May 2020 arXiv papers — page 24

Kavita Surana, Anuraag Singh, Ambuj D Sagar

Policymakers in developing countries increasingly see science, technology, and innovation (STI) as an avenue for meeting sustainable development goals (SDGs), with STI-based startups as a key part of these efforts. Market failures call for government interventions in supporting STI for SDGs and publicly-funded incubators can potentially fulfil this role. Usi

Fred Wiffen, Mohammud Z. Bocus, Angela Doufexi, Woon Hau Chin

In this work we propose a fronthaul compression scheme for distributed MIMO systems with multi-antenna receivers, in which, prior to signal quantisation, dimension reduction is performed at each receiver by matched filtering the received signal with a subset of the local user channel vectors. By choosing these matched filter vectors based on global channel i

Masayo Fujimura, Marcelina Mocanu, Matti Vuorinen

We introduce a new intrinsic metric in subdomains of a metric space and give upper and lower bounds for it in terms of well-known metrics. We also prove distortion results for this metric under quasiregular maps.

Luiz L. Lopes, Carline Biesdorf, Débora P. Menezes

In this work we study different variations of the MIT bag model. We start with the so called non-ideal bag model and discuss it in detail. Then we implement a vector interaction in the MIT bag model that simulates a meson exchange interaction and fix the quark-meson coupling constants via symmetry group theory. At the end we propose an original model, inspir

Zhongpai Gao, Guangtao Zhai, Junchi Yan, Xiaokang Yang

It has witnessed a growing demand for efficient representation learning on point clouds in many 3D computer vision applications. Behind the success story of convolutional neural networks (CNNs) is that the data (e.g., images) are Euclidean structured. However, point clouds are irregular and unordered. Various point neural networks have been developed with is

Junchao Shi, Wenjin Wang, Xinping Yi, Xiqi Gao

In this paper, we consider massive multiple-input-multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downlink precoding with imperfect channel state information (CSI). By exploiting both instantaneous and statistical CSI, we aim to design precoding vectors to maximize the ergodic rate (

Yanliang Zhu, Dongchun Ren, Mingyu Fan, Deheng Qian

Trajectory forecasting, or trajectory prediction, of multiple interacting agents in dynamic scenes, is an important problem for many applications, such as robotic systems and autonomous driving. The problem is a great challenge because of the complex interactions among the agents and their interactions with the surrounding scenes. In this paper, we present a

Bradraj Pandey, Ling-Fang Lin, Rahul Soni, Nitin Kaushal

The magnetic and electronic phase diagram of a model for the quasi-one-dimensional alkali metal iron selenide compound Na$_2$FeSe$_2$ is presented. The novelty of this material is that the valence of iron is Fe$^{2+}$ contrary to most other iron-chain compounds with valence Fe$^{3+}$. Using first-principles techniques, we developed a three-orbital tight-bind

Tyler R. Scott, Michael Shvartsman, Karl Ridgeway

Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set classification is useful for benchmarking methods, but is often unrealistic in practical settings due to the compositionality of actions, resulting in a functionally infinite-cardinal

Guang Chen, Shiwen Shen, Longyin Wen, Si Luo

Pig counting is a crucial task for large-scale pig farming, which is usually completed by human visually. But this process is very time-consuming and error-prone. Few studies in literature developed automated pig counting method. Existing methods only focused on pig counting using single image, and its accuracy is challenged by several factors, including pig

Claudia-F. López-Cámara, Albert Jordà Juanós, William A. Sirignano

This study presents methane-air counterflow simulations, in computationally efficient similar form, allowing combustible mixtures to flow from one or both directions in order to learn more about multi-branched propagating flame structures (e.g., a triple flame). These structures with both premixed and non-premixed flames are commonly seen in more practical c

Kais Feki

Let $A$ be a positive bounded linear operator on a complex Hilbert space $\mathcal{H}$ and $\mathcal{B}_{A}(\mathcal{H})$ be the subspace of all operators which admit $A$-adjoints operators. In this paper, we establish some inequalities involving the commutator and the anticommutator of operators in semi-Hilbert spaces, i.e. spaces generated by positive semi

