October 2022 arXiv papers — page 24

Michael Baur, Franz Weißer, Benedikt Böck, Wolfgang Utschick

Classical methods for model order selection often fail in scenarios with low SNR or few snapshots. Deep learning-based methods are promising alternatives for such challenging situations as they compensate lack of information in the available observations with training on large datasets. This manuscript proposes an approach that uses a variational autoencoder

Sabah Al-Fedaghi

In software engineering, conceptual modeling focuses on creating representations of the world that are as faithful and rich as possible, with the aim of guiding the development of software systems. In contrast, in the computing realm, the notion of ontology has been characterized as being closely related to conceptual modeling and is often viewed as a specif

Fang Shan, Zhenhua Chai, Baochang Shi, Meng Zhao

The displacement of multiphase fluid flow in a pore doublet is a fundamental problem, and is also of importance in understanding of the transport mechanisms of multiphase flows in the porous media. During the displacement of immiscible two-phase fluids in the pore doublet, the transport process is not only influenced by the capillary and viscous forces, but

G. Tkachov

Many-body transport has emerged as an efficient tool for understanding interaction effects in quantum materials with a multi-band electronic structure. This paper proposes a formula for the two-particle transmission coefficient for Cooper-pair transport between multi-band normal and superconducting materials. The approach employs a tight-binding nonequilibri

Marcelo Muniz Alves, Tiago Luiz Ferrazza

In this work we investigate partial actions of a Hopf algebra H on nonunital algebras and the associated partial smash products. We show that our partial actions correspond to nonunital algebras in the category of partial representations of H. The central problem of existence of a globalization for a partial action is studied in detail, and we provide suffic

François Le Gall, Daiki Suruga

The main conceptual contribution of this paper is investigating quantum multiparty communication complexity in the setting where communication is \emph{oblivious}. This requirement, which to our knowledge is satisfied by all quantum multiparty protocols in the literature, means that the communication pattern, and in particular the amount of communication exc

Shanshan Zhong, Wushao Wen, Jinghui Qin, Zhongzhan Huang

More and more empirical and theoretical evidence shows that deepening neural networks can effectively improve their performance under suitable training settings. However, deepening the backbone of neural networks will inevitably and significantly increase computation and parameter size. To mitigate these problems, we propose a simple-yet-effective Recurrent

Zijie Yue, Miaojing Shi, Shuai Ding

Facial video-based remote physiological measurement aims to estimate remote photoplethysmography (rPPG) signals from human face videos and then measure multiple vital signs (e.g. heart rate, respiration frequency) from rPPG signals. Recent approaches achieve it by training deep neural networks, which normally require abundant facial videos and synchronously

João Rino-Silvestre, Santiago González-Gaitán, Marko Stalevski, Majda Smole

Dust is a major component of the interstellar medium. Through scattering, absorption and thermal re-emission, it can profoundly alter astrophysical observations. Models for dust composition and distribution are necessary to better understand and curb their impact on observations. A new approach for serial and computationally inexpensive production of such mo

Zhendong Li, Wen Chen, Ziwei Liu, Hongying Tang

In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be regarded as a new type of multi-antenna sys

Tsz Kin Lam, Shigehiko Schamoni, Stefan Riezler

Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples to build new training instances. Continued training with such augmented data is able to improve off-the-shelf Transformer and Conformer models that were optimized on the original d

Liang Cheng

In this paper we study the pseudolocality theorems of Ricci flows on incomplete manifolds. We prove that if a ball with its closure contained in an incomplete manifold has the small scalar curvature lower bound and almost Euclidean isoperimetric constant, or almost Euclidean local $\boldsymbol{\nu}$ constant, then we can construct a solution of Ricci flow in

Ilyass Moummad, Nicolas Farrugia

Methods based on supervised learning using annotations in an end-to-end fashion have been the state-of-the-art for classification problems. However, they may be limited in their generalization capability, especially in the low data regime. In this study, we address this issue using supervised contrastive learning combined with available metadata to solve mul

