August 2022 arXiv papers — page 28

Kisung You, Ilmun Kim, Ick Hoon Jin, Minjeong Jeon

The latent space model is one of the well-known methods for statistical inference of network data. While the model has been much studied for a single network, it has not attracted much attention to analyze collectively when multiple networks and their latent embeddings are present. We adopt a topology-based representation of latent space embeddings to learn

Fan Wen

The fundamental geometry of self-similar sets becomes significantly more complex when the generating contractive maps include non-trivial rotational components. A well-known family exemplifying this complexity is that of the dragon curves in the plane. In this paper, we prove that every dragon curve has a polygonal convex hull. Moreover, we completely charac

Daochen Zha, Kwei-Herng Lai, Qiaoyu Tan, Sirui Ding

Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the minority class. While numerous over-sampling algorithms have been proposed, they heavily rely on heuristics

Tan Nhat Pham, Minh N. Dao, Rakibuzzaman Shah, Nargiz Sultanova

In this paper, we consider a class of structured nonconvex nonsmooth optimization problems, in which the objective function is formed by the sum of a possibly nonsmooth nonconvex function and a differentiable function whose gradient is Lipschitz continuous, subtracted by a weakly convex function. This type of structured problems has many practical applicatio

Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Markus Wagner, Jon A. R. Liisberg

Most literature surrounding optimal bidding strategies for aggregators in European day-ahead market (DAM) considers only hourly orders. While other order types (e.g., block orders) may better represent the temporal characteristics of certain sources of flexibility (e.g., behind-the-meter flexibility), the increased combinations from these orders make it hard

Geoffrey W. Marcy, Nathaniel K. Tellis, Edward H. Wishnow

A region 140 square degrees toward the Galactic Centre was searched for monochromatic optical light, both pulses shorter than 1 sec and continuous emission. A novel instrument was constructed that acquires optical spectra of every point within 6 square degrees every second, able to distinguish lasers from astrophysical sources. The system consists of a modif

Seong-Sik Kim, Hyun Min Lee, Adriana G. Menkara, Kimiko Yamashita

We propose a novel model which extends the Standard Model (SM) by introducing a $SU(2)_D$ gauge symmetry. In this model, a dark $SU(2)_D$ Higgs doublet and a Higgs bi-doublet can contribute to the muon $g-2$ anomaly and the $W$ boson mass, remaining in tune with the recent experimental results. At the same time, the isospin charged gauge boson of $SU(2)_D$ b

Mohit Kumar Baghel, Punit Sharma

For a given class of structured matrices $\mathbb S$, we find necessary and sufficient conditions on vectors $x,w\in \C^{n+m}$ and $y,z \in \C^{n}$ for which there exists $\Delta=[\Delta_1~\Delta_2]$ with $\Delta_1 \in \mathbb S$ and $\Delta_2 \in \C^{n,m}$ such that $\Delta x=y$ and $\Delta^*z=w$. We also characterize the set of all such mappings $\Delta$ a

Prashant Pandey, Mustafa Chasmai, Tanuj Sur, Brejesh Lall

Despite the tremendous progress made by deep learning models in image semantic segmentation, they typically require large annotated examples, and increasing attention is being diverted to problem settings like Few-Shot Learning (FSL) where only a small amount of annotation is needed for generalisation to novel classes. This is especially seen in medical doma

Yuan Mao, Lei Shi, Zheng-Chu Guo

In this paper, we consider the coefficient-based regularized distribution regression which aims to regress from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS), where the regularization is put on the coefficients and kernels are assumed to be indefinite. The algorithm involves two stages of sampling, the first sta

Boris Shekhtman, Brian Tuesink

An ideal projector on the space of polynomials $\mathbb{C} [\mathbf{x}]=\mathbb{C} [x_{1},\ldots ,x_{d}]$ is a projector whose kernel is an ideal in $\mathbb{C}[ \mathbf{x}]$. The question of characterization of ideal projectors that are limits of Lagrange projector was posed by Carl de Boor. In this paper we make a contribution to this problem. Every ideal

Xiao-yu Chen, Maoke Miao, Rui Yin, Jiantao Yuan

We use quantum entanglement witnesses derived from Gaussian operators to study the separable criteria of continuous variable states. We transform the validity of a Gaussian witness to a Bosonic Gaussian channel problem. It follows that the maximal means of two-mode and some four-mode Gaussian operators over product pure states are achieved by vacuum (or cohe

