April 2024 arXiv papers — page 167

Ryoji Tanabe

Differential evolution (DE) generally requires parameter control methods (PCMs) for the scale factor and crossover rate. Although a better understanding of PCMs provides a useful clue to designing an efficient DE, their effectiveness is poorly understood in mixed-integer black-box optimization. In this context, this paper benchmarks PCMs in DE on the mixed-i

Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu

By leveraging the retrieval of information from external knowledge databases, Large Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge-intensive tasks. However, due to the inherent flaws of current retrieval systems, there might exist irrelevant information within those retrieving top-ranked passages. In this work, we prese

Fangru Lin, Daniel Altshuler, Janet B. Pierrehumbert

Scalar adjectives pertain to various domain scales and vary in intensity within each scale (e.g. certain is more intense than likely on the likelihood scale). Scalar implicatures arise from the consideration of alternative statements which could have been made. They can be triggered by scalar adjectives and require listeners to reason pragmatically about the

Ilane Schroetter, Nicolas F. Bouché, Johannes Zabl, Martin Wendt

Absorption line spectroscopy using background quasars can provide strong constraints on galactic outflows. In this paper, we investigate possible scaling relations between outflow properties, namely outflow velocity \Vout, the mass ejection rate $\dot M_{\rm out}$, and the mass loading factor $\eta$ and the host galaxy properties, such as star formation rate

Aditya Shankar, Hans Brouwer, Rihan Hai, Lydia Chen

Synthetic tabular data is crucial for sharing and augmenting data across silos, especially for enterprises with proprietary data. However, existing synthesizers are designed for centrally stored data. Hence, they struggle with real-world scenarios where features are distributed across multiple silos, necessitating on-premise data storage. We introduce SiloFu

Robert Henschel, Jonas Lindemann, Anders Follin, Bernd Dammann

High Performance Research Desktops are used by HPC centers and research computing organizations to lower the barrier of entry to HPC systems. These Linux desktops are deployed alongside HPC systems, leveraging the investments in HPC compute and storage infrastructure. By serving as a gateway to HPC systems they provide users with an environment to perform se

Benedikt Jahnel, Utkir Rozikov

We investigate the finite-state $p$-solid-on-solid model, for $p=\infty$, on Cayley trees of order $k\geq 2$ and establish a system of functional equations where each solution corresponds to a (splitting) Gibbs measure of the model. Our main result is that, for three states, $k=2,3$ and increasing coupling strength, the number of translation-invariant Gibbs

Yanda Li, Dixuan Wang, Jiaqing Liang, Guochao Jiang

Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning. One non-negligible reason for LLMs' suboptimal performance on logical reasoning is their overlooking of understanding logical fallacies correctly. To evaluate LLMs' capability of l

Cheeun Hong, Kyoung Mu Lee

Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neural networks, it has yet limited versatile applications due to the substantial computational costs. Since different input images for SR face different restoration difficulties, adapting computational costs based on the input image, referred to as adap

Boyang Chen, Andrea Coladangelo, Or Sattath

In this work, we focus on the following question: what are the cryptographic implications of having access to an oracle that provides a single Haar random quantum state? We find that the study of such a model sheds light on several aspects of the notion of quantum pseudorandomness. Pseudorandom states (PRS) are a family of states for which it is hard to dist

Álvaro Otero Sánchez, Daniel Camazón, Juan Antonio López Ramos

The aim of this article is to solve the system $XA=Y$ where $A=(a_{ij})\in M_{m\times n}(S)$, $Y\in S^{m}$ and $X$ is an unknown vector of size $n$, being $S$ an additively idempotent semiring. If the system has solutions then we completely characterize its maximal one, and in the particular case where $S$ is a generalized tropical semiring a complete charac

Yeongrak Kim, Hyunsuk Moon, Euisung Park

Let $X$ be a non-degenerate projective irreducible variety of dimension $n \ge 1$, degree $d$, and codimension $e \ge 2$ over an algebraically closed field $\mathbb{K}$ of characteristic $0$. Let $\beta_{p,q} (X)$ be the $(p,q)$-th graded Betti number of $X$. M. Green proved the celebrating $\mathcal K_{p,1}$-theorem about the vanishing of $\beta_{p,1} (X)$

