March 2023 arXiv papers — page 19

Yongquan Hu, Dawen Zhang, Mingyue Yuan, Kaiqi Xian

Generative Artificial Intelligence (GenAI) has emerged as a fundamental component of intelligent interactive systems, enabling the automatic generation of multimodal media content. The continuous enhancement in the quality of Artificial Intelligence-Generated Content (AIGC), including but not limited to images and text, is forging new paradigms for its appli

D. V. Shaykin, A. M. Kamchatnov

We study propagation of high-frequency wave packets along a large-scale background wave which evolves according to dispersionless hydrodynamic equations for two variables (fluid density and flow velocity). Influence of the wave packet on evolution of the background wave is neglected, so the large-scale evolution can be found independently of the wave packet'

Tamás Aladics, Péter Hegedűs, Rudolf Ferenc

The presence of software vulnerabilities is an ever-growing issue in software development. In most cases, it is desirable to detect vulnerabilities as early as possible, preferably in a just-in-time manner, when the vulnerable piece is added to the code base. The industry has a hard time combating this problem as manual inspection is costly and traditional m

Kauntey Acharya, Parth Bambhaniya, Pankaj S. Joshi, Kshitij Pandey

In this paper, we investigate particle acceleration and high-energy collisions in the Joshi-Malafarina-Narayan (JMN-1) naked singularity, which, in the absence of an event horizon, allows infalling particles to turn back under specific angular momentum conditions. These outgoing particles can then collide with infalling ones, enabling the JMN-1 singularity t

S. A. Kadam, Santosh V Lohakare, B. Mishra

The stable critical points and their corresponding cosmology are derived in the teleparallel gravity with an added Gauss-Bonnet topological invariant term. We have analyzed the dynamics of the Universe by presenting two cosmological viable models, showing the potential to describe different phases of the evolution of the Universe. The value of the decelerati

Mahum Naseer, Muhammad Shafique

Owing to their remarkable learning (and relearning) capabilities, deep neural networks (DNNs) find use in numerous real-world applications. However, the learning of these data-driven machine learning models is generally as good as the data available to them for training. Hence, training datasets with long-tail distribution pose a challenge for DNNs, since th

Marat Akhmet, Madina Tleubergenova, Akylbek Zhamanshin

The paper considers a stochastic differential equation of Duffing type with Markov coefficients. The existence of unpredictable solutions is considered. The unpredictability is a property of bounded functions characterized by unbounded sequences of moments of divergence and convergence in Bebutov dynamics. Markov components of the equation coefficients admit

M. C. Braun, T. Decker, N. Hegemann, S. F. Kerstan

We present a method to model a discretized time evolution of probabilistic networks on gate-based quantum computers. We consider networks of nodes, where each node can be in one of two states: good or failed. In each time step, probabilities are assigned for each node to fail (switch from good to failed) or to recover (switch from failed to good). Furthermor

Piotr Michał Bies, Michał Gaczkowski, Przemysław Górka

We study the Hardy-Littlewood maximal operator in the Musielak-Orlicz-Sobolev space $W^{1,\varphi}(\mathbb{R}^n)$. Under some natural assumptions on $\varphi$ we show that the maximal function is bounded and continuous in $W^{1,\varphi}(\mathbb{R}^n)$.

R. Vilela Mendes

It is argued, as a working hypothesis, that "normal" and dark matter interactions can only be T and CP violating. One way to implement this idea is to consider that time reversal in dark matter is implemented, not by an antiunitary operator, but by a unitary operator. It is shown how this occurs naturally in the context of complex spacetime with an extended

El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets. We develop quantum reinforcement learning methods based on policy-search and distributional actor-critic algo

Tobias Bernd Gäbler, Patrick Hendra, Nitish Jain, Markus Gräfe

Fluorescence excitation by absorption of entangled photon pairs offers benefits compared to classical imaging techniques, such as the attainment of higher signal levels at low excitation power while simultaneously mitigating photo-toxicity. However, current entangled photon pair sources are unreliable for fluorescence detection. In order to address this limi

