January 2022 arXiv papers — page 11

Elahe Arani, Fahad Sarfraz, Bahram Zonooz

Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting. The complementary learning system (CLS) theory suggests that the interplay between rapid instance-based learning and slow structured learning in the brain is crucial for accumulating and retaini

Wioletta M. Ruszel, Debleena Thacker

Consider a generalized time-dependent P\'olya urn process defined as follows. Let $d\in \mathbb{N}$ be the number of urns/colors. At each time $n$, we distribute $\sigma_n$ balls randomly to the $d$ urns, proportionally to $f$, where $f$ is a valid reinforcement function. We consider a general class of positive reinforcement functions $\mathcal{R}$ assuming

Chuan-Yung Tsai, Graham W. Taylor

Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we aim to improve the generative testing of software by directly augmenting the random number generator (RNG) with a deep reinforcement learning (RL) agent using an efficient, automatic

Simon Plouffe

By using an asymptotic formula known for the numbers of Euler and Bernoulli it is possible to obtain an explicit expression of the nth digit of $\pi$ in decimal or in binary, it also makes it possible to obtain the $n^{\rm th}$ digit of powers of $\pi^n$.

Humian Zhou, Hailong Li, Dong-Hui Xu, Chui-Zhen Chen

Recently,a half-quantized Hall conductance (HQHC) plateau is experimentally observed in a semi-magnetic topological insulator heterostructure. However,the heterostructure is metallic with a nonzero longitudinal conductance, which contradicts the common belief that quantized Hall conductance is usually observed in insulators.In this work,we systematically stu

Qizheng Sun, Caili Guo, Yang Yang, Jiujiu Chen

Conventional image compression methods typically aim at pixel-level consistency while ignoring the performance of downstream AI tasks.To solve this problem, this paper proposes a Semantic-Assisted Image Compression method (SAIC), which can maintain semantic-level consistency to enable high performance of downstream AI tasks.To this end, we train the compress

Ahmed Farag Ali, Barun Majumder, Prabir Rudra

Motivated by generalized uncertainty principle, we derive a discrete picture of the space that respects Lorentz symmetry as well as gauge symmetry through setting an equivalency between linear GUP correction term and electromagnetic interaction term in Dirac equation. We derived a wavefunction solution that satisfies this equivalency. This discreteness may e

S. L. Cacciatori, F. Canfora, M. Lagos, F. Muscolino

We extend the (gauged) Skyrme model to the case in which the global isospin group (which usually is taken to be $SU(N)$) is a generic compact connected Lie group $G$. We analyze the corresponding field equations in (3+1) dimensions from a group theory point of view. Several solutions can be constructed analytically and are determined by the embeddings of thr

Domenic Rosati

Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language "hard to pronounce" by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-level transformer model on this task acr

Yuan Wu, Diana Inkpen, Ahmed El-Roby

Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant di

Yan Chen, Lu Lin

The issues of bias-correction and robustness are crucial in the strategy of divide-and-conquer (DC), especially for asymmetric nonparametric models with massive data. It is known that quantile-based methods can achieve the robustness, but the quantile estimation for nonparametric regression has non-ignorable bias when the error distribution is asymmetric. Th

Zejun Li, Zhihao Fan, Huaixiao Tou, Jingjing Chen

Previous vision-language pre-training models mainly construct multi-modal inputs with tokens and objects (pixels) followed by performing cross-modality interaction between them. We argue that the input of only tokens and object features limits high-level semantic alignment like phrase-to-region grounding. Meanwhile, multi-level alignments are inherently cons

Andrea Addazi, Giulia Ricciardi, Simone Scarlatella, Rahul Srivastava

In light of the recent $R_{K^{(*)}}$ data on neutral current flavour anomalies in $B \to K^{(*)} \ell^+ \ell^-$ decays, we re-examine their quantitative interpretation in terms of an extended 331 gauge theory framework. We achieve this by adding two extra lepton species with novel 331 charges, while ensuring that the model remains anomaly free. In contrast t

