October 2020 arXiv papers — page 107

Shixiang Feng, Beibei Liu, Ya Zhang, Xiaoyun Zhang

Differentiating Vertebral Compression Fractures (VCFs) associated with trauma and osteoporosis (benign VCFs) or those caused by metastatic cancer (malignant VCFs) are critically important for treatment decisions. So far, automatic VCFs diagnosis is solved in a two-step manner, i.e. first identify VCFs and then classify it into benign or malignant. In this pa

M. Gabriella Kuhn, Sandra Saliani, Tim Steger

Let $π$ be an irreducible unitary representation of a finitely generated nonabelian free group $Γ$; suppose $π$ is weakly contained in the regular representation. In 2001 the first and third authors conjectured that such a representation must be either odd or monotonous or duplicitous. In 2004 they introduced the class of multiplicative representations: this

Eugenio Mauri, Simona Cocco, Rémi Monasson

Describing the evolution of a population of genomes evolving in a complex fitness landscape is generally very hard. We here introduce an approximate Gaussian closure scheme to characterize analytically the statistics of a genomic population in the so-called Quasi--Linkage Equilibrium (QLE) regime, applicable to generic values of the rates of mutation or reco

Beren Millidge, Alexander Tschantz, Anil Seth, Christopher L Buckley

The recently proposed Activation Relaxation (AR) algorithm provides a simple and robust approach for approximating the backpropagation of error algorithm using only local learning rules. Unlike competing schemes, it converges to the exact backpropagation gradients, and utilises only a single type of computational unit and a single backwards relaxation phase.

Simon Jenni, Paolo Favaro

Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses. In contrast, we propose an approach that can exploit small annotated data sets by fine-tuning networks pre-trained via self-supervised learning on (large) unlabeled data sets

Congqi Cao, Yajuan Li, Qinyi Lv, Peng Wang

Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work in this area recently, most of the existing work is based on image classification tasks. Video-based few-shot action recognition has not been explored well and remains challenging:

Peng-Cheng Wang, Oriol Pietx-Casas, Mohsen Falamarzi Askarani, Gustavo Castro do Amaral

A proposal for fast-switching broadband frequency-shifting technology making use of frequency conversion in a nonlinear crystal is set forth, whereby the shifting is imparted to the converted photons by creating a bank of frequency-displaced pump modes that can be selected by a photonic switch and directed to the nonlinear crystal. Proof-of-principle results

Maxime Peyrard, Robert West

The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization researchers have given considerably more attention to relevance than to background knowledge. In contrast, this work puts background knowledge in the foreground. Building on the realiz

Junming Ma, Chaofan Yu, Aihui Zhou, Bingzhe Wu

We present S3ML, a secure serving system for machine learning inference in this paper. S3ML runs machine learning models in Intel SGX enclaves to protect users' privacy. S3ML designs a secure key management service to construct flexible privacy-preserving server clusters and proposes novel SGX-aware load balancing and scaling methods to satisfy users&#39

Khang Manh Huynh

We explore some properties of flows with strongly adapted 1-forms, originally discovered in (Tao 2017), which can be used to embed Turing machines into dynamical systems. In particular, we discuss some relations to geodesible flows, and show that even a slight modification of the dynamical system, such as homogeneity, can lead to an intermediate class of flo

Shujun Wang, Lequan Yu, Kang Li, Xin Yang

Deep convolutional neural networks have significantly boosted the performance of fundus image segmentation when test datasets have the same distribution as the training datasets. However, in clinical practice, medical images often exhibit variations in appearance for various reasons, e.g., different scanner vendors and image quality. These distribution discr

Zunwu He, Bobo Hua

For an infinite penny graph, we study the finite-dimensional property for the space of harmonic functions, or ancient solutions of the heat equation, of polynomial growth. We prove the asymptotically sharp dimensional estimate for the above spaces.