Samuel Bouaziz--Ermann, Alex B. Grilo, Damien Vergnaud

The subset cover problem for $k \geq 1$ hash functions, which can be seen as an extension of the collision problem, was introduced in 2002 by Reyzin and Reyzin to analyse the security of their hash-function based signature scheme HORS. The security of many hash-based signature schemes relies on this problem or a variant of this problem (e.g. HORS, SPHINCS, S

Marco Console, Leonid Libkin, Liat Peterfreund

Queries with aggregation and arithmetic operations, as well as incomplete data, are common in real-world database, but we lack a good understanding of how they should interact. On the one hand, systems based on SQL provide ad-hoc rules for numerical nulls, on the other, theoretical research largely concentrates on the standard notions of certain and possible

Michał Barej, Adam Bzdak

We calculate the baryon number cumulants within acceptance with short-range correlations and global baryon number conservation in terms of cumulants in the whole system without baryon conservation. We extract leading and next-to-leading order terms of the large baryon number limit approximation. Our results extend the findings of Refs. [1,2]. These approxima

Rishabh Gupta, Qi Zhang

Decision-making problems are commonly formulated as optimization problems, which are then solved to make optimal decisions. In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model. This statistical learning problem is referred to as data-

He Wang, Lin Wan, He Tang

Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels

Nathan Couchet, Robert Yuncken

Polyhomogeneous symbols, defined by Kohn-Nirenberg and H\"ormander in the 60's, play a central role in the symbolic calculus of most pseudodifferential calculi. We prove a simple characterisation of polyhomogeneous functions which avoids the use of asymptotic expansions. Specifically, if $U$ is open subset of $\mathbb{R}^d$, then a polyhomogeneous symbol on

Xinzhu Liang, Shangda Yang, Simon L. Cotter, Kody J. H. Law

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work builds upon a recently introduced multi-index Sequential Monte Carlo (SMC) ratio estimator, which provably enjo

Song Jiang, Chunhui Zhou

We prove the existence and stability of smooth solutions to the steady Navier-Stokes equations near plane Poiseuille-Couette flow. Consequently, we also provide the zero viscosity limit of the 2D steady Navier-Stokes equations to the steady Euler equations. First, in the absence of any external force, we prove that there exist smooth solutions to the steady

Anowar Shaikh, Shubhalaxmi Rath, Sadhana Dash, Binata Panda

We have computed the charge and heat transport coefficients of hot QCD matter by solving the relativistic Boltzmann transport equation using the BGK model approximation with a modified collision integral in the weak magnetic field regime. This modified collision integral enhances both charge and heat transport phenomena which can be understood by the large v

Eun Jung Yeo, Kwanghee Choi, Sunhee Kim, Minhwa Chung

Automatic assessment of dysarthric speech is essential for sustained treatments and rehabilitation. However, obtaining atypical speech is challenging, often leading to data scarcity issues. To tackle the problem, we propose a novel automatic severity assessment method for dysarthric speech, using the self-supervised model in conjunction with multi-task learn

M. Andres-Carcasona, O. Ballester, O. Blanch, J. Campos

A novel instrumented baffle surrounding the suspended end mirror in the input mode cleaner cavity of the Virgo interferometer was installed in spring 2021. Since then, the device has been regularly operated in the experiment and the obtained results indicate a good agreement with simulations of the stray light inside the optical cavity. The baffle will opera

Kwanghee Choi, Eun Jung Yeo

Self-supervised models, namely, wav2vec and its variants, have shown promising results in various downstream tasks in the speech domain. However, their inner workings are poorly understood, calling for in-depth analyses on what the model learns. In this paper, we concentrate on the convolutional feature encoder where its latent space is often speculated to r

Ruijie Tao, Kong Aik Lee, Rohan Kumar Das, Ville Hautamäki

We study a novel neural architecture and its training strategies of speaker encoder for speaker recognition without using any identity labels. The speaker encoder is trained to extract a fixed-size speaker embedding from a spoken utterance of various length. Contrastive learning is a typical self-supervised learning technique. However, the quality of the spe

