November 2022 arXiv papers — page 142

Xuefeng Liu, Tomáš Vejchodský

For conforming finite element approximations of the Laplacian eigenfunctions, a fully computable guaranteed error bound in the $L^2$ norm sense is proposed. The bound is based on the a priori error estimate for the Galerkin projection of the conforming finite element method, and has an optimal speed of convergence for the eigenfunctions with the worst regula

Qingyun Dou, Mark Gales

Deliberation networks are a family of sequence-to-sequence models, which have achieved state-of-the-art performance in a wide range of tasks such as machine translation and speech synthesis. A deliberation network consists of multiple standard sequence-to-sequence models, each one conditioned on the initial input and the output of the previous model. During

Chao Pan, Eli Chien, Olgica Milenkovic

As the demand for user privacy grows, controlled data removal (machine unlearning) is becoming an important feature of machine learning models for data-sensitive Web applications such as social networks and recommender systems. Nevertheless, at this point it is still largely unknown how to perform efficient machine unlearning of graph neural networks (GNNs);

David Bodesheim, Robert Biele, Gianaurelio Cuniberti

The Hofstadter butterfly is one of the first and most fascinating examples of the fractal and self-similar quantum nature of free electrons in a lattice pierced by a perpendicular magnetic field. However, the direct experimental verification of this effect on single-layer materials is still missing as very strong and inaccessible magnetic fields are necessar

Emmett B. Kendall, Jonathan P. Williams, Gudmund H. Hermansen, Frederic Bois

Multistate Markov models are a canonical parametric approach for data modeling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical data, for example. Assuming that a continuous-time Markov process is t

Marcin Wieśniak

Classifying states as entangled or separable is a highly challenging task, while it is also one of the foundations of quantum information processing theory. This task is higly nontrivial even for relatively simple cases, such as two-qutrit Bell-diagonal states, i.e., mixture of nine mutually orthogonal maximally entangled states. In this article we apply the

Sharan Mourya

Quantum computers can outperform classical computers in certain tasks. However, there are still many challenges to the current quantum computers such as decoherence and fault tolerance, and other drawbacks such as portability and accessibility. In this study, we circumvent these issues by realizing an analog quantum emulation device (AQED) where each qubit s

Christiaan G. A. Viviers, Joel de Bruijn, Lena Filatova, Peter H. N. de With

Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to so

Han Zhang

In this note, I give a method to construct rational Seifert surface for those smooth or piece-wise linear oriented knots in Lens space. I assume that the oriented knot has a regular projection on Heegaard torus and then construct rational Seifert surface on twist toroidal diagram.

Gunnar Stefansson, Jamie Lentin, Anna Helga Jonsdottir, Eyþór Eiríksson

In response to COVID-19, a new project was started to allow students to solve computerised math drills outside of school. In 9 months it has gone from zero to one thousand students, in co-operation with ten community libraries in various slums and low-income regions in Kenya. The program uses the tutor-web as a study environment and access is provided by don

Siddharth Bhela, Abhishek Banerjee, Ulrich Muenz, Joachim Bamberger

Inverter-dominated microgrids are quickly becoming a key building block of future power systems. They rely on centralized controllers that can provide reliability and resiliency in extreme events. Nonetheless, communication failures due to cyber-physical attacks or natural disasters can make autonomous operation of islanded microgrids challenging. This paper

Christoph Gorgulla

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and co

Adam Chudecki

4-dimensional spaces equipped with congruences of null strings are considered. It is assumed that a space admits a congruence of expanding self-dual null strings and its self-dual part of the Weyl tensor is algebraically degenerate. Different Petrov-Penrose types of such spaces are analyzed. A special attention is paid to para-K\"ahler Einstein spaces. All p

Josep Díaz, Öznur Yaşar Diner, Maria Serna, Oriol Serra

Vertex bisection is a graph partitioning problem in which the aim is to find a partition into two equal parts that minimizes the number of vertices in one partition set that have a neighbor in the other set. We are interested in giving upper bounds on the vertex bisection width of random $d$-regular graphs for constant values of $d$. Our approach is based on

Karl Svozil

We review some semantical aspects of probability bounds from Boole's "conditions on possible experience" violated by quantum mechanics. We also speculate about emerging space-time categories as an epiphenomenon of quantization and the resulting breakdown of relativity theory by non-unitary and non-linear processes.

