999,590 Works

An Efficient and Adaptive Granular-ball Generation Method in Classification Problem

Shuyin Xia, Xiaochuan Dai, Guoyin Wang, Xinbo Gao & Elisabeth Giem
Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve the efficiency of granular-ball generation while ensuring the accuracy similar to the existing method. Besides, a new adaptive method for the granular-ball generation is proposed by considering granular-ball's overlap eliminating and some...

One-Loop Renormalization of the Higgs Sector of the Electroweak Chiral Lagrangian extended by N Scalar Singlets

Andreas Lindner & Khoirul Faiq Muzakka
The framework of the electroweak chiral Lagrangian with a light Higgs is extended by an additional scalar and then generalized to N scalars in the Higgs sector. Divergences from scalar fluctuations are renormalized up to one loop using the background field method. The results are crosschecked against the case of one scalar. A subset of the divergences is demonstrated and crosschecked diagrammatically. Together with the complete one-loop renormalization of the electroweak chiral theory with one...

Monte-Carlo tool SANCphot for polarized $γγ$ collision simulation

Serge Bondarenko, Lidia Kalinovskaya & Andrey Sapronov
Our study of theoretical uncertainties for the four bosons processes at one-loop level including the case of the transverse polarization is presented. The calculations are based on helicity amplitudes approach for 4-boson SM interactions through a fermion and boson loops. The computation takes into account nonzero mass of loop particles. The obtained predictions are equally suitable for a wide range of energies and for arbitrary, including extreme, regions of the phase volume. Uncertainty estimates are...

Reconfiguration of Spanning Trees with Degree Constraint or Diameter Constraint

Nicolas Bousquet, Takehiro Ito, Yusuke Kobayashi, Haruka Mizuta, Paul Ouvrard, Akira Suzuki & Kunihiro Wasa
We investigate the complexity of finding a transformation from a given spanning tree in a graph to another given spanning tree in the same graph via a sequence of edge flips. The exchange property of the matroid bases immediately yields that such a transformation always exists if we have no constraints on spanning trees. In this paper, we wish to find a transformation which passes through only spanning trees satisfying some constraint. Our focus is...

Establishment and morphological characterization of a core collection of pathogenic fungal strains isolated from wilting Medicago sativa plants

Amani BEN ALAYA, Bilel KHIARI, Imen BEN SLIMENE & Naceur DJEBALI
The study focused on the isolation, characterization and conservation of fungal strains isolated form Medicago sativa plants showing wilt disease symptoms in southern Tunisian oases. A total of 79 fungal strains were isolated from different plant organs, including roots, collars, and stems. Morphological identification of fungal strains was based on the morphology of culture on PDA medium and of the conidia under microscope. All the fungal strains were stored on PDA slant covered with mineral...

Kohler-Jobin meets Ehrhard: the sharp lower bound for the Gaussian principal frequency while the Gaussian torsional rigidity is fixed, via rearrangements

Orli Herscovici & Galyna V. Livshyts
In this note, we provide an adaptation of the Kohler-Jobin rearrangement technique to the setting of the Gauss space. As a result, we prove the Gaussian analogue of the Kohler-Jobin's resolution of a conjecture of Pólya-Szegö: when the Gaussian torsional rigidity of a (convex) domain is fixed, the Gaussian principal frequency is minimized for the half-space. At the core of this rearrangement technique is the idea of considering a "modified" torsional rigidity, with respect to...

Neighboring Backdoor Attacks on Graph Convolutional Network

Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li & Zibin Zheng
Backdoor attacks have been widely studied to hide the misclassification rules in the normal models, which are only activated when the model is aware of the specific inputs (i.e., the trigger). However, despite their success in the conventional Euclidean space, there are few studies of backdoor attacks on graph structured data. In this paper, we propose a new type of backdoor which is specific to graph data, called neighboring backdoor. Considering the discreteness of graph...

Defeating Eavesdroppers with Ambient Backscatter Communications

Nguyen Van Huynh, Nguyen Quang Hieu, Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang & Eryk Dutkiewicz
Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter communications in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active...

Comparative Study of Acoustic Echo Cancellation Algorithms for Speech Recognition System in Noisy Environment

Urmila Shrawankar
Traditionally, adaptive filters have been deployed to achieve AEC by estimating the acoustic echo response using algorithms such as the Normalized Least-Mean-Square (NLMS) algorithm. Several approaches have been proposed over recent years to improve the performance of the standard NLMS algorithm in various ways for AEC. These include algorithms based on Time Domain, Frequency Domain, Fourier Transform, Wavelet Transform Adaptive Schemes, Proportionate Schemes, Proportionate Adaptive Filters, Combination Schemes, Block Based Combination, Sub band Adaptive Filtering,...

