November 2022 arXiv papers — page 145

Bang L. H. Nguyen, Tuyen Vu, Thai-Thanh Nguyen, Mayank Panwar

This paper presents a 1-D convolutional graph neural network for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type

Oliver Knitter, James Stokes, Shravan Veerapaneni

Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resources to accelerate this task, the computational advantage of VQAs over wholly classical algorithms has not bee

Bang L. H. Nguyen, Mayank Panwar, Rob Hovsapian, Yashodhan Agalgaokar

This paper presents the operation modes and control architecture of the grid-connected hydrogen electrolyzer systems for the provision of frequency and voltage supports. The analysis is focused on the primary and secondary loops in the hierarchical control scheme. At the power converter inner control loop, the voltage- and current-control modes are analyzed.

Shubhranshu Shekhar, Jetson Leder-Luis, Leman Akoglu

The US federal government spends more than a trillion dollars per year on health care, largely provided by private third parties and reimbursed by the government. A major concern in this system is overbilling, waste and fraud by providers, who face incentives to misreport on their claims in order to receive higher payments. In this paper, we develop novel ma

Konrad Staniszewski

The exact complexity of solving parity games is a major open problem. Several authors have searched for efficient algorithms over specific classes of graphs. In particular, Obdr\v{z}\'{a}lek showed that for graphs of bounded tree-width or clique-width, the problem is in $\mathrm{P}$, which was later improved by Ganardi, who showed that it is even in $\mathrm

Joseanne Viana, Hamed Farkhari, Pedro Sebastiao, Sandra Lagen

Synthetic datasets are beneficial for machine learning researchers due to the possibility of experimenting with new strategies and algorithms in the training and testing phases. These datasets can easily include more scenarios that might be costly to research with real data or can complement and, in some cases, replace real data measurements, depending on th

Kilian Raschel, Pierre Tarrago

Random walks in cones have the double interest of being at the heart of many probabilistic problems and of being related to many mathematical fields, such as spectral theory, combinatorics, or discrete complex analysis. In this article, we present some key ideas associated with these processes: we will discuss their definition, the link with Brownian motion

Hamed Farkhari, Joseanne Viana, Pedro Sebastiao, Luis Miguel Campos

Only increasing accuracy without considering uncertainty may negatively impact Deep Neural Network (DNN) decision-making and decrease its reliability. This paper proposes five combined preprocessing and post-processing methods for time-series binary classification problems that simultaneously increase the accuracy and reliability of DNN outputs applied in a

Feng Zhou, Tao Chen, Baiying Lei

There are still many challenges of emotion recognition using physiological data despite the substantial progress made recently. In this paper, we attempted to address two major challenges. First, in order to deal with the sparsely-labeled physiological data, we first decomposed the raw physiological data using signal spectrum analysis, based on which we extr

Negar Erfanian, Santiago Segarra, Maarten de Hoop

Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often used to mathematically model these point events. However, previous approaches have faced numerous challenges, particularly when attempting to

Kornikar Sen, Adithi Ajith, Saronath Halder, Ujjwal Sen

The superposition principle provides us the opportunity to unfold many surprising facts. One such fact leads to the generation of entanglement which may allow one to teleport an unknown quantum state from one location to another. We try to understand the role of superposition in the process of quantum teleportation. We consider, within the scenario of quantu

Bailey Andrew, David Westhead, Luisa Cutillo

This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimodal datasets such as those encountered i

Mingming Wu, Yue Xiao, Yulan Gao, Ming Xiao

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, th

Xiao Chi

Epistemic graphs are a generalization of the epistemic approach to probabilistic argumentation. Hunter proposed a 2-way generalization framework to learn epistemic constraints from crowd-sourcing data. However, the learnt epistemic constraints only reflect users' beliefs from data, without considering the rationality encoded in epistemic graphs. Meanwhile, t

