April 2023 arXiv papers — page 115

Liqun Zhao, Konstantinos Gatsis, Antonis Papachristodoulou

Reinforcement learning (RL) has demonstrated impressive performance in various areas such as video games and robotics. However, ensuring safety and stability, which are two critical properties from a control perspective, remains a significant challenge when using RL to control real-world systems. In this paper, we first provide definitions of safety and stab

Debasish Bhattacharjee, Tapasvini Roy, Santanu Acharjee, Tarini Kumar Dutta

The predator-prey dynamic appertaining to two species is explored, wherein the predator species is structured into different stages. As evidenced from natural documentation, the immature predators possess the potential to predate albeit not as competently as the adults. Nevertheless, this potentiality is not acquired immediately after their incipience of lif

Sayantan Choudhury, Sudhakar Panda, M. Sami

We consider Galileon inflation in the Effective Field Theory (EFT) framework and examine the possibility for PBH formation during slow roll (SR) to ultra slow roll (USR) transitions. We show that loop corrections to the power spectrum, in this case, do not impose additional constraints on the masses of PBHs produced. We indicate that the remarkable non-renor

Frederik Benirschke

Strata of exact differentials are moduli spaces for differentials on Riemann surfaces with vanishing absolute periods. Our main result is that classes of closures of strata of exact differentials inside the moduli space of multi-scale differentials lie in the divisorial tautological ring. By relating exact differentials to rational functions we obtain a new

A. Amparan, I. Baragaña, S. Marcaida, A. Roca

We solve the problem of characterizing the existence of a polynomial matrix of fixed degree when its eigenstructure (or part of it) and some of its rows (columns) are prescribed. More specifically, we present a solution to the row (column) completion problem of a polynomial matrix of given degree under different prescribed invariants: the whole eigenstructur

Giorgio Morales, John Sheppard

Response curves exhibit the magnitude of the response of a sensitive system to a varying stimulus. However, response of such systems may be sensitive to multiple stimuli (i.e., input features) that are not necessarily independent. As a consequence, the shape of response curves generated for a selected input feature (referred to as "active feature") might dep

Kai Zhang, John A. Lincoln, Xiaoqian Jiang, Elmer V. Bernstam

Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale (EDSS), composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slow

Khalef Yaddaden

To describe the double shuffle relations between multiple polylogarithm values at $N$th roots of unity, Racinet attached to each finite cyclic group $G$ of order $N$ and each group embedding $\iota : G \to \mathbb{C}^{\times}$, a $\mathbb{Q}$-scheme $\mathsf{DMR}^{\iota}$ which associates to each commutative $\mathbb{Q}$-algebra $\mathbf{k}$, a set $\mathsf{

Phuong D. Nguyen, Thinh D. Le, Duong Q. Nguyen, Binh Nguyen

In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative method for regenerating flawed facial structures, yielding 3D printable outputs that effectively support physicians in their patient treatment process. Our results highlight the model'

Lie Ju, Yicheng Wu, Wei Feng, Zhen Yu

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution \textit{e.g.,} classes and domains. However, in prac

Abhijith Jayakumar, Marc Vuffray, Andrey Y. Lokhov

Efficient representation of quantum many-body states on classical computers is a problem of enormous practical interest. An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest. A number of machine learning approac

Eduard Rohan, Vladimír Lukeš

The paper presents a new type of weakly nonlinear two-scale model of controllable periodic porous piezoelectric structures saturated by Newtonian fluids. The flow is propelled by peristaltic deformation of microchannels which is induced due to piezoelectric segments embedded in the microstructure and locally actuated by voltage waves. The homogenization is e

Mengtian Guo, David Gotz, Yue Wang

Documents in the health domain are often annotated with semantic concepts (i.e., terms) from controlled vocabularies. As the volume of these documents gets large, the annotation work is increasingly done by algorithms. Compared to humans, automatic indexing algorithms are imperfect and may assign wrong terms to documents, which affect subsequent search tasks