Rafael Diez Sanhueza, Stephan Smit, Jurriaan Peeters, Rene Pecnik

This paper presents a machine learning methodology to improve the predictions of traditional RANS turbulence models in channel flows subject to strong variations in their thermophysical properties. The developed formulation contains several improvements over the existing Field Inversion Machine Learning (FIML) frameworks described in the literature, as well

ALICE Collaboration

Charge-dependent two- and three-particle correlations measured in Xe-Xe collisions at $\sqrt{s_{\mathrm{NN}}} = 5.44$ TeV are presented. Results are obtained for charged particles in the pseudorapidity range $|\eta|<0.8$ and transverse momentum interval $0.2 \le p_{\rm T}<5.0$ GeV/$c$ for different collision centralities. The three-particle correlator ${\gam

Richard M. Höfer, Amina Mecherbet, Richard Schubert

We consider a suspension of spherical inertialess particles in a Stokes flow on the torus $\mathbb T^3$. The particles perturb a linear extensional flow due to their rigidity constraint. Due to the singular nature of this perturbation, no mean-field limit for the behavior of the particle orientation can be valid. This contrasts with widely used models in the

Yuqing Cheng, Mengtao Sun

Optical properties of coupled metallic particles (MNPs) have been widely reported due to their unique characteristics such as peak shift/splitting of the coupling spectra and electromagnetic enhancement at sub-wavelength scale, etc. In a previous work, we have investigated the coupling spectra of two coupled MNPs with parallel polarized excitation. In this s

Anand Natarajan, Chinmay Nirkhe

It is a long-standing open question in quantum complexity theory whether the definition of $\textit{non-deterministic}$ quantum computation requires quantum witnesses $(\textsf{QMA})$ or if classical witnesses suffice $(\textsf{QCMA})$. We make progress on this question by constructing a randomized classical oracle separating the respective computational com

Guobing Gan, Peng Zhang, Sunzhu Li, Xiuqing Lu

In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capability of tensor products, we propose a word embedding compressio

Denis S. Grebenkov, Alexei T. Skvortsov

Spiky coatings (also known as nanoforests or Fakir-like surfaces) have found many applications in chemical physics, material sciences and biotechnology, such as superhydrophobic materials, filtration and sensing systems, selective protein separation, to name but a few. In this paper, we provide a systematic study of steady-state diffusion towards a periodic

Miriam Anschütz, Tobias Eder, Georg Groh

People post their opinions and experiences on social media, yielding rich databases of end-users' sentiments. This paper shows to what extent machine learning can analyze and structure these databases. An automated data analysis pipeline is deployed to provide insights into user-generated content for researchers in other domains. First, the domain expert can

Y. Díaz, L. Hernández-García, P. Arévalo, E. López-Navas

An interesting feature in active galactic nuclei (AGN) accreting at low rate is the weakness of the reflection features in their X-ray spectra, which can result from the gradual disappearance of the torus with decreasing accretion rates. It has been suggested that low luminosity AGN (LLAGN) would have a different reflector configuration compared with high lu

Tjark Koopmann, Christian Neurohr, Lina Putze, Lukas Westhofen

The verification and validation of automated driving systems at SAE levels 4 and 5 is a multi-faceted challenge for which classical statistical considerations become infeasible. For this, contemporary approaches suggest a decomposition into scenario classes combined with statistical analysis thereof regarding the emergence of criticality. Unfortunately, thes

Rohit Choudhary, Mansi Sharma, Rithvik Anil

Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction

Jing Xiong, Zhongwei Wan, Xiping Hu, Min Yang

Math word problems (MWPs) is a task that automatically derives solution expression from a giving math problems in text. The previous studies suffer from spurious correlations between input text and output expression. To mitigate this issue, we propose a self-consistent reasoning framework called SCR, which attempts to adopt a pruning strategy to correct the

