December 2020 arXiv papers — page 55

Piotr Antonik, Marc Haelterman, Serge Massar

Introduction. Reservoir Computing is a bio-inspired computing paradigm for processing time-dependent signals. The performance of its hardware implementation is comparable to state-of-the-art digital algorithms on a series of benchmark tasks. The major bottleneck of these implementation is the readout layer, based on slow offline post-processing. Few analogue

Morimichi Kawasaki, Shuhei Maruyama

In this paper, we characterize the second bounded characteristic classes of foliated bundles in terms of the non-descendible quasi-morphisms on the universal covering of the structure group. As its application, we study the boundedness of obstruction classes for (contact) Hamiltonian fibrations and show the non-existence of foliated structures on some Hamilt

Chang-Chun Chen, Patrick H. Diamond, Rameswar Singh, Steven M. Tobias

Tangled magnetic fields, often coexisting with an ordered mean field, have a major impact on turbulence and momentum transport in many plasmas, including those found in the solar tachocline and magnetic confinement devices. We present a novel mean field theory of potential vorticity mixing in $\beta$-plane magnetohydrodynamic (MHD) and drift wave turbulence.

Anirudh Koul, Siddha Ganju, Meher Kasam, James Parr

Traditionally, academic labs conduct open-ended research with the primary focus on discoveries with long-term value, rather than direct products that can be deployed in the real world. On the other hand, research in the industry is driven by its expected commercial return on investment, and hence focuses on a real world product with short-term timelines. In

Bruce Bartlett, V. Hosana Ranaivomanana

The Wigner derivative is the partial derivative of dihedral angle with respect to opposite edge length in a tetrahedron, all other edge lengths remaining fixed. We compute the inverse Wigner derivative for spherical tetrahedra, namely the partial derivative of edge length with respect to opposite dihedral angle, all other dihedral angles remaining fixed. We

Tao Gui, Jiacheng Ye, Qi Zhang, Zhengyan Li

Conditional random fields (CRF) for label decoding has become ubiquitous in sequence labeling tasks. However, the local label dependencies and inefficient Viterbi decoding have always been a problem to be solved. In this work, we introduce a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally

Juan Ruben Gomez-Solano

A colloidal particle embedded in a fluid can be used as a microscopic heat engine by means of a sequence of cyclic transformations imposed by an optical trap. We investigate a model for the operation of such kind of Brownian engines when the surrounding medium is viscoelastic, which endows the particle dynamics with memory friction. We analyze the effect of

Satvik Singh

One often distinguishes between a line and a plane by saying that the former is one-dimensional while the latter is two. But, what does it mean for an object to have $d-$dimensions? Can we define a consistent notion of dimension rigorously for arbitrary objects, say a snowflake, perhaps? And must the dimension always be integer-valued? After highlighting som

Yuichi Ito, Masahiro Ikoma

Recent exoplanet statistics indicate that photo-evaporation has a great impact on the mass and bulk composition of close-in low-mass planets. While there are many studies addressing photo-evaporation of hydrogen-rich or water-rich atmospheres, no detailed investigation regarding rocky vapor atmospheres (or mineral atmospheres) has been conducted. Here, we de

Hanfu Wang, Yanjun Guo, Kaiwu Peng, Weiguo Chu

A numerical modeling study based on 3D finite element method (FEM) simulation and 1D analytical solutions has been carried out to evaluate the capabilities of two ac methods for measuring in-plane thermal conductivity of thin film deposited on the back of a suspended SiNx membrane setup. Two parallel metal strips are present on the top of the dielectric memb

Yoshihiro Michishita, Robert Peters

Nonlinear responses in condensed matter are intensively studied because they provide rich information about materials and hold the possibility of being applied in diodes or high-frequency optical devices. While nonlinear responses in noninteracting models have been explored widely, the effect of strong correlations on the nonlinear response is still poorly u

