May 2023 arXiv papers — page 72

Justin Eilertsen, Wylie Stroberg

The linear noise approximation (LNA) describes the random fluctuations from the mean-field concentrations of a chemical reaction network due to intrinsic noise. It is also used as a test probe to determine the accuracy of reduced formulations of the chemical master equation and to understand the relationship between timescale disparity and model reduction in

Ayushi Awasthi, O. S. K. S Sastri

In this paper, the np - scattering phase shifts and cross section for S,P and D partial waves have been obtained for energies below the pion threshold, by considering Deng-Fan potential as model of interaction. The radial time independent Schr\"odinger equation has been analytically solved using Nikiforov - Uvarov method to obtain the energy expression for g

Proyag Pal, Brian Thompson, Yogesh Virkar, Prashant Mathur

To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We also introduce auxiliary counters to help the decoder to kee

P. A. D. Gonçalves, F. Javier García de Abajo

Plasmons can be excited during photoemission and produce spectral photoelectron features that yield information on the nanoscale optical response of the probed materials. However, these so-called plasmon satellites have so far been observed only for planar surfaces, while their potential for the characterization of nanostructures remains unexplored. Here, we

Sebastian Kölle, Manuel Jäger, Markus Müller, Wladimir Schoch

We experimentally test a recently proposed holographic method for imaging coherent light scatterers which are distributed over a 2-dimensional grid. In our setup the scatterers consist of a back-illuminated, opaque mask with submicron-sized holes. We study how the imaging fidelity depends on various parameters of the set-up. We observe that a few hundred sca

Ping Li, Xiaoyun Li

In this paper, we develop a series of differential privacy (DP) algorithms from a family of random projections (RP) for general applications in machine learning, data mining, and information retrieval. Among the presented algorithms, iDP-SignRP is remarkably effective under the setting of ``individual differential privacy'' (iDP), based on sign random projec

Emily Adlam

Most existing proposals to explain the temporal asymmetries we see around us are sited within an approach to physics based on time evolution, and thus they typically put the asymmetry in at the beginning of time in the form of a special initial state. But there may be other possibilities for explaining temporal asymmetries if we don't presuppose the time evo

Shunkai Mao, Peng Qu

We consider the Cauchy problem for the isentropic compressible Euler-Maxwell equations under general pressure laws in a three-dimensional periodic domain. For any smooth initial electron density away from the vacuum and smooth equilibrium-charged ion density, we could construct infinitely many $\alpha$-H\"older continuous entropy solutions emanating from the

Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang

Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses. In real-life applications, user utterances are noisier, and thus it is more difficult to accurately track dialog states and correctly secure relevant knowledge. Recently, a progres

Marta R. Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti

We introduce a multilingual extension of the HOLISTICBIAS dataset, the largest English template-based taxonomy of textual people references: MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50 languages distributed across all 13 demographic axes. Source sentences are built from combinations of 118 demographic descriptors and three pat

Zehan Li, Yanzhao Zhang, Dingkun Long, Pengjun Xie

Recently, various studies have been directed towards exploring dense passage retrieval techniques employing pre-trained language models, among which the masked auto-encoder (MAE) pre-training architecture has emerged as the most promising. The conventional MAE framework relies on leveraging the passage reconstruction of decoder to bolster the text representa

Michael H. Mertens, Mark A. Norfleet

We construct a weight $1/2$ multiplier system for the group $\Gamma_0^+(p)$, the normalizer of the congruence subgroup $\Gamma_0(p)$ where $p$ is an odd prime, and we define an analogue of the eta function and Rademacher symbol and relate it to the geometry of edge paths in a triangulation of the upper half plane.

