November 2022 arXiv papers — page 102

Jasper Verbree, Ashish Cherukuri

This paper considers variational inequalities (VI) defined by the conditional value-at-risk (CVaR) of uncertain functions and provides three stochastic approximation schemes to solve them. All methods use an empirical estimate of the CVaR at each iteration. The first algorithm constrains the iterates to the feasible set using projection. To overcome the comp

Elias Zikkos

A set of complex numbers $\Lambda=\{\lambda_n,\mu_n\}_{n=1}^{\infty}$ with multiple terms \[ \{\lambda_n,\mu_n\}_{n=1}^{\infty}:= \{\underbrace{\lambda_1,\lambda_1,\dots,\lambda_1}_{\mu_1 - times}, \underbrace{\lambda_2,\lambda_2,\dots,\lambda_2}_{\mu_2 - times},\dots, \underbrace{\lambda_k,\lambda_k,\dots,\lambda_k}_{\mu_k - times},\dots\} \] is said to bel

Luhong Su, Cui-Xian Guo, Yongliang Wang, Li Li

The non-Hermitian systems with the non-Hermitian skin effect (NHSE) are very sensitive to the imposed boundary conditions and lattice size, which leads to size-dependent non-Hermitian skin effects. Here, we report the experimental observation of NHSE with different boundary conditions and different lattice size in a unidirectional hopping model based on a ci

Emma D'Aniello, Martina Maiuriello

We prove the spaceability of the set of hypercyclic vectors for {\em shifts-like operators}. Shift-like operators appear naturally as composition operators on $L^p(X)$, when the underlying space $X$ is dissipative. In the process of proving the main theorem, we provide, among other results of independent interest, a characterization of weakly mixing dissipat

Konstantinos Alexopoulos, Bryn Davies

Designing devices composed of many small resonators is a challenging problem that can easily incur significant computational cost. Can asymptotic techniques be used to overcome this often limiting factor? Integral methods and asymptotic techniques have been used to derive concise characterisations for scattering by resonators, but can these be generalised to

Nicola Mariella, Sergiy Zhuk

Swap mapping is a quantum compiler optimization that, by introducing SWAP gates, maps a logical quantum circuit to an equivalent physically implementable one. The physical implementability of a circuit is determined by the fulfillment of the hardware connectivity constraints. Therefore, the placement of the SWAP gates can be interpreted as a discrete optimiz

M Agouri, A Waqdim, A Abbassi, S Taj

The electronic, elastic, structural, thermoelectric and optical parameters of the Pb-based cubic perovskites $PbREO_3\; (RE=Ce,\; Gd,\; Nd)$ were calculated using the WIEN2k Package. The mechanical optimization and structural stabilities were evaluated. The found results are in full agreement with the experimental ones. The obtained elastic results show that

Bruno Mazorra, Nicolás Della Penna

We consider the social welfare that can be facilitated by a constant function market maker (CFMM). When there is sufficient liquidity available to the CFMM, it can approximate the optimal social welfare when all users transactions are executed. When one of the agent has the role of proposing the block, and blockspace is scarce, they can obtain higher expecte

Dan Yang, Laurent Gizon, Hélène Barucq

Helioseismic holography is a useful method to detect active regions on the Sun's far side and improve space weather forecasts. We aim to improve helioseismic holography by using a clear formulation of the problem, an accurate forward solver in the frequency domain, and a better understanding of the noise properties. Building on the work of Lindsey et al., we

Jie Fu, Zhili Chen, XinPeng Ling

Differential privacy (DP) provides a formal privacy guarantee that prevents adversaries with access to machine learning models from extracting information about individual training points. Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition. However, existing DPSGD sche

Chang-Zheng Yuan

The discovery of hadronic states beyond the conventional two-quark meson and three-quark baryon picture in the last two decades is one of the most amazing accomplishments in fundamental physics research. Many experiments contributed to this field despite of the original goals of the design. We review the experimental progress on the study of the quarkoniumli

Igor Konnov, Markus Kuppe, Stephan Merz

Using an algorithm due to Safra for distributed termination detection as a running example, we present the main tools for verifying specifications written in TLA+. Examining their complementary strengths and weaknesses, we suggest a workflow that supports different types of analysis and that can be adapted to the desired degree of confidence.