F. Aharonian, Q. An, Axikegu, L. X. Bai

As a sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A is mainly designed to cover a large fraction of the northern sky to hunt for gamma-ray sources at energies above 10 TeV. Even though the detector construction is still underway, a half of the KM2A array has been operating stably since the end of 2019. In this paper, we present th

Roman Sagaidak

Highly ionized heavy evaporation residues (ERs) resulting from heavy ion (HI) fusion-evaporation reactions are knocked out from solid targets to rarefied gas of gas-filled recoil separators. In gas, these ERs undergo charge-changing collisions on their way to a detection system. An equilibrium between the loss of charge (electron capture) and the gain of cha

Puneet Kumar, Sidharth Jain, Balasubramanian Raman, Partha Pratim Roy

In this paper, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition. The proposed system learns the embeddings from the emotional information of the speech utterances. The learned embeddings are used to recognize the emotions portrayed by given speech samples of various lengths. Th

Warit Sirichotedumrong, Hitoshi Kiya

Various visual information protection methods have been proposed for privacy-preserving deep neural networks (DNNs). In contrast, attack methods on such protection methods have been studied simultaneously. In this paper, we evaluate state-of-the-art visual protection methods for privacy-preserving DNNs in terms of visual security against ciphertext-only atta

Luyang Wang, Kai Huang, Jiao Wang, Shengsheng Huang

Drive-thru is a popular sales channel in the fast food industry where consumers can make food purchases without leaving their cars. Drive-thru recommendation systems allow restaurants to display food recommendations on the digital menu board as guests are making their orders. Popular recommendation models in eCommerce scenarios rely on user attributes (such

S. Krishnendu, B. N. Bharath, Navneet Garg, Vimal Bhatia

Design of distributed caching mechanisms is considered as an active area of research due to its promising solution in reducing data load in the backhaul link of a cellular network. In this paper, the problem of distributed content caching in a small-cell Base Stations (sBSs) wireless network that maximizes the cache hit performance is considered. Most of the

Katsuya T. Abe, Yuichiro Tada, Ikumi Ueda

The standard model of particle physics is known to be intriguingly successful. However their rich phenomena represented by the phase transitions (PTs) have not been completely understood yet, including the possibility of the existence of unknown dark sectors. In this Letter, we investigate the measurement of the equation of state parameter $w$ and the sound

Mikhail I. Gomoyunov, Nikolai Yu. Lukoyanov, Anton R. Plaksin

Motivated by optimal control problems and differential games for functional differential equations of retarded type, the paper deals with a Cauchy problem for a path-dependent Hamilton--Jacobi equation with a right-end boundary condition. Minimax solutions of this problem are studied. The existence and uniqueness result is obtained under assumptions that are

Takayuki Nishio, Yusuke Koda, Jihong Park, Mehdi Bennis

This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-RF data

Nahid Parvez Farazi, Tanvir Ahamed, Limon Barua, Bo Zou

Deep reinforcement learning (DRL) is an emerging methodology that is transforming the way many complicated transportation decision-making problems are tackled. Researchers have been increasingly turning to this powerful learning-based methodology to solve challenging problems across transportation fields. While many promising applications have been reported

Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik, Noam Slonim

Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information. Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments. In additio

Daijiro Suematsu

We consider a simple extension of the standard model, which could give a solution to its $CP$ issues through both the Peccei-Quinn mechanism and the Nelson-Barr mechanism. Its low energy effective model coincides with the scotogenic model in the leptonic sector. Although leptogenesis is known not to work well at lower reheating temperature than $10^9$ GeV in

ADMX Collaboration, C. Bartram, T. Braine, R. Cervantes

Searching for axion dark matter, the ADMX collaboration acquired data from January to October 2018, over the mass range 2.81--3.31 $μ$eV, corresponding to the frequency range 680--790 MHz. Using an axion haloscope consisting of a microwave cavity in a strong magnetic field, the ADMX experiment excluded Dine-Fischler-Srednicki-Zhitnisky (DFSZ) axions at 100%

