May 2023 arXiv papers — page 74

Jiaxi Yang, Binyuan Hui, Min Yang, Bailin Wang

Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, with the expectation that a single iteration of demonstrations processing can generalize effectively to a given test sample. However, this perspective overlooks the potential benefit

Yihua Zhu, Hidetoshi Shimodaira

The main objective of Knowledge Graph (KG) embeddings is to learn low-dimensional representations of entities and relations, enabling the prediction of missing facts. A significant challenge in achieving better KG embeddings lies in capturing relation patterns, including symmetry, antisymmetry, inversion, commutative composition, non-commutative composition,

Stefano De Paoli

Large Language Models (LLMs) have emerged as powerful generative Artificial Intelligence solutions which can be applied to several fields and areas of work. This paper presents results and reflection of an experiment done to use the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic Analysis. Previous research on this subject has largely wo

Mostafizur Rahman, Anjan A Sen, Sunil Singh Bohra

The ghost-free bi-metric gravity theory is a viable theory of gravity that explores the interaction between a massless and a massive graviton and can be described in terms of two dynamical metrics. In this paper, we present an exact static, spherically symmetric vacuum solution within this theory. The solution is spatially Schwarzschild-de Sitter, with the v

Hannu Reittu, Lasse Leskelä, Tomi Räty

Multilayer networks are in the focus of the current complex network study. In such networks multiple types of links may exist as well as many attributes for nodes. To fully use multilayer -- and other types of complex networks in applications, the merging of various data with topological information renders a powerful analysis. First, we suggest a simple way

Wijnand Broer, Rudolf Podgornik

We theoretically investigate the combined effects of the chirality and the finite total thickness of nematic cholesteric liquid crystals on the Casimir-Lifshitz torque. We find that, the larger the thickness, the more sinusoidal the angular dependence of the torque becomes. We use a Fourier decomposition to quantify this result. The general direction of the

Bertrand Toën, Gabriele Vezzosi

We introduce a notion of \emph{infinitesimal derived foliation}. We prove it is related to the classical notion of infinitesimal cohomology, and satisfies some formal integrability properties. We also provide some hints on how infinitesimal derived foliations compare to our previous notion of derived foliations.

Michael Hassid, Tal Remez, Tu Anh Nguyen, Itai Gat

Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically

Alexandru Ionita, Denis-Andrei Banu, Iulian Oleniuc

We propose a method of optimizing monotone Boolean circuits by re-writing them in a simpler, equivalent form. We use in total six heuristics: Hill Climbing, Simulated Annealing, and variations of them, which operate on the representation of the circuit as a logical formula. Our main motivation is to improve performance in Attribute-Based Encryption (ABE) sch

Federico Dalmao, José R. León

We consider the number of roots of linear combinations of a system of $n$ orthogonal eigenfunctions of a Sturm-Liouville initial value problem with i.i.d. standard Gaussian coefficients. We prove that its distribution inherits the asymptotic behavior of the number of roots of Quall's random trigonometric polynomials. This result can be thought as a robustnes

Li-Yi Hsu

Bell nonlocality and uncertainty relations are distinct features of quantum theory from classical physics. Bell nonlocality concerns the correlation strength among local observables on different quantum particles, whereas the uncertainty relations set the lower bound of the sum or product of the variance square of observables. Here we establish the statistic

Daniel Barlet, Jon Ingolfur Magnusson

The aim of this book is to show that the use of f-analytic families of finite type cycles (cycles having finitely many irreducible components, but not compact in general) in a given complex space may be useful in complex geometry, despite the fact that the corresponding functor is not, in general, representable, in contrast to the compact case. This study le

Martin Albrecht, Ondřej Hort, Michaela Kozlová, Miroslav Krůs

Many applications of short-wavelength radiation impose strong requirements on the coherence properties of the source. However, the measurement of such properties poses a challenge, mainly due to the lack of high-quality optics and source fluctuations that often violate assumptions necessary for multi-shot or cumulative techniques. In this article, we present

