defined,

A. A. Saharian, G. V. Chalyan, L. Sh. Grigoryan, H. F. Khachatryan

We investigate the radiation of surface polaritons by an annular beam that coaxially encloses a cylindrical waveguide surrounded by a homogeneous medium. By using the Green dyadic, the electromagnetic potentials and the electric and magnetic fields are found inside and outside the waveguide. The expression for the energy losses is derived for the general cas

Majid Haghparast, Enrique Moguel, Jose Garcia-Alonso, Tommi Mikkonen

Quantum computing is an emerging field that promises to revolutionize various domains, such as simulation optimization, data processing, and more, by leveraging the principles of quantum mechanics. This paper outlines innovative pedagogical strategies developed by university lecturers in Finland and Spain for teaching quantum computer programming and quantum

Marcelina Mocanu

We study in the setting of a metric space $\left( X,d\right) $ some generalizations of four hyperbolic-type metrics defined on open sets $G$ with nonempty boundary in the $n-$dimensional Euclidean space, namely Gehring-Osgood metric, Dovgoshey- Hariri-Vuorinen metric, Nikolov-Andreev metric and Ibragimov metric. In the definitions of these generalizations, t

Nigar Sultana, Joanna Krynski, Jin Gyu Lim, John Floyd

Single-photon avalanche photodiodes (SPADs) based on silicon are widely considered for quantum satellite communications but suffer from an increasing dark count rate (DCR) due to displacement damage in their active areas induced by proton radiation. When the DCR of SPADs exceeds a certain threshold, they become unusable for quantum communication protocols. P

E. T. Baskoro, C. Dalfó, M. A. Fiol, R. Simanjuntak

In this paper, we construct two infinite families of graphs $G(d,c)$ and $G^+(d,c)$, where, in both cases, a vertex label is $x_1x_2\ldots x_c$ with $x_i\in\{1,2,\ldots, d\}$. We provide a lower bound on the metric dimension, tight on $G^+(d,c)$. Moreover, we give the definition and properties of the supertoken graphs, a generalization of the well-known toke

Branislav Sazdovic

In previous two articles we postulated that field equations for arbitrary spin and helicity are Casimir eigenvalue equations. In massive case, from such principle equation, we derived spin-$0$ Klein-Gordon, spin-$\frac{1}{2}$ Dirac and spin-$1$ vector equations. In the present article we will derived spin-$\frac{3}{2}$ Rarita-Schwinger equation, which is non

Andrew T. Karl

The Hausman specification test assesses the random-effects specification by comparing the random-effects estimator with a fixed-effects alternative. This note shows how a recently proposed bias diagnostic for linear mixed models can complement that test in random-effects panel-data applications. The diagnostic delivers parameter-specific internal estimates o

Andreas Schlatter, R. E. Kastner

A key tenet of the Transactional Interpretation of Quantum Mechanics is the idea that photon absorption localizes the absorbing material system. In doing so, it measures the location of the absorber and hence reduces information entropy, which in turn needs to be balanced by appropriate entropy production, if there is a link between information entropy and t

Arseniy Andreyev, Pierfrancesco Beneventano

Recent findings by Cohen et al., 2021, demonstrate that when training neural networks using full-batch gradient descent with a step size of $\eta$, the largest eigenvalue $\lambda_{\max}$ of the full-batch Hessian consistently stabilizes around $2/\eta$. These results have significant implications for convergence and generalization. This, however, is not the

Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana, Yongyi Yang

Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However, given the open-ended nature of LLMs, e.g., their ability to in-context learn, we can ask whether models alter these pretraining semantics to adopt alternative, context-specified

Shashank Shekhar Mishra, Thakur Prasad Yadav

In the present investigation, the chemical leaching of the poly-grain Al65Cu15Co20 and Al65Cu20Co15 decagonal quasicrystalline alloy have been studied. The polished surfaces of as-cast alloys were leached with 10 mole NaOH solution for 0.5- 8 hours. The x-ray diffraction, scanning electron microscopy and transmission electron microscopy techniques have been