Deep convolutional neural network for shape optimization using level-set approach

Wrik Mallik, Neil Farvolden, Jasmin Jelovica & Rajeev K. Jaiman
This article presents a reduced-order modeling methodology via deep convolutional neural networks (CNNs) for shape optimization applications. The CNN provides a nonlinear mapping between the shapes and their associated attributes while conserving the equivariance of these attributes to the shape translations. To implicitly represent complex shapes via a CNN-applicable Cartesian structured grid, a level-set method is employed. The CNN-based reduced-order model (ROM) is constructed in a completely data-driven manner thus well suited for non-intrusive applications....

An Improved Reinforcement Learning Algorithm for Learning to Branch

Qingyu Qu, Xijun Li, Yunfan Zhou, Jia Zeng, Mingxuan Yuan, Jie Wang, Jinhu Lv, Kexin Liu & Kun Mao
Most combinatorial optimization problems can be formulated as mixed integer linear programming (MILP), in which branch-and-bound (B\&B) is a general and widely used method. Recently, learning to branch has become a hot research topic in the intersection of machine learning and combinatorial optimization. In this paper, we propose a novel reinforcement learning-based B\&B algorithm. Similar to offline reinforcement learning, we initially train on the demonstration data to accelerate learning massively. With the improvement of the...

On convergence of occupational measures sets of a discrete-time stochastic control system, with applications to averaging of hybrid systems

Lucas Gamertsfelder
In the first part of the paper, we consider a discrete-time stochastic control system. We show that, under certain conditions, the set of random occupational measures generated by the state-control trajectories of the system as well as the set of their mathematical expectations converge (as the time horizon tends to infinity) to a convex and compact (non-random) set, which is shown to coincide with the set of stationary probabilities of the system. In the second...

An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets

Yuqiao Wen, Guoqing Luo & Lili Mou
Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such overlapping can be exploited to obtain fake state-of-the-art performance. Finally, we address this issue by cleaning these datasets and setting up a proper data processing procedure for future research.

Efficient DNN Training with Knowledge-Guided Layer Freezing

Yiding Wang, Decang Sun, Kai Chen, Fan Lai & Mosharaf Chowdhury
Training deep neural networks (DNNs) is time-consuming. While most existing solutions try to overlap/schedule computation and communication for efficient training, this paper goes one step further by skipping computing and communication through DNN layer freezing. Our key insight is that the training progress of internal DNN layers differs significantly, and front layers often become well-trained much earlier than deep layers. To explore this, we first introduce the notion of training plasticity to quantify the training...

Targeted Optimal Treatment Regime Learning Using Summary Statistics

Jianing Chu, Wenbin Lu & Shu Yang
Personalized decision-making, aiming to derive optimal individualized treatment rules (ITRs) based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services, and economics. Current literature mainly focuses on estimating ITRs from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, ITRs learned by existing methods may not generalize well to the target population. Due to...

Experimental Observation of Maximum Density Fluctuation in Liquid Te

Yukio Kajihara, Masanori Inui, Kazuhiro Matsuda & Koji Ohara
We performed small-angle X-ray scattering measurements of liquid Te using a synchrotron radiation facility and observed the maximum scattering intensity near 620 K in the supercooled region (melting temperature 723 K). This result is an experimental observation of the ridge line of the critical density fluctuation associated with the liquid-liquid phase transition that is argued to exist in the supercooled region and verifies the existence of the transition. Similar results have been reported for supercooled...

Inferential Theory for Granular Instrumental Variables in High Dimensions

Saman Banafti & Tae-Hwy Lee
The Granular Instrumental Variables (GIV) methodology exploits panels with factor error structures to construct instruments to estimate structural time series models with endogeneity even after controlling for latent factors. We extend the GIV methodology in several dimensions. First, we extend the identification procedure to a large $N$ and large $T$ framework, which depends on the asymptotic Herfindahl index of the size distribution of $N$ cross-sectional units. Second, we treat both the factors and loadings as...

Drift vs Shift: Decoupling Trends and Changepoint Analysis

Haoxuan Wu, Sean Ryan & David S. Matteson
We introduce a new approach for decoupling trends (drift) and changepoints (shifts) in time series. Our locally adaptive model-based approach for robustly decoupling combines Bayesian trend filtering and machine learning based regularization. An over-parameterized Bayesian dynamic linear model (DLM) is first applied to characterize drift. Then a weighted penalized likelihood estimator is paired with the estimated DLM posterior distribution to identify shifts. We show how Bayesian DLMs specified with so-called shrinkage priors can provide smooth...

