August 2022 arXiv papers — page 26

G. M. Birajdar, N. D. Sangle

In this paper, we study subclass of analytic function with negative coefficient defined by integral operator in the unit disc $U = \left\{ {z \in C:\left| z \right| < 1} \right\}$. The results are included coefficient estimates, closure theorem and distortion theorems of functions belonging to this subclass. Also, we presented detailed study of uniformly con

Timothy Castiglia, Shiqiang Wang, Stacy Patterson

We propose Flexible Vertical Federated Learning (Flex-VFL), a distributed machine algorithm that trains a smooth, non-convex function in a distributed system with vertically partitioned data. We consider a system with several parties that wish to collaboratively learn a global function. Each party holds a local dataset; the datasets have different features b

Nakamura, R., Baumjohann, W.

We report a unique conjugate observation of fast flows and associated current sheet disturbances in the near-Earth magnetotail by MMS (Magnetospheric Multiscale) and Cluster preceding a positive bay onset of a small substorm at ~14:10 UT, Sep. 8, 2018. MMS and Cluster were located both at X ~-14 RE. A dipolarization front (DF) of a localized fast flow was de

D. Alesini, D. Babusci, C. Braggio, G. Carugno

A haloscope of the QUAX--$a\gamma$ experiment, composed of an high-Q resonant cavity immersed in a 8 T magnet and cooled to $\sim 4.5$~K is operated to search for galactic axion with mass $m_a\simeq42.8~\mu\text{eV}$. The design of the cavity with hollow dielectric cylinders concentrically inserted in a OFHC Cu cavity, allowed us to maintain a loaded quality

Yifan Sun, Pedro Parra-Rivas, Mario Ferraro, Fabio Mangini

We analyze the stability and dynamics of dissipative Kerr solitons in the presence of a parabolic potential. This potential stabilizes oscillatory and chaotic regimes, favoring the generation of static DKSs. Furthermore, the potential induces the emergence of new dissipative structures, such as asymmetric breathers and chimera-like states. Based on a mode de

Michel Cahen, Jean Gutt, Simone Gutt

We define a transverse Dolbeault cohomology associated to any almost complex structure $j$ on a smooth manifold $M$. This we do by extending the notion of transverse complex structure and by introducing a natural j-stable involutive limit distribution with such a transverse complex structure. We relate this transverse Dolbeault cohomology to the generalized

Oleg Aristov

We introduce a notion of a length function exponentially distorted on a (compactly generated) subgroup of a locally compact group. We prove that for a connected linear complex Lie group there is a maximum equivalence class of length functions exponentially distorted on a normal integral subgroup lying between the exponential and nilpotent radicals. Moreover,

Kaushal Santosh Bhogale, Abhigyan Raman, Tahir Javed, Sumanth Doddapaneni

End-to-end (E2E) models have become the default choice for state-of-the-art speech recognition systems. Such models are trained on large amounts of labelled data, which are often not available for low-resource languages. Techniques such as self-supervised learning and transfer learning hold promise, but have not yet been effective in training accurate models

Joohoon Kim, Junhwa Seong, Wonjoong Kim, Gun-Yeal Lee

Metalenses, which exhibit superior light-modulating performance with sub-micrometer-scale thicknesses, are suitable alternatives to conventional bulky refractive lenses. However, fabrication limitations, such as a high cost, low throughput, and small patterning area, hinder their mass production. Here, we demonstrate the mass production of low-cost, high-thr

Anil Kumar Yerrapragada, Jeeva Keshav S, Ankit Gautam, Radha Krishna Ganti

5G cellular systems depend on the timely exchange of feedback control information between the user equipment and the base station. Proper decoding of this control information is necessary to set up and sustain high throughput radio links. This paper makes the first attempt at using Machine Learning techniques to improve the decoding performance of the Physic

Richard Evans

Medical researchers have solved the problem of estimating the sensitivity and specificity of binary medical diagnostic tests without gold standard tests for comparison. That problem is the same as estimating confusion matrices for classifiers on unlabeled data. This article describes how to modify the diagnostic test solutions to estimate confusion matrices

