July 2022 arXiv papers — page 136

Pratik Mishra, Rekha Pitchumani, Yang Suk Kee

Conventional object-stores are built on top of traditional OS storage stack, where I/O requests typically transfers through multiple hefty and redundant layers. The complexity of object management has grown dramatically with the ever increasing requirements of performance, consistency and fault-tolerance from storage subsystems. Simply stated, more number of

M. Lunkenheimer, A. Kracklauer, G. Klinkova, M. Grabinski

In many models in economics or business a dominantly self-interested homo economicus is assumed. Unfortunately (or fortunately), humans are in general not homines economici as e.g. the ultimatum game shows. This leads to the fact that all these models are at least doubtful. Moreover, economists started to set a quantitative value for the feeling of social ju

Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods. TabPFN performs in-context learning (ICL), it learns to make predictions using sequences of labeled examples (x, f(x)) given in the inp

Ikuro Sato, Ryota Yamada, Masayuki Tanaka, Nakamasa Inoue

It has been intensively investigated that the local shape, especially flatness, of the loss landscape near a minimum plays an important role for generalization of deep models. We developed a training algorithm called PoF: Post-Training of Feature Extractor that updates the feature extractor part of an already-trained deep model to search a flatter minimum. T

Yugang Zhang, Zefang Li, Jing Zhang, Long Zhang

The domain dynamics and spin-spin correlation of 2D ferromagnets Cr2X2Te6 (X=Si, Ge) are investigated by a composite magnetoelectric method. The magnetic field-temperature phase diagrams for both in-plane and out-of-plane magnetic fields disclose a triple point around TC and 1 kOe, where ferromagnetic, paramagnetic, and spin-polarized phases coexist. The mag

Shivansh Beohar, Andrew Melnik

The practical application of learning agents requires sample efficient and interpretable algorithms. Learning from behavioral priors is a promising way to bootstrap agents with a better-than-random exploration policy or a safe-guard against the pitfalls of early learning. Existing solutions for imitation learning require a large number of expert demonstratio

Chen Huang, Walter Talbott, Navdeep Jaitly, Josh Susskind

Self-attention mechanisms model long-range context by using pairwise attention between all input tokens. In doing so, they assume a fixed attention granularity defined by the individual tokens (e.g., text characters or image pixels), which may not be optimal for modeling complex dependencies at higher levels. In this paper, we propose ContextPool to address

Marwa Chafii, Lina Bariah, Sami Muhaidat, Merouane Debbah

The research in the sixth generation of communication networks needs to tackle new challenges in order to meet the requirements of emerging applications in terms of high data rate, low latency, high reliability, and massive connectivity. To this end, the entire communication chain needs to be optimized, including the channel and the surrounding environment,

Zhizhong Chai, Huangjing Lin, Luyang Luo, Pheng-Ann Heng

Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotation is very labor-intensive and time-consuming because radiologists need to investigate and annotate the rib fractures on a slice-by-slice basis. Although a few studies have propose

Vinod S. Khandkar, Manjesh K. Hanawal, Sameer G Kulkarni

Security and Privacy are crucial in modern Internet services. Transport Layer Security (TLS) has largely addressed the issue of security. However, information about the type of service being accessed goes in plain-text in the initial handshakes of vanilla TLS, thus potentially revealing the activity of users and compromising privacy. The ``Encrypted ClientHe

Tomoya Yamanokuchi, Yuhwan Kwon, Yoshihisa Tsurumine, Eiji Uchibe

Many works have recently explored Sim-to-real transferable visual model predictive control (MPC). However, such works are limited to one-shot transfer, where real-world data must be collected once to perform the sim-to-real transfer, which remains a significant human effort in transferring the models learned in simulations to new domains in the real world. T

Tatsuhiro Furuta, Keisuke Hamada, Masaru Oda, Kazuma Nakamura

We present a hidden Markov model analysis for fluorescent time series of quantum dots. A fundamental quantity to measure optical performance of the quantum dots is a distribution function for the light-emission duration. So far, to estimate it, a threshold value for the fluorescent intensity was introduced, and the light-emission state was evaluated as a sta

