May 2023 arXiv papers — page 45

Nicolas Zucchet, Robert Meier, Simon Schug, Asier Mujika

Online learning holds the promise of enabling efficient long-term credit assignment in recurrent neural networks. However, current algorithms fall short of offline backpropagation by either not being scalable or failing to learn long-range dependencies. Here we present a high-performance online learning algorithm that merely doubles the memory and computatio

Murray Shanahan, Kyle McDonell, Laria Reynolds

As dialogue agents become increasingly human-like in their performance, it is imperative that we develop effective ways to describe their behaviour in high-level terms without falling into the trap of anthropomorphism. In this paper, we foreground the concept of role-play. Casting dialogue agent behaviour in terms of role-play allows us to draw on familiar f

Xueliang Zhao, Wenda Li, Lingpeng Kong

Large language models~(LLMs) present an intriguing avenue of exploration in the domain of formal theorem proving. Nonetheless, the full utilization of these models, particularly in terms of demonstration formatting and organization, remains an underexplored area. In an endeavor to enhance the efficacy of LLMs, we introduce a subgoal-based demonstration learn

Mingzheng Li, Pengjie Zhang, Wen Zhao

The cosmological principle has been verified using electromagnetic (EM) observations. However its verification with high accuracy is challenging due to various foregrounds and selection effects, and possible violation of the cosmological principle has been reported in the literature. In contrast, gravitational wave (GW) observations are free of these foregro

Joachim Winther Pedersen, Sebastian Risi

Biological nervous systems consist of networks of diverse, sophisticated information processors in the form of neurons of different classes. In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is usually shared between all neurons within a layer or even the whole network; training of ANNs focuses on syna

Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari

Some of the most successful knowledge graph embedding (KGE) models for link prediction -- CP, RESCAL, TuckER, ComplEx -- can be interpreted as energy-based models. Under this perspective they are not amenable for exact maximum-likelihood estimation (MLE), sampling and struggle to integrate logical constraints. This work re-interprets the score functions of t

Patrik Andersson, Mathias Lindholm

This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two-step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variation

Anthony Knittel, Morris Antonello, John Redford, Subramanian Ramamoorthy

The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high risk for driving, and it is important to identify these cases. Typical predictors use the trajectory history to predict future motion, however in cases of motion initiation, motion

Liang Xu, Haoming Sun, Denis Eremin, Sathya Ganta

In this work, the rotating spoke mode in the radio frequency (RF) magnetron discharge, which features the potential hump and the RF-modulated ionization, is observed and analyzed by means of the two dimensional axial-azimuthal (z-y) particle-in-cell/Monte Carlo collision method. The kinetic model combined with the linear analysis of the perturbation reveals

Chenglin Yao, Jianfeng Ren, Ruibin Bai, Heshan Du

Detecting 3D mask attacks to a face recognition system is challenging. Although genuine faces and 3D face masks show significantly different remote photoplethysmography (rPPG) signals, rPPG-based face anti-spoofing methods often suffer from performance degradation due to unstable face alignment in the video sequence and weak rPPG signals. To enhance the rPPG

Ambre Chabert

We build a smooth time-dependent real potential on the two-dimensional torus, decaying as time tends to infinity in Sobolev norms along with all its time derivative, and we exhibit a smooth solution to the associated Schr\"odinger equation on the two-dimensional torus whose $H^s$ norms nevertheless grow logarithmically as time tends to infinity. We use Fouri

Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander Gasnikov

This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities. Our approach covers scenarios for both non-convex and strongly convex minimization problems. To achieve an optimal (linear) d

Leanne Nortje, Benjamin van Niekerk, Herman Kamper

We propose a visually grounded speech model that acquires new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken query, we ask the model which image depicts the query word. Previous work has simplified this problem by either using an artificial setting with digit word-image pairs or by using a

Panagiotis Misiakos, Chris Wendler, Markus Püschel

We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a linear SEM can be viewed as a linear transform that, in prior work, computes the data from a dense input vector of random valued root causes (as we will call them) associated with the

