February 2024 arXiv papers — page 166

Diego Alonso-Orán, Daniel Sánchez-Simón del Pino, Juan J. L. Velázquez

In this work, we study the well-posedness of the three dimensional magneto-hydrostatic equation under Grad-Rubin boundary value conditions. The proof relies on a fixed point argument to construct solutions to an elliptic-hyperbolic problem in a perturbative regime by means of pseudo-differential operators with symbols with limited regularity in H\"older spac

Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi

In Bayesian persuasion, an informed sender strategically discloses information to a receiver so as to persuade them to undertake desirable actions. Recently, a growing attention has been devoted to settings in which sender and receivers interact sequentially. Recently, Markov persuasion processes (MPPs) have been introduced to capture sequential scenarios wh

Álvaro Martín-Cortinas, Daniel Sáez-Trigueros, Iván Vallés-Pérez, Biel Tura-Vecino

Large Language Models (LLMs) are one of the most promising technologies for the next era of speech generation systems, due to their scalability and in-context learning capabilities. Nevertheless, they suffer from multiple stability issues at inference time, such as hallucinations, content skipping or speech repetitions. In this work, we introduce a new self-

Min Zhang, Jinjiang Li, Fei Xue

In this paper, it is established that every sufficiently large positive integer $n$ subject to $n\equiv0\pmod2$ can be represented as a sum of one square of prime and seventeen fifth powers of primes, which gives an enhancement upon the previous result of Br\"{u}dern and Kawada [1].

Ronghui Liu, Yanqi Yang, Shuangping Tao

In this paper, we are devoted to studying some sharp bounds for Hardy type operators on mixed radial-angular type function spaces. In addition, we will establish the sharp weak-type estimates for the fractional Hardy operator and its conjugate operator, respectively.

Chuan Fan, Jianxian Qiu, Zhuang Zhao

In this paper, a fifth-order moment-based Hermite weighted essentially non-oscillatory scheme with unified stencils (termed as HWENO-U) is proposed for hyperbolic conservation laws. The main idea of the HWENO-U scheme is to modify the first-order moment by a HWENO limiter only in the time discretizations using the same information of spatial reconstructions,

Laura Manenti, Carlo Pepe, Isaac Sarnoff, Tengiz Ibrayev

Superconducting transition-edge sensors (TESs) are a type of quantum sensor known for its high single-photon detection efficiency and low background. This makes them ideal for particle physics experiments searching for rare events. In this work, we present a comprehensive characterization of the background in optical TESs, distinguishing three types of event

Carlos A. Velazquez-Vargas, Isaac Ray Christian, Jordan A. Taylor, Sreejan Kumar

We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills. Using a grid navigation paradigm, we tested whether contextual cues implemented as different "grid worlds", allow participants to learn two distinct key-mappings more efficiently. Our results indicate that when contextual information is provided, task performance i

Dinesh Wagle, Anish Rai, Mojtaba T. Kaffash, M. Benjamin Jungfleisch

The tunability of magnons enables their interaction with various other quantum excitations, including photons, paving the route for novel hybrid quantum systems. Here, we study magnon-photon coupling using a high-quality factor split-ring resonator and single-crystal yttrium iron garnet (YIG) spheres at room temperature. We investigate the dependence of the

Felipe Isaule, Abel Rojo-Francàs, Bruno Juliá-Díaz

We study two mobile bosonic impurities immersed in a one-dimensional optical lattice and interacting with a bosonic bath. We employ the exact diagonalization method for small periodic lattices to study stationary properties and dynamics. We consider the branch of repulsive interactions that induce the formation of bound impurities, akin to the bipolaron prob

Sebastiaan A. Terwijn

We give a quick survey of the various fixed point theorems in computability theory, partial combinatory algebra, and the theory of numberings, as well as generalizations based on those. We also point out several open problems connected to these.