Xin Jing, Yi Chang, Zijiang Yang, Jiangjian Xie

Deep learning has led to considerable advances in text-to-speech synthesis. Most recently, the adoption of Score-based Generative Models (SGMs), also known as Diffusion Probabilistic Models (DPMs), has gained traction due to their ability to produce high-quality synthesized neural speech in neural speech synthesis systems. In SGMs, the U-Net architecture and

Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez

Reliable automatic evaluation of summarization systems is challenging due to the multifaceted and subjective nature of the task. This is especially the case for languages other than English, where human evaluations are scarce. In this work, we introduce SEAHORSE, a dataset for multilingual, multifaceted summarization evaluation. SEAHORSE consists of 96K summ

Ankit Satpute, André Greiner-Petter, Moritz Schubotz, Norman Meuschke

This demo paper presents the first tool to annotate the reuse of text, images, and mathematical formulae in a document pair -- TEIMMA. Annotating content reuse is particularly useful to develop plagiarism detection algorithms. Real-world content reuse is often obfuscated, which makes it challenging to identify such cases. TEIMMA allows entering the obfuscati

Jiahao Xu, Wei Shao, Lihui Chen, Lemao Liu

This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption. Specifically, for the first perspective, we identify that the dropout noise from negative pairs affects the model's performance. Therefore, we propose a simple yet effective method to deal with such type of noise.

Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini

Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize additional entity types. A simple approach is to re-annotate entire dataset with both existing and additional entity types and then train the model on the re-annotated dataset. However, th

Daniela Inclezan

This paper introduces a framework for assisting policy authors in refining and improving their policies. In particular, we focus on authorization and obligation policies that can be encoded in Gelfond and Lobo's AOPL language for policy specification. We propose a framework that detects the statements that make a policy inconsistent, underspecified, or ambig

Lorenzo Perini, Jesse Davis

Anomaly detection aims at detecting unexpected behaviours in the data. Because anomaly detection is usually an unsupervised task, traditional anomaly detectors learn a decision boundary by employing heuristics based on intuitions, which are hard to verify in practice. This introduces some uncertainty, especially close to the decision boundary, that may reduc

Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito

Factors models are routinely used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods which scale poorly as the number of studies, observations, or measured variables increase. To address this issue, we propose variational inference algorithms to

Evgenii Chzhen, Sholom Schechtman

We consider the problem of unconstrained minimization of finite sums of functions. We propose a simple, yet, practical way to incorporate variance reduction techniques into SignSGD, guaranteeing convergence that is similar to the full sign gradient descent. The core idea is first instantiated on the problem of minimizing sums of convex and Lipschitz function

Xinyuan Lu, Liangming Pan, Qian Liu, Preslav Nakov

Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present SCITAB, a challenging evaluation dataset consisting of 1.2K expert-verified scientific claims that 1) originate from authentic scientific publications and 2) require compositional r

Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang, Nino Vieillard

Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performing practical RL algorithms. However, despite the use of function approximation in practice, the theoretical understanding of MDVI has been limited to tabular Markov decision proces

Francesco Mezzadri, Henry Taylor

We introduce the first random matrix model of a complex $\beta$-ensemble. The matrices are tridiagonal and can be thought of as the non-Hermitian analogue of the Hermite $\beta$-ensembles discovered by Dumitriu and Edelman (J. Math. Phys., Vol. 43, 5830 (2002)). The main feature of the model is that the exponent $\beta$ of the Vandermonde determinant in the

Zixing Wang, Ahmed H. Qureshi

Recent research efforts have yielded significant advancements in manipulating objects under homogeneous settings where the robot is required to either manipulate rigid or deformable (soft) objects. However, the manipulation under heterogeneous setups that involve both rigid and one-dimensional (1D) deformable objects remains an unexplored area of research. S

Coleridge Faraday, Antonia Grindrod, W. A. Horowitz

We present the first leading hadron suppression predictions in $\mathrm{Pb}+\mathrm{Pb}$ and $\mathrm{p}+\mathrm{Pb}$ collisions from a convolved radiative and collisional energy loss model in which partons propagate through a realistic background and in which the inelastic energy loss receives a short pathlength correction. We find that the short pathlength