Thibault Rolland, Fabrizio De Vico Fallani

In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of th

Daniel Ceverino, Nir Mandelker, Gregory F. Snyder, Sharon Lapiner

The evolution of star-forming galaxies at high redshifts is very sensitive to the strength and nature of stellar feedback. Using two sets of cosmological, zoom-in simulations from the VELA suite, we compare the effects of two different models of feedback: with and without kinetic feedback from the expansion of supernovae shells and stellar winds. At a fixed

Zachary M. C. Baum, Yipeng Hu, Dean Barratt

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes

Fan-Lin Wang, Yao-Fei Cheng, Hung-Shin Lee, Yu Tsao

Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation performance, and cannot meet the requirement of daily use. In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by pr

Hollis Williams, James Sprittles, Juan C. Padrino, Petr Denissenko

Formation of a splash crown and a cavity following the impact of a sphere on a body of liquid is a classical problem. In the related problem of a droplet splashing on a flat surface, it has been established that the properties of the surrounding gas can influence the splashing threshold. At lower impact speeds, this is due mainly to the influence of gas kine

Li-Wei Chen, Yao-Fei Cheng, Hung-Shin Lee, Yu Tsao

The lack of clean speech is a practical challenge to the development of speech enhancement systems, which means that there is an inevitable mismatch between their training criterion and evaluation metric. In response to this unfavorable situation, we propose a training and inference strategy that additionally uses enhanced speech as a target by improving the

Andrea Orizzonte

We carry out the complete variational analysis of the Barbero--Immirzi--Holst Lagrangian, which is the Holst Lagrangian expressed in terms of the triad of fields $(\theta, A, \kappa)$, where $\theta$ is the solder form/spin frame, $A$ is the spacetime Barbero--Immirzi connection, and $\kappa$ is the extrinsic spacetime field. The Holst Lagrangian depends on

Yuanbo Hou, Siyang Song, Chuang Yu, Yuxin Song

Most existing deep learning-based acoustic scene classification (ASC) approaches directly utilize representations extracted from spectrograms to identify target scenes. However, these approaches pay little attention to the audio events occurring in the scene despite they provide crucial semantic information. This paper conducts the first study that investiga

Gopi Krishna Erabati, Helder Araujo

Inspired by recent advances in vision transformers for object detection, we propose Li3DeTr, an end-to-end LiDAR based 3D Detection Transformer for autonomous driving, that inputs LiDAR point clouds and regresses 3D bounding boxes. The LiDAR local and global features are encoded using sparse convolution and multi-scale deformable attention respectively. In t

Rui Liu, Haolin Zuo, De Hu, Guanglai Gao

Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the process of TTS is a very interesting research direction, and has attracted more and more attention. Recent work design a speaker-adversarial loss to disentangle the speaker and accent in

Atul Kumar Shriwastva, R. S. Selvaraj

Given a regular multiset $M$ on $[n]=\{1,2,\ldots,n\}$, a partial order $R$ on $M$, and a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i) = k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, we define a pomset block metric $d_{(Pm,\pi)}$ on the direct sum $ \mathbb{Z}_{m}^{k_1} \oplus \mathbb{Z}_{m}^{k_2} \oplus \ldots \oplus \mathbb{Z}_{m}^{k_n}$ of $\m

Sally Khaidem, Mansi Sharma, Abhipraay Nevatia

A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the brightness change's time, location, and polarit

Norihide Tokushige

A family of $k$-element subsets of an $n$-element set is called 3-wise intersecting if any three members in the family have non-empty intersection. We determine the maximum size of such families exactly or asymptotically. One of our results shows that for every $\epsilon>0$ there exists $n_0$ such that if $n>n_0$ and $\frac25+\epsilon<\frac kn<\frac 12-\epsi

Yifan Hu, Rui Liu, Guanglai Gao, Haizhou Li

Conversational Text-to-Speech (TTS) aims to synthesis an utterance with the right linguistic and affective prosody in a conversational context. The correlation between the current utterance and the dialogue history at the utterance level was used to improve the expressiveness of synthesized speech. However, the fine-grained information in the dialogue histor