Aditi Dudeja

In this note, we revisit the rounding algorithm of Wajc. Wajc gave a fully-adaptive randomized algorithm that rounds a dynamic fractional matching in an unweighted bipartite graph to an integral matching of nearly the same value in $O(\text{poly}(\log n,\frac{1}{\varepsilon}))$ update time. We give show that the guarantees of this algorithm hold for general

Darija Medvecki, Bojana Bašaragin, Adela Ljajić, Nikola Milošević

This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance on partially preprocessed short text in

Marko Stošić

We propose a generalized version of knots-quivers correspondence, where the quiver series variables specialize to arbitrary powers of the knot HOMFLY-PT polynomial series variable. We explicitely compute quivers for large classes of knots, as well as many homologically thick 9- and 10-crossings knots, including the ones with the super-exponential growth prop

Nick Early, Anaëlle Pfister, Bernd Sturmfels

Minimal kinematics identifies likelihood degenerations where the critical points are given by rational formulas. These rest on the Horn uniformization of Kapranov-Huh. We characterize all choices of minimal kinematics on the moduli space $\mathcal{M}_{0,n}$. These choices are motivated by the CHY model in physics and they are represented combinatorially by 2

Kevin J. Mitchell, Vytautas Gradauskas, Jack Radford, Ilya Starshynov

The guiding and transport of energy, for example of electromagnetic waves underpins many technologies that have shaped modern society, ranging from long distance optical fibre telecommunications to on-chip optical processors. Traditionally, a mechanism is required that exponentially localises the waves or particles in the confinement region, e.g. total inter

Valentin Bartier, Nicolas Bousquet, Moritz Mühlenthaler

Given a graph $G$ and two independent sets of $G$, the independent set reconfiguration problem asks whether one independent set can be transformed into the other by moving a single vertex at a time, such that at each intermediate step we have an independent set of $G$. We study the complexity of this problem for $H$-free graphs under the token sliding and to

Brendan Hassett, Yuri Tschinkel, Zhijia Zhang

We study equivariant geometry and rationality of moduli spaces of points on the projective line, for twists associated with permutations of the points.

Anis Chiout, Agnès Tempez, Thomas Carlier, Marc Chaigneau

Straintronics involves the manipulation and regulation of the electronic characteristics of 2D materials through the use of macro- and nano-scale strain engineering. In this study, we utilized an atomic force microscope (AFM) coupled with an optical system to perform indentation measurements and tip-enhanced photoluminescence (TEPL), allowing us to extract t

Hyunmin Choi, Jihun Kim, Seungho Kim, Seonhye Park

Homomorphic encryption (HE) enables privacy-preserving deep learning by allowing computations on encrypted data without decryption. However, deploying convolutional neural networks (CNNs) with HE is challenging due to the need to convert input data into a two-dimensional matrix for convolution using the im2col technique, which rearranges the input for effici

Junyeong L. Kim, Aidan I. Brown

The endoplasmic reticulum (ER) is a network of sheet-like and tubular structures that spans much of a cell and contains molecules undergoing diffusive searches for targets, such as unfolded proteins searching for chaperones and recently-folded proteins searching for export sites. By applying a Brownian dynamics algorithm to simulate molecule diffusion, we de

Marvin Tammen, Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani

Although mask-based beamforming is a powerful speech enhancement approach, it often requires manual parameter tuning to handle moving speakers. Recently, this approach was augmented with an attention-based spatial covariance matrix aggregator (ASA) module, enabling accurate tracking of moving speakers without manual tuning. However, the deep neural network m

Leon von Detten, Vadim Baru, Christoph Hanhart, Qian Wang

In recent years many vector charmonium(-like) states were reported by different electron-positron collider experiments above $4.2$ GeV. However, so far, there not only exists sizable tension in the parameters of those states, but there is also no consensus on the number of the vector states in this energy range. To some extend, this might be caused by the fa

Alex Kaltenbach, Michael Růžička

In this paper, we derive quasi-optimal a priori error estimates for the kinematic pressure for a Local Discontinuous Galerkin (LDG) approximation of steady systems of $p$-Navier-Stokes type in the case of shear-thickening, i.e., in the case $p>2$, imposing a new mild Muckenhoupt regularity condition.