Kaiyi Du, Yong Shi, Zhi-Yu Zhang, Qiusheng Gu

We investigate the extended-Schmidt (ES) law in volume densities ($\rho_{\rm SFR}$ $\propto$ $(\rho_{\rm gas}\rho_{\rm star}^{0.5})^{\alpha^{\rm VES}}$) for spatially-resolved regions in spiral, dwarf, and ultra-diffuse galaxies (UDGs), and compare to the volumetric Kennicutt-Schmidt (KS) law ($\rho_{\rm SFR}$ $\propto$ $\rho_{\rm gas}^{\alpha^{\rm VKS}}$).

Yifan Lu, Quanhao Li, Baoan Liu, Mehrdad Dianati

Collaborative 3D object detection exploits information exchange among multiple agents to enhance accuracy of object detection in presence of sensor impairments such as occlusion. However, in practice, pose estimation errors due to imperfect localization would cause spatial message misalignment and significantly reduce the performance of collaboration. To all

Hiroki Ikegami, Yasunobu Nakamura

We present experimental investigations of transport properties in the insulating phase of two-dimensional Josephson-junction arrays (JJAs) by systematically changing the ratio of Josephson energy $E_\mathrm{J}$ and charging energy $E_\mathrm{C}$. The observed temperature dependence of resistance indicates that the JJAs do not show a sharp phase transition bu

Lufaldy Ernanda, Jhon Pieter Sitanggang, Rini Setiyawati, Siti Wahyuni

Global warming has been changing the planet climate pattern. Carbon emission is one of the big potentials to push the greenhouse gas effect which contributes to global warming. Indonesia has strengthened its climate commitments through NDC with equitable emission reduction targets and strengthened alignment between climate goals and country development goals

Matthieu Dussaule, Longmin Wang, Wenyuan Yang

Let $\Gamma$ be a non-elementary relatively hyperbolic group with a finite generating set. Consider a finitely supported admissible and symmetric probability measure $\mu$ on $\Gamma$ and a probability measure $\nu$ on $\mathbb{N}$ with mean $r$. Let $\mathrm{BRW}(\Gamma,\nu,\mu)$ be the branching random walk on $\Gamma$ with offspring distribution $\nu$ and

Adil Rengim Cetingoz, Jean-David Fermanian, Olivier Guéant

Modern portfolio theory has provided for decades the main framework for optimizing portfolios. Because of its sensitivity to small changes in input parameters, especially expected returns, the mean-variance framework proposed by Markowitz (1952) has however been challenged by new construction methods that are purely based on risk. Among risk-based methods, t

Gian Marti, Christoph Studer

Multi-antenna (MIMO) processing is a promising solution to the problem of jammer mitigation. Existing methods mitigate the jammer based on an estimate of its subspace (or receive statistics) acquired through a dedicated training phase. This strategy has two main drawbacks: (i) it reduces the communication rate since no data can be transmitted during the trai

Lingfeng Qiao, Chen Wu, Ye Liu, Haoyuan Peng

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos. Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical. Previous researches on pre-trained language models and video-language models have achieved significa

Maxime Beauchamp, Joseph Thompson, Hugo Georgenthum, Quentin Febvre

The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations. Optimal interpolation (OI), also referred to as kriging, provides a theoret

Nadim Ghaddar, Shouvik Ganguly, Lele Wang, Young-Han Kim

Coding schemes for several problems in network information theory are constructed starting from point-to-point channel codes that are designed for symmetric channels. Given that the point-to-point codes satisfy certain properties pertaining to the rate, the error probability, and the distribution of decoded sequences, bounds on the performance of the coding

Roberto Grimaudo, Antonio S. M. de Castro, Antonino Messina, Enrique Solano

A two-interacting-qubit quantum Rabi-like model with vanishing transverse fields on the qubit-pair is studied. Independently of the coupling regime, this model can be exactly and unitarily reduced to two independent single-spin quantum Rabi models, where the spin-spin coupling plays the role of the transverse field. This transformation and the analytical tre

Danilo Carastan dos Santos

This paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a benchmark tool to evaluate the speed and energy consumption of DL algorithms in HPC environments. We exploited hardware counters a