Liu Liu, Ziyang Tang, Lanqing Li, Dijun Luo

We consider offline Imitation Learning from corrupted demonstrations where a constant fraction of data can be noise or even arbitrary outliers. Classical approaches such as Behavior Cloning assumes that demonstrations are collected by an presumably optimal expert, hence may fail drastically when learning from corrupted demonstrations. We propose a novel robu

Lin-Peng Zhang, Ligong Wang

A linear $r$-uniform hypergraph is called acycilc if it can be constructed starting from one single edge then at each step adding a new edge that intersect the union of the vertices of the previous edges in at most one vertex. Recently, Gy\'{a}rf\'{a}s, Ruszink\'{o} and S\'{a}rk\''{o}zy initiated the study of the linear Tur\'{a}n numbers of acyclic linear tr

Marcus T. Cicerone, Kelly Badilla-Nunez, Jessica Zahn, John P. Stoppelman

Recent simulation and model system studies suggest that local structural excitations play an important role in the dynamics of liquids and glasses. Here, for the first time, we quantify excitation populations in real liquids, showing that their temperature-dependent population can be predicted from entropy and enthalpy of melting. We further show that the ex

Jiangjun Peng, Yao Wang, Hongying Zhang, Jianjun Wang

It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (\textbf{LSS}) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankne

Ziyang Chen, Pengjie Zhang, Xiaohu Yang

We carry out a thermal energy census of hot baryons at $z < 1$, by cross-correlating the \emph{Planck} MILCA y-map with 0.8 million clusters/groups selected from the Yang et.al (2021) catalog. The thermal Sunyaev-Zel'dovich (tSZ) effect around these clusters/groups are reliably obtained, which enables us to make our model constraints based on one-halo (1h) a

Christopher Blöcker, Juan Carlos Nieves, Martin Rosvall

To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To stu

Jinbao Wang, Guoyang Xie, Yawen Huang, Yefeng Zheng

The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in the diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is impractical, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. Previously, the misa

Elena Fuchs, Matthew Litman, Joseph H. Silverman, Austin Tran

Let $\mathcal{W}\subset\mathbb{P}^1\times\mathbb{P}^1\times\mathbb{P}^1$ be a surface given by the vanishing of a $(2,2,2)$-form. These surfaces admit three involutions coming from the three projections $\mathcal{W}\to\mathbb{P}^1\times\mathbb{P}^1$, so we call them $\textit{tri-involutive K3 (TIK3) surfaces}$. By analogy with the classical Markoff equation,

Yanrong Zhang, Jie Shen

The scalar auxiliary variable (SAV) approach \cite{shen2018scalar} and its generalized version GSAV proposed in \cite{huang2020highly} are very popular methods to construct efficient and accurate energy stable schemes for nonlinear dissipative systems. However, the discrete value of the SAV is not directly linked to the free energy of the dissipative system,

Xiang Chen, Li Li, Ying Cui, Junping Yang

To reconstruct the impact parameter distributions from the selected events sample or centrality, which is defined by two-observables, at intermediate energy heavy ion collisions, we extend the approach proposed by Das \textit{et al.} [Phys. Rev. C 97, 014905 (2018)], Rogly \textit{et al.} [Phys. Rev. C 98, 024902 (2018)], and Frankland \textit{et al.} [Phys.

