February 2023 arXiv papers — page 5

Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito, Luca Iocchi

One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address this problem and improve learning efficiency, we consider a linear hierarchy of abstraction layers of the Markov Decision Process (MDP) underlying the target domain. Each layer is an

Lars Reimann, Günter Kniesel-Wünsche

Data Scientists often use notebooks to develop Data Science (DS) pipelines, particularly since they allow to selectively execute parts of the pipeline. However, notebooks for DS have many well-known flaws. We focus on the following ones in this paper: (1) Notebooks can become littered with code cells that are not part of the main DS pipeline but exist solely

Yannick Wack, Sylvain Serra, Martine Baelmans, Jean-Michel Reneaume

The widespread use of optimization methods in the design phase of District Heating Networks is currently limited by the availability of scalable optimization approaches that accurately represent the network. In this paper, we compare and benchmark two different approaches to non-linear topology optimization of District Heating Networks in terms of computatio

Chintan Tundia, Rajiv Kumar, Om Damani, G. Sivakumar

Change detection for aerial imagery involves locating and identifying changes associated with the areas of interest between co-registered bi-temporal or multi-temporal images of a geographical location. Farm ponds are man-made structures belonging to the category of minor irrigation structures used to collect surface run-off water for future irrigation purpo

Marcelo Schiffer

In this paper, we consider angular momentum fluctuations of a Schwartzschild black hole in thermal equilibrium with radiation which, for the sake of simplicity is here modeled by a scalar field. Important, we do not set the black hole angular momentum $J$ identically to zero at the outset; we allow it to have a small value (in the sense that $J/M<<1$) and th

Yana Stoyanova, Soroush Ghandi, Maryam Tavakol

Deep neural networks are in the limelight of machine learning with their excellent performance in many data-driven applications. However, they can lead to inaccurate predictions when queried in out-of-distribution data points, which can have detrimental effects especially in sensitive domains, such as healthcare and transportation, where erroneous prediction

Raúl Morral-Yepes, Frank Pollmann, Izabella Lovas

We study measurement-induced symmetry-protected topological (SPT) order in a wide class of quantum random circuit models by combining calculations within the stabilizer formalism with tensor network simulations. We construct a family of quantum random circuits, generating the out-of-equilibrium version of all generalized cluster models, and derive a set of n

B. Yu. Irgashev

The article constructs a general solution of a degenerate equation with a fractional derivative of Dzhrbashyan-Nersesyan. Particular solutions are presented through the Kilbas-Saigo function.

Ali Beikmohammadi, Sindri Magnússon

Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions. This paper introduces a novel human-inspired framework to enhance RL algorithm sample efficiency. It achieves this by initially exposing the learning agent to simpler tasks that

Geoffrey Compère, Adrien Druart, Justin Vines

We establish the existence of a deformation of the usual Carter constant which is conserved along the motion in a fixed Kerr background of a spinning test body possessing the spin-induced quadrupole coupling of a black hole. The conservation holds perturbatively up to second order in the test body's spin. This constant of motion is obtained through the expli

Lars Reimann, Günter Kniesel-Wünsche

Due to the long runtime of Data Science (DS) pipelines, even small programming mistakes can be very costly, if they are not detected statically. However, even basic static type checking of DS pipelines is difficult because most are written in Python. Static typing is available in Python only via external linters. These require static type annotations for par

Badal Mondal, Marcel Kröner, Thilo Hepp, Kerstin Volz

Tuning the bandgap in ternary III-V semiconductors via modification of the composition or the strain in the material is a major approach for the design of optoelectronic materials. Experimental approaches screening a large range of possible target structures are hampered by the tremendous effort to optimize the material synthesis for every target structure.

