December 2020 arXiv papers — page 154

Amine Mezghani, Robert W. Heath

Nonlinear precoding and pulse shaping are jointly considered in multi-user massive multiple-input multiple-output (MIMO) systems with low-resolution D/A-converters (DACs) in terms of algorithmic approach as well as large system performance. Two design criteria are investigated: the mean {squared} error (MSE) with active constellation extension (ACE) and the

Parbati Sahoo, P. H. R. S. Moraes, Marcelo M. Lapola, P. K. Sahoo

We present a traversable wormhole solution using the traceless $f(R,T)$ theory of gravity. In the $f(R,T)$ gravity, the Ricci scalar $R$ in the Einstein-Hilbert action is replaced by a function of $R$ and trace of the energy momentum tensor $T$. The traceless version of the $f(R,T)$ gravity gives rise to a possible wormhole geometry without need for "exo

Andrew Shepley, Greg Falzon, Paul Kwan

Confluence is a novel non-Intersection over Union (IoU) alternative to Non-Maxima Suppression (NMS) in bounding box post-processing in object detection. It overcomes the inherent limitations of IoU-based NMS variants to provide a more stable, consistent predictor of bounding box clustering by using a normalized Manhattan Distance inspired proximity metric to

Samuel A. Stein, Ryan L'Abbate, Wenrui Mu, Yue Liu

Deep neural network powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily weighted network requires a tremendous amount of computing resources. Especially in the post-Moore's Law era, the limit of semiconductor fabrication technology

Tsz-Kiu Aaron Chow

Consider a compact manifold $M$ with smooth boundary $\partial M$. Suppose that $g$ and $\tilde{g}$ are two Riemannian metrics on $M$. We construct a family of metrics on $M$ which agrees with $g$ outside a neighborhood of $\partial M$ and agrees with $\tilde{g}$ in a neighborhood of $\partial M$. We prove that the family of metrics preserves various natural

Wee Chaimanowong, Paul Norbury, Michael Swaddle, Mehdi Tavakol

An embedded curve in a symplectic surface $\Sigma\subset X$ defines a smooth deformation space $\mathcal{B}$ of nearby embedded curves. A key idea of Kontsevich and Soibelman arXiv:1701.09137 [math.AG], is to equip the symplectic surface $X$ with a foliation in order to study the deformation space $\mathcal{B}$. The foliation, together with a vector space $V

Huahui Qiu, Meng Xiao, Fan Zhang, Chunyin Qiu

Discovering new topological phases of matter is a major theme in fundamental physics and materials science. Dirac semimetal provides an exceptional platform for exploring topological phase transitions under symmetry breaking. Recent theoretical studies have revealed that a three-dimensional Dirac semimetal can harbor fascinating hinge states, a higher-order

Byung Gyu Chae

The viewing-angle enlargement of a holographic image is a crucial factor for realizing the holographic display. The numerical aperture (NA) of digital hologram other than a pixel specification has been known to determine the angular field extent of image. Here, we provide a valid foundation for the dependence of viewing angle on the hologram numerical apertu

Yuri Latushkin, Selim Sukhtaiev

This work offers a new prospective on asymptotic perturbation theory for varying self-adjoint extensions of symmetric operators. Employing symplectic formulation of self-adjointness we obtain a new version of Krein formula for resolvent difference which facilitates asymptotic analysis of resolvent operators via first order expansion for the family of Lagrang

Insung Park

For a post-critically finite branched covering of the sphere that is a subdivision map of a finite subdivision rule, we define non-expanding spines which determine the existence of a Levy cycle in a non-exhaustive semi-decidable algorithm. Especially when a finite subdivision rule has polynomial growth of edge subdivisions, the algorithm terminates very quic

Qianli Zhou, Yong Deng

The total uncertainty measurement of basic probability assignment (BPA) in Dempster-Shafer evidence theory (DSET) has always been an open issue. Although some scholars put forward various measurements and entropies of BPA, due to the existence of discord and non-specificity, there is no method can measure BPA reasonably. In order to utilize BPA to practical