Miriam Oliva, Vladimir Kaganer, Maximilian Pudelski, Sebastian Meister

Ultrathin GaN nanowires (NWs) are attractive to maximize surface effects and as building block in high-frequency transistors. Here, we introduce a facile route for the top-down fabrication of ordered arrays of GaN NWs with aspect ratios exceeding $10$ and diameters below $20\,$nm. Highly uniform thin GaN NWs are first obtained by using electron beam lithogra

Andrea Cavaglià, Nikolay Gromov, Julius Julius, Michelangelo Preti

We present a systematic method for the derivation of a relation which connects the correlation function of operators on the straight Maldacena-Wilson line with the integrability data for the cusp anomalous dimension. As we show, the derivation requires very careful treatment of the UV divergences. Our method opens a way to derive infinitely many constraints

Antonios Marios Christonasis, Stef van Eijndhoven, Peter Duin

With big data becoming increasingly available, IoT hardware becoming widely adopted, and AI capabilities becoming more powerful, organizations are continuously investing in sensing. Data coming from sensor networks are currently combined with sensor fusion and AI algorithms to drive innovation in fields such as self-driving cars. Data from these sensors can

Lorenzo Speri, Nataliya K. Porayko, Mikel Falxa, Siyuan Chen

The search for gravitational waves using Pulsar Timing Arrays (PTAs) is a computationally expensive complex analysis that involves source-specific noise studies. As more pulsars are added to the arrays, this stage of PTA analysis will become increasingly challenging. Therefore, optimizing the number of included pulsars is crucial to reduce the computational

M. Khalid Hossain, S. M. Kamrul Hasan, M. Imran Hossain, Ranjit C. Das

In the future, when fossil fuels are exhausted, alternative energy sources will be essential for everyday needs. Hydrogen-based energy can play a vital role in this aspect. This energy is green, clean, and renewable. Elec-trochemical hydrogen devices have been used extensively in nuclear power plants to manage hydrogen-based renewable fuel. Doped zirconate m

Dominik Filipiak, Anna Fensel, Agata Filipowska

We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. We evaluate it with different combinations of embeddings in a few-shot image classification task. We also construct a new knowledge source - Wikidata embeddings - and evaluate it with KGTN

Di Luo, Shunyue Yuan, James Stokes, Bryan K. Clark

Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function that obeys gauge symmetry. In this paper, we have developed gauge equivariant neural network wave function techni

Andriy Panasyuk, Adam Szereszewski

In 1989 Mason and Newman proved that there is a 1-1-correspondence between self-dual metrics satisfying Einstein vacuum equation (in complex case or in neutral signature) and pairs of commuting parameter depending vector fields $X_1(\lambda),X_2(\lambda)$ which are divergence free with respect to some volume form. Earlier (in 1975) Pleba\'nski showed instanc

Md. Ferdous Rahman, M. J. A. Habib, Md. Hasan Ali, M. H. K. Rubel

Inorganic CdTe and FeSi2-based solar cells have recently drawn a lot of attention because they offer superior thermal stability and good optoelectronic properties compared to conventional solar cells. In this work, a unique alternative technique is presented by using FeSi2 as a secondary absorber layer and In2S3 as the window layer for improving photovoltaic

Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou, Alkiviadis Koukos

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often c

Lukas Graf, Tobias Harks

We study dynamic traffic assignment with side-constraints. We first give a counter-example to a key result from the literature regarding the existence of dynamic equilibria for volume-constrained traffic models in the classical edge-delay model. Our counter-example shows that the feasible flow space need not be convex and it further reveals that classical in

Lakshmi A. Ghantasala, Risi Jaiswal, Supriyo Datta

Protein structure prediction is a critical problem linked to drug design, mutation detection, and protein synthesis, among other applications. To this end, evolutionary data has been used to build contact maps which are traditionally minimized as energy functions via gradient descent based schemes like the L-BFGS algorithm. In this paper we present what we c