Haoran Xu, Mehrana R. Nejad, Julia M. Yeomans, Yilin Wu

Interaction between active materials and the boundaries of geometrical confinement is key to many emergent phenomena in active systems. For living active matter consisting of animal cells or motile bacteria, the confinement boundary is often a deformable interface, and it has been unclear how activity-induced interface dynamics might lead to morphogenesis an

Anastasiia Zalogina, Javid Javadzade, Roman Savelev, Filipp Komissarenko

The development of nanophotonics systems for the manipulation of the luminescent properties of single quantum emitters is essential for quantum communication and computing. Dielectric nanosystems enable various opportunities for light control through inherent electric and magnetic resonances, however their full potential has not yet been discovered. Here, th

Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu

The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes are available, how to improve their robustness is a key to achieve replicable and sustainable graph semi-supervised learning. Though self-training has been shown to be powerful fo

Yiyang Zhi, Andrew Lininger, Giuseppe Strangi

Random lasers are highly configurable light sources that are promising for imaging and photonic integration. In this study, random lasing action was generated by optically pumping MBBA liquid crystals infiltrated with gold nanoparticles and laser dye (pyrromethene 597). By varying the pump energy near lasing threshold, we show that it is possible to control

Jinwei Zeng, Jinrun Zhang, Yajuan Dong, Jian Wang

The full-dimensional spatial light meta-modulator requires simultaneous, arbitrary and independent manipulation of spatial phase, amplitude, and polarization. It is an essential step towards harnessing complete dimensional resources of light. However, full-dimensional meta-modulation can be challenging due to the need of multiple independent control factors.

Shi-Xue Zhang, Xiaobin Zhu, Lei Chen, Jie-Bo Hou

Arbitrary shape text detection is a challenging task due to the significantly varied sizes and aspect ratios, arbitrary orientations or shapes, inaccurate annotations, etc. Due to the scalability of pixel-level prediction, segmentation-based methods can adapt to various shape texts and hence attracted considerable attention recently. However, accurate pixel-

Ziquan Zhuang

We show that direct summands (or more generally, pure images) of klt type singularities are of klt type. As a consequence, we give a different proof of a recent result of Braun, Greb, Langlois and Moraga that reductive quotients of klt type singularities are of klt type.

Zhengxin Guan, Jun Li, Han Wu, Xiaohong Chen

Metal-organic frameworks (MOFs) possessing many unique features have been utilized in several fields in recent years. However, their application in field emission (FE) vacuum electronic device is hindered by their poor electrical conductivity. Herein, a novel conductive MOF of Cu-catecholate (Cu-CAT) with the nanorod length of 200 nm and conductivity of 0.01

P. Sedaghatnia, H. Hassanabadi, W. S. Chung, B. C. Lütfüoğlu

Recent studies have shown that the use of Dunkl derivatives instead of ordinary derivatives leads to deriving parity-dependent dynamic solutions. According to this motivation in this manuscript, we formulate the Dunkl-Schr\"odinger equation within the position-dependent mass formalism and derive an algebraic solution out of it. Our systematic approach lets u

Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti

Music tagging and content-based retrieval systems have traditionally been constructed using pre-defined ontologies covering a rigid set of music attributes or text queries. This paper presents MuLan: a first attempt at a new generation of acoustic models that link music audio directly to unconstrained natural language music descriptions. MuLan takes the form

Hanwei Liu, Baochuan Wang, Ning Wang, Zhonghai Sun

Recent progress has shown that the dramatically increased number of parameters has become a major issue in tuning of multi-quantum dot devices. The complicated interactions between quantum dots and gate electrodes cause the manual tuning process to no longer be efficient. Fortunately, machine learning techniques can automate and speed up the tuning of simple

Zi'an Xu, Yin Dai, Fayu Liu, Boyuan Wu

Parotid gland tumor is a common type of head and neck tumor. Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. Recently deep learning has developed r