Dongjiang Li, Jinyu Miao, Xuesong Shi, Yuxin Tian

Feature extraction plays an important role in visual localization. Unreliable features on dynamic objects or repetitive regions will interfere with feature matching and challenge indoor localization greatly. To address the problem, we propose a novel network, RaP-Net, to simultaneously predict region-wise invariability and point-wise reliability, and then ex

Cheng Lin, Changjian Li, Yuan Liu, Nenglun Chen

We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to

Bing Xu, Jinfeng Wang, Zhongjie Li, Chengdong Xu

Acute lower respiratory infections caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses. Our aim was to identify the dominant meteorological factors and to model their effects on common r

Saulo Carneiro, Cássio Pigozzo

It is argued that the quantum of area between consecutive, high overtones quasinormal modes of a black hole horizon coincides with the area gap predicted by Loop Quantum Gravity, as long as the horizon is isolated and the Barbero-Immirzi parameter is $γ\approx \sqrt{3}/6$, in agreement with the value derived from the Bekenstein-Hawking horizon entropy.

Jihao Fan, Jun Li, Jianxin Wang, Zhihui Wei

In this paper, we present a new construction of asymmetric quantum codes (AQCs) by combining classical concatenated codes (CCs) with tensor product codes (TPCs), called asymmetric quantum concatenated and tensor product codes (AQCTPCs) which have the following three advantages. First, only the outer codes in AQCTPCs need to satisfy the orthogonal constraint

Baijun Li, Şahin. K. Özdemir, Xun-Wei Xu, Lin Zhang

We propose a spinning nonlinear resonator as an experimentally accessible platform to achieve nonreciprocal control of optical solitons. Nonreciprocity here results from the relativistic Sagnac-Fizeau optical drag effect, which is different for pump fields propagating in the spinning direction or in the direction opposite to it. We show that in a spinning Ke

Célia Borlido, Brett McLean

We exhibit an adjunction between a category of abstract algebras of partial functions and a category of set quotients. The algebras are those atomic algebras representable as a collection of partial functions closed under relative complement and domain restriction; the morphisms are the complete homomorphisms. This generalises the discrete adjunction between

Yu Chen

We developed a perturbative calculation for entropy dynamics considering a sudden coupling between a system and a bath. The theory we developed can work in general environment without Markovian approximation. A perturbative formula is given for bosonic environment and fermionic environment, respectively. We find the Renyi entropy response is only related to

Qingyin Ma, John Stachurski, Alexis Akira Toda

We propose a new approach to solving dynamic decision problems with unbounded rewards based on the transformations used in Q-learning. In our case, the objective of the transform is to convert an unbounded dynamic program into a bounded one. The approach is general enough to handle problems for which existing methods struggle, and yet simple relative to othe

Shatil Rahman, Steven L. Waslander

Most mobile robots follow a modular sense-planact system architecture that can lead to poor performance or even catastrophic failure for visual inertial navigation systems due to trajectories devoid of feature matches. Planning in belief space provides a unified approach to tightly couple the perception, planning and control modules, leading to trajectories

Natalie C. Behague, Trent Marbach, Pawel Pralat

Zero forcing is a deterministic iterative graph colouring process in which vertices are coloured either blue or white, and in every round, any blue vertices that have a single white neighbour force these white vertices to become blue. Here we study probabilistic zero forcing, where blue vertices have a non-zero probability of forcing each white neighbour to

Petros Papadopoulos, David Coit, Ahmed Aziz Ezzat

Operations and Maintenance (O&M) constitute a major contributor to offshore wind's cost of energy. Due to the harsh and remote environment in which offshore turbines operate, there has been a growing interest in opportunistic maintenance scheduling for offshore wind farms, wherein grouping maintenance tasks is incentivized at times of opportunity. Our su

Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan

We present an unsupervised learning approach for optical flow estimation by improving the upsampling and learning of pyramid network. We design a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels. Moreover, we propose a pyramid distillation loss to add supervision for intermediate levels