Roman Neomenko

In this work the observational constraints on interaction coupling parameter between dynamical dark energy and cold dark matter were obtained using CMB, BAO and SN Ia data. The dark energy in considered models is dynamical and evolution of its equation of state parameter depends on dark coupling and internal properties of the dark energy. Such model is belie

Shriprakash Sinha

Achaete-scute complex homolog 2 (ASCL2) codes a part of the basic helix-loop-helix (BHLH) transcription factor family. WNTs have been found to directly affect the stemness of the tumor cells via regulation of ASCL2. Switching off the ASCL2 literally blocks the stemness process of the tumor cells and vice versa. In colorectal cancer (CRC) cells treated with E

Zhenqiao Cheng, Chongjun Ouyang, Xingqi Zhang

A novel frequency diverse array (FDA)-assisted secure transmission framework is proposed, which leverages additional frequency offsets to enhance physical layer security. Specifically, an FDA-assisted wiretap channel is considered, where the transmit beamforming and frequency offsets at each antenna are jointly optimized. A novel alternating optimization-bas

Chirantan Chowdhury

This article is the last of the series of articles where we reprove the foundational ideas of abstract six-functor formalisms developed by Liu-Zheng. We prove the theorem of partial adjoints, which is a simplicial technique of encoding various functors altogether by taking adjoints along specific directions. Combined with the $\infty$-categorical compactific

Eric D'Hoker, Thomas T. Dumitrescu, Efrat Gerchkovitz, Emily Nardoni

We argue that adjoint QCD in 3+1 dimensions, with any $SU(N)$ gauge group and two Weyl fermion flavors (i.e. one adjoint Dirac fermion), confines and spontaneously breaks its chiral symmetries via the condensation of a fermion bilinear. We flow to this theory from pure $\mathscr{N}=2$ SUSY Yang-Mills theory with the same gauge group, by giving a SUSY-breakin

Chenqi Meng, Xinping Yang, Tian Lan, Zhengcheng Gu

We investigate (1+1)d symmetry-protected topological (SPT) phases with fusion category symmetries. We emphasize that the UV description of an anomaly-free fusion category symmetry must include the fiber functor, giving rise to a local symmetry action, a charge category and a trivial phase. We construct an ``onsite'' matrix-product-operator (MPO) version of t

Ranim Khojah, Francisco Gomes de Oliveira Neto, Mazen Mohamad, Philipp Leitner

Large Language Models (LLMs) are increasingly used by software engineers for code generation. However, limitations of LLMs such as irrelevant or incorrect code have highlighted the need for prompt programming (or prompt engineering) where engineers apply specific prompt techniques (e.g., chain-of-thought or input-output examples) to improve the generated cod

Alexander Braverman, Michael Finkelberg, David Kazhdan, Roman Travkin

We establish an $S$-duality converse to the one studied by the 1st, 2nd and 4th authors; this is also a case of a twisted version of the relative Langlands duality of Ben Zvi, Sakellaridis and Venkatesh.. Namely, we prove that the $S$-dual of $\text{SO}(2n+1)\times \text{Sp}(2n)$ acting on the tensor product of their tautological representations is the sympl

Evandro C. R. Rosa, Jerusa Marchi, Eduardo I. Duzzioni, Rafael de Santiago

We present a method for optimizing quantum circuit compilation by automating the allocation of auxiliary qubits for multi-qubit gate decompositions. This approach is implemented and evaluated within the high-level quantum programming platform Ket. Our results indicate that the decomposition of multi-qubit gates is more effectively handled by the compiler, wh

Arun Kumar Kuchibhotla

In this note, we improve some concentration inequalities for martingales with bounded increments. These results recover the missing factor in Freedman-style inequalities and are near optimal. We also provide minor refinements of concentration inequalities for functions of independent random variables. These proofs use techniques from the works of Bentkus and

Arinbjörn Kolbeinsson, Benedikt Kolbeinsson

Generative language models are increasingly used for contract drafting and enhancement, creating a scenario where competing parties deploy different language models against each other. This introduces not only a game-theory challenge but also significant concerns related to AI safety and security, as the language model employed by the opposing party can be u

Thomas Ehrhard, Claudia Faggian, Michele Pagani

We uncover a strong correspondence between Bayesian Networks and (Multiplicative) Linear Logic Proof-Nets, relating the two as a representation of a joint probability distribution and at the level of computation, so yielding a proof-theoretical account of Bayesian Inference.