Chin-Yu Hsiao, Xiaoshan Li, George Marinescu

Let $M$ be a relatively compact connected open subset with smooth connected boundary of a complex manifold $M'$. Let $(L,h^L)\rightarrow M'$ be a positive line bundle over $M'$. Suppose that $M'$ admits a holomorphic $\mathbb{R}$-action which preserves the boundary of $M$ and lifts to $L$. We establish the asymptotic expansion of a partial Bergman kernel ass

Jingcheng Lu, Eitan Tadmor

We study the long-time hydrodynamic behavior of systems of multi-species which arise from agent-based description of alignment dynamics. The interaction between species is governed by an array of symmetric communication kernels. We prove that the crowd of different species flock towards the mean velocity if (i) cross-interactions form a heavy-tailed connecte

Shrutina Agarwal, Sriram Ganapathy, Naoya Takahashi

In this paper, we propose a model to perform style transfer of speech to singing voice. Contrary to the previous signal processing-based methods, which require high-quality singing templates or phoneme synchronization, we explore a data-driven approach for the problem of converting natural speech to singing voice. We develop a novel neural network architectu

Young Heon Kim, Hye Jeong Lee, Hyng-Yong Lee, S. -R. Eric Yang

We investigate interacting disordered zigzag nanoribbons at low doping, using the Hubbard model to treat electron interactions within the density matrix renormalization group and Hartree-Fock method. Extra electrons that are inserted into an interacting disordered zigzag nanoribbon divide into anyons. Furthermore, the fractional charges form a new disordered

Yuki Endo

Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (e.g., translation and rotation), and it is laborious to identify appropri

Luyining Gan, Sejong Kim

In this paper we introduce the limit, unique solution of the nonlinear equations, geodesic property, tolerance relations and pinch on the spectral geometric mean for two positive definite operators. We show that the spectral geometric mean is a geodesic with respect to some semi-metric. We also prove that the tolerance relation on determinant one matrices ca

Tom McKinley, Boris Shekhtman, Brian Tuesink

To the best of our knowledge this paper is the first attempt to introduce and study polynomial interpolation of the polynomial data given on arbitrary varieties. In the first part of the paper we present results on the solvability of such problems. In the second part of the paper we relate the interpolation problem to polynomial solution of some boundary val

K. Brandenburg, G. Hamad, Z. Meisel, C. R. Brune

We present results from direct measurements of the $^{27}{\rm Al}(\alpha,n)$ thick target yield from laboratory incident energies $E_{\alpha}\approx$ 3$-$5~MeV, performed with the $^{3}$HeBF$_{3}$ Giant Barrel (HeBGB) neutron detector at the Edwards Accelerator Laboratory. Our measurements have a small energy cadence in order to address discrepancies and spa

Matthew J. Conder, Jeroen Schillewaert

Let $K$ be a non-archimedean local field with residue field of characteristic $p$. We give necessary and sufficient conditions for a two-generator subgroup $G$ of ${\rm PSL_2}(K)$ to be discrete, where either $K=\mathbb{Q}_p$ or $G$ contains no elements of order $p$. We give a practical algorithm to decide whether such a subgroup $G$ is discrete. We also giv

Vasu Sharma, Prasoon Goyal, Kaixiang Lin, Govind Thattai

We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-o

Danfei Xu, Yuxiao Chen, Boris Ivanovic, Marco Pavone

Simulation is the key to scaling up validation and verification for robotic systems such as autonomous vehicles. Despite advances in high-fidelity physics and sensor simulation, a critical gap remains in simulating realistic behaviors of road users. This is because, unlike simulating physics and graphics, devising first principle models for human-like behavi

J Humberto Ramos

A Schmidt filter is a modification of the Kalman filter that allows to append system parameters as states and considers their uncertainty effect in the filtering process without attempting to estimate such parameters. The states that are only considered but not estimated, are generally known as \textit{consider} or \textit{considered} states. The main contri

Jeffrey Willette, Seanie Lee, Bruno Andreis, Kenji Kawaguchi

Recent work on mini-batch consistency (MBC) for set functions has brought attention to the need for sequentially processing and aggregating chunks of a partitioned set while guaranteeing the same output for all partitions. However, existing constraints on MBC architectures lead to models with limited expressive power. Additionally, prior work has not address