Eduardo Guendelman

Standard general coordinate invariance for the volume element is extended to general coordinate transformations that have a negative jacobian. This is possible by introducing a non Riemannian Measure of integration, which transforms according to the jacobian of the coordinate transformation, not the absolute value of the jacobian of the coordinate transforma

Nima Noii, Thomas Wick, Amirreza Khodadadian

In this work, Bayesian inversion with global-local forwards models is used to identify the parameters based on hydraulic fractures in porous media. It is well-known that using Bayesian inversion to identify material parameters is computationally expensive. Although each sampling may take more than one hour, thousands of samples are required to capture the ta

Anna Glazkova

The paper describes a transformer-based system designed for SemEval-2023 Task 9: Multilingual Tweet Intimacy Analysis. The purpose of the task was to predict the intimacy of tweets in a range from 1 (not intimate at all) to 5 (very intimate). The official training set for the competition consisted of tweets in six languages (English, Spanish, Italian, Portug

Raphael Boleslavsky

This paper studies a dynamic model of information acquisition, in which information might be secretly manipulated. A principal must choose between a safe action with known payoff and a risky action with uncertain payoff, favoring the safe action under the prior belief. She may delay her decision to acquire additional news that reveals the risky action's payo

Zihao Fu, Wai Lam, Qian Yu, Anthony Man-Cho So

The sequence-to-sequence (seq2seq) task aims at generating the target sequence based on the given input source sequence. Traditionally, most of the seq2seq task is resolved by the Encoder-Decoder framework which requires an encoder to encode the source sequence and a decoder to generate the target text. Recently, a bunch of new approaches have emerged that a

George Watkins, Giovanni Montana, Juergen Branke

The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour. In this work we investigate whether deep reinforcement learning can be used to discover a competitive construction heuristic for graph colouring. Our proposed approach, ReLCol, uses deep Q-learning togethe

Dennis Gallenmüller, Piotr Gwiazda, Agnieszka Świerczewska-Gwiazda, Jakub Woźnicki

We consider a combined system of Euler--Korteweg and Euler--Poisson equations with friction and exponential pressure with exponent $\gamma > 1$. We show the existence of dissipative measure-valued solutions in the cases of repulsive and attractive potential in Euler--Poisson system. The latter case requires additional restriction on $\gamma$. Furthermore in

Xingcheng Xu

This paper proposes a novel deep generative model, called BSDE-Gen, which combines the flexibility of backward stochastic differential equations (BSDEs) with the power of deep neural networks for generating high-dimensional complex target data, particularly in the field of image generation. The incorporation of stochasticity and uncertainty in the generative

Maxim Khomiakov, Michael Riis Andersen, Jes Frellsen

In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design. While these problems are frequently addressed using semantic segmentation, which requires additional post-processing to vectorize objects in a non-trivial way, we present

Grigori Rozenblum

We justify the Weyl asymptotic formula for the eigenvalues of the Poincar\'e-Steklov spectral problem for a domain bounded by a Lipschitz surface.

Haiwei Xie, Federica Bellizio, Jochen L. Cremer, Goran Strbac

Due to the increasing system stability issues caused by the technological revolutions of power system equipment, the assessment of the dynamic security of the systems for changing operating conditions (OCs) is nowadays crucial. To address the computational time problem of conventional dynamic security assessment tools, many machine learning (ML) approaches h

Gregory Seregin

In the note, the Euler scaling is used to study a certain scenario of potential Type II blowups of solutions to the Navier-Stokes equations.

