November 2020 arXiv papers — page 47

Charles Manière, Elisa Torresani, Eugene A. Olevsky

This work addresses the two great challenges of the spark plasma sintering (SPS) process: the sintering of complex shapes and the simultaneous production of multiple parts. A new controllable interface method is employed to concurrently consolidate two nickel gear shapes by SPS. A graphite deformable sub-mold is specifically designed for the mutual densifica

Charles Manière, Tony Zahrah, Eugene A. Olevsky

Direct microwave heating of 3Y-ZrO 2 is studied at frequency of 2.45 GHz. Different conditions of input power, sample position and size are tested. For the first time, the experimentally known instability of microwave sintering is explained coupling the effective medium approximation and finite-element method. We show how the material dielectric permittivity

Evangelos A. Latos

This paper studies the non-negative solutions of the Keller-Segel model with a nonlocal nonlinear source in a bounded domain. The competition between the aggregation and the nonlocal reaction term is highlighted: when the growth factor is stronger than the dampening effect, with the help of the nonlocal term, the model admits a classical solution which is un

Mingshan Jia, Bogdan Gabrys, Katarzyna Musial

The classic clustering coefficient and the lately proposed closure coefficient quantify the formation of triangles from two different perspectives, with the focal node at the centre or at the end in an open triad respectively. As many networks are naturally rich in triangles, they become standard metrics to describe and analyse networks. However, the advanta

Charles Manière, Elia Saccardo, Geuntak Lee, Joanna Mckittrick

One of the main challenges of the sintering of sterling silver is the phenomenon of swelling causing de-densification and a considerable reduction of the sintering kinetics. This swelling phenomenon opposes sintering and it needs to be addressed by a well-controlled processing atmosphere. In the present study, the pressure-less sintering behavior of sterling

Gabriel Kerbart, Charles Manière, Christelle Harnois, Sylvain Marinel

Grain growth has a definitive impact on the quality of transparent sintered materials in areas such as ballistics, biomaterials, jewelry, etc. Controlling the sintering trajectory at the precise moment of final stage sintering is one of the main sintering challenges for obtaining highperformance, fully-dense nano-ceramics. However, the final stage of sinteri

Yanghyun Byun, Julius Korbas, Peter Zvengrowski

We develop strong lower bounds for the span of the projective Stiefel manifolds $X_{n,r}=O(n)/(O(n-r)\times \mathbb Z/2)$, which enable very accurate (in many cases exact) estimates of the span. The technique, for the most part, involves elementary stability properties of vector bundles. However, the case $X_{n,2}$ with $n$ odd presents extra difficulties, w

Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb, Erik Goodman

"Innovization" is a task of learning common relationships among some or all of the Pareto-optimal (PO) solutions in multi- and many-objective optimization problems. Recent studies have shown that a chronological sequence of non-dominated solutions obtained in consecutive iterations during an optimization run also possess salient patterns that can be used to

Faizaan Sakib, Tilo Burghardt

We propose a first great ape-specific visual behaviour recognition system utilising deep learning that is capable of detecting nine core ape behaviours.

Aranyak Chakravarty, Mahesh V. Panchagnula, Alladi Mohan, Neelesh A. Patankar

Pulmonary drug delivery systems rely on inhalation of drug-laden aerosols produced from aerosol generators such as inhalers, nebulizers etc. On deposition, the drug molecules diffuse in the mucus layer and are also subjected to mucociliary advection which transports the drugs away from the initial deposition site. The availability of the drug at a particular

Gioele Zardini, Andrea Censi, Emilio Frazzoli

Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of co-

Shifan Zuo, Jixia Li, Yichao Li, Das Santanu

The Tianlai project is a 21cm intensity mapping experiment aimed at detecting dark energy by measuring the baryon acoustic oscillation (BAO) features in the large scale structure power spectrum. This experiment provides an opportunity to test the data processing methods for cosmological 21cm signal extraction, which is still a great challenge in current radi