Sannat Singh Bhasin, Vaibhav Holani, Divij Sanjanwala

Graph neural networks (GNNs) have gained traction over the past few years for their superior performance in numerous machine learning tasks. Graph Convolutional Neural Networks (GCN) are a common variant of GNNs that are known to have high performance in semi-supervised node classification (SSNC), and work well under the assumption of homophily. Recent liter

Hou Lihang, Gao Suogang, Kang Na, Hou Bo

Let $2.O_{m+1}$ denote the doubled Odd graph with vertex set $X$ on a set of cardinality $2m+1$, where $m\geq 1$. Fix a vertex $x_0\in X$. Let $\mathcal{A}:=\mathcal{A}(x_0)$ denote the centralizer algebra of the stabilizer of $x_0$ in the automorphism group of $2.O_{m+1}$, and $T:=T(x_0)$ the Terwilliger algebra of $2.O_{m+1}$. In this paper, we first give

Jingyi Guo, Min Zheng, Yajin Zhou, Haoyu Wang

Vetting security impacts introduced by third-party libraries in iOS apps requires a reliable library detection technique. Especially when a new vulnerability (or a privacy-invasive behavior) was discovered in a third-party library, there is a practical need to precisely identify the existence of libraries and their versions for iOS apps. However, few studies

Qing-Hua Zhu

Since it was confirmed two decades ago that the expansion of the Universe is accelerating, it would be of theoretical interests to figure out what is the influence from cosmological constant on detection of stochastic gravitational wave background. This paper studies the overlap reduction functions in de-Sitter space-time for a pair of one-way tracking gravi

Tanim Firdoshi, Sujit Garain, Suman Mondal, Ashok K. Mohapatra

Rydberg EIT-based microwave sensing has limited microwave-to-optical conversion bandwidth due to fundamental limitation in the optical pumping rate to its dark state. We demonstrate a parametric six-wave mixing of optical probe and coupling fields driving the atoms to a Rydberg state via two-photon excitation and two microwave fields with frequency offset of

Yiqiu Wang, Rahul Yesantharao, Shangdi Yu, Laxman Dhulipala

This paper presents ParGeo, a multicore library for computational geometry. ParGeo contains modules for fundamental tasks including $k$d-tree based spatial search, spatial graph generation, and algorithms in computational geometry. We focus on three new algorithmic contributions provided in the library. First, we present a new parallel convex hull algorithm

Tekin Dereli, Yorgo Senikoglu

Non-gauge generated exact solutions of simple supergravity field equations that describe pp-waves in Rosen coordinates are presented in the language of complex quaternion valued exterior differential forms.

Yi Lei, Shan Yang, Jian Cong, Lei Xie

The zero-shot scenario for speech generation aims at synthesizing a novel unseen voice with only one utterance of the target speaker. Although the challenges of adapting new voices in zero-shot scenario exist in both stages -- acoustic modeling and vocoder, previous works usually consider the problem from only one stage. In this paper, we extend our previous

Jaewon Lee, Kwang Pyo Choi, Kyong Hwan Jin

Image warping aims to reshape images defined on rectangular grids into arbitrary shapes. Recently, implicit neural functions have shown remarkable performances in representing images in a continuous manner. However, a standalone multi-layer perceptron suffers from learning high-frequency Fourier coefficients. In this paper, we propose a local texture estimat

Luca Paolo Merlino, Nicole Tabasso

We study the diffusion of a true and a false message (misinformation) when agents are biased and able to verify messages. As a recipient of a false message who verifies it becomes informed of the truth, a higher prevalence of misinformation can increase the prevalence of the truth. We uncover conditions such that this happens and discuss policy implications.

Qiao Song, Xianyong Bai, Bo Chen, Xiuqing Hu

The extreme ultraviolet (EUV) observations are widely used in solar activity research and space weather forecasting since they can observe both the solar eruptions and the source regions of the solar wind. Flat field processing is indispensable to remove the instrumental non-uniformity of a solar EUV imager in producing high-quality scientific data from orig

Daniz Teymouri, Omid Sedehi, Lambros S. Katafygiotis, Costas Papadimitriou

Structural identification and damage detection can be generalized as the simultaneous estimation of input forces, physical parameters, and dynamical states. Although Kalman-type filters are efficient tools to address this problem, the calibration of noise covariance matrices is cumbersome. For instance, calibration of input noise covariance matrix in augment