Amir Subba, Rafiqul Rahaman

Entanglement, rooted in the non-deterministic, non-local nature of quantum mechanics, serves as a fundamental correlation. High-energy particle colliders offer a unique platform for exploring entanglement in the relativistic regime. The recent observation of entanglement in $t\bar{t}$ production by ATLAS has sparked significant interest in investigating enta

D. Vukadinović, H. N. Smitha, A. Korpi-Lagg, M. van Noort

For many transitions, atomic data, such as the oscillator strength (log(gf)) and the central wavelength of the line, are poorly constrained or even unknown. We present and test a new inversion method that infers atomic line parameters and the height stratification of the atmospheric parameters from spatially resolved spectropolarimetric observations of the S

Michael Sucker, Jalal Fadili, Peter Ochs

We use the PAC-Bayesian theory for the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable generalization guarantees (PAC-Bayesian bounds) and explicit trade-off between convergence guarantees and convergence speed, which contrasts with the typical worst-case analysis. O

Igor N. Karnaukhov

The fractional quantum Hall effect (FQHE) is studied in the semiclassical limit in the framework of the Hofstadter model with a short-range interaction between fermions. In the mean-field approximation, the repulsion between fermions leads to a periodic potential. Numerical calculations show that in the case of the periodic potential with a period that is a

Jean-Marc Sac-Épée

The contribution of this work is to provide tables of Salem numbers with trace -3 and small degrees, namely degrees 2d = 34, 36, 38, and 40. The implemented method also generates a list of totally positive polynomials of degrees d = 17, 18, 19, and 20, with trace 2d - 3, a single root strictly greater than 4, and all other roots strictly between 0 and 4.

Lars-Åke Näslund, Esko Kokkonen, Martin Magnuson

One of the most explored MXenes is Ti3C2Tx, where Tx is designated to inherently form termination species. Among many applications, Ti3C2Tx is a promising material for energy storage, energy conversion, and CO2-capturing devices. However, active sites for adsorption and surface reactions on the Ti3C2Tx-surface are still open questions to explore, which have

Lukas J. Furtak, Adi Zitrin, Johan P. Richard, Dominique Eckert

MACS J0600.1-2008 (MACS0600) is an X-ray luminous, massive galaxy cluster at $z_{\mathrm{d}}=0.43$, studied previously by the REionization LensIng Cluster Survey (RELICS) and ALMA Lensing Cluster Survey (ALCS) projects which revealed a complex, bimodal mass distribution and an intriguing high-redshift object behind it. Here, we report on the results of a com

Bikshapathi Gouda, Antti Arvola, Italo Atzeni, Antti Tölli

We consider a cell-free massive multiple-input multiple-output system with multi-antenna access points (APs) and user equipments (UEs), where the UEs can be served in both the downlink (DL) and uplink (UL) within a resource block. We tackle the combined optimization of the DL precoders and combiners at the APs and DL UEs, respectively, together with the UL c

B. O. Sadiq, O. S. Zakariyya, M. D. Buhari, A. N. Shuaibu

Telemedicine networks have seen significant changes in their capacity, monitoring, management, and control framework during the previous decades. The evolution of network capacity, control, and management for Unmanned Aerial Vehicle (UAV) & Software-Defined Networks (SDN) as support to telemedicine, artificial intelligence in telemedicine networks, and capab

Anna Michael, Yuri Santos Rego, Petra Schwer, Olga Varghese

We combinatorially characterize the number $\mathrm{cc}_2$ of conjugacy classes of involutions in any Coxeter group in terms of higher rank odd graphs. This notion naturally generalizes the concept of odd graphs, used previously to count the number of conjugacy classes of reflections. We provide uniform bounds and discuss some extremal cases, where the numbe

Tom Lorenz Klein, Clemens Thielen

Most activities in hospitals require the presence of the patient. Delays in patient transport can disrupt operations, potentially resulting in idle staff, underutilized equipment, and postponed procedures, which in turn lead to lost revenue, unnecessary costs across many different areas and departments, and lower patient satisfaction. Consequently, patient t

Fabio Bernasconi, Andrea Fanelli, Julia Schneider, Susanna Zimmermann

We prove the Sarkisov program for projective surfaces over excellent base rings, including the case of non-perfect base fields $k$ of characteristic $p>0$. We classify the Sarkisov links between Mori fibre spaces and their relations for regular surfaces, generalising work of Iskovskikh. As an application, we discuss rationality problems for regular surfaces