Vadim Alekseev, Max Schmidt, Andreas Thom

In this note we state a conjecture that characterizes unital C*-algebras for which the unitary group is amenable as a topological group in the norm topology. We prove the conjecture for simple, separable, stably finite, unital, $\mathcal Z$-stable, UCT C*-algebras with torsionfree K_0 using the progress on the Elliott classification program for nuclear C*-al

J. D. Soler, C. Zucker, J. E. G. Peek, M. Heyer

We present a study of the three-dimensional (3D) distribution of interstellar dust derived from stellar extinction observations toward the Taurus molecular cloud (MC) and its relation with the neutral atomic hydrogen (HI) emission at 21 cm wavelength and the carbon monoxide $^{12}$CO and $^{13}$CO emission in the $J=1\rightarrow0$ transition. We used the his

Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi

In this paper, we evaluate the capability of transformer-based language models in making inferences over uncertain text that includes uncertain rules of reasoning. We cover both Pre-trained Language Models (PLMs) and generative Large Language Models (LLMs). Our evaluation results show that both generations of language models struggle with reasoning over unce

Miroslav Korbelář, Jiří Tolar

The paper is devoted to projective Clifford groups of quantum $N$-dimensional systems. Clearly, Clifford gates allow only the simplest quantum computations which can be simulated on a classical computer (Gottesmann-Knill theorem). However, it may serve as a cornerstone of full quantum computation. As to its group structure it is well-known that -- in $N$-dim

Vineet Kumar, Prashant Shukla, Abhijit Bhattacharyya

In this work, we review the experimental and theoretical developments of bottomonia production in proton+proton and heavy-ion collisions. The bottomonia production process is proving to be one of the most robust processes to investigate the fundamental aspects of Quantum Chromodynamics at both low and high temperatures. The LHC experiments in the last decade

A. V. Valov, E. V. Dontsov

This paper analyses the problem of a semi-infinite fluid-driven fracture propagating through multiple stress layers in a permeable elastic medium. Such a problem represents the tip region of a planar hydraulic fracture. When the hydraulic fracture crosses a stress layer, the use of a standard tip asymptotic solution may lead to a considerable reduction of ac

Hiroaki Yamagiwa, Momose Oyama, Hidetoshi Shimodaira

This study utilizes Independent Component Analysis (ICA) to unveil a consistent semantic structure within embeddings of words or images. Our approach extracts independent semantic components from the embeddings of a pre-trained model by leveraging anisotropic information that remains after the whitening process in Principal Component Analysis (PCA). We demon

Javier Cuerda, Jani M. Taskinen, Nicki Källman, Leo Grabitz

We experimentally observe the quantum geometric tensor, namely the quantum metric and the Berry curvature, for a square lattice of radiatively coupled plasmonic nanoparticles. We observe a non-zero Berry curvature and show that it arises solely from non-Hermitian effects. The quantum metric is found to originate from a pseudospin-orbit coupling. The long-ran

Shuting He, Henghui Ding, Wei Jiang

Zero-shot instance segmentation aims to detect and precisely segment objects of unseen categories without any training samples. Since the model is trained on seen categories, there is a strong bias that the model tends to classify all the objects into seen categories. Besides, there is a natural confusion between background and novel objects that have never

Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to efficiently alter the behavior of LLMs within a specific domain without negatively impacting performance across ot

Maksim Lednev, Francisco J. García-Vidal, Johannes Feist

Despite significant theoretical efforts devoted to studying the interaction between quantized light modes and matter, the so-called ultra-strong coupling regime still presents significant challenges for theoretical treatments and prevents the use of many common approximations. Here we demonstrate an approach that can describe the dynamics of hybrid quantum s

Edward McDaid, Sarah McDaid

Overlapping instruction subsets derived from human originated code have previously been shown to dramatically shrink the inductive programming search space, often by many orders of magnitude. Here we extend the instruction subset approach to consider direct instruction-instruction applications (or instruction digrams) as an additional search heuristic for in