Bahareh Tasdighi, Manuel Haussmann, Nicklas Werge, Yi-Shan Wu

Reinforcement learning (RL) for continuous control under delayed rewards is an under-explored problem despite its significance in real-world applications. Many complex skills are based on intermediate ones as prerequisites. For instance, a humanoid locomotor must learn how to stand before it can learn to walk. To cope with delayed reward, an agent must perfo

Deepak Kumar, Tuhin Malik, Hiranmaya Mishra

We investigate the intricate relationships between the non-radial \(f\) mode oscillation frequencies of neutron stars (NS)s and the corresponding nuclear matter equation of state (EOS) using a machine learning (ML) approach within the ambit of the relativistic mean field (RMF) framework for nuclear matter. With two distinct parameterizations of the Walecka m

Husein Zolkepli, Aisyah Razak, Kamarul Adha, Ariff Nazhan

In this work, we present a comprehensive exploration of finetuning Malaysian language models, specifically Llama2 and Mistral, on embedding tasks involving negative and positive pairs. We release two distinct models tailored for Semantic Similarity and Retrieval-Augmented Generation (RAG). For Semantic Similarity, our 600 million parameter Llama2 model outpe

Theo Douvropoulos, Matthieu Josuat-Vergès

The cluster complex on one hand, parking functions on the other hand, are two combinatorial (po)sets that can be associated to a finite real reflection group. Cluster parking functions are obtained by taking an appropriate fiber product (over noncrossing partitions). There is a natural structure of simplicial complex on these objects, and our main goal is to

Pierre Bourdillon, Linnea Evanson

Recent advances in material technology and in micro- and nano-electronics have profoundly changed the design of intracranial electrophysiology electrodes. It is now possible to manufacture electrodes that record cortical activity at a spatial resolution that was previously unthinkable. This high spatial resolution enables recording of the functional structur

Rupak Raj Ghimire, Bal Krishna Bal, Prakash Poudyal

In this paper, we examine the research conducted in the field of Nepali Automatic Speech Recognition (ASR). The primary objective of this survey is to conduct a comprehensive review of the works on Nepali Automatic Speech Recognition Systems completed to date, explore the different datasets used, examine the technology utilized, and take account of the obsta

Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu

In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct high-quality instruction datasets, many instruction processing approaches have been proposed, aiming to achieve a delicate balance between data quantity and data quality. Nevertheless

Zewen Yang, Songbo Dong, Armin Lederer, Xiaobing Dai

This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating under switching communication topologies. The approach leverages a correlation-aware cooperative algorithm framework built upon Gaussian process regression, which adeptly captures inte

Yunfang Niu, Dong Yi, Lingxiang Wu, Zhiwei Liu

Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision. However, to achieve high-fidelity try-on performance, most state-of-the-art methods still rely on accurate segmentation masks, which are often produced by near-perfect parsers or manual labeling. To ov

Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin

In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark,

Micha Christoph, Anders Martinsson, Raphael Steiner, Yuval Wigderson

A graph $G$ is said to be Ramsey for a tuple of graphs $(H_1,\dots,H_r)$ if every $r$-coloring of the edges of $G$ contains a monochromatic copy of $H_i$ in color $i$, for some $i$. A fundamental question at the intersection of Ramsey theory and the theory of random graphs is to determine the threshold at which the binomial random graph $G_{n,p}$ becomes a.a

Avraham Moriel, David Richard, Edan Lerner, Eran Bouchbinder

Materials typically fail under complex stress states, essentially involving dilatational (volumetric) components that eventually lead to material decohesion/separation. It is therefore important to understand dilatational irreversible deformation -- i.e., dilatational plasticity -- en route to failure. In the context of glasses, much focus has been given to

Mohammad N. S. Jahromi, Satya. M. Muddamsetty, Asta Sofie Stage Jarlner, Anna Murphy Høgenhaug

Explainable AI (XAI) aids in deciphering 'black-box' models. While several methods have been proposed and evaluated primarily in the image domain, the exploration of explainability in the text domain remains a growing research area. In this paper, we delve into the applicability of XAI methods for the text domain. In this context, the 'Similarity Difference

Qiaoyan Peng, Qingqing Wu, Wen Chen, Shaodan Ma

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cram\'{e}r-Rao bound

Xuzheng Chen, Jie Zhang, Ting Fu, Yifan Shen

Network speeds grow quickly in the modern cloud, so SmartNICs are introduced to offload network processing tasks, even application logic. However, typical multicore SmartNICs such as BlueFiled-2 are only capable of processing control-plane tasks with their embedded processors that have limited memory bandwidth and computing power. On the other hand, cloud ap