Vladyslav M. Kuchkin, Nikolai S. Kiselev

We show that competition between local interactions in monoaxial chiral magnets provides the stability of two-dimensional (2D) solitons with identical energies but opposite topological charges. These skyrmions and antiskyrmions represent metastable states in a wide range of parameters above the transition into the saturated ferromagnetic phase. The symmetry

Zhengxiang Shi, Francesco Tonolini, Nikolaos Aletras, Emine Yilmaz

Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks. Currently, there are two popular approaches to make use of unlabelled data: Self-training (ST) and Task-adaptive pre-training (TAPT). ST uses a teacher model to assign pseudo-lab

C Cuny, J Dedecker, F Merlevède

We give rates of convergence in the almost sure invariance principle for sums of dependent random variables with semi exponential tails, whose coupling coefficients decrease at a subexponential rate. We show that the rates in the strong invariance principle are in powers of log n. We apply our results to iid products of random matrices.

Ying Zhang, Hidetaka Kamigaito, Manabu Okumura

Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task. However, these models still suffer from a prediction bias due to their unidirectional decoding. Thus, we propose a bidirectional Transformer reranker (BTR), that re-estimates the probability of each candidate sentence generated by the pre-trained seq2s

Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou

Coverage path planning with unmanned aerial vehicles (UAVs) is a core task for many services and applications including search and rescue, precision agriculture, infrastructure inspection and surveillance. This work proposes an integrated guidance and gimbal control coverage path planning (CPP) approach, in which the mobility and gimbal inputs of an autonomo

Michal Neoral, Jonáš Šerých, Jiří Matas

We propose MFT -- Multi-Flow dense Tracker -- a novel method for dense, pixel-level, long-term tracking. The approach exploits optical flows estimated not only between consecutive frames, but also for pairs of frames at logarithmically spaced intervals. It selects the most reliable sequence of flows on the basis of estimates of its geometric accuracy and the

Sinda Ammous, Jérôme Dedecker, Céline Duval

A new multivariate density estimator for stationary sequences is obtained by Fourier inversion of the thresholded empirical characteristic function. This estimator does not depend on the choice of parameters related to the smoothness of the density; it is directly adaptive. We establish oracle inequalities valid for independent, $\alpha$-mixing and $\tau$-mi

Xinchi Qiu, Ilias Leontiadis, Luca Melis, Alex Sablayrolles

Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference. On-device models are typically less accurate when compared to their server counterparts due to the fact that (1) they typical

Kaiyu Li, Yiming Yang, Xiaoyuan Cheng, Yi He

Control variates are variance reduction techniques for Monte Carlo estimators. They play a critical role in improving Monte Carlo estimators in scientific and machine learning applications that involve computationally expensive integrals. We introduce multilevel control functionals (MLCFs), a novel and widely applicable extension of control variates that com

Rakesh R. Menon, Kerem Zaman, Shashank Srivastava

Understanding the internal reasoning behind the predictions of machine learning systems is increasingly vital, given their rising adoption and acceptance. While previous approaches, such as LIME, generate algorithmic explanations by attributing importance to input features for individual examples, recent research indicates that practitioners prefer examining

Julian Moosmann, Marco Giordano, Christian Vogt, Michele Magno

This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed quantized network arch

Zixiang Han, Lincong Han, Xiaozhou Zhang, Yajuan Wang

A novel multistatic multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in cellular networks is proposed. It can make use of widespread base stations (BSs) to perform cooperative sensing in wide area. This system is important since the deployment of sensing function can be achieved based on the existing mobile communicati

Francesco Tarantelli, Stefano Scopa

We investigate the nonequilibrium dynamics of quantum spin chains during a round-trip protocol that slowly drives the system across a quantum first-order transition. Out-of-equilibrium scaling behaviors \`a la Kibble-Zurek for the single-passage protocol across the first-order transition have been previously determined. Here, we show that such scaling relati