Peng Jiang, Pengcheng Zhu, Jiamin Li, Dongming Wang

A future millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) system may serve hundreds or thousands of users at the same time; thus, research on multiple access technology is particularly important.Moreover, due to the short-wavelength nature of a mmWave, large-scale arrays are easier to implement than microwaves, while their directivi

Maria Krantz, Oliver Niggemann

Rotary Indexing Machines (RIMs) are widely used in manufacturing due to their ability to perform multiple production steps on a single product without manual repositioning, reducing production time and improving accuracy and consistency. Despite their advantages, little research has been done on diagnosing faults in RIMs, especially from the perspective of t

Hossein A. Rahmani, Xi Wang, Yue Feng, Qiang Zhang

The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Asking Clarification Questions (ACQs) to reveal users' true intent from their queries or utterances arise as an essential task. However, it is noticeable that a key limitation of the e

Hyeon-dong Han

The elastic $\pi N$ scattering is investigated for the $I=3/2$ channel dominated by the $\Delta(1232)$ resonance at finite baryon density, employing the effective Lagrangian approach at the tree-level Born approximation. The quark-meson coupling (QMC) model is employed to describe the in-medium baryon properties that are constructed at the quark level, such

Jie He, Simon Chi Lok U, Víctor Gutiérrez-Basulto, Jeff Z. Pan

Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in their scope. A popular approach to UCR is to fine-tune language models with external knowledge (e.g., knowledge graphs), but this usually requires a large number of training examples.

André Haug, Dan Shahar

More than 25 years ago, unexpected metallic behavior was discovered on the superconducting side of the superconductor-to-insulator transition. To this day, the origin of this behavior is unclear. In this work, we present resistance and broadband voltage noise measurements in the kilohertz regime in amorphous indium oxide. We find that the metallic behavior g

João Helis Bernardo, Daniel Alencar da Costa, Uirá Kulesza, Christoph Treude

Continuous Integration (CI) is a software development practice that builds and tests software frequently (e.g., at every push). One main motivator to adopt CI is the potential to deliver software functionalities more quickly than not using CI. However, there is little empirical evidence to support that CI helps projects deliver software functionalities more

Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Haitham Bou Ammar

Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian optimisation by leveraging data from related tasks. While previous methods successfully meta-learn either a surrogate model or an acquisition function independently, joint training of both components remains an open challenge. This paper proposes the first end-to-end diffe

Juan Manuel Toro

Current large language models, such as OpenAI's ChatGPT, have captured the public's attention because how remarkable they are in the use of language. Here, I demonstrate that ChatGPT displays phonological biases that are a hallmark of human language processing. More concretely, just like humans, ChatGPT has a consonant bias. That is, the chatbot has a tenden

Paolo Leonetti

We define the notion of ideal convergence for sequences $(x_n)$ with values in topological spaces $X$ with respect to a family $\{F_\eta: \eta \in X\}$ of subsets of $X$ with $\eta \in F_\eta$. Each set $F_\eta$ quantifies the degree of accuracy of the convergence toward $\eta$. After proving that this is really a new notion, we provide some properties of th

Vy Vo, Trung Le, Tung-Long Vuong, He Zhao

Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge. This is because, in the presence of latent variables, both the likelihood function and posterior distribution are intractable without assumptions about structural dependencies or model classes. While existing learning methods are fundament

Pouya Houshmand, Jiacong Sun, Marian Verhelst

In-memory-computing is emerging as an efficient hardware paradigm for deep neural network accelerators at the edge, enabling to break the memory wall and exploit massive computational parallelism. Two design models have surged: analog in-memory-computing (AIMC) and digital in-memory-computing (DIMC), offering a different design space in terms of accuracy, ef

Peng Jiang, Jiafei Fu, Pengcheng Zhu, Jiamin Li

This paper investigates the resource allocation problem combined with fronthaul precoding and access link sparse precoding design in cloud radio access network (C-RAN) wireless fronthaul systems.Multiple remote antenna units (RAUs) in C-RAN systems can collaborate in a cluster through centralized signal processing to realize distributed massive multiple-inpu