Yiyuan Zhang, Yuhao Kang, Zhixin Zhang, Xiaohan Ding

We introduce $\textit{InteractiveVideo}$, a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images or text, our framework is designed for dynamic interaction, allowing users to instruct the generative model through various intuitive mechanisms during the whole generation proces

V. M. Jiménez

In Continuum Mechanic a simple material body $\mathcal{B}$ is represeted by a three-dimensional differentiable manifold and the configuration space is given by the space of embeddings $Emb \left( \mathcal{B} , \mathbb{R}^{n} \right)$. We use the topology of infinite-dimensional manifold of this space, to present the first variation formula for Lagrangian mec

Branislav Pecher, Ivan Srba, Maria Bielikova, Joaquin Vanschoren

In few-shot learning, the selection of samples has a significant impact on the performance of the model. While effective sample selection strategies are well-established in supervised settings, research on large language models largely overlooks them, favouring strategies specifically tailored to individual in-context learning settings. In this paper, we pro

Mustafa Halilsoy, Chia-Li Hsieh

At certain time the Nariai spacetime is split into two parts. The resulting cosmology consists of the original Nariai and a new component with topology $(flat)_2\times S^2$, that is equivalent to a cloud of strings. We explore the properties of our hybrid cosmological model.

Federico Clazzer, Farouk Amri, Marcel Grec

A vast population of low-cost low-power transmitters sporadically sending small amounts of data over a common wireless medium is one of the main scenarios for Internet of things (IoT) data communications. At the medium access, the use of grant-free solutions may be preferred to reduce overhead even at the cost of multiple-access interference. Unsourced multi

Samuel D. Slöetjes, Matías P. Grassi, Vassilios Kapaklis

We investigate the magnetization dynamics in nanomagnet vertices often found in artificial spin ices. Our analysis involves creating a simplified model that depicts edge magnetization using magnetic charges. We utilize the model to explore the energy landscape, its associated curvatures, and the fundamental modes. Our study uncovers specific magnonic regimes

Nils-Erik Bomark, Reidun Renstrøm

It is very common to introduce quantum physics in an historical context. Though there are advantages to this, it is a problem that many of the stories that have become central to the physics lore are mere pseudo-histories far detached from the real events. It is about time that we stop uncritically copying these stories and instead make an effort to present

Vladimir Vovk

This note states a simple property of optimality of the Bayes-Kelly algorithm for conformal testing and poses a related open problem.

Johannes Schneider, Rene Abraham, Christian Meske

Generative Artificial Intelligence (GenAI) like ChatGPT has swiftly entered organizations without adequate governance, posing both opportunities and risks. Limited research addresses organizational governance from both technical and business perspectives. This gap is particularly relevant for international businesses, where differences in regulation, languag

Anna Logioti, Barbara Niethammer, Matthias Röger, Juan J. L. Velázquez

We consider a parabolic non-local free boundary problem that has been derived as a limit of a bulk-surface reaction-diffusion system which models cell polarization. In previous papers, we have established well-posedness of this problem and derived conditions on the initial data that imply continuity of the free boundary as $t\to 0$. In this paper we extend t

Adrien. A. D. Petrillo, Mouad Fattouhi, Adriano Di Pietro, Marta Alerany Solé

Spin waves, known for their ability to propagate without the involvement of moving charges, hold immense promise for on-chip information transfer and processing, offering a path toward post-CMOS computing technologies. This study investigates the potential synergy between propagating Damon-Eshbach spin waves and voltage-controlled magnetization in the pursui

Alexandre Borovik, Sukru Yalcinkaya

This paper provides an example of structural proxies for black box rings encrypting rings of 2 by 2 matrices of finite fields of odd order.