Kai Yi, Laurent Condat, Peter Richtárik

Federated Learning is an evolving machine learning paradigm, in which multiple clients perform computations based on their individual private data, interspersed by communication with a remote server. A common strategy to curtail communication costs is Local Training, which consists in performing multiple local stochastic gradient descent steps between succes

Shayne Longpre, Gregory Yauney, Emily Reif, Katherine Lee

Pretraining is the preliminary and fundamental step in developing capable language models (LM). Despite this, pretraining data design is critically under-documented and often guided by empirically unsupported intuitions. To address this, we pretrain 28 1.5B parameter decoder-only models, training on data curated (1) at different times, (2) with varying toxic

Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song

Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understand and leverage multi-behavior data from users. Despite recent efforts towards making use of heterogeneous data, multi-behavior recommendation still faces great challenges. Firstly

Yuqi Zhu, Xiaohan Wang, Jing Chen, Shuofei Qiao

This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, the

Xingjian He, Sihan Chen, Fan Ma, Zhicheng Huang

Large-scale image-text contrastive pre-training models, such as CLIP, have been demonstrated to effectively learn high-quality multimodal representations. However, there is limited research on learning video-text representations for general video multimodal tasks based on these powerful features. Towards this goal, we propose a novel video-text pre-training

Elena Cordero, Gianluca Giacchi

Modulation spaces were originally introduced by Feichtinger in 1983. Since the 2000s there have been thousands of contributions using them as correct framework; they range from PDEs, pseudodifferential operators, quantum mechanics, signal analysis. This justifies a deep study of such spaces and the related Wiener ones. Recently, metaplectic Wigner distributi

Peter Súkeník, Marco Mondelli, Christoph Lampert

Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount of experimental evidence has pointed to the propagation of NC to earlier layers of neural networks. However, while the NC in the last layer is well studied theoretically, much les

Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri

Logic synthesis is the first and most vital step in chip design. This steps converts a chip specification written in a hardware description language (such as Verilog) into an optimized implementation using Boolean logic gates. State-of-the-art logic synthesis algorithms have a large number of logic minimization heuristics, typically applied sequentially base

D. Kleiner, P. Serra, F. M. Maccagni, M. A. Raj

We present MeerKAT Fornax Survey atomic hydrogen (HI) observations of the dwarf galaxies located in the central ~2.5 x 4 deg$^2$ of the Fornax galaxy cluster. The HI images presented in this work have a $3\sigma$ column density sensitivity between 2.7 and 50 x 10$^{18}$ cm$^{-2}$ over 25 km s$^{-1}$ for spatial resolution between 4 and 1 kpc. We are able to

Marc Brooker, Mike Danilov, Chris Greenwood, Phil Piwonka

AWS Lambda is a serverless event-driven compute service, part of a category of cloud compute offerings sometimes called Function-as-a-service (FaaS). When we first released AWS Lambda, functions were limited to 250MB of code and dependencies, packaged as a simple compressed archive. In 2020, we released support for deploying container images as large as 10Gi

Chenghong Bian, Yulin Shao, Deniz Gunduz

This paper presents a novel vision transformer (ViT) based deep joint source channel coding (DeepJSCC) scheme, dubbed DeepJSCC-l++, which can be adaptive to multiple target bandwidth ratios as well as different channel signal-to-noise ratios (SNRs) using a single model. To achieve this, we train the proposed DeepJSCC-l++ model with different bandwidth ratios

Boshi Wang, Xiang Yue, Huan Sun

Large language models (LLMs) such as ChatGPT and GPT-4 have shown impressive performance in complex reasoning tasks. However, it is difficult to know whether the models are reasoning based on deep understandings of truth and logic, or leveraging their memorized patterns in a relatively superficial way. In this work, we explore testing LLMs' reasoning by enga

