July 2023 arXiv papers — page 93

Elisa Maria Gandolfo, José Roberto Brandao Oliveira, Luigi Campajola, Dimitra Pierroutsakou

The G-NUMEN array is the future gamma spectrometer of the NUMEN experiment (Nuclear Matrix Element for the Neutrinoless double beta decay), to be installed around the object point of the MAGNEX magnetic spectrometer at the INFN-LNS laboratory. This project aims at exploring Double Charge Exchange (DCE) reactions in order to obtain crucial information about t

Domènec Ruiz-Balet, Enrique Zuazua

Inspired by normalizing flows, we analyze the bilinear control of neural transport equations by means of time-dependent velocity fields restricted to fulfill, at any time instance, a simple neural network ansatz. The L^1 approximate controllability property is proved, showing that any probability density can be driven arbitrarily close to any other one in an

Jihao Andreas Lin, Gergely Flamich, José Miguel Hernández-Lobato

This paper studies the qualitative behavior and robustness of two variants of Minimal Random Code Learning (MIRACLE) used to compress variational Bayesian neural networks. MIRACLE implements a powerful, conditionally Gaussian variational approximation for the weight posterior $Q_{\mathbf{w}}$ and uses relative entropy coding to compress a weight sample from

Stefania Boffa, Davide Ciucci

We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc. Our results highlight new connections be

Peihong Lin, Pengfei Wang, Xu Zhou, Wei Xie

Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended for testing specific targets (e.g., the potential buggy code). Despite numerous techniques proposed to enhance directedness, the existing DGF techniques still face challenges, such as taking into account the difficulty of reaching different basic blocks when designing the fitness metric, and

P. Risse, N. Derakhshanian, P. Duwentäster, T. Ježo

We discuss the foundation for a new global nCTEQ nuclear PDF analysis, combining a number of our previous analyses into one consistent framework with updates to the underlying theoretical treatment as well as the addition of new available data. In particular, the new global release will be the first nCTEQ release containing neutrino DIS scattering data in a

Emanuele Regnani

This work aims to combine these two fields together by presenting a practical implementation of machine learning to the particular form of mental training that is the art of memory, taken in its competitive version called "Memory Sports". Such a fusion, on the one hand, strives to raise awareness about both realms, while on the other it seeks to encourage re

Pietro Bonazzi, Thomas Ruegg, Sizhen Bian, Yawei Li

Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency efficiency due to the typically heavy computational load they impose on edge platforms.This work leverages one of the first "AI in sensor" vision platforms, IMX500 by Sony, to achieve ultra-fast and ultra-low-power end-to-end edge vision applications. We evaluate the

F. F. Karpeshin, L. F. Vitushkin

The most probable candidate for the role of a nuclear optical standard is the 8.338-eV isomer of the 229mTh isotope of the thorium nucleus. Ways of using the resonance properties of the electron shell as an optical resonator to create laser-nuclear technologies necessary for the optical pumping of nuclear isomers and other manipulations of atomic nuclei lead

Mennatullah Siam, Rezaul Karim, He Zhao, Richard Wildes

Few-shot video segmentation is the task of delineating a specific novel class in a query video using few labelled support images. Typical approaches compare support and query features while limiting comparisons to a single feature layer and thereby ignore potentially valuable information. We present a meta-learned Multiscale Memory Comparator (MMC) for few-s

Kamer Ali Yuksel

This paper proposes a novel meta-learning approach to optimize a robust portfolio ensemble. The method uses a deep generative model to generate diverse and high-quality sub-portfolios combined to form the ensemble portfolio. The generative model consists of a convolutional layer, a stateful LSTM module, and a dense network. During training, the model takes a

Edward Pearce-Crump, William J. Knottenbelt

Permutation equivariant neural networks are typically used to learn from data that lives on a graph. However, for any graph $G$ that has $n$ vertices, using the symmetric group $S_n$ as its group of symmetries does not take into account the relations that exist between the vertices. Given that the actual group of symmetries is the automorphism group Aut$(G)$

Owen Garnier

We consider a particular class of Garside groups, which we call circular groups. We mainly prove that roots are unique up to conjugacy in circular groups. This allows us to completely classify these groups up to isomorphism. As a consequence, we obtain the uniqueness of roots up to conjugacy in complex braid groups of rank 2. We also consider a generalizatio