A. Tesi, L. Moleri, S. Leardini, A. Breskin

Cryogenic versions of Resistive WELL (RWELL) and Resistive Plate WELL (RPWELL) detectors have been developed, aimed at stable avalanche multiplication of ionization electrons in dual-phase TPCs. In the RWELL, a thin resistive layer deposited on top of an insulator is inserted in between the electron multiplier (THGEM) and the readout anode; in the RPWELL, a

Chanwoo Lee, Miaoyan Wang

Higher-order tensor datasets arise commonly in recommendation systems, neuroimaging, and social networks. Here we develop probable methods for estimating a possibly high rank signal tensor from noisy observations. We consider a generative latent variable tensor model that incorporates both high rank and low rank models, including but not limited to, simple h

Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis

We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observations have shown that deep ensemble often return overconfident estimates outside the training domain, which is a major limitation because shifted distributions are often encountered

Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique

To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant

Joseph Schlessinger, Kiran Garimella, Maurice Jakesch, Dean Eckles

In addition to more personalized content feeds, some leading social media platforms give a prominent role to content that is more widely popular. On Twitter, "trending topics" identify popular topics of conversation on the platform, thereby promoting popular content which users might not have otherwise seen through their network. Hence, "trending topics" pot

Tingting Zhao, Megan Simons, Devin A. Matthews

The extension of least-squares tensor hypercontracted second- and third-order M{\o}ller-Plessett perturbation theory (LS-THC-MP2 and LS-THC-MP3) to open-shell systems is an important development due to the scaling reduction afforded by THC and the ubiquity of molecular ions, radicals, and other open-shell reactive species. The complexity of wavefunction-base

Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under unsupervised settings and low operational power/energy due to their bio-plausible computations. Previous studies identified that DRAM-based off-chip memory accesses dominate the energy consumption of SNN processing. However, state-of-the-art works do not optimize the DRAM

Lixin Yang, Jian Xu, Licheng Zhong, Xinyu Zhan

Enable neural networks to capture 3D geometrical-aware features is essential in multi-view based vision tasks. Previous methods usually encode the 3D information of multi-view stereo into the 2D features. In contrast, we present a novel method, named POEM, that directly operates on the 3D POints Embedded in the Multi-view stereo for reconstructing hand mesh

Kai Song, Shaofeng Wang, Ziwei Xie, Shanyu Wang

In the user targeting and expanding of new shows on a video platform, the key point is how their embeddings are generated. It's supposed to be personalized from the perspective of both users and shows. Furthermore, the pursue of both instant (click) and long-time (view time) rewards, and the cold-start problem for new shows bring additional challenges. Such

Toshiki Tsuda, Masaaki Imaizumi

We investigate the high-dimensional linear regression problem in the presence of noise correlated with Gaussian covariates. This correlation, known as endogeneity in regression models, often arises from unobserved variables and other factors. It has been a major challenge in causal inference and econometrics. When the covariates are high-dimensional, it has

S. Kovacevic, W. Ali, E. Martínez-Pañeda, J. LLorca

A phase-field model is developed to simulate the corrosion of Mg alloys in body fluids. The model incorporates both Mg dissolution and the transport of Mg ions in solution, naturally predicting the transition from activation-controlled to diffusion-controlled bio-corrosion. In addition to uniform corrosion, the presented framework captures pitting corrosion

Charles Champagne Cossette, Mohammed Ayman Shalaby, David Saussié, James Richard Forbes

This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other's position relative to themselves. The use of pseudomeasurements is introduced as a means of modelling such relationships between robo

Ryan Hynd

We consider Meissner polyhedra in $\mathbb{R}^3$. These are constant width bodies whose boundaries consist of pieces of spheres and spindle tori. We define these shapes by taking appropriate intersections of congruent balls and show that they are dense within the space of constant width bodies in the Hausdorff topology. This density assertion was essentially

Xianfei Zhang, Ling-Ling Wang, Shi-Di Huang

We report a pore-scale numerical study of salt finger convection in porous media, with a focus on the influence of the porosity in the non-Darcy regime, which has received little attention in previous research. The numerical model is based on the lattice Boltzmann method with a multiple-relaxation-time scheme and employs an immersed boundary method to descri