Gioele Zardini, Dejan Milojevic, Andrea Censi, Emilio Frazzoli

We consider the problem of co-designing embodied intelligence as a whole in a structured way, from hardware components such as propulsion systems and sensors to software modules such as control and perception pipelines. We propose a principled approach to formulate and solve complex embodied intelligence co-design problems, leveraging a monotone co-design th

Jacques Magnaudet, Micheline Abbas

Hydrodynamic forces acting on a neutrally-buoyant spherical particle immersed in a wall-bounded axisymmetric stagnation point flow (Hiemenz-Homann flow) are predicted, based on a suitable form of the reciprocal theorem. An approximate algebraic form of the undisturbed velocity field is set up, mimicking the gradual transition of the actual carrying flow thro

Libuše Hannah Vepřek, Patricia Seymour, Pietro Michelucci

With humans increasingly serving as computational elements in distributed information processing systems and in consideration of the profit-driven motives and potential inequities that might accompany the emerging thinking economy[1], we recognize the need for establishing a set of related ethics to ensure the fair treatment and wellbeing of online cognitive

Avik Pal, Jonah Philion, Yuan-Hong Liao, Sanja Fidler

For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules" include rules that drivers are required to follow by law -- such as the requirement that vehicles stop at red lights -- as well as more subtle social rules -- such as the implicit

Azhar Shaikh, Nishant Sinha

Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. We present a new framework, Learn to Bind and Grow, which lea

Baudouin Denis de Senneville, Mario Ries, Wilbert Bartels, Chrit Moonen

High Intensity Focused Ultrasound (HIFU) can be used to achieve a local temperature increase deep inside the human body in a non-invasive way. MRI guidance of the procedure allows in situ target definition. In addition, MRI can be used to provide continuous temperature mapping during HIFU for spatial and temporal control of the heating procedure and predicti

Marco Zoli

A method is proposed to select the suitable sets of potential parameters for a one-dimensional mesoscopic Hamiltonian model, first introduced to describe the DNA melting transition and later extended to investigate thermodynamic and dynamical properties of nucleic acids. The DNA base pair fluctuations are considered as time dependent trajectories whose initi

Mina Zaminkar, Fateme Sarkohaki, Reza Fotohi

Internet of Things (IoT) provides the possibility for milliards of devices throughout the world to communicate with each other, and data is collected autonomously. The big data generated by the devices should be managed securely. Due to security challenges, like malicious nodes, many approaches cannot respond to these concerns. In this paper, a robust hybrid

C. Xing, X. Cheng, M. D. Ding

Coronal mass ejections (CMEs) are large-scale explosions of the coronal magnetic field. It is believed that magnetic reconnection significantly builds up the core structure of CMEs, a magnetic flux rope, during the eruption. However, the quantitative evolution of the flux rope, particularly its toroidal flux, is still unclear. In this paper, we study the evo

Dongkwan Kim, Eunsoo Kim, Sang Kil Cha, Sooel Son

Binary code similarity analysis (BCSA) is widely used for diverse security applications, including plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches fo

Ernesto Berríos-Caro, Tobias Galla

We propose a $\tau$-leaping simulation algorithm for stochastic systems subject to fast environmental changes. Similar to conventional $\tau$-leaping the algorithm proceeds in discrete time steps, but as a principal addition it captures environmental noise beyond the adiabatic limit. The key idea is to treat the input rates for the $\tau$-leaping as (clipped

Mengjin Zhao, Guangyan Jia

To achieve robustness of risk across different assets, risk parity investing rules, a particular state of risk contributions, have grown in popularity over the previous few decades. To generalize the concept of risk contribution from the simple covariance matrix case to the continuous-time case in which the terminal variance of wealth is used as the risk mea

Slavko Simic

In this article we give some improvements and generalizations of the famous Jensen's and Jensen-Mercer inequalities for twice differentiable functions, where convexity property of the target function is not assumed in advance. They represents a refinement of these inequalities in the case of convex/concave functions with numerous applications in Theory of Me

Anne-Sophie Høyer, Giulio Vignoli, Thomas Mejer Hansen, Le Thanh Vu

Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and on the estimation of facies-level structural uncertainty. Less attention is paid to the input data and the construction of Training Images (TIs). E.g. even though the TI should capture a