Bo Jiang, Tao Li, Zhen Chen

With the constraint from Onsager reciprocity relations, here we generalize the Klemens model for phonon-phonon Umklapp scattering from isotropic to anisotropic. Combined with the anisotropic Debye dispersion, this anisotropic Klemens model leads to analytical expressions for heat transfer along both ab-plane (k_ab) and c-axis (k_c), suitable for both layered

Junyu Li, Han Huang, Dong Ni, Wufeng Xue

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor diagnosis. However, the classification of benign and malignant renal tumors can still be very challenging due to the highly h

Nzar Rauf Abdullah, Hemn Gharib Hussein, Vidar Gudmundsson

The electronic, thermal, magnetic and optical properties of BN-codoped strontium oxide (SrO) monolayers are studied taking into account the interaction effects between the B and the N dopant atoms. The indirect band gap of a pure two dimensional SrO is modified to a narrow direct band gap by tuning the B-N attractive interaction. The B or N separately doped

Xunmeng Wu, Zai Yang, Zhiqiang Wei, Zongben Xu

This paper addresses the problem of direction-of-arrival (DOA) estimation for constant modulus (CM) source signals using a uniform or sparse linear array. Existing methods typically exploit either the Vandermonde structure of the steering matrix or the CM structure of source signals only. In this paper, we propose a structured matrix recovery technique (SMAR

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Yousif Hussein Azeez, Chi-Shung Tang

We investigate the electronic, the thermal, and the optical properties of BN-codoped MgO monolayers taking into account the interaction effects between the B and the N dopant atoms. The relatively wide indirect band gap of a pure MgO nanosheet can be changed to a narrow direct band gap by tuning the B-N attractive interaction. The band gap reduction does not

Yifan Cai, Tao Wang, Liu Zhao

The general covariant Fokker-Planck equations associated with the two different versions of covariant Langevin equation in Part I of this series of work are derived, both lead to the same reduced Fokker-Planck equation for the non-normalized one particle distribution function (1PDF). The relationship between various distribution functions is clarified in thi

Markos Karameris

Let $S_{k}(\Gamma_0(N),\chi)$ denote the space of holomorphic cuspforms with Dirichlet character $\chi$ and modular subgroup $\Gamma_0(N)$. We will characterize the space of newforms $S_{k}^{new}(\Gamma_0(N),\chi)$ as the intersection of eigenspaces of a particular family of Hecke operators, generalizing the work of Baruch-Purkait to forms with non-trivial c

Alexander S. Gusev

The regularity in the distribution of young stellar groups along the spiral arms of galaxies, first discovered by Bruce and Debra Elmegreen in 1983, was considered a rather rare phenomenon. However, recent studies of the spatial regularities in the distribution of the young stellar populations along the arms of the spiral galaxies NGC 628, NGC 895, NGC 4321,

Xunmeng Wu, Zai Yang, Zongben Xu

Multichannel frequency estimation with incomplete data and miscellaneous noises arises in array signal processing, modal analysis, wireless communications, and so on. In this paper, we consider maximum-likelihood(-like) optimization methods for frequency estimation in which proper objective functions are adopted subject to observed data patterns and noise ty

V. V. Ilyushin, H. S. P. Müller, J. K. Jørgensen, S. Bauerecker

Solar-type prestellar cores and protostars display large amounts of deuterated organic molecules. Recent findings on CHD$_2$OH and CD$_3$OH toward IRAS 16293-2422 suggest that even fully deuterated methanol, CD$_3$OD, may be detectable as well. However, searches for CD$_3$OD are hampered in particular by the lack of intensity information from a spectroscopic

R. Cecchini, A. Fabrizi, C. Paternoster, W. Zhang

The present work illustrates a novel approach for the maskless and resistless fabrication of nanopatterned metal layers on Si substrates, based on the combination of nanomechanical surface modification techniques (such as nanoindentation and nanoscratching) and electrodeposition. Single crystal (100) n-doped Si substrates were first cleaned from native oxide

