August 2022 arXiv papers — page 9

Tian-Yu Ye, Jiang-Yuan Lian

By using d-level single-particle states, the first multi-party semiquantum private comparison (MSQPC) protocol which can judge the size relationship of private inputs from more than two classical users within one execution of protocol is put forward. This protocol requires the help of one quantum third party (TP) and one classical TP, both of whom are allowe

Yuchen Liang, Dmitry Krotov, Mohammed J. Zaki

The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverag

E. O. Rodrigues, L. O. Rodrigues, L. S. N. Oliveira, A. Conci

This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericar

É. O. Rodrigues, V. H. A. Pinheiro, P. Liatsis, A. Conci

We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segment

Cecilia Pagliantini, Gian Luca Delzanno, Stefano Markidis

We propose a spectral method for the 1D-1V Vlasov-Poisson system where the discretization in velocity space is based on asymmetrically-weighted Hermite functions, dynamically adapted via a scaling $\alpha$ and shifting $u$ of the velocity variable. Specifically, at each time instant an adaptivity criterion selects new values of $\alpha$ and $u$ based on the

Bing Zhu

In this paper, model predictive control (MPC) strategies are proposed for dead-beat control of linear systems with and without state and control constraints. In unconstrained MPC, deadbeat performance can be guaranteed by setting the control horizon to the system dimension, and adding an terminal equality constraint. It is proved that the unconstrained deadb

Pascal Peter, Karl Schrader, Tobias Alt, Joachim Weickert

Diffusion-based inpainting can reconstruct missing image areas with high quality from sparse data, provided that their location and their values are well optimised. This is particularly useful for applications such as image compression, where the original image is known. Selecting the known data constitutes a challenging optimisation problem, that has so far

Kai Koehler

An explicit formula for analytic torsion forms for fibrations by projective curves is given. In particular one obtains a formula for direct images in Arakelov geometry in the corresponding setting. The main tool is a new description of Bismut's equivariant Bott-Chern current in the case of isolated fixed points.

Partha Das, Sezer Karaoglu, Arjan Gijsenij, Theo Gevers

Intrinsic image decomposition (IID) is an under-constrained problem. Therefore, traditional approaches use hand crafted priors to constrain the problem. However, these constraints are limited when coping with complex scenes. Deep learning-based approaches learn these constraints implicitly through the data, but they often suffer from dataset biases (due to n

Guoqi Yan

We propose a new method to compute the $C_{2^n}$-equivariant homotopy groups of the Eilenberg-Mac Lane spectrum $H\underline{\mathbb{Z}}$ as a $RO(C_{2^n})$-graded Green functor using the generalized Tate squares. As an example, we completely compute the $C_4$ case and investigate two $\mathscr{P}$-homotopy limit spectral sequences for the family $\mathscr{P

Vijay Prakash, Sicheng Xie, Danny Yuxing Huang

Smart home IoT devices are known to be breeding grounds for security and privacy vulnerabilities. Although some IoT vendors deploy updates, the update process is mostly opaque to researchers. It is unclear what software components are on devices, whether and when these components are updated, and how vulnerabilities change alongside the updates. This opaquen

Shuanglin Yan, Hao Tang, Liyan Zhang, Jinhui Tang

Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing

Roberto Perera, Vinamra Agrawal

Fracture is one of the main causes of failure in engineering structures. Phase field methods coupled with adaptive mesh refinement (AMR) techniques have been widely used to model crack propagation due to their ease of implementation and scalability. However, phase field methods can still be computationally demanding making them unfeasible for high-throughput

Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis

Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant appr

J. E. Rodríguez Camargo

We compute the geometric Sen operator for arbitrary Shimura varieties in terms of equivariant vector bundles of flag varieties and the Hodge-Tate period map. As an application, we obtain the rational vanishing of completed cohomology in the Calegari-Emerton conjectures.