Ron M. Roth

Let $[q\rangle$ denote the integer set $\{0,1,\ldots,...,q-1\}$ and let $\mathbb{B}=\{0,1\}$. The problem of implementing functions $[q\rangle\rightarrow\mathbb{B}$ on content-addressable memories (CAMs) is considered. CAMs can be classified by the input alphabet and the state alphabet of their cells; for example, in binary CAMs, those alphabets are both $\m

Robert Cardona

Given an embedded stable hypersurface in a four-dimensional symplectic manifold, we prove that it is stable isotopic to a $C^0$-close stable hypersurface with the following property: $C^\infty$-nearby hypersurfaces are generically unstable. This shows that the stability property is neither open nor generic, independently of the isotopy class of hypersurfaces

Anna Rosławska, Katharina Kaiser, Michelangelo Romeo, Eloïse Devaux

Many natural and artificial reactions including photosynthesis or photopolymerization are initiated by stimulating organic molecules into an excited state, which enables new reaction paths. Controlling light-matter interaction can influence this key concept of photochemistry, however, it remained a challenge to apply this strategy to control photochemical re

A. Mariani

The vortex in the $(2+1)$-dimensional $\mathrm{O}(2)$ model is studied via numerical simulations in a fully non-perturbative lattice regularization. We compute the vortex condensate and susceptibility to determine its critical exponents and a renormalized condensate in the continuum limit. Together with recent results on the vortex mass, this gives a complet

Naoto Nakatsuji, Takuto Kawakami, Mikito Koshino

We present comprehensive theoretical studies on the lattice relaxation and the electronic structures in general non-symemtric twisted trilayer graphenes. By using an effective continuum model, we show that the relaxed lattice structure forms a patchwork of moir\'e-of-moir\'e domains, where a moir\'e pattern given by layer 1 and 2 and another pattern given by

Thomas Mildner, Merle Freye, Gian-Luca Savino, Philip R. Doyle

Interest in unethical user interfaces has grown in HCI over recent years, with researchers identifying malicious design strategies referred to as ''dark patterns''. While such strategies have been described in numerous domains, we lack a thorough understanding of how they operate in social networking services (SNSs). Pivoting towards regulations against such

Xiaoyu Wang, Rui Pan, Renjie Pi, Jipeng Zhang

Bilevel optimization has found successful applications in various machine learning problems, including hyper-parameter optimization, data cleaning, and meta-learning. However, its huge computational cost presents a significant challenge for its utilization in large-scale problems. This challenge arises due to the nested structure of the bilevel formulation,

Maximilian Ofner, Siegfried Hörmann

This paper studies linear reconstruction of partially observed functional data which are recorded on a discrete grid. We propose a novel estimation approach based on approximate factor models with increasing rank taking into account potential covariate information. Whereas alternative reconstruction procedures commonly involve some preliminary smoothing, our

Jérémie Laydevant, Danijela Markovic, Julie Grollier

Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training methods with Ising machine physics, even

Y. H. Chen, Thomas Y. He, F. Tang, J. J. Wei

Recently, Andrews introduced separable integer partition classes and analyzed some well-known theorems. In this paper, we investigate partitions with parts separated by parity introduced by Andrews with the aid of separable integer partition classes with modulus $2$. We also extend separable integer partition classes with modulus $1$ to overpartitions, calle

N. W. Hendrickx, L. Massai, M. Mergenthaler, F. Schupp

Spin qubits defined by valence band hole states comprise an attractive candidate for quantum information processing due to their inherent coupling to electric fields enabling fast and scalable qubit control. In particular, heavy holes in germanium have shown great promise, with recent demonstrations of fast and high-fidelity qubit operations. However, the me

Matteo Buzzegoli, Kirill Tuchin

We compute the chiral magnetic effect (CME) in a cylindrical region coaxial with the external magnetic field. As the boundary condition we require vanishing of the radial component of the electric current on the cylinder side wall. We find that when the magnetic length is comparable to or larger than the cylinder radius, the CME is suppressed compared to the