Linghan Huang, Shi Shu, Ying Yang

The Poisson-Boltzmann equation is a nonlinear elliptic equation with Dirac distribution sources, which has been widely applied to the prediction of electrostatics potential of biological biomolecular systems in solution. In this paper, we discuss and analysis the virtual element method for the Poisson-Boltzmann equation on general polyhedral meshes. Under th

Hang Yin, Zihao Wang, Weizhi Fei, Yangqiu Song

To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential because they are capable of generalizing over unobserved knowledge. Therefore, an appropriate dataset is fundamental to both obtaining and evaluating such methods under this paradigm. In

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Yousif Hussein Azeez, Vidar Gudmundsson

The electronic, the thermal, and the optical properties of hexagonal MgX monolayers (where X=C, N, and O) are investigated via first principles studies. Ab-initio molecular dynamic, AIMD, simulations using NVT ensembles are performed to check the thermodynamic stability of the monolayers. We find that an MgO monolayer has semiconductor properties with a good

Minoo Sayyadpour, Ali Nazarizadeh

Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting

Francesco Fazzini, Carlo Rovelli, Farshid Soltani

A metric that describes a collapsing star and the surrounding black hole geometry accounting for quantum gravity effects has been derived independently by different research groups. There is consensus regarding this metric up until the star reaches its minimum radius, but there is disagreement about what happens past this event. The discrepancy stems from th

Mengyu Zhao, Shirin Jalali

Snapshot compressive imaging (SCI) systems have gained significant attention in recent years. While previous theoretical studies have primarily focused on the performance analysis of Gaussian masks, practical SCI systems often employ binary-valued masks. Furthermore, recent research has demonstrated that optimized binary masks can significantly enhance syste

Vinay Sharma, Rajeev Nepal, Ramesh C. Budhani

Antiferromagnetic Weyl semimetals with spin chirality offer excellent platforms to address the Berry phase physics, which manifests prominently in several of their electro-optical and electro-magnetic responses including as a large anomalous Hall effect (AHE) and spin Hall conductivity. Here, we report measurements of magneto-transport in c-axis textured Mn3

Sahel Azizpour, Viola Priesemann, Johannes Zierenberg, Anna Levina

Tasks that require information about the world imply a trade-off between the time spent on observation and the variance of the response. In particular, fast decisions need to rely on uncertain information. However, standard estimates of information processing capabilities, such as the dynamic range, are defined based on mean values that assume infinite obser

Nzar Rauf Abdullah, Hunar Omar Rashid, Botan Jawdat Abdullah, Chi-Shung Tang

The electrical and optical properties of flat and planar buckled siligraphene (SiC) monolayer are examined using a first principles approach. Buckling between the Si and the C atoms in SiC structures influences and impacts the properties of the 2D nanomaterial, according to our results. The electron density of a planar SiC monolayer is calculated, as well as

Zikang Yuan, Fengtian Lang, Tianle Xu, Ruiye Ming

Existing LiDAR-inertial state estimation assumes that the state at the beginning of current sweep is identical to the state at the end of last sweep. However, if the state at the end of last sweep is not accurate, the current state cannot satisfy the constraints from LiDAR and IMU consistently, ultimately resulting in local inconsistency of solved state (e.g

Mengyuan Liu, Hong Liu, Tianyu Guo

Considering the instance-level discriminative ability, contrastive learning methods, including MoCo and SimCLR, have been adapted from the original image representation learning task to solve the self-supervised skeleton-based action recognition task. These methods usually use multiple data streams (i.e., joint, motion, and bone) for ensemble learning, meanw

Zhizhong Huang, Siteng Ma, Junping Zhang, Hongming Shan

Recent works for face editing usually manipulate the latent space of StyleGAN via the linear semantic directions. However, they usually suffer from the entanglement of facial attributes, need to tune the optimal editing strength, and are limited to binary attributes with strong supervision signals. This paper proposes a novel adaptive nonlinear latent transf

Enrico Arbarello, Giulia Saccà

This survey is a continuation of the study undertaken in \cite{AS18}. We examine the local structure of Bridgeland moduli spaces $M_\sigma(v,\D)$, where the relevant triangulated category $\D$ is either the bounded derived category $\D=\D^b(X)$ of a K3 surface $X$, or the Kuznetsov component $\D=\Ku(Y)\subset \D ^b(Y)$ of a smooth cubic fourfold $Y\subset \P