Elad Korngut, Jason Hindes, Michael Assaf

Understanding the spread of diseases through complex networks is of great interest where realistic, heterogeneous contact patterns play a crucial role in the spread. Most works have focused on mean-field behavior -- quantifying how contact patterns affect the emergence and stability of (meta)stable endemic states in networks. On the other hand, much less is

Gefei Niu, Jianchen Lu, Jianqun Geng, Shicheng Li

Strain engineering is a vital way to manipulate the electronic properties of two-dimensional (2D) materials. As a typical representative of transition metal mono-chalcogenides (TMMs), a honeycomb CuSe monolayer features with one-dimensional (1D) moir\'e patterns owing to the uniaxial strain along one of three equivalent orientations of Cu(111) substrates. He

Gongping Chen, Lu Zhou, Jianxun Zhang, Xiaotao Yin

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and

Chenyang Lei, Xudong Jiang, Qifeng Chen

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment w

Xiaohan Jia, Guofang Wang, Chao Xia, Xuwen Zhang

In this paper, we show that any embedded capillary hypersurface in the half-space with anisotropic constant mean curvature is a truncated Wulff shape. This extends Wente's result \cite{Wente80} to the anisotropic case and He-Li-Ma-Ge's result \cite{HLMG09} to the capillary boundary case. The main ingredients in the proof are a new Heintze-Karcher inequality

Arushi Gupta, Nikunj Saunshi, Dingli Yu, Kaifeng Lyu

Saliency methods compute heat maps that highlight portions of an input that were most {\em important} for the label assigned to it by a deep net. Evaluations of saliency methods convert this heat map into a new {\em masked input} by retaining the $k$ highest-ranked pixels of the original input and replacing the rest with \textquotedblleft uninformative\textq

Peichao Wei, Muhuo Liu

For a simple connected graph $G=(V,E)$, let $d(u)$ be the degree of the vertex $u$ of $G$. The general Sombor index of $G$ is defined as $$SO_{\alpha}(G)=\sum_{uv\in E} \left[d(u)^2+d(v)^2\right]^\alpha$$ where $SO(G)=SO_{0.5}(G)$ is the recently invented Sombor index. In this paper, we show that in the class of connected graphs with a fixed degree sequence

Youjian Song, Defeng Zou, Omri Gat, Minglie Hu

When a laser cavity supports the propagation of several ultrashort pulses, these pulses interact and can form compact bound states called soliton molecules. Soliton molecules are fascinating objects of nonlinear science, which present striking analogies with their matter molecules counterparts. The soliton pair, composed of two identical pulses, constitutes

Archia Ghiasabadi, Stefan Reppen

Let $X$ be a smooth projective curve of genus $g$, defined over an algebraically closed field $k$, and let $G$ be a connected reductive group over $k$. We say that a $G$-torsor is essentially finite if it admits a reduction to a finite group, generalising the notion of essentially finite vector bundles to arbitrary groups $G$. We give a Tannakian interpretat

Victor Y. Wang, Max Wenqiang Xu

Let $P(x)\in \mathbb{Z}[x]$ be a polynomial with at least two distinct complex roots. We prove that the number of solutions $(x_1, \dots, x_k, y_1, \dots, y_k)\in [N]^{2k}$ to the equation \[ \prod_{1\le i \le k} P(x_i) = \prod_{1\le j \le k} P(y_j)\neq 0 \] (for any $k\ge 1$) is asymptotically $k!N^{k}$ as $N\to +\infty$. This solves a question first propos

Zilu Zhang, Liping Sun, Zhaoxin Li

Over a field of characteristic p>2, firstly, the structure of Kac modules of Lie superalgebra $\tilde{P}(2)$ and the weight space decompositions are given. Secondly, the weight-derivations of $\tilde{P}(2)$ to its Kac modules are computed. Finally, the first cohomology of $\tilde{P}(2)$ with coefficients in Kac modules is determined.