Atanu Rajak, Sei Suzuki, Amit Dutta, Bikas K. Chakrabarti

In this review, after providing the basic physical concept behind quantum annealing (or adiabatic quantum computation), we present an overview of some recent theoretical as well as experimental developments pointing to the issues which are still debated. With a brief discussion on the fundamental ideas of continuous and discontinuous quantum phase transition

Zhenyu Xiao, Kohei Kawabata, Xunlong Luo, Tomi Ohtsuki

Symmetries associated with complex conjugation and Hermitian conjugation, such as time-reversal symmetry and pseudo-Hermiticity, have great impact on eigenvalue spectra of non-Hermitian random matrices. Here, we show that time-reversal symmetry and pseudo-Hermiticity lead to universal level statistics of non-Hermitian random matrices on and around the real a

Edwin Langmann, Christian Hainzl, Robert Seiringer, Alexander V. Balatsky

We present a necessary condition for odd-frequency (odd-f) superconductivity (SC) to occur in a large class of materials described by Eliashberg theory. We use this condition to prove a no-go theorem ruling out the occurrence of odd-f SC in standard one-band superconductors with pairing interactions mediated by phonon exchange. We also present a correspondin

Eoghan McDowell

This paper investigates partitions which have neither parts nor hook lengths divisible by $p$, referred to as $p$-core $p'$-partitions. We show that the largest $p$-core $p'$-partition corresponds to the longest walk on a graph with vertices $\{0, 1, \ldots, p-1\}$ and labelled edges defined via addition modulo $p$. We also exhibit an explicit family of larg

Bin Li, Yixuan Weng, Ziyu Ma, Bin Sun

This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided into two phases: multi-modal context understanding and response generation. To fully leverage the visual information for both scene understanding and dialogue generation, we propos

Annette Spooner, Gelareh Mohammadi, Perminder S. Sachdev, Henry Brodaty

Healthcare datasets present many challenges to both machine learning and statistics as their data are typically heterogeneous, censored, high-dimensional and have missing information. Feature selection is often used to identify the important features but can produce unstable results when applied to high-dimensional data, selecting a different set of features

Zhihao Yuan, Xu Yan, Zhuo Li, Xuhao Li

Recent progress in 3D scene understanding has explored visual grounding (3DVG) to localize a target object through a language description. However, existing methods only consider the dependency between the entire sentence and the target object, ignoring fine-grained relationships between contexts and non-target ones. In this paper, we extend 3DVG to a more f

S. Sudhakar, S. Vignesh Raja, A. Govindarajan, K. Batri

In the context of $\mathcal{PT}$-symmetric fiber Bragg gratings, tailoring the nonlinear profile along the propagation coordinate serves to be a new direction for realizing low-power all-optical switches. The scheme is fruitful only when the nonlinearity profile will be either linearly decreasing or increasing form. If the rate of variation of the nonlineari

Yu-Hsueh Chen, Ke Hsu, Wei-Lin Tu, Hyun-Yong Lee

We propose a simple and generic construction of the variational tensor network operators to study the quantum spin systems by the synergy of ideas from the imaginary-time evolution and variational optimization of trial wave functions. By applying these operators to simple initial states, accurate variational ground state wave functions with extremely few par

Takaki Akiba, Youhi Morii, Kaoru Maruta

The Harrow, Hassidim, Lloyd (HHL) algorithm is a quantum algorithm expected to accelerate solving large-scale linear ordinary differential equations (ODEs). To apply the HHL to non-linear problems such as chemical reactions, the system must be linearized. In this study, Carleman linearization was utilized to transform nonlinear first-order ODEs of chemical r

S. Miyake, T. Koi, Y. Muraki, Y. Matsubara

In association with a large solar flare on November 7, 2004, the solar neutron detectors located at Mt. Chacaltaya (5,250m) in Bolivia and Mt. Sierra Negra (4,600m) in Mexico recorded very interesting events. In order to explain these events, we have performed a calculation solving the equation of motion of anti-protons inside the magnetosphere. Based on the

Tom Stindl, Feng Chen

Epidemic-Type Aftershock Sequence (ETAS) models are point processes that have found prominence in seismological modeling. Its success has led to the development of a number of different versions of the ETAS model. Among these extensions is the RETAS model which has shown potential to improve the modeling capabilities of the ETAS class of models. The RETAS mo