Carles Balsells-Rodas, Yixin Wang, Yingzhen Li

The identifiability of latent variable models has received increasing attention due to its relevance in interpretability and out-of-distribution generalisation. In this work, we study the identifiability of Switching Dynamical Systems, taking an initial step toward extending identifiability analysis to sequential latent variable models. We first prove the id

Ilan Naiman, Nimrod Berman, Omri Azencot

Unsupervised disentanglement is a long-standing challenge in representation learning. Recently, self-supervised techniques achieved impressive results in the sequential setting, where data is time-dependent. However, the latter methods employ modality-based data augmentations and random sampling or solve auxiliary tasks. In this work, we propose to avoid tha

James Wurster, Connar Rowan

What is the nature of a star forming clump? Observations reveal these to be chaotic environments being modified and influenced by many physical processes. However, numerical simulations often define these initial star forming clumps to be idealised objects. In this paper, we define and analyse 109 star forming clumps extracted from our previous low-mass star

Butler, Tom, Espinoza-Limón, Angelina

The comprehension and adoption of Artificial Intelligence (AI) are beset with practical and ethical problems. This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM) to assist practitioners in AI comprehension and adoption. These practical tools were developed with business executives, technologist

Maurizio Proietti, Francesca Toni

We propose a novel approach to logic-based learning which generates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. These ABA frameworks can be mapped onto logic programs with negation as failure that may be non-stratified. Whereas existing argumentation-based methods learn exceptions t

Daniele Lanzoni, Olivier Pierre-Louis, Francesco Montalenti

Generative Adversarial Networks (GANs) have shown immense potential in fields such as text and image generation. Only very recently attempts to exploit GANs to statistical-mechanics models have been reported. Here we quantitatively test this approach by applying it to a prototypical stochastic process on a lattice. By suitably adding noise to the original da

A. Boselli, M. Fossati, P. Cote, J. C. Cuillandre

We use a complete set of deep narrow-band imaging data for 384 galaxies gathered during the VESTIGE survey to derive the first Halpha luminosity function (LF) of the Virgo cluster within R200. The data allow us to cover the whole dynamic range of the Halpha LF (10^36<LHa<10^42 erg s^-1). After they are corrected for [NII] contamination and dust attenuation,

Andres Ruiz-Chamorro, Daniel Cano, Aida Garcia-Callejo, Veronica Fernandez

Quantum Key Distribution (QKD) is a cutting-edge communication method that enables secure communication between two parties. Continuous-variable QKD (CV-QKD) is a promising approach to QKD that has several advantages over traditional discrete-variable systems. Despite its potential, CV-QKD systems are highly sensitive to optical and electronic component impa

Leif Eriksson, Victor Lagerkvist

Partially ordered models of time occur naturally in applications where agents or processes cannot perfectly communicate with each other, and can be traced back to the seminal work of Lamport. In this paper we consider the problem of deciding if a (likely incomplete) description of a system of events is consistent, the network consistency problem for the poin

Tengfei Cui, Xiangqiang Chu

The utilization of spin-echo small angle neutron scattering (SESANS) for the analysis of structures in soft matter is becoming increasingly prevalent. In this context, the Gaussian chain model and the corresponding framework for calculating the theoretical SESANS correlation function are presented briefly. This work provides a novel theoretical derivation to

Bertrand Cloez, Lucas Journel, Pierre Monmarché, Boris Nectoux

We review some recent results of quantitative long-time convergence for the law of a killed Markov process conditioned to survival toward a quasi-stationary distribution, and on the analogous question for the particle systems used in practice to sample these distributions. With respect to the existing literature, one of the novelties of these works is the de