Lohit Kayal, Saswata Basak, Ratul Dasgupta

Numerical studies of dimple and jet formation from a collapsing cavity often model the initial cavity shape as a truncated sphere, mimicking a bursting bubble. In this study, we present a minimal model containing only nonlinear inertial and capillary forces, which produces dimples and jets from a collapsing, capillary wave trough. The trough develops from an

Mohammad Farzanullah, Hung V. Vu, Tho Le-Ngoc

We propose joint user association, channel assignment and power allocation for mobile robot Ultra-Reliable and Low Latency Communications (URLLC) based on multi-connectivity and reinforcement learning. The mobile robots require control messages from the central guidance system at regular intervals. We use a two-phase communication scheme where robots can for

Zhen Zhang, Jun Ding, Evan Ma

Plastic flow in amorphous solids is known to be carried by localized shear transformations (STs) which have been proposed to preferentially initiate from some defect units in the structure, akin to dislocations and point defects in crystalline solids. Despite the central role of STs in the mechanical deformation of metallic glasses (MGs), our knowledge of th

Jonas M. Lindert, Daniele Lombardi, Marius Wiesemann, Giulia Zanderighi

We consider $W^\pm Z$ production in hadronic collisions and present high-precision predictions in QCD and electroweak (EW) perturbation theory matched to parton showers. To this end, we match next-to-next-to-leading order QCD corrections to parton showers using the MiNNLO$_{\rm PS}$ method and consistently combine them with next-to-leading order EW correctio

Michael Facci, Jason Metcalfe

We explore the global existence of solutions to systems of quasilinear wave equations satisfying the null condition when the initial data are sufficiently small. We adapt an approach of Keel, Smith, and Sogge, which relies on integrated local energy estimates and a weighted Sobolev estimate that yields decay in $|x|$, by using the $r^p$-weighted local energy

Kevin M. Douglas, Daniel Lucas, Catherine Walsh, Niclas A. West

The first experimental study of the low-temperature kinetics of the gas-phase reaction of NH2 with formaldehyde (CH2O) has been performed. This reaction has previously been suggested as a source of formamide (NH2CHO) in interstellar environments. A pulsed Laval nozzle equipped with laser-flash photolysis and laser-induced fluorescence spectroscopy was used t

Dharm Veer Singh, Vinod Kumar Bhardwaj, Sudhaker Upadhyay

We obtain an exact solution of $AdS$ black hole solution in Einstein-Gauss-Bonnet (EGB) gravity coupled with nonlinear electrodynamics. It interpolates with the $AdS$ regular black hole and $AdS$ EGB black hole in the absence of the Gauss-Bonnet coupling constant and both magnetic monopole charge and deviation parameter, respectively. Based on horizon thermo

Chen Yang, Wang Ziyue, Fang Zijie, Bian Hao

The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance is often unsatisfactory when tested data

Gaurav Bhatnagar

Ramanujan's $q$-continued fractions are a central part of Ramanujan's development of basic hypergeometric series. They appear in Chapter 16 of Part III and Chapter 32 of Part V of {\em Ramanujan's Notebooks} edited by Berndt, and in Volume I of Andrews and Berndt's {\em Ramanujan's Lost Notebook}. In these references the continued fractions as presented in t

Jianing Li, Jiaming Liu, Xiaobao Wei, Jiyuan Zhang

Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity scenario since traditional cameras can only capture blurred images. To deal with this problem, the spike camera is designed to capture the pixel-wise luminance intensity at high frame

Dao-Neng Gao, Xi Gong

Within the standard model, we have investigated rare $Z$-boson decays into double heavy quarkonia, $Z\to VV$ and $Z\to VP$ with $V$ denoting vector and $P$ denoting pseudoscalar quarkonia, respectively. It is assumed that the leading-order QCD diagrams would give the dominant contributions to these processes, and the corresponding branching fractions, for in

Maciej Styczen, Bing-Jyue Chen, Ya-Wen Teng, Yvonne-Anne Pignolet

When spreading information over social networks, seeding algorithms selecting users to start the dissemination play a crucial role. The majority of existing seeding algorithms focus solely on maximizing the total number of reached nodes, overlooking the issue of group fairness, in particular, gender imbalance. To tackle the challenge of maximizing informatio