T. Peitzmann, J. Alme, R. Barthel, A. van Bochove

A prototype of a new type of calorimeter has been designed and constructed, based on a silicon-tungsten sampling design using pixel sensors with digital readout. It makes use of the Alpide MAPS sensor developed for the ALICE ITS upgrade. A binary readout is possible due to the pixel size of $\approx 30 \times 30 \, \mu \mathrm{m}^2$. This prototype has been

Robert Turnbull

Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB d

Jeiyoon Park, Kiho Kwoun, Chanhee Lee, Heuiseok Lim

As the number of video content has mushroomed in recent years, automatic video summarization has come useful when we want to just peek at the content of the video. However, there are two underlying limitations in generic video summarization task. First, most previous approaches read in just visual features as input, leaving other modality features behind. Se

Yun Zhou, Boying Gong, Tao Jiang, Ting Xu

Recent years have seen substantial advances in the development of biofunctional materials using synthetic polymers. The growing problem of elusive sequence-functionality relations for most biomaterials has driven researchers to seek more effective tools and analysis methods. In this study, statistical models are used to study sequence features of the recentl

Biplab Basak, Manisha Binjola

We define the notion of $(p_0,p_1,\dots,p_d)$-type semi-equivelar gems for closed connected PL $d$-manifolds, related to the regular embedding of gems $\Gamma$ representing $M$ on a surface $S$ such that the face-cycles at all the vertices of $\Gamma$ on $S$ are of the same type. The term is inspired by semi-equivelar maps of surfaces. Given a surface $S$ ha

Bibin Wilson, Rajiv Kumar, Narayanarao Bhogapurapu, Anand Singh

Traditional survey methods for finding surface resistivity are time-consuming and labor intensive. Very few studies have focused on finding the resistivity/conductivity using remote sensing data and deep learning techniques. In this line of work, we assessed the correlation between surface resistivity and Synthetic Aperture Radar (SAR) by applying various de

Levi H. Dudte, Gary P. T. Choi, Kaitlyn P. Becker, L. Mahadevan

We present an additive approach for the inverse design of kirigami-based mechanical metamaterials by focusing on the empty (negative) spaces instead of the solid tiles. By considering each negative space as a four-bar linkage, we identify a simple recursive relationship between adjacent linkages, yielding an efficient method for creating kirigami patterns. T

Yiren Wang, Fatima Tuz-Zahra, Rong Zablocki, Chongzhi Di

Cohort studies are increasingly using accelerometers for physical activity and sedentary behavior estimation. These devices tend to be less error-prone than self-report, can capture activity throughout the day, and are economical. However, previous methods for estimating sedentary behavior based on hip-worn data are often invalid or suboptimal under free-liv

Liang Li, Siwei Wang, Xinwang Liu, En Zhu

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a set of base kernels. Constructing precise and local kernel matrices is proved to be of vital significance in applications since the unreliable distant-distance similarity estimation would degrade clustering per-formance. Although existing localized MKC algorithms exh

Yadi Zhong, Ujjwal Guin

Due to the adoption of horizontal business models following the globalization of semiconductor manufacturing, the overproduction of integrated circuits (ICs) and the piracy of intellectual properties (IPs) can lead to significant damage to the integrity of the semiconductor supply chain. Logic locking emerges as a primary design-for-security measure to count

Raghunathan Srianand, Neeraj Gupta, Patrick Petitjean, Emmanuel Momjian

We report the emergence of a new HI 21-cm absorption at z_abs = 1.172635 in the damped Lyman-alpha absorber (DLA) towards the gamma-ray blazar PKS 2355-106 (z_em~1.639) using science verification observations (June 2020) from the MeerKAT Absorption Line Survey (MALS). Since 2006, this DLA is known to show a narrow HI 21-cm absorption at z_abs = 1.173019 coin

Xin Jin, Xinning Li, Hao Lou, Chenyu Fan

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, w

Jiawei Yang, Hanbo Chen, Yu Zhao, Fan Yang

Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datas

Gabriela Jaramillo

We study the existence of target patterns in oscillatory media with weak local coupling and in the presence of an impurity, or defect. We model these systems using a viscous eikonal equation posed on the plane, and represent the defect as a perturbation. In contrast to previous results we consider large defects, which we describe using a function with slow a