Saroj Sahoo, Matthew Berger

We present a method for learning neural representations of flow maps from time-varying vector field data. The flow map is pervasive within the area of flow visualization, as it is foundational to numerous visualization techniques, e.g. integral curve computation for pathlines or streaklines, as well as computing separation/attraction structures within the fl

Ramazan Akgün

In this work we obtain a transference theorem for Lebesgue spaces with $A_{\infty }$ weights, namely, starting from some uniform-norm inequalities it is possible to obtain similar inequalities in Lebesgue spaces with $A_{\infty }$ weights. This transference technic allows us to obtain some weighted norm inequalities easily. Also transference result gives pos

Nilotpal Sanyal

We propose an iterative variable selection scheme for high-dimensional data with binary outcomes. The scheme adopts a structured screen-and-select framework and uses non-local prior-based Bayesian model selection within the same. The structured screening is based on the association of the independent variables with the outcome which is measured in terms of t

Laura Peralta, Daniele Mazierli, Alberto Gomez, Joseph V Hajnal

Coherent multi-transducer ultrasound (CoMTUS) creates an extended effective aperture through the coherent combination of multiple arrays, which results in images with enhanced resolution, extended field-of-view, and higher sensitivity. The subwavelength localization accuracy of the multiple transducers required to coherently beamform the data is achieved by

Alexey Gorlach, Matan Even Tzur, Michael Birk, Michael Krüger

High harmonic generation (HHG) is an extreme nonlinear process where intense pulses of light drive matter to emit high harmonics of the driving frequency, reaching the extreme ultraviolet (XUV) and x-ray spectral ranges. So far, the HHG process was always generated by intense laser pulses that are well described as a classical electromagnetic field. Advances

Ahmed Hossain, Xiaoduan Sun, Raju Thapa, Julius Codjoe

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three

Zafir Stojanovski, Karsten Roth, Zeynep Akata

Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts. In addition, subsequent fine-tuning can considerably improve performance on a selected downstream task. However, through naive fine-tuning, these zero-shot models lose their gener

Yonatan Messica, Dmitri. B. Gutman, Pavel M. Ostrovsky

We study the anomalous Hall effect in a disordered Weyl semimetal. While the intrinsic contribution is expressed solely in terms of Berry curvature, the extrinsic contribution is given by a combination of the skew scattering and side jump terms. For the model of small size impurities, we are able to express the skew scattering contribution in terms of scatte

Shoaib Imran, Muhammad Tahir, Zubair Khalid, Momin Uppal

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of

Nawid Keshtmand, Raul Santos-Rodriguez, Jonathan Lawry

A recent popular approach to out-of-distribution (OOD) detection is based on a self-supervised learning technique referred to as contrastive learning. There are two main variants of contrastive learning, namely instance and class discrimination, targeting features that can discriminate between different instances for the former, and different classes for the

Qun Wang, Ke Zhang

Treschev made the remarkable discovery that there exists formal power series describing a billiard with locally linearizable dynamics. We show that if the frequency for the linear dynamics is Diophanine, the Treschev example is $(1+ \alpha)$-Gevrey for some $\alpha > 0$. Our proof is based on an iterative scheme that further clarifies the structure and symme

Ayisha Fayomi, Yannis Pantazis, Michail Tsagris, Andrew T. A. Wood

Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA can be formulated in terms of operations on a multivariate Gaussian likelihood. As a consequence of the implied Gaussian formulation, the principal components are not robust to outli

Merav Hadad

The Marolf-Maxfield topological toy model for 2D gravity gives the full spectrum of boundary theories, but can not describe any evolution. In order to obtain the expected evolution from Hartle-Hawking state to one of the superselection sectors, we suggest to consider a correspondence between models of evaporating black holes and models of collapsing wave fun

Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla, Charalampos Kontoes

To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that.