Timothée Goubault de Brugière, Simon Martiel

We devise greedy heuristics tailored for synthesizing quantum circuits that implement a specified set of Pauli rotations. Our heuristics are designed to minimize either the count of entangling gates or the depth of entangling gates, and they can be adjusted to either maintain or loosen the ordering of rotations. We present benchmark results demonstrating a d

Giacomo Bacci, Antonio Alberto D'Amico, Luca Sanguinetti

Large-scale MIMO systems with a massive number N of individually controlled antennas pose significant challenges for minimum mean square error (MMSE) channel estimation, based on uplink pilots. The major ones arise from the computational complexity, which scales with $N^3$, and from the need for accurate knowledge of the channel statistics. This paper aims t

Liam Cripwell, Joël Legrand, Claire Gardent

Text simplification intends to make a text easier to read while preserving its core meaning. Intuitively and as shown in previous works, these two dimensions (simplification and meaning preservation) are often-times inversely correlated. An overly conservative text will fail to simplify sufficiently, whereas extreme simplification will degrade meaning preser

Preeti P. Bhatt, Jitendra V. Nasriwala, Rakesh R. Savant

Handwritten font generation is important for preserving cultural heritage and creating personalized designs. It adds an authentic and expressive touch to printed materials, making them visually appealing and establishing a stronger connection with the audience. This paper aims to design a framework for generating handwritten fonts in the Gujarati script, mim

Marco Arazzi, Serena Nicolazzo, Antonino Nocera

The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increasing systems' complexity, and the need for reliability and autonomy. In this context, both smart objects and services are often provided by third parties which do not give full tra

Yuchen Liu, Luigi Palmieri, Sebastian Koch, Ilche Georgievski

Recent advancements in Large Language Models (LLMs) have sparked a revolution across many research fields. In robotics, the integration of common-sense knowledge from LLMs into task and motion planning has drastically advanced the field by unlocking unprecedented levels of context awareness. Despite their vast collection of knowledge, large language models m

Se-Wook Yoo, E In Son, Seung-Woo Seo

Autonomous navigation in extreme mountainous terrains poses challenges due to the presence of mobility-stressing elements and undulating surfaces, making it particularly difficult compared to conventional off-road driving scenarios. In such environments, estimating traversability solely based on exteroceptive sensors often leads to the inability to reach the

Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar, Gilles Gasso

Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown that it provides performances similar to its non-smoothed (non-private) counterpart. However, the computationaland statistical properties of such a metric have not yet been well-establishe

Elodie Germani, Camille Maumet, Elisa Fromont

We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines used to compute fMRI statistic maps can be considered as a style component and we propose to use different generative models, among which, Generative Adversarial Netwo

Min Jae Song

We show that $L^2$-accurate score estimation, in the absence of strong assumptions on the data distribution, is computationally hard even when sample complexity is polynomial in the relevant problem parameters. Our reduction builds on the result of Chen et al. (ICLR 2023), who showed that the problem of generating samples from an unknown data distribution re

Luc Angelelli, Danilo Carastan-Santos, Pierre-François Dutot

The energy consumption of an exascale High-Performance Computing (HPC) supercomputer rivals that of tens of thousands of people in terms of electricity demand. Given the substantial energy footprint of exascale HPC systems and the increasing strain on power grids due to climate-related events, electricity providers are starting to impose power caps during cr

Christophe Godin, Frédéric Boudon

In the past 50 years, the formalism of L-systems has been successfully used and developed to model the growth of filamentous and branching biological forms. These simulations take place in classical 2-D or 3-D Euclidean spaces. However, various biological forms actually grow in curved, non-Euclidean, spaces. This is for example the case of vein networks grow

Mewen Crespo, Guy Casale, Loïc Le Marrec

A new geometrically exact micro-structured model is constructed using a generalisation of the notion of Riemann-Cartan manifolds and fibre bundle theory of rank 3. This model is based around the concept of two different length scales: a macroscopic scale -- of dimensions 1, 2, or 3 -- and a microscopic one -- of dimension 3. As they interact with each other,

Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang

Incorporating Search and Recommendation (S&R) services within a singular application is prevalent in online platforms, leading to a new task termed open-app motivation prediction, which aims to predict whether users initiate the application with the specific intent of information searching, or to explore recommended content for entertainment. Studies have sh

Florent Thomas Fougères

This paper's objective is to improve the existing proof of the derivation of the Rayleigh--Boltzmann equation from the nonideal Rayleigh gas [6], yielding a far faster convergence rate. This equation is a linear version of the Boltzmann equation, describing the behavior of a small fraction of tagged particles having been perturbed from thermodynamic equilibr

Leon Maximilian Kolditz, Thomas Wick

In this work, the matrix-free solution of quasi-static phase-field fracture problems is further investigated. More specifically, we consider a quasi-monolithic formulation in which the irreversibility constraint is imposed with a primal-dual active set method. The resulting nonlinear problem is solved with a line-search assisted Newton method. Therein, the a

Yuting He, Fuxiang Huang, Xinrui Jiang, Yuxiang Nie

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial intelligence (AI) models, breaking the contradiction between limited AI models and diverse healthcare practices. Much more widespread healthcare scenarios will benefit from the devel

Sean Farhat, Deming Chen

In this paper, we propose that small models may not need to absorb the cost of pre-training to reap its benefits. Instead, they can capitalize on the astonishing results achieved by modern, enormous models to a surprising degree. We observe that, when distilled on a task from a pre-trained teacher model, a small model can achieve or surpass the performance i

Nicolò Lo Piparo, William J. Munro, Kae Nemoto

Advanced quantum networking systems rely on efficient quantum error correction codes for their optimal realization. The rate at which the encoded information is transmitted is a fundamental limit that affects the performance of such systems. Quantum aggregation allows one to increase the transmission rate by adding multiple paths connecting two distant users

Mozib Bin Awal, Prabwal Phukon

We study the Restricted Phase Space Thermodynamics (RPST) of magnetically charged Anti de Sitter (AdS) black holes sourced by nonlinear electrodynamics(NED). The first law and the corresponding Euler relation are examined using the scaling properties. While the mass is homogeneous in the first order, the intensive variables are observed to follow zeroth orde

Kosuke Fujiwara, Takahiro Morimoto

We study the effect of the magnon-magnon interaction on the nonlinear magnon transport. The magnonmagnon interaction induces nonreciprocal magnon decay when the time-reversal symmetry is broken and leads to nonlinear thermal responses of magnons. We construct a theoretical framework to study the nonlinear thermal responses due to the nonreciprocal magnon dec

Gaëtan Robillard

In 1960 in Stuttgart, Max Bense published the book Programming the Beautiful [Programmierung des Sch{\"o}nen]. Bense looks in cybernetics for scientific concepts and instigates the thought of programming in the field of literature. His information aesthetics influences a whole generation of scientists and artists - including the Stuttgart Circle, which takes

Chen Li, Huidong Tang, Jinli Zhang, Xiujing Guo

Aspect-based sentiment analysis predicts sentiment polarity with fine granularity. While graph convolutional networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigat

Shota Fukushima, Yong-Gwan Ji, Hyeonbae Kang, Xiaofei Li

If two conducting or insulating inclusions are closely located, the gradient of the solution may become arbitrarily large as the distance between inclusions tends to zero, resulting in high concentration of stress in between two inclusions. This happens if the bonding of the inclusions and the matrix is perfect, meaning that the potential and flux are contin

Hao Tian, Chao Liu, Changqing Luo, Xiang-Xiang Xue

Thanks to the precise astrometric measurements of proper motions by the Gaia mission, a new tidal stellar stream has been discovered in the northern hemisphere. The distribution of star count shows that the stream is approximately $80$ degrees long and $1.70$ degrees wide. Observations of $21$ member stars, including 14 RR Lyrae stars, indicate that the stre

Sho Inayoshi, Aji Resindra Widya, Satoshi Ozaki, Junji Otsuka

Model pre-training has become essential in various recognition tasks. Meanwhile, with the remarkable advancements in image generation models, pre-training methods utilizing generated images have also emerged given their ability to produce unlimited training data. However, while existing methods utilizing generated images excel in classification, they fall sh