Meng Ai, Biao Li, Heyang Gong, Qingwei Yu

Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and platform revenue. Despite its proven effectiveness, these marketing incentives incur an inevitable cost and might result in a low ROI (Return on Investment) if not used properly. On

Elvin Isufi, Maosheng Yang

This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC). SCs are mathematical tools that not only capture pairwise relationships as graphs but account also for higher-order network structures. These filters are built by following the shift-and-sum principle of the convolution operation and rely on the

Saurabh, Parth Bambhaniya, Pankaj S. Joshi

Recent observations of the Milky-way galactic center at various frequencies suggest a supermassive compact object. Generally, that supermassive compact object is assumed to be a `Black Hole', having more than four million solar masses. In this work, we study the observational appearance at $230$ GHz and probe the nature of Sagittarius-A* (Sgr A*) as the nake

Ziqin Zhou, Xiaoyang Li, Changsheng You, Kaibing Huang

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing enables controllable sensing and transmission processes for multiple types of data in a single device. In this paper, a typical user is considered that is

Devsi Bantva, Samir Vaidya, Sanming Zhou

A radio labelling of a graph $G$ is a mapping $f : V(G) \rightarrow \{0, 1, 2,\ldots\}$ such that $|f(u)-f(v)| \geq diam(G) + 1 - d(u,v)$ for every pair of distinct vertices $u,v$ of $G$, where $diam(G)$ is the diameter of $G$ and $d(u,v)$ is the distance between $u$ and $v$ in $G$. The radio number $rn(G)$ of $G$ is the smallest integer $k$ such that $G$ ad

Xiaoyang Li, Fan Liu, Ziqin Zhou, Guangxu Zhu

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data communication, the integrated sensing and communication (ISAC) technique has broken the barriers between da

Jacob Bamberger, Daniel T. Wise

The main result in this paper is the failure of the finitely generated intersection property (FGIP) of ascending HNN extensions of non-cyclic finite rank free groups. This class of group consists of free-by-cyclic groups and properly ascending HNN extensions of free groups. We also give a sufficient condition for the failure of the FGIP in the context of rel

Chi Zhang, Karan K Mehta, Jonathan P Home

We describe the design of a surface-electrode ion trap junction, which is a key element for large-scale ion trap arrays. A bi-objective optimization method is used for designing the electrodes, which maintains the total pseudo-potential curvature while minimizing the axial pseudo-potential gradient along the ion transport path. To facilitate the laser beam d

Pengyao Cui

The production of strange~($K^0_S$, $\Lambda$) and multi-strange~($\Xi$ and $\Omega$) hadrons in jets and underlying events in \pp and \pPb collisions is studied with ALICE at the LHC. Transverse momentum ($p_\rm{T}$) differential density distribution of particles produced in a jet is compared to that of inclusive particle production and that in underlying e

John Chiang

In this work, we propose an interesting method that aims to approximate an activation function over some domain by polynomials of the presupposing low degree. The main idea behind this method can be seen as an extension of the ordinary least square method and includes the gradient of activation function into the cost function to minimize.

John Chiang

In this work, we present a novel matrix-encoding method that is particularly convenient for neural networks to make predictions in a privacy-preserving manner using homomorphic encryption. Based on this encoding method, we implement a convolutional neural network for handwritten image classification over encryption. For two matrices $A$ and $B$ to perform ho

Jinbo Xing, Wenbo Hu, Tien-Tsin Wong

Conventional social media platforms usually downscale the HR images to restrict their resolution to a specific size for saving transmission/storage cost, which leads to the super-resolution (SR) being highly ill-posed. Recent invertible image downscaling methods jointly model the downscaling/upscaling problems and achieve significant improvements. However, t

Lei Du, Yao-Tong Chen, Yan Zhang, Yong Li

We study the decay dynamics of a two-level giant atom that is coupled to a waveguide with time-dependent coupling strengths. In the non-Markovian regime where the retardation effect cannot be ignored, we show that the dynamics of the atom depends on the atom-waveguide coupling strengths at an earlier time. This allows one to tailor the decay dynamics of the

D. E. Maison, L. V. Skripnikov

The francium atom is considered as a prospective candidate system to search for the T,P-violating electron electric dipole moment [T. Aoki et al Quantum Sci. Technol. 6, 044008 (2021)]. We demonstrate that the same experiment can be used for axionlike particles (ALP) search. For this, we calculate electronic structure constants of ALP-mediated interaction fo