Martin Rosalie, Serge Chaumette

With the constant increase of the number of autonomous vehicles and connected objects, tools to understand and reproduce their mobility models are required. We focus on chaotic dynamics and review their applications in the design of mobility models. We also provide a review of the nonlinear tools used to characterize mobility models, as it can be found in th

Enrico Lipparini, Stefan Ratschan

For typical first-order logical theories, satisfying assignments have a straightforward finite representation that can directly serve as a certificate that a given assignment satisfies the given formula. For non-linear real arithmetic augmented with trigonometric and exponential functions (NTA), however, there is no known direct representation of satisfying

S. A. N. Nouwens, M. M. Paulides, W. P. M. H. Heemels

Reducing the computation time of model predictive control (MPC) is important, especially for systems constrained by many state constraints. In this paper, we propose a new online constraint removal framework for linear systems, for which we coin the term constraint-adaptive MPC (ca-MPC). In so-called exact ca-MPC, we adapt the imposed constraints by removing

Shenyuan Gao, Chunluan Zhou, Jun Zhang

Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain. However, existing one-stream trackers always let the template interact with all parts inside the search region throughout all the encoder layers. This could p

Gergely Kiss, Gábor Somlai, Tamás Terpai

The main purpose of this paper is to prove that the positive real numbers can be decomposed into finitely many disjoint pieces which are also closed under addition and multiplication. As a byproduct of the argument we determine all the possible decompositions of the transcendental extension of the rational field of rank one into two pieces. Further, we prove

Goutam Das

In this article, we have studied threshold effects on rapidity distributions of massive gauge bosons ($Z, W^{\pm}$) in the Standard Model at the Large Hadron Collider. By exploiting the universal behavior of soft gluon emissions in the threshold region, we resum the large threshold logarithms arising in the rapidity distribution at next-to-next-to leading lo

Yusuke Wakuta, Michael Mior, Teruyoshi Zenmyo, Yuya Sasaki

In this paper, we propose a schema optimization method for time-dependent workloads for NoSQL databases. In our proposed method, we migrate schema according to changing workloads, and the estimated cost of execution and migration are formulated and minimized as a single integer linear programming problem. Furthermore, we propose a method to reduce the number

Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret

Text-to-Image synthesis is the task of generating an image according to a specific text description. Generative Adversarial Networks have been considered the standard method for image synthesis virtually since their introduction. Denoising Diffusion Probabilistic Models are recently setting a new baseline, with remarkable results in Text-to-Image synthesis,

Liying Bao, Bo Qi, Franco Nori, Daoyi Dong

Unique properties of multimode non-Hermitian lattice dynamics can be utilized to construct exponentially sensitive sensors. However, the impact of noise remains unclear, which may severely degrade their sensitivity. We analytically characterize and highlight the impact of loss and gain on the sensitivity revival and stability of non-Hermitian sensors. Defyin

Yuning Wang, Pu Zhang, Lei Bai, Jianru Xue

Predicting the future trajectories of the traffic agents is a gordian technique in autonomous driving. However, trajectory prediction suffers from data imbalance in the prevalent datasets, and the tailed data is often more complicated and safety-critical. In this paper, we focus on dealing with the long-tail phenomenon in trajectory prediction. Previous meth

J. P. Wittmer, A. N. Semenov, J. Baschnagel

Strain correlation functions in two-dimensional isotropic elastic bodies are shown both theoretically (using the general structure of isotropic tensor fields) and numerically (using a glass-forming model system) to depend on the coordinates of the field variable (position vector r in real space or wavevector q in reciprocal space) and thus on the direction o

Ayse Arik, Andrew J. G. Cairns, Erengul Dodd, Angus S. Macdonald

We propose a methodology to quantify the impact on breast cancer mortality of diagnostic delays caused by public health measures introduced as a response to the COVID-19 pandemic. These measures affected cancer pathways by halting cancer screening, delaying diagnostic tests, and reducing the numbers of patients starting treatment. We introduce a semi-Markov

