October 2020 arXiv papers — page 90

Peng Cui, Le Hu, Yuanchao Liu

Sentence matching is a fundamental task of natural language processing with various applications. Most recent approaches adopt attention-based neural models to build word- or phrase-level alignment between two sentences. However, these models usually ignore the inherent structure within the sentences and fail to consider various dependency relationships amon

G. Qin, S. -S. Wu

Forbush decreases (Fds) in galactic cosmic ray intensity are related to interplanetary coronal mass ejections (ICMEs). The parallel diffusion of particles is reduced because the magnetic turbulence level in sheath region bounded by ICME's leading edge and shock is high. Besides, in sheath and magnetic cloud (MC) energetic particles would feel enhanced ma

Sinya Aoki, Tetsuya Onogi, Shuichi Yokoyama

We propose a new class of vector fields to construct a conserved charge in a general field theory whose energy momentum tensor is covariantly conserved. We show that there always exists such a vector field in a given field theory even without global symmetry. We also argue that the conserved current constructed from the (asymptotically) time-like vector fiel

Ole Steuernagel, Popo Yang, Ray-Kuang Lee

We study the formation of lines in phase space in Wigner's distribution $W.$ Whereas lines in phase space do not form in classical systems, unless special initial states are chosen, we find, for large classes of systems and initial states of quantum systems that $W$ tends to form straight line patterns crisscrossing phase space. These arise from the states'

Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas

Explainable AI is an emerging field providing solutions for acquiring insights into automated systems' rationale. It has been put on the AI map by suggesting ways to tackle key ethical and societal issues. Existing explanation techniques are often not comprehensible to the end user. Lack of evaluation and selection criteria also makes it difficult for th

Shakshi Sharma, Rajesh Sharma

Online Social Media (OSM) platforms such as Twitter, Facebook are extensively exploited by the users of these platforms for spreading the (mis)information to a large audience effortlessly at a rapid pace. It has been observed that the misinformation can cause panic, fear, and financial loss to society. Thus, it is important to detect and control the misinfor

NA62 Collaboration

The NA62 experiment at the CERN SPS reports a study of a sample of $4 \times10^{9}$ tagged $π^0$ mesons from $K^+ \to π^+ π^0 (γ)$, searching for the decay of the $π^0$ to invisible particles. No signal is observed in excess of the expected background fluctuations. An upper limit of $4.4 \times10^{-9}$ is set on the branching ratio at 90% confidence level, i

Maximilian P Oppelt, Maximilian Riehl, Felix P Kemeth, Jan Steffan

An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features

Juan Manuel Carmona-Loaiza, Zamaan Raza

The goal of Specular Neutron and X-ray Reflectometry is to infer materials Scattering Length Density (SLD) profiles from experimental reflectivity curves. This paper focuses on investigating an original approach to the ill-posed non-invertible problem which involves the use of Artificial Neural Networks (ANN). In particular, the numerical experiments describ

Eugenio Giannelli, Stacey Law, Jason Long

Let $p$ be any prime. Let $P_n$ be a Sylow $p$-subgroup of the symmetric group $S_n$. Let $ϕ$ and $ψ$ be linear characters of $P_n$ and let $N$ be the normaliser of $P_n$ in $S_n$. In this article we show that the inductions of $ϕ$ and $ψ$ to $S_n$ are equal if, and only if, $ϕ$ and $ψ$ are $N$--conjugate. This is an analogue for symmetric groups of a result

Akanksha Jaiswal, Arpan Chattopadhyay

In this paper, the minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source-equipped remote sensing setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a time-varying wireless link. At any discrete-time instant,

K. Oláh, Zs. Kővári, M. N. Günther, K. Vida

We aim to give a reliable estimate of the number of flaring giant stars in the Kepler field. By analyzing the flaring activity of these stars we explore their flare statistics and the released flare energies. The role of oscillation in suppressing magnetic activity is also investigated. On a sample of flaring giant stars we search for flaring specialities wh

Yao Zhang, Hongru Liang, Adam Jatowt, Wenqiang Lei

Knowledge graphs are essential for numerous downstream natural language processing applications, but are typically incomplete with many facts missing. This results in research efforts on multi-hop reasoning task, which can be formulated as a search process and current models typically perform short distance reasoning. However, the long-distance reasoning is

Barbara Bosnjak

Let $G_n$ denote either the group $Sp(2n, F)$ or $SO(2n+1, F)$ over a non-archimedean local field $F$. We determine the composition series of representations of $G_n$ induced from cuspidal and ladder representations such that the minimal exponent in the cuspidal support of the ladder representation is greater than or equal to $\frac{1}{2}$.