Alexander Anferov, Shannon P. Harvey, Fanghui Wan, Jonathan Simon

Current state-of-the-art superconducting microwave qubits are cooled to extremely low temperatures to avoid sources of decoherence. Higher qubit operating temperatures would significantly increase the cooling power available, which is desirable for scaling up the number of qubits in quantum computing architectures and integrating qubits in experiments requir

Zengzhao Xu, Weige Xi, Ligong Wang

Let $G$ be a connected graph with order $n$ and size $m$. Let $D(G)$ and $Tr(G)$ be the distance matrix and diagonal matrix with vertex transmissions of $G$, respectively. For any real $\alpha\in[0,1]$, the generalized distance matrix $D_\alpha(G)$ of $G$ is defined as $$D_\alpha(G)=\alpha Tr(G)+(1-\alpha)D(G).$$ The largest eigenvalue of $D_{\alpha}(G)$ is

Chih Wei Ling, Cheuk Ting Li

We construct a randomized vector quantizer which has a smaller maximum error compared to all known lattice quantizers with the same entropy for dimensions 5, 6, ..., 48, and also has a smaller mean squared error compared to known lattice quantizers with the same entropy for dimensions 35, ..., 47, in the high resolution limit. Moreover, our randomized quanti

D. E. Ferreyra, F. E. Levis, R. P. Moas, H. H. Zhu

Rao and Mitra in 1972 introduced two different types of constraints to extend the concept of Bott-Duffin inverse and defined a new constrained inverse. Mary in 2011 defined the inverse along an element that generalizes the Moore-Penrose and Drazin inverses in a semigroup. Drazin in 2012 introduced the $(b,c)$-inverse generalizing the Mary inverse. In 2017, R

Martin Eigel, Charles Miranda

A novel approach to approximate solutions of Stochastic Differential Equations (SDEs) by Deep Neural Networks is derived and analysed. The architecture is inspired by the notion of Deep Operator Networks (DeepONets), which is based on operator learning in function spaces in terms of a reduced basis also represented in the network. In our setting, we make use

Ivan S. Gerasimov, Oleg . V. Egorov, Alexei V. Moiseev, Alexei Yu. Kniazev

We investigated the ionised and atomic gas kinematics and excitation state in the central region of ongoing star formation of the nearby low-metallicity dwarf galaxy Sextans B. The analysis is based on the new observations performed in Ha emission line with high resolution ($R \sim 16000$) scanning Fabry-Perot interferometer at the 6-m BTA SAO RAS telescope,

Theo Diamantakis, James Woodfield

Stochastic modelling necessitates an interpretation of noise. In this paper, we describe the loss of deterministically stable behaviour in a fundamental fluid mechanics problem, conditional to whether noise is introduced in the sense of It\^o, Stratonovich or a limit of Wong-Zakai type. We examine this comparison in the wider context of discretising stochast

Yuanyi Wang, Wei Tang, Haifeng Sun, Zirui Zhuang

Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this paper, we present a propagation perspective to

Dalibor Djukanovic, Georg von Hippel, Harvey B. Meyer, Konstantin Ottnad

We present an update of our determination of the isovector charges $g_A^{u-d}$, $g_S^{u-d}$ and $g_T^{u-d}$, and the isovector twist-2 forward matrix elements $\langle x\rangle_{u-d}$, $\langle x\rangle_{\Delta u-\Delta d}$ and $\langle x\rangle_{\delta u-\delta d}$ on the $N_\mathrm{f}=2+1$ gauge ensembles generated by the Coordinated Lattice Simulations (C

Ismael Ribeiro de Assis, Ingrid Mertig, Börge Göbel

Magnetic skyrmions are nano-scale magnetic whirls that can be driven by currents via spin torques. They are promising candidates for spintronic devices such as the racetrack memory, where a motion along the uniform current is typically desired. However, for spin torque nano-oscillators in Corbino disks, the goal is to achieve a circular motion, perpendicular

Ben Knudsen

We survey two decades of work on the (sequential) topological complexity of configuration spaces of graphs (ordered and unordered), aiming to give an account that is unifying, elementary, and self-contained. We discuss the traditional approach through cohomology, with its limitations, and the more modern approach through asphericity and the fundamental group

Juncai He, Liangchen Liu, Yen-Hsi Richard Tsai

This paper investigates the impact of multiscale data on machine learning algorithms, particularly in the context of deep learning. A dataset is multiscale if its distribution shows large variations in scale across different directions. This paper reveals multiscale structures in the loss landscape, including its gradients and Hessians inherited from the dat