Z. Ge, T. Eronen, A. deRoubin, K. S. Tyrin

A precise determination of the ground state $^{111}$In ($9/2^+$) electron capture to ground state of $^{111}$Cd ($1/2^+$) $Q$ value has been performed utilizing the double Penning trap mass spectrometer, JYFLTRAP. A value of 857.63(17) keV was obtained, which is nearly a factor of 20 more precise than the value extracted from the Atomic Mass Evaluation 2020

Keehang Kwon, Hyung Joon Kwon

Proofs (sequent calculus, natural deduction) and imperative algorithms (pseudocodes) are two well-known coexisting concepts. Then what is their relationship? Our answer is that \[ imperative\ algorithms\ =\ proofs\ with\ cuts \] This observation leads to a generalization to pseudocodes which we call {\it logical pseudocodes}. It is similar to natural deducti

Yinfeng Sun, Dapeng Xia, Zichun Gao, Zhenhao Wang

The operating conditions of the power system have become more complex and changeable. This paper proposes a probabilistic load flow based on the cumulant method (PLF-CM) for the voltage sourced converter high voltage direct current (VSC-HVDC) hybrid system containing photovoltaic grid-connected systems. Firstly, the corresponding control mode is set for the

Asif Khan, Alexander I. Cowen-Rivers, Antoine Grosnit, Derrick-Goh-Xin Deik

Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antibodies. However, the combinatorial nature

Chao Qu, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi

We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in

Gaël Poux-Médard, Julien Velcin, Sabine Loudcher

The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on similar issues, according to underlying temporal dynamics. However, it can be challenging to retrieve meaningful information when textual information conveys little. Furthermore, the t

Ziyi Chen, Hua Hua, Yuxiang Zhang, Ming Li

The voice conversion task is to modify the speaker identity of continuous speech while preserving the linguistic content. Generally, the naturalness and similarity are two main metrics for evaluating the conversion quality, which has been improved significantly in recent years. This paper presents the HCCL-DKU entry for the fake audio generation task of the

Rehab S. Hassan

This paper proposes a novel wireless, battery-free, and label-free biosensor for minimally invasive and non-invasive permittivity sensing for applications such as detecting glucose levels in the interstitial dermal fluid. The miniaturized, fully passive sensor is based on two symmetric parallel 0.8 mm$^3$ LC (inductor-capacitor) resonators. Each inductor is

Guangji Chen, Qingqing Wu, Chong He, Wen Chen

We investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming

Wagner A. Barbosa, Sérgio Luiz E. F. da Silva, Erick de la Barra, João M. de Araújo

The estimation of physical parameters from data analysis is a crucial point for the description and modeling of many complex systems. Based on R\'enyi $\alpha$-Gaussian distribution and patched Green's function (PGF) techniques, we propose a robust framework for data inversion using a wave-equation based methodology named full-waveform inversion (FWI). We sh

Daniel Perez, Lewis Gudgeon

The meteoric rise of Decentralized Finance (DeFi) has been accompanied by a plethora of frequent and often financially devastating attacks on its protocols There have been over 70 exploits of DeFi protocols, with the total of lost funds amounting to approximately 1.5bn USD. In this paper, we introduce a new approach to minimizing the frequency and severity o

Artem S. Chefranov, Sergey G. Chefranov, Georgy S. Golitsyn

An exact turbulence universal scaling law-8/3 for the density fluctuations of cosmic ray (CR) is obtained on the basis of a new analytical compressible turbulence theory and known two-fluid model of the CR dynamics. It is shown that the origin of this scaling law may be due to the breaking of the nonlinear simple waves in CR medium near the scale of their La

Annamaria Ortu

We give a general construction of extremal Kaehler metrics on the total space of certain holomorphic submersions, extending results of Dervan-Sektnan, Fine, and Hong. We consider submersions whose fibres admit a degeneration to Kaehler manifolds with constant scalar curvature, in a way that is compatible with the fibration structure. Thus we allow fibres tha