Jonas Rothfuss, Martin Josifoski, Vincent Fortuin, Andreas Krause

Meta-Learning aims to speed up the learning process on new tasks by acquiring useful inductive biases from datasets of related learning tasks. While, in practice, the number of related tasks available is often small, most of the existing approaches assume an abundance of tasks; making them unrealistic and prone to overfitting. A central question in the meta-

Antonin Voyez, Tristan Allard, Gildas Avoine, Pierre Cauchois

The collection of electrical consumption time series through smart meters grows with ambitious nationwide smart grid programs. This data is both highly sensitive and highly valuable: strong laws about personal data protect it while laws about open data aim at making it public after a privacy-preserving data publishing process. In this work, we study the uniq

Karl-Ludwig Besser, Eduard A. Jorswieck, Justin P. Coon

We consider a multi-user two-ray ground reflection scenario with unknown distances between transmitter and receivers. By using two frequencies per user in parallel, we can mitigate possible destructive interference and ensure ultra-reliability with only very limited knowledge at the transmitter. In this work, we consider the problem of assigning two frequenc

Hengne Li, LHCb collaboration

Measurements of quarkonia production in ultra-peripheral heavy-ion collisions are of important value to study photon-photon and photon-nucleus interactions, the partonic structure of nuclei, and mechanisms of vector-meson production. LHCb has studied both coherent $J/\psi$ and $\psi(2S)$ mesons in ultra-peripheral collisions using PbPb data at forward rapidi

Cao Vien Phung, Andre Drummond, Admela Jukan

The emerging dynamic Virtual Reality (VR) applications are the best candidate applications in high bandwidth indoor Terahertz (THz) wireless networks, with the Reconfigurable Intelligent Surface (RIS) devices presenting a breakthrough solution in extending the typically short THz communication range and alleviating line-of-sight link blockages. In future sma

Dexin Liao, Tao Jiang, Feng Wang, Lin Li

Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR). However, the main-stream architecture for Automatic Speaker Verification (ASV) is

Giulio Cerbai, Anders Claesson

The in-order traversal provides a natural correspondence between binary trees with a decreasing vertex labeling and endofunctions on a finite set. By suitably restricting the vertex labeling we arrive at a class of trees that we call Fishburn trees. We give bijections between Fishburn trees and other well-known combinatorial structures that are counted by th

Simon Fabiunke, Christian Fillafer, Matthias F. Schneider

The existence of acoustic pulse propagation in lipid monolayers at the air-water interface is well known. These pulses are controlled by the thermodynamic state of the lipid membrane. Nevertheless, the role of acoustic pulses for intra- and intercellular communication are still a matter of debate. Herein, we used the dye di 4- -ANEPPDHQ, which is known to be

Haokui Zhang, Wenze Hu, Xiaoyu Wang

Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows. In contrast, we propose a different approach that aims to improve the performance of transformer-based architectures by densifying the attention pattern.

Stéphane André, Camille Noûs

Finite Element codes used for solving the mechanical equilibrium equations in transient problems associated to (time-dependent) viscoelastic media generally relies on time-discretized versions of the selected constitutive law. Recent concerns about the use of non-integer differential equations to describe viscoelasticity or well-founded ideas based upon the

Michel Balazard

A study of the greatest possible ratio of the smallest absolute value of a higher derivative of some function, defined on a bounded interval, to the L p-norm of the function.

René Haas, Stella Graßhof, Sami S. Brandt

In this paper, we present an approach for combining non-rigid structure-from-motion (NRSfM) with deep generative models,and propose an efficient framework for discovering trajectories in the latent space of 2D GANs corresponding to changes in 3D geometry. Our approach uses recent advances in NRSfM and enables editing of the camera and non-rigid shape informa

F. Sattin, D. F. Escande

Stochastic heating is a well-known mechanism through which magnetized particles may be energized by low-frequency electromagnetic waves. In its simplest version, under spatially homogeneous conditions, it is known to be operative only above a threshold in the normalized wave amplitude, which may be a demanding requisite in actual scenarios, severely restrict