Lizi Liao, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on consider

Fei Liu, Shanhe Su

Based on the notion of quantum trajectory, we present a stochastic theoretical framework for Floquet quantum heat engines. As an application, the large deviation functions of two types of stochastic efficiencies for a two-level Floquet quantum heat engine are investigated. We find that the statistics of one efficiency agree well with the predictions of the u

Xiaoying Ding, Zhenzhong Chen

Traditional 3D mesh saliency detection algorithms and corresponding databases were proposed under several constraints such as providing limited viewing directions and not taking the subject's movement into consideration. In this work, a novel 6DoF mesh saliency database is developed which provides both the subject's 6DoF data and eye-movement data. Different

Srivatsan Sridhar, Yong-Seon Song, Ashley J. Ross, Rongpu Zhou

A photometric redshift sample of Luminous Red Galaxies (hereafter LRGs) obtained from The DECam Legacy Survey (DECaLS) is analysed to probe cosmic distances by exploiting the wedge approach of the two-point correlation function. Although the cosmological information is highly contaminated by the uncertainties existing in the photometric redshifts from the ga

Kelechi Nwaike, Licheng Jiao

We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements. While Transfer learning is already being appli

Matthew DelVecchio, Vanessa Arndorfer, William C. Headley

Adversarial evasion attacks have been very successful in causing poor performance in a wide variety of machine learning applications. One such application is radio frequency spectrum sensing. While evasion attacks have proven particularly successful in this area, they have done so at the detriment of the signal's intended purpose. More specifically, for real

Matthew DelVecchio, Bryse Flowers, William C. Headley

Recent work has shown the impact of adversarial machine learning on deep neural networks (DNNs) developed for Radio Frequency Machine Learning (RFML) applications. While these attacks have been shown to be successful in disrupting the performance of an eavesdropper, they fail to fully support the primary goal of successful intended communication. To remedy t

Wael Jaafar, Shimaa Naser, Sami Muhaidat, Paschalis C. Sofotasios

Recently, interest on the utilization of unmanned aerial vehicles (UAVs) has aroused. Specifically, UAVs can be used in cellular networks as aerial users for delivery, surveillance, rescue search, or as an aerial base station (aBS) for communication with ground users in remote uncovered areas or in dense environments requiring prompt high capacity. Aiming to

Jeremie S. Kim, Minesh Patel, A. Giray Yaglikci, Hasan Hassan

In order to shed more light on how RowHammer affects modern and future devices at the circuit-level, we first present an experimental characterization of RowHammer on 1580 DRAM chips (408x DDR3, 652x DDR4, and 520x LPDDR4) from 300 DRAM modules (60x DDR3, 110x DDR4, and 130x LPDDR4) with RowHammer protection mechanisms disabled, spanning multiple different t

Shuyue Guan, Murray Loew, Hanseok Ko

In machine learning, the performance of a classifier depends on both the classifier model and the dataset. For a specific neural network classifier, the training process varies with the training set used; some training data make training accuracy fast converged to high values, while some data may lead to slowly converged to lower accuracy. To quantify this p

Zehao Lin, Shaobo Cui, Guodun Li, Xiaoming Kang

Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially

Peng Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu

Since real-world ubiquitous documents (e.g., invoices, tickets, resumes and leaflets) contain rich information, automatic document image understanding has become a hot topic. Most existing works decouple the problem into two separate tasks, (1) text reading for detecting and recognizing texts in images and (2) information extraction for analyzing and extract

Chengwei Zhang, Yunlu Xu, Zhanzhan Cheng, Shiliang Pu

Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Therefore, methods based on spatial transformers are extensively studied. However, chromatic difficulties in com

Jing Lu, Baorui Zou, Zhanzhan Cheng, Shiliang Pu

In object recognition applications, object images usually appear with different quality levels. Practically, it is very important to indicate object image qualities for better application performance, e.g. filtering out low-quality object image frames to maintain robust video object recognition results and speed up inference. However, no previous works are e