Mario Chahoud, Hani Sami, Azzam Mourad, Safa Otoum

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model conver

Sarosh Adenwalla

A permutation of the integers avoiding monotone arithmetic progressions of length $6$ was constructed in (Geneson, 2018). We improve on this by constructing a permutation of the integers avoiding monotone arithmetic progressions of length $5$. We also construct permutations of the integers and the positive integers that improve on previous upper and lower de

Thomas Fernique, Daria Pchelina

We consider ternary disc packings of the plane, i.e. the packings using discs of three different radii. Packings in which each ''hole'' is bounded by three pairwise tangent discs are called triangulated. There are 164 pairs $(r,s)$, $1{>}r{>}s$, allowing triangulated packings by discs of radii 1, $r$ and $s$. In this paper, we enhance existing methods of dea

Lu Bai, Lixin Cui, Yue Wang, Ming Li

In this work, we propose a family of novel quantum kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), for un-attributed graphs. Different from most existing classical graph kernels, the proposed HAQJSK kernels can incorporate hierarchical aligned structure information between graphs and transform graphs of random sizes into fix

Yongmao Zhang, Heyang Xue, Hanzhao Li, Lei Xie

End-to-end singing voice synthesis (SVS) model VISinger can achieve better performance than the typical two-stage model with fewer parameters. However, VISinger has several problems: text-to-phase problem, the end-to-end model learns the meaningless mapping of text-to-phase; glitches problem, the harmonic components corresponding to the periodic signal of th

C. M. Adair, K. Altenmüller, V. Anastassopoulos, S. Arguedas Cuendis

The CAST-CAPP axion haloscope, operating at CERN inside the CAST dipole magnet, has searched for axions in the 19.74 $\mu$eV to 22.47 $\mu$eV mass range. The detection concept follows the Sikivie haloscope principle, where Dark Matter axions convert into photons within a resonator immersed in a magnetic field. The CAST-CAPP resonator is an array of four indi

Jagadish Pine

In this article we will construct a universal moduli space of stable parabolic vector bundles over the moduli space of marked Deligne-Mumford stable curves $\overline{M}_{_{g, n}}$. The objects that appear over the boundary of $\overline{M}_{_{g, n}}$ i.e., over singular curves will remain vector bundles. The total space and the fibers over $\overline{M}_{_{

Ryoma Yataka, Kazuki Hirashima, Masashi Shiraishi

Recently, studies on machine learning have focused on methods that use symmetry implicit in a specific manifold as an inductive bias. Grassmann manifolds provide the ability to handle fundamental shapes represented as shape spaces, enabling stable shape analysis. In this paper, we present a novel approach in which we establish the theoretical foundations for

Rui Yu, Yifeng Li, Wenpeng Lu, Longbing Cao

In natural language processing (NLP), the context of a word or sentence plays an essential role. Contextual information such as the semantic representation of a passage or historical dialogue forms an essential part of a conversation and a precise understanding of the present phrase or sentence. However, the standard attention mechanisms typically generate w

Jin-Han Xie

We explore the spatial-temporal structure functions of Burgers turbulence driven by a temporal Ornstein-Uhlenbeck (OU) process, where the characteristic time scale of the OU process is much larger than that of the energy flux across spatial scales. Based on the K\'arm\'an-Howarth-Monin equation, we obtain an expression for the third-order spatial-temporal st

C. Di Maio, Q. Changeat, S. Benatti, G. Micela

Characterising the properties of exoplanet atmospheres relies on several interconnected parameters, which makes it difficult to determine them independently. Planetary mass plays a role in determining the scale height of atmospheres, similarly to the contribution from the average molecular weight of the gas. We investigate the relevance of planetary mass kno

Qi Wu, Yuanxin Zheng, Shidong Liu, Gang Li

The light hadron decay processes of $Z_b(10610)/Z_b(10650)$ provide us a way to study their nature and decay mechanism. In this work, we evaluate the branching ratios of $Z_b(10610)/Z_b(10650) \to VP$ ($V$ and $P$ stand for light vector and pseudoscalar mesons, respectively) using an effective Lagrangian approach, in which the contributions of intermediate b