Evgeniy Petrov

Weak similarities form a special class of mappings between semimetric spaces. Two semimetric spaces $X$ and $Y$ are weakly similar if there exists a weak similarity $\Phi\colon X\to Y$. We find a structural characteristic of finite ultrametric spaces for which the isomorphism of its representing trees implies a weak similarity of the spaces. We also find con

Qucheng Peng, Ce Zheng, Zhengming Ding, Pu Wang

Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. However, it suffers from the lack of labeled diverse real-world datasets due to the time- and labor-intensive annotation. To cope with the label deficiency issue, one common solution is to train the HPE mode

Daniel Palenicek, Michael Lutter, João Carvalho, Daniel Dennert

Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. This paper empirically investigates potential sample efficiency gains from improved dynamics models in model-based value expansion methods. Our study reveals two key findings when u

Sonu Karayat, Prashant K. Purohit, L. Mahadevan, Arvind Gopinath

We use Brownian dynamics simulations and theory to study the over-damped spatiotemporal dynamics and pattern formation in a fluid-permeated array of equally spaced, active, elastic filaments that are pinned at one end and free at the other. The filaments are modeled as connected colloidal chains with activity incorporated via compressive follower forces acti

Soham Bakshi, Yiling Huang, Snigdha Panigrahi, Walter Dempsey

Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical inference for a data-dependent model obtained from a fitted regression tree. We introduce Randomized Regression Trees (RRT), a novel selective inference method that adds independent Ga

Vincent Labarre, Michal Shavit

Using weak wave turbulence theory analysis, we distinguish three main regimes for 2D stratified fluids in the dimensionless parameter space defined by the Froude number and the Reynolds number: discrete wave turbulence, weak wave turbulence, and strong nonlinear interaction. These regimes are investigated using direct numerical simulations (DNS) of the 2D Bo

Evgeniy Petrov

The problems of continuation of a partially defined metric and a partially defined ultrametric were considered in (O. Dovgoshey, O. Martio and M. Vuorinen, Metrization of weighted graphs, Ann. Comb., 17:455--476, 2013) and (A. A. Dovgoshey and E. A. Petrov, Subdominant pseudoultrametric on graphs, Sb. Math., 204:1131--1151, 2013), respectively. Using the lan

Chiyu Cheng, Chang Zhou, Yang Zhao

The exponential growth of data-intensive applications has placed unprecedented demands on modern storage systems, necessitating dynamic and efficient optimization strategies. Traditional heuristics employed for storage performance optimization often fail to adapt to the variability and complexity of contemporary workloads, leading to significant performance

Chiyu Cheng, Chang Zhou, Yang Zhao, Jin Cao

The exponential growth of data storage demands has necessitated the evolution of hierarchical storage management strategies [1]. This study explores the application of streaming machine learning [3] to revolutionize data prefetching within multi-tiered storage systems. Unlike traditional batch-trained models, streaming machine learning [5] offers adaptabilit

Purnendu Ray, Kingshuk Sarkar

In this study, we conducted a comprehensive review and analysis of the thermoelectric responses exhibited by monolayer pristine graphene in response to temperature variations. Employing the Boltzmann transport theory, we rigorously examined and evaluated various thermoelectric coefficients, with particular emphasis on elucidating their behavior under differe

José Solano, Quentin Rossi, Jerome Robert, Marc Lenertz

An electric current flowing in a ferromagnetic metal carries spin angular momentum, i.e. it is spin-polarized. Here, we measure the spin-wave Doppler shift induced by the transfer of angular momentum from the diffusive spin-polarized electric current onto coherent spin waves in epitaxial MgO/Fe/MgO thin films. We follow this Doppler shift as function of the

Chiyu Cheng, Chang Zhou, Yang Zhao, Jin Cao

The growing demand for efficient cloud storage solutions has led to the widespread adoption of Solid-State Drives (SSDs) for caching in cloud block storage systems. The management of data writes to SSD caches plays a crucial role in improving overall system performance, reducing latency, and extending the lifespan of storage devices. A critical challenge ari

Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from

A. Korenev, G. Belokrylov, B. Lodonova, A. Novokhrestov

This article will discuss the use of attacks on a neural network trained on audio data, as well as possible methods of protection against these attacks. FGSM, PGD and CW attacks, as well as data poisoning, will be considered. Within the framework of protection, Art-IBM and advertorch libraries will be considered. The obtained accuracy metrics within the fram

G. Belokrylov, A. Korenev, B. Lodonova, A. Novokhrestov

The article describes an attempt to apply an ensemble of binary classifiers to solve the problem of speech assessment in medicine. A dataset was compiled based on quantitative and expert assessments of syllable pronunciation quality. Quantitative assessments of 7 selected metrics were used as features: dynamic time warp distance, Minkowski distance, correlat

Chau-Jian Huang

Web crawling is a critical technique for extracting online data, yet it poses challenges due to webpage diversity and anti-scraping mechanisms. This study investigates the integration of generative AI tools Claude AI (Sonnet 3.5) and ChatGPT4.0 with prompt engineering to automate web scraping. Using two prompts, PROMPT I (general inference, tested on Yahoo N

Md. Imran Khan, Sayantani Ghosh, Arnold D. Kim

The flexibility and versatility of nanoassembled plasmonic structures provide platforms for mesoscale tunable optical modulation. Our recently developed model for these nanoassembled plasmonic structures is composed of a dielectric spherical core surrounded by a concentric spherical shell containing a random distribution of AuNPs. This model provides a usefu

Evgeniy A. Petrov, Ruslan R. Salimov

In 2017 M. Bessenyei and Z. P\'ales introduced a definition of a triangle function which generates a concept of a generalized triangle inequality in semimetric spaces. Inspired by this concept we discuss already known inequalities in metric spaces that relate the six distances determined by four points and introduce a definition of a generalized four-point i

Anirban Kundu, Poulami Mondal, Gilbert Moultaka

We provide a possible interpretation of excesses reported by ATLAS and CMS at around 95GeV, 650GeV and possibly 320GeV, in terms of CP-even scalars. In particular, the combined {\sl global} statistical significances of independent indications for a 650GeV object reach the $4\sigma$ level! While this seems sufficient incentive for a further investigation, thi

Anatolij Zubow, Yannik Pilz, Sascha Rösler, Falko Dressler

Network simulators are indispensable tools for the advancement of wireless network technologies, offering a cost-effective and controlled environment to simulate real-world network behavior. However, traditional simulators, such as the widely used ns-3, exhibit limitations in accurately modeling indoor and outdoor scenarios due to their reliance on simplifie

Neil De La Fuente, Miquel Noguer i Alonso, Guim Casadellà

This paper explores advanced topics in complex multi-agent systems building upon our previous work. We examine four fundamental challenges in Multi-Agent Reinforcement Learning (MARL): non-stationarity, partial observability, scalability with large agent populations, and decentralized learning. The paper provides mathematical formulations and analysis of rec

Yifei Liu, Zhihang Zhong, Yifan Zhan, Sheng Xu

While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality. To mitigate this issue, improvements have been made by pruning unnecessary Gaussians, either through a hand-crafted criterion or by using learn

Thomas Alessandro Ciarfuglia, Ionut Marian Motoi, Leonardo Saraceni, Daniele Nardi

In table grape cultivation, harvesting depends on accurately assessing fruit quality. While some characteristics, like color, are visible, others, such as Soluble Solid Content (SSC), or sugar content measured in degrees Brix ({\deg}Brix), require specific tools. SSC is a key quality factor that correlates with ripeness, but lacks a direct causal relationshi

Xingshuai Huang, Di Wu, Benoit Boulet

Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it generally fails to learn well-qualified policies in suboptimal datasets. To address datasets with insufficient optimal demonstrations, we introduc

Lourens van Niekerk, Dhiraj Kumar, Aasish Kumar Sharma, Tino Meisel

This paper compares quantum computing simulators running on a single CPU or GPU-based HPC node using the Quantum Volume benchmark commonly proposed for comparing NISQ systems. As simulators do not suffer from noise, the metric used in the comparison is the time required to simulate a set Quantum Volume. The results are important to estimate the feasibility o