Marvin Brieger, Stefan Mitsch, André Platzer

This paper presents a dynamic logic $d\mathcal{L}_\text{CHP}$ for compositional deductive verification of communicating hybrid programs (CHPs). CHPs go beyond the traditional mixed discrete and continuous dynamics of hybrid systems by adding CSP-style operators for communication and parallelism. A compositional proof calculus is presented that modularly veri

Tom Rainforth, Adam Foster, Desi R Ivanova, Freddie Bickford Smith

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effe

Shunsaku Nagasawa, Takahiro Minami, Shin Watanabe, Tadayuki Takahashi

We propose a new strip configuration for CdTe X-ray detectors, named "Wide-gap CdTe strip detector", in which the gap between adjacent strips is much wider than the width of each strip. It has been known that the observed energies of an incoming photon in adjacent strips can be utilized to achieve a position resolution finer than the strip pitch, if and only

Himanshu, Jinraj V Pushpangathan, Harikumar Kandath

In this paper, an algorithm for Unmanned Aircraft Systems Traffic Management (UTM) for a finite number of unmanned aerial vehicles (UAVs) is proposed. This algorithm is developed by combining the Rapidly-Exploring Random Trees (RRT) and Velocity Obstacle (VO) algorithms and is referred to as the RRT-VO UTM algorithm. Here, the RRT algorithm works offline to

Pablo L. Saldanha

We propose an electrodynamic Aharonov-Bohm (AB) scheme where a nonzero AB phase difference appears even if the interferometer paths do not enclose a magnetic flux and are subjected to negligible scalar potential differences during the propagation of the quantum charged particle. In the proposal, the current in a solenoid outside the interferometer varies in

Marko Kostic, Stevan Pilipovic, Milica Zigic

The main subject of this paper is the analysis of sequences of exponentially bounded integrated semigroups which are related to Cauchy problems \begin{equation}\label{jed} \frac{\partial}{\partial t}u(t,x)-a(D)u(t,x)=f(t,x), \quad u(0,x)=u_0(x), \quad t\geq 0, \ x\in \mathbb R^d, \end{equation} with a distributional initial data $u_0$ and a distributional ri

Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk, Kathrin Klamroth

Shape optimization with constraints given by partial differential equations (PDE) is a highly developed field of optimization theory. The elegant adjoint formalism allows to compute shape gradients at the computational cost of a further PDE solve. Thus, gradient descent methods can be applied to shape optimization problems. However, gradient descent methods

A. J. D. Farias Junior, Herondy F. Santana Mota

In this paper the Casimir energy density, loop corrections, and generation of topological mass are investigated for a system consisting of two interacting real and complex scalar fields. The interaction considered is the quartic interaction in the form of a product of the modulus square of the complex field and the square of the real field. In addition, it i

Sarah Krebber, Marvin Kopp, Charu Garg, Kurt Kummer

We investigate single crystals of the trigonal antiferromagnet EuZn$_2$P$_2$ ($P\overline{3}m1$) by means of electrical transport, magnetization measurements, X-ray magnetic scattering, optical reflectivity, angle-resolved photoemission spectroscopy (ARPES) and ab-initio band structure calculations (DFT+U). We find that the electrical resistivity of EuZn$_2$

Sudhanwa Patra, S. T. Petcov, Prativa Pritimita, Purushottam Sahu

We discuss a left-right (L-R) symmetric model with the double seesaw mechanism at the TeV scale generating Majorana masses for the active left-handed (LH) flavour neutrinos $\nu_{\alpha L}$ and the heavy right-handed (RH) neutrinos $N_{\beta R}$, $\alpha,\beta = e,\mu,\tau$, which in turn mediate lepton number violating processes, including neutrinoless doub

P. Laskos-Patkos, Ch. C. Moustakidis

The observation and distinction of two compact stars with an identical mass but a different radius would be a clear sign of hadron-quark phase transition in nuclear matter. Motivated by studies searching for significant deviations in the observables of twin stars, we investigate the differences that manifest in their r-mode instability windows and spin-down