Zhe Liu, Yun Li, Lina Yao, Xiaojun Chang

The task of Compositional Zero-Shot Learning (CZSL) is to recognize images of novel state-object compositions that are absent during the training stage. Previous methods of learning compositional embedding have shown effectiveness in closed-world CZSL. However, in Open-World CZSL (OW-CZSL), their performance tends to degrade significantly due to the large ca

Li Wang, Qiang Zhao, Wei Wang

Recently, malevolent user hacking has become a huge problem for real-world companies. In order to learn predictive models for recommender systems, factorization techniques have been developed to deal with user-item ratings. In this paper, we suggest a broad architecture of a factorization model with adversarial training to get over these issues. The effectiv

Aref Einizade, Sepideh Hajipour Sardouie

Graph Learning (GL) is at the core of inference and analysis of connections in data mining and machine learning (ML). By observing a dataset of graph signals, and considering specific assumptions, Graph Signal Processing (GSP) tools can provide practical constraints in the GL approach. One applicable constraint can infer a graph with desired frequency signat

Kathleen M. Lewis, John Guttag

Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To our knowledge, our paper presents the first method for generating images of garments and models in a new target size to tackle the size under-representation problem. Our primary te

Jinan Wang, Hung N. Do, Kushal Koirala, Yinglong Miao

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides and antibodies. Notably, drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling appro

Ribhu Paul

We consider the modification to Kruglov's non-linear electrodynamics in the presence of minimal length ($l_p$), by considering a specific formulation of generalized uncertainty principle (GUP). The presence of minimal length has been motivated by several candidate theories of quantum-gravity (both perturbative and non-perturbative). We show that, such minima

Roland Guttenberg, Mikhail Raskin, Javier Esparza

Vector Addition Systems (VAS), aka Petri nets, are a popular model of concurrency. The reachability set of a VAS is the set of configurations reachable from the initial configuration. Leroux has studied the geometric properties of VAS reachability sets, and used them to derive decision procedures for important analysis problems. In this paper we continue the

Moritz Haas, Bedartha Goswami, Ulrike von Luxburg

Network-based analyses of dynamical systems have become increasingly popular in climate science. Here we address network construction from a statistical perspective and highlight the often ignored fact that the calculated correlation values are only empirical estimates. To measure spurious behaviour as deviation from a ground truth network, we simulate time-

Eric Gaidos, Teruyuki Hirano, Rena A. Lee, Hiroki Harakawa

We report a search for excess absorption in the 1083.2 nm line of ortho (triplet) helium during transits of TOI-1807b and TOI-2076b, 1.25 and 2.5R$_{\rm Earth}$ planets on 0.55- and 10.4-day orbits around nearby $\sim$200~Myr-old K dwarf stars. We limit the equivalent width of any transit-associated absorption to $<$4 and $<$8 mA, respectively. We limit the

Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia, Xiaoyue Jiang

Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model trained to recognize images of animals can be reprogrammed to recognize medical images by embedding an adversarial program in the images provided as inputs. This attack can be perpet

Yizhi Li, Ge Zhang, Bohao Yang, Chenghua Lin

Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional bias in language models (LMs), a long-standing global discrimination problem, still remains unexplored. This paper bridges the gap by analysing the regional bias learned by the pre-

Pierre A. Pantaleon, Alejandro Jimeno-Pozo, Hector Sainz-Cruz, Vo Tien Phong

Twisted bilayer graphene has a rich phase diagram, including superconductivity. Recently, an unexpected discovery has been the observation of superconductivity in non-twisted graphene bilayers and trilayers. In this Perspective, we give an overview of the search for uncommon phases in non-twisted graphene systems. We first contextualize these recent results

Ke Li, Renzhi Chen, Xin Yao

Many real-world problems are usually computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach for tackling expensive black-box optimization problems in a static environment whereas it has rarely been studied under dynamic environments. T