Aashna Chawla, Deepak Kumar

Soft and biological matter come in a variety of shapes and geometries. When soft surfaces that do not fit into each other due to a mismatch in Gaussian curvatures form an interface, beautiful geometry-induced patterns emerge. In this paper, we study the effect of geometry on the dynamical response of soft surfaces moving relative to each other. Using a novel

Chen Li, Ye Zhu, Yang Cao, Jinli Zhang

The computation of the skyline provides a mechanism for utilizing multiple location-based criteria to identify optimal data points. However, the efficiency of these computations diminishes and becomes more challenging as the input data expands. This study presents a novel algorithm aimed at mitigating this challenge by harnessing the capabilities of Apache S

Yin Li, Qi Chen, Kai Wang, Meige Li

Multi-modality magnetic resonance imaging(MRI) data facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC). The lack of publicly available, comprehensive datasets limits advancements in diagnosis, treatment planning, and the development of machine learning algorithms for NPC. Addressing thi

Barsha G. Chowdhury, Justin R. David, Semanti Dutta, Jyotirmoy Mukherjee

We consider linear superpositions of single particle excitations in a scalar field theory on $AdS_3$ and evaluate their contribution to the bulk entanglement entropy across the Ryu-Takayanagi surface. We compare the entanglement entropy of these excitations obtained using the Faulkner-Lewkowycz-Maldacena formula to the entanglement entropy of linear superpos

Maik Wischow, Patrick Irmisch, Anko Boerner, Guillermo Gallego

Autonomous machines must self-maintain proper functionality to ensure the safety of humans and themselves. This pertains particularly to its cameras as predominant sensors to perceive the environment and support actions. A fundamental camera problem addressed in this study is noise. Solutions often focus on denoising images a posteriori, that is, fighting sy

Akira Okazaki, Shuichi Kawano

Multi-task learning (MTL) aims to improve estimation and prediction performance by sharing common information among related tasks. One natural assumption in MTL is that tasks are classified into clusters based on their characteristics. However, existing MTL methods based on this assumption often ignore outlier tasks that have large task-specific components o

Antonio J. Durán

Write $\zeta_m(n)$, $1\le m\le n-1$, for the negative zeros of the $n$-th Bell polynomial, ordered in decreasing size. In this paper, we prove the following asymptotic: for a positive integer $m$ we have $$ \lim_{n\to \infty}\frac{\zeta_m(n)}{-m\left(\displaystyle\frac{m}{m+1}\right)^{n-1}}=1. $$ The approach used to find this asymptotic applies to many othe

Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, na

Barbara Klotz, Verena Schenzinger, Michael Schwarzmann, Axel Kreuter

A study for the comprehensive information of current UV exposure for the area of Germany, based on the method for near real time calculation of UV Index maps used in the framework of the Austrian UV Monitoring Network, is presented. For the area of Germany about 22.000 surface UV Index maps were calculated for the year 2022 via the radiative transfer model l

Divyansh Shrimali, Biswaranjan Panda, Arun Kumar Pati

How fast an observable can evolve in time is answered by so-called ``observable speed limit". Here, we prove a stronger version of the observable speed limit and show that the previously obtained bound is a special case of the new bound. The stronger quantum speed limit for the state also follows from the stronger quantum speed limit for observables (SQSLO).

Yinji Li, Zhiwei Wang, Xiangyu Zhou

Let $(X,\omega)$ be a compact Hermitian manifold of complex dimension $n$. Let $\beta$ be a smooth real closed $(1,1)$ form such that there exists a function $\rho \in \mbox{PSH}(X,\beta)\cap L^{\infty}(X)$. We study the range of the complex non-pluripolar Monge-Amp\`ere operator $\langle(\beta+dd^c\cdot)^n\rangle$ on weighted Monge-Amp\`ere energy classes o

Sunita Jain, Nagaradhesh Yeleswarapu, Hasan Al Maruf, Rita Gupta

Compute Express Link (CXL) is a rapidly emerging coherent interconnect standard that provides opportunities for memory pooling and sharing. Memory sharing is a well-established software feature that improves memory utilization by avoiding unnecessary data movement. In this paper, we discuss multiple approaches to enable memory sharing with different generati