Suchit Negi, Alexandra Carvalho, Maxim Trushin, A. H. Castro Neto

2D water, confined by atomically flat layered materials, may transit into various crystalline phases even at room temperature. However, to gain full control over the crystalline state, we should not only confine water in the out of plane direction but also restrict its in plane motion, forming 2D water clusters or ribbons. One way to do this is by using an e

Li Zhang, Yue Li, Huan Zhao, Qing Wang

This paper describes the NPU system submitted to Spoofing Aware Speaker Verification Challenge 2022. We particularly focus on the \textit{backend ensemble} for speaker verification and spoofing countermeasure from three aspects. Firstly, besides simple concatenation, we propose circulant matrix transformation and stacking for speaker embeddings and counterme

Mahabubul Alam, Satwik Kundu, Swaroop Ghosh

Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building block of a QNN, consists of several layers of single-qubit rotations and multi-qubit entanglement operations. T

Samuel Drapeau, Liming Yin

In this work, we study the extremal functions of the log-Sobolev functional on compact metric measure spaces satisfying the $\mathrm{RCD}^*(K,N)$ condition for $K$ in $\mathbb{R}$ and $N$ in $(2,\infty)$. We show the existence, regularity and positivity of non-negative extremal functions. Based on these results, we prove a Li-Yau type estimate for the logari

Sanwar Alam, Mohammad N. Murshed

Loewner framework is a technique that uses frequency response data to construct a reduced order model of a given system. In the past, it has been employed in many different synthetic problems and applications like beams. In this work, we exploit the tool on data pertaining to the structural vibrations in the Russian Service module. As per our analysis, the L

Zhi Chen, Yadan Luo, Sen Wang, Jingjing Li

Generalized Zero-Shot Learning (GZSL) aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. However, due to the generation

Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, Jiaya Jia

Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency be

Jesse Haviland, Peter Corke

Manipulator kinematics is concerned with the motion of each link within a manipulator without considering mass or force. In this article, which is the first in a two-part tutorial, we provide an introduction to modelling manipulator kinematics using the elementary transform sequence (ETS). Then we formulate the first-order differential kinematics, which lead

Ke Xu, Yao Xiao, Zhaoheng Zheng, Kaijie Cai

Adversarial patch attacks mislead neural networks by injecting adversarial pixels within a local region. Patch attacks can be highly effective in a variety of tasks and physically realizable via attachment (e.g. a sticker) to the real-world objects. Despite the diversity in attack patterns, adversarial patches tend to be highly textured and different in appe

Jesse Haviland, Peter Corke

This is the second and final article on the tutorial on manipulator differential kinematics. In Part 1, we described a method of modelling kinematics using the elementary transform sequence (ETS), before formulating forward kinematics and the manipulator Jacobian. We then described some basic applications of the manipulator Jacobian including resolved-rate m

Yuan Liang, Bingjie Yu, Xiaojian Zhang, Yi Lu

Ridesourcing is popular in many cities. Despite its theoretical benefits, a large body of studies have claimed that ridesourcing also brings (negative) externalities (e.g., inducing trips and aggravating traffic congestion). Therefore, many cities are planning to enact or have already enacted policies to regulate its use. However, these policies' effectivene

Adnan Ali, Jinlong Li

Self-Supervised learning aims to eliminate the need for expensive annotation in graph representation learning, where graph contrastive learning (GCL) is trained with the self-supervision signals containing data-data pairs. These data-data pairs are generated with augmentation employing stochastic functions on the original graph. We argue that some features c

Bhanuday Sharma, Rakesh Kumar, Savitha Pareek, Ashish Singh

Extensive research has been carried out in the past to estimate the bulk viscosity of dense monatomic fluids; however, little attention has been paid to estimate the same in the dilute gas regime. In this work, we perform precise Green-Kubo calculations in molecular dynamics simulations to estimate the bulk viscosity of dilute argon gas. The investigated tem

Balamurali Murugesan, Sriprabha Ramanarayanan, Sricharan Vijayarangan, Keerthi Ram

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin with, we propose reconsynergynet (RSN), a network that combines the complementary benefits of independently operating on b