Ping Yang, Wanxiang Feng, Xiaodong Zhou, Xiuxian Yang

The second-order magneto-optical effects, represented by Voigt and Sch\"{a}fer-Hubert effects, are effective methods to detect the spin textures in antiferromagnets, whereas the previous studies are usually limited to collinear antiferromagnets. In noncollinear antiferromagnets, the spin textures characterized by spin chirality have been revealed to play a c

Ram M. Kripa, Nandini Ramesh, William R. Boos

There is an increasing need for precise location information on historical disasters, such as mass casualty events caused by weather or earthquakes, but existing disaster datasets often do not provide geographic coordinates of past events. Here we describe a new tool, the Wrangler for the Emergency Events Database (WEED), that associates latitude and longitu

Florian Huber, Artem Yushchenko, Benedikt Stratmann, Volker Steinhage

Accurate prediction of crop yield before harvest is of great importance for crop logistics, market planning, and food distribution around the world. Yield prediction requires monitoring of phenological and climatic characteristics over extended time periods to model the complex relations involved in crop development. Remote sensing satellite images provided

Sander De Coninck, Wei-Cheng Wang, Sam Leroux, Pieter Simoens

Camera sensors are increasingly being combined with machine learning to perform various tasks such as intelligent surveillance. Due to its computational complexity, most of these machine learning algorithms are offloaded to the cloud for processing. However, users are increasingly concerned about privacy issues such as function creep and malicious usage by t

Suman Kunwar

In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applic

Claudia de Rham, Laura Engelbrecht, Lavinia Heisenberg, Alice Lüscher

Assuming unitarity, locality, causality, and Lorentz invariance of the, otherwise unknown, UV completion, we derive a new set of constraints on the effective field theory coefficients for the most general, ghost-free Generalized Proca and Proca Nuevo massive vector models. For the Generalized Proca model, we include new interactions that had not been previou

Miguel A. Mendez

This chapter describes modal decompositions in the framework of matrix factorizations. We highlight the differences between classic space-time decompositions and 2D discrete transforms and discuss the general architecture underpinning \emph{any} decomposition. This setting is then used to derive simple algorithms that complete \emph{any} linear decomposition

David A. Vargas, Robert D. Falgout, Stefanie Günther, Jacob B. Schroder

As CPU clock speeds have stagnated and high performance computers continue to have ever higher core counts, increased parallelism is needed to take advantage of these new architectures. Traditional serial time-marching schemes can be a significant bottleneck, as many types of simulations require large numbers of time-steps which must be computed sequentially

Martin Kolář

Research and applications in Machine Learning are limited by computational resources, while 1% of the world's electricity goes into calculating 34 billion billion SHA-256 hashes per second, four orders of magnitude more than the 200 petaflop power of the world's most powerful supercomputer. The work presented here describes how a simple soft fork on Bitcoin

Charl Maree, Christian W. Omlin

The proliferation of artificial intelligence is increasingly dependent on model understanding. Understanding demands both an interpretation - a human reasoning about a model's behavior - and an explanation - a symbolic representation of the functioning of the model. Notwithstanding the imperative of transparency for safety, trust, and acceptance, the opacity

Kevin Ivan Piterman, Volkmar Welker

Let $V$ be a finite dimensional vector space equipped with a non-degenerate Hermitian form over a field $\mathbb{K}$. Let $\mathcal{G}(V)$ be the graph with vertex set the $1$-dimensional non-degenerate subspaces of $V$ and adjacency relation given by orthogonality. We give a complete description of when $\mathcal{G}(V)$ is connected in terms of the dimensio

Simon Roburin, Charles Corbière, Gilles Puy, Nicolas Thome

Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts. The presence of spurious correlations in training datasets leads ERM-trained models to display high loss when evaluated on minority groups not presenting such correlations. Extensive attempts have been made to d

Hanna Müller, Vlad Niculescu, Tommaso Polonelli, Michele Magno

Nano-size drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for on-board computation and sensing, making fully autonomous flight extremely challen

Baomin Wang, Geng Hu, Dan Chen, Lihua Hu

Accurately and quickly binuclear cell (BC) detection plays a significant role in predicting the risk of leukemia and other malignant tumors. However, manual microscopy counting is time-consuming and lacks objectivity. Moreover, with the limitation of staining quality and diversity of morphology features in BC microscopy whole slide images (WSIs), traditional