Clément Stahl, Benoit Famaey, Rodrigo Ibata, Oliver Hahn

For the last decade, several probes have pointed to a cosmological tension between the amplitude of density fluctuations extrapolated from the cosmic microwave background within the standard cosmological model and the one encapsulated by the $S_8$ parameter from large scale structure. The origin of this $S_8$ tension has not yet been elucidated and may hint

Alberto Ohashi, Francesco Russo, Alan Teixeira

We prove existence and uniqueness of solutions of a semilinear PDE driven by a Bessel type generator$L^\delta$ with low dimension $0 < \delta < 1$. $L^\delta$ is a local operator, whose drift is thederivative of $x \mapsto \log (\vert x\vert)$:in particular it is a Schwartz distribution, whichis not the derivative of a continuous function.The solutions are i

Tianwei Chen, Yusuke Hirota, Mayu Otani, Noa Garcia

We investigate the impact of deep generative models on potential social biases in upcoming computer vision models. As the internet witnesses an increasing influx of AI-generated images, concerns arise regarding inherent biases that may accompany them, potentially leading to the dissemination of harmful content. This paper explores whether a detrimental feedb

Stefano Galatolo, Davide Faranda

We prove that if a nonautonomous system has in a certain sense a fast convergence to equilibrium (faster than any power law behavior) then the time $\tau _{r}(x,y)$ needed for a typical point $x$ to enter for the first time in a ball $B(y,r)$ centered in $y$, with small radius \ $r $ scales as the local dimension of the equilibrium measure \ $\mu $ at $y$, i

Zakaria Elabid, Daniel Busby, Abdenour Hadid

Physics-informed neural networks (PINNs) have gained significant prominence as a powerful tool in the field of scientific computing and simulations. Their ability to seamlessly integrate physical principles into deep learning architectures has revolutionized the approaches to solving complex problems in physics and engineering. However, a persistent challeng

Rukshani Somarathna, Gelareh Mohammadi

Emotion understanding is a complex process that involves multiple components. The ability to recognise emotions not only leads to new context awareness methods but also enhances system interaction's effectiveness by perceiving and expressing emotions. Despite the attention to discrete and dimensional models, neuroscientific evidence supports those emotions a

Borisa Kuzeljevic, Dilip Raghavan

We survey some recent results about the order structure of various kinds of ultrafilters. More precisely, we study Rudin-Keisler and Tukey reducibility in classes of selective, stable ordered-union, and P-point ultrafilters. Although these reductions are fundamentally different, there are connections between them. On the other hand, even though the classes o

Hong-Xuan Jiang, Yosuke Mizuno, Indu K. Dihingia, Antonios Nathanail

Flux ropes erupting from the vicinity of the black hole are thought to be a potential model for the flares observed in Sgr\,A$^*$. In this study, we examine the radiative properties of flux ropes that emerged from the vicinity of the black hole. We have performed three-dimensional two-temperature General Relativistic Magnetohydrodynamic (GRMHD) simulations o

Haoyang Wang, Huihong Yuan, Qiang Zeng, Lai Zhou

Single-photon source is the cornerstone for modern quantum information processing. The present work derives the theoretical limit of single-photon purity for general parametric heralded single-photon sources, and subsequently demonstrates a bright, gigahertz-pulsed heralded source with the purity saturating the limit. By stimulating spontaneous four-wave mix

Henrik Sigstad

How robust are analyses based on marginal treatment effects (MTE) to violations of Imbens and Angrist (1994) monotonicity? In this note, I present weaker forms of monotonicity under which popular MTE-based estimands still identify the parameters of interest.

Alexander Avdoshkin

Understanding the geometric information contained in quantum states is valuable in various branches of physics, particularly in solid-state physics when Bloch states play a crucial role. While the Fubini-Study metric and Berry curvature form offer comprehensive descriptions of non-degenerate quantum states, a similar description for degenerate states did not

Hongsheng Hu, Shuo Wang, Tian Dong, Minhui Xue

Machine unlearning has become a promising solution for fulfilling the "right to be forgotten", under which individuals can request the deletion of their data from machine learning models. However, existing studies of machine unlearning mainly focus on the efficacy and efficiency of unlearning methods, while neglecting the investigation of the privacy vulnera

Gauhar Abbas, Neelam Singh

We discuss models of the flavour problem and dark matter based on the discrete $\mathcal{Z}_{\rm N} \times \mathcal{Z}_{\rm M} \times \mathcal{Z}_{\rm P}$ flavour symmetry. A new class of dark-matter emerges out of these models, which is defined as the flavonic dark matter. An ultra-violet completion of these models based on the dark-technicolour paradigm is

Shigeru Yamagami

Radial representations of finitely generated free groups are studied. The associated C*-algebra is located between the reduced and full group C*-algebras and its primitive ideal space is described concretely as a topological space.