Nouraldin Jaber, Christopher Wagner, Swen Jacobs, Milind Kulkarni

Distributed agreement-based (DAB) systems use common distributed agreement protocols such as leader election and consensus as building blocks for their target functionality. While automated verification for DAB systems is undecidable in general, recent work identifies a large class of DAB systems for which verification is efficiently-decidable. Unfortunately

Javier Montaño-Domínguez, Héctor Novales-Sánchez, Mónica Salinas, J. Jesús Toscano

Lorentz-violating Yukawa couplings lying within the renormalizable part of the SME generate Lorentz-invariant one-loop contributions to electromagnetic moments of fermions. These proceedings provide a discussion on such contributions and present bounds on SME coefficients from experimental data on electromagnetic moments. Constraints as restrictive as $\sim1

Lichen Jia, Bowen Tang, Chenggang Wu, Zhe Wang

The binary code similarity detection (BCSD) method measures the similarity of two binary executable codes. Recently, the learning-based BCSD methods have achieved great success, outperforming traditional BCSD in detection accuracy and efficiency. However, the existing studies are rather sparse on the adversarial vulnerability of the learning-based BCSD metho

Xixi Wang, Xiao Wang, Bo Jiang, Bin Luo

Few-shot classification which aims to recognize unseen classes using very limited samples has attracted more and more attention. Usually, it is formulated as a metric learning problem. The core issue of few-shot classification is how to learn (1) consistent representations for images in both support and query sets and (2) effective metric learning for images

Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by cau

Wei Xu, Zaifeng Gao, Liezhong Ge

This paper proposes the innovative concept of "human factors science" to characterize engineering psychology, human factors engineering, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common approach: "human-centered design." In the AI era, the human-machine relationship presents a trans-e

Ye Wang, Qi Zhao, Wenyan Wu, Ailsa Willis

This paper presents a case study of the operational management of the Robinvale high-pressure piped irrigation water delivery system (RVHPS) in Australia. Based on datasets available, improved pump setpoint selection using a calibrated hydraulic model is investigated. The first step was to implement pre-processing of measured flow and pressure data to identi

Kehinde Olobatuyi

In this paper, I propose a new class of Zero-Inflated Poisson models into the family of Cluster Weighted Models (CWMs) called Zero-Inflated Poisson CWMs (ZIPCWM). ZIPCWM extends Poisson cluster weighted models and other mixture models. I propose an Expectation-Maximization (EM) algorithm via an iteratively reweighted least squares for the model. I theoretica

Sami Yamani, Gareth H. McKinley

The FENE-P (Finitely-Extensible Nonlinear Elastic) dumbbell constitutive equation is widely used in simulations and stability analyses of free and wall-bounded viscoelastic shear flows due to its relative simplicity and accuracy in predicting macroscopic properties of dilute polymer solutions. The model contains three independent material parameters, which e

Beomsik Park, Ranggi Hwang, Dongho Yoon, Yoonhyuk Choi

The widespread deployment of machine learning (ML) is raising serious concerns on protecting the privacy of users who contributed to the collection of training data. Differential privacy (DP) is rapidly gaining momentum in the industry as a practical standard for privacy protection. Despite DP's importance, however, little has been explored within the comput

Anne-Marie Aubert, Yujie Xu

We develop a general strategy for constructing the explicit Local Langlands Correspondences for $p$-adic reductive groups via reduction to LLC for supercuspidal representations of proper Levi subgroups, using Hecke algebra techniques. As an example of our general strategy, we construct the explicit Local Langlands Correspondence for the exceptional group $G_

Nelly Elsayed, Zag ElSayed, Navid Asadizanjani, Murat Ozer

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowadays, the usage of Intelligent Virtual Personal Assistants (IVA) has

Tomoyuki Morimae, Takashi Yamakawa

Assume that Alice can do only classical probabilistic polynomial-time computing while Bob can do quantum polynomial-time computing. Alice and Bob communicate over only classical channels, and finally Bob gets a state $|x_0\rangle+|x_1\rangle$ with some bit strings $x_0$ and $x_1$. Is it possible that Alice can know $\{x_0,x_1\}$ but Bob cannot? Such a task,

Minoru Nishimura, Kazuya Matsubayashi, Takashi Murayama, Yoshiaki Taniguchi

In order to understand the formation and evolution of galaxies fully, it is important to study their three-dimensional gravitational potential for a large sample of galaxies. Since polar-ring galaxies (PRGs) provide useful laboratories for this investigation, we have started our detailed study of a sample of known PRGs by using the data set obtained by the H