Philipp Otto, Osman Doğan, Süleyman Taşpınar

This article introduces a dynamic spatiotemporal stochastic volatility (SV) model with explicit terms for the spatial, temporal, and spatiotemporal spillover effects. Moreover, the model includes time-invariant site-specific constant log-volatility terms. Thus, this formulation allows to distinguish between spatial and temporal interactions, while each locat

Rahul Mourya, João F. C. Mota

End-to-end deep neural networks (DNNs) have become the state-of-the-art (SOTA) for solving inverse problems. Despite their outstanding performance, during deployment, such networks are sensitive to minor variations in the testing pipeline and often fail to reconstruct small but important details, a feature critical in medical imaging, astronomy, or defence.

Sunayana Dutta, Axel U. J. Lode, Ofir E. Alon

The theoretical investigation of rotating Bose-Einstein condensates has mainly focused on the emergence of quantum vortex states and the condensed properties of such systems. In the present work, we concentrate on other facets by examining the impact of rotation on the ground state of weakly interacting bosons confined in anharmonic potentials computed both

Amirreza Rouhi, Matt K. Fu, Dileep Chandran, Andrea Zampiron

Turbulent drag reduction through streamwise travelling waves of spanwise wall oscillation is investigated over a wide range of Reynolds numbers. Here, in Part 1, wall-resolved large-eddy simulations in a channel flow are conducted to examine how the frequency and wavenumber of the travelling wave influence the drag reduction at friction Reynolds numbers $Re_

Yibin Wu, Jian Kuang, Xiaoji Niu, Jens Behley

A reliable pose estimator robust to environmental disturbances is desirable for mobile robots. To this end, inertial measurement units (IMUs) play an important role because they can perceive the full motion state of the vehicle independently. However, it suffers from accumulative error due to inherent noise and bias instability, especially for low-cost senso

Stefan Kuhn, Carlos Cobas, Agustin Barba, Simon Colreavy-Donnelly

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by one. Therefo

Klaus Thomsen

It is shown that the pairing of the K00 group of a C*-algebra with the densely defined traces of the algebra can be extended to a pairing with the densely defined weights. For traces the pairing can be extended to the K0 group without the semi-continuity assumption occurring in the work of Connes and Elliott.

Naimul Khan, Md Niaz Imtiaz

R-peak detection is crucial in electrocardiogram (ECG) signal processing as it is the basis of heart rate variability analysis. The Pan-Tompkins algorithm is the most widely used QRS complex detector for the monitoring of many cardiac diseases including arrhythmia detection. However, the performance of the Pan-Tompkins algorithm in detecting the QRS complexe

František Knapp, Panagiota Papakonstantinou, Petr Veselý, Giovanni De Gregorio

We investigate the differences and analogies between the equation of motion phonon method (EMPM) and second Tamm-Dancoff and random-phase approximations (STDA and SRPA) paying special attention to the problem of spurious center-of-mass (c.m.) admixtures. In order to compare them on an equal footing, we perform self-consistent calculations of the multipole st

Jiechao Zhang, Hadi Beik-Mohammadi, Leonel Rozo

Dexterous and autonomous robots should be capable of executing elaborated dynamical motions skillfully. Learning techniques may be leveraged to build models of such dynamic skills. To accomplish this, the learning model needs to encode a stable vector field that resembles the desired motion dynamics. This is challenging as the robot state does not evolve on

Manuel Alberto M. Ferreira

Solving a Riccati equation, induced by the study of the transient behaviour of the MGInf queue system, a collection of service times distributions is determined. For the MGInf queue, which service time distribution is a member of that collection, the busy period and busy cycle probabilistic studies are performed. In extra, the properties of that distribution

Lorenzo Ciardo, Stanislav Živný

We show that approximate graph colouring is not solved by the lift-and-project hierarchy for the combination of linear programming and linear Diophantine equations. The proof is based on combinatorial tensor theory.