Gabriel Montes de Oca

Plamenevskaya defined an invariant of transverse links as a distinguished class in the even Khovanov homology of a link. We define an analog of Plamenevskaya's invariant in the odd Khovanov homology of Ozsváth, Rasmussen, and Szabó. We show that the analog is also an invariant of transverse links and has similar properties to Plamenevskaya's invarian

Bhupesh Kumar, Melanie Lebental, Patrick Sebbah

Light scattering and localization in strongly scattering disordered systems is governed by the nature of the underlying eigenmodes, specially their spatial extension within the system. One of the main challenges in studying experimentally Anderson like localized states resides in the difficulty to excite these states independently and observe them individual

Kojo Sarfo Gyamfi, James Brusey, Elena Gaura

We propose a differential radial basis function (RBF) network termed RBF-DiffNet -- whose hidden layer blocks are partial differential equations (PDEs) linear in terms of the RBF -- to make the baseline RBF network robust to noise in sequential data. Assuming that the sequential data derives from the discretisation of the solution to an underlying PDE, the d

Anwaar Ulhaq, Oliver Burmeister

To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy

Yibo Yang, Hongyang Li, Shan You, Fei Wang

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate connections. Current gradient-based NAS methods commonly ignore the constraint of sparsity in the search phase, but project the optimized solution onto a sparse one by post-processing. As a result, the dense super-net for search

Xin Xing, Yu Gui, Chenguang Dai, Jun S. Liu

Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks. However, the DNN framework itself cannot inform the user which features are more or less relevant for making the prediction, which limits its applicability in many scientific fields. We introduce neural Gaussian mirrors (NGMs), in which mirr

Xiaoshuai Zhu, Thomas Wiegelmann, Bernd Inhester

Context. Understanding the 3D magnetic field as well as the plasma in the chromosphere and transition region is important. One way is to extrapolate the magnetic field and plasma from the routinely measured vector magnetogram on the photosphere based on the assumption of the magnetohydrostatic (MHS) state. However, photospheric data may be inconsistent with

Graham G. Brown, Dong Hyuk Ko, Chunmei Zhang, P. B. Corkum

Measuring the delay for an electron to emerge from different states is one of the major achievements of attosecond science. This delay can have two origins - the electron wave packet is reshaped during departure by the electrostatic field of the ionizing medium or it is modified by dynamic interaction with the remaining electrons. Most experiments have obser

Songming Yu, Yongpan Liu, Lu Zhang, Jingyu Wang

Resistive Random Access Memory (RRAM) is an emerging device for processing-in-memory (PIM) architecture to accelerate convolutional neural network (CNN). However, due to the highly coupled crossbar structure in the RRAM array, it is difficult to exploit the sparsity of the network in RRAM-based CNN accelerator. To optimize the weight mapping of sparse networ

Beixiong Zheng, Changsheng You, Rui Zhang

To achieve the more promising passive beamforming gains in the double-intelligent reflecting surface (IRS) assisted system over the conventional single-IRS system, channel estimation is practically indispensable but also a more challenging problem to tackle, due to the presence of not only the single- but also double-reflection links that are intricately cou

Thomas Fung, Eugene Seneta

We derive the asymptotic rate of decay to zero of the tail dependence of the bivariate skew Variance Gamma (VG) distribution under the equal-skewness condition, as an explicit regularly varying function. Our development is in terms of a slightly more general bivariate skew Generalized Hyperbolic (GH) distribution. Our initial reduction of the bivariate probl

Jialin Wang, Hai-Ning Liang, Diego Monteiro, Wenge Xu

Virtual Reality (VR) technology has been proliferating in the last decade, especially in the last few years. However, Simulator Sickness (SS) still represents a significant problem for its wider adoption. Currently, the most common way to detect SS is using the Simulator Sickness Questionnaire (SSQ). SSQ is a subjective measurement and is inadequate for real

Xi Chen, Nan Ding, Tomer Levinboim, Radu Soricut

Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements. At the same time, it has been argued that contextualized word representations exhibit sub-optimal statistical properties for e