Cheng-Xiang Wang, Xiaohu You, Xiqi Gao, Xiuming Zhu

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards or

M. Coraiola, D. Z. Haxell, D. Sabonis, H. Weisbrich

In hybrid Josephson junctions with three or more superconducting terminals coupled to a semiconducting region, Andreev bound states may form unconventional energy band structures, or Andreev matter, which are engineered by controlling superconducting phase differences. Here we report tunnelling spectroscopy measurements of three-terminal Josephson junctions

Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang

We present Spacerini, a tool that integrates the Pyserini toolkit for reproducible information retrieval research with Hugging Face to enable the seamless construction and deployment of interactive search engines. Spacerini makes state-of-the-art sparse and dense retrieval models more accessible to non-IR practitioners while minimizing deployment effort. Thi

Rajiv Kumar, G. Sivakumar

Requirements of large amounts of data is a difficulty in training many GANs. Data efficient GANs involve fitting a generators continuous target distribution with a limited discrete set of data samples, which is a difficult task. Single image methods have focused on modeling the internal distribution of a single image and generating its samples. While single

Yueqi Xie, Jingqi Gao, Peilin Zhou, Qichen Ye

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representat

Hien D Nguyen, Mayetri Gupta

In recent years, empirical Bayesian (EB) inference has become an attractive approach for estimation in parametric models arising in a variety of real-life problems, especially in complex and high-dimensional scientific applications. However, compared to the relative abundance of available general methods for computing point estimators in the EB framework, th

Baptiste Blachier, Pierre Auclair, Christophe Ringeval, Vincent Vennin

We revisit how super-Hubble cosmological fluctuations induce, at any time in the cosmic history, a non-vanishing spatial curvature of the local background metric. The random nature of these fluctuations promotes the curvature density parameter to a stochastic quantity for which we derive novel non-perturbative expressions for its mean, variance, higher momen

Alberto Lanconelli, Christopher S. A. Lauria

We consider the problem of tracking an unknown time varying parameter that characterizes the probabilistic evolution of a sequence of independent observations. To this aim, we propose a stochastic gradient descent-based recursive scheme in which the log-likelihood of the observations acts as time varying gain function. We prove convergence in mean-square err

Wenyu Wang, Wu-Long Xu, Yang Xu, Xu-Dong Yang

A freely falling chain from a cup at certain height can jump. The process can be divided into two parts: a stable suspension and an accelerating procedure. Variational principle and force analysis demonstrate that the shape of stable suspension is an inverted catenary. The requirement of the jumping and the parameters to describe the jumping catenary have be

Dino Peran

We give normal forms for strongly hyperbolic logarithmic transseries f = z^r + ... (r is a positive real number nonequal to 1), with respect to parabolic logarithmic normalizations. These normalizations are obtained using fixed point theorems, and are given algorithmically, as limits of Picard sequences in appropriate formal topologies. The results are appli

Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger

In the framework of reduced basis methods, we recently introduced a new certified hierarchical and adaptive surrogate model, which can be used for efficient approximation of input-output maps that are governed by parametrized partial differential equations. This adaptive approach combines a full order model, a reduced order model and a machine-learning model

Ivan Ovsyannikov, Jens D. M. Rademacher, Roland Welter, Bingying Lu

Revisiting the Lorenz '63 equations in the regime of large of Rayleigh number, we study the occurrence of periodic solutions and quantify corresponding time averages of selected quantities. Perturbing from the integrable limit of infinite $\rho$, we provide a full proof of existence and stability of symmetric periodic orbits, which confirms previous partial

Lawrence M. Widrow

We explore the interplay between in-plane and vertical dynamics in stellar discs within the framework of the shearing box approximation. Julian and Toomre used the shearing sheet to show that leading density waves are amplified as they swing into a trailing ones. We extend their formalism into the dimension perpendicular to the disc and obtain explicit solut

Jocelyn Huang, Evelina Bakhturina, Oktai Tatanov

Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech (TTS) pipeline. However, G2P conversion is difficult for languages that contain heteronyms -- words that have one spelling but can be pronounced in multiple ways. G2P datasets with annotated heteronyms are limited in size and expensive to create, as human labeling remains the prima