Chenxu Pang, Xiaojie Wang

In the field of computational finance, one is commonly interested in the expected value of a financial derivative whose payoff depends on the solution of stochastic differential equations (SDEs). For multi-dimensional SDEs with non-commutative diffusion coefficients in the globally Lipschitz setting, a kind of one-half order truncated Milstein-type scheme wi

Jacopo Di Iorio, Simone Vantini

Nowadays, more and more problems are dealing with data with one infinite continuous dimension: functional data. In this paper, we introduce the funLOCI algorithm which allows to identify functional local clusters or functional loci, i.e., subsets/groups of functions exhibiting similar behaviour across the same continuous subset of the domain. The definition

Shohei Yoda, Hayato Tsukagoshi, Ryohei Sasano, Koichi Takeda

Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a vector space can express only a part of the diverse information that sentences have, such as asymmetrical rel

Eleonora Di Valentino, Stefano Gariazzo, William Giarè, Olga Mena

Model-independent mass limits assess the robustness of current cosmological measurements of the neutrino mass scale. Consistency between high-multipole and low-multiple Cosmic Microwave Background observations measuring such scale further valuate the constraining power of present data. We derive here up-to-date limits on neutrino masses and abundances exploi

Ruben Ohana, Daniel Hesslow, Daniel Brunner, Sylvain Gigan

We introduce a novel method to perform linear optical random projections without the need for holography. Our method consists of a computationally trivial combination of multiple intensity measurements to mitigate the information loss usually associated with the absolute-square non-linearity imposed by optical intensity measurements. Both experimental and nu

Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk, Joey Öhman

This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3. We cover all parts of the development process, from data collection and processing, training configuration and instruction finetuning, to evaluation and considerations for release strategies. We hope that this paper can serve as a

Junhui Li, Xingsong Hou, Huake Wang, Shuhao Bi

The learned denoising-based approximate message passing (LDAMP) algorithm has attracted great attention for image compressed sensing (CS) tasks. However, it has two issues: first, its global measurement model severely restricts its applicability to high-dimensional images, and its block-based measurement method exhibits obvious block artifacts; second, the d

Haoyang Cao, Haotian Gu, Xin Guo

Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. It has enjoyed numerous empirical successes and inspired a growing number of theoretical studies. This paper addresses the feasibility issue of transfer learning. It begins by establishing the necessary mathematical co

Xiaoyi Tian, Qingxiang Xu, Chunhong Fu

The aim of this paper is to give new characterizations of some fundamental issues about idempotents. In the general setting of adjointable operators on Hilbert $C^*$-modules, a new term of quasi-projection pair is introduced. For each idempotent $Q$, a projection $m(Q)$, called the matched projection of $Q$, is constructed. It is shown that $Q$ and $m(Q)$ as

Michael Kranl, Hubert Ramsauer, Bernhard Knapp

One of the main tasks of an autonomous agent in a vehicle is to correctly perceive its environment. Much of the data that needs to be processed is collected by optical sensors such as cameras. Unfortunately, the data collected in this way can be affected by a variety of factors, including environmental influences such as inclement weather conditions (e.g., r

Horst Foidl, Tanja Rindler-Daller, Werner Zeilinger

(abridged) We present simulations of halo formation and evolution in scalar field dark matter (SFDM) cosmologies in the Thomas-Fermi regime, aka ``SFDM-TF", where a strong repulsive 2-particle self-interaction (SI) is included, being a valuable alternative to CDM, with the potential to resolve its ``cusp-core" problem. In general, SFDM behaves like a quantum

Pedro Abdalla

We consider the problem of estimating the covariance matrix of a random vector by observing i.i.d samples and each entry of the sampled vector is missed with probability $p$. Under the standard $L_4-L_2$ moment equivalence assumption, we construct the first estimator that simultaneously achieves optimality with respect to the parameter $p$ and it recovers th