Iana Sudreau, Marion Servel, Eric Freyssingeas, François Liénard

Boehmite, an aluminum oxide hydroxide $\gamma$-AlO(OH), is broadly used in the form of particulate dispersions in industrial applications, e.g., for the fabrication of ceramics and catalyst supports or as a binder for extrusion processes. Under acidic conditions, colloidal boehmite dispersions at rest form gels, i.e., space-spanning percolated networks that

J. P. Wittmer, A. N. Semenov, J. Baschnagel

Correlation functions of components of second-order tensor fields in isotropic systems can be reduced to an isotropic forth-order tensor field characterized by a few invariant correlation functions (ICFs). It is emphasized that components of this field depend in general on the coordinates of the field vector variable and thus on the orientation of the coordi

Karim Knaebel, Jonas Schult, Alexander Hermans, Bastian Leibe

Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach. However, it remains open whether such a framework generalizes to the unique challenges of 3D point clouds. To answer this question, we extend data2vec to the point cloud domain and report encouraging result

Matthew Dyer, Christophe Hohlweg, Susanna Fishel, Alice Mark

Given an arbitrary Coxeter system $(W,S)$ and a nonnegative integer $m$, the $m$-Shi arrangement of $(W,S)$ is a subarrangement of the Coxeter hyperplane arrangement of $(W,S)$. The classical Shi arrangement ($m=0$) was introduced in the case of affine Weyl groups by Shi to study Kazhdan-Lusztig cells for $W$. As two key results, Shi showed that each region

Narayanan Arvind

In the shipping industry, document classification plays a crucial role in ensuring that the necessary documents are properly identified and processed for customs clearance. OCR technology is being used to automate the process of document classification, which involves identifying important documents such as Commercial Invoices, Packing Lists, Export/Import C

Boris Altshuler

It is shown that following experimentally viable expressions for quark mixing angles $\theta_{12}$, $\theta_{23}$, $\theta_{13}$ and CP-violating phase $\delta$: $\sin\theta_{12} = \sqrt{m_{d} / |m_{s}|}$, $\sin\theta_{23} = 2 \, |m_{s}| / m_{b}$, $\sin\theta_{13} \approx 2 \, m_{d} / m_{b}$, $\tan\delta = m_{b}^{2} \, m_{c} / 6 \, m_{t} \, m_{s}^{2}$ may be

Elis Stefansson, Karl H. Johansson

In this paper, we consider a planning problem for a large-scale system modelled as a hierarchical finite state machine (HFSM) and develop a control algorithm for computing optimal plans between any two states. The control algorithm consists of two steps: a preprocessing step computing optimal exit costs for each machine in the HFSM, with time complexity scal

Felix Finster, Eduardo Guendelman, Claudio F. Paganini

We compare the structures of the theory of causal fermion systems (CFS), an approach to unify quantum theory with general relativity (GR), with those of modified measure theories (MMT), which are a set of modified gravity theories. Classical spacetimes with MMT can be obtained as the continuum limit of a CFS. This suggests that MMT could serve as effective d

Xuechao Zou, Kai Li, Junliang Xing, Pin Tao

Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application. Although existing deep cloud removal models have achieved notable outcomes, they scarcely consider contextual information. This study introduces a high-performance cloud removal architecture, termed Progressiv

Rebecca S Stone, Nishant Ravikumar, Andrew J Bulpitt, David C Hogg

The fairness of a deep neural network is strongly affected by dataset bias and spurious correlations, both of which are usually present in modern feature-rich and complex visual datasets. Due to the difficulty and variability of the task, no single de-biasing method has been universally successful. In particular, implicit methods not requiring explicit knowl

Ke Zhang

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this p

Haoqi Yuan, Chi Zhang, Hongcheng Wang, Feiyang Xie

We study building multi-task agents in open-world environments. Without human demonstrations, learning to accomplish long-horizon tasks in a large open-world environment with reinforcement learning (RL) is extremely inefficient. To tackle this challenge, we convert the multi-task learning problem into learning basic skills and planning over the skills. Using