Noah Schaffer, Boaz Cogan, Ethan Manilow, Max Morrison

Despite phenomenal progress in recent years, state-of-the-art music separation systems produce source estimates with significant perceptual shortcomings, such as adding extraneous noise or removing harmonics. We propose a post-processing model (the Make it Sound Good (MSG) post-processor) to enhance the output of music source separation systems. We apply our

Adam Hepworth, Aya Hussein, Darryn Reid, Hussein Abbass

Contemporary swarm indicators are often used in isolation, focused on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members, and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about

Wanming Hao, Xiaobei You, Fuhui Zhou, Zheng Chu

Terahertz (THz) and intelligent reflecting surface (IRS) have been regarded as two promising technologies to improve the capacity and coverage for future 6G networks. Generally, IRS is usually equipped with large-scale elements when implemented at THz frequency. In this case, the near-field model and beam squint should be considered. Therefore, in this paper

Tim Berenc, Tomasz Plawski, Dmitry Teytelman

This volume contains contributions presented at LLRF2022: the 10th Low-Level RF Workshop held in Brugg-Windisch, Switzerland on October 10-13, 2022.

Wolfgang Schramm

All schoolchildren know how often they breathe, but even experts don't know exactly why. The aim of this publication is to develop a model of the resting spontaneous breathing rate using physiological, physical and mathematical methods with the aid of the principle that evolution pushes physiology in a direction that is as economical as possible. The respira

Y. Kubota, F. Nabeshima, K. Nakayama, H. Ohsumi

Lattice and electronic states of thin FeSe films on LaAlO$_3$ substrates are investigated in the vicinity of the nematic phase transition. No evidence of structural phase transition is found by x-ray diffraction below $T^\ast \sim 90$ K, while results obtained from resistivity measurement and angle-resolved photoemission spectroscopy clearly show the appeara

Suman Kumar Kundu, Arnab Pradhan, Carl Rosenzweig

The cosmological constant presents one of the most fascinating and confounding problems in physics. A straightforward, seemingly robust prediction of quantum mechanics and general relativity is that the vacuum energy gravitates. Therefore, the cosmological constant should be enormous. It is minuscule. Since there is no understanding of why the cosmological c

Sinya Aoki

This essay is dedicated to Prof. K.K. Phua on the occasion of his 80th birthday. While the contents of this essay are based on our recent papers$^{1,2}$ published in the International Journal of Modern Physics A, I have added many personal opinions, so that I am solely responsible for all statements in this essay. 1. S. Aoki, T. Onogi and S. Yokoyama, Int. J

Özge Sahin, Claudia Czado

High-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula based (quantile) regression is an important tool. However, the current vine copula based regression approaches do not scale up to high and ultra-high dimensions. To p

Timothy L Staples

Change in language use is driven by cultural forces; it is unclear whether that extends to programming languages. They are designed to be used by humans, but interaction with computer hardware rather than a human audience may limit opportunities for evolution of the lexicon of used terms. I tested this in R, an open source, mature and commonly used programmi

Qing Wang, Jian Zheng, Huawei Huang, Jianru Lin

Consensus is unnecessary when the truth is available. In this paper, we present a new perspective of rebuilding the blockchain without consensus. When the consensus phase is eliminated from a blockchain, transactions could be canonized quickly using a well-defined universal rule without consuming hashing power. Thus, the transactions per second(TPS) metric o

Zonghai Yao, Yi Cao, Zhichao Yang, Vijeta Deshpande

Language Models (LMs) have performed well on biomedical natural language processing applications. In this study, we conducted some experiments to use prompt methods to extract knowledge from LMs as new knowledge Bases (LMs as KBs). However, prompting can only be used as a low bound for knowledge extraction, and perform particularly poorly on biomedical domai

Subeom Kang, Wan-il Park, Dong-han Yeom

We find a novel phenomenon in the solution to the Wheeler-DeWitt equation by solving numerically the equation assuming $O(4)$-symmetry and imposing the Hartle-Hawking wave function as a boundary condition. In the slow-roll limit, as expected, the numerical solution gives the most dominant steepest-descent that describes the probability distribution for the i