Virendra Sule

This paper addresses the computational problem of deciding invertibility (or one to one-ness) of a Boolean map $F$ in $n$-Boolean variables. This problem is a special case of deciding invertibilty of a map $F:\mathbb{F}_{q}^n\rightarrow\mathbb{F}_{q}^n$ over the finite field $\mathbb{F}_q$ for $q=2$. Algebraic condition for invertibility of $F$ is well known

Hong Kuan Ng, Du Xiang, Ady Suwardi, Guangwei Hu

In this reply, we include new experimental results and verify that the observed non-linearity in rippled-MoS$_2$ (leading to mobility kink) is an intrinsic property of a disordered system, rather than contact effects (invasive probes) or other device issues. Noting that Peng Wu's hypothesis is based on a highly ordered ideal system, transfer curves are expec

Nzar Rauf Abdullah, Botan Jawdat Abdullah, Hunar Omar Rashid, Vidar Gudmundsson

The structural, electronic, and optical properties of flat and buckled AlN monolayers are investigated using first-principles approaches. The band gap of a flat AlN monolayer is changed from an indirect one to a direct one, when the planar buckling increases, primarily due to diminishing sp$^2$ overlapping and bond symmetry breaking in the conversion to sp$^

Liam Hodgkinson, Chris van der Heide, Robert Salomone, Fred Roosta

The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical information criteria typically consider the large-data limit, penalizing model size. However, these criteria are not appropriate in modern settings where overparameterized models tend to per

Ignace Aristide Minlend

We prove the existence of nontrivial unbounded domains $\O$ in the Euclidean space $\R^d$ for which the Dirichlet eigenvalue problem for the Laplacian on $\Omega$ admits sign-changing eigenfunctions with constant Neumann values on $\partial \Omega$. We also establish a similar result by studying a partially overdetermined problem on domains with two boundary

Fabiano L. Ribeiro, Yunfei Li, Stefan Born, Diego Rybski

Many physical, biological, and social systems exhibit emergent properties that arise from the interactions between their components (cells). In this study, we systematically treat every-pair interactions (a) that exhibit power-law dependence on the Euclidean distance and (b) act in structures that can be characterized using fractal geometry. We analytically

Zhihao Wen, Yuan Fang

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serious concern for supervised learning. Meanwhile, many text dat

Guilherme C. M. Gomes, Clément Legrand-Duchesne, Reem Mahmoud, Amer E. Mouawad

We study the problem of reconfiguring one minimum $s$-$t$-separator $A$ into another minimum $s$-$t$-separator $B$ in some $n$-vertex graph $G$ containing two non-adjacent vertices $s$ and $t$. We consider several variants of the problem as we focus on both the token sliding and token jumping models. Our first contribution is a polynomial-time algorithm that

Christof Tinnes

The existence of trace links between artifacts of the software development life cycle can improve the efficiency of many activities during software development, maintenance and operations. Unfortunately, the creation and maintenance of trace links is time-consuming and error-prone. Research efforts have been spent to automatically compute trace links and lat

Wolfgang Dahmen, Olga Mula

The criticality problem in nuclear engineering asks for the principal eigenpair of a Boltzmann operator describing neutron transport in a reactor core. Being able to reliably design, and control such reactors requires assessing these quantities within quantifiable accuracy tolerances. In this paper we propose a paradigm that deviates from the common practice

Raimondo Cecchini, Christian Martella, Claudia Wiemer, Alessio Lamperti

Synthetic two-dimensional (2D) mono-elemental crystals, namely X-enes, have recently emerged as a new frontier for atomically thin nanomaterials with on-demand properties. Among X-enes, antimonene, the \b{eta}-phase allotrope of antimony, is formed by atoms arranged in buckled hexagonal rings bearing a comparatively higher environmental stability with respec

Yuri Shtanov, Valery I. Zhdanov

In cosmological $N$-body simulations of warm dark matter, thermal velocities of dark-matter particles are sometimes taken into account by adding random initial velocities to the particles of simulation. However, a particle in the $N$-body system represents a huge collection of dark-matter particles, whose average thermal velocity is very close to zero. We co