Yanzhao Wang, Jianpo Cui, Yonghao Gao, Jianzhong Gu

To search for new candidates of the true and simultaneous two-proton ($2p$) radioactivity, the $2p$ decay energies (\textit{Q}$_{2p}$) are extracted by the Weizsäcker-Skyrme-4 (WS4) model, the finite-range droplet model (FRDM), the Kourra-Tachibaba-Uno-Yamada (KTUY) model and the Hartree-Fock-Bogoliubov mean-field model with the BSk29 Skyrme interaction (HFB

Jinho Choi

We consider multiple access with re-transmission diversity (RTxD) based on a hybrid automatic request (HARQ) protocol with incremental redundancy (IR) in this paper. In order to mitigate multiple access interference (MAI), we employ successive interference cancellation (SIC) at a receiver and derive conditions that all the signals from active users can be su

Abu Naser, Mehran Sadeghi Lahijani, Cong Wu, Mohsen Gavahi

In order for High-Performance Computing (HPC) applications with data security requirements to execute in the public cloud, the cloud infrastructure must ensure the privacy and integrity of data. To meet this goal, we consider incorporating encryption in the Message Passing Interface (MPI) library. We empirically evaluate four contemporary cryptographic libra

Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei

While large scale pre-trained language models such as BERT have achieved great success on various natural language understanding tasks, how to efficiently and effectively incorporate them into sequence-to-sequence models and the corresponding text generation tasks remains a non-trivial problem. In this paper, we propose to address this problem by taking two

Farjana Sultana Mim, Naoya Inoue, Paul Reisert, Hiroki Ouchi

Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-tr

Jielin Wang, Wanzhou Zhang, Tian Hua, Tzu-Chieh Wei

Machine learning methods have been recently applied to learning phases of matter and transitions between them. Of particular interest is the topological phase transition, such as in the XY model, which can be difficult for unsupervised learning such as the principal component analysis. Recently, authors of [Nature Physics \textbf{15},790 (2019)] employed the

Lei Shi, Yahui Li, Rajeev Alur, Boon Thau Loo

Writing classification rules to identify malicious network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both expressiveness

Sung-Hong Park, K. D. Leka, Kanya Kusano

A hemispheric preference in the dominant sign of magnetic helicity has been observed in numerous features in the solar atmosphere: i.e., left-handed/right-handed helicity in the northern/southern hemisphere. The relative importance of different physical processes which may contribute to the observed hemispheric sign preference (HSP) of magnetic helicity is s

Jianquan Li, Xiaokang Liu, Honghong Zhao, Ruifeng Xu

Pre-trained language models (e.g., BERT) have achieved significant success in various natural language processing (NLP) tasks. However, high storage and computational costs obstruct pre-trained language models to be effectively deployed on resource-constrained devices. In this paper, we propose a novel BERT distillation method based on many-to-many layer map

Jian-Bin Chen, Meng Zhang, Li-Li Xing, Tai-Fu Feng

Applying the effective Lagrangian method, we study the Flavor Changing Neutral Current $b\to sγ$ within the minimal supersymmetric extension of the standard model where baryon and lepton numbers are local gauge symmetries. Constraints on the parameters are investigated numerically with the experimental data on branching ratio of $\bar{B}\to X_sγ$. Additional

Clara Vania, Ruijie Chen, Samuel R. Bowman

Large-scale natural language inference (NLI) datasets such as SNLI or MNLI have been created by asking crowdworkers to read a premise and write three new hypotheses, one for each possible semantic relationships (entailment, contradiction, and neutral). While this protocol has been used to create useful benchmark data, it remains unclear whether the writing-b

Shijian Li, Qiangqiang Wang, Xian Wei, Zhurong Cao

To indirectly evaluate the asymmetry of the radiation drive under limited measurement conditions in inertial confinement fusion research, we have proposed an integral method to approximate the three-dimensional self-radiation distribution of the compressed plasma core using only four pinhole images from a single laser entrance hole at a maximum projection an