Dabao Wang, Hang Feng, Siwei Wu, Yajin Zhou

The prosperity of decentralized finance motivates many investors to profit via trading their crypto assets on decentralized applications (DApps for short) of the Ethereum ecosystem. Apart from Ether (the native cryptocurrency of Ethereum), many ERC20 (a widely used token standard on Ethereum) tokens obtain vast market value in the ecosystem. Specifically, th

Richard Y. Zhang

We consider minimizing a twice-differentiable, $L$-smooth, and $\mu$-strongly convex objective $\phi$ over an $n\times n$ positive semidefinite matrix $M\succeq0$, under the assumption that the minimizer $M^{\star}$ has low rank $r^{\star}\ll n$. Following the Burer--Monteiro approach, we instead minimize the nonconvex objective $f(X)=\phi(XX^{T})$ over a fa

L. A. Rachmeler, J. Trujillo Bueno, D. E. McKenzie, R. Ishikawa

The CLASP2 (Chromospheric LAyer SpectroPolarimeter 2) sounding rocket mission was launched on 2019 April 11. CLASP2 measured the four Stokes parameters of the Mg II h & k spectral region around 2800 Angstroms along a 200 arcsecond slit at three locations on the solar disk, achieving the first spatially and spectrally resolved observations of the solar polari

Weizhe Shen

We prove that if $P$ is a $(1,1)$-pattern knot, the two inequalities $\dim \widehat{HFK} (P(K)) \geqslant \dim \widehat{HFK} (P(U))$ and $\dim \widehat{HFK} (P(K)) \geqslant \dim \widehat{HFK} (K)$ hold for the unknot $U\subset S^3$ and any companion knot $K\subset S^3$.

Alessandro Stabile, Vladimir V. Yotov, Guglielmo S. Aglietti, Pasquale De Francesco

Isolation of spacecraft microvibrations is essential for the successful deployment of instruments relying on high-precision pointing. Hexapod platforms represent a promising solution, but the difficulties associated with attaining desirable 3D dynamics within acceptable mass and complexity budgets have led to a minimal practical adoption. This paper addresse

Xiaoyan Yang, Jingwen Shen

Let $\mathfrak{a}$ be a proper ideal of a commutative noetherian ring $R$ and $d$ a positive integer. We answer Hartshorne's question on cofinite complexes completely in the cases $\mathrm{dim}R=d$ or $\mathrm{dim}R/\mathfrak{a}=d-1$ or $\mathrm{ara}(\mathfrak{a})=d-1$, show that if $d\leq2$ then an $R$-complex $X\in\mathrm{D}_\sqsubset(R)$ is $\mathfrak{a}$

Jiachen Lu, Junge Zhang, Xiatian Zhu, Jianfeng Feng

Vision transformers (ViTs) have pushed the state-of-the-art for visual perception tasks. The self-attention mechanism underpinning the strength of ViTs has a quadratic complexity in both computation and memory usage. This motivates the development of approximating the self-attention at linear complexity. However, an in-depth analysis in this work reveals tha

Yi Xie, Jonathan Macoskey, Martin Radfar, Feng-Ju Chang

We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization. Our architecture creates sparse computation pathways dynamically at inference time, resulting in selective use of compute resources throughout decoding, enabling significant reductio

Isac M. Lacerda, Eber A. Schmitz, Jayme L. Szwarcfiter, Rosiane de Freitas

This paper presents an empirical performance analysis of three project scheduling algorithms dealing with maximizing projects' net present value with unrestricted resources. The selected algorithms, being the most recently cited in the literature, are: Recursive Search (RS), Steepest Ascent Approach (SAA) and Hybrid Search (HS). The main motivation for this

Mira Seo, Hong Bae Ann

We present the structural parameters of $\sim910$ dwarf elliptical-like galaxies in the local universe ($z\lesssim0.01$) derived from the $r-$band images of the Sloan Digital Sky Survey (SDSS). We examine the dependence of structural parameters on the morphological types (dS0, dE,dE$_{bc}$, dSph, and dE$_{blue}$). There is a significant difference in the str

Jun Wu, Jingrui He

Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a stationary task distribution, thereby leading to the sub-optimal performance for dynamic tasks drawn from a non-stationary task distribution in real scenarios. To bridge this gap,