Haolin Zuo, Rui Liu, Jinming Zhao, Guanglai Gao

Multimodal emotion recognition leverages complementary information across modalities to gain performance. However, we cannot guarantee that the data of all modalities are always present in practice. In the studies to predict the missing data across modalities, the inherent difference between heterogeneous modalities, namely the modality gap, presents a chall

Jason Hoelscher-Obermaier, Edward Stevinson, Valentin Stauber, Ivaylo Zhelev

The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors for useful terms that are infrequent or newly emerging. We demonstrate how \gls{lsi} can address this problem by imputing embeddings for domain-specific words from up-to-date knowled

Zhou Rui, Jia-Ming Li, Chao-Qi Zhang

The penguin-dominated two-body weak decay of $\Lambda_b\rightarrow \Lambda \phi$ is studied based on the perturbative QCD approach. In addition to the penguin emission diagrams, the penguin exchange and $W$ exchange ones are also accounted for. It is found that the penguin exchange contribution is in fact important and comparable to the penguin emission one,

Xuanxiang Huang, Martin C. Cooper, Antonio Morgado, Jordi Planes

Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction. In some applications, and besides asking for an explanation, it is also critical to understand whether sensitive features can occur in some explanation, or whether a non-interesting feature must occur in all explanati

Axel Marmoret, Jérémy E. Cohen, Frédéric Bimbot

Music Structure Analysis (MSA) consists of representing a song in sections (such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval of a simplified organization of the song. This work presents a new algorithm, called Convolutive Block-Matching (CBM) algorithm, devoted to MSA. In particular, the CBM algorithm is a dynamic programming al

Cheryl E. Praeger, Enoch Suleiman

We give a short proof for an explicit upper bound on the proportion of permutations of a given prime order $p$, acting on a finite set of given size $n$, which is sharp for certain $n$ and $p$. Namely, we prove that if $n\equiv k\pmod{p}$ with $0\leq k\leq p-1$, then this proportion is at most $(p\cdot k!)^{-1}$ with equality if and only if $p\leq n<2n$.

Harish Kishnani, Rijubrata Kundu, Sumit Chandra Mishra

Given integers $k,l\geq 2$, where either $l$ is odd or $k$ is even, let $n(k,l)$ denote the largest integer $n$ such that each element of $A_n$ is a product of $k$ many $l$-cycles. In 2008, M. Herzog, G. Kaplan and A. Lev conjectured that $\lfloor \frac{2kl}{3} \rfloor \leq n(k,l)\leq \lfloor \frac{2kl}{3}\rfloor+1$. It is known that the conjecture holds whe

Andrew J. Wren, Pasquale Minervini, Luca Franceschi, Valentina Zantedeschi

Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. However, a number of techniques for fully discrete backpropagation could instead be applied. In this paper, we explore that direction and propose DAG-DB, a framework for learning DAGs b

Bochao Chen, Yixian Gao, Yong Li, Hongyu Liu

This paper is devoted to establishing the resonant modal expansion of the low-frequency part of the scattered field for acoustic bubbles embedded in elastic materials in the time domain. Due to the nano-bubble with damping, Minnaert resonance can be induced at certain discrete resonant frequencies, which forms the fundamental basis of effectively constructin

Dongzhe Li, Soumyajyoti Haldar, Tim Drevelow, Stefan Heinze

We investigate the impact of mechanical strain, stacking order, and external electric fields on the magnetic interactions of a two-dimensional (2D) van der Waals (vdW) heterostructure in which a 2D ferromagnetic metallic Fe$_3$GeTe$_2$ monolayer is deposited on germanene. Three distinct computational approaches based on \textit{ab initio} methods are used, a

Dimity Stephen

A key feature of questionable journals is a lack of adequate peer review of their articles. Content of thus unknown quality may be utilised by unsuspecting practitioners or incorporated into peer-reviewed research, becoming legitimised. It is therefore necessary to examine the citation patterns of articles in questionable journals to understand the impact an