Senad Beadini, Iacopo Masi

We offer a study that connects robust discriminative classifiers trained with adversarial training (AT) with generative modeling in the form of Energy-based Models (EBM). We do so by decomposing the loss of a discriminative classifier and showing that the discriminative model is also aware of the input data density. Though a common assumption is that adversa

Wutao Si, P. -A. Absil, Wen Huang, Rujun Jiang

In recent years, the proximal gradient method and its variants have been generalized to Riemannian manifolds for solving optimization problems with an additively separable structure, i.e., $f + h$, where $f$ is continuously differentiable, and $h$ may be nonsmooth but convex with computationally reasonable proximal mapping. In this paper, we generalize the p

David Belius, Leon Fröber, Justin Ko

Spin glass models involving multiple replicas with constrained overlaps have been studied in [FPV92; PT07; Pan18a]. For the spherical versions of these models [Ko19; Ko20] showed that the limiting free energy is given by a Parisi type minimization. In this work we show that for Sherrington-Kirkpatrick (i.e. 2-spin) interactions, it can also be expressed in t

Zhenying Wang, George Karapetsas, Prashant Valluri, Chihiro Inoue

Droplet spreading is ubiquitous and plays a significant role in liquid-based energy systems, thermal management devices, and microfluidics. While the spreading of non-volatile droplets is quantitatively understood, the spreading and flow transition in volatile droplets remains elusive due to the complexity added by interfacial phase change and non-equilibriu

Lama Alkhaled, Tosin Adewumi, Sana Sabah Sabry

We introduce bipol, a new metric with explainability, for estimating social bias in text data. Harmful bias is prevalent in many online sources of data that are used for training machine learning (ML) models. In a step to address this challenge we create a novel metric that involves a two-step process: corpus-level evaluation based on model classification an

Morteza Maleknia, Majid Soleimani-damaneh

We propose a descent subgradient algorithm for minimizing a real function, assumed to be locally Lipschitz, but not necessarily smooth or convex. To find an effective descent direction, the Goldstein subdifferential is approximated through an iterative process. The method enjoys a new two-point variant of Mifflin line search in which the subgradients are arb

Sihwa Park, Seongjun Kim, Doeyoung Kwon, Yohan Jang

Panoramic radiography (Panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, PX only provides a flattened 2D image, lacking in a 3D view of the oral structure. In this paper, we propose NeBLa (Neural Beer-Lambert) to estimate 3D oral structures from real-world PX. NeBLa tackles full 3D reconstruction for varying subjects (pa

Dávid Šuba, Marek Šuppa, Jozef Kubík, Endre Hamerlik

Named Entity Recognition (NER) is a fundamental NLP tasks with a wide range of practical applications. The performance of state-of-the-art NER methods depends on high quality manually anotated datasets which still do not exist for some languages. In this work we aim to remedy this situation in Slovak by introducing WikiGoldSK, the first sizable human labelle

Himani Garg, Lei Wang

In the presence of elastic forces, even dilute polymer suspensions can exhibit erratic flow fluctuations even when the viscous forces dominate over the inertial forces, which occur at vanishing-low Reynolds numbers (Re). This phenomenon is called Elastic Turbulence (ET). ET can be generated in small-scale laboratory settings and is relevant to enhancing mixi

Giordano Giambartolomei, Frederik Mallmann-Trenn, Raimundo Saona

Prophet inequalities are a central object of study in optimal stopping theory. A gambler is sent values in an online fashion, sampled from an instance of independent distributions, in an adversarial, random or selected order, depending on the model. When observing each value, the gambler either accepts it as a reward or irrevocably rejects it and proceeds to

Binqian Xu, Xiangbo Shu, Jiachao Zhang, Rui Yan

Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action recognition. To achieve these positive and negative pairs, existing weak/strong data augmentation methods have to randomly change the appearance of skeletons for indirectly pursuing s