Benjamin Sick, Michael Walter, Jochen Abhau

3D object detection with point clouds and images plays an important role in perception tasks such as autonomous driving. Current methods show great performance on detection and pose estimation of standard-shaped vehicles but lack behind on more complex shapes as e.g. semi-trailer truck combinations. Determining the shape and motion of those special vehicles

M. Thamban Nair, P. Danumjaya

It is well-known that the backward heat conduction problem of recovering the temperature $u(\cdot, t)$ at a time $t\geq 0$ from the knowledge of the temperature at a later time, namely $g:= u(\cdot, \tau)$ for $\tau>t$, is ill-posed, in the sense that small error in $g$ can lead to large deviation in $u(\cdot, t)$. However, in the case of a time fractional b

Guobiao Li, Sheng Li, Meiling Li, Xinpeng Zhang

Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are proposed recently, which are shown to be promising to achieve good performance. Unlike the traditional handcrafted steganographic tools, a steganographic network is relatively large in

Tom Kocmi, Christian Federmann

We describe GEMBA, a GPT-based metric for assessment of translation quality, which works both with a reference translation and without. In our evaluation, we focus on zero-shot prompting, comparing four prompt variants in two modes, based on the availability of the reference. We investigate nine versions of GPT models, including ChatGPT and GPT-4. We show th

Ole Fredrik Brevig, Athanasios Kouroupis

The following extension of Bohr's theorem is established: If a somewhere convergent Dirichlet series $f$ has an analytic continuation to the half-plane $\mathbb{C}_\theta = \{s = \sigma+it\,:\, \sigma>\theta\}$ that maps $\mathbb{C}_\theta$ to $\mathbb{C} \setminus \{\alpha,\beta\}$ for complex numbers $\alpha \neq \beta$, then $f$ converges uniformly in $\m

Ibrahim Issa, Amedeo Roberto Esposito, Michael Gastpar

We adopt an information-theoretic framework to analyze the generalization behavior of the class of iterative, noisy learning algorithms. This class is particularly suitable for study under information-theoretic metrics as the algorithms are inherently randomized, and it includes commonly used algorithms such as Stochastic Gradient Langevin Dynamics (SGLD). H

Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso, Flavio P. Calmon

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Gaussian noise to clipped gradients). We demonstrate that such randomization incurs predictive multiplicit

Darren Ramsook, Anil Kokaram

Image based Deep Feature Quality Metrics (DFQMs) have been shown to better correlate with subjective perceptual scores over traditional metrics. The fundamental focus of these DFQMs is to exploit internal representations from a large scale classification network as the metric feature space. Previously, no attention has been given to the problem of identifyin

Leonard Göke, Alexander Wimmers, Christian von Hirschhausen

Many governments are considering constructing new nuclear power plants to support the decarbonization of the energy system. On the one hand, dispatchable nuclear plants can complement fluctuating generation from wind and PV. On the other hand, escalating construction costs and times raise economic concerns. In this paper, we extensively review construction c

Minseok Ryu, Kibaek Kim

This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints. In DO, each agent iteratively solves a local optimization model constructed by its own data and communicates some information (e.g., a local solution) with its neighbo

Tanmay Kumar Poddar, Anish Ghoshal, Gaetano Lambiase

We derive in detail the orbital period loss of a compact binary system in presence of a fifth force and radiation of ultralight particles for a general eccentric Keplerian orbit. We obtain constraints on fifth force strength $\alpha\lesssim 1.11\times 10^{-3}$ from the orbital period decay of compact binary systems. We derive constraints on the gauge couplin

Edward Coltman, Martin Schneider, Rainer Helmig

Presented in this work is a framework for the data-driven determination of multi-scale porous media parametrizations. Simulations of flow and transport in a porous medium at the REV scale, although efficient, require well defined parameters that represent pore-scale phenomena to maintain their accuracy. Determining the optimal parameters for this often requi