Jutta Kunz, Yakov Shnir

We obtain charged spherically symmetric black holes in the two-component scalar Einstein-Maxwell-Friedberg-Lee-Sirlin model with a symmetry breaking potential. These asymptotically flat black holes carry resonant scalar Q-hair. As expected, these hairy black holes give rise to non-uniqueness. When comparing these solutions with the corresponding charged boso

Selim Yilmaz, Sevil Sen, Emre Aydogan

In RPL security, intrusion detection (ID) plays a vital role, especially given its susceptibility to attacks, particularly those carried out by insider threats. While numerous studies in the literature have proposed intrusion detection systems (IDS) utilizing diverse techniques, the placement of such systems within RPL topology remains largely unexplored. Th

Roisin Holmes, Xuebin B. Yang, David J. Wood, Giuseppe Tronci

UV-cured collagen-based hydrogels hold promise in skeletal muscle regeneration due to their soft elastic properties and porous architecture. However, the complex triple helix conformation of collagen and environmental conditions, i.e. molecular oxygen, pose risks to reaction controllability, wet-state integrity and reproducibility. To address this challenge,

Dario Fiore Mosca, Hermann Schnait, Lorenzo Celiberti, Markus Aichhorn

Spin-orbit coupling has been reported to be responsible for the insulating nature of the 5d$^1$ osmate double perovskite Ba$_2$NaOsO$_6$ (BNOO). However, whether spin-orbit coupling indeed drives the metal-to-insulator transition (MIT) in this compound is an open question. In this work we investigate the impact of relativistic effects on the electronic prope

Mingqing Liu, Fei Gao, Zhuangzhuang Cui, Sofie Pollin

Joint communication and sensing (JCAS) technology has been regarded as one of the innovations in the 6G network. With the channel modeling proposed by the 3rd Generation Partnership Project (3GPP) TR 38.901, this paper investigates the sensing capability using the millimeter-wave (mmWave) band with an orthogonal frequency division multiplexing (OFDM) wavefor

L. Chen, C. Deng, M. H. Duong, T. A. Han

In this paper, we consider the replicator-mutator dynamics for pairwise social dilemmas where the payoff entries are random variables. The randomness is incorporated to take into account the uncertainty, which is inevitable in practical applications and may arise from different sources such as lack of data for measuring the outcomes, noisy and rapidly changi

Hong-Jun Choi, Dongbin Na, Kyungjin Cho, Byunguk Bae

This study presents a novel approach to bone age assessment (BAA) using a multi-view, multi-task classification model based on the Sauvegrain method. A straightforward solution to automating the Sauvegrain method, which assesses a maturity score for each landmark in the elbow and predicts the bone age, is to train classifiers independently to score each regi

M. Lencsés, G. Mussardo, G. Takács

Integrable Quantum Field Theories can be solved exactly using bootstrap techniques based on their elastic and factorisable S-matrix. While knowledge of the scattering amplitudes reveals the exact spectrum of particles and their on-shell dynamics, the expression of the matrix elements of the various operators allows the reconstruction of off-shell quantities

Haiqi Liu, Jiajie Sun, Xuqi Zhang, Fanqin Meng

Multisensor track-to-track fusion for target tracking involves two primary operations: track association and estimation fusion. For estimation fusion, lossless measurement transformation of sensor measurements has been proposed for single target tracking. In this paper, we investigate track association which is a fundamental and important problem for multita

Maicol Ciani, Stefano Bonato, Rafail Psiakis, Angelo Garofalo

Autonomous Micro Aerial Vehicles (MAVs), with a form factor of 10cm in diameter, are an emerging technology thanks to the broad applicability enabled by their onboard intelligence. However, these platforms are strongly limited in the onboard power envelope for processing, i.e., less than a few hundred mW, which confines the onboard processors to the class of

Kei-ichi Maeda, Priti Gupta, Hirotada Okawa

We discuss motion of a binary system around a supermassive black hole. Using Fermi-Walker transport, we construct a local inertial reference frame and set up a Newtonian binary system. Assuming a circular geodesic observer around a Schwarzschild black hole, we write down the equations of motion of a binary. Introducing a small acceleration of the observer, w