Ataru Tanikawa, Tomoya Kinugawa, Takashi Yoshida, Kotaro Hijikawa

GW190521 is a merger of two black holes (BHs), wherein at least one BH lies within the pair-instability (PI) mass gap, and it is difficult to form because of the effects of PI supernovae (PISNe) and pulsational PI (PPI). In this study, we examined the formation of GW190521-like BH-BHs under Population (Pop) III environments by binary population synthesis cal

George De Ath, Richard M. Everson, Jonathan E. Fieldsend

Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource utilisation. To maximise resource allocation, we develop a novel asynchronous BO method, AEGiS (Asynchronous $ε$-Greedy G

Estephe Arnaud, Arnaud Dapogny, Kevin Bailly

For a number of machine learning problems, an exogenous variable can be identified such that it heavily influences the appearance of the different classes, and an ideal classifier should be invariant to this variable. An example of such exogenous variable is identity if facial expression recognition (FER) is considered. In this paper, we propose a dual exoge

Manuele Tettamanti, Iacopo Carusotto, Alberto Parola

We provide a joint numerical-analytical study of the physics of a flowing atomic Bose-Einstein condensate in the combined presence of an external trap and a step potential which accelerates the atoms out of the condensate creating a pair of neighbouring black- and white- hole horizons. In particular, we focus on the rapidly growing density modulation pattern

Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn

Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on "how to choose," the layerwise sparsities are mostly selected algorithm-by-algorithm, often resorting to handc

Alessandro Casalino, Bruno Sanna, Lorenzo Sebastiani, Sergio Zerbini

In this paper, working in a Friedman-Lemaitre-Robertson-Walker (FLRW), first, in the flat case, we recover the generalized Friedman equation of Quantum Loop cosmology, and therefore the cosmological bounce, in the framework of modified teleparallel gravity $f(T)$-model, $T$ being the torsion scalar introduced in teleparallel gravity approach, without invokin

Bo Pang, Deming Zhai, Junjun Jiang, Xianming Liu

Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. Unsupervised person ReID attracts a lot of attention recently, due to it works without intensive manual annotation and thus shows great potential of adapting to new conditions. Representation learning plays a critical role in unsupervised pers

Gionnieve Lim, Simon T. Perrault

Fake news is a prevalent problem, particularly in digital media, that undermines trust and cooperation among people. As a variety of global mitigation efforts arise, the understanding of how people consider fake news becomes important, especially in local contexts. To that end, we carried out a survey with 75 participants in Singapore to understand people's

Liang Li, Can Ma, Yinliang Yue, Linjun Shou

Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems still overlooked. The first is that the values recorded in many tables are mostly numbers in practice. The existing approach

Dongjun Kim, Kyungwoo Song, YoonYeong Kim, Yongjin Shin

Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulation models on real-world datasets. As the likelihood evaluation is inaccessible, previous papers train the amortized neural network to estimate the ground-truth posterior for the s

Yury A. Kutoyants

The partially observed linear Gaussian system of stochastic differential equations with low noise in observations is considered. A kernel-type estimators are used for estimation of the quadratic variation of the derivative of the limit of the observed process. Then this estimator is used for nonparametric estimation of the integral of the square of volatilit

Ahmet M. Elbir, Gokhan Gurbilek, Burak Soner, Anastasios K. Papazafeiropoulos

As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a bett

Ludwig Kürzinger, Nicolas Lindae, Palle Klewitz, Gerhard Rigoll

Many end-to-end Automatic Speech Recognition (ASR) systems still rely on pre-processed frequency-domain features that are handcrafted to emulate the human hearing. Our work is motivated by recent advances in integrated learnable feature extraction. For this, we propose Lightweight Sinc-Convolutions (LSC) that integrate Sinc-convolutions with depthwise convol

Tetsuo Hyodo, Masayuki Niiyama

The strange quark plays a unique role in QCD, reflecting its intermediate mass between the light and heavy quarks. In recent years, remarkable progress has been made in the spectroscopy of baryons with strangeness. Many new features of the strange baryon spectrum have been revealed by accurate experimental data with novel techniques, as well as systematic de