Nicholas Roberts, Xintong Li, Tzu-Heng Huang, Dyah Adila

Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with aggregating multiple noisy-but-cheap label estimates expressed by labeling functions (LFs). While it has been used successfully in many domains, weak supervision's application scope

Mirda Prisma Wijayanto, Emir Syahreza Fadhilla, Fiki Taufik Akbar, Bobby Eka Gunara

In this paper we prove the global existence of classical static solutions of Einstein gravitational theory coupled to a real scalar field where the spacetime admits spherically symmetry. The equations of motions can then be reduced into a single first-order integro-differential equation. First, we obtain the decay estimates of the solutions. Then, in order t

Mostafa Mehdipour Ghazi, Mads Nielsen

Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols are different. Methods robust to these variations are most needed. A novel deep learning method is proposed for fast and a

Philipp Sibbertsen, Timm Lampert, Karsten Müller, Michael Taktikos

The coefficients of the regular continued fraction for random numbers are distributed by the Gauss-Kuzmin distribution according to Khinchin's law. Their geometric mean converges to Khinchin's constant and their rational approximation speed is Khinchin's speed. It is an open question whether these theorems also apply to algebraic numbers of degree $>2$. Sinc

Matthew Dobson, Abdel Kader Geraldo

We prove that incompressible two dimensional nonequilibrium Langevin dynamics (NELD) converges exponentially fast to a steady-state limit cycle. We use automorphism remapping periodic boundary conditions (PBCs) techniques such as Lees-Edwards PBCs and Kraynik-Reinelt PBCs to treat respectively shear flow and planar elongational flow. After rewriting NELD in

Tianyuan Yao, Chang Qu, Jun Long, Quan Liu

With the rapid development of self-supervised learning (e.g., contrastive learning), the importance of having large-scale images (even without annotations) for training a more generalizable AI model has been widely recognized in medical image analysis. However, collecting large-scale task-specific unannotated data at scale can be challenging for individual l

Fredrik Dahlqvist, Renato Neves

Programs with a continuous state space or that interact with physical processes often require notions of equivalence going beyond the standard binary setting in which equivalence either holds or does not hold. In this paper we explore the idea of equivalence taking values in a quantale V, which covers the cases of (in)equations and (ultra)metric equations am

Chang-Bin Jeon, Kyogu Lee

Nowadays, commercial music has extreme loudness and heavily compressed dynamic range compared to the past. Yet, in music source separation, these characteristics have not been thoroughly considered, resulting in the domain mismatch between the laboratory and the real world. In this paper, we confirmed that this domain mismatch negatively affect the performan

Philipp Sibbertsen, Timm Lampert, Karsten Müller, Michael Taktikos

Enrico Bombieri proved that the ABC Conjecture implies Roth's theorem in 1994. This paper concerns the other direction. In making use of Bombieri's and Van der Poorten's explicit formula for the coefficients of the regular continued fractions of algebraic numbers, we prove that Roth's theorem implies a weakened non-effective version of the ABC Conjecture in

Karunesh K. Mishra, Stefan Ataman

In this paper we address the problem of optimizing an unbalanced Mach-Zehnder interferometer, for a given pure input state and considering a specific detection scheme. While the optimum transmission coefficient of the first beam splitter can be uniquely determined via the quantum Fisher information only [Phys. Rev. A 105, 012604 (2022)], the second beam spli

Érick Oliveira Rodrigues, Felipe Fernandes Cordeiro de Morais, Aura Conci

The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads

Tom Braden, Carl Mautner

For any field $k$, we give an algebraic description of the category $\mathrm{Perv}_\mathscr{S}(S^n (\mathbb{C}^2),k)$ of perverse sheaves on the $n$-fold symmetric product of the plane $S^n(\mathbb{C}^2)$ constructible with respect to its natural stratification and with coefficients in $k$. In particular, we show that it is equivalent to the category of modu

Matteo Giordano

Besov priors are nonparametric priors that can model spatially inhomogeneous functions. They are routinely used in inverse problems and imaging, where they exhibit attractive sparsity-promoting and edge-preserving features. A recent line of work has initiated the study of their asymptotic frequentist convergence properties. In the present paper, we consider