Matthew Barthet, Ahmed Khalifa, Antonios Liapis, Georgios N. Yannakakis

This paper introduces a paradigm shift by viewing the task of affect modeling as a reinforcement learning (RL) process. According to the proposed paradigm, RL agents learn a policy (i.e. affective interaction) by attempting to maximize a set of rewards (i.e. behavioral and affective patterns) via their experience with their environment (i.e. context). Our hy

Jingyu Dong, Ziwen Pan, Tianyi Yang, Zebin Lin

A new muon source has been proposed to conduct muon spin rotation/relaxation/resonance ($\mu$SR) measurements at China Spallation Neutron Source (CSNS). Only 1 Hz of the CSNS proton beams (25 Hz in total) will be allocated for muon production. To make better use of muons in every pulse, an ultrahigh-array $\mu$SR spectrometer (UHAM) with thousands of detecto

Antonio Mandarino

A three-terminal device, able to control the heat currents flowing through it, is known as a quantum thermal transistor whenever it amplifies two output currents as a response to the external source acting on its third terminal. Several efforts have been proposed in the direction of addressing different engineering options of the configuration of the system.

Dhananjay Saikumar

Direct gravitational simulations of n-body systems have a time complexity O(n^2), which gets computationally expensive as the number of bodies increases. Distributing this workload to multiple cores significantly speeds up the computation and is the fundamental principle behind parallel computing. This project simulates (evolves) our solar system for the nex

Masoud Khalkhali, Ilya Shapiro

This paper is an introduction to Hopf cyclic cohomology with an emphasis on its most recent developments. We cover three major areas: the original definition of Hopf cyclic cohomology by Connes and Moscovici as an outgrowth of their study of transverse index theory on foliated manifolds, the introduction of Hopf cyclic cohomology with coefficients by Hajac-K

Arjen van der Wel, Josha van Houdt, Rachel Bezanson, Marijn Franx

Dynamical models for $673$ galaxies at $z=0.6-1.0$ with spatially resolved (long-slit) stellar kinematic data from LEGA-C are used to calibrate virial mass estimates defined as $M_{\rm{vir}}=K \sigma'^2_{\star,\rm{int}} R$, with $K$ a scaling factor, $\sigma'_{\star,\rm{int}}$ the spatially-integrated stellar velocity second moment from the LEGA-C survey and

Davide Vaccaro, Hiroki Akamatsu, Luciano Gottardi, Jan van der Kuur

We report our most recent progress and demonstration of a frequency domain multiplexing (FDM) readout technology for transition-edge sensor (TES) arrays, both of which we have been developing, in the framework of the X-IFU instrument on board the future Athena X-ray telescope. Using Ti/Au TES micro-calorimeters, high-Q LC filters and analog/digital electroni

Shi-Bei Kong, Haximjan Abdusattar, Hongsheng Zhang, Ya-Peng Hu

We study the thermodynamic properties of the Friedmann-Robertson-Walker (FRW) universe in the brane world scenario, concentrating on the Randall-Sundrum II model. From the first law of thermodynamics for the FRW universe, we find that the work density W can be identified with the thermodynamic pressure P. We construct the equation of state P=P(V,T) for the F

Hugues Thomas, Jian Zhang, Timothy D. Barfoot

We present a method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future semantic information of real dynamic scenes. We present an auto-labeling process that creates SOGMs from noisy real navigation data. We use a 3D-2D feedforward architecture, trained to predict the future time steps of SOGMs, given 3D lidar

Zhongkai Li, Haihua Wei

The purpose of the paper is to study the operators on the weighted Bergman spaces on the unit disk ${\mathbb{D}}$, denoted by $A^{p}_{\lambda,w}({\mathbb{D}})$, that are associated with a class of generalized analytic functions, named the $\lambda$-analytic functions, and with a class of radial weight functions $w$. For $\lambda\ge0$, a $C^2$ function $f$ on

Matthew Barthet, Ahmed Khalifa, Antonios Liapis, Georgios N. Yannakakis

Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large. One of the most promising methods for achieving that long-standing goal is the use of generative AI agents, namely procedural personas, that attempt to imitate particul