Ben-Hur Francisco Cardoso, Sebastián Gonçalves, José Roberto Iglesias

Aiming to describe the wealth distribution evolution, several models consider an ensemble of interacting economic agents that exchange wealth in binary fashion. Intriguingly, models that consider an unbiased market, that gives to each agent the same chances to win in the game, are always out of equilibrium until the perfect inequality of the final state is a

L. K. Eraso-Hernandez, A. P. Riascos, T. M. Michelitsch, J. Wang-Michelitsch

In this paper, we explore the reduction of functionality in a complex system as a consequence of cumulative random damage and imperfect reparation, a phenomenon modeled as a dynamical process on networks. We analyze the global characteristics of the diffusive movement of random walkers on networks where the walkers hop considering the capacity of transport o

Mehdi Ouaki

The statistical description of the scalar conservation law of the form $ρ_t=H(ρ)_x$ with $H: \mathbb{R} \rightarrow \mathbb{R}$ a smooth convex function has been an object of interest when the initial profile $ρ(\cdot,0)$ is random. The special case when $H(ρ)=\frac{ρ^2}{2}$ (Burgers equation) has in particular received extensive interest in the past and is

Xing Gu

We determine a subring of the Chow ring and the cohomology of $BPGL_n$, the classifying space of the projective linear group of degree $n$ over complex numbers, and explain a way in which this computation might play a role in the period-index problem. In addition, we show that the Chow ring of $BPGL_n$ is not generated by the Chern classes of linear represen

Carlos Améndola, Lukas Gustafsson, Kathlén Kohn, Orlando Marigliano

We study multivariate Gaussian models that are described by linear conditions on the concentration matrix. We compute the maximum likelihood (ML) degrees of these models. That is, we count the critical points of the likelihood function over a linear space of symmetric matrices. We obtain new formulae for the ML degree, one via Schubert calculus, and another

Amin Naseri, Yutao Hu, Wenchen Luo

It is shown that the eigenproblem of any $2\times 2$ matrix Hamiltonian with discrete eigenvalues is involved with a supersymmetric quantum mechanics. The energy dependence of the superalgebra marks the disparity between the deduced supersymmetry and the standard supersymmetric quantum mechanics. The components of an eigenspinor are superpartners\textemdash

Luca Saglietti, Lenka Zdeborová

In recent years the empirical success of transfer learning with neural networks has stimulated an increasing interest in obtaining a theoretical understanding of its core properties. Knowledge distillation where a smaller neural network is trained using the outputs of a larger neural network is a particularly interesting case of transfer learning. In the pre

Muhammad Usman

The internet of things refers to the network of devices connected to the internet and can communicate with each other. The term things is to refer non-conventional devices that are usually not connected to the internet. The network of such devices or things is growing at an enormous rate. The security and privacy of the data flowing through these things is a

Anand Jayarajan, Kimberly Hau, Andrew Goodwin, Gennady Pekhimenko

Hospitals around the world collect massive amounts of physiological data from their patients every day. Recently, there has been an increase in research interest to subject this data to statistical analysis to gain more insights and provide improved medical diagnoses. Such analyses require complex computations on large volumes of data, demanding efficient da

Sven Buechel, Luise Modersohn, Udo Hahn

Research in emotion analysis is scattered across different label formats (e.g., polarity types, basic emotion categories, and affective dimensions), linguistic levels (word vs. sentence vs. discourse), and, of course, (few well-resourced but much more under-resourced) natural languages and text genres (e.g., product reviews, tweets, news). The resulting hete

Lucas A. Cieza, Camilo González-Ruilova, Antonio S. Hales, Paola Pinilla

We present 1.3 mm continuum ALMA long-baseline observations at 3-5 au resolution of 10 of the brightest discs from the Ophiuchus DIsc Survey Employing ALMA (ODISEA) project. We identify a total of 26 narrow rings and gaps distributed in 8 sources and 3 discs with small dust cavities (r $<$10 au). We find that two discs around embedded protostars lack the cle