Stephen McAleese

Superhuman artificial general intelligence could be created this century and would likely be a significant source of existential risk. Delaying the creation of superintelligent AI (ASI) could decrease total existential risk by increasing the amount of time humanity has to work on the AI alignment problem. However, since ASI could reduce most risks, delaying

Haoyu Zuo, Qianzhi Jing, Tianqi Song, Huiting Liu

Data-driven design and innovation is a process to reuse and provide valuable and useful information. However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information. Besides, existing studies build the edges of a semantic network only on either statistical or semantic relationships, whi

Shashi Bhushan Kotwal, Chinmoy Kundu, Sudhakar Modem, Ankit Dubey

Node selection is a simple technique to achieve diversity and thereby enhance the physical layer security in future wireless communication systems which require low complexity. High-speed data transmission often encounters frequency selective fading. In this context, we evaluate the exact closed-form expression for the ergodic secrecy rate (ESR) of the optim

Sean G. Sellers, Ryan O. Milligan, R. T. James McAteer

We studied an X1.6 solar flare produced by NOAA AR 12602 on 2014 October 22. The entirety of this event was covered by RHESSI, IRIS, and Hinode/EIS, allowing analysis of the chromospheric response to a nonthermal electron driver. We derived the energy contained in nonthermal electrons via RHESSI spectral fitting, and linked the time-dependent parameters of t

Sary Drappeau, Asbjørn Christian Nordentoft

In the present paper we study the central values of additive twists of Maa{\ss} forms $L$-series. In the case of the modular group, we show that the additive twists (when averaged over denominators) are asymptotically normally distributed. This supplements the recent work of Petridis--Risager which settled an averaged version of a conjecture of Mazur--Rubin

Peiling Lu, Xu Tan, Botao Yu, Tao Qin

Human usually composes music by organizing elements according to the musical form to express music ideas. However, for neural network-based music generation, it is difficult to do so due to the lack of labelled data on musical form. In this paper, we develop MeloForm, a system that generates melody with musical form using expert systems and neural networks.

Aakash Sharma, Vivek M. Bhasi, Sonali Singh, Rishabh Jain

We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud. We have implemented the profiler by extending prior work to additionally estimate two types of communication stalls - interconnect and network stalls.

Stéphane Brull, Marwa Shahine, Philippe Thieullen

In the following work, we consider the Boltzmann equation that models a polyatomic gas by representing the microscopic internal energy by a continuous variable I. Under some convenient assumptions on the collision cross-section $\mathcal{B}$, we prove that the linearized Boltzmann operator $\mathcal{L}$ of this model is a Fredholm operator. For this, we writ

Zeinab Akhavan, Mona Esmaeili, Babak Badnava, Mohammad Yousefi

Owing to the resource-constrained feature of Internet of Things (IoT) devices, offloading tasks from IoT devices to the nearby mobile edge computing (MEC) servers can not only save the energy of IoT devices but also reduce the response time of executing the tasks. However, offloading a task to the nearest MEC server may not be the optimal solution due to the

Petar Radanliev, David De Roure

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The a

Maarten Beens, Koen de Mare, Rembert Duine, Bert Koopmans

A `toy model' - aimed at capturing the essential physics - is presented that jointly describes spin-polarized hot electron transport and spin pumping driven by local heating. These two processes both contribute to spin-current generation in laser-excited magnetic heterostructures. The model is used to compare the two contributions directly. The spin-polarize

Caroline VanBlargan, Yi Wang

In this paper, we consider the stability of quermassintegral inequalities along a inverse curvature flow. We choose a special rescaling of the flow such that the $k$-th quermassintegral is decreasing and the $k-1$-th quermassintegral is preserved. Along this rescaled flow, we prove that the decreasing rate of the $k$-th quermassintegral is faster than the Fr

J. C. del Valle

In order to find the spectrum associated with the one-dimensional Schr\"oodinger equation, we discuss the Lagrange Mesh method (LMM) and its numerical implementation for bound states. After presenting a general overview of the theory behind the LMM, we introduce the LagrangeMesh package: the numerical implementation of the LMM in Mathematica. Using few lines