Zikai Wei, Bo Dai, Dahua Lin

Active investing aims to construct a portfolio of assets that are believed to be relatively profitable in the markets, with one popular method being to construct a portfolio via factor-based strategies. In recent years, there have been increasing efforts to apply deep learning to pursue "deep factors'' with more active returns or promising pipelines for asse

Juan Guerrero Montero, Andres Karjus, Kenny Smith, Richard A. Blythe

Language change is a cultural evolutionary process in which variants of linguistic variables change in frequency through processes analogous to mutation, selection and genetic drift. In this work, we apply a recently-introduced method to corpus data to quantify the strength of selection in specific instances of historical language change. We first demonstrat

Shivam Sharma, Ramaneswaran S, Udit Arora, Md. Shad Akhtar

Memes are a powerful tool for communication over social media. Their affinity for evolving across politics, history, and sociocultural phenomena makes them an ideal communication vehicle. To comprehend the subtle message conveyed within a meme, one must understand the background that facilitates its holistic assimilation. Besides digital archiving of memes a

Wenlin Chen, Hong Ge

We introduce a novel approach for analyzing the training dynamics of ReLU networks by examining the characteristic activation boundaries of individual ReLU neurons. Our proposed analysis reveals a critical instability in common neural network parameterizations and normalizations during stochastic optimization, which impedes fast convergence and hurts general

Pengcheng Shi, Xutao Guo, Yanwu Yang, Chenfei Ye

Convolutional neural networks (CNN) and Transformer variants have emerged as the leading medical image segmentation backbones. Nonetheless, due to their limitations in either preserving global image context or efficiently processing irregular shapes in visual objects, these backbones struggle to effectively integrate information from diverse anatomical regio

Tran N. Hung, Cao H. Nam

We study the topological defects in the thermodynamics of regular black strings (from a four-dimensional perspective) that is symmetric under the double Wick rotation and constructed in the high-dimensional spacetime with an extra dimension compactified on a circle. We observe that the thermodynamic phases of regular black strings can be topologically classi

Hantao Yao, Lu Yu, Jifei Luo, Changsheng Xu

Object Re-identification (ReID) aims to retrieve the probe object from many gallery images with the ReID model inferred based on a stationary camera-free dataset by associating and collecting the identities across all camera views. When deploying the ReID algorithm in real-world scenarios, the aspect of storage, privacy constraints, and dynamic changes of ca

Seyed Mahed Mousavi, Simone Caldarella, Giuseppe Riccardi

Longitudinal Dialogues (LD) are the most challenging type of conversation for human-machine dialogue systems. LDs include the recollections of events, personal thoughts, and emotions specific to each individual in a sparse sequence of dialogue sessions. Dialogue systems designed for LDs should uniquely interact with the users over multiple sessions and long

Yifan Luo, Bin Dong

In this paper, we studied two identically-trained neural networks (i.e. networks with the same architecture, trained on the same dataset using the same algorithm, but with different initialization) and found that their outputs discrepancy on the training dataset exhibits a "double descent" phenomenon. We demonstrated through extensive experiments across vari

Oleksiy Kashuba, Thomas L. Schmidt, Fabian Hassler, Andreas Haller

The calculation of the full counting statistics of the charge within a finite interval of an interacting one-dimensional system of electrons is a fundamental, yet as of now unresolved problem. Even in the non-interacting case, charge counting turns out to be more difficult than anticipated because it necessitates the calculation of a nontrivial determinant a

Yi Yuan, Haohe Liu, Xubo Liu, Xiyuan Kang

Foley sound presents the background sound for multimedia content and the generation of Foley sound involves computationally modelling sound effects with specialized techniques. In this work, we proposed a system for DCASE 2023 challenge task 7: Foley Sound Synthesis. The proposed system is based on AudioLDM, which is a diffusion-based text-to-audio generatio

Sebastian Vincent, Robert Flynn, Carolina Scarton

Efficient utilisation of both intra- and extra-textual context remains one of the critical gaps between machine and human translation. Existing research has primarily focused on providing individual, well-defined types of context in translation, such as the surrounding text or discrete external variables like the speaker's gender. This work introduces MTCue,