Yangyang Guo, Hao Wang, Lei He, Witold Pedrycz

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calcul

Emanuele Solfiti, Di Wan, Ambra Celotto, Nicola Solieri

Flexible graphite (FG) with 1 - 1.2 g/cm$^3$ density is employed as beam energy absorber material in the CERN's Large Hadron Collider (LHC) beam dumping system. However, the increase of energy deposited expected for new HL-LHC (High-Luminosity LHC) design demanded for an improvement in reliability and safety of beam dumping devices, and the need for a calibr

Yermek Aldabergenov, Ignatios Antoniadis, Auttakit Chatrabhuti, Hiroshi Isono

We study in more detail the cubic constraints for N=1 chiral superfields proposed in the earlier work Eur. Phys. J. C 81, 523 (2021), which describe low-energy goldstino-axion dynamics in global non-linearly realized supersymmetry. We generalize the constraint (i) by gauging the abelian symmetry that shifts the axion, and (ii) to supergravity. In the former

Rui Graca, Brian McReynolds, Tobi Delbruck

Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise. Photon and electron shot-noise cause a high rate of non-informative events that reduce Signal to Noise ratio. DVS noise performance depends not only on the scene illumination, but also on the user-controllable biasing of the camera. In this p

M. S. Asif, Mahesh Raveendranatha Panicker

Digital radiography (DR) is becoming popular for the point of care imaging in the recent past. To reduce the radiation exposure, controlled radiation based on as low as reasonably achievable (ALARA) principle is employed and this results in low contrast images. To address this issue, post-processing algorithms such as the Multiscale Image Contrast Amplificat

Huimin Zeng, Jie Huang, Jiacheng Li, Zhiwei Xiong

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally

Shinkook Choi, Junkyeong Choi

As deep learning advances, edge devices and lightweight neural networks are becoming more important. To reduce latency in the AI accelerator, it's essential to not only reduce FLOPs but also enhance hardware performance. We proposed an arithmetic intensity balancing convolution (ABConv) to address the issue of the overall intensity being limited by the small

Siqiang Luo, Zulun Zhu

SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data management tasks. These tasks often involve single-source SimRank computation that evaluates the SimRank values between a source node $s$ and all other nodes. Due to its high computation complexity, s

Anil Yesilkaya, Hanaa Abumarshoud, Harald Haas

This chapter is devoted to discussing the integration of intelligent reflecting surfaces (IRSs), or intelligent walls, in optical wireless communication (OWC) systems. IRS technology is a revolutionary concept that enables communication systems to harness the surrounding environment to control the propagation of light signals. Based on this, specific key per

Serena Della Corte, Antonia Diana, Carlo Mantegazza

In this note, our aim is to show that families of smooth hypersurfaces of $\mathbb R^{n+1}$ which are all $C^1$--close enough to a fixed compact, embedded one, have uniformly bounded constants in some relevant inequalities for mathematical analysis, like Sobolev, Gagliardo-Nirenberg and ``geometric'' Calder\'on-Zygmund inequalities. This technical result is

Shuangkang Fang, Yufeng Wang, Yi Yang, Weixin Xu

Neural Radiance Fields (NeRF) have been widely adopted as practical and versatile representations for 3D scenes, facilitating various downstream tasks. However, different architectures, including the plain Multi-Layer Perceptron (MLP), Tensors, low-rank Tensors, Hashtables, and their combinations, entail distinct trade-offs. For instance, representations bas

Antonia Diana, Nicola Fusco, Carlo Mantegazza

We study the global existence and stability of surface diffusion flow (the normal velocity is given by the Laplacian of the mean curvature) of smooth boundaries of subsets of the $n$--dimensional flat torus. More precisely, we show that if a smooth set is ``close enough'' to a strictly stable critical set for the Area functional under a volume constraint, th

Alberto Bordino, Stefano Favaro, Sandra Fortini

There is a recent and growing literature on large-width asymptotic and non-asymptotic properties of deep Gaussian neural networks (NNs), namely NNs with weights initialized as Gaussian distributions. For a Gaussian NN of depth $L\geq1$ and width $n\geq1$, it is well-known that, as $n\rightarrow+\infty$, the NN's output converges (in distribution) to a Gaussi