Andrea Pinamonti, Simone Verzellesi

In this paper we achieve a first concrete step towards a better understanding of the so-called Bernstein problem in higher dimensional Heisenberg groups. Indeed, in the sub-Riemannian Heisenberg group $\mathbb{H}^n$, with $n\geq 2$, we show that the only entire hypersurfaces with vanishing horizontal symmetric second fundamental form are hyperplanes. This re

Xiangcheng Hu, Jin Wu, Jianhao Jiao, Ruoyu Geng

Evaluating simultaneous localization and mapping (SLAM) algorithms necessitates high-precision and dense ground truth (GT) trajectories. But obtaining desirable GT trajectories is sometimes challenging without GT tracking sensors. As an alternative, in this paper, we propose a novel prior-assisted SLAM system to generate a full six-degree-of-freedom ($6$-DOF

Minhao Hong, Heguang Liu, Fangjun Xu

Under certain mild conditions, limit theorems for additive functionals of some $d$-dimensional self-similar Gaussian processes are obtained. These limit theorems work for general Gaussian processes including fractional Brownian motions, sub-fractional Brownian motions and bi-fractional Brownian motions. To prove these results, we use the method of moments an

Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy

One of the major sources of perturbation in the solar cycle amplitude is believed to be the emergence of anomalous active regions which do not obey Hale's polarity law and Joy's law of tilt angles. Anomalous regions containing high magnetic flux that disproportionately impact the polar field are sometimes referred to as ``rogue regions". In this study -- uti

Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang

Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i

Zangwei Zheng, Xiaozhe Ren, Fuzhao Xue, Yang Luo

Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with significant computational costs. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs. Our approach begins by tapping into the potential of LL

Manali Jeste, Helmut Wiesemeyer, Karl M. Menten, Friedrich Wyrowski

Aims: The study at hand aims to describe the distribution of atomic carbon, C0, throughout the envelope, in support of an improved understanding of its photo-chemistry. Additionally, we also briefly discuss the observation of [CII] emission towards the star. Methods: We obtain spectra of the [CI] $\mathrm{^3P_1} \rightarrow \mathrm{^3P_0}$ fine structure lin

Lu Xu, Lidong Bing, Wei Lu

Distantly supervised named entity recognition (DS-NER) has been proposed to exploit the automatically labeled training data instead of human annotations. The distantly annotated datasets are often noisy and contain a considerable number of false negatives. The recent approach uses a weighted sampling approach to select a subset of negative samples for traini

Enric Boix-Adsera, Etai Littwin

We study when the neural tangent kernel (NTK) approximation is valid for training a model with the square loss. In the lazy training setting of Chizat et al. 2019, we show that rescaling the model by a factor of $\alpha = O(T)$ suffices for the NTK approximation to be valid until training time $T$. Our bound is tight and improves on the previous bound of Chi

Bohong Wu, Fei Yuan, Hai Zhao, Lei Li

Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT). However, these non-autoregressive (NAR) models still struggle to generate high-quality texts compared with autoregressive (AR) models. Considering that encoder-based models have th

Gui-Geng Liu, Subhaskar Mandal, Peiheng Zhou, Xiang Xi

Quantum Hall systems host chiral edge states extending along the one-dimensional boundary of any two-dimensional sample. In solid state materials, the edge states serve as perfectly robust transport channels that produce a quantised Hall conductance; due to their chirality, and the topological protection by the Chern number of the bulk bandstructure, they ca

Yongzan Liu, Lin Liang, Smaine Zeroug

Characterizing the fluid-driven fracture tip advancing process presents a significant challenge due to the difficulty of replicating real-world conditions in laboratory experiments and the lack of precise field measurements. However, recent advances in low-frequency distributed acoustic sensing (LF-DAS) technology offer new opportunities to investigate the d