Piotr T. Chruściel, Tomasz Smołka

We analyse the Noether charges for scalar and Maxwell fields on light cones on a de Sitter, Minkowski, and anti-de Sitter backgrounds. Somewhat surprisingly, under natural asymptotic conditions all charges for the Maxwell fields on both the de Sitter and anti-de Sitter backgrounds are finite. On the other hand, one needs to renormalise the charges for the co

Xinjing Yuan, Lingjun Pu, Lei Jiao, Xiaofei Wang

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performanc

Nicola Clinco, Michele Girfoglio, Annalisa Quaini, Gianluigi Rozza

We present a filter stabilization technique for the mildly compressible Euler equations that relies on a linear or nonlinear indicator function to identify the regions of the domain where artificial viscosity is needed and determine its amount. For the realization of this technique, we adopt a three step algorithm called Evolve-Filter-Relax (EFR), which at e

Adam D. Smith, Sumner B. Harris, Renato P. Camata, Da Yan

Recently a relationship between the Debye temperature $\Theta_D$ and the superconducting transition temperature $T_c$ of conventional superconductors has been proposed [npj Quantum Materials $\mathbf{3}$, 59 (2018)]. The relationship indicates that $T_c \le A \Theta_D$ for phonon-mediated BCS superconductors, with $A$ being a pre-factor of order $\sim 0.1$.

Ming-Hao Juan, Pu-Jen Cheng, Hui-Neng Hsu, Pin-Hsin Hsiao

Textual data are commonly used as auxiliary information for modeling user preference nowadays. While many prior works utilize user reviews for rating prediction, few focus on top-N recommendation, and even few try to incorporate item textual contents such as title and description. Though delivering promising performance for rating prediction, we empirically

Matteo Acclavio

In this paper we explore the design of sequent calculi operating on graphs. For this purpose, we introduce a set of logical connectives allowing us to extend the correspondence between cographs and classical propositional formulas to any graph. We then provide sequent calculi operating on these formulas, we prove cut-elimination and that formula encoding the

Wenxiong Li, Iair Arcavi, Ehud Nakar, Alexei V. Filippenko

We search the archival Zwicky Transient Facility public survey for rapidly evolving transient (RET) candidates based on well-defined criteria between 2018 May and 2021 December. The search yielded 19 bona-fide RET candidates, corresponding to a discovery rate of $\sim 5.2$ events per year. Even with a Galactic latitude cut of $20^\circ$, 8 of the 19 events (

M. A. Masharin, D. Khmelevskaia, V. I. Kondratiev, D. I. Markina

Deeply subwavelength lasers (or nanolasers) are highly demanded for compact on-chip bioimaging and sensing at the nanoscale. One of the main obstacles for the development of single-particle nanolasers with all three dimensions shorter than the emitting wavelength in the visible range is the high lasing thresholds and the resulting overheating. Here we exploi

Hanting Chen, Yunhe Wang, Jianyuan Guo, Dacheng Tao

At the heart of foundation models is the philosophy of "more is different", exemplified by the astonishing success in computer vision and natural language processing. However, the challenges of optimization and inherent complexity of transformer models call for a paradigm shift towards simplicity. In this study, we introduce VanillaNet, a neural network arch

James Stovold

Neural Cellular Automata (NCAs) are a model of morphogenesis, capable of growing two-dimensional artificial organisms from a single seed cell. In this paper, we show that NCAs can be trained to respond to signals. Two types of signal are used: internal (genomically-coded) signals, and external (environmental) signals. Signals are presented to a single pixel

Kiarn T. Laverick, Prahlad Warszawski, Areeya Chantasri, Howard M. Wiseman

State smoothing is a technique to estimate a state at a particular time, conditioned on information obtained both before (past) and after (future) that time. For a classical system, the smoothed state is a normalized product of the $\textit{filtered state}$ (a state conditioned only on the past measurement information and the initial preparation) and the $\t