Weixing Wei, Peilin Li, Yi Yu, Wei Li

While neural network models are making significant progress in piano transcription, they are becoming more resource-consuming due to requiring larger model size and more computing power. In this paper, we attempt to apply more prior about piano to reduce model size and improve the transcription performance. The sound of a piano note contains various overtone

Pierrick Pochelu, Serge G. Petiton, Bruno Conche

Deep Reinforcement Learning (or just "RL") is gaining popularity for industrial and research applications. However, it still suffers from some key limits slowing down its widespread adoption. Its performance is sensitive to initial conditions and non-determinism. To unlock those challenges, we propose a procedure for building ensembles of RL agents to effici

Wadie Skaf, Tomáš Horváth

Anomalies in time-series provide insights of critical scenarios across a range of industries, from banking and aerospace to information technology, security, and medicine. However, identifying anomalies in time-series data is particularly challenging due to the imprecise definition of anomalies, the frequent absence of labels, and the enormously complex temp

Gianmichele Blasi, Francesco Giazotto, Géraldine Haack

We propose and theoretically investigate the behavior of a ballistic Aharonov-Bohm (AB) ring when embedded in a N-S two-terminal setup, consisting of a normal metal (N) and superconducting (S) leads. This device is based on available current technologie and we show in this work that it constitutes a promising hybrid quantum thermal device, as quantum heat en

Xueli Bai, Fang Li, Maolin Zhou

In this paper, we investigate the issue of maximizing the total equilibrium population with respect to resources distribution m(x) and diffusion rates d under the prescribed total amount of resources in a logistic model with nonlocal dispersals. Among other things, we show that for $d\ge1$, there exist $C_0, C_1>0$, depending on the $\|m\|_{L^1}$ only, such

Mohammad Javaheri

Given a sequence ${\bf g}: g_0,\ldots, g_{m}$, in a finite group $G$ with $g_0=1_G$, let ${\bf \bar g}: \bar g_0,\ldots, \bar g_{m}$, be the sequence defined by $\bar g_0=1_G$ and $\bar g_i=g_{i-1}^{-1}g_i$ for $1\leq i \leq m$. We say that $G$ is doubly sequenceable if there exists a sequence ${\bf g}$ in $G$ such that every element of $G$ appears exactly t

Yi-Yun Huang, Hai-Ming Zhang, Kai Yan, Ruo-Yu Liu

GRB 220627A, detected by Fermi Gamma-ray Burst Monitor (GBM), shows two episodes of gamma-ray emission, which are separated by a {$\sim$700} s long quiescent phase. Due to similar temporal shapes and spectra in the two episodes, GRB 220627A is speculated to be a gravitationally lensed gamma-ray burst (GRB). We analyze the Fermi Large Area Telescope (LAT) dat

Atakan Kara, F. Serhan Daniş, Günce Keziban Orman, Sultan Nezihe Turhan

The term "sector" in professional business life is a vague concept since companies tend to identify themselves as operating in multiple sectors simultaneously. This ambiguity poses problems in recommending jobs to job seekers or finding suitable candidates for open positions. The latter holds significant importance when available candidates in a specific sec

Ovidiu Costin, Philip Ehrlich

Conway's real closed field $\mathbf{No}$ of surreal numbers is a sweeping generalization of the real numbers and the ordinals to which a number of elementary functions such as log and exponentiation have been shown to extend. The problems of identifying significant classes of functions that can be so extended and of defining integration for them have proven

Saadia Nasir, Walter J. Smith, Thomas E. Beechem, Stephanie Law

Bi$_2$Se$_3$ is a widely studied 3D topological insulator having potential applications in optics, electronics, and spintronics. When the thickness of these films decrease to less than approximately 6 nm, the top and bottom surface states couple, resulting in the opening of a small gap at the Dirac point. In the 2D limit, Bi$_2$Se$_3$ may exhibit quantum spi

Jiabei Yang, Ann W. Mwangi, Rami Kantor, Issa J. Dahabreh

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes

Shahrokh Hamidi

In this paper, we present 3D high resolution radar imaging at millimeter-Wave (mmWave) frequencies by means of a combination of virtual Multiple Input Multiple Output (MIMO) Frequency Modulated Continuous Wave (FMCW) Radars and Synthetic Aperture Radar (SAR) which results in a compact, low-cost, and high-speed 3D mmWave imagery system with low complexity.