Mitsuhito Itoh, Hiroyasu Satoh

The spherical Fourier transform on a harmonic Hadamard manifold $(X^n, g)$ of positive volume entropy is studied. If $(X^n, g)$ is of hypergeometric type, namely spherical functions of $X$ are represented by the Gauss hypergeometric functions, the inversion formula, the convolution rule together with the Plancherel theorem are shown by the representation of

Ke Xu, Anton S. Tremsin, Jiaqi Li, Daniela M. Ushizima

There is renewed interest in using advanced techniques to characterize ancient Roman concrete. In the present work, samples were drilled from the "Hospitium" in Pompeii and were analyzed by synchrotron microtomography (uCT) and neutron radiography to study how the microstructure, including the presence of induced cracks, affects their water adsorption. The w

Justin Finkel, Dorian Abbot, Jonathan Weare

Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe \emph{transition path theory} (TPT), a framework originally developed

Haley Grant Yifan Zhang, Lu Li, Yan Wang, Satomi Kawamoto

Obesity increases significantly cancer risk in various organs. Although this has been recognized for decades, the mechanism through which this happens has never been explained. Here, we show that the volumes of kidneys, pancreas, and liver are strongly correlated (median correlation = 0.625; P-value<10-47) with the body mass index (BMI) of an individual. We

Kyle Swanson, Lili Yu, Tao Lei

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal tran

Clifford Broni-Bediako, Yuki Murata, Luiz Henrique Mormille, Masayasu Atsumi

The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexi

Kalliopi Basioti, George V. Moustakides

When images are statistically described by a generative model we can use this information to develop optimum techniques for various image restoration problems as inpainting, super-resolution, image coloring, generative model inversion, etc. With the help of the generative model it is possible to formulate, in a natural way, these restoration problems as Stat

Shushman Choudhury, Jayesh K. Gupta, Mykel J. Kochenderfer, Dorsa Sadigh

We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making

Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi

LWF chain graphs combine directed acyclic graphs and undirected graphs. We present a PC-like algorithm that finds the structure of chain graphs under the faithfulness assumption to resolve the problem of scalability of the proposed algorithm by Studeny (1997). We prove that our PC-like algorithm is order dependent, in the sense that the output can depend on

Daniel E. Spector, Scott J. Spector

In this note two results are established for energy functionals that are given by the integral of $ W(\mathbf x,\nabla \mathbf u(\mathbf x))$ over $\Omega \subset\mathbb{R}^n$ with $\nabla \mathbf u \in BMO(\Omega;{\mathbb R}^{N\times n})$, the space of functions of Bounded Mean Oscillation of John & Nirenberg. A version of Taylor's theorem is first shown to

Zichao Wang, Yi Gu, Andrew Lan, Richard Baraniuk

We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors. Such uncertainty information is useful, for example, for an adaptive testing scenario, where additional tests can be administered if the mod

W. C. Soares, A. L. Moura, Askery Canabarro, E. De Lima

The orbital angular momentum conservation of light reveals different diffraction patterns univocally dependent on the topological charge of the incident light beam when passing through a triangular aperture. It is demonstrated that these patterns, which are accessed by observing the far field measurement of the diffracted light, can also be obtained using fe

Mee Seong Im, Venkat R. Dasari

Genetic algorithms are modeled after the biological evolutionary processes that use natural selection to select the best species to survive. They are heuristics based and low cost to compute. Genetic algorithms use selection, crossover, and mutation to obtain a feasible solution to computational problems. In this paper, we describe our genetic optimization a

Kanae Mukai, Naomichi Hatano

We define the discrete-time quantum walk on complex networks and utilize it for community detection. We numerically show that the quantum walk with the Fourier coin is localized in a community to which the initial node belongs. Meanwhile, the quantum walk with the Grover coin tends to be localized around the initial node, not over a community. The probabilit

Daniel Liang, Li Li, Stefan Leichenauer

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices. We analyze the algorithm as a bang-bang protocol with fixed total time and a randomized greedy optimization scheme. We investigate the performance of bang-bang QAOA on MAX-2-SAT, finding the appearance of phase transiti