Jeronimo G. C. Martinez, Brittany Kamai

A MHz gravitational wave search for harmonic sources was conducted using a 704-hr dataset obtained from the Holometer, a pair of 40-meter power recycled Michelson interferometers. Our search was designed to look for cosmic string loops and eccentric black hole binaries in an entirely unexplored frequency range from 1 to 25 MHz. The measured cross-spectral de

Bing Zha, Alper Yilmaz

In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent geolocalization of object. Inspired by human's ability to both be aware of distance and direction of self-motion in navigation, our trajectory learning method learns a pattern represent

Marco Negrete, Jesús Savage, José Avendaño

Manipulation is a key capability in domestic service robots, as can be seen in the rulebooks of last Robocup@Home editions. Currently, object recognition is performed based mostly on visual information. Some robots use also 3D information such as point clouds or laser scans but, to the knowledge of authors, robots don't use physical properties to improve

Joe Hellmers

This document presents a numerical study of the effects of high density nuclear reactions, pycnonuclear reactions, on the evolution of white dwarfs that accrete matter from a companion star. Primarily we seek observable properties that might be different when we include these nuclear reactions with varying microphysical assumptions. These different assumptio

Shubham Sharma, Alan H. Gee, David Paydarfar, Joydeep Ghosh

Fairness in machine learning is crucial when individuals are subject to automated decisions made by models in high-stake domains. Organizations that employ these models may also need to satisfy regulations that promote responsible and ethical A.I. While fairness metrics relying on comparing model error rates across subpopulations have been widely investigate

Saravanan Venkatachalam, Jonathon M. Smereka

Heterogeneous unmanned vehicles (UVs) are used in various defense and civil applications. Some of the civil applications of UVs for gathering data and monitoring include civil infrastructure management, agriculture, public safety, law enforcement, disaster relief, and transportation. This paper presents a two-stage stochastic model for a fuel-constrained UV

Jingjing He, Xuefei Guan

A Bayesian inference method for problems with small samples and sparse data is presented in this paper. A general type of prior ($\propto 1/σ^{q}$) is proposed to formulate the Bayesian posterior for inference problems under small sample size. It is shown that this type of prior can represents a broad range of priors such as classical noninformative priors a

Tadakatsu Sakai, Masashi Zenkai

We study the contact terms that appear in the correlation functions of exactly marginal operators using the AdS/CFT correspondence. It is known that CFT with an exactly marginal deformation requires the existence of the contact terms with their coefficients having a geometrical interpretation in the context of conformal manifolds. We show that the AdS/CFT co

Julia Qin, Jared Vanasse

Cold Helium atoms are a unique system where a single excited three-body Efimov state occurs, naturally, without the need for an external magnetic field. While three-body bound state energies of cold Helium atoms have previously been investigated, recent experimental techniques have allowed their structure to also be studied. The weak interaction between Heli

G. Potel, F. Barranco, E. Vigezzi, R. A. Broglia

While Josephson-like junctions, transiently established in heavy ion collisions ($τ_{coll}\approx10^{-21}$ s) between superfluid nuclei --through which Cooper pair tunneling ($Q$-value $Q_{2n}$) proceeds mainly in terms of successive transfer of entangled nucleons-- is deprived from the macroscopic aspects of a supercurrent, it displays many of the special e

Jie Mei, Christian Desrosiers, Johannes Frasnelli

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult

Felipe Farias, Teresa Ludermir, Carmelo Bastos-Filho

We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads t

Xue Dong, Cong Ma, Xiaokun Zhao, Xing Li

Time-digital Converter (TDC) aims to measure the arrival time of the leading edge of the pulse signal. Our recent work presented a high resolution multi-phase TDC based on the Kintex-7 Field Programmable Gate Array (FPGA) device. A simple I/O tile based circular input buffer is employed to oscillate the input signal periodically, and then a multi-phase TDC b