Josep Maria Batllori, Yikun Gu, Dieter Horns, Marios Maroudas

A novel table-top experiment is introduced to detect photon-axion conversion: WISP Searches on a Fiber Interferometer (WISPFI). The setup consists of a Mach-Zehnder-type interferometer with a fiber placed inside an external magnetic field, where mixing occurs and is detected by measuring changes in amplitude. Hollow-core photonic crystal fibers (HC-PCF) will

Deng-Shan Wang, Peng Yan

The rigorous asymptotic analysis for the Riemann problem of the defocusing nonlinear Schr\"{o}dinger hydrodynamics is a very interesting problem with many challenges. To date, the full analysis of this problem remains open. In this work, the long-time asymptotics for the defocusing nonlinear Schr\"{o}dinger equation with general step-like initial data is inv

Soutrik Bandyopadhyay, Shubhendu Bhasin

This paper proposes a safe reinforcement learning (RL) algorithm that approximately solves the state-constrained optimal control problem for continuous-time uncertain nonlinear systems. We formulate the safe RL problem as the minimization of a Lagrangian that includes the cost functional and a user-defined barrier Lyapunov function (BLF) encoding the state c

Zheng Chen, Yulun Zhang, Ding Liu, Bin Xia

Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribut

A. D'Alise, G. Fabiano, D. Frattulillo, S. Hohenegger

We analyse the impact of positivity conditions on static spherically symmetric deformations of the Schwarzschild space-time. The metric is taken to satisfy, at least asymptotically, the Einstein equation in the presence of a non-trivial stress-energy tensor, on which we impose various physicality conditions. We systematically study and compare the impact of

Yang Bai, Jingyao Wang, Min Cao, Chen Chen

Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explore TBPS without parallel image-text data

Madison Russell, Alber Aqil, Marie Saitou, Omer Gokcumen

With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks

Jiazheng Li, Lin Gui, Yuxiang Zhou, David West

Providing explainable and faithful feedback is crucial for automated student answer assessment. In this paper, we introduce a novel framework that explores using ChatGPT, a cutting-edge large language model, for the concurrent tasks of student answer scoring and rationale generation. We identify the appropriate instructions by prompting ChatGPT with differen

Mitchell Keren Taraday, Chaim Baskin

Traditional methods for learning with the presence of noisy labels have successfully handled datasets with artificially injected noise but still fall short of adequately handling real-world noise. With the increasing use of meta-learning in the diverse fields of machine learning, researchers leveraged auxiliary small clean datasets to meta-correct the traini

Desmond Y. M. Tang

The backpropagation algorithm, despite its widespread use in neural network learning, may not accurately emulate the human cortex's learning process. Alternative strategies, such as the Forward-Forward Algorithm (FFA), offer a closer match to the human cortex's learning characteristics. However, the original FFA paper and related works on the Forward-Forward

Xiaoxiao Sheng, Zhiqiang Shen, Gang Xiao

We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal representations. Specifically, dense point cl

Jonas Soenen, Elia Van Wolputte, Vincent Vercruyssen, Wannes Meert

Most anomaly detection systems try to model normal behavior and assume anomalies deviate from it in diverse manners. However, there may be patterns in the anomalies as well. Ideally, an anomaly detection system can exploit patterns in both normal and anomalous behavior. In this paper, we present AD-MERCS, an unsupervised approach to anomaly detection that ex

Wentao Zhang, Yang Shi, Baoyong Zhang, Deming Yuan

In this paper, we consider a distributed online convex optimization problem over a time-varying multi-agent network. The goal of this network is to minimize a global loss function through local computation and communication with neighbors. To effectively handle the optimization problem with a high-dimensional and structural constraint set, we develop a distr