Leonardo Gigli, B Ravi Kiran, Thomas Paul, Andres Serna

Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsampling image based representations of den

Vahid Azimi, Mojtaba Sharifi, Seyed Fakoorian, Thang Tien Nguyen

This paper presents a state estimation-based robust optimal control strategy for influenza epidemics in an interactive human society in the presence of modeling uncertainties. Interactive society is influenced by the random entrance of individuals from other human societies whose effects can be modeled as a non-Gaussian noise. Since only the number of expose

Marieme Ngom, Oana Marin

We present a Fourier neural network (FNN) that can be mapped directly to the Fourier decomposition. The choice of activation and loss function yields results that replicate a Fourier series expansion closely while preserving a straightforward architecture with a single hidden layer. The simplicity of this network architecture facilitates the integration with

Sahib Singh, Harshvardhan Sikka, Sasikanth Kotti, Andrew Trask

In this paper we measure the effectiveness of $\epsilon$-Differential Privacy (DP) when applied to medical imaging. We compare two robust differential privacy mechanisms: Local-DP and DP-SGD and benchmark their performance when analyzing medical imagery records. We analyze the trade-off between the model's accuracy and the level of privacy it guarantees, and

Zhe Cheng, Fengwen Mu, Tiangui You, Wenhui Xu

The ultra-wide bandgap, high breakdown electric field, and large-area affordable substrates make \b{eta}-Ga2O3 promising for applications of next-generation power electronics while its thermal conductivity is at least one order of magnitude lower than other wide/ultrawide bandgap semiconductors. To avoid the degradation of device performance and reliability

Murat A. Erdogdu, Rasa Hosseinzadeh

We study sampling from a target distribution ${\nu_* = e^{-f}}$ using the unadjusted Langevin Monte Carlo (LMC) algorithm. For any potential function $f$ whose tails behave like ${\|x\|^\alpha}$ for ${\alpha \in [1,2]}$, and has $\beta$-H\"older continuous gradient, we prove that ${\widetilde{\mathcal{O}} \Big(d^{\frac{1}{\beta}+\frac{1+\beta}{\beta}(\frac{2

Sha Yuan, Zhou Shao, Yu Zhang, Xingxing Wei

Science of science (SciSci) is an emerging discipline wherein science is used to study the structure and evolution of science itself using large data sets. The increasing availability of digital data on scholarly outcomes offers unprecedented opportunities to explore SciSci. In the progress of science, the previously discovered knowledge principally inspires

J. R. Nascimento, A. Yu. Petrov, P. J. Porfirio, A. R. Soares

In this work, we calculate the deflection angle of light in a spacetime that interpolates between regular black holes and traversable wormholes, depending on the free parameter of the metric. Afterwards, this angular deflection is substituted into the lens equations which allows to obtain physically measurable results, such as the position of the relativisti

Nilufer Tuptuk, Stephen Hailes

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process mo

Baoxu Shi, Jaewon Yang, Feng Guo, Qi He

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract imp

Sohini Roy, Arunabha Sen

Monitoring and automation of the critical infrastructures like the power grid is improvised by the support of an efficient and secure communication net-work. Due to the low cost, low power profile, dynamic nature, improved ac-curacy and scalability, wireless sensor networks (WSN) became an attractive choice for the Information and Communication Technology (I

Aditya Rawal, Joel Lehman, Felipe Petroski Such, Jeff Clune

Neural Architecture Search (NAS) explores a large space of architectural motifs -- a compute-intensive process that often involves ground-truth evaluation of each motif by instantiating it within a large network, and training and evaluating the network with thousands of domain-specific data samples. Inspired by how biological motifs such as cells are sometim

Pedro Araújo, Fábio Botler, Guilherme Oliveira Mota

Alon and Yuster proved that the number of orientations of any $n$-vertex graph in which every $K_3$ is transitively oriented is at most $2^{\lfloor n^2/4\rfloor}$ for $n \geq 10^4$ and conjectured that the precise lower bound on $n$ should be $n \geq 8$. We confirm their conjecture and, additionally, characterize the extremal families by showing that the bal