William M. Campbell, Ben T. McAllister, Maxim Goryachev, Eugene N. Ivanov

We present a way to search for light scalar dark matter (DM), seeking to exploit putative coupling between dark matter scalar fields and fundamental constants, by searching for frequency modulations in direct comparisons between frequency stable oscillators. Specifically we compare a Cryogenic Sapphire Oscillator (CSO), Hydrogen Maser (HM) atomic oscillator

Ronan Gautier, Hepeng Yao, Laurent Sanchez-Palencia

Quasicrystals exhibit exotic properties inherited from the self-similarity of their long-range ordered, yet aperiodic, structure. The recent realization of optical quasicrystal lattices paves the way to the study of correlated Bose fluids in such structures, but the regime of strong interactions remains largely unexplored, both theoretically and experimental

Camille Carvalho, Zoïs Moitier

It is well-known that classical optical cavities can exhibit localized phenomena associated to scattering resonances, leading to numerical instabilities in approximating the solution. This result can be established via the ``quasimodes to resonances'' argument from the black-box scattering framework. Those localized phenomena concentrate at the inner boundar

Jean Kieffer

Existing algorithms to compute genus 2 theta constants in quasi-linear time use Borchardt sequences, an analogue of the arithmetic-geometric mean for four complex numbers. In this paper, we show that these Borchardt sequences are given by good choices of square roots only, as in the genus 1 case. This removes the sign indeterminacies in the algorithm without

Danijel Jurman, Hrvoje Nikolic

We develop a general theory of the time distribution of quantum events, applicable to a large class of problems such as arrival time, dwell time and tunneling time. A stopwatch ticks until an awaited event is detected, at which time the stopwatch stops. The awaited event is represented by a projection operator $π$, while the ideal stopwatch is modeled as a s

Dan Garber, Ben Kretzu

Projection-free optimization algorithms, which are mostly based on the classical Frank-Wolfe method, have gained significant interest in the machine learning community in recent years due to their ability to handle convex constraints that are popular in many applications, but for which computing projections is often computationally impractical in high-dimens

Ctirad Klimcik

We argue that the T-duality phenomenon is not exclusively a stringy effect but it is relevant also in the context of the standard point particle dynamics. To illustrate the point, we construct a four-parametric family of four-dimensional electro-gravitational backgrounds such that the dynamics of a charged point particle in those backgrounds is insensitive t

Sinan Bugu, Shimpei Nishiyama, Kimihiko Kato, Yongxun Liu

We have embedded a physically defined p-channel silicon MOS quantum dot (QD) device into an impedance transformer RC circuit. To decrease the parasitic capacitance and surpass the cutoff frequency of the device which emerges in MOS devices that have a top gate and act as RC low-pass filter, we fabricate a new device to reduce the device's top gate area f

Junfu Wang, Yunhong Wang, Zhen Yang, Liang Yang

Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning. Unfortunately, current GNNs usually rely on loading the entire attributed graph into network for processing. This implicit assumption may not be satisfied with limited memory resources, especially when the attributed graph is large. In this paper, we pioneer to pr

Sung Mook Lee, Kin-ya Oda, Seong Chan Park

We propose a scenario of spontaneous leptogenesis in Higgs inflation with help from two additional operators: the Weinberg operator (Dim 5) and the derivative coupling of the Higgs field and the current of lepton number (Dim 6). The former is responsible for lepton number violation and the latter induces chemical potential for lepton number. The period of ra

Jinyue Guo, Aozhi Liu, Jing Xiao

We proposed a model for the Conference of Music and Technology (CSMT2020) data challenge of melody classification. Our model used the Performance Event Vector as the input sequence to build a Bidirectional RNN network for classfication. The model achieved a satisfying performance on the development dataset and Wikifonia dataset. We also discussed the effect

Masato Nozawa

The static and spherically symmetric solutions in $n(\ge 4)$-dimensional Einstein-phantom-scalar system fall into three family: (i) the Fisher solution, (ii) the Ellis-Gibbons solution, and (iii) the Ellis-Bronnikov solution. We exploit these solutions as seed to generate a bunch of corresponding asymptotically (A)dS spacetimes, at the price of introducing t

Masato Nozawa

We present a method to generate static solutions in the Einstein-Maxwell system with a (phantom) dilaton field in $n(\ge 4)$-dimensions, based upon the symmetry of the target space for the nonlinear sigma model. Unlike the conventional Einstein-Maxwell-dilaton system, there appears a critical value of the coupling constant for a phantom dilaton field. In the