Cinzia Bisi, Jonathan D. Hauenstein, Tuyen Trung Truong

We study a family of birational maps of smooth affine quadric 3-folds, {over the complex numbers}, of the form $x_1x_4-x_2x_3=$ constant, which seems to have some (among many others) interesting/unexpected characters: a) they are cohomologically hyperbolic, b) their second dynamical degree is an algebraic number but not an algebraic integer, and c) the logar

Lang Deng, Jianfei Yang, Shenghai Yuan, Han Zou

As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait rec

J. Blümlein, P. Marquard, C. Schneider, K. Schönwald

We calculate the massless unpolarized Wilson coefficients for deeply inelastic scattering for the structure functions $F_2(x,Q^2), F_L(x,Q^2), x F_3(x,Q^2)$ in the $\overline{\sf MS}$ scheme and the polarized Wilson coefficients of the structure function $g_1(x,Q^2)$ in the Larin scheme up to three--loop order in QCD in a fully automated way based on the met

Simone Zanotti, Hai Son Nguyen, Momchil Minkov, Lucio Claudio Andreani

We present a formalism for studying the radiation-matter interaction in multilayered dielectric structures with active semiconductor quantum wells patterned with an in-plane periodic lattice. The theory is based on the diagonalization of the generalized Hopfield matrix, and it includes loss channels in a non-Hermitian formulation. Hybrid elementary excitatio

P. Ferracin, G. Ambrosio, D. Arbelaez, L. Brouwer

The most effective way to achieve very high collision energies in a circular particle accelerator is to maximize the field strength of the main bending dipoles. In dipole magnets using Nb-Ti superconductor the practical field limit is considered to be 8-9 T. When Nb3Sn superconductor material is utilized, a field level of 15-16 T can be achieved. To further

Harry Shomer, Wei Jin, Juanhui Li, Yao Ma

Knowledge graphs (KGs) have gained prominence for their ability to learn representations for uni-relational facts. Recently, research has focused on modeling hyper-relational facts, which move beyond the restriction of uni-relational facts and allow us to represent more complex and real-world information. However, existing approaches for learning representat

Vladimir I. Kolobov, Robert R. Arslanbekov

A hybrid kinetic-fluid model is used to study ionization waves (striations) in a low-current plasma column of DC discharges in noble gases. Coupled solutions of a kinetic equation for electrons, a drift-diffusion equation of ions, and a Poisson equation for the electric field are obtained to clarify the nature of plasma stratification in the positive column

Tomoki Wada, Kunihito Ioka

A fireball of radiation plasma created near the surface of a neutron star (NS) expands under its own pressure along magnetic field lines, and produces photon emission and relativistic matter outflow. We comprehensively classify the expanding fireball evolution into five cases and obtain the photospheric luminosity and the kinetic energy of the outflow, takin

Chengyuan Li, Zhifang Qiu, Zhangrui Yan, Meifu Li

With the mass construction of Gen III nuclear reactors, it is a popular trend to use deep learning (DL) techniques for fast and effective diagnosis of possible accidents. To overcome the common problems of previous work in diagnosing reactor accidents using deep learning theory, this paper proposes a diagnostic process that ensures robustness to noisy and cr

Jintao Xu, Chenglong Bao, Wenxun Xing

Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure. The alternating minimization (AM) approaches split the composition structure of DNNs and have drawn great interest in the deep learning and optimization communities. In this paper, we propose a un