Ruoyu Zhao, Yushu Zhang, Tao Wang, Wenying Wen

Vision is the most important sense for people, and it is also one of the main ways of cognition. As a result, people tend to utilize visual content to capture and share their life experiences, which greatly facilitates the transfer of information. Meanwhile, it also increases the risk of privacy violations, e.g., an image or video can reveal different kinds

J. Khalouf-Rivera, Qian Wang, Lea F. Santos, J. E. García Ramos

Excited-state quantum phase transitions (ESQPTs) strongly influence the spectral properties of collective many-body quantum systems, changing degeneracy patterns in different quantum phases. Level degeneracies, in turn, affect the system's dynamics. We analyze the degeneracy dependence on the size of two-level boson models with a $u(n+1)$ dynamical algebra,

Xiangyu Li, Xiaolong Yin, Nathan Wiebe, Jaehun Chun

Numerical simulation of turbulent fluid dynamics needs to either parameterize turbulence-which introduces large uncertainties-or explicitly resolve the smallest scales-which is prohibitively expensive. Here we provide evidence through analytic bounds and numerical studies that a potential quantum exponential speedup can be achieved to simulate the Navier-Sto

E. Orozco-Acosta, A. Riebler, A. Adin, M. D. Ugarte

Short-term disease forecasting at specific discrete spatial resolutions has become a high-impact decision-support tool in health planning. However, when the number of areas is very large obtaining predictions can be computationally intensive or even unfeasible using standard spatio-temporal models. The purpose of this paper is to provide a method for short-t

Deyue Li

This paper studies an infinite horizon optimal control problem for discrete-time linear system and quadratic criteria, both with random parameters which are independent and identically distributed with respect to time. In this general setting, we apply the policy gradient method, a reinforcement learning technique, to search for the optimal control without r

Vladimir N. Potapov

The logarithm of the number of binary n-variable bent functions is asymptotically less than $11(2^n)/32$ as n tends to infinity. Keywords: boolean function, Walsh--Hadamard transform, plateaued function, bent function, upper bound

Wei-Jie Li, Jie Zhou, Zi Li, Yun-Liang Zhu

Beryllium is an ablator material in the inertial-confinement fusion and hypervelocity impact studies. The thermoelastic properties, structure factors, and optical properties of beryllium are important in these studies. In this paper, the static structure factors, ion-ion dynamic structure factors, adiabatic velocity, and optical properties of beryllium along

Mingqing Wang, Jiawei Li, Zhenyang Li, Chengxiao Luo

Unsupervised anomaly detection (UAD) has been widely implemented in industrial and medical applications, which reduces the cost of manual annotation and improves efficiency in disease diagnosis. Recently, deep auto-encoder with its variants has demonstrated its advantages in many UAD scenarios. Training on the normal data, these models are expected to locate

Michael Hanke, Roswitha März

Accurate initial conditions have the task of precisely capturing and fixing the free integration constants of the flow considered. This is trivial for regular ordinary differential equations, but a complex problem for differential-algebraic equations (DAEs) because, for the latter, these free constants are hidden in the flow. We deal with linear time-varying

Mehdi Haghshenas, Parisa Ramezani, Emil Björnson

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that, with its unique features, is considered to be a game changer for future wireless networks. Channel estimation is one of the most critical challenges for the realization of RIS-assisted communications. Non-parametric channel estimation techniques are inefficient due to the huge pilot

Christian S. Kern, Andreas Windischbacher, Peter Puschnig

Driven by recent developments in time-resolved photoemission spectroscopy, we extend the successful method of photoemission orbital tomography (POT) to excitons. Our theory retains the intuitive orbital picture of POT, while respecting both the entangled character of the exciton wave function and the energy conservation in the photoemission process. Analyzin

Wei-Jie Li, Zi Li, Zhe Ma, Jie Zhou

The accurate thermoelastic properties and thermal conductivity are crucial in understanding the thermal evolution of the Martian core. A fitting method based on the ab initio calculated pressure-volume-temperature data is proposed in the formulation of the equation of state with high accuracy, by which the pressure and temperature dependent thermoelastic pro