Puskar Mondal, Shing-Tung Yau

Here we prove a global gauge-invariant radiation estimates for the perturbations of the $3+1$ dimensional Minkowski spacetime in the presence of Yang-Mills sources. In particular, we obtain a novel gauge invariant estimate for the Yang-Mills fields coupled to gravity in a double null framework in the Causal complement of a compact set of a Cauchy slice. A co

Anwita Bhowmik, Rupam Barman

For a prime $p\equiv 3\pmod 4$ and a positive integer $t$, let $q=p^{2t}$. The Peisert graph of order $q$ is the graph with vertex set $\mathbb{F}_q$ such that $ab$ is an edge if $a-b\in\langle g^4\rangle\cup g\langle g^4\rangle$, where $g$ is a primitive element of $\mathbb{F}_q$. In this paper, we construct a similar graph with vertex set as the commutativ

Parth Kothari, Danya Li, Yuejiang Liu, Alexandre Alahi

Deep motion forecasting models have achieved great success when trained on a massive amount of data. Yet, they often perform poorly when training data is limited. To address this challenge, we propose a transfer learning approach for efficiently adapting pre-trained forecasting models to new domains, such as unseen agent types and scene contexts. Unlike the

Ehtibar N. Dzhafarov, Janne V. Kujala

A noncontextual system of random variables may become contextual if one adds to it a set of new variables, even if each of them is obtained by the same context-wise function of the old variables. This fact follows from the definition of contextuality, and its demonstration is trivial for inconsistently connected systems (i.e. systems with disturbance). Howev

Selomit Ramírez-Uribe, Roger J. Hernández-Pinto, Germán Rodrigo, German F. R. Sborlini

Characterizing multiloop topologies is an important step towards developing novel methods at high perturbative orders in quantum field theory. In this article, we exploit the Loop-Tree Duality (LTD) formalism to analyse multiloop topologies that appear for the first time at five loops. Explicitly, we open the loops into connected trees and group them accordi

Ronilo J. Ragodos, Tong Wang, Qihang Lin, Xun Zhou

While deep reinforcement learning has proven to be successful in solving control tasks, the "black-box" nature of an agent has received increasing concerns. We propose a prototype-based post-hoc policy explainer, ProtoX, that explains a blackbox agent by prototyping the agent's behaviors into scenarios, each represented by a prototypical state. When learning

Yakov Nekrich, Saladi Rahul

In the orthogonal range reporting problem we must pre-process a set $P$ of multi-dimensional points, so that for any axis-parallel query rectangle $q$ all points from $q\cap P$ can be reported efficiently. In this paper we study the query complexity of multi-dimensional orthogonal range reporting in the pointer machine model. We present a data structure that

Ao Xu, Bo-Tao Li

We assess the performance of the hybrid Open Accelerator (OpenACC) and Message Passing Interface (MPI) approach for multi-graphics processing units (GPUs) accelerated thermal lattice Boltzmann (LB) simulation. The OpenACC accelerates computation on a single GPU, and the MPI synchronizes the information between multiple GPUs. With a single GPU, the two-dimens

Martina Hofmanová, Florian Bechtold

Recent years have seen spectacular progress in the mathematical study of hydrodynamic equations. Novel tools from convex integration in particular prove extremely versatile in establishing non-uniqueness results. Motivated by this 'pathological' behavior of solutions in the deterministic setting, stochastic models of fluid dynamics have enjoyed growing inter

Olivier Dauchot, François Ladieu, C. Patrick Royall

We highlight certain key achievements in experimental work on molecular, colloidal and granular glassformers. This short review considers these three classes of experimental systems and focusses largely on the work of the authors and their coworkers and thus is far from exhaustive. Our goal is rather to discuss particular experimental results from these clas

Kyle A. Kilian, Christopher J. Ventura, Mark M. Bailey

Artificial Intelligence (AI) is one of the most transformative technologies of the 21st century. The extent and scope of future AI capabilities remain a key uncertainty, with widespread disagreement on timelines and potential impacts. As nations and technology companies race toward greater complexity and autonomy in AI systems, there are concerns over the ex

Wentao Liu, Xiongjun Fang, Jiliang Jing, Jieci Wang

We have studied the quasinormal modes (QNMs) of a slowly rotating black hole with Lorentz-violating parameter in Einstein-bumblebee gravity. We analyse the slow rotation approximation of the rotating black hole in the Einstein-bumblebee gravity, and obtain the master equations for scalar perturbation, vector perturbation and axial gravitational perturbation,