Alexander Soen, Hisham Husain, Richard Nock

We introduce a boosting algorithm to pre-process data for fairness. Starting from an initial fair but inaccurate distribution, our approach shifts towards better data fitting while still ensuring a minimal fairness guarantee. To do so, it learns the sufficient statistics of an exponential family with boosting-compliant convergence. Importantly, we are able t

B. A. S. D. Chrispim, R. C. L. Bruni, M. S. Guimaraes

The theory of massive photons in the presence of axions is studied as the effective theory describing the electromagnetic response of semimetals when a particular quartic fermionic pairing perturbation triggers the formation of charged chiral condensates, giving rise to an axionic superconductor. We investigate corrections to the Yukawa-like potential mediat

Lijun Ding, Madeleine Udell

In this paper, we provide an elementary, geometric, and unified framework to analyze conic programs that we call the strict complementarity approach. This framework allows us to establish error bounds and quantify the sensitivity of the solution. The framework uses three classical ideas from convex geometry and linear algebra: linear regularity of convex set

Shubham Kanodia, Joe P. Ninan, Andrew J. Monson, Suvrath Mahadevan

The NEID spectrograph is a R $\sim$ 120,000 resolution fiber-fed and highly stabilized spectrograph for extreme radial velocity (RV) precision. It is being commissioned at the 3.5 m WIYN telescope in Kitt Peak National Observatory with a desired instrumental precision of better than 30 \cms{}. NEID&#39;s bandpass of 380 -- 930 nm enables the simultaneous wav

John R. J. Thompson, W. John Braun

Understanding forest fire spread in any region of Canada is critical to promoting forest health, and protecting human life and infrastructure. Quantifying fire spread from noisy images, where regions of a fire are separated by change-point boundaries, is critical to faithfully estimating fire spread rates. In this research, we develop a statistically consist

Runlin Zhang

Let G be a semisimple group over rational numbers and H is a subgroup over rational numbers. Given a representation of G and an integral vector x whose stabilizer is equal to H. In this paper we investigate the asymptotic of integral points on Gx with bounded height. We find its asymptotic up to an implicit constant when H is large in G but we allow the pres

Sylvain Marinel, Charles Manière, Anthony Bilot, Christelle Bilot

Microwave energy can be advantageously used for materials processing as it provides high heating rates and homogeneous temperature field distribution. These features are partly due to the large microwave penetration depth into dielectric materials which is, at room temperature, a few centimeters in most dielectric materials. However, up to now, this technolo

Robin Elliott

Let X be a simply connected Riemannian manifold. Until now, quantitative topology has used Sullivan&#39;s rational homotopy theory as the bridge between geometric information on X and torsion-free homotopy theoretic information on X. In this paper we introduce Chen&#39;s iterated integrals on the based loop space of X as a new bridge between these two areas.

Dariusz Stolicki, Stanisław Szufa, Nimrod Talmon

We describe the PArticipatory BUdgeting LIBrary website (in short, Pabulib), which can be accessed via http://pabulib.org/, and which is a library of participatory budgeting data. In particular, we describe the file format (.pb) that is used for instances of participatory budgeting.

Huansheng Ning, Feifei Shi

With the overwhelming advances in Artificial Intelligence (AI), brain science and neuroscience, robots are developing towards a direction of much more human-like and human-friendly. We can&#39;t help but wonder whether robots could be regarded as humans in future? In this article, we propose a novel perspective to analyze the essential difference between hum

Charles Manière, Shirley Chan, Geuntak Lee, Joanna Mckittrick

Volume shrinkage, grain growth, and their interaction are major events occurring during free sintering of ceramics. A high temperature sintering dilatometry curve is influenced by these both phenomena. It is shown that the continuum theory of sintering can be utilized in the format enabling the extraction of the maximum amount of information on the densifica

Akshay Pal, Jayanta K Bhattacharjee

We consider the transmission dynamics of COVID-19 which is characterized by two distinct features. One is the existence of asymptomatic carriers which is a hidden variable in the problem. The other is the issue of latency which means that among the symptomatic carriers there could be a fraction whose symptoms develop after a couple of days. Our modelling is