Lin Li, Wendong Ding, Yongkun Wen, Yufei Liang

Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding correct correspondences from the two point clouds. However, the low overlap between input point clouds causes the registration to fail easily, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping r

Mengying Lei, Lijun Sun

Bayesian optimization (BO) primarily uses Gaussian processes (GP) as the key surrogate model, mostly with a simple stationary and separable kernel function such as the squared-exponential kernel with automatic relevance determination (SE-ARD). However, such simple kernel specifications are deficient in learning functions with complex features, such as being

Y. Ladghami, B. Asfour, A. Bouali, A. Errahmani

In this paper, we study thermodynamics of charged and uncharged 4-Dimension Einstein-Gauss-Bonnet (4D-EGB) black holes. The context of this study is the Visser's holographic thermodynamics with a fixed anti-de Sitter radius and a variable Newton constant known as restricted phase space thermodynamics (RPST). Our setup is constructed by using the AdS/CFT corr

Bohao Qu, Xiaofeng Cao, Jielong Yang, Hechang Chen

Markov Decision Process (MDP) presents a mathematical framework to formulate the learning processes of agents in reinforcement learning. MDP is limited by the Markovian assumption that a reward only depends on the immediate state and action. However, a reward sometimes depends on the history of states and actions, which may result in the decision process in

Robert Reynolds

In this work we derive a bilateral generating function involving the product of an Appell-type product of the Bernoulli and Euler polynomials over independent indices and orders. This function is expressed in terms of the Hurwitz zeta function and special cases in terms of the finite sum of the Hurwitz zeta function and integral formula are derived.

Joakim Linja, Joonas Hämäläinen, Antti Pihlajamäki, Paavo Nieminen

Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications. This need goes beyond the basic knowledge of the number and types of atoms, their chemical composition, and the character of the chemical interactions. The bigger picture takes place on the quan

Maya Lavie, Tehila Caspi, Omer Lev, Roei Zivan

In scenarios with numerous emergencies that arise and require the assistance of various rescue units (e.g., medical, fire, \& police forces), the rescue units would ideally be allocated quickly and distributedly while aiming to minimize casualties. This is one of many examples of distributed settings with service providers (the rescue units) and service requ

Alexander Nicolas, Florian Zentgraf, Mark Linne, Andreas Dreizler

The performance of a wavelet-based optical flow velocimetry (wOFV) algorithm to extract high accuracy and high resolution velocity fields from particle images in wall-bounded turbulent flows is assessed. wOFV is first evaluated using synthetic particle images generated from a channel flow DNS of a turbulent boundary layer. The sensitivity of wOFV to the regu

Giovanni Brigati, Gabriel Stoltz

We study time averages for the norm of solutions to kinetic Fokker--Planck equations associated with general Hamiltonians. We provide fully explicit and constructive decay estimates for systems subject to a confining potential, allowing fat-tail, sub-exponential and (super-)exponential local equilibria, which also include the classic Maxwellian case. The key

Arturo Villegas, Marcello H. M. Passos, Silvania F. Pereira, Juan P. Torres

We show a general method to estimate with optimum precision, i.e., the best precision determined by the light-matter interaction process, a set of parameters that characterize a phase object. The method derives from ideas presented by Pezze et al., [Phys. Rev. Lett. 119, 130504 (2017)]. Our goal is to illuminate the main characteristics of this method as wel

Hyemin Ahn, Esteve Valls Mascaro, Dongheui Lee

After many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this paper presents a study of employing diffusion probabi

Zican Dong, Tianyi Tang, Junyi Li, Wayne Xin Zhao

Modeling long texts has been an essential technique in the field of natural language processing (NLP). With the ever-growing number of long documents, it is important to develop effective modeling methods that can process and analyze such texts. However, long texts pose important research challenges for existing text models, with more complex semantics and s