Aliaksandr Hubin, Georg Heinze, Riccardo De Bin

We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified Mode Jumping Markov Chain Monte Carlo algorithm. The universality of the Bayesian Generalized Nonlinear Models allows us to employ a Bayesian version of the fractional polynomials

Ho Lee, Ernesto Nungesser, John Stalker, Paul Tod

We consider the Einstein-Boltzmann system for massless particles in the Bianchi I space-time with scattering cross-sections in a certain range of soft potentials. We assume that the space-time has an initial conformal gauge singularity and show that the initial value problem is well posed with data given at the singularity. This is understood by considering

Piyushi Manupriya, Rachit Keerti Das, Sayantan Biswas, Saketha Nath Jagarlapudi

Given samples from two joint distributions, we consider the problem of Optimal Transportation (OT) between them when conditioned on a common variable. We focus on the general setting where the conditioned variable may be continuous, and the marginals of this variable in the two joint distributions may not be the same. In such settings, standard OT variants c

Suraj, Shashwat Rathkanthiwar, Srinivasan Raghavan, Shankar Kumar Selvaraja

In this work, we report the realization of a polarization-insensitive grating coupler, single-mode waveguide, and ring resonator in the GaN-on-Sapphire platform. We provide a detailed demonstration of the material characterization, device simulation, and experimental results. We achieve a grating coupler efficiency of -5.2 dB/coupler with a 1dB and 3dB bandw

Yetong Sha, Huan Xiong

In 2016, Nath and Sellers proposed a conjecture regarding the precise largest size of ${(s,ms-1,ms+1)}$-core partitions. In this paper, we prove their conjecture. One of the key techniques in our proof is to introduce and study the properties of generalized-$\beta$-sets, which extend the concept of $\beta$-sets for core partitions. Our results can be interpr

Kyungyun Lee, Jeonghun Seo, Keunwoo Choi, Sangmoon Lee

In real-world acoustic scenarios, there often are multiple sound sources present in a room. These sources are situated in various locations and produce sounds that reach the listener from multiple directions. The presence of multiple sources in a room creates new challenges in estimating the room impulse response (RIR) as each source has a unique RIR, depend

Zanis Ali Khan, Donghwan Shin, Domenico Bianculli, Lionel Briand

Software systems log massive amounts of data, recording important runtime information. Such logs are used, for example, for log-based anomaly detection, which aims to automatically detect abnormal behaviors of the system under analysis by processing the information recorded in its logs. Many log-based anomaly detection techniques based on deep learning model

Yutao Cui, Tianhui Song, Gangshan Wu, Limin Wang

Transformer-based trackers have achieved strong accuracy on the standard benchmarks. However, their efficiency remains an obstacle to practical deployment on both GPU and CPU platforms. In this paper, to overcome this issue, we propose a fully transformer tracking framework, coined as \emph{MixFormerV2}, without any dense convolutional operation and complex

Weihang Zhang, Ovidiu Serban, Jiahao Sun, Yi-ke Guo

Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive power of KGC methods is often limited by the completeness of the existing knowledge graphs from different sources and languages. In monolingu

Seolhwa Lee, Anders Søgaard

Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential pri

Iain Duncan, David Alonso, Anže Slosar, Kate Storey-Fisher

Galaxy peculiar velocities can be used to trace the growth of structure on cosmological scales. In the radial direction, peculiar velocities cause redshift space distortions, an established cosmological probe, and can be measured individually in the presence of an independent distance indicator. In the transverse direction, peculiar velocities cause proper m

Hanchong Zhang, Jieyu Li, Lu Chen, Ruisheng Cao

The cross-domain text-to-SQL task aims to build a system that can parse user questions into SQL on complete unseen databases, and the single-domain text-to-SQL task evaluates the performance on identical databases. Both of these setups confront unavoidable difficulties in real-world applications. To this end, we introduce the cross-schema text-to-SQL task, w