Chiara Arina, Benjamin Fuks, Luca Mantani, Hanna Mies

A comprehensive analysis of cosmological and collider constraints is presented for three simplified models characterised by a dark matter candidate (real scalar, Majorana fermion and real vector) and a coloured mediator (fermion, scalar and fermion respectively) interacting with the right-handed up quark of the Standard Model. Constraints from dark matter di

Alon Z. Shapira, Nir Gavish, Hannes Uecker, Arik Yochelis

Optimizing the properties of the mosaic morphology of bulk heterojunction (BHJ) organic photovoltaics (OPV) is not only challenging technologically but also intriguing from the mechanistic point of view. Among the recent breakthroughs is the identification and utilization of a three-phase (donor/mixed/acceptor) BHJ, where the (intermediate) mixed-phase can i

Laura Oberländer, Roman Klinger

Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli ("Is the stimulus category A or B?"), (2) as sequence labeling

Hyun-Jin Jeong, Yong-Jae Moon, Eunsu Park, Harim Lee

Solar magnetic fields play a key role in understanding the nature of the coronal phenomena. Global coronal magnetic fields are usually extrapolated from photospheric fields, for which farside data is taken when it was at the frontside, about two weeks earlier. For the first time we have constructed the extrapolations of global magnetic fields using frontside

Jan Giesselmann, Elena Mäder-Baumdicker, David Jakob Stonner

We provide a posteriori error estimates in the energy norm for temporal semi-discretisations of wave maps into spheres that are based on the angular momentum formulation. Our analysis is based on novel weak-strong stability estimates which we combine with suitable reconstructions of the numerical solution. We present time-adaptive numerical simulations based

Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler

Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge, a benchmark for single-camera

Uria Mor, Boris Shustin, Haim Avron

The Trust Region Subproblem is a fundamental optimization problem that takes a pivotal role in Trust Region Methods. However, the problem, and variants of it, also arise in quite a few other applications. In this article, we present a family of iterative Riemannian optimization algorithms for a variant of the Trust Region Subproblem that replaces the inequal

Masakazu Yoshimura, Satoshi Ogata

Age estimation from images can be used in many practical scenes. Most of the previous works targeted on the estimation from images in which only one face exists. Also, most of the open datasets for age estimation contain images like that. However, in some situations, age estimation in the wild and for multi-person is needed. Usually, such situations were sol

Saurav Prakash, Hanieh Hashemi, Yongqin Wang, Murali Annavaram

Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, sharing of local model updates can also reveal information of clients' local datasets. Trusted execution environments (TEEs) within the FL server have been recently deployed by companie

Emna Ben Yacoub

A quantized message passing decoding algorithm for low-density parity-check codes is presented. The algorithm relies on the min approximation at the check nodes, and on modelling the variable node inbound messages as observations of an extrinsic discrete memoryless channel. The performance of the algorithm is analyzed and compared to quantized min-sum decodi

Stefan Friedl, Clara Loeh

We show that the epimorphism problem is solvable for targets that are virtually cyclic or a product of an Abelian group and a finite group.

Vidhi Rana, Remi A. Chou, Hyuck Kwon

In this paper, we study an information-theoretic secret sharing problem, where a dealer distributes shares of a secret among a set of participants under the following constraints: (i) authorized sets of users can recover the secret by pooling their shares, and (ii) non-authorized sets of colluding users cannot learn any information about the secret. We assum

Brendon G. Anderson, Somayeh Sojoudi

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in the literature are designed for adversarial input uncertainty, but researchers have recently shown a need for methods that consider random uncertainty. In this paper, we propose a novel robustness cert

Tarek Mealy, Filippo Capolino

A scheme to characterize the dynamics of the electron beam-electromagnetic power exchange along a traveling wave tube (TWT) is proposed. The method is based on defining a state vector at discrete periodic locations along the TWT and determining the transfer matrix of the unit-cell of the "hot" slow-wave structure (SWS) that takes into account the int

Senthil Velan S

The proponents of Aspect Oriented Software Development (AOSD) methodology have done a tremendous amount of work to bring out the positive effects of its adoption using quantitative assessment. A structured assessment of the methodology requires a well-defined quality model. In this paper, an AI agent based quality model has been proposed to evaluate the effe