Victor S. Gerasimchuk, Yuri I. Gorobets, Oksana Yu. Gorobets, Igor V. Gerasimchuk

We report a theoretical study of the localized spatial magnetization configuration, which is a confined spin configuration of the target skyrmion/hopfion type in an antiferromagnet with perpendicular magnetic anisotropy, and then we solve the particular problem of self-oscillations of such a topological spin texture. Using the energy approach, a self-consist

Paolo Lipparini

If $X$ is a closure space with closure $K$, we consider the semilattice $(\mathcal P(X), \cup)$ endowed with further relations $ x \sqsubseteq y_1, y_2, \dots, y_n$ (a distinct $n+1$-ary relation for each $n \geq 1$), whose interpretation is $x \subseteq Ky_1 \cup Ky_2 \cup \dots \cup Ky_n $. We present axioms for such "multi-argument specialization semi

Christian Manasseh, Razvan Veliche, Jared Bennett, Hamilton Clouse

Humans learn from the occurrence of events in a different place and time to predict similar trajectories of events. We define Loosely Decoupled Timeseries (LDT) phenomena as two or more events that could happen in different places and across different timelines but share similarities in the nature of the event and the properties of the location. In this work

Yu-Peng Chen, Shu Zhang, Long Ji, Shuang-Nan Zhang

A type-I burst could influence the accretion process through radiation pressure and Comptonization both for the accretion disk and the corona/boundary layer of an X-ray binary, and vice versa. We investigate the temporal evolution of a bright photospheric radius expansion (PRE) burst of 4U 1608-52 detected by Insight-HXMT in 1-50 keV, with the aim of studyin

Lance Cooley, David Larbalestier, Kathleen Amm

We take a comprehensive look at conductors used in superconducting magnets for the accelerator sector and explore the ramifications of the present marketplace for supply of conductor to future accelerator facilities. While there are thousands of superconductors, many of which have promising properties for applications, we outline the journey a promising mate

Vasudha Bharathram

We study two classical representations of Artin's braid group and their modulo $p$ reductions. We use topological methods to show that the Gassner representation $\tau_n: B_n\to\text{GL}_n(\mathbb{Z}[t_1^{\pm 1}, \ldots, t_n^{\pm 1}])$ is faithful for all $n$, and furthermore that it is faithful modulo $p$ for all integers $p>1$. We then give a novel proof t

Nils Bruin, Linden Disney-Hogg, Wuqian Effie Gao

We present an algorithm which uses Fujiwara's inequality to bound algebraic functions over ellipses of a certain type, allowing us to concretely implement a rigorous Gauss-Legendre integration method for algebraic functions over a line segment. We consider path splitting strategies to improve convergence of the method and show that these yield significant pr

Evan Coleman, Ronak M Soni, Sungyeon Yang

We study how entanglement spreads in the boundary duals of finite-cutoff three-dimensional theories with positive, negative and zero cosmological constant, the $T \bar{T} + \Lambda_{2}$ two-dimensional theories. We first study the Hawking-Page transition in all three cases, and find that there is a transition in all three scenarios at the temperature where t

David Galvin, Yufei Zhang

Let ${\bf a}=(a_1, a_2, \ldots, a_n)$ and ${\bf e}=(e_1, e_2, \ldots, e_n)$ be real sequences. Denote by $M_{{\bf e}\rightarrow {\bf a}}$ the $(n+1)\times(n+1)$ matrix whose $(m,k)$ entry ($m, k \in \{0,\ldots, n\}$) is the coefficient of the polynomial $(x-a_1)\cdots(x-a_k)$ in the expansion of $(x-e_1)\cdots(x-e_m)$ as a linear combination of the polynomia

Tanguy Bertrand, Melinda Kahre, Richard Urata, Anni Määttänen

Coagulation of particles occurs when two particles collide and stick together. In the Martian atmosphere, dust coagulation would increase the effective particle size, as small particles accrete to larger particles. Murphy et al. (1990) showed that Brownian coagulation of dust in the Martian atmosphere was not significant, due to the low dust particle mixing

Fabio Giardina, S Ganga Prasath, L Mahadevan

Cooperative task execution, a hallmark of eusociality, is enabled by local interactions between the agents and the environment through a dynamically evolving communication signal. Inspired by the collective behavior of social insects whose dynamics is modulated by interactions with the environment, we show that a robot collective can successfully nucleate a