Piotr Pikul

In a paper from 2012 Jab{\l}o\'nski, Jung and Stochel introduced the weighted shifts on directed trees, a generalisation of well known weighted shift operators on $\ell^2$. In the last decade this class has proven itself handy for finding counterexamples in operator theory. Properties of underlying graph structure had essential influence on the operator. It

Bilal T. Bilalov, Natavan P. Nasibova, Lubomira G. Softova, Salvatore Tramontano

We consider a non-local boundary value problem for the Laplace equation in unbounded studding the weak and strong solvability of that problem in the framework of the weighted Sobolev space $W^{1,p}_\nu$, with a Muckenhoupt weight. We proved that if any weak solution belongs to the space $W_{\nu}^{2,p}$, then it is also a strong solution and satisfies the cor

Enzo Vitillaro

The paper deals with three evolution problems arising in the physical modelling of acoustic phenomena of small amplitude in a fluid, bounded by a surface of extended reaction. The first one is the widely studied wave equation with acoustic boundary conditions, which derivation from the physical model is not fully mathematically satisfactory. The other two mo

Igor G. Korepanov

We introduce new algebraic structures associated with heptagon relations -- higher analogue of the well-known pentagon. The main points we deal with are: (i) polygon relations as algebraic imitations of Pachner moves, on the example of heptagon, (ii) parameterization of heptagon relations by simplicial 3-cocycles, (iii) applications to invariants of pairs "p

Ilan Doron-Arad, Ariel Kulik, Hadas Shachnai

We study a family of matroid optimization problems with a linear constraint (MOL). In these problems, we seek a subset of elements which optimizes (i.e., maximizes or minimizes) a linear objective function subject to (i) a matroid independent set, or a matroid basis constraint, (ii) additional linear constraint. A notable member in this family is budgeted ma

Michaela Mlynarikova

The large dataset of about 3000 fb$^{-1}$ that will be collected by both the ATLAS and CMS experiments at the High Luminosity LHC (HL-LHC) will be used to measure Higgs boson properties in detail. Studies based on current analyses have been carried out to understand the expected precision and limitations of these measurements. The large dataset will also all

Banghee So

The property and casualty (P&C) insurance industry faces challenges in developing claim predictive models due to the highly right-skewed distribution of positive claims with excess zeros. To address this, actuarial science researchers have employed "zero-inflated" models that combine a traditional count model and a binary model. This paper investigates the u

Yiran Huang, Yexu Zhou, Till Riedel, Likun Fang

Deep learning has proven to be an effective approach in the field of Human activity recognition (HAR), outperforming other architectures that require manual feature engineering. Despite recent advancements, challenges inherent to HAR data, such as noisy data, intra-class variability and inter-class similarity, remain. To address these challenges, we propose

Konstantinos T. Gkikas

Let $s\in(0,1),$ $1<p<\frac{N}{s}$ and $\Omega\subset\mathbb{R}^N$ be an open bounded set. In this work we study the existence of solutions to problems ($E_\pm$) $Lu\pm g(u)=\mu$ and $u=0$ a.e. in $\mathbb{R}^N\setminus\Omega,$ where $g\in C(\mathbb{R})$ is a nondecreasing function, $\mu$ is a bounded Radon measure on $\Omega$ and $L$ is an integro-different

Sarosij Bose, Saikat Sarkar, Amlan Chakrabarti

Classifying player actions from soccer videos is a challenging problem, which has become increasingly important in sports analytics over the years. Most state-of-the-art methods employ highly complex offline networks, which makes it difficult to deploy such models in resource constrained scenarios. Here, in this paper we propose a novel end-to-end knowledge

Chuhan Wang, Xuehu Zhu, Lixing Zhu

This paper proposes a Robust One-Step Estimator(ROSE) to solve the Byzantine failure problem in distributed M-estimation when a moderate fraction of node machines experience Byzantine failures. To define ROSE, the algorithms use the robust Variance Reduced Median Of the Local(VRMOL) estimator to determine the initial parameter value for iteration, and commun

J. I. Villaseñor, D. J. Lennon, A. Picco, T. Shenar

Recent studies of massive binaries with putative black hole companions have uncovered a phase of binary evolution that has not been observed before, featuring a bloated stripped star that very recently ceased transferring mass to a main-sequence companion. In this study, we focus on the candidate system VFTS 291, a binary with an orbital period of 108 d and