Kaiwen Wang, Travis Dick, Maria-Florina Balcan

This paper introduces the first provably accurate algorithms for differentially private, top-down decision tree learning in the distributed setting (Balcan et al., 2012). We propose DP-TopDown, a general privacy preserving decision tree learning algorithm, and present two distributed implementations. Our first method NoisyCounts naturally extends the single

Markus Kreer, Ayse Kizilersu, Anthony W. Thomas

In a previous analysis the problem of "zero-inflated" time data (caused by high frequency trading in the electronic order book) was handled by left-truncating the inter-arrival times. We demonstrated, using rigorous statistical methods, that the Weibull distribution describes the corresponding stochastic dynamics for all inter-arrival time differences except

Rupei Xu, Warren Shull

Connectivity is a central notion of graph theory and plays an important role in graph algorithm design and applications. With emerging new applications in networks, a new type of graph connectivity problem has been getting more attention--hedge connectivity. In this paper, we consider the model of hedge graphs without hedge overlaps, where edges are partitio

Jairo Capilla-Valdepeña, Rubén Cordero, Felipe Maya-Ordóñez

We investigate the cosmological consequences of a two brane system embedded in a higher dimensional background spacetime with a compactified extra dimension described with an action that include the intrinsic curvature for each brane. We find that the dynamics of each brane is related to the other by means of cosmological restrictions that involve the scale

Daniel Leykam, Dimitris G Angelakis

The machine learning technique of persistent homology classifies complex systems or datasets by computing their topological features over a range of characteristic scales. There is growing interest in applying persistent homology to characterize physical systems such as spin models and multiqubit entangled states. Here we propose persistent homology as a too

Vidya A. Chhabria, Yanqing Zhang, Haoxing Ren, Ben Keller

Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vecto

Christopher Corley, Andrew Ledoan

Let $\{\eta_{j}\}_{j = 0}^{N}$ be a sequence of independent and identically distributed complex normal random variables with mean zero and variances $\{\sigma_{j}^{2}\}_{j = 0}^{N}$. Let $\{f_{j} (z)\}_{j = 0}^{N}$ be a sequence of holomorphic functions that are real-valued on the real line. The purpose of the present study is that of examining the number of

Jaehun Jung, Jinhong Jung, U Kang

Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. Naturally, automatic TKG completion has drawn much research interests for a more realistic modeling of relational reasoning. However, most of the existing mod-els for TKG completion extend static KG embeddings that donot fu

Sanmitra Banerjee, Mahdi Nikdast, Krishnendu Chakrabarty

Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehens

R. Díaz, F. Gómez, M. Pinilla, P. Salgado

We evaluate a 5-dimensional Randall Sundrum type metric in the Lagrangian of the Einstein-Chern-Simons gravity, and then we derive an action and its corresponding field equations, for a 4-dimensional brane embedded in the 5-dimensional space-time of the theory, which in the limit l--0 leads to the 4-dimensional general relativity with cosmological constant.

Yibin Wu, Jian Kuang, Xiaoji Niu

A reliable self-contained navigation system is essential for autonomous vehicles. Based on our previous study on Wheel-INS \cite{niu2019}, a wheel-mounted inertial measurement unit (Wheel-IMU)-based dead reckoning (DR) system, in this paper, we propose a multiple IMUs-based DR solution for the wheeled robots. The IMUs are mounted at different places of the w

Lixing Tan, Zhaohui Zhu, Jinjin Zhang

After a few decades of development, computational argumentation has become one of the active realms in AI. This paper considers extension-based concrete and abstract semantics of argumentation. For concrete ones, based on Grossi and Modgil's recent work, this paper considers some issues on graded extension-based semantics of abstract argumentation framework

Manuel Santana, Jonathan Mousley, David Brown, Leroy Beasley

Recently L. B. Beasley introduced $(2,3)$-cordial labelings of directed graphs in [1]. He made two conjectures which we resolve in this article. He conjectured that every orientation of a path of length at least five is $(2,3)$ cordial, and that every tree of max degree $n =3$ has a cordial orientation. We show these two conjectures to be false. We also disc