Distributions of Hook lengths in integer partitions

Michael Griffin, Ken Ono & Wei-Lun Tsai
Motivated by the many roles that hook lengths play in mathematics, we study the distribution of the number of $t$-hooks in the partitions of $n$. We prove that the limiting distribution is normal with mean $μ_t(n)\sim \frac{\sqrt{6n}}π-\frac{t}{2}$ and variance $σ_t^2(n)\sim \frac{(π^2-6)\sqrt{6n}}{2π^3}.$ Furthermore, we prove that the distribution of the number of hook lengths that are multiples of a fixed $t\geq 4$ in partitions of $n$ converge to a shifted Gamma distribution with parameter $k=(t-1)/2$ and...

Optimal trend following portfolios

Sebastien Valeyre
This paper derives an optimal portfolio that is based on trend-following signal. Building on an earlier related article, it provides a unifying theoretical setting to introduce an autocorrelation model with the covariance matrix of trends and risk premia. We specify practically relevant models for the covariance matrix of trends. The optimal portfolio is decomposed into four basic components that yield four basic portfolios: Markowitz, risk parity, agnostic risk parity, and trend following on risk parity....

Supercurrent in the presence of direct transmission and a resonant localized state

Hristo Barakov & Yuli V. Nazarov
Inspired by recent experimental findings that will be presented elsewhere, we formulate and investigate a model of a superconducting junction that combines the electron propagation in a quantum channel with an arbitrary transmission, and that through a localized state. Interesting situation occurs if the energy of the localized state is close to Fermi level, that is, the state is in resonant tunnelling regime. Since this energy is affected by the gate voltage, we expect a...

Parallel server systems under an extended heavy traffic condition: A lower bound

Rami Atar, Eyal Castiel & Martin I. Reiman
The standard setting for studying parallel server systems (PSS) at the diffusion scale is based on the heavy traffic condition (HTC), which assumes that the underlying static allocation linear program (LP) is critical and has a unique solution. This solution determines the graph of basic activities, which identifies the set of activities (i.e., class-server pairs) that are operational. In this paper we explore the extended HTC, where the LP is merely assumed to be critical....

Transient Nature of Fast Relaxation in Metallic Glass

Leo Zella, Jaeyun Moon, David Keffer & Takeshi Egami
Metallic glasses exhibit fast mechanical relaxations at temperatures well below the glass transition, one of which shows little variation with temperature known as nearly constant loss (NCL). Despite the important implications of this phenomenon to in aging and deformation, the origin of the relaxation is unclear. Through molecular dynamics simulations of a model metallic glass, Cu_64.5Zr_35.5, we implement dynamic mechanical analysis with system stress decomposed into atomic-level stresses to identify the group of atoms responsible...

PDE-Based Optimal Strategy for Unconstrained Online Learning

Zhiyu Zhang, Ashok Cutkosky & Ioannis Paschalidis
Unconstrained Online Linear Optimization (OLO) is a practical problem setting to study the training of machine learning models. Existing works proposed a number of potential-based algorithms, but in general the design of these potential functions relies heavily on guessing. To streamline this workflow, we present a framework that generates new potential functions by solving a Partial Differential Equation (PDE). Specifically, when losses are 1-Lipschitz, our framework produces a novel algorithm with anytime regret bound $C\sqrt{T}+||u||\sqrt{2T}[\sqrt{\log(1+||u||/C)}+2]$,...

THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-Offs

M. Mahdi Azari, Sourabh Solanki, Symeon Chatzinotas & Mehdi Bennis
Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few. If such requirements are satisfactorily met, it can create novel applications and enable highly reliable and harmonized integration of UAVs in the 6G network ecosystem. Towards this, terahertz (THz) bands are perceived as a prospective technological enabler for various improved functionalities such as ultra-high throughput and enhanced sensing capabilities. This...

Registration Year

  • 2022
    999,590

Resource Types

  • Preprint
    999,590

Affiliations

  • University of Massachusetts Amherst
    20
  • Institute for Advanced Study
    4
  • National Institute for Demographic Studies
    3
  • Keele University
    2
  • Sewanee: The University of the South
    2
  • St. Francis College
    1
  • University of Strasbourg
    1
  • Australian National University
    1
  • Goldsmiths University of London
    1
  • Nankai University
    1