Jin-Xin Li, Xue-Jing Feng, Ying-Ying Zhang, Jing-Xue Liu

We systematically investigate unconventional superfluid phases of fermionic dipolar particles lying in a double-wire setup with laser-assisted interwire tunneling. Our numerical simulations, based on the nonlocal Kohn-Sham Bogoliubov-de Gennes equation, reveal the existence of a large Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) region with a stripe phase under a

Yan-Qiu Zhang, Hao-Xiang Lin, Shao-Lin Xiong, Zhuo Li

Gamma-ray bursts (GRBs) are believed to launch relativistic jets, which generate prompt emission by internal processes, and produce long-lasting afterglows by driving external shocks into surrounding medium. However, how the jet powers the external shock is poorly known. The unprecedented observations of the keV-MeV emission with GECAM and the TeV emission w

Qiang Zeng, Huihong Yuan, Haoyang Wang, Lai Zhou

Nonlocal correlation represents the key feature of quantum mechanics, and is an exploitable resource in quantum information processing. However, the loophole issues and the associated applicability compromises hamper the practical applications. We report the first time-bin entangled detection-loophole-free steering nonlocality demonstration in a fully chip-f

Xingran Chen, Navid NaderiAlizadeh, Alejandro Ribeiro, Shirin Saeedi Bidokhti

We study real-time sampling and estimation of autoregressive Markovian sources in decentralized and dynamic multi-hop networks that share similar structures. Nodes cache neighboring samples and communicate over wireless collision channels. The objective is to minimize the time-average estimation error and/or the age of information under decentralized policie

Yiqing Wang, Xiaoyan Liu, Hailong Yang, Xinyu Yang

As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by such heterogeneity. Consequently, task-based runtime systems have been proposed as an intermediate layer to hide the complex heterogeneity from the application programmers. The core f

Xu Wang, Tian Ye, Rajgopal Kannan, Viktor Prasanna

Deep Learning (DL) Models for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), while delivering improved performance, have been shown to be quite vulnerable to adversarial attacks. Existing works improve robustness by training models on adversarial samples. However, by focusing mostly on attacks that manipulate images randomly, they neglect

Vincent Abbott, Gioele Zardini

The flow of information through a complex system can be readily understood with category theory. However, negative information (e.g., what is not possible) does not have an immediately evident categorical representation. The formalization of nategories using unconventional composition addresses this issue, and lets imposed limitations on categories be consid

Kelei Wang, Guangzeng Yi

This is the first in a series of papers devoted to the blow up analysis for the quenching phenomena in a parabolic MEMS equation. In this paper, we first give an optimal H\"{o}lder estimate for solutions to this equation by using the blow up method and some Liouville theorems on stationary two-valued caloric functions, and then establish a convergence theory

Zhoujian Sun, Cheng Luo, Ziyi Liu, Zhengxing Huang

The development of large language models (LLMs) has brought unprecedented possibilities for artificial intelligence (AI) based medical diagnosis. However, the application perspective of LLMs in real diagnostic scenarios is still unclear because they are not adept at collecting patient data proactively. This study presents a LLM-based diagnostic system that e

Alvaro Carbonero, Shaowen Mao, Mohamed Mehana

To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Under

Petre Birtea, Zohreh Ravanpak, Cornelia Vizman

We generalize double bracket vector fields, originally defined on semisimple Lie algebras, to Poisson manifolds equipped with a pseudo-Riemannian metric by utilizing a symmetric contravariant 2-tensor field. We extend the normal metric on an adjoint orbit of a compact semisimple Lie algebra to ensure that these vector fields become gradient vector fields on