Matan Mussel, Christian Fillafer, Gal Ben-Porath, Matthias F. Schneider

Electric pulses in biological cells (action potentials) have been reported to be accompanied by a propagating cell-surface deformation with a nano-scale amplitude. Typically, this cell surface is covered by external layers of polymer material (extracellular matrix, cell wall material etc.). It was recently demonstrated in excitable plant cells (Chara Braunii

S. Hasibul Hassan Chowdhury, Talal Ahmed Chowdhury

In this paper, we provide a mathematically and physically consistent minimal prescription for a charged spinless point particle coupled to a constant magnetic field in a 2-dimensional noncommutative plane. It turns out to be a gauge invariant prescription in contrast to the widely and carelessly used naive minimal prescription in the context of 2-dimensional

Ying Hu, Jianhui Huang, Wenqiang Li

We introduce a new type of reflected backward stochastic differential equations (BSDEs) for which the reflection constraint is imposed on its main solution component, denoted as $Y$ by convention, but in terms of its conditional expectation $\mathbb{E}[Y_t|\mathcal{G}_{t}]$ on a general sub-filtration $\{\mathcal{G}_{t}\}.$ We thus term such equation as cond

Baoshun Shi, Ke Jiang, Shaolei Zhang, Qiusheng Lian

Recent deep learning-based methods have achieved promising performance for computed tomography metal artifact reduction (CTMAR). However, most of them suffer from two limitations: (i) the domain knowledge is not fully embedded into the network training; (ii) metal artifacts lack effective representation models. The aforementioned limitations leave room for f

Carina S. Fedosejevs, Matthias F. Schneider

The origin of nonlinear responses in cells has been suggested to be crucial for various cell functions including the propagation of the nervous impulse. In physics nonlinear behavior often originates from phase transitions. Evidence for such transitions on the single cell level, however, has so far not been provided leaving the field unattended by the biolog

Victor Kalt, Georges Sadaka, Ionut Danaila, Frédéric Hecht

We present finite-element numerical algorithms for the identification of vortices in quantum fluids described by a macroscopic complex wave function. Their implementation using the free software FreeFem++ is distributed with this paper as a post-processing toolbox that can be used to analyse numerical or experimental data. Applications for Bose-Einstein cond

Piotr Krasnowski, Jerome Lebrun, Bruno Martin

Motivated by an increasing need for privacy-preserving voice communications, we investigate here the original idea of sending encrypted data and speech in the form of pseudo-speech signals in the audio domain. Being less constrained than military ``Crypto Phones'' and allowing genuine public evaluation, this approach is quite promising for public unsecured v

Meni Orenbach, Bar Raveh, Alon Berkenstadt, Yan Michalevsky

Applications running in Trusted Execution Environments (TEEs) commonly use untrusted external services such as host File System. Adversaries may maliciously alter the normal service behavior to trigger subtle application bugs that would have never occurred under correct service operation, causing data leaks and integrity violations. Unfortunately, existing m

Youngrong Lim, Changhun Oh

Quasiprobability representation is an important tool for analyzing a quantum system, such as a quantum state or a quantum circuit. In this work, we propose classical algorithms specialized for approximating outcome probabilities of a linear optical circuit using $s$-parameterized quasiprobability distributions. Notably, we can reduce the negativity bound of

Eloy Merlijn de Kinkelder, Elisabeth Fischer-Friedrich, Sebastian Aland

During division in animal cells, the actomyosin cortex has been found to exhibit counter-rotating cortical flows, also known as chiral flows, along the axis of division. Furthermore, such chiral surface flows were shown to influence cellular rearrangements and drive the left-right symmetry breaking in developing organisms. In spite of this prospective biolog

Liyang Lu, Wenbo Xu, Yue Wang, Zhi Tian

Compressive sensing (CS) has recently emerged as an extremely efficient technology of the wideband spectrum sensing. In compressive spectrum sensing (CSS), it is necessary to know the sparsity or the noise information in advance for reliable reconstruction. However, such information is usually absent in practical applications. In this paper, we propose a bli

Aaron Philip, Guoqing Zhou, Benjamin Nebgen

Machine Learning Inter-atomic Potentials (MLIPs) have become a common tool in use by computational chemists due to their combination of accuracy and speed. Yet, it is still not clear how well these tools behave at or near transitions states found in complex molecules. Here we investigate the applicability of MLIPs in evaluating the transition barrier of two,