Alex S. Leong, Arunselvan Ramaswamy, Daniel E. Quevedo, Holger Karl

In many Cyber-Physical Systems, we encounter the problem of remote state estimation of geographically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors have to be transmitted to a central gateway over a wirel

John Antonioli, Soonjo Hong, Anthony Quas

In this article, we continue the structural study of factor maps betweeen symbolic dynamical systems and the relative thermodynamic formalism. Here, one is studying a factor map from a shift of finite type $X$ (equipped with a potential function) to a sofic shift $Z$, equipped with a shift-invariant measure $\nu$. We study relative equilibrium states, that i

Hongye Yu, Frank Wilczek, Biao Wu

We present a quantum algorithm for approximating maximum independent sets of a graph based on quantum non-Abelian adiabatic mixing in the sub-Hilbert space of degenerate ground states, which generates quantum annealing in a secondary Hamiltonian. For both sparse and dense graphs, our quantum algorithm on average can find an independent set of size very close

Nathaniel Rupprecht, Dervis Vural

Here we study the operation efficiency of a finite-size finite-response-time Maxwell's demon, who can make future predictions. We compare the heat and mass transport rate of predictive demons to non-predictive ones and find that predictive demons can achieve higher mass and heat transport rates over longer periods of time. We determine how the demon performa

Guanming Zhang, Romain Mueller, Amin Doostmohammadi, Julia M. Yeomans

The collective behaviour of confluent cell sheets is strongly influenced both by polar forces, arising through cytoskeletal propulsion and by active inter-cellular forces, which are mediated by interactions across cell-cell junctions. We use a phase-field model to explore the interplay between these two contributions and compare the dynamics of a cell sheet

Xiaoqing Gao, Yali Wang, Xuehao He, Mengjun Xu

Developing angular trapping methods, which will enable optical tweezers to rotate a micronized bead, is of great importance for the studies of biomacromolecules during a wide range of torque-generation processes. Here we report a novel controlled angular trapping method based on composite Janus particles. We used a chemically synthesized Janus particle, whic

Naoki Narisawa, Nicolas Chauvet, Mikio Hasegawa, Makoto Naruse

By exploiting ultrafast and irregular time series generated by lasers with delayed feedback, we have previously demonstrated a scalable algorithm to solve multi-armed bandit (MAB) problems utilizing the time-division multiplexing of laser chaos time series. Although the algorithm detects the arm with the highest reward expectation, the correct recognition of

Kai Shu, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah

Email remains one of the most frequently used means of online communication. People spend a significant amount of time every day on emails to exchange information, manage tasks and schedule events. Previous work has studied different ways for improving email productivity by prioritizing emails, suggesting automatic replies or identifying intents to recommend

Laura Polimeno, Antonio Fieramosca, Giovanni Lerario, Marco Cinquino

Two dimensional (2D) perovskites are promising materials for photonic applications, given their outstanding nonlinear optical properties, ease of fabrication and versatility. In particular, exploiting their high oscillator strength, the crystalline form of 2D perovskites can be used as excitonic medium in optical microcavities, allowing for the study of thei

P. Huillery, J. Leibold, T. Delord, L. Nicolas

We use nominally forbidden electron-nuclear spin transitions in nitrogen-vacancy (NV) centers in diamond to demonstrate coherent manipulation of a nuclear spin ensemble using microwave fields at room temperature. We show that employing an off-axis magnetic field with a modest amplitude($\approx$ 0.01 T) at an angle with respect to the NV natural quantization

Niny Arcila-Maya

Let $\mathcal{A}$ be a topological Azumaya algebra of degree $mn$ over a CW complex $X$. We give conditions for the positive integers $m$ and $n$, and the space $X$ so that $\mathcal{A}$ can be decomposed as the tensor product of topological Azumaya algebras of degrees $m$ and $n$. Then we prove that if $m<n$ and the dimension of $X$ is higher than $2m+1$, $