S. A. Khrapak, A. G. Khrapak

Exact mechanisms of thermal conductivity in liquids are not well understood, despite rich research history. A vibrational model of energy transfer in dense simple liquids with soft pairwise interactions seems adequate to partially fill this gap. The purpose of the present paper is to define its applicability domain and to demonstrate how well it works within

Lei Wang, Xiuyuan Yuan, Tom Gedeon, Liang Zheng

Effectively extracting motions from video is a critical and long-standing problem for action recognition. This problem is very challenging because motions (i) do not have an explicit form, (ii) have various concepts such as displacement, velocity, and acceleration, and (iii) often contain noise caused by unstable pixels. Addressing these challenges, we propo

Jonah Elias Nitschke, Michael Gutnikov, Karl Schiller, Eugenio Coronado

Excitations between localized 3d states of transition metal ions within crystalline solids, commonly known as d-d transitions, play a pivotal role in diverse phenomena across solid state physics, materials science, and chemistry. These transitions contribute to the coloration in transition metal oxides, catalytic processes on oxide surfaces, and high-tempera

Georgios Tsoumplekas, Vladislav Li, Panagiotis Sarigiannidis, Vasileios Argyriou

Despite the widespread success of deep learning, its intense requirements for vast amounts of data and extensive training make it impractical for various real-world applications where data is scarce. In recent years, Few-Shot Learning (FSL) has emerged as a learning paradigm that aims to address these limitations by leveraging prior knowledge to enable rapid

Shuntaro Yamamoto, Nobuyuki Yoshioka

Quantum Signal Processing (QSP), together with the quantum singular value transformation, is one of the central quantum algorithms due to its efficiency and generality in many fields including quantum simulation, quantum machine learning, and quantum cryptography. The largest bottleneck of QSP and its family is its difficulty in finding the phase angle seque

Dipayan Chakraborty, Annegret K. Wagler

Using dominating sets to separate vertices of graphs is a well-studied problem in the larger domain of identification problems. In such problems, the objective is to choose a suitable dominating set $C$ of a graph $G$ which is also separating in the sense that the neighbourhoods of any two distinct vertices of $G$ have distinct intersections with $C$. Such a

Supriyo Ghosh, Karish Grover, Jimmy Wong, Chetan Bansal

Despite significant reliability efforts, large-scale cloud services inevitably experience production incidents that can significantly impact service availability and customer's satisfaction. Worse, in many cases one incident can lead to multiple downstream failures due to cascading effects that creates several related incidents across different dependent ser

Zewen Yang, Xiaobing Dai, Akshat Dubey, Sandra Hirche

This paper introduces an innovative approach to enhance distributed cooperative learning using Gaussian process (GP) regression in multi-agent systems (MASs). The key contribution of this work is the development of an elective learning algorithm, namely prior-aware elective distributed GP (Pri-GP), which empowers agents with the capability to selectively req

Lucrezia Manieri, Alessandro Falsone, Maria Prandini

This paper addresses non-convex constrained optimization problems that are characterized by a scalar complicating constraint. We propose an iterative bisection method for the dual problem (DualBi Algorithm) that recovers a feasible primal solution, with a performance that is progressively improving throughout iterations. Application to multi-agent problems w

Bakhrom Omirov, Gulkhayo Solijanova

In this paper, we provide a complete description of complex maximal solvable extensions for a certain class of nilpotent Lie algebras. In particular, we show that, up to isomorphism, a solvable extension of a $d$-locally diagonalizable nilpotent Lie algebra is unique and is realized as the semidirect product of its nilradical with a maximal torus. This resul

Vitalii Emelianov, Michaël Perrot

We theoretically study how differential privacy interacts with both individual and group fairness in binary linear classification. More precisely, we focus on the output perturbation mechanism, a classic approach in privacy-preserving machine learning. We derive high-probability bounds on the level of individual and group fairness that the perturbed models c

Junpeng Wang, Sergio Estrada

Let $R$ be a ring with Gwgldim$(R)<\infty$. We obtain a triangle-equivalence $\mathrm{K}(R\text{-}\mathrm{GProj})\simeq \mathrm{K}(R\text{-}\mathrm{GInj})$ which restricts to a triangle-equivalence $\mathrm{K}(R\text{-}\mathrm{Proj})$ $\simeq \mathrm{K}(R\text{-}\mathrm{Inj})$. This class of rings includes, among others, (left) Gorenstein rings, Ding-Chen ri