Stan Tendijck, Philip Jonathan, David Randell, Jonathan Tawn

We develop two models for the temporal evolution of extreme events of multivariate $k$th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan & Tawn (2004), and it naturally extends the work of Winter & Tawn (2016,2017) and Tendijck et al. (2019) to include multivariate random variables. We use cross-v

Chong Fu, Xuhong Zhang, Shouling Ji, Ting Wang

Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs. However, if the input contains a particular trigger, the trojaned model will have attacker-chosen abnormal behavior. Although many backdoor detection methods exist, most of them as

Victoria Hoskins

Many moduli spaces are constructed as quotients of group actions; this paper surveys the classical theory, as well as recent progress and applications. We review geometric invariant theory for reductive groups and how it is used to construct moduli spaces, and explain two new developments extending this theory to non-reductive groups and to stacks, which ena

Zongyu Xu, Yihao Zhang, Long Yuan, Yuwen Qian

Community search over bipartite graphs has attracted significant interest recently. In many applications such as user-item bipartite graph in E-commerce, customer-movie bipartite graph in movie rating website, nodes tend to have attributes, while previous community search algorithm on bipartite graphs ignore attributes, which makes the returned results with

Spyridon Pougkakiotis, Jacek Gondzio, Dionysios S. Kalogerias

In this paper we present an efficient active-set method for the solution of convex quadratic programming problems with general piecewise-linear terms in the objective, with applications to sparse approximations and risk-minimization. The method exploits the structure of the piecewise-linear terms appearing in the objective in order to significantly reduce it

Kaito Kobayashi, Yukitoshi Motome

Physical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them fro

Aydin Shoja, Joost van der Neut, Kees Wapenaar

Least-Squares Reverse-Time Migration (LSRTM) is a method that seismologists utilize to compute a high-resolution subsurface image. Nevertheless, LSRTM is a computationally demanding problem. One way to reduce the computational costs of the LSRTM is to choose a small region of interest and compute the image of that region. However, finding representations tha

Elmurod Kuriyozov, Ulugbek Salaev, Sanatbek Matlatipov, Gayrat Matlatipov

Text classification is an important task in Natural Language Processing (NLP), where the goal is to categorize text data into predefined classes. In this study, we analyse the dataset creation steps and evaluation techniques of multi-label news categorisation task as part of text classification. We first present a newly obtained dataset for Uzbek text classi

Ankit Kumar, Amruta Mishra

We investigate the mass modifications of open strange mesons (vector $K^*$ and axial vector $K_1$) in (magnetized) isospin asymmetric nuclear matter using quantum chromodynamics sum rule (QCDSR) approach. The in-medium decay widths of $K^*$ $\rightarrow$ $K\pi$ and $K_1$ $\rightarrow$ $K^*\pi$ are studied from the mass modifications of $K_1$, $K^*$ and $K$ m

M. C. Crabb

An elementary result in point-set topology is used, with knowledge of the mod $2$ cohomology of real projective spaces, to establish classical results of Lebesgue and Knaster-Kuratowski-Mazurkiewicz, as well as the topological central point theorem of Karasev, which is applied to deduce results of Helly-Lov\'asz, B\'ar\'any and Tverberg

Ashvni Narayanan

The Euler--Riemann zeta function is a largely studied numbertheoretic object, and the birthplace of several conjectures, such as the Riemann Hypothesis. Different approaches are used to study it, including $p$-adic analysis : deriving information from $p$-adic zeta functions. A generalized version of $p$-adic zeta functions (Riemann zeta function) are $p$-ad

Clemens Pollak, David Kügler, Martin Reuter

Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted

Frederik Lohof, Johannes Michl, Alexander Steinhoff, Bo Han

We revisit and extend the standard bosonic interpretation of interlayer excitons in the moir\'e potential of twisted heterostructures of transition-metal dichalcogenides. In our experiments, we probe a high quality MoSe$_2$/WSe$_2$ van der Waals bilayer heterostructure via density-dependent photoluminescence spectroscopy and reveal strongly developed, unconv