Dang Minh Nguyen, Luu Anh Tuan

Recent studies on adversarial images have shown that they tend to leave the underlying low-dimensional data manifold, making them significantly more challenging for current models to make correct predictions. This so-called off-manifold conjecture has inspired a novel line of defenses against adversarial attacks on images. In this study, we find a similar ph

Shangsheng Wen, Yingcong Chen

With the development of wireless communications and the increasing computing power of variety mobile devices, LBS (Location Based Service) technologies getting more and more attention as it can provide most flexibility and convenience in modern people' s life. For this survey, we will first give a comprehensive introduction about LBS, including definition, a

Ria Joseph, Manushan Thenabadu, Channa Hatharasinghe, Jesse Fulton

Wigner's friend paradoxes highlight contradictions between measurements made by Friends inside a laboratory and superobservers outside a laboratory, who have access to an entangled state of the measurement apparatus. The contradictions lead to no-go theorems for observer-independent facts, thus challenging concepts of objectivity. Here, we examine the parado

Luca Stendardo, Athanasios Milionis, George Kokkoris, Christos Stamatopoulos

Rapid and sustained condensate droplet departure from a surface is key towards achieving high heat transfer rates in condensation, a physical process critical to a broad range of industrial and societal applications. Despite progress in enhancing condensation heat transfer through inducing its dropwise mode with hydrophobic materials, sophisticated surface e

Sergey A. Dyachenko, Vera Mikyoung Hur, Denis A. Silantyev

Numerically computed with high accuracy are periodic traveling waves at the free surface of a two dimensional, infinitely deep, and constant vorticity flow of an incompressible inviscid fluid, under gravity, without the effects of surface tension. Of particular interest is the angle the fluid surface of an almost extreme wave makes with the horizontal. Numer

Mane Margaryan, Matthias Seibold, Indu Joshi, Mazda Farshad

Data augmentation is a valuable tool for the design of deep learning systems to overcome data limitations and stabilize the training process. Especially in the medical domain, where the collection of large-scale data sets is challenging and expensive due to limited access to patient data, relevant environments, as well as strict regulations, community-curate

Julien Trevisan

We study the error of the number of points of the lattice $\mathbb{Z}^{d}$ that fall into a dilated and translated hypercube centred around $0$ and whose axis are parallel to the axis of coordinates. We show that if $t$, the factor of dilatation, is distributed according to the probability measure $\frac{1}{T} \rho(\frac{t}{T}) dt$ with $\rho$ being a probab

Riku Funada, María Santos, Ryuichi Maniwa, Junya Yamauchi

This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopte

Maciej Lewenstein, Guillem Müller-Rigat, Jordi Tura, Anna Sanpera

Physical transformations are described by linear maps that are completely positive and trace preserving (CPTP). However, maps that are positive (P) but not completely positive (CP) are instrumental to derive separability/entanglement criteria. Moreover, the properties of such maps can be linked to entanglement properties of the states they detect. Here, we e

Jan M. Wolf, Lennart Werner, Philip Bergmann, Clemens Riegler

The Daedalus 2 mission aboard REXUS 29 is a technology demonstrator for an alternative descent mechanism for very high altitude drops based on auto-rotation. It consists of two probes that are ejected from a sounding rocket at an altitude of about 80 km and decelerate to a soft landing using only a passive rotor with pitch control. This type of autonomous, s

Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas

With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent of weather or lighting conditions. Usage of SAR, however

P Supriya Patro, Tripti Goel, S A VaraPrasad, M Tanveer

Structural alterations have been thoroughly investigated in the brain during the early onset of schizophrenia (SCZ) with the development of neuroimaging methods. The objective of the paper is an efficient classification of SCZ in 2 different classes: Cognitive Normal (CN), and SCZ using magnetic resonance imaging (MRI) images. This paper proposed a lightweig