Ulf-G. Meißner, Bernard Ch. Metsch

We study the dependence of primordial nuclear abundances on fundamental nuclear observables such as binding energies, scattering lengths, neutron lifetime, \textit{etc.} by varying these quantities. The numerical computations were performed with four publicly available codes, thus facilitating an investigation of the model-dependent (systematic) uncertaintie

Haixin Huang, Haoyuan Qiu, Hanzhe Wu, Yihong Ji

Fourier light-field microscopy (FLFM) uses a micro-lens array (MLA) to segment the Fourier Plane of the microscopic objective lens to generate multiple two-dimensional perspective views, thereby reconstructing the three-dimensional(3D) structure of the sample using 3D deconvolution calculation without scanning. However, the resolution of FLFM is still limite

Serafina Lapenta, Giuseppe Metere, Luca Spada

Let $A$ be a homological category and $U\colon B\to A$ be a faithful conservative right adjoint. We introduce the notion of relative ideal with respect to $U$, and we show that, under suitable conditions, any object of $A$ can be seen as a relative ideal of some object in $B$. We then develop a case study. We first prove that the category of hoops is semi-ab

Chandan Bothra, Jianfei Gao, Sanjay Rao, Bruno Ribeiro

In this paper, we seek to answer what-if questions - i.e., given recorded data of an existing deployed networked system, what would be the performance impact if we changed the design of the system (a task also known as causal inference). We make three contributions. First, we expose the complexity of causal inference in the context of adaptive bit rate video

Arne Gevaert, Yvan Saeys

Because of their strong theoretical properties, Shapley values have become very popular as a way to explain predictions made by black box models. Unfortuately, most existing techniques to compute Shapley values are computationally very expensive. We propose PDD-SHAP, an algorithm that uses an ANOVA-based functional decomposition model to approximate the blac

Miguel García-Bravo, Toni Ikonen, Zheng Zhu

In complete metric measure spaces equipped with a doubling measure and supporting a weak Poincar\'e inequality, we investigate when a given Banach-valued Sobolev function defined on a subset satisfying a measure-density condition is the restriction of a Banach-valued Sobolev function defined on the whole space. We investigate the problem for Haj{\l}asz- and

J. Redolat, M. Camarena-Pérez, A. Griol, M. Kovylina

Controlled integration of metallic nanoparticles (NPs) onto photonic nanostructures enables realization of complex devices for extreme light confinement and enhanced light-matter interaction. This can be achieved combining Nanoparticle-on-Mirror (NPoM) nanocavities with the light manipulation capabilities of micron-scale metallic antennas and/or photonic int

Adel Alameh

The ability to approach a physical phenomenon and grasp its major importance is a remarkable quality of understanding. This paper presents a rather elegant and novel way of looking at the resonance phenomenon, which among others shares a common conceptual basis in various fields of physics. For the sake of simplicity, the discussion will be restricted to the

Gal Vardi

Gradient-based deep-learning algorithms exhibit remarkable performance in practice, but it is not well-understood why they are able to generalize despite having more parameters than training examples. It is believed that implicit bias is a key factor in their ability to generalize, and hence it was widely studied in recent years. In this short survey, we exp

Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo

Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery. A key application of machine learning in the molecular sciences is to learn potential energy surfaces or force fields

Tim Sonnekalb, Bernd Gruner, Clemens-Alexander Brust, Patrick Mäder

Transformer networks such as CodeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved. However, code clone detection is not a trivial task. Semantic code clones, in particular, are challenging to detect. We show that the generalizability of CodeBERT decreases by ev

Mostafa Jahanifar, Adam Shephard, Neda Zamanitajeddin, Simon Graham

Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algo

Vasily Sazonov

In these notes, we develop a path integral approach for the partial differential equations with random initial conditions. Then, we apply it to the dynamics of the spiked tensor model and show that the large-$N$ saddle point equations are dominated by the melonic type diagrams.