Xu Guo, Han Yu

Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circumventing the need to customize complex designs for specific tasks. However, most current work focus on finetuning PLMs on a domain-specific datasets, ignoring the fact that the do

Daniil Stepanenko, Igor Volovich

The Schwarzschild black hole metric with mass $M$ has the limit of the vanishing mass when one get the Minkowski space. We study behavior of the entanglement entropy by using the island formula and the limit of the vanishing black hole mass. The black hole information problem appears if one considers the lowering its mass. It was noted recently that the form

Oscar M Cumbicus-Pineda, Iker Gutiérrez-Fandiño, Itziar Gonzalez-Dios, Aitor Soroa

In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme. Instead of directly computing the best simplification given a complex text, the re-ranking method also considers the probability of the simple sentence to produce the complex counterpart, as well as the probability of the simple text i

Tu Le-Xuan, Trung Tran-Quang, Thi Ngoc Hien Doan, Thanh-Hai Tran

3D hand pose estimation from RGB images suffers from the difficulty of obtaining the depth information. Therefore, a great deal of attention has been spent on estimating 3D hand pose from 2D hand joints. In this paper, we leverage the advantage of spatial-temporal Graph Convolutional Neural Networks and propose LG-Hand, a powerful method for 3D hand pose est

Iskander Aliev, Martin Henk, Mark Hogan, Stefan Kuhlmann

Given a rational pointed $n$-dimensional cone $C$, we study the integer Carath\'{e}odory rank $\operatorname{CR}(C)$ and its asymptotic form $\operatorname{CR^{\rm a}}(C)$, where we consider ``most'' integer vectors in the cone. The main result significantly improves the previously known upper bound for $\operatorname{CR^{\rm a}}(C)$. We also study bounds on

Ye Gao, Jason Jabbour, Eunjung Ko, Lahiru Nuwan Wijayasingha

To demonstrate the value of machine learning based smart health technologies, researchers have to deploy their solutions into complex real-world environments with real participants. This gives rise to many, oftentimes unexpected, challenges for creating technology in a lab environment that will work when deployed in real home environments. In other words, li

Pratinav Seth, Adil Khan, Ananya Gupta, Saurabh Kumar Mishra

Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of predictive uncertainty. Quantifying model

Jade E. Holliman, H. Todd Schaef, B. Peter McGrail, Quin R. S. Miller

In the past two decades, artificial structures known as metamaterials have been found to exhibit extraordinary material properties that enable the unprecedented manipulation of electromagnetic waves, elastic waves, molecules, and particles. Phenomena such as negative refraction, bandgaps, near perfect wave absorption, wave focusing, negative Poissons ratio,

Guillaume Ducoffe

Let $G=(V,E)$ be a graph with unit-length edges and nonnegative costs assigned to its vertices. Being given a list of pairwise different vertices $S=(s_1,s_2,\ldots,s_p)$, the {\em prioritized Voronoi diagram} of $G$ with respect to $S$ is the partition of $G$ in $p$ subsets $V_1,V_2,\ldots,V_p$ so that, for every $i$ with $1 \leq i \leq p$, a vertex $v$ is

Martin Robin, Ruben Guis, Mustafa Umit Arabul, Zili Zhou

Conoscopic interferometry is a promising detection technique for ultrafast acoustics. By focusing a probe beam through a birefringent crystal before passing it through a polarizer, conoscopic interferences sculpt the spatial profile of the beam. The use of these patterns for acoustic wave detection revealed a higher detection sensitivity over existing techni

Ye Gao, Zhendong Chu, Hongning Wang, John Stankovic

In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new domain with a different distribution than its training set. Domain Adaptation (DA) is used to mitigate this problem. One approach of existing DA algorithms is to find domain invari

Tomas Kacetl, Jan Kacetl, Nima Tashakor, Stefan M. Goetz

Dynamically reconfigurable batteries merge battery management with output formation in ac and dc batteries, increasing the available charge, power, and life time. However, the combined ripple generated by the load and the internal reconfiguration can degrade the battery. This paper introduces that the frequency range of the ripple matters for degradation and