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

By analyzing 2.93 fb$^{-1}$ of $e^+e^-$ collision data taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, the branching fractions of the inclusive decays $D^+\to K^0_S X$ and $D^0\to K^0_S X$ are measured to be $(32.78\pm 0.13\pm 0.27)\%$ and $(20.54\pm 0.12\pm 0.18)\%$, respectively, where the first uncertainties are statistical and t

Sudeep Kumar Sahoo, Sathish Chalasani, Abhishek Joshi, Kiran Nanjunda Iyer

Real-world vision based applications require fine-grained classification for various area of interest like e-commerce, mobile applications, warehouse management, etc. where reducing the severity of mistakes and improving the classification accuracy is of utmost importance. This paper proposes a method to boost fine-grained classification through a hierarchic

Gianluca D'Amico, Mauro Marinoni, Federico Nesti, Giulio Rossolini

The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A prom

Peng Zheng, Jie Qin, Shuo Wang, Tian-Zhu Xiang

Co-Salient Object Detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework

Michela Lai

DEAP-3600 is the largest running dark matter detector filled with liquid argon, set at SNOLAB, in Sudbury, Canada, 2 km underground. The experiment holds the most stringent exclusion limit in argon for WIMPs above 20 GeV/c$^2$. In the most recent published analysis, the background events due to alpha-induced scintillation in the neck of the detector limited

Sangwoo Mo, Jong-Chyi Su, Chih-Yao Ma, Mido Assran

Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-cla

Felix M. Haehl, Charles Marteau, Wyatt Reeves, Moshe Rozali

We discuss spectral correlations in coarse-grained chaotic two-dimensional CFTs with large central charge. We study a partition function describing the dense part of the spectrum of primary states in a way that disentangles the chaotic properties of the spectrum from those which are a consequence of Virasoro symmetry and modular invariance. We argue that ran

Sébastien Labbé, Jana Lepšová

We extend the well-known Dumont--Thomas numeration systems to $\mathbb{Z}$ using an approach inspired by the two's complement numeration system. Integers in $\mathbb{Z}$ are canonically represented by a finite word (starting with $\mathtt{0}$ when nonnegative and with $\mathtt{1}$ when negative). The systems are based on two-sided periodic points of substitu

Peter Mészáros

We study cosmological perturbations in a universe with only one matter component described by a triplet of fields. Configuration of these fields is the same as for body coordinates of a solid, and they enter the matter Lagrangian only through the kinetic term. We restrict ourselves only to cases with constant pressure to energy density ratio $w$. Superhorizo

Alyzia-Maria Konsta, Beatrice Spiga, Alberto Lluch Lafuente, Nicola Dragoni

Graphical security models constitute a well-known, user-friendly way to represent the security of a system. These kinds of models are used by security experts to identify vulnerabilities and assess the security of a system. The manual construction of these models can be tedious, especially for large enterprises. Consequently, the research community is trying

Koichi Takahashi

We discuss scenarios and branch points to four major possible consequences regarding future machine intelligence; 1) the singleton scenario where the first and only super-intelligence acquires a decisive strategic advantage, 2) the multipolar scenario where the singleton scenario is not technically denied but political or other factors in human society or mu

Susumu Ariki, Linliang Song, Qi Wang

We first investigate a connected quiver consisting of all dominant maximal weights for an integrable highest weight module in affine type A. This quiver provides an efficient method to obtain all dominant maximal weights. Then, we completely determine the representation type of cyclotomic Khovanov-Lauda-Rouquier algebras of arbitrary level in affine type A,

Amit Hazi

The two-colored Temperley-Lieb algebra $2\mathrm{TL}_R({}_s {n})$ is a generalization of the Temperley-Lieb algebra. The analogous two-colored Jones-Wenzl projector $\mathrm{JW}_R({}_s {n}) \in 2\mathrm{TL}_R({}_s {n})$ plays an important role in the Elias-Williamson construction of the diagrammatic Hecke category. We give conditions for the existence and ro