Rishabh Sharma, Fernando Antonio Flor, Sibaram Behera, Chitrasen Jena

We study the production of light nuclei in Au+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 7.7 - 200 GeV and Pb+Pb collisions at $\sqrt{s_\mathrm{NN}}$ = 2.76 and 5.02 TeV within a flavour-dependent freeze-out framework, assuming different flavoured hadrons undergo separate chemical freeze-out. Using the Thermal-FIST package, thermal parameters extracted from f

Claudia Testi, Emanuele Pontecorvo, Chiara Bartoli, Chiara Marzaro

Brillouin Microscopy is a novel label-free optical technique that enables the measurement of a material's mechanical properties at the sub-micron scale in a non-invasive and non-contact way; in the last few years, its applications in the life sciences have extensively expanded. To date, many custom-built Brillouin Microscopes suffer from temporal instabiliti

Marina Sanino, Isaac Carvalho, Vivian França

We investigate the single-site von Neumann entropy along a harmonically confined superfluid chain, as described by the one-dimensional fermionic Hubbard model with strongly attractive interactions. We find that by increasing the confinement (or equivalently the particle filling) the system undergoes a quantum phase transition from a superfluid (SF) to a non-

Paolo Antonelli, David N Reynolds

We study the well known Schr\"odinger-Lohe model for quantum synchronization with non-identical natural frequencies. The main results are related to the characterization and convergence to phase-locked states for this quantum system. The results of this article are four-fold. Via a characterization of the fixed points of the system of correlations, we uncove

Yi Dai

In this paper, we aim to study the smallest normalized signless $\infty$-Laplacian eigenvalue $\mu_{\infty}$, a generalisation of the smallest signless Laplacian eigenvalue. For a non-bipartite connected graph, we show that the invariant $\mu_{\infty}$ equals to the reciprocal of the minimal $\infty$-norm of the generalized inverses of the weighted signless

Paul Boniol, Qinghua Liu, Mingyi Huang, Themis Palpanas

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health

Rishabh Ballal, Albert Much, Rainer Verch

A deformation technique, known as the warped convolution, takes quantum fields in Minkowski spacetime to quantum fields in noncommutative Minkowski space-time. Since a quantum field is an operator valued regular distribution and the warped convolution is (weakly) an oscillatory integral of Rieffel type, we prove that the symbol classes introduced by Hormande

Riku Green, Grant Stevens, Zahraa Abdallah, Telmo M. Silva Filho

A key aspect of temporal domains is the ability to make predictions multiple time steps into the future, a process known as multi-step forecasting (MSF). At the core of this process is selecting a forecasting strategy, however, with no existing frameworks to map out the space of strategies, practitioners are left with ad-hoc methods for strategy selection. I

Cristian Castiglione, Alexandre Segers, Lieven Clement, Davide Risso

Single-cell RNA sequencing allows the quantification of gene expression at the individual cell level, enabling the study of cellular heterogeneity and gene expression dynamics. Dimensionality reduction is a common preprocessing step critical for the visualization, clustering, and phenotypic characterization of samples. This step, often performed using princi

Dorota Węgłowska, Michał Czerwiński, Robert Dzienisiewicz, Paweł Perkowski

Non-chiral liquid crystals (LCs) exhibiting ferroelectricity, distinguished by their dynamic responsiveness to external stimuli and high spontaneous polarization, provide renewed impetus for research into this area of soft matter and open novel application possibilities. Consequently, identifying structural elements within LC compounds that promote ferroelec

Bohdan Turbal, Anastasiia Mazur, Jiaxu Zhao, Mykola Pechenizkiy

We investigate the adversarial robustness of LLMs in transfer learning scenarios. Through comprehensive experiments on multiple datasets (MBIB Hate Speech, MBIB Political Bias, MBIB Gender Bias) and various model architectures (BERT, RoBERTa, GPT-2, Gemma, Phi), we reveal that transfer learning, while improving standard performance metrics, often leads to in

Takashi Ito, Arika Higuchi

We describe the result of our numerical orbit simulation which traces dynamical evolution of new comets coming from the Oort Cloud. We combine two dynamical models for this purpose. The first one is semi-analytic, and it models an evolving comet cloud under galactic tide and encounters with nearby stars. The second one numerically deals with planetary pertur