Oswaldo Diéguez, Massimiliano Stengel

Macroscopic descriptions of ferroelectrics have an obvious appeal in terms of efficiency and physical intuition. Their predictive power, however, has often been thwarted by the lack of a systematicp rocedure to extract the relevant materials parameters from the microscopics. Here we address this limitation by establishing an unambiguous two-way mapping betwe

Mathieu Dubied, Mike Michelis, Andrew Spielberg, Robert Katzschmann

An accurate, physically-based, and differentiable model of soft robots can unlock downstream applications in optimal control. The Finite Element Method (FEM) is an expressive approach for modeling highly deformable structures such as dynamic, elastomeric soft robots. In this paper, we compare virtual robot models simulated using differentiable FEM with measu

Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong

Batch Normalization (BN) and its variants has been extensively studied for neural nets in various computer vision tasks, but relatively little work has been dedicated to studying the effect of BN in continual learning. To that end, we develop a new update patch for BN, particularly tailored for the exemplar-based class-incremental learning (CIL). The main is

Samuele Marro, Luca Donno

We introduce a model of greenhouse gas emissions due to on-chain activity on Ethereum, focusing on cryptoart. We also estimate the impact of individual transactions on the environment, both before and after the London hard fork. We find that with the current fee mechanism, spending one dollar on transaction fees corresponds to emitting at least the equivalen

Xue Yang, Yue Zhou, Gefan Zhang, Jirui Yang

Differing from the well-developed horizontal object detection area whereby the computing-friendly IoU based loss is readily adopted and well fits with the detection metrics. In contrast, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. In this paper, we propose an effective approximate

Huy Phan, Thi Ngoc Tho Nguyen, Philipp Koch, Alfred Mertins

Polyphonic events are the main error source of audio event detection (AED) systems. In deep-learning context, the most common approach to deal with event overlaps is to treat the AED task as a multi-label classification problem. By doing this, we inherently consider multiple one-vs.-rest classification problems, which are jointly solved by a single (i.e. sha

Arata Yamamoto

Although the Hamiltonian formalism is so far favored for quantum computation of lattice gauge theory, the path integral formalism would never be useless. The advantages of the path integral formalism are the knowledge and experience accumulated by classical lattice simulation and manifest Lorentz invariance. We discuss quantum computation of lattice gauge th

Sarath Chandra Varma, Abhineet Singh Rajput, Aloke Kumar

Dynamics of the pendant drop coalescing with a sessile drop to form a single daughter droplet is known to form a bridge. The bridge evolution begins with a point contact between the two drops leading to a liquid neck of size comparable to the diameter of the drops. To probe this phenomenon in polymeric fluids, we quantify the neck radius growth during coales

Xiangchun Tian, Yundong Zhang, Yu Duan, Yong Zhou

We propose a scheme to study the nonlinear propagation properties of nonlinear surface plasmon polaritons (SPPs) in a three level $\Lambda$ type electromagnetically induced transparency (EIT) system with modulation of a rectangular barrier. Based on the multi scale method, the nonlinear Schr\"odinger equation (NLSE) describing nonlinear propagation of SPPs i

Yan-Jin Wang, Jing-Zhao Qi, Bo Wang, Jing-Fei Zhang

Although the cosmic curvature has been tightly constrained in the standard cosmological model using observations of cosmic microwave background anisotropies, it is still of great importance to independently measure this key parameter using only late-universe observations in a cosmological model-independent way. The distance sum rule in strong gravitational l

Tuhin Malik, Márcio Ferreira, B. K. Agrawal, Constança Providência

The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density dependent relativistic mean field description of nuclear matter. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and

Soumen Ash, Moumita Naskar, Ravi Shankar P. N., Nityasagar Jena

We report a detailed study of superconductivity in polycrystalline SnTaS$_2$ using electrical transport, magnetization and heat capacity measurements. SnTaS$_2$ crystallizes in centrosymmetric hexagonal structure with space group $P6_3/mmc$. Electrical resistivity, magnetization and specific heat data suggest SnTaS$_2$ to be a weakly coupled, type-II superco