Hooman Asgari, Andrea Munari, Gianluigi Liva

The present paper focuses on an IoT setting in which a large number of devices generate time-stamped updates addressed to a common gateway. Medium access is regulated following a grant-free approach, and the system aims at maintaining an up-to-date knowledge at the receiver, measured through the average network age of information (AoI). In this context, we p

Andrea Giusti, Serena Giardino, Valerio Faraoni

We refine and slightly enlarge the recently proposed first-order thermodynamics of scalar-tensor gravity to include gravitational scalar fields with timelike and past-directed gradients. The implications and subtleties arising in this situation are discussed and an exact cosmological solution of scalar-tensor theory in first-order thermodynamics is revisited

Haotian Wu, Yulin Shao, Chenghong Bian, Krystian Mikolajczyk

This paper presents a vision transformer (ViT) based joint source and channel coding (JSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) systems, called ViT-MIMO. The proposed ViT-MIMO architecture, in addition to outperforming separation-based benchmarks, can flexibly adapt to different channel conditions without requiri

Jens Hönen, Johann L. Hurink, Bert Zwart

The current trend towards more renewable and sustainable energy generation leads to an increased interest in new energy management systems and the concept of a smart grid. One important aspect of this is local energy trading, which is an extension of existing electricity markets by including prosumers, who are consumers also producing electricity. Prosumers

David R. Baños, Salvador Ortiz-Latorre, Oriol Zamora Font

We consider the stochastic volatility model obtained by adding a compound Hawkes process to the volatility of the well-known Heston model. A Hawkes process is a self-exciting counting process with many applications in mathematical finance, insurance, epidemiology, seismology and other fields. We prove a general result on the existence of a family of equivale

Francesco Di Colandrea, Lorenzo Amato, Roberto Schiattarella, Alexandre Dauphin

An optical waveplate rotating light polarization can be modeled as a single-qubit unitary operator, whose action can be experimentally determined via quantum process tomography. Standard approaches to tomographic problems rely on the maximum-likelihood estimation, providing the most likely transformation to yield the same outcomes as a set of experimental pr

Mingchen Xia

In this paper, we develop several pluripotential-theoretic techniques for singular metrics on vector bundles. We first introduce the theory of non-pluripolar products on holomorphic vector bundles on complex manifolds. Then we define and study a special class of singularities of Hermitian metrics on vector bundles, called $\mathcal{I}$-good singularities, pa

Marco Abbadini, Vincenzo Marra, Luca Spada

We extend Yosida's 1941 version of Stone-Gelfand duality to metrically complete unital lattice-ordered groups that are no longer required to be real vector spaces. This calls for a generalised notion of compact Hausdorff space whose points carry an arithmetic character to be preserved by continuous maps. The arithmetic character of a point is (the complete i

Eric V. Strobl, Thomas A. Lasko

Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome. In prior work, we defined sample-specific root causes of disease using exogenous error terms that predict a diagnosis in a structural equation model. We rigorously quantified predictivity using Shapley values. However, the associated algorithms for inferr

Anthony Lee, Steven J. Miller

The German Tank Problem dates back to World War II when the Allies used a statistical approach to estimate the number of enemy tanks produced or on the field from observed serial numbers after battles. Assuming that the tanks are labeled consecutively starting from 1, if we observe $k$ tanks from a total of $N$ tanks with the maximum observed tank being $m$,

Alexander Yu. Vlasov

The Witting configuration with 40 complex rays was suggested as a possible reformulation of Penrose model with two spin-3/2 systems based on geometry of dodecahedron and used for analysis of nonlocality and contextuality in quantum mechanics. Yet another configuration with 120 quantum states is considered in presented work. Despite of different number of sta

Nikolay Shakura, Konstantin Postnov, Dmitry Kolesnikov, Galina Lipunova

We revisit the modal analysis of small perturbations in Keplerian ideal gas flows leading to magneto-rotational instability (MRI) using the non-local approach. We consider the case of constant vertical background magnetic field, as well as the case of radially dependent background Alfv\'en velocity. In the case of constant Alfv\'en velocity, MRI modes are de