Francesco Sovrano, Giulio Masetti

The AI Act has been recently proposed by the European Commission to regulate the use of AI in the EU, especially on high-risk applications, i.e. systems intended to be used as safety components in the management and operation of road traffic and the supply of water, gas, heating and electricity. On the other hand, IEC 61508, one of the most adopted internati

Sagnick Mukherjee, Jonathan J. Fortney, Natasha E. Batalha, Theodora Karalidi

Evidence of disequilibrium chemistry due to vertical mixing in the atmospheres of many T and Y-dwarfs has been inferred due to enhanced mixing ratios of CO and reduced NH$_3$. Atmospheric models of planets and brown dwarfs typically parameterize this vertical mixing phenomenon with the vertical eddy diffusion coefficient, $K_{\rm zz}$. While $K_{\rm zz}$ can

Tobias Schräder, Torben Stolte, Inga Jatzkowski, Robert Graubohm

A major challenge in the development of a fully automated vehicle is to enable a large variety of users to use the vehicle independently and safely. Particular demands arise from user groups who rely on human assistance when using conventional cars. For the independent use of a vehicle by such groups, the vehicle must compensate for the absence of an accompa

Guillaume Fertin, Géraldine Jean, Anthony Labarre

In this paper, we study the problem of sorting unichromosomal linear genomes by prefix double-cut-and-joins (or DCJs) in both the signed and the unsigned settings. Prefix DCJs cut the leftmost segment of a genome and any other segment, and recombine the severed endpoints in one of two possible ways: one of these options corresponds to a prefix reversal, whic

Dongdong Ge, Qi Huangfu, Zizhuo Wang, Jian Wu

Cardinal Optimizer is a high-performance mathematical programming solver for efficiently solving largescale optimization problem. This documentation provides basic introduction to the Cardinal Optimizer.

Louis Esser, Federico Scavia

Let $G$ be a finite group, $X$ be a smooth complex projective variety with a faithful $G$-action, and $Y$ be a resolution of singularities of $X/G$. Larsen and Lunts asked whether $[X/G]-[Y]$ is divisible by $[\mathbb{A}^1]$ in the Grothendieck ring of varieties. We show that the answer is negative if $BG$ is not stably rational and affirmative if $G$ is abe

K. Zberecki, M. Wilczynnski, M. Wierzbicki

In this work we investigated adequacy of the Heisenberg model application to novel two-dimensional magnetic materials, on an example of monolayer CrI3. We introduced the concept of the mean tensor invariant under symmetry operations of the magnetic structure, which allows the number of parameters of the anisotropic tensor Heisenberg model to be significantly

Jonathan Berrisch, Sven Pappert, Florian Ziel, Antonia Arsova

We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy prices (natural gas, coal, and oil). We propose a probabilistic multivariate conditional time series model with a VECM-Copula-GARCH structure which exploits key characteristics of the data. Data are normalized with respect to inf

Tanjung Krisnanda, Su-Yong Lee, Changsuk Noh, Jaewan Kim

The minimum time required for a quantum system to evolve to a distinguishable state is set by the quantum speed limit, and consequently influences the change of quantum correlations and other physical properties. Here we study the time required to maximally entangle two principal systems interacting either directly or via a mediating ancillary system, under

Fernanda Couto, Luís Cunha, Diego Ferraz

A tree $t$-spanner of a graph $G$ is a spanning tree $T$ in which the distance between any two adjacent vertices of $G$ is at most $t$. The smallest $t$ for which $G$ has a tree $t$-spanner is called tree stretch index. The $t$-admissibility problem aims to decide whether the tree stretch index is at most $t$. Regarding its optimization version, the smallest

Tobias Mistele

Modified gravity models often contain modes that couple to normal matter and propagate with slightly less than the speed of light. High-energy cosmic rays then lose energy due to Cherenkov radiation, which constrains such models. This is also true for some MOND (Modified Newtonian Dynamics) models. However, these constraints are difficult to make precise bec