Mahkameh Zarekarizi, K. Joel Roop-Eckart, Sanjib Sharma, Klaus Keller

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example

Yibin Wu, Xiaoji Niu, Jian Kuang

A self-contained autonomous dead reckoning (DR) system is desired to complement the Global Navigation Satellite System (GNSS) for land vehicles, for which odometer-aided inertial navigation system (ODO/INS) is a classical solution. In this study, we use a wheel-mounted MEMS IMU (Wheel-IMU) to substitute the odometer, and further, investigate three types of m

Vedangi Pathak, Stephan Plugge, Marcel Franz

Majorana quasi-particles may arise as zero-energy bound states in vortices on the surface of a topological insulator that is proximitized by a conventional superconductor. Such a system finds its natural realization in the iron-based superconductor FeTe$_{0.55}$Se$_{0.45}$ that combines bulk $s$-wave pairing with spin helical Dirac surface states, and which

Litao Zhao, He Gao, WeiHua Lei, Lin Lan

The successful operation of dedicated detectors has brought us valuable information for understanding the central engine and the progenitor of gamma-ray bursts (GRBs). For instance, the giant X-ray and optical bumps found in some long-duration GRBs (e.g. GRBs 121027A and 111209A) imply that some extended central engine activities, such as the late X-ray flar

Robert Dicks

Suppose that $\ell \geq 5$ is prime. For a positive integer $N$ with $4 \mid N$, previous works studied properties of half-integral weight modular forms on $\Gamma_0(N)$ which are supported on finitely many square classes modulo $\ell$, in some cases proving that these forms are congruent to the image of a single variable theta series under some number of it

Jianze Liang, Chengqi Zhao, Mingxuan Wang, Xipeng Qiu

Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-fit to the target domain. To mitigate the issue, we propose Prune-Tune, a novel domain adaptation method via gradual pruning. It learns tiny domain-specific sub-networks during fin

Takato Matsui, Makoto Katori, Tomoyuki Shirai

The bulk scaling limit of eigenvalue distribution on the complex plane ${\mathbb{C}}$ of the complex Ginibre random matrices provides a determinantal point process (DPP). This point process is a typical example of disordered hyperuniform system characterized by an anomalous suppression of large-scale density fluctuations. As extensions of the Ginibre DPP, we

Asaf Ferber, Matthew Kwan, Lisa Sauermann

List-decodability of Reed-Solomon codes has received a lot of attention, but the best-possible dependence between the parameters is still not well-understood. In this work, we focus on the case where the list-decoding radius is of the form $r=1-\varepsilon$ for $\varepsilon$ tending to zero. Our main result states that there exist Reed-Solomon codes with rat

Li Zhao, Xiangyi Cui, Wenbo Ma, Yingjie Fan

PandaX-4T, the new generation of the PandaX detector, is a xenon dual-phase detector with about six tons of liquid xenon as target for dark matter search. A cryogenics and xenon handling system is designed to liquify and purify this large detector. In this paper, the results on the commission of the cryogenics and gas handling system are reported. The maximu

Yining Hong, Qing Li, Daniel Ciao, Siyuan Huang

Previous neural solvers of math word problems (MWPs) are learned with full supervision and fail to generate diverse solutions. In this paper, we address this issue by introducing a \textit{weakly-supervised} paradigm for learning MWPs. Our method only requires the annotations of the final answers and can generate various solutions for a single problem. To bo

Yueling Yang, Lan Lang, Xule Zhao, Jinshu Huang

Considering the $B$ mesonic distribution amplitude ${\phi}_{B2}$, we reinvestigated the $B$ ${\to}$ $PP$ (where $P$ $=$ ${\pi}$ and $K$) decays with the perturbative QCD (pQCD) approach based on the $k_{T}$ factorization for three scenarios. It is found that the contributions of ${\phi}_{B2}$ to formfactors $F_{0}^{B{\to}P}(0)$ and branching ratios are compa