Yabin Wang, Zhiwu Huang, Xiaopeng Hong

Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfake researchers have been making a great upending to create deepfake artworks (deeparts), which are further closing the gap between reality and fantasy. To address potentially appea

Emmanuel Dror Farjoun, Sergei O. Ivanov

We consider the terminal monad among those preserving the objects of a subcategory, and in particular preserving the image of a monad. Several common monads are shown to be uniquely characterized by the property of being terminal objects in the category of co-augmented endo-functors. Once extended to infinity categories, this gives, for example, a complete c

Francesco Alesiani

Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy-preserving AI. Integrating bilevel mathematical programming within deep learning is thus an essential objective for the Machine Learning community. Previously proposed approaches only consider single-level programming. In

Donghuo Zeng, Jianming Wu, Gen Hattori, Yasuhiro Takishima

Watching TV not only provides news information but also gives an opportunity for different generations to communicate. With the proliferation of smartphones, PC, and the Internet, increase the opportunities for communication in front of the television is also likely to diminish. This has led to some problems further from face-to-face such as a lack of self-c

Théo Guyard, Gilles Monnoyer, Clément Elvira, Cédric Herzet

We introduce a new methodology dubbed ``safe peeling'' to accelerate the resolution of L0-regularized least-squares problems via a Branch-and-Bound (BnB) algorithm. Our procedure enables to tighten the convex relaxation considered at each node of the BnB decision tree and therefore potentially allows for more aggressive pruning. Numerical simulations show th

Aleksandra Franz, Barbara Solenthaler, Nils Thuerey

We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training. In the absence of ground truth data we

Susie Xi Rao, Yilei Tu, Peter H. Egger

We present SAINE, an Scientific Annotation and Inference ENgine based on a set of standard open-source software, such as Label Studio and MLflow. We show that our annotation engine can benefit the further development of a more accurate classification. Based on our previous work on hierarchical discipline classifications, we demonstrate its application using

Sung-Hyun Lee, Wook-Hyeon Kwon, Yoon-Seop Lim, Yong-Hwa Park

In this paper, an automatic calibration algorithm is proposed to reduce the depth error caused by internal stray light in amplitude-modulated continuous wave (AMCW) coaxial scanning light detection and ranging (LiDAR). Assuming that the internal stray light generated in the process of emitting laser is static, the amplitude and phase delay of internal stray

Alcides Buss, Diego Martínez

In this paper we study the nuclearity and weak containment property of reduced cross-sectional C*-algebras of Fell bundles over inverse semigroups. In order to develop the theory, we first prove an analogue of Fell's absorption trick in the context of Fell bundles over inverse semigroups. In parallel, the approximation property of Exel can be reformulated in

Vignesh V Menon, Prajit T Rajendran, Reza Farahani, Klaus Schoeffmann

The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. We propose a Discrete Cosine Transform (DCT)-energy-based VQA with t

Simon Z. Lahme, Pekka Pirinen, Lucija Rončević, Antti Lehtinen

While lab courses are an integral part of studying physics aiming at a huge variety of learning objectives, research has shown that typical lab courses do not reach all the desired goals. While diverse approaches by lab instructors and researchers try to increase the effectiveness of lab courses, experimental tasks remain the core of any lab course. To keep

Federico Ruilova, Aleksandar Yonchev

This project aims to study the feasibility and cost-effectiveness of using edge computing for stream data processing in the context of Internet of Things (IoT) in manufacturing in Europe. Two scenarios were considered: using edge computing to reduce latency and using a popular public cloud provider. Both scenarios demonstrated high throughput, with the edge

João Henrique Inacio de Souza, José Carlos Marinello Filho, Taufik Abrão, Cristiano Panazio

In this article, we study the uplink (UL) channel of a cellular network of Internet of Things (IoT) devices assisted by a reconfigurable intelligent surface (RIS) with a limited number of reflecting angle configurations. Firstly, we derive an expression of the required transmit power for the machine-type devices (MTDs) to attain a target signal-to-noise rati