Kari Ali Noriy, Xiaosong Yang, Jian Jun Zhang

The increasing adoption of text-to-speech technologies has led to a growing demand for natural and emotive voices that adapt to a conversation's context and emotional tone. The Emotive Narrative Storytelling (EMNS) corpus is a unique speech dataset created to enhance conversations' expressiveness and emotive quality in interactive narrative-driven systems. T

J. E. Méndez-Delgado, C. Esteban, J. García-Rojas, K. Z. Arellano-Córdova

We present a first study based on the analysis of the DEep Spectra of Ionized REgions Database (DESIRED). This is a compilation of 190 high signal-to-noise ratio optical spectra of HII regions and other photoionized nebulae, mostly observed with 8-10m telescopes and containing $\sim$29380 emission lines. We find that the electron density --$n_{\rm e}$-- of t

Xiuzhan Guo, Wei Huang, Min Luo, Priya Rangarajan

In this paper, we study the geospatial ontologies that we are interested in together as a geospatial ontology system, consisting of a set of the geospatial ontologies and a set of geospatial ontology operations, without any internal details of the geospatial ontologies and their operations being needed, algebraically. A homomorphism between two geospatial on

Kananart Kuwaranancharoen, Shreyas Sundaram

The optimization problem concerning the determination of the minimizer for the sum of convex functions holds significant importance in the realm of distributed and decentralized optimization. In scenarios where full knowledge of the functions is not available, limiting information to individual minimizers and convexity parameters -- either due to privacy con

Xi Cen, Xiang Li, Dunyan Yan

The main questions raised in this paper are to find the sufficient conditions that make multi-sublinear operators $T$ and their commutators ${T_{\prod \vec b }}$, ${T_{\sum {\vec b} }}$ to be bounded on three kinds of generalized weighted Morrey spaces. We give the main theorems of this paper to solve the above related questions. As corollaries of the main t

David W. Kanaar, J. P. Kestner

Spin qubits in semiconductor quantum dots are a promising platform for quantum computing, however scaling to large systems is hampered by crosstalk and charge noise. Crosstalk here refers to the unwanted off-resonant rotation of idle qubits during the resonant rotation of the target qubit. For a three-qubit system with crosstalk and charge noise, it is diffi

Toni Kodzoman, Eric Lescano

We construct non-commutative theories with the Moyal-Weyl product in the Double Field Theory (DFT) framework. We deform the infinitesimal generalized diffeomorphisms and the Leibniz rule in a consistent way. The prescription requires a generalized star metric, which can be thought of as the fundamental double metric, in order to construct the action. Finally

Mounir Bensalem, Erkan Ipek, Admela Jukan

With rapid advances in containerization techniques, the serverless computing model is becoming a valid candidate execution model in edge networking, similar to the widely used cloud model for applications that are stateless, single purpose and event-driven, and in particular for delay-sensitive applications. One of the cloud serverless processes, i.e., the a

Dennis Eriksson, Gerard Freixas i Montplet

This article is part of a series of works by the authors with the goal of completing a far-reaching program propounded by Deligne, aiming to extend the codimension one part of the Grothendieck-Riemann-Roch theorem from isomorphism classes of line bundles to isomorphisms thereof. The paper develops a relative functorial intersection theory with values in line

Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad

Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more. However, training such models usually requires large amounts of clean signals which are often difficult or impossible to obtain. In this work, we propose a novel training technique for gene

Anju Rani, Pooja Chandravanshi, Jayanth Ramakrishnan, Pravin Vaity

Quantum Key Distribution (QKD) offers unconditional security in principle. Many QKD protocols have been proposed and demonstrated to ensure secure communication between two authenticated users. Continuous variable (CV) QKD offers many advantages over discrete variable (DV) QKD since it is cost-effective, compatible with current classical communication techno