Yifan Feng, Yuxuan Tang

We consider a preference learning setting where every participant chooses an ordered list of $k$ most preferred items among a displayed set of candidates. (The set can be different for every participant.) We identify a distance-based ranking model for the population's preferences and their (ranked) choice behavior. The ranking model resembles the Mallows mod

Kazuhiro Seki, Seiji Yunoki

We propose a method to calculate finite-temperature properties of a quantum many-body system for a microcanonical ensemble by introducing a pure quantum state named here an energy-filtered random-phase state, which is also a potentially promising application of near-term quantum computers. In our formalism, a microcanonical ensemble is specified by two param

Huaqing Huang, Linxin Guo, Yunbiao Zhao, Xinwei Wang

The performance and radiation tolerance of the proton detector based on MAPbBr3 perovskite single crystal are investigated here with 3MeV protons. The detector can monitor fluence rate and dose quantificationally at a low applied bias electric field(0.01$V/{\mu}m$) within a dose range of 45 kGy. The detector can also be worked at zero bias due to the Dember

Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese

Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only f

Rufeng Zhang, Tao Kong, Weihao Wang, Xuan Han

It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estimation problem. We propose a multi-layer t

Suraj Kothawade, Donna Roy, Michele Fenzi, Elmar Haussmann

Retrieving images with objects that are semantically similar to objects of interest (OOI) in a query image has many practical use cases. A few examples include fixing failures like false negatives/positives of a learned model or mitigating class imbalance in a dataset. The targeted selection task requires finding the relevant data from a large-scale pool of

Ryan Hamerly, Alexander Sludds, Saumil Bandyopadhyay, Zaijun Chen

This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modul

Hai-Liang Li, Tong Yang, Mingying Zhong

The pointwise space-time behavior of the Green's function of the one-dimensional Vlasov-Maxwell-Boltzmann (VMB) system is studied in this paper. It is shown that the Green's function consists of the macroscopic diffusive waves and Huygens waves with the speed $\pm \sqrt{5/3}$ at low-frequency, the hyperbolic waves with the speed $\pm 1$ at high-frequency, th

Shintaro Eijima, Osamu Seto, Takashi Shimomura

We reexamine sterile neutrino dark matter in gauged $U(1)_{B-L}$ model. Improvements have been made by tracing and careful evaluation of the evolution of the number densities of sterile neutrinos $N$ and extra neutral gauge bosons $Z'$. As a result, the cosmologically-interesting gauge coupling of $U(1)_{B-L}$ for freeze-in sterile neutrinos turns out to be

Peize Ding, Tilman Schwemmer, Ching Hua Lee, Xianxin Wu

We study the quasiparticle interference (QPI) pattern emanating from a pair of adjacent impurities on the surface of a gapped superconductor (SC). We find that hyperbolic fringes (HF) in the QPI signal can appear due to the loop contribution of the two-impurity scattering, where the location of the two impurities are the hyperbolic focus points. For a single

Lei Zhang, Mukesh Ghimire, Wenlong Zhang, Zhe Xu

Finding Nash equilibrial policies for two-player differential games requires solving Hamilton-Jacobi-Isaacs (HJI) PDEs. Self-supervised learning has been used to approximate solutions of such PDEs while circumventing the curse of dimensionality. However, this method fails to learn discontinuous PDE solutions due to its sampling nature, leading to poor safety

Thong Nguyen, Cong-Duy Nguyen, Xiaobao Wu, See-Kiong Ng

With the burgeoning amount of data of image-text pairs and diversity of Vision-and-Language (V\&L) tasks, scholars have introduced an abundance of deep learning models in this research domain. Furthermore, in recent years, transfer learning has also shown tremendous success in Computer Vision for tasks such as Image Classification, Object Detection, etc., an

Kaan Ozkara, Antonious M. Girgis, Deepesh Data, Suhas Diggavi

A distinguishing characteristic of federated learning is that the (local) client data could have statistical heterogeneity. This heterogeneity has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. There have been various personalization methods proposed in literature, with seemingly very

Yi-Tong Wang, Shu-Min Zhao, Tong-Tong Wang, Xi Wang

$U(1)_X$SSM is the extension of the minimal supersymmetric standard model (MSSM) and its local gauge group is $SU(3)_C\times SU(2)_L \times U(1)_Y \times U(1)_X$. We study lepton flavor violating (LFV) decays $Z\rightarrow{{l_i}^{\pm}{l_j}^{\mp}}$($Z\rightarrow e{\mu}$, $Z\rightarrow e{\tau}$, and $Z\rightarrow {\mu}{\tau}$) and $h\rightarrow{{l_i}^{\pm}{l_j