Damien Bouchabou, Sao Nguyen, Christophe Lohr, Benoit Leduc

Activity recognition in smart homes is essential when we wish to propose automatic services for the inhabitants. However, it poses challenges in terms of variability of the environment, sensorimotor system, but also user habits. Therefore, endto-end systems fail at automatically extracting key features, without extensive pre-processing. We propose to tackle

Guido Sanguinetti

We consider the problem of estimating the reproduction number $R_t$ of an epidemic for populations where the probability of detection of cases depends on a known covariate. We argue that in such cases the normal empirical estimator can fail when the prevalence of cases among groups changes with time. We propose a Bayesian strategy to resolve the problem, as

Yajuan Qi, Chunyin Qiu, Meng Xiao, Hailong He

A quadrupole topological insulator, being one higher-order topological insulator with nontrivial quadrupole quantization, has been intensely investigated very recently. However, the tight-binding model proposed for such emergent topological insulators demands both positive and negative hopping coefficients, which imposes an obstacle in practical realizations

Wei Zhou, Zhibo Chen

Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN-based quality assessment models by exploiting efficient multi-scale features. In this paper, motivated by the human visu

Weilu Gao, Cunxi Yu, Ruiyang Chen

Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with unprecedented performance. The incorporation of nanomaterials into the system can further improve the performance thanks to their extraordinary properties, but the non-uniformity

Albert Senen-Cerda, Jaron Sanders

We analyze the convergence rate of gradient flows on objective functions induced by Dropout and Dropconnect, when applying them to shallow linear Neural Networks (NNs) - which can also be viewed as doing matrix factorization using a particular regularizer. Dropout algorithms such as these are thus regularization techniques that use 0,1-valued random variable

Leyu Dai, He Zhu, Dianbo Liu

Patient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes the pairwise similarity between patients. Procedures of typical a patient similarity study can be divided into several steps including data integration, similarity measurement, and

Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Zobaida Edib

Collecting sufficient labelled training data for health and medical problems is difficult (Antropova, et al., 2018). Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009). However, machine learning algo

Yi Liu, Li Zhang, Ning Ge, Guanghao Li

As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption mechanism. The clients train their local model, and the server aggregates models until convergence. In this process, the server uses an incentive mechanism t

Thomas Dalton, Mauritius Schmidtler, Alireza Hadj Khodabakhshi

We propose a deep learning approach for identifying malware families using the function call graphs of x86 assembly instructions. Though prior work on static call graph analysis exists, very little involves the application of modern, principled feature learning techniques to the problem. In this paper, we introduce a system utilizing an executable&#39;s func

Ramón I. Verdés Kairuz, Yury Orlov, Luis T. Aguilar

Gain tuning is given for the twisting controller to ensure that the closed-loop trajectories of the perturbed double integrator, initialized within a bounded domain and affected by uniformly bounded disturbances, settle at the origin in prescribed time.

Hadi A. Khorshidi, Uwe Aickelin

In this study, we propose a multicriteria group decision making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criter

Kipngeno Benard Kirui, Georg Ch. Pflug, Alois Pichler

We present new algorithms and fast implementations to find efficient approximations for modelling stochastic processes. For many numerical computations it is essential to develop finite approximations for stochastic processes. While the goal is always to find a finite model, which represents a given knowledge about the real data process as accurate as possib

K. B. Taranets, M. A. Fomin, L. E. Klyachkin, A. M. Malyarenko

We present a method for the measurements of the tetrahertz (THz) resonance response of DNA oligonucleotides deposited on a silicon nanosandwich (SNS). It is shown that the SNS device can be used to generate a THz resonance response within living biotissue. The technique we propose measures changes of the longitudinal conductance and the lateral voltage with

András Pál

While Transiting Exoplanet Survey Satellite (TESS) covers a considerable area of the sky during routine observations and the pointing schedule is easy to follow, it is not obvious to retrieve the current and/or predicted visibility of a bulk amount of objects, considering both stationary and moving Solar System targets like asteroids or comets. The program `

Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem

We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who

Bartolomeo Silvestri, Alessandro Rinaldi, Antonella Berardi, Michele Roccotelli

Serious Games (SGs) represent a new approach to improve learning processes more effectively and economically than traditional methods. This paper aims to present a SG approach for the electro-mobility context, in order to encourage the use of electric light vehicles. The design of the SG is based on the typical elements of the classic &#34;game&#34; with a r

Andrei Rodin

In a series of lectures given in 2003 soon after receiving the Fields Medal for his results in the Algebraic Geometry Vladimir Voevodsky (1966-2017) identifies two strategic goals for mathematics, which he plans to pursue in his further research. The first goal is to develop a &#34;computerised library of mathematical knowledge&#34;, which supports an automa

Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch

We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is constructed with the TensorFlow C API and is integrated into OpenFOAM as an application that may be linked at run time. Notably, our formulation precludes any r

Yiran Zhong, Charles Loop, Wonmin Byeon, Stan Birchfield

Although deep learning-based methods have dominated stereo matching leaderboards by yielding unprecedented disparity accuracy, their inference time is typically slow, on the order of seconds for a pair of 540p images. The main reason is that the leading methods employ time-consuming 3D convolutions applied to a 4D feature volume. A common way to speed up the

Zhe Liu, Fuchun Peng

We introduce federated marginal personalization (FMP), a novel method for continuously updating personalized neural network language models (NNLMs) on private devices using federated learning (FL). Instead of fine-tuning the parameters of NNLMs on personal data, FMP regularly estimates global and personalized marginal distributions of words, and adjusts the

Peng Wu, Joerg Appenzeller

Negative capacitance field-effect transistors (NC-FETs) have attracted wide interest as promising candidates for steep-slope devices, and sub-60 mV/decade switching has been demonstrated in NC-FETs with various device structures and material systems. However, the detailed mechanisms of the observed steep-slope switching in some of these experiments are under

Charlotte Van Hulse

The LHCb collaboration has measured central exclusive production of $J/ψ$, $ψ(2S)$, and $Υ$ mesons as well as $J/ψJ/ψ$, $J/ψψ(2S)$, $ψ(2S)ψ(2S)$, and $χ_cχ_c$ meson pairs in proton-proton collisions. The analyses of $Υ$ and charmonium pairs are performed at the centre-of-mass energies of 7 TeV and 8 TeV, and those of $J/ψ$ and $ψ(2S)$ are done at 7 TeV and 1

Cheng Tan, Wen-Qiang Xie, Guolin Zheng, Nuriyah Aloufi

Magnetic van der Waals (vdW) materials, including ferromagnets (FM) and antiferromagnets (AFM), have given access to the investigation of magnetism in two-dimensional (2D) limit and attracted broad interests recently. However, most of them are semiconducting or insulating and the vdW itinerant magnets, especially vdW itinerant AFM, are very rare. Here, we st

Saurav K. Shastri, Rizwan Ahmad, Philip Schniter

For magnetic resonance imaging (MRI), recently proposed &#34;plug-and-play&#34; (PnP) image recovery algorithms have shown remarkable performance. These PnP algorithms are similar to traditional iterative algorithms like FISTA, ADMM, or primal-dual splitting (PDS), but differ in that the proximal update is replaced by a call to an application-specific image

Hao Wang, Carlos Igncio Hernández Castellanos, Tome Eftimov

Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof. Multiple performance indicators, e.g., the generational distance and the hypervolume, are frequently applied when reporting the experimental data, where typically the data on ea

Arvind Ramanathan, Heng Ma, Akash Parvatikar, Chakra S. Chennubhotla

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse

Marianne Guillet, Gerhard Hiermann, Alexander Kröller, Maximilian Schiffer

Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions and CO2 emissions, if fed by clean energy sources. However, the adoption of electric vehicles so far fell short of expectations despite significant governmental incentives. One reason for this slow adoption is the drivers&#39;

Vignesh Raman

This paper introduces a new generalized superfactorial function (referable to as $n^{th}$- degree superfactorial: $sf^{(n)}(x)$) and a generalized hyperfactorial function (referable to as $n^{th}$- degree hyperfactorial: $H^{(n)}(x)$), and we show that these functions possess explicit formulae involving figurate numbers. Besides discussing additional number