Longfei Mo, Min Hua, Hongyu Sun, Hongming Xu

Testing of function safety and Safety Of The Intended Functionality (SOTIF) is important for autonomous vehicles (AVs). It is hard to test the AV's hazard response in the real world because it would involve hazards to passengers and other road users. This paper studied on virtual testing of AV on the CARLA platform and proposed a Humanoid Scenario Generation

François Swiadek, Ross Shillito, Paul Magnard, Ants Remm

The performance of a wide range of quantum computing algorithms and protocols depends critically on the fidelity and speed of the employed qubit readout. Examples include gate sequences benefiting from mid-circuit, real-time, measurement-based feedback, such as qubit initialization, entanglement generation, teleportation, and perhaps most importantly, quantu

Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti

Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-called counterfactual paths generated by conditional permutations of features. The algorithm measures feature importance by identifying seque

Ke Cao, Ruiping Liu, Ze Wang, Kunyu Peng

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a

Chiara Saffirio

The derivation of effective macroscopic theories approximating microscopic systems of interacting particles is a major question in non-equilibrium statistical mechanics. In these notes we present an approximation of systems made by many fermions interacting via inverse power law potentials in the mean-field and semiclassical regimes, reviewing the material p

Alberto S. Cattaneo, Pavel Mnev, Michele Schiavina

This note gives an overview of the BV formalism in its various incarnations and applications.

Michael Scherbela, Leon Gerard, Philipp Grohs

Obtaining accurate solutions to the Schr\"odinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently outperformed conventional approaches in terms of accuracy, but only at large computational cost. Whereas in many domains models are trained once and subsequently applied for infe

Bastián Díaz Sáez, Patricio Escalona Contreras

In addition to the Standard Model, the introduction of a singlet complex scalar field that acquires vacuum expectation value may give rise to a cosmologically stable pseudo-Nambu-Goldstone boson (pNGB), a suitable dark matter (DM) candidate. This work extends this scenario by including a second cosmologically stable particle: a fermion singlet. The pNGB and

Naichung Conan Leung, Dan Wang

A compact K\"ahler manifold $\left( M,\omega ,J\right) $ with $T$-symmetry admits a natural mixed polarization $\mathcal{P}_{\mathrm{mix}}$ whose real directions come from the $T$-action. In \cite{LW1}, we constructed a one-parameter family of K\"ahler structures $\left( \omega ,J_{t}\right) $'s with the same underlying K\"a hler form $\omega $ and $J_{0}=J$

Jize Sui

A biophysical issue how the nuclear size dynamically scales with the cellular size remains mysterious. We develop a theoretical framework in which the interactions between polydisperse biomolecules and the mechanical elasticity of the cell are precisely integrated to investigate dynamics of the nuclear-to-cellular volume ratio (N/C ratio). We surprisingly fi

Yuxiang Yang, Benjamin Yadin, Zhen-Peng Xu

Armed with quantum correlations, quantum sensors in a network have shown the potential to outclass their classical counterparts in distributed sensing tasks such as clock synchronization and reference frame alignment. On the other hand, this analysis was done for simple and idealized networks, whereas the correlation shared within a practical quantum network

Ruiping Liu, Jiaming Zhang, Kunyu Peng, Junwei Zheng

Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images. In this work, we focus on the application of GSR in assisting people with visual impairments (PVI). However, precise localization information o

Xiao Fei, Philippe Martins, Jialiang Lu

The classification of fifth-generation New-Radio (5G-NR) mobile network traffic is an emerging topic in the field of telecommunications. It can be utilized for quality of service (QoS) management and dynamic resource allocation. However, traditional approaches such as Deep Packet Inspection (DPI) can not be directly applied to encrypted data flows. Therefore

Harjinder Singh Lallie, Andrew Thompson, Elzbieta Titis, Paul Stephens

Universities hold and process a vast amount of valuable user and research data. This makes them a prime target for cyber criminals. Additionally, universities and other educational settings, such as schools and college IT systems, have become a prime target for some of their own students -- often motivated by an opportunity to cause damage to networks and we