Priya Batra, M. Harshanth Ram, T. S. Mahesh

Quantum control optimization algorithms are routinely used to generate optimal quantum gates or efficient quantum state transfers. However, there are two main challenges in designing efficient optimization algorithms, namely overcoming the sensitivity to local optima and improving the computational speed. The former challenge can be dealt with by designing h

Xiang Chen, Xiaojun Wan

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to the expensive annotation for aspect terms, we often lack labeled target domain data for fine-tuning. To address this pr

Cheol Jeong, Chang-Jae Chun, Won-Yong Shin, Il-Min Kim

We present a new adaptive resource optimization strategy that jointly allocates the subwindow and transmit power in multi-device terahertz (THz) band Internet of Things (Tera-IoT) networks. Unlike the prior studies focusing mostly on maximizing the sum distance, we incorporate both rate and transmission distance into the objective function of our problem for

Guodong Yu, Zhichen Feng, Peiyou jiang, Guoyong Fu

An optimized compact stellarator with four simple coils is obtained from direct optimization via coil shape. The new stellarator consists of two interlocking coils and two vertical field coils similar to those of the Columbia Non-neutral Torus (CNT)[Pedersen et al. Phys. Rev. Lett. 88, 205002 (2002)]. The optimized configuration has global magnetic well and

Yizheng Huang, Nana Hou, Nancy F. Chen

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. To tackle such challenges, we propose a progressive continual learning strategy for small-footprint spoken keyword spotting (PCL-KWS). Specifically, the proposed PCL-KWS framework introduces a network instantiator to generate the task-specific sub-netw

Anna Rupp, Jonas Göser, Zhijie Li, Philipp Altpeter

We report the implementation of energy dispersive X-ray spectroscopy for layered semiconductors in the form of atomically thin transition metal dichalcogenides. The technique is based on a scanning electron microscope equipped with a silicon drift detector for energy dispersive X-ray analysis. By optimizing operational parameters in numerical simulations and

Dexter I. Mercurio, Alexander A. Hernandez

Barangay is the smallest type of government in the Philippines, and it is driven and represented by its barangay authorities. The barangay officials are accountable for keeping the records of citizens health and crime incidents. It also the first-hand source of information of the national government to develop government programs, community services, and mai

Yue Song, Nicu Sebe, Wei Wang

Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationa

Sinan Wang, Yibo Wang, Xian Zhan, Ying Wang

The Android platform introduces the runtime permission model in version 6.0. The new model greatly improves data privacy and user experience, but brings new challenges for app developers. First, it allows users to freely revoke granted permissions. Hence, developers cannot assume that the permissions granted to an app would keep being granted. Instead, they

Youness Boutaib

We show how to use geometric arguments to prove that the terminal solution to a rough differential equation driven by a geometric rough path can be obtained by driving the same equation by a piecewise linear path. For this purpose, we combine some results of the seminal work of Sussmann on orbits of vector fields with the rough calculus on manifolds develope

Jacek Dziarmaga, Marek M. Rams, Wojciech H. Zurek

It is now widely accepted that quenches through the critical region of quantum phase transitions result in post-transition states populated with topological defects -- analogs of the classical topological defects. However, consequences of the very non-classical fact that the state after a quench is a {\it superposition} of distinct, broken-symmetry vacua wit

George V. Moustakides

Optimum parameter estimation methods require knowledge of a parametric probability density that statistically describes the available observations. In this work we examine Bayesian and non-Bayesian parameter estimation problems under a data-driven formulation where the necessary parametric probability density is replaced by available data. We present various

Jiaan Wang, Beiqi Zou, Zhixu Li, Jianfeng Qu

Story ending generation is an interesting and challenging task, which aims to generate a coherent and reasonable ending given a story context. The key challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper,