MyeongAh Cho, Taeoh Kim, Woo Jin Kim, Suhwan Cho

In contemporary society, surveillance anomaly detection, i.e., spotting anomalous events such as crimes or accidents in surveillance videos, is a critical task. As anomalies occur rarely, most training data consists of unlabeled videos without anomalous events, which makes the task challenging. Most existing methods use an autoencoder (AE) to learn to recons

Zhengxuan Wu, Desmond C. Ong

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a given text can have different targets (e.g., neighborhoods) and different aspects (e.g., price or safety), with different sentiment associated with each target-aspect pair. In thi

Youmi Ma, Tatsuya Hiraoka, Naoaki Okazaki

In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented. By using contextualized word embeddings, the proposed method computes representations for entity mentions and long-range dependencies without complicated hand-crafted features or neural-network architectures. We als

Martin Schlueter, Mehdi Neshat, Mohamed Wahib, Masaharu Munetomo

This contribution introduces the GTOPX space mission benchmark collection, which is an extension of GTOP database published by the European Space Agency (ESA). GTOPX consists of ten individual benchmark instances representing real-world interplanetary space trajectory design problems. In regard to the original GTOP collection, GTOPX includes three new proble

Julia Belyakova, Benjamin Chung, Jack Gelinas, Jameson Nash

Dynamic programming languages face semantic and performance challenges in the presence of features, such as eval, that can inject new code into a running program. The Julia programming language introduces the novel concept of world age to insulate optimized code from one of the most disruptive side-effects of eval: changes to the definition of an existing fu

Zihao Song, Junqiang Ge, Youjun Lu, Changshuo Yan

PG1302-102 is thought to be a supermassive binary black hole (BBH) system according to the periodical variations of its optical and UV photometry, which may be interpreted as being due to the relativistic Doppler boosting of the emission mainly from the disk around the secondary black hole (BH) modulated by its orbital motion. In this paper, we investigate s

Shao Wei Wang, Guan Jie Huang, Xiang Yu Luo

Synthetic data used for scene text detection and recognition tasks have proven effective. However, there are still two problems: First, the color schemes used for text coloring in the existing methods are relatively fixed color key-value pairs learned from real datasets. The dirty data in real datasets may cause the problem that the colors of text and backgr

Siqing Zhang

Over a field of positive characteristic, a semisimple algebraic group $G$ may have some nonreduced parabolic subgroup $P$. In this paper, we study the Schubert and Bott-Samelson-Demazure-Hansen (BSDH) varieties of $G/P$, with $P$ nonreduced, when the base field is perfect. It is shown that in general the Schubert and BSDH varieties of such a $G/P$ are not no

Alexander I. Suciu

The Bieri-Neumann-Strebel-Renz invariants $Σ^q(X,\mathbb{Z})\subset H^1(X,\mathbb{R})$ of a connected, finite-type CW-complex $X$ are the vanishing loci for Novikov-Sikorav homology in degrees up to $q$, while the characteristic varieties $\mathcal{V}^q(X) \subset H^1(X,\mathbb{C}^{\times})$ are the nonvanishing loci for homology with coefficients in rank 1

Wenge Liu, Jianheng Tang, Yi Cheng, Wenjie Li

Developing conversational agents to interact with patients and provide primary clinical advice has attracted increasing attention due to its huge application potential, especially in the time of COVID-19 Pandemic. However, the training of end-to-end neural-based medical dialogue system is restricted by an insufficient quantity of medical dialogue corpus. In

Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang

While Deep Reinforcement Learning (DRL) has emerged as a promising approach to many complex tasks, it remains challenging to train a single DRL agent that is capable of undertaking multiple different continuous control tasks. In this paper, we present a Knowledge Transfer based Multi-task Deep Reinforcement Learning framework (KTM-DRL) for continuous control

Kai Zhang, Gernot Riegler, Noah Snavely, Vladlen Koltun

Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes. NeRF fits multi-layer perceptrons (MLPs) representing view-invariant opacity and view-dependent color volumes to a set of training images, and samples no

Shounak Datta, Eduardo B. Mariottoni, David Dov, Alessandro A. Jammal

Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recurs

Alon Jacovi, Ana Marasović, Tim Miller, Yoav Goldberg

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satis

Lei Mou, Yitian Zhao, Huazhu Fu, Yonghuai Liu

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanis

Jia Guo, Minghao Chen, Yao Hu, Chen Zhu

Knowledge distillation aims at obtaining a compact and effective model by learning the mapping function from a much larger one. Due to the limited capacity of the student, the student would underfit the teacher. Therefore, student performance would unexpectedly drop when distilling from an oversized teacher, termed the capacity gap problem. We investigate th