Daoyi Dong, Chuan-Cun Shu, Jiangchao Chen, Xi Xing

We investigate two classes of quantum control problems by using frequency-domain optimization algorithms in the context of ultrafast laser control of quantum systems. In the first class, the system model is known and a frequency-domain gradient-based optimization algorithm is applied to searching for an optimal control field to selectively and robustly manip

Tony Alexandre Medeiros Silva, Guilherme Sousa Cassia, João Luiz Azevedo Carvalho

Radiomics is an emerging area, which presents a large set of computational methods and techniques to extract quantitative characteristics from magnetic resonance images. In the feature extraction stage, its outputs must be well defined and carefully evaluated, to provide imaging diagnostics, prognoses and responses to treatment therapies. In this study, we p

Sakshi Arya, Yuhong Yang

Delayed rewards problem in contextual bandits has been of interest in various practical settings. We study randomized allocation strategies and provide an understanding on how the exploration-exploitation tradeoff is affected by delays in observing the rewards. In randomized strategies, the extent of exploration-exploitation is controlled by a user-determine

Tomoya Hirota, Richard L. Plambeck, Melvyn C. H. Wright, Masahiro N. Machida

We observed polarization of the SiO rotational transitions from Orion Source I (SrcI) to probe the magnetic field in bipolar outflows from this high mass protostar. Both 43 GHz $J$=1-0 and 86 GHz $J$=2-1 lines were mapped with $\sim$20 AU resolution, using the Very Large Array (VLA) and Atacama Large Millimeter/Submillimeter Array (ALMA), respectively. The $

Eduardo José Gómez-Hernández, Pablo Antonio Martínez, Biagio Peccerillo, Sandro Bartolini

Performance has always been a hot topic in computing. However, the viable ways to achieve it have taken many forms in the different moments of computing history. Today, technological limits have pushed the adoption of increasingly parallel multi-core and many-core architectures and even the use of highly specific hardware (aka Domain-Specific Architectures,

Qifeng Li

This paper presents a novel optimization framework of formulating the three-phase optimal power flow that involves uncertainty. The proposed uncertainty-aware optimization (UaO) framework is: 1) a deterministic framework that is less complex than the existing optimization frameworks involving uncertainty, and 2) convex such that it admits polynomial-time alg

Geethu Joseph, M. Cenk Gursoy, Pramod K. Varshney

We consider the problem of detecting anomalies among a given set of processes using their noisy binary sensor measurements. The noiseless sensor measurement corresponding to a normal process is 0, and the measurement is 1 if the process is anomalous. The decision-making algorithm is assumed to have no knowledge of the number of anomalous processes. The algor

Benjamin D. Horne, Maurício Gruppi, Sibel Adalı

A major concern with text-based news veracity detection methods is that they may not generalize across countries and cultures. In this short paper, we explicitly test news veracity models across news data from the United States and the United Kingdom, demonstrating there is reason for concern of generalizabilty. Through a series of testing scenarios, we show

Cecilia Jarne

Different brain areas, such as the cortex and, more specifically, the prefrontal cortex, show great recurrence in their connections, even in early sensory areas. {Several approaches and methods based on trained networks have been proposed to model and describe these regions. It is essential to understand the dynamics behind the models because they are used t

John H. Lagergren, John T. Nardini, Ruth E. Baker, Matthew J. Simpson

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting

Jeremy Budd, Yves van Gennip

An emerging technique in image segmentation, semi-supervised learning, and general classification problems concerns the use of phase-separating flows defined on finite graphs. This technique was pioneered in Bertozzi and Flenner (2012), which used the Allen-Cahn flow on a graph, and was then extended in Merkurjev, Kostic and Bertozzi (2013) using instead the

Yongxin Liu

In this research, we propose a series of methodologies to mine transit riders travel pattern and behavioral preferences, and then we use these knowledges to adjust and optimize the transit systems. Contributions are: 1) To increase the data validity: a) we propose a novel approach to rectify the time discrepancy of data between the AFC (Automated Fare Collec