Xinwei Sun, Botong Wu, Wei Chen

Existing deepfake detection methods have reported promising in-distribution results, by accessing published large-scale dataset. However, due to the non-smooth synthesis method, the fake samples in this dataset may expose obvious artifacts (e.g., stark visual contrast, non-smooth boundary), which were heavily relied on by most of the frame-level detection me

Zhengwu Zhang, Xiao Wang, Linglong Kong, Hongtu Zhu

This paper develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or imag

F. Gómez, S. Lepe, P. Salgado

From the field equations corresponding to a 4-dimensional brane embedded in the 5-dimensional spacetime of the Einstein-Chern-Simons theory for gravity, we find cosmological solutions that describe an accelerated expansion for a flat universe. Apart from a quintessence-type evolution scheme, we obtain a transient phantom evolution, which is not ruled out by

Stefano Bianchini, Prerona Dutta, Khai T. Nguyen

The present paper first aims to study the BV-type regularity for viscosity solutions of the Hamilton-Jacobi equation \[ u_t(t,x)+H\big(D_{x} u(t,x)\big)~=~0\qquad\forall (t,x)\in ]0,\infty[\times\mathbb{R}^d \] with a coercive and uniformly directionally convex Hamiltonian $H\in\mathcal{C}^{1}(\mathbb{R}^d)$. More precisely, we establish a BV bound on the sl

Ahmet Kurt, Suat Mercan, Enes Erdin, Kemal Akkaya

Lightning Network (LN) addresses the scalability problem of Bitcoin by leveraging off-chain transactions. Nevertheless, it is not possible to run LN on resource-constrained IoT devices due to its storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions

Zhuo Wang, Xiao Wang, Wenhua Yang, Yaohong Xiao

The capability of multi-input field-to-field regression, i.e. mapping the initial field and applied conditions to the evolved field, is appealing, enabling ultra-fast physics-free simulation of various field evolvements across many disciplines. We hereby propose a y-shaped multi-input deep convolutional network, yNet, which can effectively account for combin

N. Kameswara Rao, Sutaria F., Murthy J., Ray A.

The spectral region between 1250 Angstroms - 3000 Angstroms contains important spectral lines to understand the morphological structures and evolution of planetary nebulae. This is the region sampled by UVIT through various filter bands both in the continuum and in emission lines (e.g. C IV, He I, Mg II etc.). We have mapped several planetary nebulae with di

Xuhui Meng, Hessam Babaee, George Em Karniadakis

We propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network,

Chenfei Wang, Mahroo Bahreinian, Roberto Tron

In this paper, we propose a novel approach to synthesize linear feedback controllers for navigating in polygonal environments using noisy measurements and a convex cell decomposition. Our method is based on formulating chance constraints for the convergence and collision avoidance condition. In particular, the stability and safety guarantees come from chance

Jianfeng Cheng, Lili Du

This paper deals with a two-phase fluid free boundary problem in a slot-film cooling. We give two well-posedness results on the existence and uniqueness of the incompressible inviscid two-phase fluid with a jump relation on free interface. The problem formulates the oblique injection of an incompressible ideal fluid from a slot into a free stream. From the m

Huanyin Chen, Marjan Sheibani Abdolyousefi

We introduce and study a new class of generalized inverses in rings. An element $a$ in a ring $R$ has generalized Zhou inverse if there exists $b\in R$ such that $bab=b, b\in comm^2(a), a^n-ab\in \sqrt{J(R)}$ for some $n\in {\Bbb N}$. We prove that $a\in R$ has generalized Zhou inverse if and only if there exists $p=p^2\in comm^2(a)$ such that $a^n-p\in \sqr

Julio César Hernández Arzusa

In this paper we give sufficient conditions under which a subsemigroup of a topological group is a subgroup, adding to the results given in \cite{Kosh, can, axioms, forum, Hof, cc, locally} where conditions exist (such as locally compactness, compactness, feeble compactness and sequential compactness) for a semigroup to be a group. In our work we proved that