Haorui Ji, Taojun Lin, Hongdong Li

Diffusion models demonstrate remarkable capabilities in capturing complex data distributions and have achieved compelling results in many generative tasks. While they have recently been extended to dense prediction tasks such as depth estimation and surface normal prediction, their full potential in this area remains underexplored. As target signal maps and

Xiao Wang, Qingyi Si, Jianlong Wu, Shiyu Zhu

Video Large Language Models (VideoLLMs) have made significant strides in video understanding but struggle with long videos due to the limitations of their backbone LLMs. Existing solutions rely on length extrapolation, which is memory-constrained, or visual token compression, which primarily leverages low-level temporal redundancy while overlooking the more

Leyang Xue, Zengru Di, An Zeng

Contagion dynamics in complex networks drive critical phenomena such as epidemic spread and information diffusion,but their analysis remains computationally prohibitive in large-scale, high-complexity systems. Here, we introduce the Iterative Structural Coarse-Graining (ISCG) framework, a scalable methodology that reduces network complexity while preserving

Toshimi Inoue

In the paper, we study the Gauss map of a completely immersed anisotropic minimal surface with respect to convex parametric integrand in $\mathbb{R}^3$. By a local analysis, we prove the discreteness of the critical set of the Gauss map of an anisotropic minimal surface. In particular, we may consider the Gauss map as a branched covering map from an anisotro

Zongwu Wang, Fangxin Liu, Mingshuai Li, Li Jiang

Efficient parallelization of Large Language Models (LLMs) with long sequences is essential but challenging due to their significant computational and memory demands, particularly stemming from communication bottlenecks in attention mechanisms. While sequence parallelism (SP) has been introduced as a potential solution, existing methods often suffer from limi

Toshimi Inoue

We prove the Hasanis--Koutroufiotis type inequality for the anisotropic extrinsic radius of hypersurfaces in Euclidean space involving the anisotropic mean curvatures. We also study the equality case and proved that an almost extremal hypersurface must be close to the Wulff shape in the sense of the Hausdorff distance.

Ulisse Munari

(Invited Review) According to modern definition, a symbiotic nova is an otherwise normal nova (i.e. powered by explosive thermonuclear burning) that erupts within a symbiotic star, which is a binary where a WD accretes from a cool giant companion. Guided primarily by the very well observed eruptions of RS Oph in 2006 and 2021, and that of V407 Cyg in 2010, w

Camila González, Moritz Fuchs, Daniel Pinto dos Santos, Philipp Matthies

Over time, the distribution of medical image data drifts due to factors such as shifts in patient demographics, acquisition devices, and disease manifestations. While human radiologists can adjust their expertise to accommodate such variations, deep learning models cannot. In fact, such models are highly susceptible to even slight variations in image charact

Sizhan Liu, Sandun Amarasinghe, Mo Li, Stella Chariton

Under external stimuli, lead halide perovskites exhibit large atomic fluctuations, impacting optical and electron transport properties that affect device performance in operational settings. However, a thorough understanding of the atomic basis for the underlying structural instability is still absent. Focusing on the model material CsPbBr$_3$, the inherent

Georgios Giannakopoulos, Peter Adegbenro, Maria Antonnette Perez

The evolution of Internet Protocol Television (IPTV) has transformed the landscape of digital broadcasting by leveraging high-speed internet connectivity to deliver high-quality multimedia content. IPTV provides a dynamic and interactive television experience through managed networks, ensuring superior Quality of Service (QoS) compared to open-network Intern

Chanju Park, Matteo Favoni, Biagio Lucini, Gert Aarts

Investigating the dynamics of learning in machine learning algorithms is of paramount importance for understanding how and why an approach may be successful. The tools of physics and statistics provide a robust setting for such investigations. Here we apply concepts from random matrix theory to describe stochastic weight matrix dynamics, using the framework

Narasimha Raghavan Veeraragavan, Svetlana Boudko, Jan Franz Nygård

The proliferation of real-world health data enables multi-institutional survival studies, yet privacy constraints preclude centralizing sensitive records. We present a privacy-preserving federated Kaplan--Meier framework based on threshold CKKS (Cheon-Kim-Kim-Song) homomorphic encryption that supports approximate floating-point computation and encrypted aggr