Xianghui Han, Chunli Liang, Ruiqi Liu, Xingguang Wei

With increasing availability of spectrum in the market due to new spectrum allocation and re-farming bands from previous cellular generation networks, a more flexible, efficient and green usage of the spectrum becomes an important topic in 5G-Advanced. In this article, we provide an overview on the 3rd Generation Partnership Project (3GPP) work on flexible s

Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu

Domain generalization (DG) is a prevalent problem in real-world applications, which aims to train well-generalized models for unseen target domains by utilizing several source domains. Since domain labels, i.e., which domain each data point is sampled from, naturally exist, most DG algorithms treat them as a kind of supervision information to improve the gen

Marco Saldutti, Yi Yu, Jesper Mørk

Nanolasers based on emerging dielectric cavities with deep sub-wavelength confinement of light offer a large light-matter coupling rate and a near-unity spontaneous emission factor, $\beta$. These features call for reconsidering the standard approach to identifying the lasing threshold. Here, we suggest a new threshold definition, taking into account the rec

Xuan Liu, Yaoqin Xie, Jun Cheng, Songhui Diao

Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for training, which are challenging to obtain in clinical settings. Ex

Ranajay Datta, Fabian Berressem, Friederike Schmid, Arash Nikoubashman

We investigate with numerical simulations the molecular origin of viscosity in melts of flexible and semiflexible oligomer rings in comparison to corresponding systems with linear chains. The strong increase of viscosity with ring stiffness is linked to the formation of entangled clusters, which dissolve under shear. This shear-induced breakup and alignment

An Gong, Chong-Bin Chen, Fu-Wen Shu

This paper investigates the entanglement entropy inequality and explores the presentation of mutual information and conditional mutual information in kinematic space. Specifically, we examine the regions within kinematic space responsible for computing these physical quantities, enabling a more intuitive understanding of the entanglement entropy inequality.

Sahil Mishra, Sanjaya Kumar Panda

Intelligent Systems (ISs) are technologically advanced machines, which perceive and respond to the environment around them. They are usually of various forms ranging from software to hardware. ISs are generally the fusion of Artificial Intelligence (AI), robotics and Internet of things (IoT). In order to strengthen ISs, one of the key technologies is green t

Ahmed F. AbouElhamayed, Angela Cui, Javier Fernandez-Marques, Nicholas D. Lane

Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs), espcially convolutional neural networks (CNNs). Recently, product quantization (PQ) has been applied to these workloads, replacing MACs with memory lookups to pre-computed dot products. To better understand the efficiency tradeoffs of produ

Lukas Stäcker, Shashank Mishra, Philipp Heidenreich, Jason Rambach

Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research. However, there has been surprisingly little research on radar-camera fusion with neural networks. One of the reasons is a lack of large-scale automotive datasets with radar and unmasked camera data, with the exception of the nu

Sabrina Francesca Pellegrino

We propose a spectral method based on the implementation of Chebyshev polynomials to study a model of conservation laws on network. We avoid the Gibbs phenomenon near shock discontinuities by implementing a filter in the frequency space in order to add local viscosity able to contrast the spurious oscillations appearing in the profile of the solution and we

Dario Coscia, Nicola Demo, Gianluigi Rozza

In this work, we present GAROM, a new approach for reduced order modelling (ROM) based on generative adversarial networks (GANs). GANs have the potential to learn data distribution and generate more realistic data. While widely applied in many areas of deep learning, little research is done on their application for ROM, i.e. approximating a high-fidelity mod

Oriel Perets, Nadav Rappoport

Electronic health records (EHR) often contain different rates of representation of certain subpopulations (SP). Factors like patient demographics, clinical condition prevalence, and medical center type contribute to this underrepresentation. Consequently, when training machine learning models on such datasets, the models struggle to generalize well and perfo

Jaswant Singh, Tobias Toll

The event generator Sar$t$re has been used extensively for simulations of electron-ion collisions in preparation for the Electron-Ion Collider (EIC). Sar$t$re simulates exclusive diffraction in $e$A collisions, in principle for any nuclear species and exclusive final state, usually a vector meson. The coherent and incoherent cross sections for each process a