Rishabh Batra, Rahul Jain

One-way state generators (OWSG) are natural quantum analogs to classical one-way functions. We consider statistically-verifiable OWSGs (sv-OWSG), which are potentially weaker objects than OWSGs. We show that O(n/log(n))-copy sv-OWSGs (n represents the input length) are equivalent to poly(n)-copy sv-OWSGs and to quantum commitments. Since known results show t

Sayantan Kumar, Tom Earnest, Braden Yang, Deydeep Kothapalli

INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discove

Itai Lang, Fei Xu, Dale Decatur, Sudarshan Babu

We present iSeg, a new interactive technique for segmenting 3D shapes. Previous works have focused mainly on leveraging pre-trained 2D foundation models for 3D segmentation based on text. However, text may be insufficient for accurately describing fine-grained spatial segmentations. Moreover, achieving a consistent 3D segmentation using a 2D model is highly

Qinian Jin, Qin Huang

In this paper we consider ill-posed inverse problems, both linear and nonlinear, by a heavy ball method in which a strongly convex regularization function is incorporated to detect the feature of the sought solution. We develop ideas on how to adaptively choose the step-sizes and the momentum coefficients to achieve acceleration over the Landweber-type metho

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on $(2.712\pm0.014)\times10^9$ $\psi(3686)$ events collected by the BESIII collaboration, evidence of the hadronic decay $h_c\to K_S^0K^+\pi^-+c.c.$ is found with a significance of $4.3\sigma$ in the $\psi(3686)\to\pi^0 h_c$ process. The branching fraction of $h_c\to K_S^0 K^+\pi^- +c.c.$ is measured to be $(7.3\pm0.8\pm1.8)\times10^{-4}$, where the fi

Jianming Tong, Jingtian Dang, Anupam Golder, Callie Hao

As machine learning (ML) permeates fields like healthcare, facial recognition, and blockchain, the need to protect sensitive data intensifies. Fully Homomorphic Encryption (FHE) allows inference on encrypted data, preserving the privacy of both data and the ML model. However, it slows down non-secure inference by up to five magnitudes, with a root cause of r

N. Itzhaki, U. Peleg

We argue that on-shell excitations with large negative energies are created rapidly when the string coupling increases with time. This does not indicate an inconsistency in string theory since the negative energy on-shell excitation is always entangled with an on-shell excitation with a positive energy. The total energy of this energy-EPR state vanishes. We

Walid Bousselham, Angie Boggust, Sofian Chaybouti, Hendrik Strobelt

Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However, the interpretability of these models remains a challenge. To address this, we propose LeGrad, an explainability method specifically designed for ViTs. LeGrad computes the gradient

Reda Alami, Abdalgader Abubaker, Mastane Achab, Mohamed El Amine Seddik

This paper explores the effects of various forms of regularization in the context of language model alignment via self-play. While both reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO) require to collect costly human-annotated pairwise preferences, the self-play fine-tuning (SPIN) approach replaces the rejected answe

Jesper Leong, Parada T. P. Hutauruk, Anthony W. Thomas

We investigate the neutrino elastic differential cross-section (NDCS) and corresponding mean free path for neutral current scattering in the dense matter of a neutron star. A wide range of observed neutron star (NS) masses is considered, including the presence of $\Lambda$, $\Xi^{-}$, and $\Xi^{0}$ hyperons in the heaviest stars. Their presence significantly

Markus A. G. Amano, Minoru Eto

The AdS/CFT correspondence has significantly impacted the study of strongly coupled systems, providing insights into various condensed matter phenomena through its holographic duality. This paper introduces an alternative approach to the breaking of the global $U(1)$ symmetry in the bulk in two asymptotically AdS spacetimes: AdS plus hard wall and AdS Blackb

Yan Chen, Tao Li, Xiwei Zhang

We study recursive regularized learning algorithms in the reproducing kernel Hilbert space (RKHS) with non-stationary online data streams. We introduce the concept of a random Tikhonov regularization path and decompose the tracking error of the algorithm&#39;s output for the regularization path into random difference equations in RKHS. We show that the track

Muhammad Asghar, Imran Sarwar Bajwa, Shabana Ramzan, Hina Afreen

In the field of human-computer interaction (HCI), the usability assessment of m-learning (mobile-learning) applications is a real challenge. Such assessment typically involves extraction of the best features of an application like efficiency, effectiveness, learnability, cognition, memorability, etc., and further ranking of those features for an overall asse