Avrim Blum, Shelby Heinecke, Lev Reyzin

Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient collaborative PAC learning algorithms usi

Harriet Parsons, Jessica Dempsey, Dan Bintley, Craig Walther

The James Clerk Maxwell Telescope (JCMT) is the largest single dish telescope in the world focused on sub-millimeter astronomy - and it remains at the forefront of sub-millimeter discovery space. JCMT continues itspush for higher efficiency and greater science impact with a switch to fully remote operation. This switch toremote operations occurred on Novembe

R. Dharmapalan

The movable temperature profiler is a 7 m vertical array of 24 sensors that measures cryogenic temperatures with a precision of a few mK. This precision is necessary to monitor the efficiency of re-circulation and purification of liquid-argon inside large liquid-argon based neutrino detectors. Liquid argon temperature impacts electron (signal) drift velocity

Jiasi Weng, Jian Weng, Hongwei Huang, Chengjun Cai

Data holders, such as mobile apps, hospitals and banks, are capable of training machine learning (ML) models and enjoy many intelligence services. To benefit more individuals lacking data and models, a convenient approach is needed which enables the trained models from various sources for prediction serving, but it has yet to truly take off considering three

Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian Curless

Removing objects from images is a challenging problem that is important for many applications, including mixed reality. For believable results, the shadows that the object casts should also be removed. Current inpainting-based methods only remove the object itself, leaving shadows behind, or at best require specifying shadow regions to inpaint. We introduce

Abhishek K Dubey, Michael T Young, Christopher Stanley, Dalton Lunga

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions bet

Takeshi Miyamae, Kanta Matsuura

In blockchain-based anonymous cryptocurrencies, due to their tamper-resistance and transparency characteristics, transaction data are initially required to be anonymous, with the help of various cryptographic techniques, e.g., commitment schemes and zero-knowledge proofs. Also, cryptocurrencies are different from existing anonymous messaging protocols regard

Morteza Mirzaei, Amir Asif, Hassan Rivaz

Ultrasound elastography is an emerging noninvasive imaging technique wherein pathological alterations can be visualized by revealing the mechanical properties of the tissue. Estimating tissue displacement in all directions is required to accurately estimate the mechanical properties. Despite capabilities of elastography techniques in estimating displacement

Wei-Xi Li, Juan Zeng

We consider a Fokker-Planck operator with electric potential and electromagnetic fields. We establish the sharp weighted and subelliptic estimates, involving the control of the derivatives of electric potential and electromagnetic fields. Our proof relies on a localization argument as well as a careful calculation on commutators.

Mark Taylor

The desktop GUI catalogue analysis tool TOPCAT, and its command-line counterpart STILTS, offer among other capabilities visual exploration of locally stored tables containing millions of rows or more. They offer many variations on the theme of scatter plots, density maps and histograms, which can be navigated interactively. These capabilities have now been e

Shane Lubold, Arun G. Chandrasekhar, Tyler H. McCormick

A common approach to modeling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood. More positive manifold curvature encourages more and tighter communities; negative curvature induces repulsion. We consistently estimate manifold type, dimension, and curvature from simply con

Hung Le

In this paper, we consider a class of nonlocal equations where the convolution kernel is given by a Bessel potential symbol of order $\alpha$ for $\alpha > 1$. Based on the properties of the convolution operator, we apply a global bifurcation technique to show the existence of a highest, even, $2\pi$-periodic traveling-wave solution. The regularity of this w

Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan, Junchen Jiang

Video analytics applications use edge compute servers for the analytics of the videos (for bandwidth and privacy). Compressed models that are deployed on the edge servers for inference suffer from data drift, where the live video data diverges from the training data. Continuous learning handles data drift by periodically retraining the models on new data. Ou