Hiroyasu Ando, T. Okamoto, H. Chang, T. Noguchi

Owing to recent advances in artificial intelligence and internet of things (IoT) technologies, collected big data facilitates high computational performance, while its computational resources and energy cost are large. Moreover, data are often collected but not used. To solve these problems, we propose a framework for a computational model that follows a nat

Max Jwo Lem Lee, Li-Ta Hsu, Hoi-Fung Ng, Shang Lee

Accurate smartphone-based outdoor localization system in deep urban canyons are increasingly needed for various IoT applications such as augmented reality, intelligent transportation, etc. The recently developed feature-based visual positioning system (VPS) by Google detects edges from smartphone images to match with pre-surveyed edges in their map database.

Eva A. A. Pogna, Mahdi Asgari, Valentina Zannier, Lucia Sorba

Semiconductor nanowire field-effect transistors represent a promising platform for the development of room-temperature (RT) terahertz (THz) frequency light detectors due to the strong nonlinearity of their transfer characteristics and their remarkable combination of low noise-equivalent powers (< 1 nW/Hz$^{1/2}$) and high responsivities (> 100 V/W). Nano-eng

Kai-Xin Gao, Xiao-Lei Liu, Zheng-Hai Huang, Min Wang

Second-order optimization methods have the ability to accelerate convergence by modifying the gradient through the curvature matrix. There have been many attempts to use second-order optimization methods for training deep neural networks. Inspired by diagonal approximations and factored approximations such as Kronecker-Factored Approximate Curvature (KFAC),

Deokkeun An, Timothy C. Beers

We improve the identification and isolation of individual stellar populations in the Galactic halo based on an updated set of empirically calibrated stellar isochrones in the Sloan Digital Sky Survey (SDSS) and Pan-STARRS 1 (PS1) photometric systems. Along the Galactic prime meridian ($l=0^{\circ}$ and $180^{\circ}$), where proper motions and parallaxes from

Fabrizio Colombo, Jonathan Gantner, Stefano Pinton

The aim of this paper is to give an overview of the spectral theories associated with the notions of holomorphicity in dimension greater than one. A first natural extension is the theory of several complex variables whose Cauchy formula is used to define the holomorphic functional calculus for $n$-tuples of operators $(A_1,...,A_n)$. A second way is to consi

Shweta Dahale, Balasubramaniam Natarajan

With the transition to a smart grid, we are witnessing a significant growth in sensor deployments and smart metering infrastructure in the distribution system. However, information from these sensors and meters are typically unevenly sampled at different time-scales and are incomplete. It is critical to effectively aggregate these information sources for sit

Zilong Cheng, Yulin Li, Kai Chen, Jun Ma

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to i

Johannes Erdmann, Olaf Nackenhorst, Sonja Verena Zeißner

The maximum achievable performance of strange-jet tagging at hadron colliders and the loss in performance in different detector designs is estimated based on simulated truth jets from strange-quark and down-quark hadronisation. Both jet types are classified with a recurrent neural network using long short-term memory units, at first using all available truth

Ying Chao, Pingyuan Wei, Jinqiao Duan

This work is to investigate the (top) Lyapunov exponent for a class of Hamiltonian systems under small non-Gaussian L\'evy noise. In a suitable moving frame, the linearisation of such a system can be regarded as a small perturbation of a nilpotent linear system. The Lyapunov exponent is then estimated by taking a Pinsky-Wihstutz transformation and applying t

Jon Gabriel Goiri, Anton Van der Ven

Extended defects in crystals, such as dislocations, stacking faults and grain boundaries, play a crucial role in determining a wide variety of materials properties. Extended defects can also lead to novel electronic properties in two-dimensional materials, as demonstrated by recent discoveries of emergent electronic phenomena in twisted graphene bilayers. Th

Tane Vergili, Ayse Borat

Homotopic distance $\D$ as introduced in \cite{MVML} can be realized as a pseudometric on $\mathrm{Map}(X,Y)$. In this paper, we study the topology induced by the pseudometric $\D$. In particular, we consider the space $\mathrm{Map}(S^1,S^1)$ and show that homotopic distance between any two maps in this space is 1. Moreover, while a general proof of the non-