Dominik Wagner, Ilja Baumann, Franziska Braun, Sebastian P. Bayerl

The detection of pathologies from speech features is usually defined as a binary classification task with one class representing a specific pathology and the other class representing healthy speech. In this work, we train neural networks, large margin classifiers, and tree boosting machines to distinguish between four pathologies: Parkinson's disease, laryng

Praveen Mathil, Barkha Baloda, Jitender Kumar, A. Somasundaram

Let $R$ be a commutative ring with unity. The prime ideal sum graph $\text{PIS}(R)$ of the ring $R$ is the simple undirected graph whose vertex set is the set of all nonzero proper ideals of $R$ and two distinct vertices $I$ and $J$ are adjacent if and only if $I + J$ is a prime ideal of $R$. In this paper, we study some interplay between algebraic propertie

D. Ezenkova, D. Moskalev, N. Smirnov, A. Ivanov

Josephson parametric amplifiers have emerged as a promising platform for quantum information processing and squeezed quantum states generation. Travelling wave and impedance-matched parametric amplifiers provide broad bandwidth for high-fidelity single-shot readout of multiple qubit superconducting circuits. Here, we present a quantum-limited 3-wave-mixing p

Gregory A. L. White, Kavan Modi, Charles D. Hill

From an open system perspective non-Markovian effects due to a nearby bath or neighbouring qubits are dynamically equivalent. However, there is a conceptual distinction to account for: neighbouring qubits may be controlled. We combine recent advances in non-Markovian quantum process tomography with the framework of classical shadows to characterise spatiotem

Che Liu, Rui Wang, Junfeng Jiang, Yongbin Li

In this paper, we introduce the task of learning unsupervised dialogue embeddings. Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been shown to be feasible for this task. However, these approaches typically ignore the conversational interactions between interlocutors,

A. Seljak, M. Bračko, R. Dolenec, P. Križan

Single photon sensitive detectors used in high energy physics are, in some applications, required to cover areas the size of several m2, and more specifically in very strong demand with15 an ever finer imaging and timing capability for Cherenkov Ring Imaging Detector (RICH) configurations. We are evaluating the Large Area Picosecond Photo-detector (LAPPD) pr

Nils Caci, Katarina Karlova, Taras Verkholyak, Jozef Strecka

The spin-1/2 Heisenberg antiferromagnet on the frustrated diamond-decorated square lattice is known to feature various zero-field ground-state phases, consisting of extended monomer-dimer and dimer-tetramer ground states as well as a ferrimagnetic regime. Using a combination of analytical arguments, density matrix renormalization group (DMRG), exact diagonal

Ricardo Crisostomo

We develop a comprehensive framework to measure the impact of the climate transition on investment portfolios. Our analysis is enriched by including geographical, sectoral, company and ISIN-level data to assess transition risk. We find that investment funds suffer a moderate 5.7% loss upon materialization of a high transition risk scenario. However, the risk

Johannes Preiser-Kapeller, Ewald Kislinger

The blinding of the Byzantine Emperor Constantine VI in Constantinople in August 797 and his overthrow by his mother Eirene, who then until 802 ruled as first female emperor of the Eastern Roman Empire, was used as legitimation for the coronation of the Frankish King Charlemagne as emperor of the Romans on December 25, 800, by contemporaries in Western Europ

José Manuel Gómez-Pérez

Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientific tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange

Nikolay Bobev, Sunjin Choi, Junho Hong, Valentin Reys

We study a limit of the superconformal index of the ABJM theory on $S^1\times S^2$ in which the size of the circle is much smaller than the radius of the two-sphere. We derive closed form expressions for the two leading terms in this Cardy-like limit which are valid to all orders in the $1/N$ expansion. These results are facilitated by a judicious rewriting

Paul Manuel, Bostjan Bresar, Sandi Klavzar

Given a graph $G$, a geodesic packing in $G$ is a set of vertex-disjoint maximal geodesics, and the geodesic packing number of $G$, ${\gpack}(G)$, is the maximum cardinality of a geodesic packing in $G$. It is proved that the decision version of the geodesic packing number is NP-complete. We also consider the geodesic transversal number, ${\gt}(G)$, which is