Lixiang Gu, Siteng Fan, Jiazheng Li, Stuart Bartlett

Point source spectrophotometric ("single-point") light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significan

Aleksey Cherman, Theodore Jacobson

We consider examples of long-lived false vacua in quantum field theory that arise from so-called `universes'. These false vacua are protected by a $(d-1)$-form global symmetry, where $d$ is the dimension of spacetime. The lifetimes of the false vacua are set by UV data: the tension of $(d-2)$-branes charged under a $(d-2)$-form gauge symmetry. The lifetimes

Wagno Cesar e Silva, Ilya L. Shapiro

An extremely fast exponential expansion of the Universe is typical for the stable version of the inflationary model, based on the anomaly-induced action of gravity. The total amount of exponential $e$-folds could be very large, before the transition to the unstable version and the beginning of the Starobinsky inflation. Thus, the stable exponential expansion

Aniq Ur Rahman, Agnivesh Adhikari

Drone base stations (DBSs) have recently gained wide popularity as a possible solution to provide wireless connectivity in a variety of scenarios, for example, in inaccessible terrains such as connectivity over vast areas of a water body or in rural areas where the physical deployment of base stations is not feasible at the moment, also in the case of terres

Thomas Vasileiou

The present paper develops recursive algorithms to track shifts in the resonance frequency of linear systems in real time. To date, automatic resonance tracking has been limited to non-model-based approaches, which rely solely on the phase difference between a specific input and output of the system. Instead, we propose a transformation of the system into a

Richard T. Marriott, Safa Madiouni, Sami Romdhani, Stéphane Gentric

Generative Adversarial Networks (GANs) are now capable of producing synthetic face images of exceptionally high visual quality. In parallel to the development of GANs themselves, efforts have been made to develop metrics to objectively assess the characteristics of the synthetic images, mainly focusing on visual quality and the variety of images. Little work

Alaa Awad Abdellatif, Lutfi Samara, Amr Mohamed, Aiman Erbad

Epidemic situations typically demand intensive data collection and management from different locations/entities within a strict time constraint. Such demand can be fulfilled by leveraging the intensive and easy deployment of the Internet of Things (IoT) devices. The management and containment of such situations also rely on cross-organizational and national

Yannis Papaphilippou

The content of this contribution is based on the course on numerical analysis techniques for non-linear dynamics. After introducing basic concepts as the visual analysis of trajectories in phase space and the importance of the nature of fixed points in their topology and dynamics, the motion close to a resonance is presented, with simple non-linear map examp

Latévi Mohamed Lawson

More recently, we have proposed a set of noncommutative space that describes the quantum gravity at the Planck scale [J. Phys. A: Math. Theor. 53, 115303 (2020)]. The interesting significant result we found is that, the generalized uncertainty principle induces a maximal measurable length of quantum gravity. This measurement revealed strong quantum gravitati

Jinho Choi, Jie Ding, Ngoc Phuc Le, Zhiguo Ding

Massive machine-type communication (MTC) is expected to play a key role in supporting Internet of Things (IoT) applications such as smart cities, smart factory, and connected vehicles through cellular networks. MTC is characterized by a large number of MTC devices and their sparse activities, which are difficult to be supported by conventional approaches and

Kim V. Berghaus, Peter W. Graham, David E. Kaplan, Guy D. Moore

We show that if dark energy evolves in time, its dynamical component could be dominated by a bath of dark radiation. Within current constraints this radiation could have up to $\sim 10^4$ times more energy density than the cosmic microwave background. We demonstrate particular models in which a rolling scalar field generates different forms of dark radiation

Richard T. Marriott, Sami Romdhani, Stéphane Gentric, Liming Chen

Face-morphing attacks have been a cause for concern for a number of years. Striving to remain one step ahead of attackers, researchers have proposed many methods of both creating and detecting morphed images. These detection methods, however, have generally proven to be inadequate. In this work we identify two new, GAN-based methods that an attacker may alre