Andrea Gabrio

Patient-level health economic data collected alongside clinical trials are an important component of the process of technology appraisal, with a view to informing resource allocation decisions. For end of life treatments, such as cancer treatments, modelling of cost-effectiveness/utility data may involve some form of partitioned survival analysis, where meas

Mark Philip M. Sy, Christian James M. Historillo, Allen Cris T. Conde, Ma. Yvonne Czarina R. Costelo

Purpose: The study aimed to improve the previous study covering a web-based query game for Cavite State University. The study created a new mechanic and gameplay for the students to learn Structured Query Language (SQL). The enhancements also focused on the interactions of one or more students playing the game. Method: The researchers used iterative developm

Baohua Sun, Michael Lin, Hao Sha, Lin Yang

Optical Character Recognition (OCR) has many real world applications. The existing methods normally detect where the characters are, and then recognize the character for each detected location. Thus the accuracy of characters recognition is impacted by the performance of characters detection. In this paper, we propose a method for recognizing characters with

Weixin Liang, Feiyang Niu, Aishwarya Reganti, Govind Thattai

The predominant approach to visual question answering (VQA) relies on encoding the image and question with a "black-box" neural encoder and decoding a single token as the answer like "yes" or "no". Despite this approach's strong quantitative results, it struggles to come up with intuitive, human-readable forms of justification for the prediction process. To

Andrew J. Taylor, Victor D. Dorobantu, Sarah Dean, Benjamin Recht

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in ensuring that model-based controllers transfer to real world systems. This paper develops a data-driven approach to robust co

Aleksey Korobenko, Soham Saha, Alan T. K. Godfrey, Marina Gertsvolf

High-harmonic generation is the cornerstone of nonlinear optics. It has been demonstrated in a wide range of crystalline systems including dielectrics, semiconductors, and semi-metals, as well as in gases, leaving metals out due to their low damage threshold. Here, we report on the high-harmonic generation in metallic titanium nitride (TiN) films. TiN is a r

Wei Dai, Changjian Fu

Let $\mathcal{H}$ be a hereditary abelian category over a field $k$ with finite dimensional $\operatorname{Hom}$ and $\operatorname{Ext}$ spaces. It is proved that the bounded derived category $\mathcal{D}^b(\mathcal{H})$ has a silting object iff $\mathcal{H}$ has a tilting object iff $\mathcal{D}^b(\mathcal{H})$ has a simple-minded collection with acyclic $

Ravindra Yadav, Ashish Sardana, Vinay P Namboodiri, Rajesh M Hegde

The ability to envisage the visual of a talking face based just on hearing a voice is a unique human capability. There have been a number of works that have solved for this ability recently. We differ from these approaches by enabling a variety of talking face generations based on single audio input. Indeed, just having the ability to generate a single talki

Michael Danielczuk, Arsalan Mousavian, Clemens Eppner, Dieter Fox

Robotic object rearrangement combines the skills of picking and placing objects. When object models are unavailable, typical collision-checking models may be unable to predict collisions in partial point clouds with occlusions, making generation of collision-free grasping or placement trajectories challenging. We propose a learned collision model that accept

Xiucai Ding, Hau-Tieng Wu

We systematically study the spectrum of kernel-based graph Laplacian (GL) constructed from high-dimensional and noisy random point cloud in the nonnull setup. The problem is motived by studying the model when the clean signal is sampled from a manifold that is embedded in a low-dimensional Euclidean subspace, and corrupted by high-dimensional noise. We quant

Naoya Takahashi, Yuki Mitsufuji

Tasks that involve high-resolution dense prediction require a modeling of both local and global patterns in a large input field. Although the local and global structures often depend on each other and their simultaneous modeling is important, many convolutional neural network (CNN)-based approaches interchange representations in different resolutions only a

Gopal Goel, Andrew Yao

We study a family of measures originating from the signatures of the irreducible components of representations of the unitary group, as the size of the group goes to infinity. Given a random signature $\lambda$ of length $N$ with counting measure $\mathbf{m}$, we obtain a random signature $\mu$ of length $N-1$ through projection onto a unitary group of lower