Célestin Coquidé, José Lages, Dima L. Shepelyansky

From the Bretton Woods agreement in 1944 till the present day, the US dollar has been the dominant currency in the world trade. However, the rise of the Chinese economy led recently to the emergence of trade transactions in Chinese yuan. Here, we analyze mathematically how the structure of the international trade flows would favor a country to trade whether

Julien Toulouse, Karno Schwinn, Felipe Zapata, Antoine Levitt

We consider the calculations of photoionization spectra and core resonances of open-shell systems using range-separated time-dependent density-functional theory. Specifically, we use the time-dependent range-separated hybrid (TDRSH) scheme, combining a long-range Hartree-Fock (HF) exchange potential and kernel with a short-range potential and kernel from a l

Greg Sabella

Shallow water and coastal aquatic ecosystems such as coral reefs and seagrass meadows play a critical role in regulating and understanding Earth's changing climate and biodiversity. They also play an important role in protecting towns and cities from erosion and storm surges. Yet technology used for remote sensing (drones, UAVs, satellites) cannot produce de

Michael Klug, Maggie Miller

Let $S_0$ and $S_1$ be two homotopic, oriented 2-spheres embedded in an orientable 4-manifold $X$. After discussing several operations for modifying an immersion of a 3-manifold into a 5-manifold, we discuss the Freedman--Quinn (fq) and Stong (stong) concordance obstructions. When these are defined for the pair $S_0,S_1$, they are defined in terms of the sel

Rahul Deshpande, Dileep Chandran, Alexander J. Smits, Ivan Marusic

We investigate the role of inter-scale interactions in the high-Reynolds number skin-friction drag reduction strategy reported by Marusic et al. (Nat. Commun., vol. 12, 2021). The strategy involves imposing relatively low-frequency streamwise travelling waves of spanwise velocity at the wall to actuate the drag generating outer-scales. This approach has prov

ATLAS Collaboration

Several extensions of the Standard Model predict the production of dark matter particles at the LHC. A search for dark matter particles produced in association with a dark Higgs boson decaying into $W^{+}W^{-}$ in the $\ell^\pm\nu q \bar q'$ final states with $\ell=e,\mu$ is presented. This analysis uses 139 fb$^{-1}$ of $pp$ collisions recorded by the ATLAS

Yu Shi, Shu-Yi Wei, Jian Zhou

We develop a novel Monte Carlo parton branching algorithm based on the Gribov-Levin-Ryskin (GLR) equation. The formulations of both forward evolution and backward evolution for the GLR equation are presented. The results from the Monte Carlo implementation of the GLR equation are in full agreement with its numerical solutions. Our work thus paves the way for

Jie Wang, Yuzhou Peng, Xiaodong Yang, Ting Wang

The SportsMOT dataset aims to solve multiple object tracking of athletes in different sports scenes such as basketball or soccer. The dataset is challenging because of the unstable camera view, athletes' complex trajectory, and complicated background. Previous MOT methods can not match enough high-quality tracks of athletes. To pursue higher performance of M

Bikshapathi Gouda, Italo Atzeni, Antti Tölli

We propose fully distributed multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems with modest training overhead. We target the minimization of the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with a weighted sum MSE minimization to simplify the compu

Yu Hong, Hang Dai, Yong Ding

Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e.g., Pseudo-LiDAR methods. However, the existing methods usually apply non-end-to-end training strategies and insufficiently leverage the LiDAR information, where the rich potential of the LiDAR data has not been well exploited. In

Shan Zhang, Zhichuan Wang, Dong Pan, Zhaoyu Wang

Electron interactions in quantum devices can exhibit intriguing phenomena. One example is assembling an electronic device in series with an on-chip resistor. The quantum laws of electricity of the device is modified at low energies and temperatures by dissipative interactions induced by the resistor, a phenomenon known as dynamical Coulomb blockade (DCB). Th

Yu-Biao Wu, Zhen Zheng, Xiang-Gang Qiu, Lin Zhuang

Nonlocal interaction between ultracold atoms trapped in optical lattices can give rise to interesting quantum many-body phenomena. However, its realization usually demands unconventional techniques, for example the artificial gauge fields or higher-orbit Feshbach resonances, and is not highly controllable. Here, we propose a valid and feasible scheme for rea