Falong Tan, Xu Guo, Lixing Zhu

This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of dimensionality, we introduce weighted residual empirical process-based tests, both with and without martingale transformations. The asymptotic properties of these tests are derive

Shi-fan Qi, Jun Jing

Cavity magnomechanics provides a readily-controllable hybrid system, that consisted of cavity mode, magnon mode, and phonon mode, for quantum state manipulation. To implement a fast-and-robust state transfer between the hybrid photon-magnon mode and the phonon mode, we propose two accelerated adiabatic-passage protocols individually based on the counterdiaba

Thomas Yu, Tom Hilbert, Gian Franco Piredda, Arun Joseph

Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as w

Weihao Yu, Hao Zheng, Minghui Zhang, Hanxiao Zhang

Airway segmentation is critical for virtual bronchoscopy and computer-aided pulmonary disease analysis. In recent years, convolutional neural networks (CNNs) have been widely used to delineate the bronchial tree. However, the segmentation results of the CNN-based methods usually include many discontinuous branches, which need manual repair in clinical use. A

Chun Chen, Rong-Feng Shen

Fast Blue Optical Transients (FBOTs) are luminous transients with fast evolving (typically $t_{\rm rise}<12\ \rm days$) light curve and blue color (usually $\rm {-0.2\ >\ g-r\ >\ -0.3}$) that cannot be explained by a supernova-like explosion. We propose a radiative diffusion in a time-dependent outflow model to interpret such special transients. In this mode

Xiao Huo, Dongyang Jin, Saiping Zhang, Fuzheng Yang

Hand-held light field (LF) cameras have unique advantages in computer vision such as 3D scene reconstruction and depth estimation. However, the related applications are limited by the ultra-small baseline, e.g., leading to the extremely low depth resolution in reconstruction. To solve this problem, we propose to rectify LF to obtain a large baseline. Specifi

Qian Zhang, Wenpeng Lu

Session-based recommendations (SBRs) capture items' dependencies from the sessions to recommend the next item. In recent years, Graph neural networks (GNN) based SBRs have become the mainstream of SBRs benefited from the superiority of GNN in modeling complex dependencies. Based on a strong assumption of adjacent dependency, any two adjacent items in a sessi

Xiaxia Guan, Xian'an Jin

The interior polynomial and the exterior polynomial are generalizations of valuations on $(1/\xi,1)$ and $(1,1/\eta)$ of the Tutte polynomial $T_G(x,y)$ of graphs to hypergraphs, respectively. The pair of hypergraphs induced by a connected bipartite graph are abstract duals and are proved to have the same interior polynomial, but may have different exterior

Vladimir N. Gladilin, Michiel Wouters

We investigate theoretically the evolution of the vortex number in an array of photon condensates that is brought from an incoherent low density state to a coherent high density state by a sudden change in the pumping laser intensity. We analyze how the recombination of vortices and antivortices depends on the system parameters such as the coefficients for e

Mathias Mikkelsen, Kira L. Eliasen, Niclas Lindemann, Kevin Moch

The Piezo-electric Shear Gauge (PSG) [Christensen & Olsen, Rev. Sci. Instrum. 66, 5019, 1995] is a rheometric technique developed to measure the complex shear modulus of viscous liquids near their glass transition temperature. We report recent advances to the PSG technique: 1) The data extraction procedure is optimized which extends the upper limit of the fr

Tengfei Xue, Fan Zhang, Chaoyi Zhang, Yuqian Chen

White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts to enable quantification and visualization. Most parcellation methods focus on the deep white matter (DWM), while fewer methods address the superficial white matter (SWM) due to its complexity. We propose a deep-learning-based framework, Superficial

Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang

Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Projected Gradient Descent (PGD) attack has been demonstrated to be one of the most successful adversarial attacks. However, the effect of the standard PGD attack can be easily weakened by rescaling the logits, while the original decision o