Junjie Fang, Likai Tang, Hongzhe Bi, Yujia Qin

Long-context processing is a critical ability that constrains the applicability of large language models (LLMs). Although there exist various methods devoted to enhancing the long-context processing ability of LLMs, they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments. In thi

Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato

The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayesian inference and molecular dynamics. Addressing this, we propose Diffusive Gibbs Sampling (DiGS), an innovative family of sampling methods designed for effective sampling from dis

Mike Diessner, Kevin J. Wilson, Richard D. Whalley

Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting -- an assumption that is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as temperature, humidity and wind speed. When opt

Haijiao Ji, Ning Zhang, Noah F. Q. Yuan

We investigate the properties of the two-dimensional model with Rashba-type spin-orbit coupling cubic in electron momentum. In the normal phase, edge states emerge on open boundaries. In the superconducting phase, edge states could evolve into gapped fermionic edge states. Applications to realistic materials of interface superconductors are also discussed.

Ainesh Sewak, Sandra Siegfried, Torsten Hothorn

Accurate diagnostic tests are essential for effective screening and treatment. However, individual biomarkers often fail to provide sufficient diagnostic accuracy, as they typically capture only one aspect of the complex disease process. Combining multiple biomarkers, each capturing a distinct mechanism, can help constructing more informative diagnostic test

Théo Sourget, Ahmet Akkoç, Stinna Winther, Christine Lyngbye Galsgaard

Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating datasets requires a lot of effort, researchers often use publicly available datasets, there is however no adopted standard for citing the datasets used in scientific papers, leading to

Minsheng Huang, Lidong Cheng, Wenjun Ying, Xi Deng

Solving compressible flows containing both smooth and discontinuous flow structures remains a significant challenge for finite volume methods. Godunov-type finite volume methods are commonly used for numerical simulations of compressible flows. One of the key factors in obtaining high-quality solutions is high-fidelity spatial reconstruction. In this work, w

Longwen Zhou

The intricate interplay between unitary evolution and projective measurements could induce entanglement phase transitions in the nonequilibrium dynamics of quantum many-particle systems. In this work, we uncover loss-induced entanglement transitions in non-Hermitian topological superconductors. In prototypical Kitaev chains with local particle losses and var

Martins Zubkins, Edvards Strods, Viktors Vibornijs, Anatolijs Sarakovskis

This paper reports on the deposition and photoluminescence of amorphous and crystalline thin films of zinc gallium oxide with Ga:Zn atomic ratio varied between 0.3 and 5.7. The films are prepared by reactive direct current magnetron co-sputtering from liquid/solid gallium/zinc targets onto fused quartz substrates; the temperature of the substrate is varied f

Jordan Aiko Deja, Sandi Štor, Ilonka Pucihar, Klen Čopič Pucihar

Improvisation is a vital but often neglected aspect of traditional piano teaching. Challenges such as difficulty in assessment and subjectivity have hindered its effective instruction. Technological approaches, including augmentation, aim to enhance piano instruction, but the specific application of digital augmentation for piano improvisation is under-explo

Yu-Guan Hsieh, James Thornton, Eugene Ndiaye, Michal Klein

Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. performance on another dataset, robustness, agreement with a prior). Although the simplest approach to incorporating an auxiliary loss is to sum it with the training loss as a regul

James Hefford, Matt Wilson

We identify morphisms of strong profunctors as a categorification of quantum supermaps. These black-box generalisations of diagrams-with-holes are hence placed within the broader field of profunctor optics, as morphisms in the category of copresheaves on concrete networks. This enables the first construction of abstract logical connectives such as tensor pro

Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle

Image clustering divides a collection of images into meaningful groups, typically interpreted post-hoc via human-given annotations. Those are usually in the form of text, begging the question of using text as an abstraction for image clustering. Current image clustering methods, however, neglect the use of generated textual descriptions. We, therefore, propo

Xiang Wang, Renzhi Wang, Ningzi Hu, Pinqiang Wang

The leading operational Global Ocean Forecasting Systems (GOFSs) use physics-driven numerical forecasting models that solve the partial differential equations with expensive computation. Recently, specifically in atmosphere weather forecasting, data-driven models have demonstrated significant potential for speeding up environmental forecasting by orders of m

Xander M. de Wit, Giulio Ortali, Alessandro Corbetta, Alexei A. Mailybaev

We present a study of the intermittent properties of a shell model of turbulence with unprecedented statistics, about $\sim 10^7$ eddy turn over time, achieved thanks to an implementation on a large-scale parallel GPU factory. This allows us to quantify the inertial range anomalous scaling properties of the velocity fluctuations up to the 24th order moment.