A. Torres-Signes, M. P. Frías, M. D. Ruiz-Medina

Global Fr\'echet regression is addressed from the observation of a strictly stationary bivariate curve process, evaluated in a finite--dimensional compact differentiable Riemannian manifold, with bounded positive smooth sectional curvature. The involved univariate curve processes respectively define the functional response and regressor, having the same Fr\'

Yaping Zhao, Haitian Zheng, Mengqi Ji, Ruqi Huang

In this paper, we consider the color-plus-mono dual-camera system and propose an end-to-end convolutional neural network to align and fuse images from it in an efficient and cost-effective way. Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-tempor

Debmalya Mandal, Jiarui Gan

We consider the problem of episodic reinforcement learning where there are multiple stakeholders with different reward functions. Our goal is to output a policy that is socially fair with respect to different reward functions. Prior works have proposed different objectives that a fair policy must optimize including minimum welfare, and generalized Gini welfa

Simon Dirckx, Daan Huybrechs, Robbe Ongenae

Contiguous submatrices of the Fourier matrix are known to be ill-conditioned. In a recent paper in SIAM Review A. Barnett has provided new bounds on the rate of ill-conditioning of the discrete Fourier submatrices. In this paper we focus on the corresponding singular value decomposition. The singular vectors go by the name of periodic discrete prolate sphero

Shyamal Kumar Hui, Shahroud Azami, Sujit Bhattacharyya

In this article we derive both Hamilton type and Souplet-Zhang type gradient estimations for a system of semilinear equations along a geometric flow on a weighted Riemannian manifold.

Carole Chevalier, Jérôme Dorignac, Yahaya Ibrahim, Armelle Choquet

Gene expression consists in the synthesis of proteins from the information encoded on DNA. One of the two main steps of gene expression is the translation of messenger RNA (mRNA) into polypeptide sequences of amino acids. Here, by taking into account mRNA degradation, we model the motion of ribosomes along mRNA with a ballistic model where particles advance

Ruixiang Xue, Jianqiang Wang, Zhan Ma

Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited. This is because 1) fixed receptive field of the

Romanius Ejike Ugwoke, Augustine Ubachukwu, Johnson Ozoemena Urama, Ogbonnaya Okike

We present the results of analyses of the ground level enhancements (GLEs) of cosmic ray (CR) events of 29 September 1989; 15 April 2001 and 20 January 2005. This involve examination of hourly raw CR counts of an array of neutron monitors (NMs) spread across different geographical latitudes and longitudes. Using awk script and computer codes implemented in R

Arman Mahboubi Soufiani, Robert Lee-Chin, Paul Fassl, Md Arafat Mahmud

A direct, camera-based implied open-circuit voltage (iVOC) imaging method via the novel use of a single bandpass filter (s-BPF) is developed for large-area photovoltaic solar cells and solar cell precursors. This method images the photoluminescence (PL) emission using a narrow BPF with centre energy in the high-energy tail of the PL emission taking advantage

Om Mane, Saravanakumar kandasamy

The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this stock value offers enormous arbitrage pr

Marc Goerigk, Michael Hartisch

With dramatic improvements in optimization software, the solution of large-scale problems that seemed intractable decades ago are now a routine task. This puts even more real-world applications into the reach of optimizers. At the same time, solving optimization problems often turns out to be one of the smaller difficulties when putting solutions into practi

Peijin Zhang, Jun Chen, Rui Liu, Chuanbing Wang

Magnetic reconnection preferentially takes place at the intersection of two separatrices or two quasi-separatrix layers, which can be quantified by the squashing factor Q, whose calculation is computationally expensive due to the need to trace as many field lines as possible. We developed a method (FastQSL) optimized for obtaining Q and the twist number in a

Zhang Liu, Minghui Liwang, Seyyedali Hosseinalipour, Huaiyu Dai

Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic nature of VCs (i.e., limited contact duration among vehicles), caused by vehicles' mobility, poses unique challenges to the execution of computation-intensive applications/tasks with d

Jiankang Zhang, Dong Liu, Sheng Chen, Soon Xin Ng

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical {\it{ad-hoc}} networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this paper, we develop a discrete $\epsilon$ multi-objective genetic algorithm ($\epsilon$-

H. C. Das

One of the most common assumptions has been made that the pressure inside the star is isotropic in nature. However, the pressure is locally anisotropic in nature which is a more realistic case. In this study, we investigate certain properties of anisotropic neutron stars with the scalar pressure anisotropy model. Different perfect fluid conditions are tested