Micheline Fakhoury

We show that if $1<p\neq 2<\infty$, then any isometry of the $p$-convexification of the combinatorial Banach space associated with a hereditary family of finite subsets of $\mathbb{N}$ containing the singletons is given by a signed permutation of the canonical basis. In the case of a generalized Schreier family, the result also holds for $p=2$, and every iso

Alexander Hoelzemann, Julia Lee Romero, Marius Bock, Kristof Van Laerhoven

We present a benchmark dataset for evaluating physical human activity recognition methods from wrist-worn sensors, for the specific setting of basketball training, drills, and games. Basketball activities lend themselves well for measurement by wrist-worn inertial sensors, and systems that are able to detect such sport-relevant activities could be used in ap

Matthieu Garcin

We are interested in the nonparametric estimation of the probability density of price returns, using the kernel approach. The output of the method heavily relies on the selection of a bandwidth parameter. Many selection methods have been proposed in the statistical literature. We put forward an alternative selection method based on a criterion coming from in

Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong

Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of exploration. The diffusion probability model is powerful to learn complicated multimodal distributions, which has shown promising and potential applications to RL. In this paper, we fo

M. R. Ibrahim

This study aimed to investigate how transformational leadership affects team processes, mediated by change in team members. A self-administered questionnaire was distributed to construction project team members in Abuja and Kaduna, and statistical analysis revealed a significant positive relationship between transformational leadership and team processes, tr

Liangping Ding, Giovanni Colavizza, Zhixiong Zhang

Motivation: Named Entity Recognition (NER) is a key task to support biomedical research. In Biomedical Named Entity Recognition (BioNER), obtaining high-quality expert annotated data is laborious and expensive, leading to the development of automatic approaches such as distant supervision. However, manually and automatically generated data often suffer from

Zhu Liu, Ying Liu

Word sense disambiguation (WSD), which aims to determine an appropriate sense for a target word given its context, is crucial for natural language understanding. Existing supervised methods treat WSD as a classification task and have achieved remarkable performance. However, they ignore uncertainty estimation (UE) in the real-world setting, where the data is

Daniel Kressner, Bor Plestenjak

The numerical solution of the generalized eigenvalue problem for a singular matrix pencil is challenging due to the discontinuity of its eigenvalues. Classically, such problems are addressed by first extracting the regular part through the staircase form and then applying a standard solver, such as the QZ algorithm, to that regular part. Recently, several no

Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos

Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim. In this paper we introduce AVeriTeC, a new dataset of 4,568 real-world claims covering fact-checks by 50 different organizations. Each c

Yassine Hamdi, Deniz Gündüz

In image compression, with recent advances in generative modeling, the existence of a trade-off between the rate and the perceptual quality has been brought to light, where the perception is measured by the closeness of the output distribution to the source. This leads to the question: how does a perception constraint impact the trade-off between the rate an

Hongjun Wang, Jiyuan Chen, Lun Du, Qiang Fu

Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervisi

Reza Hadi Mogavi, Chao Deng, Justin Juho Kim, Pengyuan Zhou

To foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes, it is pivotal to critically explore the perceptions and experiences of the users immersed in these contexts. In this study, we perform a thorough qualitative content analysis across four key social media platforms. Our goal is to understand the user exper

Sahar Allahkaram, Francisco A. Monteiro, Ioannis Chatzigeorgiou

Supporting ultra-reliable and low-latency communication (URLLC) is a challenge in current wireless systems. Channel codes that generate large codewords improve reliability but necessitate the use of interleavers, which introduce undesirable latency. Only short codewords can eliminate the requirement for interleaving and reduce decoding latency. This paper su

Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the exi

A. Abd-Aldaim, G. Conant, C. Terry

The $k$-dimensional functional order property ($\text{FOP}_k$) is a combinatorial property of a $(k+1)$-partitioned formula. This notion arose in work of Terry and Wolf, which identified $\text{NFOP}_2$ as a ternary analogue of stability in the context of two finitary combinatorial problems related to hypergraph regularity and arithmetic regularity. In this