Runhua Xu, James Joshi, Chao Li

Training a machine learning model over an encrypted dataset is an existing promising approach to address the privacy-preserving machine learning task, however, it is extremely challenging to efficiently train a deep neural network (DNN) model over encrypted data for two reasons: first, it requires large-scale computation over huge datasets; second, the exist

Daniel E. Ho, Alice Xiang

While there has been a flurry of research in algorithmic fairness, what is less recognized is that modern antidiscrimination law may prohibit the adoption of such techniques. We make three contributions. First, we discuss how such approaches will likely be deemed "algorithmic affirmative action," posing serious legal risks of violating equal protection, part

Pedro Pereyra

We present here the transmission of electromagnetic waves through layered structures of metallic and left-handed media. Based on the theory of finite periodic systems, we show that besides the strong influence of the incidence angle, the low transmission characteristic of a single conductor slab, for frequencies $\omega$ below the plasma frequency $\omega_p$

Richard T. Marriott, Sami Romdhani, Liming Chen

Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to learn robustness to intra-class variation. In practice, such datasets are difficult to obtain, particularly tho

Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance. The absence of trustworthy human supervision over the data collection process exposes organizations to security vulnerabilities; training data can be

Lili He

We prove global existence backwards from the scattering data posed at infinity for the Maxwell Klein Gordon equations in Lorenz gauge satisfying the weak null condition. The asymptotics of the solutions to the Maxwell Klein Gordon equations in Lorenz gauge were shown to be wave like at null infinity and homogeneous towards timelike infinity in arXiv:1803.110

Victor Gonzalez, Sumit Basu, Pedro Ladron de Guevara, Ana Marin

The specific shear viscosity, $\eta/s$, of the quark-gluon plasma formed in ultrarelativistic heavy-ion collisions at RHIC and LHC is estimated based on the progressive longitudinal broadening of transverse momentum two-particle correlators, $G_2$, reported as a function of collision centrality by the STAR and ALICE experiments. Estimates are computed as a f

Tianyu Pan, Guanyu Hu, Weining Shen

Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for spatial panel data analysis, where repeated measurements are collected over time at various spatial locations. We propose a novel class o

Islam Akef Ebeid, Majdi Hassan, Tingyi Wanyan, Jack Roper

Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely on incorporating information from different biomedical databases. These databases contain various biological e

Crichton Ogle, Nikolas Schonsheck

The completion tower of a nonunital commutative ring is a classical construction in commutative algebra. In the setting of structured ring spectra as modeled by algebras over a spectral operad, the analogous construction is the homotopy completion tower. The purpose of this brief note is to show that localization with respect to the Johnson-Wilson spectrum $

Carlo Oleari, Marco Rocco

We consider the production of a vector boson ($Z$, $W^\pm$ or $\gamma^*$) at next-to-next-to-leading order in the strong coupling constant $\alpha_{\rm S}$. We impose a transverse-momentum cutoff, $q_{\rm T}^{\rm cut}$, on the vector boson produced in the $qg$-initiated channel. We then compute the power corrections in the cutoff, up to the second power, of

Hao Guo, Behrooz Makki, Dinh-Thuy Phan-Huy, Erik Dahlman

In future wireless systems, a large number of users may access the networks via moving relays (MRs) installed on top of vehicles. One of the main challenges of MRs is rapid channel variation which may make channel estimation, and its following procedures, difficult. To address these issues, various schemes are designed, among which predictor antenna (PA) is

Naser Ahmadiniaz, James P. Edwards, José Nicasio, Christian Schubert

We examine the non-perturbative gauge dependence of arbitrary configuration space fermion correlators in quantum electrodynamics (QED). First, we study the dressed electron propagator (allowing for emission or absorption of any number of photons along a fermion line) using the first quantised approach to quantum field theory and analyse its gauge transformat

Bastian Sander, Gerhard Hensler

We numerically investigate the evolution of compact high-velocity clouds (CHVCs) passing through a hot, tenuous gas representing the highly-ionized circumgalactic medium (CGM) by applying the adaptive-mesh refinement code FLASH. The model clouds start from both hydrostatic and thermal equilibrium and are in pressure balance with the CGM. Here, we present 14