Pei-Shien Wu, Ryan Martin

Inference on the minimum clinically important difference, or MCID, is an important practical problem in medicine. The basic idea is that a treatment being statistically significant may not lead to an improvement in the patients' well-being. The MCID is defined as a threshold such that, if a diagnostic measure exceeds this threshold, then the patients are mor

Felix Joos, Dhruv Mubayi

We give asymptotically optimal constructions in generalized Ramsey theory using results about conflict-free hypergraph matchings. For example, we present an edge-coloring of $K_{n,n}$ with $2n/3 + o(n)$ colors such that each $4$-cycle receives at least three colors on its edges. This answers a question of Axenovich, F\"uredi and the second author (On general

Komal Pathade, Uday Khedker

The control flow graph (CFG) representation of a procedure used by virtually all flow-sensitive program analyses, admits a large number of infeasible control flow paths i.e., these paths do not occur in any execution of the program. Hence the information reaching along infeasible paths in an analysis is spurious. This affects the precision of the conventiona

Sandra Di Rocco, Lukas Gustafsson, Luca Schaffler

We explore the maximum likelihood degree of a homogeneous polynomial $F$ on a projective variety $X$, $\mathrm{MLD}_F(X)$, which generalizes the concept of Gaussian maximum likelihood degree. We show that $\mathrm{MLD}_F(X)$ is equal to the count of critical points of a rational function on $X$, and give different geometric characterizations of it via topolo

Walter Didimo, Michael Kaufmann, Giuseppe Liotta, Giacomo Ortali

A graph is rectilinear planar if it admits a planar orthogonal drawing without bends. While testing rectilinear planarity is NP-hard in general (Garg and Tamassia, 2001), it is a long-standing open problem to establish a tight upper bound on its complexity for partial 2-trees, i.e., graphs whose biconnected components are series-parallel. We describe a new O

Claire Voisin

We prove that for any rationally connected threefold $X$, there exists a smooth projective surface $S$ and a family of $1$-cycles on $X$ parameterized by $S$, inducing an Abel-Jacobi isomorphism ${\rm Alb}(S)\cong J^3(X)$. This statement was previously known for some classes of smooth Fano threefolds.

Martin de Wit, Luciano Gottardi, Kenichiro Nagayoshi, Hiroki Akamatsu

In the early 2030s, ESAs new X-ray observatory, Athena, is scheduled to be launched. It will carry two main instruments, one of which is the X-ray Integral Field Unit (X-IFU), an X-ray imaging spectrometer, which will consist of an array of several thousand transition-edge sensors (TESs) with a proposed energy resolution of 2.5 eV for photon energies up to 7

Mario E. Villanueva, Matthias A. Müller, Boris Houska

This paper is about robust Model Predictive Control (MPC) for linear systems with additive and multiplicative uncertainty. A novel class of configuration-constrained polytopic robust forward invariant tubes is introduced, which admit a joint parameterization of their facets and vertices. They are the foundation for the development of novel Configuration-Cons

Micha Horlboge, Erwin Quiring, Roland Meyer, Konrad Rieck

The source code of a program not only defines its semantics but also contains subtle clues that can identify its author. Several studies have shown that these clues can be automatically extracted using machine learning and allow for determining a program's author among hundreds of programmers. This attribution poses a significant threat to developers of anti

Lucas Daniel Wittwer, Felix Reichel, Paul Müller, Jochen Guck

Real-time deformability cytometry (RT-DC) is an established method that quantifies features like size, shape, and stiffness for whole cell populations on a single-cell level in real time. To extract the cell stiffness, a lookup table (LUT) disentangles the experimentally derived steady state cell deformation and the projected area, yielding the Young's modul

Jiahui Chen, Yixin Xu, Shicheng Wan, Wensheng Gan

As an important data mining technology, high utility itemset mining (HUIM) is used to find out interesting but hidden information (e.g., profit and risk). HUIM has been widely applied in many application scenarios, such as market analysis, medical detection, and web click stream analysis. However, most previous HUIM approaches often ignore the relationship b