Benedikt Bagus, Alexander Gepperth, Timothée Lesort

Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine learning where the usual assumption of stationary data distribution is relaxed or omitted. When naively applying, e.g., DNNs in CL problems, changes in the data distribution can cause the so-called catastrophic forgetting (CF) effect: an abrupt loss of previous knowled

Gergely Endrődi, Gergely Markó

We investigate the response of a hot gas of quarks to external electric fields via leading-order perturbation theory. In particular, we discuss how equilibrium is maintained in the presence of the electric field and calculate the electric susceptibility, providing its high-temperature expansion for arbitrary quark mass. Furthermore, we point out that there i

A. K. Petoukhov, V. V. Nesvizhevsky, T. Bigault, P. Courtois

In this last of a series of three papers on the development of an advanced solid-state neutron polarizer, we present the final construction of the polarizer and the results of its commissioning. The polarizer uses spin-selective reflection of neutrons by interfaces coated with polarizing super-mirrors. The polarizer is built entirely in-house for the PF1B co

Saswata Jana, Partha Sarathi Mandal

Recent advancements in unmanned aerial vehicles, also known as drones, have motivated logistics to use drones for multiple operations. Collaboration between drones and trucks in a last-mile delivery system has numerous benefits and reduces a number of challenges. In this paper, we introduce \textit{drone-delivery packing problem} (DDP), where we have a set o

Farzad Vatandoust, Hoseyn A. Amiri, Sima Mas-hafi

Size-based separation of bioparticles/cells is crucial to a variety of biomedical processing steps for applications such as exosomes and DNA isolation. Design and improvement of such microfluidic devices is a challenge to best answer the demand for producing homogeneous end-result for study and use. Deterministic lateral displacement (DLD) exploits a similar

Samet Bayram, Kenneth Barner

Adversarial machine learning is an emerging area showing the vulnerability of deep learning models. Exploring attack methods to challenge state of the art artificial intelligence (A.I.) models is an area of critical concern. The reliability and robustness of such A.I. models are one of the major concerns with an increasing number of effective adversarial att

Jiayi Cai, Pierre-Emmanuel Angeli, Jean-Marc Martinez, Guillaume Damblin

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been an amount of work aiming at improving traditional closure models, while they are still not satisfactory to some complex f

Leandro Silveri

DAMPE (Dark Matter Particle Explorer) is a satellite-born experiment launched in 2015 in a sun-synchronous orbit at 500 km altitude, and it has been taking data in stable conditions ever since. Its main goals include the spectral measurements up to very high energies, cosmic electrons/positrons and gamma rays up to tens of TeV, and protons and nuclei up to h

Matthias Liero, Alexander Mielke, Giuseppe Savaré

We study the fine regularity properties of optimal potentials for the dual formulation of the Hellinger--Kantorovich problem (HK), providing sufficient conditions for the solvability of the primal Monge formulation. We also establish new regularity properties for the solution of the Hamilton--Jacobi equation arising in the dual dynamic formulation of HK, whi

Jesper Lykke Jacobsen, Sylvain Ribault, Hubert Saleur

We determine the spaces of states of the two-dimensional $O(n)$ and $Q$-state Potts models with generic parameters $n,Q\in \mathbb{C}$ as representations of their known symmetry algebras. While the relevant representations of the conformal algebra were recently worked out, it remained to determine the action of the global symmetry groups: the orthogonal grou

Jing Wu, Shanshan Cao, Feng Li

Jet quenching parameter $\hat{q}$ is essential for characterizing the interaction strength between jet partons and nuclear matter. Based on the quark-meson (QM) model, we develop a new framework for calculating $\hat{q}$ at finite chemical potentials, in which $\hat{q}$ is related to the spectral function of the chiral order parameter. A perturbative calcula

James B. Dragan, Ivan O. Antonov, Brian C. Odom

Fast and efficient state preparation of molecules can be accomplished by optical pumping. Molecular structure that most obviously facilitates cycling involves a strong electronic transition, with favorable vibrational branching (diagonal Franck-Condon factors, aka FCFs) and without any intervening electronic states. Here, we propose important adjustments to