Jiawei Liu, Weining Wang, Sihan Chen, Xinxin Zhu

As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded. In this work, we concentrate on a rarely investigated problem of text guided sounding video generation and propose the S

Marco Petrella, Remi Abgrall, Siddhartha Mishra

We propose a novel Monte-Carlo based ab-initio algorithm for directly computing the statistics for quantities of interest in an immiscible two-phase compressible flow. Our algorithm samples the underlying probability space and evolves these samples with a sharp interface front-tracking scheme. Consequently, statistical information is generated without resort

I. A. Kudryashov, A. N. Turundaevskiy, D. E. Karmanov, I. M. Kovalev

In this paper the dependence of the spectra of cosmic ray nuclei on the charges of nuclei was studied, according to the data of the NUCLEON space experiment. First, we studied the dependence of the spectral index of magnetic rigidity spectra on the charge for abundant nuclei. Secondly, for the charge range $Z=9\div20$, the differences in the total spectra of

Valerio Briganti, Alessandro Lunghi

Machine-learning force fields enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate machine learning models with a small number of ab initio data. In this respect, act

Zichen Chen, Jianda Chen, Yuanyuan Chen, Han Yu

Language models (LMs) like GPT-4 are important in AI applications, but their opaque decision-making process reduces user trust, especially in safety-critical areas. We introduce LMExplainer, a novel knowledge-grounded explainer that clarifies the reasoning process of LMs through intuitive, human-understandable explanations. By leveraging a graph attention ne

Freya Behrens, Barbora Hudcová, Lenka Zdeborová

The cavity method is one of the cornerstones of the statistical physics of disordered systems such as spin glasses and other complex systems. It is able to analytically and asymptotically exactly describe the equilibrium properties of a broad range of models. Exact solutions for dynamical, out-of-equilibrium properties of disordered systems are traditionally

Aapo Hyvarinen, Ilyes Khemakhem, Hiroshi Morioka

A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called "disentanglement". Most approaches are heuristic and lack a proper theoretical foundation. In linear representation learning, independent component analysis (ICA) has been successful in many applications areas, and it is principled

Jose A. Carrillo, Li Wang, Chaozhen Wei

We develop structure preserving schemes for a class of nonlinear mobility continuity equation. When the mobility is a concave function, this equation admits a form of gradient flow with respect to a Wasserstein-like transport metric. Our numerical schemes build upon such formulation and utilize modern large scale optimization algorithms. There are two distin

Mehdi Naouar, Gabriel Kalweit, Ignacio Mastroleo, Philipp Poxleitner

Cancer detection and classification from gigapixel whole slide images of stained tissue specimens has recently experienced enormous progress in computational histopathology. The limitation of available pixel-wise annotated scans shifted the focus from tumor localization to global slide-level classification on the basis of (weakly-supervised) multiple-instanc

Min Hu, Zhizhong Tan, Bin Liu, Guosheng Yin

This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks. The model integrates multi-factor pricing theories with real-time market dynamics, effectively bypassing the limitations of existing methods that lack financial theory guidan

Igor Markov, Sergey Nesteruk, Andrey Kuznetsov, Denis Dimitrov

Information surrounds people in modern life. Text is a very efficient type of information that people use for communication for centuries. However, automated text-in-the-wild recognition remains a challenging problem. The major limitation for a DL system is the lack of training data. For the competitive performance, training set must contain many samples tha

Marc Carwehl, Thomas Vogel, Genaína Nunes Rodrigues, Lars Grunske

To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements formalized as temporal-logic properties. These requirements, however, may change due to system evolution or uncertainty

Thibault Lahire

Stochastic gradient descent samples uniformly the training set to build an unbiased gradient estimate with a limited number of samples. However, at a given step of the training process, some data are more helpful than others to continue learning. Importance sampling for training deep neural networks has been widely studied to propose sampling schemes yieldin

Lukas König, Sebastian Neumaier

Distributed ledger systems have become more prominent and successful in recent years, with a focus on blockchains and cryptocurrency. This has led to various misunderstandings about both the technology itself and its capabilities, as in many cases blockchain and cryptocurrency is used synonymously and other applications are often overlooked. Therefore, as a