Qiu-Shi Zhu, Long Zhou, Jie Zhang, Shu-Jie Liu

Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (ASR). However, the robustness impact of combining the two pre-training tasks and constructing different negative samples for contrastive learning still remains unclear. In this pape

Taylan Kargin, Fariborz Salehi, Babak Hassibi

The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case. It utilizes a mirror potential to influence the implicit bias of the training algorithm. In this paper we explore the performance of the SMD iterates on mean-field ensemble models. Our re

Martín Saavedra, Jorge Mira, Alberto P Muñuzuri, Luís F Seoane

Social dynamics are shaped by each person's actions, as well as by collective trends that emerge when individuals are brought together. These latter kind of influences escape anyone's control. They are, instead, dominated by aggregate societal properties such as size, polarization, cohesion, or hierarchy. Such features add nuance and complexity to social str

Onel López, Nurul Mahmood, Mohammad Shehab, Hirley Alves

Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics,

Javier Pliego

We analyse a collection of mixed moments of the Riemann zeta function and establish the validity of asymptotic formulae. Such examinations are performed both unconditionally and under the assumption of a weaker version of the $abc$ conjecture.

Jnaneshwar Baslingker

A famous result of Horn and Fitzgerald is that the $\beta$-th Hadamard power of any $n\times n$ positive semi-definite (p.s.d) matrix with non-negative entries is p.s.d $\forall \beta\geq n-2$ and is not necessarliy p.s.d for $\beta< n-2,$ with $\ \beta\notin \mathbb{N}$. In this article, we study this question for random Wishart matrix $A_n:={X_nX_n^T}$, wh

Rolf Schlichenmaier, Daniel Pitters, Juan Manuel Borrero, Matthias Schubert

The imaging spectro-polarimeter VTF (Visible Tunable Filter) will be operated at the Daniel K. Inouye Solar Telescope (DKIST). Due to its capability of resolving dynamic fine structure of smaller than 0.05'', the finite acquisition time of typically 11 s affects the measurement process and potentially causes errors in deduced physical parameters. We estimate

Sravanti Addepalli, Samyak Jain, R. Venkatesh Babu

Adversarial training of Deep Neural Networks is known to be significantly more data-hungry when compared to standard training. Furthermore, complex data augmentations such as AutoAugment, which have led to substantial gains in standard training of image classifiers, have not been successful with Adversarial Training. We first explain this contrasting behavio

Pontus Vikstål, Giulia Ferrini, Shruti Puri

Virtual distillation has been proposed as an error mitigation protocol for estimating the expectation values of observables in quantum algorithms. It proceeds by creating a cyclic permutation of $M$ noisy copies of a quantum state using a sequence of controlled-swap gates. If the noise does not shift the dominant eigenvector of the density operator away from

Gopi Krishna Erabati, Helder Araujo

3D object detection is a significant task for autonomous driving. Recently with the progress of vision transformers, the 2D object detection problem is being treated with the set-to-set loss. Inspired by these approaches on 2D object detection and an approach for multi-view 3D object detection DETR3D, we propose MSF3DDETR: Multi-Sensor Fusion 3D Detection Tr

Daniele De Sensi, Tiziano De Matteis, Konstantin Taranov, Salvatore Di Girolamo

Cloud computing represents an appealing opportunity for cost-effective deployment of HPC workloads on the best-fitting hardware. However, although cloud and on-premise HPC systems offer similar computational resources, their network architecture and performance may differ significantly. For example, these systems use fundamentally different network transport

Saayan Mukherjee

A measure theoretic approach of the problem that there exits a finite unit-distance graphs in the plane that are not five (or four) colorable.

Frank Phillipson, Niels Neumann, Robert Wezeman

As quantum computers mature, the applicability in practice becomes more important. Many uses of quantum computers will be hybrid, with classical computers still playing an important role in operating and using the quantum computer. The term hybrid is however diffuse and multi-interpretable. In this work we define two classes of hybrid quantum-classical compu