Alejandro Cárdenas-Avendaño, Alexandru Lupsasca

Black hole images are theoretically predicted (under mild astrophysical assumptions) to display a stack of lensed "photon rings" that carry information about the underlying spacetime geometry. Despite vigorous efforts, no such ring has been observationally resolved thus far. However, planning is now actively under way for space missions targeting the first (

Stefanie Walz, Mario Bijelic, Andrea Ramazzina, Amanpreet Walia

We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images. Using active and high dynamic range passive captures, Gated Stereo exploits multi-view cues alongside time-of-flight intensity cues from active gating. To this end, we propose a depth estimation method with a monocular and stereo

Zheng Li, Yuxuan Li, Penghai Zhao, Renjie Song

Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-a

Sanket Thakur, Cigdem Beyan, Pietro Morerio, Vittorio Murino

Short-term action anticipation (STA) in first-person videos is a challenging task that involves understanding the next active object interactions and predicting future actions. Existing action anticipation methods have primarily focused on utilizing features extracted from video clips, but often overlooked the importance of objects and their interactions. To

Donatella Darsena, Francesco Verde

We provide accurate approximations of the sum-rate capacity of an opportunistic time-sharing downlink, when a reconfigurable intelligent surface (RIS) assists the transmission from a single-antenna base station (BS) to single-antenna user equipments (UEs). We consider the fading effects of both the direct (i.e., BS-to-UEs) and reflection (i.e, BS-to-RIS-to-U

Pedro Henrique Luz de Araujo, Benjamin Roth

In behavioural testing, system functionalities underrepresented in the standard evaluation setting (with a held-out test set) are validated through controlled input-output pairs. Optimising performance on the behavioural tests during training (behavioural learning) would improve coverage of phenomena not sufficiently represented in the i.i.d. data and could

Fucai Luo, Saif Al-Kuwari, Haiyan Wang, Xingfu Yan

Federated learning (FL) allows a large number of clients to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. Unfortunately, recent attacks on FL demonstrate that local gradients may leak information about local trai

Nikolay Kuznetsov

The two-dimensional sloshing problem is considered; it describes the transversal free oscillations of water in an open, infinitely long canal of uniform cross-section. It is proved that the fundamental eigenfrequency is simple, whereas the corresponding velocity potential has only one nodal line connecting the free surface and the bottom; its harmonic conjug

Yifan Hong, Chen Wang

Humans can categorize with only a few samples despite the numerous features. To mimic this ability, we propose a novel dimension-reduced category representation using a mixture of probabilistic principal component analyzers (mPPCA). Tests on the ${\tt CIFAR-10H}$ dataset demonstrate that mPPCA with only a single principal component for each category effectiv

Joshua Smailes, Razvan David, Sebastian Kohler, Simon Birnbach

Recent years have seen a rapid increase in the number of CubeSats and other small satellites in orbit - these have highly constrained computational and communication resources, but still require robust secure communication to operate effectively. The QUIC transport layer protocol is designed to provide efficient communication with cryptography guarantees bui

Jan Cegin, Jakub Simko, Peter Brusilovsky

The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing? Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, modification or evaluation. For some of these tasks, models like ChatGPT can potenti

Jennifer Przybilla, Igor Pontes Duff, Peter Benner

In this article, we consider vibrational systems with semi-active damping that are described by a second-order model. In order to minimize the influence of external inputs to the system response, we are optimizing some damping values. As minimization criterion, we evaluate the energy response, that is the $\cH_2$-norm of the corresponding transfer function o

Dongfang Li, Jindi Yu, Baotian Hu, Zhenran Xu

As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks. But more research needs to be done on the interpretability capabilities of LLMs, that is, the ability to generate reasons after an answer has been given. Existing explanation datasets are mostly English-la

Germano Gabbianelli, Gergely Neu, Nneka Okolo, Matteo Papini

Offline Reinforcement Learning (RL) aims to learn a near-optimal policy from a fixed dataset of transitions collected by another policy. This problem has attracted a lot of attention recently, but most existing methods with strong theoretical guarantees are restricted to finite-horizon or tabular settings. In constrast, few algorithms for infinite-horizon se