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

Using 2.93 $\rm{fb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector at the center-of-mass energy 3.773\,GeV, we perform the first amplitude analysis of the decay $D^+\to K_S^0\pi^+\pi^0\pi^0$ and determine the relative magnitudes and phases of different intermediate processes. The absolute branching fraction of $D^+\to K_S^0\pi^+\pi^0\pi^

Bruce W. Lee, Jason Hyung-Jong Lee

Past research has identified a rich set of handcrafted linguistic features that can potentially assist various tasks. However, their extensive number makes it difficult to effectively select and utilize existing handcrafted features. Coupled with the problem of inconsistent implementation across research works, there has been no categorization scheme or gene

Imad Aouali, Victor-Emmanuel Brunel, David Rohde, Anna Korba

Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two-sided PAC-Bayes generalization bound. The bound is tractable, scalable, interpretable and provides learning

Xue-Jian Gao, Zi-Ting Sun, Ruo-Peng Yu, Xing-Yao Guo

Symmetry is a crucial factor in determining the topological properties of materials. In nonmagnetic chiral crystals, the existence of the Kramers Weyl fermions reveals the topological nature of the Kramers degeneracy at time-reversal-invariant momenta (TRIMs). However, it is not clear whether Weyl nodes can also be pinned at points of symmetry in magnetic ma

Bruce W. Lee, Benedict Florance Arockiaraj, Helen Jin

We investigate the phenomenon of an LLM's untruthful response using a large set of 220 handcrafted linguistic features. We focus on GPT-3 models and find that the linguistic profiles of responses are similar across model sizes. That is, how varying-sized LLMs respond to given prompts stays similar on the linguistic properties level. We expand upon this findi

Robert J. Lemke Oliver, Daniel Loughran, Ari Shnidman

We prove normal distribution laws for primes of bad semistable reduction in families of curves. As a consequence, we deduce that when ordered by height, $100\%$ of curves in these families have, in a precise sense, many such primes.

Tsu-Ching Hsiao, Hao-Wei Chen, Hsuan-Kung Yang, Chun-Yi Lee

Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the $SE(3)$ group, marking the first application of diffusion models to $SE(3)$ within the image domain, specifically tai

Yandan Zheng, Anran Hao, Anh Tuan Luu

Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation instances as well as the existence of simi

Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi

Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models. However, such methods are known to yield untrustworthy and misleading inference outcomes under model misspecification, thus hin

Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi

In this work, we propose a novel adversarial defence mechanism for image classification - CARSO - blending the paradigms of adversarial training and adversarial purification in a synergistic robustness-enhancing way. The method builds upon an adversarially-trained classifier, and learns to map its internal representation associated with a potentially perturb

Michael Ruderman

This paper revisits the previously proposed linear asymptotic observer of the motion state variables with nonlinear friction and provides a robust design suitable for both, transient presliding and steady-state sliding phases of the relative motion. The class of motion systems with the only measurable output displacement is considered. The reduced-order Luen

W. Ebeling, H. Krienke

In previous work we developed a new statistical method for calculating the individual activities of ions including the association of ions. Here we study multi-particle electrostatic interactions connected within higher cluster integrals and identify the ionization constants of the mass action law of associating ion clusters. In contrast to Bjerrum and Fuoss

Xuan Li, Zongsheng Zhou, Guanglei Xu, Runze Chi

Determining the low-energy eigenspectra of quantum many-body systems is a long-standing challenge in physics. In this work, we solve this problem by introducing two novel algorithms to determine low-energy eigenstates based on a compact matrix product state (MPS) representation of the multiple targeted eigenstates. The first algorithm utilizes a canonicaliza

Francesco Fusco, Diego Antognini

Extracting dense representations for terms and phrases is a task of great importance for knowledge discovery platforms targeting highly-technical fields. Dense representations are used as features for downstream components and have multiple applications ranging from ranking results in search to summarization. Common approaches to create dense representations