Souhaib Attaiki, Maks Ovsjanikov

Deep functional maps have recently emerged as a successful paradigm for non-rigid 3D shape correspondence tasks. An essential step in this pipeline consists in learning feature functions that are used as constraints to solve for a functional map inside the network. However, the precise nature of the information learned and stored in these functions is not ye

Yiheng Li, Canhui Tang, Runzhao Yao, Aixue Ye

Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate correspondences. Firstly, we propose to use sal

Shijie Bao, Qi'an Guan, Zheng Yuan

In this paper, we establish the log-plurisubharmonicity of fiberwise $\xi$-Bergman kernels for a family of variant functionals, thereby addressing a question posed by Bo Berndtsson to the authors. As an application, we prove that for a plurisubharmonic function $\phi$ and a locally finitely generated ideal sheaf $\mathscr{I}$ on the polydisc $\Delta^{n+m}=\D

G. Aileen Mendrofa, Gatot Fatwanto Hertono, Bevina Desjwiandara Handari

Pillars are important structural units used to ensure mining safety in underground hard rock mines. Therefore, precise predictions regarding the stability of underground pillars are required. One common index that is often used to assess pillar stability is the Safety Factor (SF). Unfortunately, such crisp boundaries in pillar stability assessment using SF a

Zeju Li, Xinghan Liu, Guoshun Nan, Jinfei Zhou

End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising, ESC has also been shown susceptible to the crafted physical layer adversarial perturbations due to the openness of wireless channels and the sensitivity of neural models. Previous

Po-Hsuan Huang, Yi-Hsiang Pan, Ying-Sheng Luo, Yi-Fan Chen

This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressure wound, given color images captured using readily available cameras. The accuracy of the classification is vi

Louis Mahon, Thomas Lukasiewicz

Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put i

Meirui Jiang, Holger R Roth, Wenqi Li, Dong Yang

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them togeth

Fernando Zhapa-Camacho, Robert Hoehndorf

Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the ontologies into a graph structure, i.e., introducing a set of nodes and edges for named entities and logical axioms, and then applying a graph

S. Autti, R. P. Haley, A. Jennings, G. R. Pickett

The B phase of superfluid 3He can be cooled into the pure superfluid regime, where the thermal quasiparticle density is negligible. The bulk superfluid is surrounded by a quantum well at the boundaries of the container, confining a sea of quasiparticles with energies below that of those in the bulk. We can create a non-equilibrium distribution of these state

Deep Kumar Kirtania

The main objective of this study is to conduct a bibliometric analysis of scholarly publications of Authorship Pattern. The present study covers 1723 research papers published in the area of authorship pattern and indexed in Scopus database from the year 2013 to 2022. These research publications considered for the present study have been analysed based on th

Zan Ahmad Naeem, Mohammad Shahmeer Ahmad, Mohamed Eltabakh, Mourad Ouzzani

Can foundation models (such as ChatGPT) clean your data? In this proposal, we demonstrate that indeed ChatGPT can assist in data cleaning by suggesting corrections for specific cells in a data table (scenario 1). However, ChatGPT may struggle with datasets it has never encountered before (e.g., local enterprise data) or when the user requires an explanation

Ken'yo U, Masanori Kameyama, Masaki Ogawa

To understand the evolution of the Moon, we numerically modeled mantle convection and magmatism in a two-dimensional polar rectangular mantle. Magmatism occurs as an upward permeable flow of magma generated by decompression melting through the convecting matrix. The mantle is assumed to be initially enriched in heat-producing elements (HPEs) and compositiona

Ll. Alsedà, D. Juher, J. Los, F. Mañosas

We define a family of discontinuous maps on the circle, called Bowen-Series-like maps, for geometric presentations of surface groups. The family has $2N$ parameters, where $2N$ is the number of generators of the presentation. We prove that all maps in the family have the same topological entropy, which coincides with the volume entropy of the group presentat