Shoulin Wei, Yadi Li, Wei Lu, Nan Li

Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which r

Rohit Nageshwar, Abdul Gaffar Khan, Tarun Das

In this paper, we define bi-asymptotically $c$-expansive maps on metric spaces and study its relationship with other variants of expansivity such as bi-asymptotically expansive maps and $N$-expansive maps. We also provide an example to establish that expansive homeomorphisms need not be bi-asymptotically expansive. Finally we prove a spectral decomposition t

Hai M. Nguyen, Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wireless resources. Specifically, an FL process can be fused with quantized Binomial mechanism-based updates contributed by multiple users. However, optimizing quantization parameters,

Helen Zhou, Yuwen Chen, Zachary C. Lipton

Machine learning models deployed in healthcare systems face data drawn from continually evolving environments. However, researchers proposing such models typically evaluate them in a time-agnostic manner, with train and test splits sampling patients throughout the entire study period. We introduce the Evaluation on Medical Datasets Over Time (EMDOT) framewor

Wenhao Li, Xiaoyuan Yi, Jinyi Hu, Maosong Sun

Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, severely hurting the diversity and novelty of generated text. In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer c

Yinglong Song, Jinbo Yang

Strong Scott topology introduced by X. Xu and D. Zhao is a kind of new topology which is finer than upper topology and coarser than Scott topology. Inspired by the topological characterizations of continuous domains and hypercontinuous domains, we introduce the concept of strongly continuous domains and investigate some properties of strongly continuous doma

Haie Long, Ye Zhang

Recently, the stochastic asymptotical regularization (SAR) has been developed in (\emph{Inverse Problems}, 39: 015007, 2023) for the uncertainty quantification of the stable approximate solution of linear ill-posed inverse problems. In this paper, we extend the regularization theory of SAR for nonlinear inverse problems. By combining techniques from classica

Kemal Bicakci, Yusuf Uzunay

Operating system and browser support that comes with the FIDO2 standard and the biometric user verification options increasingly available on smart phones has excited everyone, especially big tech companies, about the passwordless future. Does a dream come true, are we finally totally getting rid of passwords? In this position paper, we argue that although p

Shuo Shao, Wenyuan Yang, Hanlin Gu, Zhan Qin

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property ri

Nevil Anto, Manu Basavaraju

Gallai's path decomposition conjecture states that if $G$ is a connected graph on $n$ vertices, then the edges of $G$ can be decomposed into at most $\lceil \frac{n }{2} \rceil$ paths. A graph is said to be an odd semi-clique if it can be obtained from a clique on $2k+1$ vertices by deleting at most $k-1$ edges. Bonamy and Perrett asked if the edges of every

Kota Hayashi, Kenta Kiuchi, Koutarou Kyutoku, Yuichiro Sekiguchi

As a follow-up study of our previous work, numerical-relativity simulations for seconds-long black hole-neutron star mergers are performed for a variety of setups. Irrespective of the initial and symmetry conditions, we find qualitatively universal evolution processes: The dynamical mass ejection takes place together with a massive accretion disk formation a

Ruihan Xu, Haokui Zhang, Wenze Hu, Shiliang Zhang

Transformers have shown great potential in various computer vision tasks. By borrowing design concepts from transformers, many studies revolutionized CNNs and showed remarkable results. This paper falls in this line of studies. Specifically, we propose a new convolutional neural network, ParCNetV2, that extends position-aware circular convolution (ParCNet) w

Hongxing Wang, Pei Huang

In this paper, we introduce D-star order, T-star order and P-star order on the class of dual matrices. By applying matrix decomposition and dual generalized inverses, we discuss properties, characterizations and relations among these orders, and illustrate their relations with examples.