Liset Vázquez Romaguera, Rosalie Plantefève, Samuel Kadoury

In this work we propose a multi-scale recurrent encoder-decoder architecture to predict the breathing induced organ deformation in future frames. The model was trained end-to-end from input images to predict a sequence of motion labels. Targets were created by quantizing the displacement fields obtained from deformable image registration. We report results u

Nicolas P. D. Sawaya, Gian Giacomo Guerreschi, Adam Holmes

A primary objective of quantum computation is to efficiently simulate quantum physics. Scientifically and technologically important quantum Hamiltonians include those with spin-$s$, vibrational, photonic, and other bosonic degrees of freedom, i.e. problems composed of or approximated by $d$-level particles (qudits). Recently, several methods for encoding the

Miloslav Znojil, Denis I. Borisov

Quantum phase transitions in certain non-Hermitian systems controlled by non-tridiagonal Hamiltonian matrices are found anomalous. In contrast to the known models with tridiagonal-matrix structure in which the geometric multiplicity of the completely degenerate energy eigenvalue appears always equal to one, this multiplicity is found larger than one in the p

Ana Luisa Foguel, Eduardo Souza Fraga, Carla Bonifazi

Low-energy neutrinos are clean messengers from supernovae explosions and probably carry unique insights into the process of stellar evolution. We estimate the expected number of events considering coherent elastic scattering of neutrinos off silicon nuclei, as would happen in Charge Coupled Devices (CCD) detectors. The number of expected events, integrated o

Trevor Reed, Christopher Leon, Frank Vera, Lei Guo

The constituent counting ruling (CCR) has been found to hold for numerous hard, exclusive processes. It predicts the differential cross section at high energies and fixed $\cos \theta_{c.m.}$ should follow $\frac{d \sigma}{dt} \sim \frac{1}{s^{n-2}}$, where $n$ is the minimal number of constituents involved in the reaction. Here we provide an in-depth analys

Vahe Galstyan, Rob Phillips

Kinetic proofreading is an error correction mechanism present in the processes of the central dogma and beyond, and typically requires the free energy of nucleotide hydrolysis for its operation. Though the molecular players of many biological proofreading schemes are known, our understanding of how energy consumption is managed to promote fidelity remains in

Julia Asplund, Guðlaugur Jóhannesson, Axel Brandenburg

A handedness in the arrival directions of high-energy photons from outside our Galaxy can be related to the helicity of an intergalactic magnetic field. Previous estimates by arXiv:1310.4826 and arXiv:1412.3171 showed a hint of a signal present in the photons observed by the Fermi Large Area Telescope (LAT). An update on the measurement of handedness in Ferm

Thomas G. Zimmerman, Vito P. Pastore, Sujoy K. Biswas, Simone Bianco

Plankton provide the foundation for life on earth. To advance our understanding of the marine ecosystem, for scientific, commercial and survival purposes, more in situ continuous monitoring and analysis of plankton is required. Cost, complexity, power and data communication demands are barriers to widespread deployment of in situ plankton microscopes. We add

Matthew Inglis-Whalen, Michael Luke, Aris Spourdalakis

We re-examine observables with rapidity divergences in the context of a formulation of Soft-Collinear Effective Theory in which infrared degrees of freedom are not explicitly separated into modes. We consider the Sudakov form factor with a massive vector boson and Drell-Yan production of lepton pairs at small transverse momentum as demonstrative examples. In

Daniel Soh, Eric Chatterjee, Matt Eichenfield

We derive the optimal analytical quantum-state-transfer control solutions for two disparate quantum memory blocks. Employing the SLH formalism description of quantum network theory, we calculate the full quantum dynamics of system populations, which lead to the optimal solution for the highest quantum fidelity attainable. We show that, for the example where

Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi

Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata

Cristhian Montoya, Louis Breton

In this article the robust Stackelberg controllability (RSC) problem is studied for a nonlinear fourth-order parabolic equation, namely, the Kuramoto-Sivashinsky equation. When three external sources are acting into the system, the RSC problem consists essentially in combining two subproblems: the first one is a saddle point problem among two sources. Such a