Wing-Yin Yu, Lai-Man Po, Ray C. C. Cheung, Yuzhi Zhao

Video-based human pose transfer is a video-to-video generation task that animates a plain source human image based on a series of target human poses. Considering the difficulties in transferring highly structural patterns on the garments and discontinuous poses, existing methods often generate unsatisfactory results such as distorted textures and flickering

Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel

In this work, we propose a novel prior learning method for advancing generalization and uncertainty estimation in deep neural networks. The key idea is to exploit scalable and structured posteriors of neural networks as informative priors with generalization guarantees. Our learned priors provide expressive probabilistic representations at large scale, like

Junya Shijubo, Masaki Waga, Kohei Suenaga

We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established black-box checking (BBC) technique to address stochastic behavior. Traditional BBC primarily involves iteratively identifying an input that breaches the system's specifications by executing the following

Vyacheslav Kovalev, Anna Shkromada, Henni Ouerdane, Pavel Osinenko

Stable gait generation is a crucial problem for legged robot locomotion as this impacts other critical performance factors such as, e.g. mobility over an uneven terrain and power consumption. Gait generation stability results from the efficient control of the interaction between the legged robot's body and the environment where it moves. Here, we study how t

Claudio Guarcello, F. Sebastián Bergeret, Roberta Citro

We investigate the switching current distributions of ferromagnetic anomalous Josephson junctions subjected to a linearly increasing bias current. Our study uncovers a significant correlation between the position of the switching current distributions and crucial system parameters, such as the strength of the spin-orbit coupling and the Gilbert damping param

Steven J. Desch, Emilie T. Dunham, Ashley K. Herbst, Cayman T. Unterborn

Most meteoritic calcium-rich, aluminum-rich inclusions (CAIs) formed from a reservoir with ${}^{26}{\rm Al}/{}^{27}{\rm Al} \approx 5 \times 10^{-5}$, but some record lower $({}^{26}{\rm Al}/{}^{27}{\rm Al})_0$, demanding they sampled a reservoir without live ${}^{26}{\rm Al}$. This has been interpreted as evidence for "late injection" of supernova material

Congcong Li, Xuelei Lin, Sean Hon, Shu-Lin Wu

In this study, a novel preconditioner based on the absolute-value block $\alpha$-circulant matrix approximation is developed, specifically designed for nonsymmetric dense block lower triangular Toeplitz (BLTT) systems that emerge from the numerical discretization of evolutionary equations. Our preconditioner is constructed by taking an absolute-value of a bl

Richard Lee Lai, Jen-Cheng Hou, I-Chun Chern, Kuo-Hsuan Hung

Individuals with hearing impairments face challenges in their ability to comprehend speech, particularly in noisy environments. The aim of this study is to explore the effectiveness of audio-visual speech enhancement (AVSE) in enhancing the intelligibility of vocoded speech in cochlear implant (CI) simulations. Notably, the study focuses on a challenged scen

Torben Lange, Saswati Nandan, Joosep Pata, Laurits Tani

Identifying and reconstructing hadronic $\tau$ decays ($\tau_{\textrm{h}}$) is an important task at current and future high-energy physics experiments, as $\tau_{\textrm{h}}$ represent an important tool to analyze the production of Higgs and electroweak bosons as well as to search for physics beyond the Standard Model. The identification of $\tau_{\textrm{h}

Yeon-Koo Che, Olivier Tercieux

We study the optimal method for rationing scarce resources through a queue system. The designer controls agents' entry into a queue and their exit, their service priority -- or queueing discipline -- as well as their information about queue priorities, while providing them with the incentive to join the queue and, importantly, to stay in the queue, when reco

Bei-Lei Liu, Yue-Chao Wang, Yu Liu, Hai-Feng Liu

Strongly correlated systems containing d/f-electrons present a challenge to conventional density functional theory (DFT), such as the widely used local density approximation (LDA) or generalized gradient approximation (GGA). In this work, we developed a doubly screened Coulomb correction (DSCC) approach to perform on-site Coulomb interaction correction for s

Héber H. Arcolezi, Selene Cerna, Catuscia Palamidessi

This paper investigates the utility gain of using Iterative Bayesian Update (IBU) for private discrete distribution estimation using data obfuscated with Locally Differentially Private (LDP) mechanisms. We compare the performance of IBU to Matrix Inversion (MI), a standard estimation technique, for seven LDP mechanisms designed for one-time data collection a