Konstantin Postnov, Nikita Mitichkin

We briefly discuss the most prominent results and specific sources detected by gravitational-wave observatories LIGO-Virgo during first three O1-O3 runs, as well as possible astrophysical and cosmological channels of their formation. We show that it is possible to explain the observed correlation between the effective spin of coalescing binary black holes an

P. -A. Gourdain, I. N. Erez, M. E. Evans, H. R. Hasson

This paper shows analytically and numerically that a vortex plate coupled to a neutral density filter can deliver a true optical derivative when placed at the focal plane of a $2f$ lens pair. This technique turns spatial variations in intensity into an intensity, which square root is the spatial derivative of the initial intensity variation. More surprisingl

Fengsheng Wang, Leyi Wei

With the rapid advances of image editing techniques in recent years, image manipulation detection has attracted considerable attention since the increasing security risks posed by tampered images. To address these challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is proposed to automatically identify manipulated regions. In our MSMG-Net,

Quan Situ

We establish an algebra isomorphism between the center of the category $\mathcal{O}$ for a hybrid quantum group at a root of unity $\zeta$ and the cohomology of $\zeta$-fixed locus on affine Grassmannian. A deformed version of this isomorphism was established in the previous paper of the author. For the Steinberg block of $\mathcal{O}$, we construct an abeli

D. Nancy Kirupanithi, A. Antonidoss, G. Subathra

For data privacy, system reliability, and security, Blockchain technologies have become more popular in recent years. Despite its usefulness, the blockchain is vulnerable to cyber assaults; for example, in January 2019 a 51% attack on Ethereum Classic successfully exposed flaws in the platform's security. From a statistical point of view, attacks represent a

W. H. Huang, H. Li, H. Zhou, T. Li

The Super Tau Charm Facility (STCF) is a proposed electron-positron collider working at $\sqrt{s}=2\sim 7$ GeV, and the peak luminosity is designed to be above $0.5 \times 10^{35}cm^{-2}s^{-1}$. The huge amount of scientific data brings great challenges to the offline data processing software, including the Monte Carlo simulation, calibration, reconstruction

Tianrun Gao, Huihe Qiu, Lin Fu

Fluid-Structure Interaction (FSI) is a crucial problem in ocean engineering. The smoothed particle hydrodynamics (SPH) method has been employed recently for FSI problems in light of its Lagrangian nature and its advantage in handling multi-physics problems. The efficiency of SPH can be greatly improved with the Adaptive Particle Refinement (APR) method, whic

Reza kakooee, Benjamin Dillunberger

While reinforcement learning has been used widely in research during the past few years, it found fewer real-world applications than supervised learning due to some weaknesses that the RL algorithms suffer from, such as performance degradation in transitioning from the simulator to the real world. Here, we argue the design process is a reinforcement learning

Yumeng Zeng, Bozhen Zhou, Shu Chen

The dynamical quantum phase transition is characterized by the emergence of nonanalytic behaviors in the rate function, corresponding to the occurrence of exact zero points of the Loschmidt echo in the thermodynamical limit. In general, exact zeros of the Loschmidt echo are not accessible in a finite-size quantum system except for some fine-tuned quench para

Mengyi Tang, Wenjing Liao, Rachel Kuske, Sung Ha Kang

Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of li

Jiejing Feng, Doudou Hei, Xinmin Hou

For a fixed graph $F$, a graph $G$ is said to be $F$-saturated if $G$ does not contain a subgraph isomorphic to $F$ but does contain $F$ after the addition of any new edge. Let $M_k$ be a matching consisting of $k$ edges and $S_{n,k}$ be the join graph of a complete graph $K_k$ and an empty graph $\overline{K_{n-k}}$. In this paper, we prove that for $s \geq

Lei Hu, Guyue Li, Xuewen Qian, Aiqun Hu

Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the performance of physical-layer key generation (PKG) thanks to its ability to smartly customize the radio environments. Existing RIS-assisted PKG methods are mainly based on the idealistic assumption of an independent and identically distributed (i.i.d.) channel model at both th