Munan Ning, Yujia Xie, Dongdong Chen, Zeyin Song

This work studies how to transform an album to vivid and coherent stories, a task we refer to as "album storytelling". While this task can help preserve memories and facilitate experience sharing, it remains an underexplored area in current literature. With recent advances in Large Language Models (LLMs), it is now possible to generate lengthy, coherent text

Driss Bennis, Brahim El Alaoui

For a commutative ring $R$ with identity, the zero-divisor graph of $R$, denoted $\Gamma(R)$, is the graph whose vertices are the non-zero zero divisors of $R$ with two distinct vertices $x$ and $y$ are adjacent if and only if $xy=0$. In this paper, we are interested in partitioning the vertex set of $\Gamma(R)$ into global defensive alliances for a finite c

Emily Cheng, Mathieu Rita, Thierry Poibeau

Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitation learning has yet to be investigated. Our work explores t

Mihai Masala, Nicolae Cudlenco, Traian Rebedea, Marius Leordeanu

One of the essential human skills is the ability to seamlessly build an inner representation of the world. By exploiting this representation, humans are capable of easily finding consensus between visual, auditory and linguistic perspectives. In this work, we set out to understand and emulate this ability through an explicit representation for both vision an

Xiaoyu Wang, Mikael Johansson, Tong Zhang

In machine learning applications, it is well known that carefully designed learning rate (step size) schedules can significantly improve the convergence of commonly used first-order optimization algorithms. Therefore how to set step size adaptively becomes an important research question. A popular and effective method is the Polyak step size, which sets step

ATLAS Collaboration

A search for dark matter produced in association with a Higgs boson in final states with two hadronically decaying $\tau$-leptons and missing transverse momentum is presented. The analysis uses $139$ fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. No evidence

Ming-Ming Long, Kirill Melnikov, Jérémie Quarroz

Non-factorizable virtual corrections to Higgs boson production in weak boson fusion at next-to-next-to-leading order in QCD were estimated in the eikonal approximation [1]. This approximation corresponds to the expansion of relevant amplitudes around the forward limit. In this paper we compute the leading power correction to the eikonal limit and show that i

Igor G. Vladimirov

This paper is concerned with stochastic systems whose state is a diffusion process governed by an Ito stochastic differential equation (SDE). In the framework of a nominal white-noise model, the SDE is driven by a standard Wiener process. For a scenario of statistical uncertainty, where the driving noise acquires a state-dependent drift and thus deviates fro

Yisheng Alison Zheng, Abdallah Lakhdari, Amani Abusafia, Shing Tai Tony Lui

Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models

Atul Sharma, S. Janardhanan

This paper proposes a functional observer-based sliding mode control technique for position control of a single-link flexible manipulator. The proposed method considers the unmodelled system dynamics as uncertainty and aims to achieve accurate position control. The functional observer is used to directly estimate the sliding mode control design components an

W. C. Shiu, G. C. Lau, R. X. Zhang

Let $G=(V, E)$ be a connected graph. A bijection $f: E\to \{1, \ldots, |E|\}$ is called a local antimagic labeling if for any two adjacent vertices $x$ and $y$, $f^+(x)\neq f^+(y)$, where $f^+(x)=\sum_{e\in E(x)}f(e)$ and $E(x)$ is the set of edges incident to $x$. Thus a local antimagic labeling induces a proper vertex coloring of $G$, where the vertex $x$

Vijaya Krishna Yalavarthi, Kiran Madhusudhanan, Randolf Sholz, Nourhan Ahmed

Forecasting irregularly sampled time series with missing values is a crucial task for numerous real-world applications such as healthcare, astronomy, and climate sciences. State-of-the-art approaches to this problem rely on Ordinary Differential Equations (ODEs) which are known to be slow and often require additional features to handle missing values. To add