Inske Groenen, Stevan Rudinac, Marcel Worring

Large collections of geo-referenced panoramic images are freely available for cities across the globe, as well as detailed maps with location and meta-data on a great variety of urban objects. They provide a potentially rich source of information on urban objects, but manual annotation for object detection is costly, laborious and difficult. Can we utilize s

Qinji Yu, Kang Dang, Ziyu Zhou, Yongwei Chen

Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data. However, coarse annotations such as circles or ellipses for outlining the lesion area can be six times more efficient than pixel-level annotation. Therefore, this paper proposes an annotation refinement network to convert a co

A. Basyrov, A. -S. Suur-Uski, L. P. L. Colombo, J. R. Eskilt

We present Planck LFI frequency sky maps derived within the BeyondPlanck framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples. This ensemble allows for computationally convenient end-to-end pro

Bharath Muppasani, Cheyyur Jaya Anand, Chinmayi Appajigowda, Biplav Srivastava

The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial un-supervised machine learning based solution as a baseli

Evelina V. Permyakova, Denis S. Goldobin

Exponential time differencing methods is a power tool for high-performance numerical simulation of computationally challenging problems in condensed matter physics, fluid dynamics, chemical and biological physics, where mathematical models often possess fast oscillating or decaying modes -- in other words, are stiff systems. Practical implementation of these

Blake J. Boudreaux

We explore the relationship between the Bergman kernel of a Hartogs domain and weighted Bergman kernels over its base domain. In particular we develop a representation of the Bergman kernel of a Hartogs domain as a series involving weighted Bergman kernels over its base, as well as a "transformation" formula for some weighted Bergman kernels. Other relations

Wei Zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation

Mike Becker, Roland Meyer, Tobias Runge, Ina Schaefer

Intensive testing using model-based approaches is the standard way of demonstrating the correctness of automotive software. Unfortunately, state-of-the-art techniques leave a crucial and labor intensive task to the test engineer: identifying bugs in failing tests. Our contribution is a model-based classification algorithm for failing tests that assists the e

Shentong Mo, Pedro Morgado

Audio-visual source localization is a challenging task that aims to predict the location of visual sound sources in a video. Since collecting ground-truth annotations of sounding objects can be costly, a plethora of weakly-supervised localization methods that can learn from datasets with no bounding-box annotations have been proposed in recent years, by leve

Philipp Rüßmann, Stefan Blügel

Interfacing a topological insulator (TI) with an $s$-wave superconductor (SC) is a promising material platform that offers the possibility to realize a topological superconductor through which Majorana-based topologically protected qubits can be engineered. In our computational study of the prototypical SC/TI interface between Nb and Bi$_2$Te$_3$, we identif

Hongpeng Cao, Lukas Dirnberger, Daniele Bernardini, Cristina Piazza

6D pose recognition has been a crucial factor in the success of robotic grasping, and recent deep learning based approaches have achieved remarkable results on benchmarks. However, their generalization capabilities in real-world applications remain unclear. To overcome this gap, we introduce 6IMPOSE, a novel framework for sim-to-real data generation and 6D p

Anuj Kumar Bhagat, Ritumoni Sarma

We propose an algorithm to find a lower bound for the number of cyclic codes over any finite field with any given exponent. Besides, we give a formula to find the exponent of BCH codes.

Cong Guo, Chen Zhang, Jingwen Leng, Zihan Liu

Quantization is a technique to reduce the computation and memory cost of DNN models, which are getting increasingly large. Existing quantization solutions use fixed-point integer or floating-point types, which have limited benefits, as both require more bits to maintain the accuracy of original models. On the other hand, variable-length quantization uses low

S. I. Kruglov

Thermodynamics and phase transitions of magnetic Anti-de Sitter black holes are studied. We consider an extended phase space thermodynamics with the cosmological constant being a thermodynamic pressure and the black hole mass is treated as a chemical enthalpy. The extended phase space thermodynamics of black holes mimics the behavior of the Van der Walls liq