Imène R. Goumiri, Amanda L. Muyskens, Michael D. Schneider, Benjamin W. Priest

Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Being able to automatically classify these images is a long-standing problem in astronomy and critical to a number of different scientific analyses. Recently, the challenge of "sta

Xin Tong

We continue our study on the Hodge-Iwasawa theory which is a continuation of our previous work on Hodge-Iwasawa theory, which is aimed at higher dimensional deformation of higher dimensional Hodge structures over general analytic spaces or adic spaces. We still follow closely the approaches of Kedlaya-Liu to study our Frobenius modules over the different kin

Saptarshi Chatterjee, Shrabanti Chowdhury, Sanjib Basu

The question of association between outcome and feature is generally framed in the context of a model on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis, and inflammation, predictive of recurrence after lung resection in node-negative non-small cell lung can

David Herald, David Gault, Robert Anderson, David Dunham

Occultations of stars by asteroids have been observed since 1961, increasing from a very small number to now over 500 annually. We have created and regularly maintain a growing data-set of more than 5,000 observed asteroidal occultations. The data-set includes: the raw observations; astrometry at the 1 mas level based on centre of mass or figure (not illumin

Stefan Hrisafov, Justinas Pupeikis, Pierre-Alexis Chevreuil, Fabian Brunner

We present a near-infrared optical parametric chirped-pulse amplifier (OPCPA) and soft X-ray (SXR) high-harmonic generation system. The OPCPA produces few-cycle pulses at a center wavelength of 800 nm and operates at a high repetition rate of 100 kHz. It is seeded by fully programmable amplitude and phase controlled ultra-broadband pulses from a Ti:sapphire

Fan Lai, Xiangfeng Zhu, Harsha V. Madhyastha, Mosharaf Chowdhury

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. As a result, data characteristics and device capabilities

John Christian Ottem, Fumiaki Suzuki, with an appendix by Olivier Wittenberg

We give the first examples of $\mathcal{O}$-acyclic smooth projective geometrically connected varieties over the function field of a complex curve, whose index is not equal to one. More precisely, we construct a family of Enriques surfaces over $\mathbb{P}^{1}$ such that any multi-section has even degree over the base $\mathbb{P}^{1}$ and show moreover that

Diksha Gupta, Jared Saia, Maxwell Young

A Sybil attack occurs when an adversary controls multiple identifiers (IDs) in a system. Limiting the number of Sybil (bad) IDs to a minority is critical to the use of well-established tools for tolerating malicious behavior, such as Byzantine agreement and secure multiparty computation. A popular technique for enforcing a Sybil minority is resource burning:

Daniel Bashir, George D. Montanez, Sonia Sehra, Pedro Sandoval Segura

We present an information-theoretic framework for understanding overfitting and underfitting in machine learning and prove the formal undecidability of determining whether an arbitrary classification algorithm will overfit a dataset. Measuring algorithm capacity via the information transferred from datasets to models, we consider mismatches between algorithm

Daniel Mentiplay, Daniel J. Price, Christophe Pinte, Guillaume Laibe

We present a method for simulating the dynamics of a mixture of gas and multiple species of large Stokes number dust grains, typical of evolved protoplanetary discs and debris discs. The method improves upon earlier methods, in which only a single grain size could be represented, by capturing the differential backreaction of multiple dust species on the gas.

Amit Solomon, Alejandro Cohen, Nir Shlezinger, Yonina C. Eldar

A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations. Some of the leading testing procedures, such as those utilizing qualitative polymerase chain reaction, involve using dedicated machinery which can simultaneously process

Shiran Dudy, Steven Bedrick

Current evaluation metrics for language modeling and generation rely heavily on the accuracy of predicted (or generated) words as compared to a reference ground truth. While important, token-level accuracy only captures one aspect of a language model's behavior, and ignores linguistic properties of words that may allow some mis-predicted tokens to be use