Rui Gao, Zhi-Hui Guo, J. A. Oller, Hai-Qing Zhou

We study the mixing of the QCD/QCD-like axion and light-flavor mesons $\pi^0, \eta, \eta'$ within the framework of $U(3)$ chiral perturbation theory up to next-to-leading order in this work. The axion-meson mixing formulas are calculated order by order in the $U(3)$ $\delta$-expansion scheme, namely the joint expansions of the momentum, light-quark masses an

Jakub Byszewski, Gunther Cornelissen

We propose a correspondence between certain multiband linear cellular automata - models of computation widely used in the description of physical phenomena - and endomorphisms of certain algebraic unipotent groups over finite fields. The correspondence is based on the construction of a universal element specialising to a normal generator for any finite field

Ameneh Farhadian, Hamid Reza Fanai

In this paper, a simple explanation for the Goldbach Conjecture is given. We have shown that the probability of violating the conjecture not only for the prime numbers, but also for any subset of natural numbers whose distribution is similar to the prime numbers is negligible. This result makes it possible to generalize the conjecture to any subset of natura

Xiaojun Yang, Haoyu Zhong, Penglin Du, Keyi Zhou

Practices in the built environment have become more digitalized with the rapid development of modern design and construction technologies. However, the requirement of practitioners or scholars to gather complicated professional knowledge in the built environment has not been satisfied yet. In this paper, more than 80,000 paper abstracts in the built environm

Xin Zhou, Jinglong Wang, Yong Liu, Xingyu Wu

Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences. Although this problem shares some common issues with the conventional estimated time of arrival (ETA), it is more challenging with the following aspects: 1) Inductive inf

Alhun Aydin, Jonas Fransson, Altug Sisman

Quantum shape effect appears under the size-invariant shape transformations of strongly confined structures. Such a transformation distinctively influences the thermodynamic properties of confined particles. Due to their characteristic geometry, core-shell nanostructures are good candidates for quantum shape effects to be observed. Here we investigate the th

Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution spac

Veronika Witzke, Alexander I. Shapiro, Nadiia M. Kostogryz, Robert Cameron

The reliable characterization of planetary atmospheres with transmission spectroscopy requires realistic modeling of stellar magnetic features, since features that are attributable to an exoplanet atmosphere could instead stem from the host star's magnetic activity. Current retrieval algorithms for analysing transmission spectra rely on intensity contrasts o

Zhaobing Fan, Shaolong Han, Seok-Jin Kang, Young Rock Kim

Let $U_{q}^{-}(\mathfrak g)$ be the negative half of a quantum Borcherds-Bozec algebra $U_{q}(\mathfrak g)$ and $V(\lambda)$ be the irreducible highest weight module with $\lambda \in P^{+}$. In this paper, we investigate the structures, properties and their close connections between crystal bases and canonical bases of $U_{q}^{-}(\mathfrak g)$ and $V(\lambd

Shital Saha, Suchandan Kayal

Very recently, extended fractional cumulative residual entropy (EFCRE) has been proposed by Foroghi et al. (2022). In this paper, we introduce extended fractional cumulative past entropy (EFCPE), which is a dual of the EFCRE. The newly proposed measure depends on the logarithm of fractional order and the cumulative distribution function (CDF). Various proper

The Pierre Auger Collaboration, A. Abdul Halim, P. Abreu, M. Aglietta

In this work we present the interpretation of the energy spectrum and mass composition data as measured by the Pierre Auger Collaboration above $6 \times 10^{17}$ eV. We use an astrophysical model with two extragalactic source populations to model the hardening of the cosmic-ray flux at around $5\times 10^{18}$ eV (the so-called "ankle" feature) as a transit

Gift Khangamwa, Terence L. van Zyl, Clint J. van Alten

Missing data is a common concern in health datasets, and its impact on good decision-making processes is well documented. Our study's contribution is a methodology for tackling missing data problems using a combination of synthetic dataset generation, missing data imputation and deep learning methods to resolve missing data challenges. Specifically, we condu