Margarita Belova, Matthew Bernard

We give locally finite Markov trees in $L^p$-compact$,$ separable Hilbert$,$ supersymmetric process$:$ $[0,\infty)\!\times\!\mathbb{R}^{\lvert\mathcal{A}^{\otimes m}\rvert}/\mathcal{A}^{\otimes m}$ on quantum ${\rm U}(\lvert\mathcal{A}^{\otimes m}\rvert)$ semigroups$.$ In full automorphism group ${\rm Aut}({\rm\bf T})$ of modular subgroup$,$ asymptotic-ergod

Luca Comisso, Felipe A. Asenjo

Spinning black holes store rotational energy that can be extracted. When a black hole is immersed in an externally supplied magnetic field, reconnection of magnetic field lines within the ergosphere can generate negative energy (relative to infinity) particles that fall into the black hole event horizon while other particles escape stealing energy from the b

Calvin Brown, Artem Goncharov, Zachary Ballard, Mason Fordham

Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to grating-based spectroscopy. The system empl

Peter Wu, Yifan Zhong, Alan W Black

Existing multilingual speech NLP works focus on a relatively small subset of languages, and thus current linguistic understanding of languages predominantly stems from classical approaches. In this work, we propose a method to analyze language similarity using deep learning. Namely, we train a model on the Wilderness dataset and investigate how its latent sp

Driss Kiouach, Yassine Sabbar, Salim El Azami El-idrissi

The spread of infectious diseases is a major challenge in our contemporary world, especially after the recent outbreak of Coronavirus disease 2019 (COVID-19). The quarantine strategy is one of the important intervention measures to control the spread of an epidemic by greatly minimizing the likelihood of contact between infected and susceptible individuals.

Allison Woodruff, Yasmin Asare Anderson, Katherine Jameson Armstrong, Marina Gkiza

Algorithmic systems are increasingly deployed to make decisions in many areas of people&#39;s lives. The shift from human to algorithmic decision-making has been accompanied by concern about potentially opaque decisions that are not aligned with social values, as well as proposed remedies such as explainability. We present results of a qualitative study of a

Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as house

S. Jin, A. H. Reiman, N. J. Fisch

Because of the large mass differences between electrons and ions, the heat diffusion in electron-ion plasmas exhibits more complex behavior than simple heat diffusion found in typical gas mixtures. In particular, heat is diffused in two distinct, but coupled, channels. Conventional single fluid models neglect the resulting complexity, and can often inaccurat

Srikar Appalaraju, Yi Zhu, Yusheng Xie, István Fehérvári

Self-supervised representation learning has seen remarkable progress in the last few years. More recently, contrastive instance learning has shown impressive results compared to its supervised learning counterparts. However, even with the ever increased interest in contrastive instance learning, it is still largely unclear why these methods work so well. In

R. C. Hillyard, Yunlu Liaozheng, Sai Vineeth K. R

Much of the copious literature on the subject of sorting has concentrated on minimizing the number of comparisons and/or exchanges/copies. However, a more appropriate yardstick for the performance of sorting algorithms is based on the total number of array accesses that are required (the &#34;work&#34;). For a sort that is based on divide-and-conquer (includ

Ivan Dadić, Dubravko Klabučar, Domagoj Kuić

Within the finite-time-path out-of-equilibrium quantum field theory (QFT), we calculate direct photon emission from early stages of heavy ion collisions, from a narrow window, in which uncertainty relations are still important and they provide a new mechanism for production of photons. The basic difference with respect to earlier calculations, leading to div

Maud Formanek, Lorenzo Rovigatti, Emanuela Zaccarelli, Francesco Sciortino

By means of Langevin dynamics simulations, we investigate the gel formation of randomly functionalized polymers in solution, with the ability to form both intra- and intermolecular reversible bonds. Under highly dilute conditions, these polymers form soft nano-objects (so-called single-chain nanoparticles, SCNPs), resulting from the purely intramolecular cro