Shu-Qing Zhong, Long Li, Zi-Gao Dai

The gamma-ray burst GRB 211211A and its associated kilonova-like emission were reported recently. A significant difference between this association event and GRB 170817A/AT 2017gfo is that GRB 211211A has a very long duration. In this paper, we show that this association event may arise from a neutron star$-$white dwarf (NS$-$WD) merger if a magnetar leaves

Alberto Bordino, Stefano Favaro, Sandra Fortini

There is a growing literature on the study of large-width properties of deep Gaussian neural networks (NNs), i.e. deep NNs with Gaussian-distributed parameters or weights, and Gaussian stochastic processes. Motivated by some empirical and theoretical studies showing the potential of replacing Gaussian distributions with Stable distributions, namely distribut

Jie Yin, Tao Li, Hao Yin, Wenxian Yu

Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous Localization and Mapping (SLAM) systems draws increasing attention to a global and continuous localization solution. Nonetheless, in dense urban environments, GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS) measurements, which might lead to a sharp deterioration in l

B. S. Ham

Photon indistinguishability is an essential concept to understanding mysterious quantum features from the viewpoint of the wave-particle duality in quantum mechanics. The physics of indistinguishability lies in the manipulation of quantum superposition between orthonormal bases of a single photon such as in a quantum eraser. Here, a pure coherence approach i

Seyed Mohammad Hossein Abedy Nejad, Mohammad Amin Behzadi, Abdolrahim Taheri

Overloading in DC servo motors is a major concern in industries, as many companies face the problem of finding expert operators, and also human monitoring may not be an effective solution. Therefore, this paper proposed an embedded Artificial intelligence (AI) approach using a Convolutional Neural Network (CNN) using a new transformation to extract faults fr

Anant Khandelwal

Few Shot Class Incremental Learning (FSCIL) with few examples per class for each incremental session is the realistic setting of continual learning since obtaining large number of annotated samples is not feasible and cost effective. We present the framework MASIL as a step towards learning the maximal separable classifier. It addresses the common problem i.

Bruno Dall'Agnol de Oliveira, Thaisa Storchi-Bergmann, Raffaella Morganti, Rogemar A. Riffel

We present ALMA CO(2-1) observations of the Seyfert 2 galaxy NGC 3281 at $\sim$ 100 pc spatial resolution. This galaxy was previously known to present a bi-conical ionised gas outflow extending to 2 kpc from the nucleus. The analysis of the CO moment and channel maps, as well as kinematic modelling reveals two main components in the molecular gas: one rotati

Jai Prakash, Mohammad Yousuf Jamal

Heavy quarks serve as crucial probes for exploring the properties of the hot and dense medium formed in heavy-ion collision experiments. Understanding the modification of their energy as they traverse the medium is a focal point of research, with various authors extensively studying this phenomenon. This study specifically concentrates on the equilibrium pha

Giandomenico Palumbo

It is well known that noncommutative geometry naturally emerges in the quantum Hall states due to the presence of strong and constant magnetic fields. Here, we discuss the underlying noncommutative geometry of quantum Hall fluids in which the magnetic fields are spatially inhomogenoeus. We analyze these cases by employing symplectic geometry and Fedosov's de

E. T. Aliev, U. A. Rozikov

In this paper we consider function $f(x)={x+a\over bx+c}$, (where $b\ne 0$, $c\ne ab$, $x\ne -{c\over b}$) on three fields: the set of real, $p$-adic and complex numbers. We study dynamical systems generated by this function on each field separately and give some comparison remarks. For real variable case we show that the real dynamical system of the functio