Yu Liu, Yuan-Chuan Zou

G4.8+6.2 was proposed as a possible kilonova remnant associated with the Korean guest star of AD 1163 in our Milky Way galaxy. Its age is about 860 years according to the historical record. If a neutron star was left in the center of G4.8+6.2, this young neutron star may radiate strong continuous gravitational waves, which could beat the indirect age-based u

Junqi Shi, Ming Lu, Zhan Ma

Quantizing a floating-point neural network to its fixed-point representation is crucial for Learned Image Compression (LIC) because it improves decoding consistency for interoperability and reduces space-time complexity for implementation. Existing solutions often have to retrain the network for model quantization, which is time-consuming and impractical to

Takatoshi Ko, Kotaro Fujisawa, Toshikazu Shigeyama

We construct axisymmetric self-similar solutions of transonic outflows emanating from a point source including the effect of the rotation. The solutions are constructed exclusively on the equatorial plane. The features of solutions are determined by three parameters; the adiabatic index $\gamma$, the dimensionless coordinate of the transonic point, and the d

Zhanhe Li, Xiaoqian Li, Yingdong Wei, Chao Lu

This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal solutions can be solved with high computational efficiency by the proposed quasi-OPF. Unlike conventional OPF utilizing mathematical optimization algorithms, the proposed quasi-OPF adopts analytical mapping from load information to near-

Xiaomeng Zhao, Ganghua Yuan

In this paper, we study the phenomenon of increasing stability in the inverse boundary value problems for the biharmonic equation. By considering a linearized form, we obtain an increasing Lipschitz-like stability when k is large. Furthermore, we extend the discussion to the linearized inverse biharmonic potential problem with attenuation, where an exponenti

Yisheng Tian

We show that certain Tate--Shafarevich groups are unramified which enables us to give an obstruction to the Hasse principle for torsors under tori over p-adic function fields.

Hongmin Cai, Wenxiong Liao, Zhengliang Liu, Yiyang Zhang

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a dom

Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, whi

Khouloud Abdelli, Helmut Griesser, Christian Neumeyr, Robert Hohenleitner

Semiconductor lasers have been rapidly evolving to meet the demands of next-generation optical networks. This imposes much more stringent requirements on the laser reliability, which are dominated by degradation mechanisms (e.g., sudden degradation) limiting the semiconductor laser lifetime. Physics-based approaches are often used to characterize the degrada

Kai Wu, Yingzhou Li, Wentiao Wu, Lin Lin

Electronic band structure is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. We introduc

Guang Liu, Zhongjia Chen, Peng Wu, Hu Xu

Unconventional Weyl phonons with higher topological charges in crystalline solids have attracted increasing attention. By symmetry analysis and low-energy $k.p$ effective Hamiltonian, we propose the symmetry enforced triple hourglass Weyl phonons (THWPs) with Chern number $\mathcal C$ = $\pm$ 3 protected by $6_3$ screw rotation symmetry in chiral space group

Gunter M. Schütz

We prove a duality between the asymmetric simple exclusion process (ASEP) with non-conservative open boundary conditions and an asymmetric exclusion process with particle-dependent hopping rates and conservative reflecting boundaries. This is a reverse duality in the sense that the duality function relates the measures of the dual processes rather than expec

Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui

The issue of distribution shifts is emerging as a critical concern in graph representation learning. From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstabl

Khouloud Abdelli, Helmut Griesser, Stephan Pachnicke

Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor laser reliability. Therefore, a great de

Hongwei Jin, Shumin Xu, Hongjie Jiang, Xiaoji Liu

This paper studies the issues about the generalized inverses of tensors under the C-Product. The aim of this paper is threefold. Firstly, this paper present the definition of the Moore-Penrose inverse, Drazin inverse of tensors under the C-Product. Moreover, the inverse along a tensor is also introduced. Secondly, this paper gives some other expressions of t