Osman Tursun, Simon Denman, Sridha Sridharan, Clinton Fookes

High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techniques have been developed to generate better saliency using neural networks. However, they are often limited to specific saliency visualisation methods or saliency issues. We propos

Xucheng Wang, Dan Zeng, Qijun Zhao, Shuiwang Li

Unmanned aerial vehicle (UAV) tracking has wide potential applications in such as agriculture, navigation, and public security. However, the limitations of computing resources, battery capacity, and maximum load of UAV hinder the deployment of deep learning-based tracking algorithms on UAV. Consequently, discriminative correlation filters (DCF) trackers stan

Oleg Evnin, Karapet Mkrtchyan

We briefly review and critically compare three approaches to constructing Lagrangian theories of self-interacting Abelian chiral form fields with manifest Lorentz invariance. The first approach relies on the original ideas of Pasti, Sorokin, and Tonin (PST) and has been explored since the late 1990s. The second approach was introduced by Ashoke Sen in 2015.

Lijun Zhu, David Lujan, Xiaoqin Li

Dzyaloshinskii-Moriya interaction (DMI) is the key ingredient of chiral spintronic phenomena and the emerging technologies based on such phenomena. A nonzero DMI usually occurs at magnetic interfaces or within non-centrosymmetric single crystals. Here, we report the observation of a strong unexpected DMI within a centrosymmetric polycrystalline ferromagnet t

Jae Yong Lee, Juhi Jang, Hyung Ju Hwang

We propose a hybrid framework opPINN: physics-informed neural network (PINN) with operator learning for approximating the solution to the Fokker-Planck-Landau (FPL) equation. The opPINN framework is divided into two steps: Step 1 and Step 2. After the operator surrogate models are trained during Step 1, PINN can effectively approximate the solution to the FP

Qiang Zhao, Zhengxue Ren, Pengwei Zhao, Jie Meng

A new density-dependent point-coupling covariant density functional PCF-PK1 is proposed, where the exchange terms of the four-fermion terms are local and are taken into account with the Fierz transformation. The coupling constants of the PCF-PK1 functional are determined by empirical saturation properties and ab initio equation of state and proton-neutron Di

Xi-Jie Zhan, Xing-Gang Wu, Xu-Chang Zheng

Based on the non-relativistic quantum chromodynamics factorization framework, we study the inclusive $J/\psi$ photoproduction at the future high energy $e^+e^-$ collider, International Linear Collider(ILC), where the initial photons come from the back-scattering of laser and electron (positron). The intermediate states, $c\bar{c}$$({}^3\!S_1^{[\textbf{1}]}$,

Pan Du, Jian-Yun Nie, Yutao Zhu, Hao Jiang

Beyond topical relevance, passage ranking for open-domain factoid question answering also requires a passage to contain an answer (answerability). While a few recent studies have incorporated some reading capability into a ranker to account for answerability, the ranker is still hindered by the noisy nature of the training data typically available in this ar

Yaonan Jin, Pinyan Lu

We prove that the {\sf PoA} of {\sf First Price Auctions} is $1 - 1/e^2 \approx 0.8647$, closing the gap between the best known bounds $[0.7430,\, 0.8689]$.

Gyunpyo Lee, Taesu Kim, Hyeon-Jeong Suk

An automated design data archiving could reduce the time wasted by designers from working creatively and effectively. Though many datasets on classifying, detecting, and instance segmenting on car exterior exist, these large datasets are not relevant for design practices as the primary purpose lies in autonomous driving or vehicle verification. Therefore, we

Yanni Zhai, Xiying Yuan

Given a graph $H$ and an odd integer $t$ ($t\geq 3$), the odd-ballooning of $H$, denoted by $H(t)$, is the graph obtained from replacing each edge of $H$ by an odd cycle of length at least $t$ where the new vertices of the cycles are all distinct. In this paper, we determine the range of Tur\'{a}n numbers for odd-ballooning of bipartite graphs when $t\geq 5$