Maximilian Kleissl, Lukas Drews, Benedict B. Heyder, Julian Zabbarov

Training sophisticated machine learning (ML) models requires large datasets that are difficult or expensive to collect for many applications. If prior knowledge about system dynamics is available, mechanistic representations can be used to supplement real-world data. We present SimbaML (Simulation-Based ML), an open-source tool that unifies realistic synthet

Q. Liu, X. Yang

In the training process of the implicit 3D reconstruction network, the choice of spatial query points' sampling strategy affects the final performance of the model. Different works have differences in the selection of sampling strategies, not only in the spatial distribution of query points but also in the order of magnitude difference in the density of quer

Diksha Goel, Aneta Neumann, Frank Neumann, Hung Nguyen

We study a Stackelberg game between one attacker and one defender in a configurable environment. The defender picks a specific environment configuration. The attacker observes the configuration and attacks via Reinforcement Learning (RL trained against the observed environment). The defender's goal is to find the environment with minimum achievable reward fo

Md Saef Ullah Miah, Junaida Sulaiman, Md. Imamul Islam, Md. Masuduzzaman

Integrating renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG

Noga Alon, Shay Moran, Hilla Schefler, Amir Yehudayoff

We provide a unified framework for characterizing pure and approximate differentially private (DP) learnability. The framework uses the language of graph theory: for a concept class $\mathcal{H}$, we define the contradiction graph $G$ of $\mathcal{H}$. Its vertices are realizable datasets, and two datasets $S,S'$ are connected by an edge if they contradict e

Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu

Genetic algorithms constitute a family of black-box optimization algorithms, which take inspiration from the principles of biological evolution. While they provide a general-purpose tool for optimization, their particular instantiations can be heuristic and motivated by loose biological intuition. In this work we explore a fundamentally different approach: G

Rui-Qi Wu, Zheng-Peng Duan, Chun-Le Guo, Zhi Chai

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto

Florian Biersack

We study the class $HQ(\mathbb{D})$, the set of harmonic quasiconformal automorphisms of the unit disk $\mathbb{D}$ in the complex plane, endowed with the topology of uniform convergence. Several important topological properties of this space of mappings are investigated, such as separability, compactness, path--connectedness and completeness.

Tong Lin, Qiang Wang

Let $q$ be a prime power. We construct stable polynomials of the form $b^{m-1}(x+a)^m+c(x+a)+d$ over a finite field $\mathbb{F}_{q}$ for $m=2,3,4$ by Capelli's lemma. When $m=3$ and $q$ is even, we confirm the conjecture of Ahmadi and Monsef-Shokri [2] that the polynomial $f(x) = x^3 + x^2 + 1$ is stable over $\mathbb{F}_{2}$. Moreover, when $m=2$ and $q\equ

Frew Gashaw Asefa, Yi Ma, Qin Wu, Yedilfana Setarge Mekonnen

In this report, the evolution of the local atomic structure of the Zr50Nb50 melt was investigated by applying temperature (2600 to 300 K) and pressure (0 to 50 Gpa) using classical molecular dynamics simulations. To gain clear insight into the structural evolution during quenching, we used various methods of structural analysis such as the radial distributio

Jing Yang, Ruibo Wang, Yijun Song, Jihong Li

In the task of comparing two classification algorithms, the widely-used McNemar's test aims to infer the presence of a significant difference between the error rates of the two classification algorithms. However, the power of the conventional McNemar's test is usually unpromising because the hold-out (HO) method in the test merely uses a single train-validat

Won-Ki Seo

This paper consider inverting a holomorphic Fredholm operator pencil. Specifically, we provide necessary and sufficient conditions for the inverse of a holomorphic Fredholm operator pencil to have a simple pole and a second order pole. Based on those results, a closed-form expression of the Laurent expansion of the inverse around an isolated singularity is o

Igor S. Sergeev

For any $n$ and $k$, we provide an explicit (that is, computable in polynomial time) example of integer $B_k$-sequence of size $n$ consisting of elements bounded by $n^{k+o(k)}$.