Giovanni Gandolfi, Andrea Lapi, Tommaso Ronconi, Luigi Danese

We exploit the recent determination of cosmic star formation rate (SFR) density at redshifts $z\gtrsim 4$ to derive astroparticle constraints on three common dark matter scenarios alternative to standard cold dark matter (CDM): warm dark matter (WDM), fuzzy dark matter ($\psi$DM) and self-interacting dark matter (SIDM). Our analysis relies on the UV luminosi

Kijti Rodtes

In the year 1982, John Chollet conjectured that, for any pair of $n\times n$ positive semidefinite matrices $A,B$, $per(A)\cdot per(B)\geq per(A\circ B)$, where $A\circ B$ is the Hardamard product of $A$ and $B$. This conjecture was proved to be valid for $n=2, 3$ in the year 1987. In this paper, we show that the conjecture holds true for $n=4$.

Jianfeng Hou, Heng Li, Caihong Yang, Qinghou Zeng

An $\mathcal{F}$-saturated $r$-graph is a maximal $r$-graph not containing any member of $\mathcal{F}$ as a subgraph. Let $\mathcal{K}_{\ell + 1}^{r}$ be the collection of all $r$-graphs $F$ with at most $\binom{\ell+1}{2}$ edges such that for some $\left(\ell+1\right)$-set $S$ every pair $\{u, v\} \subset S$ is covered by an edge in $F$. Our first result sh

J. W. Lee, M. B. Tsang, C. Y. Tsang, R. Wang

Experimental information on fragment emissions is important in understanding the dynamics of nuclear collisions and in the development of transport model simulating heavy-ion collisions. The composition of complex fragments emitted in the heavy-ion collisions can be explained by statistical models, which assume that thermal equilibrium is achieved at collisi

Hongwei Jin, Peifeng Zhou, Hongjie Jiang, Xiaoji Liu

In this article, specific definitions of the Moore-Penrose inverse, Drazin inverse of the quaternion tensor and the inverse along two quaternion tensors are introduced under the T-product. Some characterizations, representations and properties of the defined inverses are investigated. Moreover, algorithms are established for computing the Moore-Penrose inver

Irakli Dochviri, Alexander Gamkrelidze, Revaz Kurdiani

In the present paper we study approximation of discs by octagons on the pixel plane. To decide which octagon approximates better the given disc we use Jaccard's distance. The table of Jaccard's distances (calculated by a software created for these purposes) are presented at the end of the paper. The results for proximity are given in the form of a graph. Som

Daniel Maître, Roi Santos-Mateos

In this article we present a method for automatic integration of parametric integrals over the unit hypercube using a neural network. The method fits a neural network to the primitive of the integrand using a loss function designed to minimize the difference between multiple derivatives of the network and the function to be integrated. We apply this method t

Yuhui Wang, Junaid Farooq

Unmanned aerial vehicles (UAVs) with on-board cameras are widely used for remote surveillance and video capturing applications. In remote virtual reality (VR) applications, multiple UAVs can be used to capture different partially overlapping angles of the ground target, which can be stitched together to provide 360{\deg} views. This requires coordinated form

Robert Lee, Jad Abou-Chakra, Fangyi Zhang, Peter Corke

Fabric manipulation is a long-standing challenge in robotics due to the enormous state space and complex dynamics. Learning approaches stand out as promising for this domain as they allow us to learn behaviours directly from data. Most prior methods however rely heavily on simulation, which is still limited by the large sim-to-real gap of deformable objects

Tao Wang, Kaihao Zhang, Jiankang Deng, Tong Lu

Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images, which is a domain-specific image restoration problem in the low-level computer vision area. The early face restoration methods mainly use statistical priors and degradation models, which are difficult to meet the requirements of real-world applications in practi

Sören Becker, Michal Klein, Alexander Neitz, Giambattista Parascandolo

Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and still mostly manual task. In this paper we make a step towards the automation of this process: we propose a transformer-based sequence-to-sequence model that recovers scalar autonom