Daniele S. M. Alves, Sergi Gonzàlez-Solís

It has been long-understood that final state rescattering effects provide $\mathcal{O}(1)$ corrections to hadronic meson decays rates, such as $\eta\to\pi\pi\pi$ and $\eta^{\prime}\to\eta\pi\pi$. Hence, one would expect that such effects would be just as important in axio-hadronic $\eta$ and $\eta^{\prime}$ decays, such as $\eta^{(\prime)}\to\pi\pi a$, where

Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello

Aligning language models with human preferences is crucial for reducing errors and biases in these models. Alignment techniques, such as reinforcement learning from human feedback (RLHF), are typically cast as optimizing a tradeoff between human preference rewards and a proximity regularization term that encourages staying close to the unaligned model. Selec

Seongmin Jeon, Henrik Shahgholian

In this paper, we study a parabolic free boundary problem in an exterior domain $$\begin{cases} F(D^2u)-\partial_tu=u^a\chi_{\{u>0\}}&\text{in }(\mathbb R^n\setminus K)\times(0,\infty),\\ u=u_0&\text{on }\{t=0\},\\ |\nabla u|=u=0&\text{on }\partial\Omega\cap(\mathbb R^n\times(0,\infty)),\\ u=1&\text{in }K\times[0,\infty).\end{cases}$$ Here, $a$ belongs to th

L. Feher

Some generalizations of spin Sutherland models descend from `master integrable systems' living on Heisenberg doubles of compact semisimple Lie groups. The master systems represent Poisson--Lie counterparts of the systems of free motion modeled on the respective cotangent bundles and their reduction relies on taking quotient with respect to a suitable conjuga

Zehang Weng, Haofei Lu, Danica Kragic, Jens Lundell

We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds. DexDiffuser includes the conditional diffusion-based grasp sampler DexSampler and the dexterous grasp evaluator DexEvaluator. DexSampler generates high-quality grasps conditioned on object point clouds by iterative denoisi

Iyas Ismail, Ludger Inhester, Tatiana Marchenko, Florian Trinter

Double-core-hole (DCH) states in isolated water and heavy water molecules, resulting from the sequential absorption of two x-ray photons, have been investigated. A comparison of the subsequent Auger emission spectra from the two isotopes provides direct evidence of ultrafast nuclear motion during the 1.5 fs lifetime of these DCH states. Our numerical results

Junjie Chu, Zeyang Sha, Michael Backes, Yang Zhang

Significant advancements have recently been made in large language models represented by GPT models. Users frequently have multi-round private conversations with cloud-hosted GPT models for task optimization. Yet, this operational paradigm introduces additional attack surfaces, particularly in custom GPTs and hijacked chat sessions. In this paper, we introdu

Maria Lyssenko, Piyush Pimplikar, Maarten Bieshaar, Farzad Nozarian

In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics are not an adequate safety indicator, recent works employ approaches to identify safety-critical VRU and back-annotate the risk to the object detector. However, those approaches do

Zihan Ma, Yongshang Li, Ronggui Ma, Chen Liang

There are two challenges presented in parsing road scenes from UAV images: the complexity of processing high-resolution images and the dependency on extensive manual annotations required by traditional supervised deep learning methods to train robust and accurate models. In this paper, a novel unsupervised road parsing framework that leverages advancements i

Balázs Pozsgay, Kohei Fukai

Recently multiple families of spin chain models were found, which have a free fermionic spectrum,even though they are not solvable by a Jordan-Wigner transformation. Instead, the free fermions emerge as a result of a rather intricate construction. In this work we consider the quantum circuit formulation of the problem. We construct circuits using local unita