Leonid Positselski, Jan Stovicek

We consider the contraderived category of left contramodules over a right linear topological ring $\mathfrak R$ with a countable base of neighborhoods of zero. Equivalently, this is the homotopy category of unbounded complexes of projective left $\mathfrak R$-contramodules. Assuming that the abelian category of discrete right $\mathfrak R$-modules is locally

Mohit Gurumukhani, Marvin Künnemann, Ramamohan Paturi

We consider a basic question on the expressiveness of $k$-CNF formulas: How well can $k$-CNF formulas capture threshold functions? Specifically, what is the largest number of assignments (of Hamming weight $t$) accepted by a $k$-CNF formula that only accepts assignments of weight at least $t$? Among others, we provide the following results: - While an optima

Hajime Inoue

We propose a scenario for mass evolution of massive black holes (MBH) in galactic nuclei, to explain both the mass correlation of the supermassive black hole (SMBH) with the bulge and the down-sizing behavior of the active galactic nuclei. Primordial gas structures to evolve galactic bulges are supposed to be formed at $z \sim$ 10 and the core region, called

Katarzyna Grabowska, Janusz Grabowski

We study regular contact manifolds $(M,\eta)$ whose Reeb vector field is complete and prove that they are canonically principal bundles with the structure group $S^1$ or $\mathbb{R}$. For compact $M$, our proof is very short and elementary and covers the celebrated Boothby-Wang theorem, but we do not assume compactness from the very beginning. However, to pr

Andreas Emil Feldmann, Arnold Filtser

Realistic metric spaces (such as road/transportation networks) tend to be much more algorithmically tractable than general metrics. In an attempt to formalize this intuition, Abraham et~al.\ (SODA 2010, JACM 2016) introduced the notion of highway dimension. A weighted graph $G$ has highway dimension $h$ if for every ball $B$ of radius $\approx4r$, there is a

Ahmed Mahfouz, Gabriella Gaias, Florio Dalla Vedova, Holger Voos

This study presents autonomous guidance and control strategies for the purpose of reconfiguring close-range multi-satellite formations. The formation under consideration includes $N$ under-actuated deputy satellites and an uncontrolled virtual or physical chief spacecraft. The guidance problem is formulated as a trajectory optimization problem that incorpora

Andrew Campbell

Initiated by a result of Gorin and Marcus [Int. Math. Res. Not., (3):883--913, 2020] and an observation of Steinerberger [Proc. Amer. Math. Soc., 147(11):4733--4744, 2019], there has been a recent growing body of literature connecting repeated differentiation of polynomials to free additive convolution semigroups in free probability. Roughly, this connection

Peijie Qiu, Wenhui Zhu, Sayantan Kumar, Xiwen Chen

Multiple signal modalities, such as vision and sounds, are naturally present in real-world phenomena. Recently, there has been growing interest in learning generative models, in particular variational autoencoder (VAE), to for multimodal representation learning especially in the case of missing modalities. The primary goal of these models is to learn a modal

Debdas Ghosh, Anshika, Jen-Chih Yao, Xiaopeng Zhao

In this article, we propose a quasi-Newton method for unconstrained set optimization problems to find its weakly minimal solutions with respect to lower set-less ordering. The set-valued objective mapping under consideration is given by a finite number of vector-valued functions that are twice continuously differentiable. To find the necessary optimality con

Takumi Kobori, Yasunari Suzuki, Yosuke Ueno, Teruo Tanimoto

Current fault-tolerant quantum computer (FTQC) architectures utilize several encoding techniques to enable reliable logical operations with restricted qubit connectivity. However, such logical operations demand additional memory overhead to ensure fault tolerance. Since the main obstacle to practical quantum computing is the limited qubit count, our primary

Songhan Zhao, Shimin Gong, Bo Gu, Lanhua Li

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel

Han-Liang Chen, Bing-Sheng Lin

We studied some geometric properties of the Moyal sphere. Using the conformal metric of the sphere in ordinary space and the matrix basis, we calculated the scalar curvature, total curvature integral and area of the Moyal sphere. We found that when the noncommutative parameter approaches to 0, the scalar curvature and area of the Moyal sphere return to those