Xianda Chen, Meixin Zhu, Kehua Chen, Pengqin Wang

Car-following is a control process in which a following vehicle (FV) adjusts its acceleration to keep a safe distance from the lead vehicle (LV). Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats

E. S. de Santana, A. S. de Arruda, M. Godoy

We have studied the mixed spin-1/2 and 1 Ising ferrimagnetic system with a random anisotropy on a triangular lattice with three interpenetrating sublattices $A$, $B$, and $C$. The spins on the sublattices are represented by $\sigma_{A}$ (states $\pm1/2$), $\sigma_{B}$ (states $\pm1/2$), and $S_{C}$ (states $\pm1$, $0$). We have performed Monte Carlo simulati

Xiao-Tong Chen, Wang-Jun Lu, Yunlan Zuo, Rui Zhang

We study quantum phase sensing with an asymmetric two-mode entangled coherent state (ECS) in which the two local amplitudes have different values. We find the phenomenon of the asymmetry-enhanced phase sensing which the asymmetry can significantly increase the precise of the phase estimation. We further study the effect of decoherence induced by the photon l

David Rumler, Reinhard Meinel

The gravitational and electromagnetic multipole moments of the charged rotating disc of dust, which is an axisymmetric, stationary solution of the Einstein-Maxwell equations in terms of a post-Newtonian expansion, are calculated and discussed. It turns out that the individual mass, angular momentum, electric and magnetic moments are ordered in the sense that

Prashant Narayanan, Lakshmi Narasimhan Theagarajan

We study the problem of decentralized power allocation in a multi-access channel (MAC) with non-cooperative users, additive noise of arbitrary distribution and a generalized power constraint, i.e., the transmit power constraint is modeled by an upper bound on $\mathbb{E}[\phi(|S|)]$, where $S$ is the transmit signal and $\phi(.)$ is some non-negative, increa

Risheng Liu, Zhu Liu, Jinyuan Liu, Xin Fan

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially for enhancing visual quality and/or extracting aggregated features for perception. However, most existing methods just consider image fusion as an individual task, thus ignoring its underlying relationship with these downstream vision problems. Furthermore, designing

Jin Guo, Alexander L. Gavrilyuk, Ilia Ponomarenko

It is proved that the Weisfeiler-Leman dimension of the class of permutation graphs is at most 18. Previously it was only known that this dimension is finite (Gru{\ss}ien, 2017).

K. Haydukivska, V. Blavatska

The present work continues our previous studies of pom-pom molecule [K. Haydukivska, O. Kalyuzhnyi, V. Blavatska, and J. Ilnytskyi, J. Mol. Liq. 328, 115456 (2021); Condens. Matter Phys. 25, 23302 (2022)]. The molecule consists of a linear backbone with two branching points at both ends, with functionalities $f_1$ and $f_2$. Here, the main attention is conce

Kanta Shimonishi, Kota Dohi, Yohei Kawaguchi

This paper proposes an unsupervised anomalous sound detection method using sound separation. In factory environments, background noise and non-objective sounds obscure desired machine sounds, making it challenging to detect anomalous sounds. Therefore, using sounds not mixed with background noise or non-purpose sounds in the detection system is desirable. We

Marwan Dhuheir, Aiman Erbad, Sinan Sabeeh

Recently, Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. In many of these applications, the UAVs capture images as well as other sensory data and then send the data processing requests to remote servers. Nevertheless, this appr

Briceyda B. Delgado

We introduce the spaces $A^p_{\alpha, \beta}(\Omega)$ of $L^p$-solutions to the Vekua equation (generalized monogenic functions) $D w=\alpha\overline{w}+\beta w$ in a bounded domain in $\mathbb{R}^n$, where $D=\sum_{i=1}^n e_i \partial_i$ is the Moisil-Teodorescu operator, $\alpha$ and $\beta$ are bounded functions on $\Omega$. The main result of this work c