Tamoghna Das

Mutual information between local stress and local non-affine deformation is proposed as a collective field variable quantifying the {\em local softness} of soft materials. The liquid-solid transition in a simple liquid is considered as a generic example of mechanical transformation through varying correlation between stress and deformation at the microscopic

Marie Dorchain, Riccardo Muolo, Timoteo Carletti

Self-organization in natural and engineered systems causes the emergence of ordered spatio-temporal motifs. In presence of diffusive species, Turing theory has been widely used to understand the formation of such patterns on continuous domains obtained from a diffusion-driven instability mechanism. The theory was later extended to networked systems, where th

Ali Azimi, R. B. Bapat, Mohammad Farrokhi Derakhshandeh Ghouchan

We present explicit formulas for Moore-Penrose inverses of some families of set inclusion matrices arising from sets, vector spaces, and designs.

Dmitry Vasilyev

The intermetallic Mn-phase, which precipitates in steels and superalloys, can noticeably soften the mechanical properties of their matrix. Despite the importance of developing superalloys and steels, the thermodynamic properties and directions of thermal expansion of the Mu-phase are still poorly studied. In this work, the thermal expansion paths, elastic, t

Luyao Cheng, Siqi Zheng, Zhang Qinglin, Hui Wang

Speaker diarization(SD) is a classic task in speech processing and is crucial in multi-party scenarios such as meetings and conversations. Current mainstream speaker diarization approaches consider acoustic information only, which result in performance degradation when encountering adverse acoustic conditions. In this paper, we propose methods to extract spe

Martin Bromberger, Chaahat Jain, Christoph Weidenbach

We show that SCL(FOL) can simulate the derivation of non-redundant clauses by superposition for first-order logic without equality. Superposition-based reasoning is performed with respect to a fixed reduction ordering. The completeness proof of superposition relies on the grounding of the clause set. It builds a ground partial model according to the fixed or

Alfredo González Lezcano, Imtak Jeon, Augniva Ray

Quantum study of supersymmetric theories on Euclidean two dimensional anti-de Sitter space (AdS$_2$) is invalid if we use the standard normalizable functional basis due to its incompatibility with supersymmetry. We cure this problem by demonstrating that supersymmetry requires complexified spectrum and constructing the supersymmetric basis for scalar and spi

Frank Mtumbuka, Steven Schockaert

Entity typing is the task of assigning semantic types to the entities that are mentioned in a text. In the case of fine-grained entity typing (FET), a large set of candidate type labels is considered. Since obtaining sufficient amounts of manual annotations is then prohibitively expensive, FET models are typically trained using distant supervision. In this p

Detlev Buchholz, Klaus Fredenhagen

Algebraic quantum field theory is a general mathematical framework for relativistic quantum physics, based on the theory of operator algebras. It comprises all observable and operational aspects of a theory. In its framework the entire state space of a theory is covered, starting from the vacuum over arbitrary configurations of particles to thermal equilibri

Jaewan Moon, Hye-young Kim, Jongwuk Lee

Linear autoencoder models learn an item-to-item weight matrix via convex optimization with L2 regularization and zero-diagonal constraints. Despite their simplicity, they have shown remarkable performance compared to sophisticated non-linear models. This paper aims to theoretically understand the properties of two terms in linear autoencoders. Through the le

Masamichi Ishihara

We studied the escort averages in microcanonical and canonical ensembles in the Tsallis statistics of entropic parameter $q>1$. The quantity $(q-1)$ is the measure of the deviation from the Boltzmann-Gibbs statistics. We derived the relation between the escort average in the microcanonical ensemble and the escort average in the canonical ensemble. Conditions

Aniket Pramanick, Yufang Hou, Saif M. Mohammad, Iryna Gurevych

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement. In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques. We define three variables to encompass diverse facets of th