D. Souami, J. Cresson, C. Biernacki, F. Pierret

We revisit the concept of sphere of gravitational activity, to which we give both a geometrical and physical meaning. This study aims to refine this concept in a much broader context that could, for instance, be applied to exo-planetary problems (in a Galactic stellar disc-Star-Planets system) to define a first order "border" of a planetary system. The metho

Andreas Blommaert, Thomas G. Mertens, Henri Verschelde

We identify the spectral properties of Hawking-Unruh radiation in the eternal black hole at ultra low energies as a probe for the chaotic level statistics of quantum black holes. Level repulsion implies that there are barely Hawking particles with an energy smaller than the level separation. This effect is experimentally accessible by probing the Unruh heat

Mallku Soldevila, Beta Ziliani, Daniel Fridlender

We provide the semantics of garbage collection (GC) for the Lua programming language. Of interest are the inclusion of finalizers(akin to destructors in object-oriented languages) and weak tables (a particular implementation of weak references). The model expresses several aspects relevant to GC that are not covered in Lua's documentation but that, neverthel

Xinwen Zhu

We formulate a Satake isomorphism for the integral spherical Hecke algebra of an unramified $p$-adic group $G$ and generalize the formulation to give a description of the Hecke algebra $H_G(V)$ of weight $V$, where $V$ is a lattice in an irreducible algebraic representation of $G$.

Shangxiong Huangfu, Zurab Guguchia, Denis Cheptiakov, Xiaofu Zhang

The nickelate Pr4Ni3O8 features quasi-two-dimensional layers consisting of three stacked square-planar NiO2 planes, in a similar way to the well-known cuprate superconductors. The mixed-valent nature of Ni and its metallic properties makes it a candidate for potentially unconventional superconductivity. We have synthesized Pr4Ni3O8 by topotactic reduction of

Somepalli Venkateswarlu, Andreas Honecker, Guy Trambly de Laissardière

Moir\'e patterns are known to confine electronic states in transition metal dichalcogenide bilayers, thus generalizing the notion of magic angles discovered in twisted bilayer graphene to semiconductors. Here, we present a revised Slater-Koster tight-binding model that facilitates the first reliable and systematic studies of such states in twisted bilayer Mo

Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Mark Kirkland

Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a multi-task learning method to relax this constraint. We regard the segmentation problem as a sequence of approximation subproblems that are recursively defined and in increasing level

Hamed Ghaemi-Dizicheh, Ali Mostafazadeh, Mustafa Sarisaman

We investigate linear and nonlinear spectral singularities in the transverse electric and transverse magnetic modes of a slab laser consisting of an active planar slab sandwiched between a pair of Graphene or Weyl semimetal thin sheets. The requirement of the presence of linear spectral singularities gives the laser threshold condition while the existence of

Sayan Banerjee, Abishek Sankararaman

We analyze an interacting queueing network on $\mathbb{Z}^d$ that was introduced in Sankararaman-Baccelli-Foss (2019) as a model for wireless networks. We show that the marginals of the minimal stationary distribution have exponential tails. This is used to furnish asymptotics for the maximum steady state queue length in growing boxes around the origin. We a

Nazanin Hoghooghi, Ryan K. Cole, Gregory B. Rieker

Broadband dual-comb spectroscopy (DCS) based on portable mode-locked fiber frequency combs is a powerful tool for in situ, calibration free, multi-species spectroscopy. While the acquisition of a single spectrum with mode-locked DCS typically takes microseconds to milliseconds, the applications of these spectrometers have generally been limited to systems an

Abijith Kowligy, David Carlson, Daniel Hickstein, Henry Timmers

We demonstrate mid-infrared (MIR) frequency combs at 10 GHz repetition rate via intra-pulse difference-frequency generation (DFG) in quasi-phase-matched nonlinear media. Few-cycle pump pulses ($\mathbf{\lesssim}$15 fs, 100 pJ) from a near-infrared (NIR) electro-optic frequency comb are provided via nonlinear soliton-like compression in photonic-chip silicon-