Morteza Ghahremani, Yonghuai Liu, Bernard Tiddeman

Scale-invariance, good localization and robustness to noise and distortions are the main properties that a local feature detector should possess. Most existing local feature detectors find excessive unstable feature points that increase the number of keypoints to be matched and the computational time of the matching step. In this paper, we show that robust a

Caglar Yildirim

Cybersickness is an unpleasant side effect of exposure to a virtual reality (VR) experience and refers to such physiological repercussions as nausea and dizziness triggered in response to VR exposure. Given the debilitating effect of cybersickness on the user experience in VR, academic interest in the automatic detection of cybersickness from physiological m

Tim Roughgarden

EIP-1559 is a proposal to make several tightly coupled additions to Ethereum&#39;s transaction fee mechanism, including variable-size blocks and a burned base fee that rises and falls with demand. This report assesses the game-theoretic strengths and weaknesses of the proposal and explores some alternative designs.

Mostafa Abdollahi, Farshad Eshghi, Manoochehr Kelarestaghi, Mozafar Bag-Mohammadi

High-speed, low latency, and heterogeneity features of 5G, as the common denominator of many emerging and classic wireless applications, have put wireless technology back in the spotlight. Continuous connectivity requirement in low-power and wide-reach networks underlines the need for more efficient routing over scarce wireless resources, in multi-hp scenari

Qian Wang, Fanlin Meng, Toby P. Breckon

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distributions or learn domain-invariant features, we directly learn a unified classifier for both domains within a high-dimensional homogeneous feature space without explicit domain adaptat

Farid Ghareh Mohammadi, Farzan Shenavarmasouleh, M. Hadi Amini, Hamid R. Arabnia

Malware detection has become a challenging task due to the increase in the number of malware families. Universal malware detection algorithms that can detect all the malware families are needed to make the whole process feasible. However, the more universal an algorithm is, the higher number of feature dimensions it needs to work with, and that inevitably ca

Konstantin Aleshkin, Kyoji Saito

We introduce primitive forms with or without higher residue structure and explore their connection with the flat structures with or without a metric and integrable hierarchies of KdV type. Just as the classical case of primitive forms with metric arXiv:1311.1659, the primitive forms without metrics are constructed as the positive part of the Birkhoff decompo

Sıla Ada, Nadia Abou Nabout, Elea McDonnell Feit

Ad exchanges, i.e., platforms where real-time auctions for ad impressions take place, have developed sophisticated technology and data ecosystems to allow advertisers to target users, yet advertisers may not know which sites their ads appear on, i.e., the ad context. In practice, ad exchanges can require publishers to provide accurate ad placement informatio

Xiang Li, Maxim Goryachev, Yiqiu Ma, Michael E. Tobar

A conventional resonant detector is often subject to a trade-off between bandwidth and peak sensitivity that can be traced back to quantum Cramer-Rao Bound. Anomalous dispersion has been shown to improve it by signal amplification and is thus more robust against decoherence, while it leads to instabilities. We propose a stable quantum amplifier applicable to

Milan Nosáľ, Jaroslav Porubän, Matúš Sulír

Pure embedding as an implementation strategy of domain-specific languages (DSLs) benefits from low implementation costs. On the other hand, it introduces undesired syntactic noise that impedes involvement of non-programming domain experts. Due to this, pure embedded DSLs are generally not intended for, nor used by, non-programmers. In this work, we try to ch

Sofian Hamad, Taoufik Yeferny

Advancements in artificial intelligence (AI), speech recognition systems (ASR), and machine learning have enabled the development of intelligent computer programs called chatbots. Many chatbots have been proposed to provide different services in many areas such as customer service, sales and marketing. However, the use of chatbot as advisers in the field of

Daniel Chen, Yekun Xu, Betis Baheri, Samuel A. Stein

Recently, there has been a surge of interest for quantum computation for its ability to exponentially speed up algorithms, including machine learning algorithms. However, Tang suggested that the exponential speed up can also be done on a classical computer. In this paper, we proposed an algorithm for slow feature analysis, a machine learning algorithm that e