Hidekazu Furusho

This paper introduces a $p$-adic analogue of Gauss's hypergeometric function, constructed via a method that is distinct from distinct from Dwork's approach. The idea of our construction is motivated by the Ohno-Zagier formula, which is elucidated through the relationship between the hypergeometric differential equation and the Knizhnik-Zamolodchikov (KZ) equ

Tuukka Korhonen, Daniel Lokshtanov

We give an algorithm that takes as input an $n$-vertex graph $G$ and an integer $k$, runs in time $2^{O(k^2)} n^{O(1)}$, and outputs a tree decomposition of $G$ of width at most $k$, if such a decomposition exists. This resolves the long-standing open problem of whether there is a $2^{o(k^3)} n^{O(1)}$ time algorithm for treewidth. In particular, our algorit

Aaron Brunk, Herbert Egger, Timileyin David Oyedeji, Yangyiwei Yang

A system of phase-field equations with strong-coupling through state and gradient dependent non-diagonal mobility matrices is studied. Existence of weak solutions is established by the Galerkin approximation and a-priori estimates in strong norms. Relative energy estimates are used to derive a general nonlinear stability estimate. As a consequence, a weak-st

Ruchi Tamang, Manoj Ghising, Mohammed Tobrej, Binay Rai

We report the X-ray spectral and timing analysis of the high mass X-ray binary EXO 2030+375 during the 2021 type II outburst. We have incorporated NuSTAR, NICER, \textit{Swift}/BAT \& \textit{Fermi}/GBM observations to carry out a comprehensive analysis of the source. Pulse profiles in different energy ranges and time intervals have been generated and analyz

Hong-Xing Li, Wei Zhou, Hong-Hai Mi

The relation between continuous functions and random vectors is revealed in the paper that the main meaning is described as, for any given continuous function, there must be a sequence of probability spaces and a sequence of random vectors where every random vector is defined on one of these probability spaces, such that the sequence of conditional mathemati

Ryuhei Mizutani

K\H{o}nig's edge-coloring theorem for bipartite graphs and Vizing's edge-coloring theorem for general graphs are celebrated results in graph theory and combinatorial optimization. Schrijver generalized K\H{o}nig's theorem to a framework defined with a pair of intersecting supermodular functions. The result is called the supermodular coloring theorem. This pa

Adam Haskard, Damith Herath, Zena Assaad

Secure robotics is a multi-disciplinary endeavour for improving the cybersecurity posture of robotic and embodied Artificial Intelligence systems. The article surveys emerging concepts and ideas encapsulating the notion of secure robotics and identifies five Secure Robotics Cybersecurity Control Implementation Layers as a crucial starting point for considera

Rie Matsumoto, Shinji Yuasa, Hiroshi Imamura

Voltage-induced dynamic switching in magnetic tunnel junctions (MTJs) is a writing technique for voltage-controlled magnetoresistive random access memory (VCMRAM), which is expected to be an ultimate non-volatile memory with ultra-low power consumption. In conventional dynamic switching, the width of sub-nanosecond write voltage pulses must be precisely cont

Wenqi Ren, Qiyu Sun, Chaoqiang Zhao, Yang Tang

Learning-based image dehazing methods are essential to assist autonomous systems in enhancing reliability. Due to the domain gap between synthetic and real domains, the internal information learned from synthesized images is usually sub-optimal in real domains, leading to severe performance drop of dehaizing models. Driven by the ability on exploring interna

Sujin Kook, Won-Yong Shin, Seong-Lyun Kim, Seung-Woo Ko

The vision of pervasive machine learning (ML) services can be realized by training an ML model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. On the other hand, high dimensional data with a heavy volume causes a significant burden to an IoT dev

Yicheng Zou, Kaitao Song, Xu Tan, Zhongkai Fu

Dialogue summarization aims to condense the lengthy dialogue into a concise summary, and has recently achieved significant progress. However, the result of existing methods is still far from satisfactory. Previous works indicated that omission is a major factor in affecting the quality of summarization, but few of them have further explored the omission prob

L. Cheng, B. Da, X. Liu, K. Shigeto

We report a promising InSiO film that allows simultaneous observation of sample morphology and Kikuchi patterns in raster scan mode of scanning electron microscopy. This new experimental observation suggests potential mechanism beyond existing diffraction theories. We find by simulation that this material has a novel cylindrically symmetric rotational crysta

Yihui He

In this paper, we introduce a new channel pruning method to accelerate very deep convolutional neural networks. Given a trained CNN model, we propose an iterative two-step algorithm to effectively prune each layer, by a LASSO regression based channel selection and least square reconstruction. We further generalize this algorithm to multi-layer and multi-bran