Anirudh R., Chakali Eswaraiah, Sihan Jiao, Jessy Jose

Fragmentation is a key step in the process of transforming clouds (and their substructures such as filaments, clumps, and cores) into protostars. The thermal gas pressure and gravitational collapse are believed to be the primary agents governing this process, referred to as the thermal Jeans fragmentation. However, the contributions of other factors (such as

Alberto Marchisio, Davide Dura, Maurizio Capra, Maurizio Martina

Transformers' compute-intensive operations pose enormous challenges for their deployment in resource-constrained EdgeAI / tinyML devices. As an established neural network compression technique, quantization reduces the hardware computational and memory resources. In particular, fixed-point quantization is desirable to ease the computations using lightweight

Anoop S. K. M., Jayalal Sarma

Computing the rotation distance between two binary trees with $n$ internal nodes efficiently (in $poly(n)$ time) is a long standing open question in the study of height balancing in tree data structures. In this paper, we initiate the study of this problem bounding the rank of the trees given at the input (defined by Ehrenfeucht and Haussler (1989) in the co

Shangfei Zheng, Hongzhi Yin, Tong Chen, Quoc Viet Hung Nguyen

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack ex

Ayhan Demiriz

We present a new data analysis perspective to determine variable importance regardless of the underlying learning task. Traditionally, variable selection is considered an important step in supervised learning for both classification and regression problems. The variable selection also becomes critical when costs associated with the data collection and storag

Rainer Niekamp, Johanna Niemann, Maximilian Reichel, Hongbin Zhang

In this contribution we propose a data-driven surrogate model for the prediction of magnetic stray fields in two-dimensional random micro-heterogeneous materials. Since data driven models require thousands of training data sets, FEM simulations appear to be too time consuming. Hence, a stochastic model based on Brownian motion, which utilizes an efficient ev

Meng Wang, Tian Lin, Lianyu Wang, Aidi Lin

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We established an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Besides assessing the probab

Elena Camuffo, Simone Milani

During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catastrophic forgetting, like knowledge distillation and self-inpainting. However, the application of continual learning paradigms to point clouds is still unexplored and investigation

Jens Kaad

In this paper, we present a characterization of compact quantum metric spaces in terms of finite dimensional approximations. This characterization naturally leads to the introduction of a matrix analogue of a compact quantum metric space. As an application, we show that matrix compact quantum metric spaces are stable under minimal tensor products and more sp

Shaina Raza

Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. To address this challenge, this s

E. Ya. Paryev

Accounting for the LHCb observation that the reported hidden-charm strange pentaquark $P_{cs}(4459)^0$ can split into two substructures, $P_{cs}(4455)^0$ and $P_{cs}(4468)^0$, with a mass difference of 13 MeV as well as the newly observed hidden-charm pentaquark resonance $P_{cs}(4338)^0$ with strangeness, we study within the double-peak scenario for the $P_

Shengbang Tong, Yubei Chen, Yi Ma, Yann Lecun

Recently, self-supervised learning (SSL) has achieved tremendous success in learning image representation. Despite the empirical success, most self-supervised learning methods are rather "inefficient" learners, typically taking hundreds of training epochs to fully converge. In this work, we show that the key towards efficient self-supervised learning is to i

A. Fialowski, K. Iohara, Y. Saito

The class of root systems, called elliptic root systems, were introduced in 1985 by K. Saito, for his studies on a normal surface singularity which contains a regular elliptic curve in its minimal resolution. He also classified such root systems when they admit a reduced affine quotient, as root system. In this note, we provide the classification of elliptic

Rumen Bachev, Tushar Tripathi, Alok C. Gupta, Pankaj Kushwaha

In this work, we report the presence of rapid intra-night optical variations in both -- flux and polarization of the blazar BL Lacertae during its unprecedented 2020--2021 high state of brightness. The object showed significant flux variability and some color changes, but no firmly detectable time delays between the optical bands. The linear polarization was