Jichao Zhang, Aliaksandr Siarohin, Yahui Liu, Hao Tang

Generative Neural Radiance Fields (GNeRF)-based 3D-aware GANs have showcased remarkable prowess in crafting high-fidelity images while upholding robust 3D consistency, particularly face generation. However, specific existing models prioritize view consistency over disentanglement, leading to constrained semantic or attribute control during the generation pro

Pablo M. Berná, Antonio Falcó

This article introduces an innovative mathematical framework designed to tackle non-linear convex variational problems in reflexive Banach spaces. Our approach employs a versatile technique that can handle a broad range of variational problems, including standard ones. To carry out the process effectively, we utilize specialized sets known as radial dictiona

Steven Chaplick, Emilio Di Giacomo, Fabrizio Frati, Robert Ganian

We present an $O(n^2)$-time algorithm to test whether an $n$-vertex directed partial $2$-tree is upward planar. This result improves upon the previously best known algorithm, which runs in $O(n^4)$ time.

Zizhao Zhang, Yifan Feng, Shihui Ying, Yue Gao

Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure. How to generate the hypergraph structure among da

Guanzhou Ke, Yongqi Zhu, Yang Yu

Multi-view representation learning is essential for many multi-view tasks, such as clustering and classification. However, there are two challenging problems plaguing the community: i)how to learn robust multi-view representation from mass unlabeled data and ii) how to balance the view consistency and the view specificity. To this end, in this paper, we prop

Hans L. Bodlaender, Carla Groenland, Michał Pilipczuk

We investigate the parameterized complexity of Binary CSP parameterized by the vertex cover number and the treedepth of the constraint graph, as well as by a selection of related modulator-based parameters. The main findings are as follows: i) Binary CSP parameterized by the vertex cover number is $\mathrm{W}[3]$-complete. More generally, for every positive

Tymoteusz Chmiel

We introduce Koszul modules associated with (graded) Kac-Moody Lie algebras. We provide a precise criterion for when these modules are of finite length. As an exemplary application we deduce a bound on the dimension of the second graded component for a certain class of graded Kac-Moody Lie algebras. We also provide an exact description of all nilpotent Kac-M

Tianyi Li, Wenyu Huang, Nikos Papasarantopoulos, Pavlos Vougiouklis

We present a system for knowledge graph population with Language Models, evaluated on the Knowledge Base Construction from Pre-trained Language Models (LM-KBC) challenge at ISWC 2022. Our system involves task-specific pre-training to improve LM representation of the masked object tokens, prompt decomposition for progressive generation of candidate objects, a

Reginald Christian Bernardo, Kin-Wang Ng

Pulsar timing offers an independent avenue to test general relativity and alternative gravity theories. This requires an understanding of how metric polarizations beyond the familiar transverse tensor ones imprint as a stochastic gravitational wave background and correlate the arrival time of radio pulses from a pair of millisecond pulsars. In this work, we

Sahar Salimpour, Jorge Peña Queralta, Tomi Westerlund

Automatic detection of visual anomalies and changes in the environment has been a topic of recurrent attention in the fields of machine learning and computer vision over the past decades. A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. The majority of existing approaches focus on an

Tommaso Pacini

We present a systematic collection of results concerning interactions between convex, subharmonic and pluri-subharmonic functions on pairs of manifolds related by a Riemannian submersion. Our results are modelled on those known in the classical complex-analytic context and represent another step in the recent Harvey-Lawson pluri-potential theory for calibrat

Erzhong Xue, Zilong Wang, Guang Gong

We generalize the three-stage process for constructing and enumerating Golay array and sequence pairs given in 2008 by Frank Fiedler et al. [A multi-dimensional approach to the construction and enumeration of Golay complementary sequences, Journal of Combinatorial Theory, Series A 115 (2008) 753-776] to $4^{q}$-QAM constellation based on para-unitary matrix

Cédric Lorcé, Bernard Pire, Qin-Tao Song

We apply the Braun-Manashov technique to improve the description of $\gamma^*(q_1) \gamma(q_2) \to M(p_1) M(p_2)$ amplitudes at large $Q^2=-q_1^2$ and small $s=(q_1+q_2)^2$. We derive the kinematical higher-twist contributions of order $s/Q^2$ and $m^2/Q^2$ to the helicity amplitudes and estimate their sizes in the kinematics accessible at Belle and Belle II