Xing Wu, Jie Cao

In this paper, we show that the solution map of the two-component Novikov system is not uniformly continuous on the initial data in Besov spaces $B_{p, r}^{s-1}(\mathbb{R})\times B_{p, r}^s(\mathbb{R})$ with $s>\max\{1+\frac{1}{p}, \frac{3}{2}\}$, $1\leq p< \infty$, $1\leq r<\infty$. Our result covers and extends the previous non-uniform continuity in Sobole

Michael Coons, James Evans

We show that missing $q$-ary digit sets $F\subseteq[0,1]$ have corresponding naturally associated countable binary $q$-automatic sequence $f$. Using this correspondence, we show that the Hausdorff dimension of $F$ is equal to the base-$q$ logarithm of the Mahler eigenvalue of $f$. In addition, we demonstrate that the standard mass distribution $\nu_F$ suppor

Chuanzheng Wang, Yinan Li, Yiming Meng, Stephen L. Smith

Control barrier functions have shown great success in addressing control problems with safety guarantees. These methods usually find the next safe control input by solving an online quadratic programming problem. However, model uncertainty is a big challenge in synthesizing controllers. This may lead to the generation of unsafe control actions, resulting in

Roland A. Matsouaka, Adrian Coles

As alternatives to the time-to-first-event analysis of composite endpoints, the {\it net benefit} (NB) and the {\it win ratio} (WR) -- which assess treatment effects using prioritized component outcomes based on clinical importance -- have been proposed. However, statistical inference of NB and WR relies on a large-sample assumptions, which can lead to an in

Erica J. Sturm, Matthew R. Carbone, Deyu Lu, Andreas Weichselbaum

The Anderson Impurity Model (AIM) is a canonical model of quantum many-body physics. Here we investigate whether machine learning models, both neural networks (NN) and kernel ridge regression (KRR), can accurately predict the AIM spectral function in all of its regimes, from empty orbital, to mixed valence, to Kondo. To tackle this question, we construct two

Anshuka Rangi, Mohammad Javad Khojasteh, Massimo Franceschetti

We study the problem of learning-based attacks in linear systems, where the communication channel between the controller and the plant can be hijacked by a malicious attacker. We assume the attacker learns the dynamics of the system from observations, then overrides the controller's actuation signal, while mimicking legitimate operation by providing fictitio

Antoine B. Jacquey, Hadrien Rattez, Manolis Veveakis

Strain localization is an instability phenomenon occurring in deformable solid materials which undergo dissipative deformation mechanisms. Such instability is characterized by the localization of the displacement or velocity fields in a zone of finite thickness and is generally associated with the failure of materials. In several fields of material engineeri

Ghurumuruhan Ganesan

Consider~\(n\) nodes~\(\{X_i\}_{1 \leq i \leq n}\) independently distributed in the unit square~\(S,\) each according to a distribution~\(f\) and let~\(K_n\) be the complete graph formed by joining each pair of nodes by a straight line segment. For every edge~\(e\) in~\(K_n\) we associate a weight~\(w(e)\) that may depend on the \emph{individual locations} o

Seungjae Jung, Kyung-Min Kim, Hanock Kwak, Young-Jin Park

Probabilistic time-series models become popular in the forecasting field as they help to make optimal decisions under uncertainty. Despite the growing interest, a lack of thorough analysis hinders choosing what is worth applying for the desired task. In this paper, we analyze the performance of three prominent probabilistic time-series models for sales forec

Chris Cummins, Hugh Leather, Zacharias Fisches, Tal Ben-Nun

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engineer from the complex interactions of program, architecture, and other optimizations, is alluring. However, most machine learning methods cannot replicate even the simplest of the ab

Elahe Aghapour, Nora Ayanian

We are interested in learning models of non-stationary environments, which can be framed as a multi-task learning problem. Model-free reinforcement learning algorithms can achieve good asymptotic performance in multi-task learning at a cost of extensive sampling, due to their approach, which requires learning from scratch. While model-based approaches are am