Ju Chen, Changshuo Yan, Youjun Lu, Yuetong Zhao

Gravitational wave (GW) signals from compact binary coalescences can be used as standard sirens to constrain cosmological parameters if their redshift can be measured independently. However, mergers of stellar binary black holes (BBHs) may not have electromagnetic counterparts and thus have no direct redshift measurements. These dark sirens may be still used

Jie Li, Ling Han, Cong Zhang, Qiyue Li

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging Virtual Reality (VR) and Augmented Reality (AR) applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the

Koorosh Sadri, Fereshte Shahbeigi, Zbigniew Puchała, Karol Życzkowski

We study the problem of accessibility in a set of classical and quantum channels admitting a group structure. Group properties of the set of channels, and the structure of the closure of the analyzed group $G$ plays a pivotal role in this regard. The set of all convex combinations of the group elements contains a subset of channels that are accessible by a d

Andy Nguyen, Ahmed E. Helal, Fabio Checconi, Jan Laukemann

Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectures. In contrast to prior work, the proposed Blocked Linearized Coordinate (BLCO) format enables efficient out-

Hao Liu, Huaping Liu

Learning multiple tasks sequentially without forgetting previous knowledge, called Continual Learning(CL), remains a long-standing challenge for neural networks. Most existing methods rely on additional network capacity or data replay. In contrast, we introduce a novel approach which we refer to as Recursive Gradient Optimization(RGO). RGO is composed of an

Hardeep Singh, A. Bhagwat

Scattering of matter waves through slits has been explored using the Feynman Path Integral formalism. We explicitly plot the near-zero probability densities to analyse the behaviour near the slit. Upon doing so, intriguing patterns emerge, most notably the braid-like structure in the case of double slits, whose complexity increases as one increases the numbe

Kaustav Bora, Adarsh Mishra, CS Kumar

Virtual models are important for training and teaching tools used in medical imaging research. We introduce a workflow that can be used to convert volumetric medical imaging data (as generated by Computer Tomography (CT)) to computer-based models where we can perform interaction of tool with the tissue. This process is broken up into two steps: image segment

Shoule Wu, Ziqiang Shi

In this paper, we propose a vocoder based on a pair of forward and reverse-time linear stochastic differential equations (SDE). The solutions of this SDE pair are two stochastic processes, one of which turns the distribution of wave, that we want to generate, into a simple and tractable distribution. The other is the generation procedure that turns this trac

Yuheng Lei, Yao Lyu, Guojian Zhan, Tao Zhang

Evolutionary algorithms (EAs) have shown promise in solving sequential decision problems (SDPs) by simplifying them to static optimization problems and searching for the optimal policy parameters in a zeroth-order way. While these methods are highly versatile, they often suffer from high sample complexity due to their ignorance of the underlying temporal str

Hirokazu Taguchi, Yuta Murakami, Akihisa Koga

We study how stable the Majorana-mediated spin transport in a quantum spin Kitaev model is against thermal fluctuations. Using the time-dependent thermal pure quantum state method, we examine finite-temperature spin dynamics in the Kitaev model. The model exhibits two characteristic temperatures $T_L$ and $T_H$, which correspond to energy scales of the local

Ju Chen, Changshuo Yan, Youjun Lu, Yuetong Zhao

The detection of gravitational waves (GWs) by ground-based laser interferometer GW observatories (LIGO/Virgo) reveals a population of stellar binary black holes (sBBHs) with (total) masses up to $\sim 150M_\odot$, which are potential sources for space-based GW detectors, such as LISA and Taiji. In this paper, we investigate in details on the possibility of d

Tian Liu, Jiahao Ding, Ting Wang, Miao Pan

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of vanilla FL method cannot b

Kosuke Akita, Yuto Miyatake, Daisuke Furihata

In data assimilation, state estimation is not straightforward when the observation operator is unknown. This study proposes a method for composing a surrogate operator when the true operator is unknown. A neural network is used to improve the surrogate model iteratively to decrease the difference between the observations and the results of the surrogate mode