Bogdan Ichim, Julio José Moyano-Fernández

In this paper we introduce the submonoids $\mathscr{R}_n$, resp. $\mathscr{C}_n$, of the monoid $\mathscr{M}_n$ of ordered score sheets of a robin-round tournament played by $n$ teams for which the order is preserved after the leader team is disqualified, resp. all principal submatrices preserve the given ordering. We study (using both theoretical and comput

Saeed Rezaee, Naveen A. Reddy, Michael W. Topping, Irene Shivaei

The $\rm{H}\alpha$-to-UV luminosity ratio ($L(\rm H\alpha)/L(\rm UV)$) is often used to probe SFHs of star-forming galaxies and it is important to validate it against other proxies for burstiness. To address this issue, we present a statistical analysis of the resolved distribution of $\Sigma_{\rm{SFR}}$ as well as stellar age and their correlations with the

Shaoor Munir, Sandra Siby, Umar Iqbal, Steven Englehardt

As third-party cookie blocking is becoming the norm in browsers, advertisers and trackers have started to use first-party cookies for tracking. We conduct a differential measurement study on 10K websites with third-party cookies allowed and blocked. This study reveals that first-party cookies are used to store and exfiltrate identifiers to known trackers eve

A. Samartzis, J. Xu, V. K. Anand, A. T. M. N. Islam

While it is established that the pinch point scattering pattern in spin ice arises from an emergent coulomb phase associated with magnetic moment that is divergence-free, more complex Hamiltonians can introduce a divergence-full part. If these two parts remain decoupled, they give rise to the co-existence of distinct features. Here we show that the moment in

Sergey Belomestnykh, Emilio A. Nanni, Hans Weise, Sergey V. Baryshev

Accelerator radio frequency (RF) technology has been and remains critical for modern high energy physics (HEP) experiments based on particle accelerators. Tremendous progress in advancing this technology has been achieved over the past decade in several areas highlighted in this report. These achievements and new results expected from continued R&D efforts c

Shahriar Golchin, Mihai Surdeanu, Nazgol Tavabi, Ata Kiapour

Domain adaptation for large neural language models (NLMs) is coupled with massive amounts of unstructured data in the pretraining phase. In this study, however, we show that pretrained NLMs learn in-domain information more effectively and faster from a compact subset of the data that focuses on the key information in the domain. We construct these compact su

Callen MacPhee, Bahram Jalali

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the trajectory of cannon balls. In modern times, this idea has been extended, for example, to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves

Brandon S. Hensley, B. T. Draine

We present a new model of interstellar dust in which large grains are a single composite material, ``astrodust,'' and nanoparticle-sized grains come in distinct varieties including polycyclic aromatic hydrocarbons (PAHs). We argue that a single-composition model for grains larger than $\sim$0.02 $\mu$m most naturally explains the lack of frequency dependence

Colin Orion Chandler

The 1950 discovery of activity emanating from asteroid (4015) Wilson-Harrington prompted astronomers to realize comet-like activity is not limited to comets. Since then < 30 active asteroids have been discovered, yet they hold clues about fundamental physical and chemical processes in the solar system. Around half of the activity is attributed to sublimation

T. de Oliveira, D. Harnett, A. Palameta, T. G. Steele

In QCD sum-rule methods, the fundamental field-theoretical quantities are correlation functions of composite operators that serve as hadronic interpolating fields. One of the challenges of loop corrections to QCD correlation functions in conventional approaches is the renormalization-induced mixing of composite operators. This involves a multi-step process o

Enrico Virgilli, Hubert Halloin, Gerald Skinner

The low-energy gamma-ray domain is an important window for the study of the high energy Universe. Here matter can be observed in extreme physical conditions and during powerful explosive events. However, observing gamma-rays from faint sources is extremely challenging with current instrumentation. With techniques used at present collecting more signal requir

Jeffrey Wong, Jasmine Nettiksimmons, Jiannan Lu, Katherine Livins

Technology companies are increasingly using randomized controlled trials (RCTs) as part of their development process. Despite having fine control over engineering systems and data instrumentation, these RCTs can still be imperfectly executed. In fact, online experimentation suffers from many of the same biases seen in biomedical RCTs including opt-in and use