Siqi Li, Guobao Wang

Combined use of PET and dual-energy CT provides complementary information for multi-parametric imaging. PETenabled dual-energy CT combines a low-energy x-ray CT image with a high-energy &γ&-ray CT (GCT) image reconstructed from time-of-flight PET emission data to enable dual-energy CT material decomposition on a PET/CT scanner. The maximumlikelihood attenuat

Wenbin Zhang

While artificial intelligence (AI)-based decision-making systems are increasingly popular, significant concerns on the potential discrimination during the AI decision-making process have been observed. For example, the distribution of predictions is usually biased and dependents on the sensitive attributes (e.g., gender and ethnicity). Numerous approaches ha

A. David-Uraz, V. Petit, M. E. Shultz, A. W. Fullerton

NGC 1624-2 is the most strongly magnetized O-type star known. Previous spectroscopic observations of this object in the ultraviolet provided evidence that it hosts a large and dense circumstellar magnetosphere. Follow-up observations obtained with the \textit{Hubble Space Telescope} not only confirm that previous inference, but also suggest that NGC 1624-2&#

Suncheng Xiang, Yuzhuo Fu, Guanjie You, Ting Liu

Person re-identification (re-ID) plays an important role in applications such as public security and video surveillance. Recently, learning from synthetic data, which benefits from the popularity of synthetic data engine, has achieved remarkable performance. However, in pursuit of high accuracy, researchers in the academic always focus on training with large

Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson

This study assesses the relative importance of time integration error in present-day climate simulations conducted with the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) at 1-degree horizontal resolution. We show that a factor-of-6 reduction of time step size in all major parts of the model leads to significant changes in t

Kyoung-Bum Huh, Kazuki Ikeda, Viktor Jahnke, Keun-Young Kim

We explore quantum phase transitions using two probes of quantum chaos: out-of-time-order correlators (OTOCs) and the $r$-parameter obtained from the level spacing statistics. In particular, we address $p$-spin models associated with quantum annealing or reverse annealing. Quantum annealing triggers first-order or second-order phase transitions, which is cru

Ye Wang, Kevin Too Yok, Wenyan Wu, Angus R. Simpson

Optimizing pump operations is a challenging task for real-time management of water distribution systems (WDSs). With suitable pump scheduling, pumping costs can be significantly reduced. In this research, a novel economic model predictive control (EMPC) framework for real-time management of WDSs is proposed. Optimal pump operations are selected based on pred

Yuan Zhou, Xiangrui Li

Optical aerial images change detection is an important task in earth observation and has been extensively investigated in the past few decades. Generally, the supervised change detection methods with superior performance require a large amount of labeled training data which is obtained by manual annotation with high cost. In this paper, we present a novel un

Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda

Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). For many models such as convolutional neural networks (CNNs), adaptive methods typically converge faster but generalize worse compared to SGD; for complex settings such as generat

Javier R. Movellan, Prasad Gabbur

We show that Transformers are Maximum Posterior Probability estimators for Mixtures of Gaussian Models. This brings a probabilistic point of view to Transformers and suggests extensions to other probabilistic cases.

Khalid Hossain, Konrad Kobuszewski, Michael McNeil Forbes, Piotr Magierski

Quantized vortices carry the angular momentum in rotating superfluids, and are key to the phenomenon of quantum turbulence. Advances in ultra-cold atom technology enable quantum turbulence to be studied in regimes with both experimental and theoretical control, unlike the original contexts of superfluid helium experiments. While much work has been performed

Vishnu Balakrishnan, David Champion, Ewan Barr, Michael Kramer

Machine learning methods are increasingly helping astronomers identify new radio pulsars. However, they require a large amount of labelled data, which is time consuming to produce and biased. Here we describe a Semi-Supervised Generative Adversarial Network (SGAN) which achieves better classification performance than the standard supervised algorithms using

Paul Corbae, Frances Hellman, Sinead M. Griffin

We investigate the possibility of using structural disorder to induce a topological phase in a solid state system. Using first-principles calculations, we introduce structural disorder in the trivial insulator BiTeI and observe the emergence of a topological insulating phase. By modifying the bonding environments, the crystal-field splitting is enhanced and