Rajiv Raman, Karamjeet Singh

Let $\mathcal{H}=(X,\mathcal{E})$ be a hypergraph. A support is a graph $Q$ on $X$ such that for each $E\in\mathcal{E}$, the subgraph of $Q$ induced on the elements in $E$ is connected. In this paper, we consider hypergraphs defined on a host graph. Given a graph $G=(V,E)$, with $c:V\to\{\mathbf{r},\mathbf{b}\}$, and a collection of connected subgraphs $\mat

Jiadong Wang, Xinyuan Qian, Malu Zhang, Robby T. Tan

Talking face generation, also known as speech-to-lip generation, reconstructs facial motions concerning lips given coherent speech input. The previous studies revealed the importance of lip-speech synchronization and visual quality. Despite much progress, they hardly focus on the content of lip movements i.e., the visual intelligibility of the spoken words,

Kingman Cheung, C. J. Ouseph

We study the potential of the future Higgs factories, including the ILC, CEPC, and FCC-ee with $\sqrt{s}$ = 240-250 GeV on discovering axion-like particles (ALPs) through various production channels in the leptonic final states, $e^+e^- \to f\bar{f} a$, where $f=e,\mu,\nu$. We show that the $e^+e^- \to e^+e^- a$ with $a \to \gamma\gamma$ provides the best bo

Hao-Wei Chen, Yu-Syuan Xu, Min-Fong Hong, Yi-Min Tsai

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggr

Cristina Ballantine, Hannah Burson, William Craig, Amanda Folsom

Motivated in part by hook-content formulas for certain restricted partitions in representation theory, we consider the total number of hooks of fixed length in odd versus distinct partitions. We show that there are more hooks of length $2$, respectively $3$, in all odd partitions of $n$ than in all distinct partitions of $n$, and make the analogous conjectur

Jinseok Park, Hyung Yong Kim, Jihwan Park, Byeong-Yeol Kim

Language identification (LID) recognizes the language of a spoken utterance automatically. According to recent studies, LID models trained with an automatic speech recognition (ASR) task perform better than those trained with a LID task only. However, we need additional text labels to train the model to recognize speech, and acquiring the text labels is a co

Pierre Ablin, Simon Vary, Bin Gao, P. -A. Absil

Orthogonality constraints naturally appear in many machine learning problems, from principal component analysis to robust neural network training. They are usually solved using Riemannian optimization algorithms, which minimize the objective function while enforcing the constraint. However, enforcing the orthogonality constraint can be the most time-consumin

Timothy Cai

Recently, a practical and publicly accessible satellite standard called the SmallSat has amplified public involvement in orbital research. This allows for flexible and efficient deployments of impactful low-earth-orbit experiments that would otherwise never be flown. However, the launch industry responsible for flying these experiments is not flexible nor ef

Animesh Karnewar, Andrea Vedaldi, David Novotny, Niloy Mitra

Diffusion models have emerged as the best approach for generative modeling of 2D images. Part of their success is due to the possibility of training them on millions if not billions of images with a stable learning objective. However, extending these models to 3D remains difficult for two reasons. First, finding a large quantity of 3D training data is much m

Harald Monsuur, Rob Stevenson

We consider an ultra-weak first order system discretization of the Helmholtz equation. When employing the optimal test norm, the `ideal' method yields the best approximation to the pair of the Helmholtz solution and its scaled gradient w.r.t.~the norm on $L_2(\Omega)\times L_2(\Omega)^d$ from the selected finite element trial space. On convex polygons, the `

Hieu H. Pham, Khiem H. Le, Tuan V. Tran, Ha Q. Nguyen

The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels. To address the issue of subjectivity in labeling with a single annotator, we introduce a simple and effective approach that aggregates annotations

Avraam Bardos, Nikolaos Mylonas, Ioannis Mollas, Grigorios Tsoumakas

Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especially when it can directly impact human well-being. Although model-agnostic techniques exist for multi-target regression, specific techniques t

Nils Hanke, Olaf Stursberg

In the context of studying periodic processes, this paper investigates first under which conditions switching affine systems in the plane generate stable limit cycles. Based on these conditions, a design methodology is proposed by which the phase portraits of the switching systems are determined to obtain globally stable limit cycles from simple specificatio