Tomer Goldfriend, Jorge Kurchan

We discuss a quantum Kolmogorov-Sinai entropy defined as the entropy production per unit time resulting from coupling the system to a weak, auxiliary bath. The expressions we obtain are fully quantum, but require that the system is such that there is a separation between the Ehrenfest and the correlation timescales. We show that they reduce to the classical

Rahul Krishna, Md Shahriar Iqbal, Mohammad Ali Javidian, Baishakhi Ray

Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cause unexpected non-functional faults. This paper proposes CADET (short for Causal Debugging Toolkit) that enables users to identify, explain, and fix the root cause of non-functiona

Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri

There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Books. Yet, most works do not study the factors affecting each domain language application deeply. Additionally, t

R. García-Delgado, G. Salgado, O. A. Sánchez-Valenzuela

Hom-Lie algebras defined on central extensions of a given quadratic Lie algebra that in turn admit an invariant metric, are studied. It is shown how some of these algebras are naturally equipped with other symmetric, bilinear forms that satisfy an invariant condition for their twisted multiplication maps. The twisted invariant bilinear forms so obtained rese

Alexandre Toubiana, Laura Sberna, Andrea Caputo, Giulia Cusin

GW190521 is the compact binary with the largest masses observed to date, with at least one in the pair-instability gap. This event has also been claimed to be associated with an optical flare observed by the Zwicky Transient Facility in an Active Galactic Nucleus (AGN), possibly due to the post-merger motion of the merger remnant in the AGN gaseous disk. We

Zhiyu Xiao, Hao Shi, Shiwei Zhang

We present a method to construct pseudo-BCS wave functions from the one-body density matrix. The resulting many-body wave function, which can be produced for any fermion systems, including those with purely repulsive interactions, has the form of a number-projected BCS form, or antisymmetrized germinal power (AGP). Such wave functions provide a better ansatz

Yongtao Zhan, Hoi-Kwong Lo

Entanglement witness is an effective method to detect entanglement in unknown states without doing full tomography. One of the most widespread schemes of witnessing entanglement is measuring its fidelity with respect to a pure entangled state. Recently, a large class of states whose entanglement can not be detected with the fidelity witness has been discover

X. Gratens, V. Chitta

The magnetization steps of quintet clusters, consisting of five identical magnetic ions coupled by isotropic nearest neighbors antiferromagnetic exchange interaction in the hcp lattice, have been investigated. In that model, called the $J_1$ model, there are 17 types of quintets. The values of the magnetic field of the magnetization steps of the clusters hav

Wei Li, Martin Z. Bazant, Juner Zhu

One of the obstacles hindering the scaling-up of the initial successes of machine learning in practical engineering applications is the dependence of the accuracy on the size of the database that "drives" the algorithms. Incorporating the already-known physical laws into the training process can significantly reduce the size of the required database.

Scott W. McIntosh, Robert J. Leamon, Ricky Egeland, Mausumi Dikpati

We investigate the occurrence of the "extended solar cycle" (ESC) as it occurs in a host observational data spanning 140 years. Investigating coronal, chromospheric, photospheric and interior diagnostics we develop a consistent picture of solar activity migration linked to the 22-year Hale (magnetic) cycle using superposed epoch analysis (SEA) using

Kasia Jankiewicz, Kevin Schreve

The Tits Conjecture, proved by Crisp and Paris, states that squares of the standard generators of any Artin group generate an obvious right-angled Artin subgroup. We consider a larger set of elements consisting of all the centers of the irreducible spherical special subgroups of the Artin group, and conjecture that sufficiently large powers of those elements

S. J. Jepeal, A. Danagoulian, L. A. Kesler, D. A. Korsun

The bulk irradiation of materials with 10-30 MeV protons promises to advance the study of radiation damage for fission and fusion power plants. Intermediate energy proton beams can now be dedicated to materials irradiation within university-scale laboratories. This paper describes the first such facility, with an Ionetix ION-12SC cyclotron producing 12 MeV p