Laurent Chauvin

The contributions of this thesis stem from technology developed to analyse large sets of volumetric images in terms of atom-like features extracted in 3D image space, following SIFT algorithm in 2D image space. New feature properties are introduced including a binary feature sign, analogous to an electrical charge, and a discrete set of symmetric feature ori

Racin Nygaard

We have built a bare-metal testbed in order to perform large-scale, reproducible evaluations of erasure coding algorithms. Our testbed supports at least 1000 Ethereum Swarm peers running on 30 machines. Running experimental evaluation is time-consuming and challenging. Researchers must consider the experimental software's limitations and artifacts. If not co

Zahra Varaminybahnemiry, Jessie Galasso, Houari Sahraoui

Model transformations play an essential role in the Model-Driven Engineering paradigm. Writing a correct transformation program requires to be proficient with the source and target modeling languages, to have a clear understanding of the mapping between the elements of the two, as well as to master the transformation language to properly describe the transfo

Juliet Aygun, Jeremy Miller

The degree of a based graph is the number of essential nonbasepoint vertices after generic perturbation. Hatcher--Vogtmann's degree theorem states that the subcomplex of Auter space of graphs of degree at most d is (d-1)-connected. We extend the definition of degree to the simplicial closure of Auter space and prove a version of Hatcher--Vogtmann's result in

Chen Greif, Yunhui He

We consider the problem of iteratively solving large and sparse double saddle-point systems arising from the stationary Stokes-Darcy equations in two dimensions, discretized by the Marker-and-Cell (MAC) finite difference method. We analyze the eigenvalue distribution of a few ideal block preconditioners. We then derive practical preconditioners that are base

Eduard Zubchuk, Mikhail Arhipkin, Dmitry Menshikov, Aleksandr Karaush

We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trai

Erick Schulz, Andy T. S. Wan

We introduce an extension to the Discrete Multiplier Method (DMM), called Minimal $\ell_2$ Norm Discrete Multiplier Method (MN-DMM), where conservative finite difference schemes for dynamical systems with multiple conserved quantities are constructed procedurally, instead of analytically as in the original DMM. For large dynamical systems with multiple conse

Mohammad Mohammadi Amiri, Frederic Berdoz, Ramesh Raskar

We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test accuracy on a validation set, that may not exist in practice. In this work, we focus on task-agnostic data valuation without any validation requirements. The data buyer has access

Langtian Liu, Chen Chen, Craig D. Roberts

Using a Poincar\'e-covariant quark+diquark Faddeev equation, we provide structural information on the four lightest $(I,J^P)=(\tfrac{1}{2},\tfrac{3}{2}^\mp)$ baryon multiplets. These systems may contain five distinct types of diquarks; but in order to obtain reliable results, it is sufficient to retain only isoscalar-scalar and isovector-axialvector correlat

Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz

The ability to generalize out-of-domain (OOD) is an important goal for deep neural network development, and researchers have proposed many high-performing OOD generalization methods from various foundations. While many OOD algorithms perform well in various scenarios, these systems are evaluated as ``black-boxes''. Instead, we propose a flexible framework th

Marco Bravin, Šárka Nečasová

In this paper we study the evolution of a small rigid body in a viscous incompressible fluid, in particular we show that a small particle is not accelerated by the fluid in the limit when its size converges to zero under a lower bound on its mass. This result is based on a new a priori estimate on the velocities of the centers of mass of rigid bodies that ho

Xinxing Wu, Chong Peng, Gregory Jicha, Donna Wilcock

The disruption of circadian rhythm is a cardinal symptom for Alzheimer's disease (AD) patients. The full circadian rhythm orchestration of gene expression in the human brain and its inherent associations with AD remain largely unknown. We present a novel comprehensive approach, PRIME, to detect and analyze rhythmic oscillation patterns in untimed high-dimens

Xinxing Wu, Chong Peng, Peter T. Nelson, Qiang Cheng

Alzheimer's disease (AD), as a progressive brain disease, affects cognition, memory, and behavior. Similarly, limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined common neurodegenerative disease that mimics the clinical symptoms of AD. At present, the risk factors implicated in LATE and those distinguishing LATE from AD are larg