Hussein Sibai, Yangge Li, Sayan Mitra

We presentSceneChecker, a tool for verifying scenarios involving vehicles executing complex plans in large cluttered workspaces. SceneChecker converts the scenario verification problem to a standard hybrid system verification problem, and solves it effectively by exploiting structural properties in the plan and the vehicle dynamics. SceneChecker uses symmetr

Lintao Ye, Aritra Mitra, Shreyas Sundaram

We study a fundamental problem in Bayesian learning, where the goal is to select a set of data sources with minimum cost while achieving a certain learning performance based on the data streams provided by the selected data sources. First, we show that the data source selection problem for Bayesian learning is NP-hard. We then show that the data source selec

Yanguang Chen, Yuqing Long

For a long time, many methods are developed to make temporal signal analyses based on time series. However, for geographical systems, spatial signal analyses are as important as temporal signal analyses. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fra

C. D. Omorog, R. P. Medina

Purpose. This paper examines the Internet security perception of Filipinos to establish a need and sense of urgency on the part of the government to create a culture of cybersecurity for every Filipino. Method. A quantitative survey was conducted through traditional, online, and phone interviews among 252 respondents using a two-page questionnaire that cover

Xiaoyi Qin, Yaogen Yang, Lin Yang, Xuyang Wang

In this paper, we focus on improving the performance of the text-dependent speaker verification system in the scenario of limited training data. The speaker verification system deep learning based text-dependent generally needs a large scale text-dependent training data set which could be labor and cost expensive, especially for customized new wake-up words.

Kareem M. Attiah, Foad Sohrabi, Wei Yu

This paper proposes a deep learning approach to channel sensing and downlink hybrid beamforming for massive multiple-input multiple-output systems operating in the time division duplex mode and employing either single-carrier or multicarrier transmission. The conventional precoding design involves a two-step process of first estimating the high-dimensional c

Blake E. Zimmerman, Sara L. Johnson, Henrik A. Odéen, Jill E. Shea

Advances in imaging and early cancer detection have increased interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. MRgFUS ablation treatments could reduce surgical risks, preserve organ tissue/function, and improve patient quality of life. However, surgical resection and histological analysis remain the go

Sarath Sreedharan, Tathagata Chakraborti, Yara Rizk, Yasaman Khazaeni

A new design of an AI assistant that has become increasingly popular is that of an "aggregated assistant" -- realized as an orchestrated composition of several individual skills or agents that can each perform atomic tasks. In this paper, we will talk about the role of planning in the automated composition of such assistants and explore how concepts in autom

Mark R. Saddler, Andrew Francl, Jenelle Feather, Kaizhi Qian

Contemporary speech enhancement predominantly relies on audio transforms that are trained to reconstruct a clean speech waveform. The development of high-performing neural network sound recognition systems has raised the possibility of using deep feature representations as 'perceptual' losses with which to train denoising systems. We explored their utility b

Suhas S. Jain, Ali Mani

In this article, we propose a novel scalar-transport model for the simulation of scalar quantities in two-phase flows with a phase-field method (diffuse-interface method). In a two-phase flow, the scalar quantities typically have disparate properties in two phases, which results in effective confinement of the scalar quantities in one of the phases, in the t

Weixin Liang, James Zou

Recent advances in deep learning have made the use of large, deep neural networks with tens of millions of parameters. The sheer size of these networks imposes a challenging computational burden during inference. Existing work focuses primarily on accelerating each forward pass of a neural network. Inspired by the group testing strategy for efficient disease

David Urbanik

Let $\mathbb{V}$ be a motivic variation of Hodge structure on a $K$-variety $S$, let $\mathcal{H}$ be the associated $K$-algebraic Hodge bundle, and let $\sigma \in \textrm{Aut}(\mathbb{C}/K)$ be an automorphism. The absolute Hodge conjecture predicts that given a Hodge vector $v \in \mathcal{H}_{\mathbb{C}, s}$ above $s \in S(\mathbb{C})$ which lies inside