Timjan Kalajdzievski, Nicolás Quesada

We gather and examine in detail gate decomposition techniques for continuous-variable quantum computers and also introduce some new techniques which expand on these methods. Both exact and approximate decomposition methods are studied and gate counts are compared for some common operations. While each having distinct advantages, we find that exact decomposit

Eduardo Santana

In this work, we construct Markov structures for zooming systems adapted to holes of a special type. Our construction is based on backward contractions provided by zooming times. These Markov structures may be used to code the open zooming systems. In the context of open zooming systems, possibly with the presence of a critical/singular set, we prove the exi

Ali Devran Kara, Serdar Yuksel

In the theory of Partially Observed Markov Decision Processes (POMDPs), existence of optimal policies have in general been established via converting the original partially observed stochastic control problem to a fully observed one on the belief space, leading to a belief-MDP. However, computing an optimal policy for this fully observed model, and so for th

Wen-Hua Cai, Qing-Wu Wang

The properties of strange quark stars are studied within the quasi-particle model. Taking into account the chemical equilibrium and charge neutrality, the EOS of $ (2+1) $-flavor quark matter is obtained. We illustrate the parameter spaces with constraints from two aspects: the one is based on the astronomical results of PSR J$ 0740+6620 $ and GW$ 170817 $,

Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang

Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analy

Jaouhar Fattahi, Mohamed Mejri

In this paper, we put forward a new tool, called SpaML, for spam detection using a set of supervised and unsupervised classifiers, and two techniques imbued with Natural Language Processing (NLP), namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). We first present the NLP techniques used. Then, we present our classifiers and th

Stefan Nevlacsil, Paul Muellner, Alejandro Maese-Novo, Moritz Eggeling

In this paper, we present a novel concept for a multi-channel swept source optical coherence tomography (OCT) system based on photonic integrated circuits (PICs). At the core of this concept is a low-loss polarization dependent path routing approach allowing for lower excess loss compared to previously shown PIC-based OCT systems, facilitating a parallelizat

Steven J O'Hare, Richard D Connors, David P Watling

Studies of the effect of network structure on performance have, thus far, been restricted to examining ensembles of synthetic networks generated by canonical models from the Network Science literature, which do not plausibly represent real road networks. Furthermore disparate ranges of parameter settings for demand and supply structure are often used, making

Julian Berger, Maximilian Böther, Vanja Doskoč, Jonathan Gadea Harder

In language learning in the limit, the most common type of hypothesis is to give an enumerator for a language. This so-called $W$-index allows for naming arbitrary computably enumerable languages, with the drawback that even the membership problem is undecidable. In this paper we use a different system which allows for naming arbitrary decidable languages, n

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni

Prior research notes that BERT's computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document

Ritu Arora, Xiaosong Li, Bonnie Hurwitz, Daniel Fay

The CSSI 2019 workshop was held on October 28-29, 2019, in Austin, Texas. The main objectives of this workshop were to (1) understand the impact of the CSSI program on the community over the last 9 years, (2) engage workshop participants in identifying gaps and opportunities in the current CSSI landscape, (3) gather ideas on the cyberinfrastructure needs and

Amir Shanehsazzadeh, David Belanger, David Dohan

The Basic Local Alignment Search Tool (BLAST) is currently the most popular method for searching databases of biological sequences. BLAST compares sequences via similarity defined by a weighted edit distance, which results in it being computationally expensive. As opposed to working with edit distance, a vector similarity approach can be accelerated substant

Chun-Teh Chen, Grace X. Gu

We introduce a de novo elastography method to learn the elasticity of solids from measured strains. The deep neural network in our new method is supervised by the theory of elasticity and does not require labeled data for training. Results show that the proposed method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy

Katherine Goode, Daniel Ries, Joshua Zollweg

Optical spectral-temporal signatures extracted from videos of explosions provide information for identifying characteristics of the corresponding explosive devices. Currently, the identification is done using heuristic algorithms and direct subject matter expert review. An improvement in predictive performance may be obtained by using machine learning, but t

Miguel Baritto, Md Mashum Billal, S. M. Muntasir Nasim, Rumana Afroz Sultana

The rapid growth of Industry 4.0 technologies such as big data, cloud computing, smart sensors, machine learning (ML), radio-frequency identification (RFID), robotics, 3D-printing, and Internet of Things (IoT) offers Small and Medium Enterprises (SMEs) the chance to improve productivity and efficiency, reduce cost and provide better customer experience, amon