Artur B. Saturnino

Using the local picture of the degeneration of sequences of minimal surfaces developed by Chodosh, Ketover and Maximo we show that in any closed Riemannian 3-manifold $(M,g)$, the genus of an embedded CMC surface can be bounded only in terms of its index and area, independently of the value of its mean curvature. We also show that if $M$ has finite fundament

Mingzhi Zheng, Dinghan Shen, Yelong Shen, Weizhu Chen

Masked Language Model (MLM) framework has been widely adopted for self-supervised language pre-training. In this paper, we argue that randomly sampled masks in MLM would lead to undesirably large gradient variance. Thus, we theoretically quantify the gradient variance via correlating the gradient covariance with the Hamming distance between two different mas

Martín Muñoz, Cristian Riveros

Some of the most relevant document schemas used online, such as XML and JSON, have a nested format. In the last decade, the task of extracting data from nested documents over streams has become especially relevant. We focus on the streaming evaluation of queries with outputs of varied sizes over nested documents. We model queries of this kind as Visibly Push

Mark McConnell, Robert MacPherson

We present an algorithm to compute the Hecke operators on the equivariant cohomology of an arithmetic subgroup $Γ$ of the general linear group $\mathrm{GL}_n$. This includes $\mathrm{GL}_n$ over a number field or a finite-dimensional division algebra. As coefficients, we may use any finite-dimensional local coefficient system. Unlike earlier methods, the alg

Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core ch

Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel

Pre-trained models have revolutionized natural language understanding. However, researchers have found they can encode artifacts undesired in many applications, such as professions correlating with one gender more than another. We explore such gendered correlations as a case study for how to address unintended correlations in pre-trained models. We define me

Travis Dillon

$S$-gap shifts are a well-studied class of shift spaces, which has led to several proposed generalizations. This paper introduces a new class of shift spaces called $\mathcal{S}$-graph shifts whose essential structure is encoded in a novel way, as a finite directed graph with a set of natural numbers assigned to each vertex. $\mathcal{S}$-graph shifts contai

Jiahui Yu, Wei Han, Anmol Gulati, Chung-Cheng Chiu

Streaming automatic speech recognition (ASR) aims to emit each hypothesized word as quickly and accurately as possible, while full-context ASR waits for the completion of a full speech utterance before emitting completed hypotheses. In this work, we propose a unified framework, Dual-mode ASR, to train a single end-to-end ASR model with shared weights for bot

Lvzhou Chen, Alexander J. Rasmussen

The loop graph of an infinite type surface is an infinite diameter hyperbolic graph first studied in detail by Juliette Bavard. An important open problem in the study of infinite type surfaces is to describe the boundary of the loop graph as a space of geodesic laminations. We approach this problem by constructing the first examples of 2-filling rays on infi

Yu-Ting Chen

We study the multi-strategy stochastic evolutionary game with death-birth updating in expanding spatial populations of size $N\to \infty$. The model is a voter model perturbation. For typical populations, we require perturbation strengths satisfying $1/N\ll w\ll 1$. Under appropriate conditions on the space, the limiting density processes of strategy are pro

Sreenath K. Manikandan, Étienne Jussiau, Andrew N. Jordan

We propose a quantum absorption refrigerator using the quantum physics of resonant tunneling through quantum dots. The cold and hot reservoirs are fermionic leads, tunnel coupled via quantum dots to a central fermionic cavity, and we propose configurations in which the heat absorbed from the (very hot) central cavity is used as a resource to selectively tran

Paul-Henry Leemann, Mikael de la Salle

We characterize the finitely generated groups that admit a Cayley graph whose only automorphisms are the translations, confirming a conjecture by Watkins from 1976. The proof relies on random walk techniques. As a consequence, every finitely generated group admits a Cayley graph with countable automorphism group. We also treat the case of directed graphs.

Douglas Guilbeault, Samuel Woolley, Joshua Becker

The digital spread of misinformation is one of the leading threats to democracy, public health, and the global economy. Popular strategies for mitigating misinformation include crowdsourcing, machine learning, and media literacy programs that require social media users to classify news in binary terms as either true or false. However, research on peer influe