James Ren Hou Lee, Maya Pavlova, Mahmoud Famouri, Alexander Wong

Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective skin cancer detection due to strong prognosis when treated at an early stage,

Hiroyasu Koizumi

Although the standard theory of superconductivity based on the BCS theory is a successful one, there are several experimental results that indicate the necessity for fundamental revisions. One of them is the mass in the London moment. Experiments indicate the mass in the London moment is the free electron mass although the BCS theory and its extension predic

Alexandros Haridis

A set $L$ of straight lines and a set $P$ of points in the Euclidean plane define an arrangement $\mathcal{A}$ = ($L$, $P$) of construction lines and registration marks, if and only if: (1) any point in $P$ is a point of intersection of at least two lines in $L$, and (2) any two nonparallel lines in $L$ have a unique point of intersection in $P$. This exposi

Akimasa Hirata, Sachiko Kodera, Kensuke Sasaki, Jose Gomez-Tames

International guidelines/standards for human protection from electromagnetic fields have been revised recently, especially for frequencies above 6 GHz where new wireless communication systems have been deployed. Above this frequency a new physical quantity "absorbed/epithelia power density" has been adopted as a dose metric. Then, the permissible level of ex

Shihong Fang, Anna Choromanska

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs) and many interpretation methods generate saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image

Elhousni Mahdi, Zhang Ziming, Huang Xinming

Deep learning provides a powerful new approach to many computer vision tasks. Height prediction from aerial images is one of those tasks that benefited greatly from the deployment of deep learning which replaced old multi-view geometry techniques. This letter proposes a two-stage approach, where first a multi-task neural network is used to predict the height

Sebastien Vievard, Aurelie Bonnefois, Frederic Cassaing, Joseph Montri

Focal plane wavefront sensing is an appealing technique to cophase multiple aperture telescopes. Phase diversity, operable with any aperture configuration or source extension, generally suffers from high computing load. In this Letter, we introduce, characterize and experimentally validate the LAPD algorithm, based on a fast linearized phase diversity algori

Michał Dereziński, Zhenyu Liao, Edgar Dobriban, Michael W. Mahoney

For a tall $n\times d$ matrix $A$ and a random $m\times n$ sketching matrix $S$, the sketched estimate of the inverse covariance matrix $(A^\top A)^{-1}$ is typically biased: $E[(\tilde A^\top\tilde A)^{-1}]\ne(A^\top A)^{-1}$, where $\tilde A=SA$. This phenomenon, which we call inversion bias, arises, e.g., in statistics and distributed optimization, when a

Cesar Lema, Anna Choromanska

Quantum theory has been remarkably successful in providing an understanding of physical systems at foundational scales. Solving the Schr\"odinger equation provides full knowledge of all dynamical quantities of the physical system. However closed form solutions to this equation are only available for a few systems and approximation methods are typically used

Jordan Broussard

We will use overlays and templates derived from two-dimensional recurrence relations to build the arrays, and we will study the structure of the overlays, including initial conditions and basis arrays.

Athanasios Sourmelidis, Jörn Steuding, Ade Irma Suriajaya

We study Riemann-type functional equations with respect to value-distribution theory and derive implications for their solutions. In particular, for a fixed complex number $a\neq0$ and a function from the Selberg class $\mathcal{L}$, we prove a Riemann-von Mangoldt formula for the number of a-points of the $\Delta$-factor of the functional equation of $\math

Atsushi Nakayashiki

We study the tau function of the KP-hierarchy associated with an (n,1) curve $y^n=x-\alpha$. If $\alpha=0$ the corresponding tau function is 1. On the other hand if $\alpha\neq 0$ the tau function becomes the exponential of a quadratic function of the time variables. By applying vertex opertaors to the latter we obtain soliton solutions on non-zero constant

Parshan Pakiman, Boxiao Chen, Selvaprabu Nadarajah, Stefanus Jasin

We study dynamic pricing of a product with an unknown demand distribution over a finite horizon. Departing from the standard no-regret learning environment in which prices can be adjusted at any time, we restrict price changes to predetermined points in time to reflect common retail practice. This constraint, coupled with demand model ambiguity and an unknow