Xin Hua, Zhijiang Du, Hongjian Yu, Jixin Ma

Cochlear implantation is currently the most effective treatment for patients with severe deafness, but mastering cochlear implantation is extremely challenging because the temporal bone has extremely complex and small three-dimensional anatomical structures, and it is important to avoid damaging the corresponding structures when performing surgery. The spati

Humphrey O. Obie, Hung Du, Kashumi Madampe, Mojtaba Shahin

Human values such as honesty, social responsibility, fairness, privacy, and the like are things considered important by individuals and society. Software systems, including mobile software applications (apps), may ignore or violate such values, leading to negative effects in various ways for individuals and society. While some works have investigated differe

Lotfi Hermi, Naoki Saito

We reformulate all general real coupled self-adjoint boundary value problems as integral operators and show that they are all finite rank perturbations of the free space Green's function on the real line. This free space Green's function corresponds to the nonlocal boundary value problem proposed earlier by Saito [N. Saito, Appl. Comput. Harmonic Anal., 25,

Tom Meyerovitch, Shrey Sanadhya, Yaar Solomon

We show that translational tiling problems in a quotient of $\mathbb{Z}^d$ can be effectively reduced or ``simulated'' by translational tiling problems in $\mathbb{Z}^d$. In particular, for any $d \in \mathbb{N}$, $k < d$ and $N_1,\ldots,N_k \in \mathbb{N}$ the existence of an aperiodic tile in $\mathbb{Z}^{d-k} \times (\mathbb{Z} / N_1\mathbb{Z} \times \ldo

F. A. M. Mulki, H. Wulandari, T. Hidayat

We propose noncanonical domain walls as a new dark energy model inspired by grand unified theories (GUTs). We investigate the cosmic dynamics and discover that the domain walls act as either dark energy or dark matter at different times, depending on the velocity v in the observer's comoving frame. We find a single stable solution to the dynamics, i.e., only

Wenyuan Yang, Shuo Shao, Yue Yang, Xiyao Liu

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in secure FL framework prevents th

Muhammad Asif Khan, Hamid Menouar, Ridha Hamila

Video surveillance using drones is both convenient and efficient due to the ease of deployment and unobstructed movement of drones in many scenarios. An interesting application of drone-based video surveillance is to estimate crowd densities (both pedestrians and vehicles) in public places. Deep learning using convolution neural networks (CNNs) is employed f

Mohammadreza Sadeghi, Hadi Hojjati, Narges Armanfard

Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent advancements in deep learning have revived interest in this field. Contrastive clustering (CC) models are a staple of deep clustering in which positive and negative pairs of each dat

Monsurul Khan, Rishabh V. More, Luca Brandt, Arezoo M. Ardekani

We explain the origins of yield stress, shear-thinning, and normal stress differences in rigid fiber suspensions. We investigate the interplay between the hydrodynamic, colloidal attractive and repulsive, and inter-fiber contact interactions. The shear-thinning viscosity and finite yield stress obtained from the computational model are in quantitative agreem

Monsurul Khan, Rishabh V. More, Arezoo M. Ardekani

We propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coeffi

Jinyeop Lee, Alessandro Michelangeli

Fragmented Bose-Einstein condensates are large systems of identical bosons displaying \emph{multiple} macroscopic occupations of one-body states, in a suitable sense. The quest for an effective dynamics of the fragmented condensate at the leading order in the number of particles, in analogy to the much more controlled scenario for complete condensation in on

Yi Li, Honghao Lin, David P. Woodruff

In the $\ell_p$-subspace sketch problem, we are given an $n\times d$ matrix $A$ with $n>d$, and asked to build a small memory data structure $Q(A,\epsilon)$ so that, for any query vector $x\in\mathbb{R}^d$, we can output a number in $(1\pm\epsilon)\|Ax\|_p^p$ given only $Q(A,\epsilon)$. This problem is known to require $\tilde{\Omega}(d\epsilon^{-2})$ bits o

Pengyuan Zhai

The quantum circuit Born machine (QCBM) is a quantum physics inspired implicit generative model naturally suitable for learning binary images, with a potential advantage of modeling discrete distributions that are hard to simulate classically. As data samples are generated quantum-mechanically, QCBMs encompass a unique optimization landscape. However, pionee