Sara Lombardo, Casper Oelen

We present normal forms of elliptic automorphic Lie algebras with dihedral symmetry of order 4, which arise naturally in the context of Landau-Lifshitz type of equations. These normal forms provide a transparent description and allow a classification of such Lie algebras over $\mathbb{C}$. Using this perspective, we show that a Lie algebra introduced by Uglo

Kensuke Asai, Jun-ya Gotoh

The EM (Expectation-Maximization) algorithm is regarded as an MM (Majorization-Minimization) algorithm for maximum likelihood estimation of statistical models. Expanding this view, this paper demonstrates that by choosing an appropriate probability distribution, even nonstatistical optimization problem can be cast as a negative log-likelihood-like minimizati

Minjae Seong, Jisong Kim, Geonho Bang, Hawook Jeong

Accurate 3D perception is essential for understanding the environment in autonomous driving. Recent advancements in 3D semantic occupancy prediction have leveraged camera-LiDAR fusion to improve robustness and accuracy. However, current methods allocate computational resources uniformly across all voxels, leading to inefficiency, and they also fail to adequa

Xiao Lu, Hiroyuki Sagawa, Shan-Gui Zhou

We study the soft electric dipole ($E1$) response of deformed halo nuclei $^{31}$Ne and $^{37}$Mg using a deformed Woods-Saxon potential, with the potential depth adjusted to reproduce empirical separation energy of last neutron orbit, i.e., 150 keV for $^{31}$Ne and 220 keV for $^{37}$Mg. The configuration dependence of the $E1$ strength near the neutron th

Ayanendu Dutta

We discuss fermion tunneling from the black hole exterior to white hole exterior geometry using the spin foam technique in background-independent Loop Quantum Gravity. The fermion transition time is equal, longer, or shorter than the black hole tunneling time, depending on the Euclidean dihedral angle. For a locally pinched negative curvature, fermions accum

Yi Yang, Xuchen Wang, Richard M. Voyles, Xin Ma

This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges. The PTC-NT-FOZNN, stemming from a novel iteration within the variable-gain ZNN spectrum, known as FOZNNs, features diminishing gains over time

Yao Tong, Weijun Li, Xuanli He, Haolan Zhan

Model NLP models are commonly trained (or fine-tuned) on datasets from untrusted platforms like HuggingFace, posing significant risks of data poisoning attacks. A practical yet underexplored challenge arises when such backdoors are discovered after model deployment, making retraining-required defenses less desirable due to computational costs and data constr

Sheng Cen, Qiying Pan, Yifei Zhu, Bo Li

Low-earth-orbit (LEO) satellite communication networks have evolved into mega-constellations with hundreds to thousands of satellites inter-connecting with inter-satellite links (ISLs). Network planning, which plans for network resources and architecture to improve the network performance and save operational costs, is crucial for satellite network managemen

Atsuhisa Ota

We study stimulated emission and absorption of gravitons in a squeezed vacuum state immersed in a thermal radiation bath. Employing one-loop interaction-picture perturbation theory, we track the time evolution of the graviton number operator and its expectation value in the squeezed vacuum, which characterizes the inflationary graviton state. In a Minkowski

Xi Chen, Yu Ji, Cong Xia, Wen Wu

Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associated with children's lower levels of externalizing problems. The current study aims to evaluate and improve the

Yu-Hsiang Huang, Wei Wang, Sheng-Yu Huang, Yu-Chiang Frank Wang

Recent advancements in object-centric text-to-3D generation have shown impressive results. However, generating complex 3D scenes remains an open challenge due to the intricate relations between objects. Moreover, existing methods are largely based on score distillation sampling (SDS), which constrains the ability to manipulate multiobjects with specific inte

Sumeet Khatri

The quantum internet is one of the frontiers of quantum information science. It will revolutionize the way we communicate and do other tasks, and it will allow for tasks that are not possible using the current, classical internet. The backbone of a quantum internet is entanglement distributed globally in order to allow for such novel applications to be perfo