David Roscoe

It has been reported that the application of convolutional neural-network techniques to infer the Dark Matter distribution in the local IGM has revealed how it follows the hierarchical distribution of galaxies in the locality, rather than exhibiting homogeneity. This result makes it natural to consider the possibility that, on scales at least as big as $20 \

Linqing Li, Zhifeng Wang

Presently, knowledge graph-based recommendation algorithms have garnered considerable attention among researchers. However, these algorithms solely consider knowledge graphs with single relationships and do not effectively model exercise-rich features, such as exercise representativeness and informativeness. Consequently, this paper proposes a framework, nam

Andrew Coates, Fethi M. Ramazanoğlu

We demonstrate some shortcomings of "fixing the equations," an increasingly popular remedy for time evolution problems of effective field theories (EFTs). We compare the EFTs and their "fixed" versions to the UV theories from which they can be derived in two cases: K-essence and nonlinear Proca theory. We find that when an EFT breaks down due to loss of hype

Yi-Syuan Chen, Yun-Zhu Song, Cheng Yu Yeo, Bei Liu

Large Pre-trained Transformers exhibit an intriguing capacity for in-context learning. Without gradient updates, these models can rapidly construct new predictors from demonstrations presented in the inputs. Recent works promote this ability in the vision-language domain by incorporating visual information into large language models that can already make in-

Joonyoung Kim, Kangwook Lee, Haebin Shin, Hurnjoo Lee

The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short, and there are just too many to remember. In such a case, the users may want to ask contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot

Akram Alishahi, Eugene Gorsky, Beibei Liu

In this paper, we study the skein exact sequence for links via the exact surgery triangle of link Floer homology and compare it with other skein exact sequences given by Ozsv\'ath and Szab\'o. As an application, we use the skein exact sequence to study the splitting number and splitting maps for links. In particular, we associate the splitting maps for the t

Joohyung Lee, Vladimir Lifschitz, Ravi Palla

Safe first-order formulas generalize the concept of a safe rule, which plays an important role in the design of answer set solvers. We show that any safe sentence is equivalent, in a certain sense, to the result of its grounding -- to the variable-free sentence obtained from it by replacing all quantifiers with multiple conjunctions and disjunctions. It foll

Mohammad Dehghani, Zahra Yazdanparast

Sentiment analysis is the process of identifying and categorizing people's emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was c

Samin Jamalabadi, Uwe Schwiegelshohn

\textit{Weighted shortest processing time first} (WSPT) is one of the best known algorithms for total weighted completion time scheduling problems. For each job $J_j$, it first combines the two independent job parameters weight $w_j$ and processing time $p_j$ by simply forming the so called Smith ratio $w_j/p_j$. Then it schedules the jobs in order of decrea

Anqi Dong, Tryphon T. Georgiou, Allen Tannenbaum

Biological networks often encapsulate promotion/inhibition as signed edge-weights of a graph. Nodes may correspond to genes assigned expression levels (mass) of respective proteins. The promotion/inhibition nature of co-expression between nodes is encoded in the sign of the corresponding entry of a sign-indefinite adjacency matrix, though the strength of suc

Mohsen Abbaspour Onari, Isel Grau, Marco S. Nobile, Yingqian Zhang

This empirical study proposes a novel methodology to measure users' perceived trust in an Explainable Artificial Intelligence (XAI) model. To do so, users' mental models are elicited using Fuzzy Cognitive Maps (FCMs). First, we exploit an interpretable Machine Learning (ML) model to classify suspected COVID-19 patients into positive or negative cases. Then,

Martin Gebser, Joohyung Lee, Yuliya Lierler

By introducing the concepts of a loop and a loop formula, Lin and Zhao showed that the answer sets of a nondisjunctive logic program are exactly the models of its Clark's completion that satisfy the loop formulas of all loops. Recently, Gebser and Schaub showed that the Lin-Zhao theorem remains correct even if we restrict loop formulas to a special class of

Victor Gallego

Recently, large multimodal models, such as CLIP and Stable Diffusion have experimented tremendous successes in both foundations and applications. However, as these models increase in parameter size and computational requirements, it becomes more challenging for users to personalize them for specific tasks or preferences. In this work, we address the problem