Fumihiko Nakamura, Michael C. Mackey

In this paper we give a new sufficient condition for asymptotic periodicity of Frobenius-Perron operator corresponding to two--dimensional maps. The result of the asymptotic periodicity for strictly expanding systems, that is, all eigenvalues of the system are greater than one, in a high-dimensional dynamical systems was already known. Our new theorem enable

Xinwei Yao, Ohad Fried, Kayvon Fatahalian, Maneesh Agrawala

We present a text-based tool for editing talking-head video that enables an iterative editing workflow. On each iteration users can edit the wording of the speech, further refine mouth motions if necessary to reduce artifacts and manipulate non-verbal aspects of the performance by inserting mouth gestures (e.g. a smile) or changing the overall performance st

Gowri Somanath, Daniel Kurz

We present a method to estimate an HDR environment map from a narrow field-of-view LDR camera image in real-time. This enables perceptually appealing reflections and shading on virtual objects of any material finish, from mirror to diffuse, rendered into a real physical environment using augmented reality. Our method is based on our efficient convolutional n

Kazuya Shinjo, Shigetoshi Sota, Takami Tohyama

Optical measurements in doped Mott insulators have discovered the emergence of spectral weights at mid-infrared (MIR) upon chemical doping and photodoping. MIR weights may have a relation to string-type excitation of spins, which is induced by a doped hole generating misarranged spins with respect to their sublattice. There are two types of string effects: o

Albert V. Paytaren

This study aims to develop a personalized Recommender System that helps to address the problems encountered by the faculty members of Higher Education Institutions in the selection of Seminar and Training Programs (STP). The researcher used the Descriptive Developmental Method of research to gather information relevant to the current problems and challenges

Ross Glandon, Mahesh Narayanamurthi, Adrian Sandu

Time integration methods for solving initial value problems are an important component of many scientific and engineering simulations. Implicit time integrators are desirable for their stability properties, significantly relaxing restrictions on timestep size. However, implicit methods require solutions to one or more systems of nonlinear equations at each t

Ian Covert, Scott Lundberg, Su-In Lee

Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another. We describe a new unified class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature's influence. These methods var

Faiq Khalid, Syed Rafay Hasan, Sara Zia, Osman Hasan

Traditional learning-based approaches for run-time Hardware Trojan detection require complex and expensive on-chip data acquisition frameworks and thus incur high area and power overhead. To address these challenges, we propose to leverage the power correlation between the executing instructions of a microprocessor to establish a machine learning-based run-t

Hao-Zhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang

Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists an

Vrindavan Harrison, Juraj Juraska, Wen Cui, Lena Reed

This report describes Athena, a dialogue system for spoken conversation on popular topics and current events. We develop a flexible topic-agnostic approach to dialogue management that dynamically configures dialogue based on general principles of entity and topic coherence. Athena's dialogue manager uses a contract-based method where discourse constraints ar

Bolin Gao, Lacra Pavel

In this paper, we provide exponential rates of convergence to the interior Nash equilibrium for continuous-time dual-space game dynamics such as mirror descent (MD) and actor-critic (AC). We perform our analysis in $N$-player continuous concave games that satisfy certain monotonicity assumptions while possibly also admitting potential functions. In the first

T. C. Wesselink, P. Borja, J. M. A. Scherpen

In this work, we propose a passivity-based controller that addresses the problem of set point regulation for planar robots with two links and flexible joints. Moreover, the controller is saturated and does not require velocity measurements. Additionally, we present experiments that corroborate the theoretical results of this note.

Xiaochu Wang, Wenyuan Tang

The customer baseline is required to assign rebates to participants in baseline-based demand response (DR) programs. The average baseline method has been widely accepted in practice due to its simplicity and reliability. However, the customer's baseline manipulation is little-known in the literature. We start from a customer's perspective and establish a Mar

Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami

Current low-precision quantization algorithms often have the hidden cost of conversion back and forth from floating point to quantized integer values. This hidden cost limits the latency improvement realized by quantizing Neural Networks. To address this, we present HAWQV3, a novel mixed-precision integer-only quantization framework. The contributions of HAW