Marco Badran, Manuel del Pino

We consider the magnetic Ginzburg-Landau equations in a compact manifold $N$ $$ \begin{cases} -\varepsilon^2 \Delta^{A} u=\frac{1}{2}(1-|u|^2)u,\\ \varepsilon^2 d^*dA=\langle\nabla^A u,iu\rangle \end{cases} $$ formally corresponding to the Euler-Lagrange equations for the energy functional $$ E(u,A)=\frac{1}{2}\int_{N}\varepsilon^2|\nabla^Au|^{2}+\varepsilon

Quan Situ

We study the representation theory of a hybrid quantum group at root of unity $\zeta$ introduced by Gaitsgory. After discussing some basic properties of its category $\mathcal{O}$, we study deformations of the category $\mathcal{O}$. For subgeneric deformations, we construct the endomorphism algebra of big projective object and compute it explicitly. Our mai

Bin Chen, Xinmin Hou

Let $\mathscr{H}$ be a family of digraphs. A digraph $D$ is \emph{$\mathscr{H}$-free} if it contains no isomorphic copy of any member of $\mathscr{H}$. For $k\geq2$, we set $C_{\leq k}=\{C_{2}, C_{3},\ldots,C_{k}\}$, where $C_{\ell}$ is a directed cycle of length $\ell\in\{2,3,\ldots,k\}$. Let $D_{n}^{k}(\xi,\zeta)$ denote the family of \emph{${C}_{\le k}$-f

Le-Chen Qu, Hong-Yue Jiang, Yu-Xiao Liu

We examine the multifold complexity and Loschmidt echo for an inverted harmonic oscillator. We give analytic expressions for any number of precursors, implementing multiple backward and forward time evolutions of the quantum state, at the leading order in the perturbation. We prove that complexity is dominated by the longest permutation of the given time com

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu

A reconstruction attack on a private dataset $D$ takes as input some publicly accessible information about the dataset and produces a list of candidate elements of $D$. We introduce a new class of data reconstruction attacks based on randomized methods for non-convex optimization. We empirically demonstrate that our attacks can not only reconstruct full rows

Huayi Zhou, Fei Jiang, Jiaxin Si, Lili Xiong

Each student matters, but it is hardly for instructors to observe all the students during the courses and provide helps to the needed ones immediately. In this paper, we present StuArt, a novel automatic system designed for the individualized classroom observation, which empowers instructors to concern the learning status of each student. StuArt can recogniz

Srishti Mishra, Srinjoy Das

Leveraging civic data, divided into 3 categories spending, infrastructure and citizen feedback, can present a clear picture of the priorities, performance, and pain-points of a city. Data driven insights highlight the current issues faced by citizens as well as disparity between government spending and quality of work, and can aid in providing effective solu

Arnab K. Ray

The GDP growth of national economies is modelled by the logistic function. Applying it on the GDP data of the World Bank till the year 2020, we forecast the outcome of the competitive GDP growth of Japan, Germany, UK and India, all of whose current GDPs are very close to one another. Fulfilling one of the predictions, in 2022 the GDP of India has indeed over

Chenjie Fan, Gigliola Staffilani, Zehua Zhao

In this paper we discuss quantitative (pointwise) decay estimates for solutions to the 3D cubic defocusing Nonlinear Schr\"odinger equation with various initial data, deterministic and random. We show that nonlinear solutions enjoy the same decay rate as the linear ones. The regularity assumption on the initial data is much lower than in previous results (se

Md Sariful Sheikh, Tushar Kanti Bhowmik, Alo Dutta, Sujoy Saha

The magnetic and transport properties of BiFeO3/La2NiMnO6 (BFO/LNMO) composite have been investigated both experimentally and theoretically. Unlike the normal rhombohedral (R3c) phase, BFO in the composites is crystallized in the triclinic phase (P1). Interestingly, the composites demonstrate a significant enhancement in the magnetization, magnetoelectric co

Konstantin Ushenin, Maksim Dzhigil, Vladislav Dordiuk

A computational anatomy atlas is a set of internal organ geometries. It is based on data of real patients and complemented with virtual cases by using a some numerical approach. Atlases are in demand in computational physiology, especially in cardiological and neurophysiological applications. Usually, atlas generation uses explicit object representation, suc