Md. Monowar Anjum, Noman Mohammed

During pandemics, health officials usually recommend access monitoring and regulation protocols/systems in places that are major activity centres. As organizations adhere to those recommendations, they often fail to implement proper privacy requirements to prevent privacy loss of the users of those protocols or systems. This is a very timely issue as health

Matthew Sinclair, Andreas Schuh, Karl Hahn, Kersten Petersen

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and do not guarantee a plausible topology. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts

Thi-Huong Nguyen, Nikolay Medvedev, Mihaela Delcea, Andreas Greinacher

Antibodies recognizing complexes of the chemokine platelet factor 4 (PF4-CXCL4) and polyanions (P) opsonize PF4-coated bacteria hereby mediating bacterial host defense. A subset of these antibodies may activate platelets after binding to PF4-heparin complexes, causing the prothrombotic adverse drug reaction heparin-induced thrombocytopenia (HIT). In autoimmu

Ding Ding, H. Howie Huang

This work investigates the problem of multi-agents trajectory prediction. Prior approaches lack of capability of capturing fine-grained dependencies among coordinated agents. In this paper, we propose a spatial-temporal trajectory prediction approach that is able to learn the strategy of a team with multiple coordinated agents. In particular, we use graph-ba

Scott Workman, Nathan Jacobs

Our goal is to use overhead imagery to understand patterns in traffic flow, for instance answering questions such as how fast could you traverse Times Square at 3am on a Sunday. A traditional approach for solving this problem would be to model the speed of each road segment as a function of time. However, this strategy is limited in that a significant amount

Thi-Huong Nguyen, Raghavendra Palankar, Van-Chien Bui, Nikolay Medvedev

Little is known about mechanics underlying the interaction among platelets during activation and aggregation. Although the strength of a blood thrombus has likely major biological importance, no previous study has measured directly the adhesion forces of single platelet-platelet interaction at different activation states. Here, we filled this void first, by

Elena Bykova, Maxim Bykov, Stella Chariton, Vitali B. Prakapenka

We synthesized two C-S-H compounds from a mixture of carbon and sulfur in hydrogen and from sulfur in mixed methane-hydrogen fluids at 4 GPa. X-ray synchrotron single-crystal diffraction and Raman spectroscopy have been applied to these samples up to 58 and 143 GPa, respectively. Both samples show a similar Al2Cu type I4/mcm basic symmetry, while the hydroge

Una Janke, Martin Kulke, Ina Buchholz, Norman Geist

Integrins are transmembrane proteins involved in hemostasis, wound healing, immunity and cancer. In response to intracellular signals and ligand binding, integrins adopt different conformations: the bent (resting) form; the intermediate extended form; and the ligand-occupied active form. An integrin undergoing such conformational dynamics is the heterodimeri

Srini Bhagavan, Saravanan Balasubramanian, Prasad Reddy Annem, Thuan Ngo

Recent advancements in the cloud computing domain have resulted in huge strides toward simplifying the procurement of hardware and software for diverse needs. By moving enterprise workloads to managed cloud offerings (private, public, hybrid), customers are delegating mundane tasks and labor-intensive maintenance activities related to network connectivity, p

Elena Arseneva, Pilar Cano, Linda Kleist, Tamara Mchedlidze

We study upward planar straight-line embeddings (UPSE) of directed trees on given point sets. The given point set $S$ has size at least the number of vertices in the tree. For the special case where the tree is a path $P$ we show that: (a) If $S$ is one-sided convex, the number of UPSEs equals the number of maximal monotone paths in $P$. (b) If $S$ is in gen

Paul Schwahn

We prove the linear stability with respect to the Einstein-Hilbert action of the symmetric spaces $\mathrm{SU}(n)$, $n\geq3$, and $E_6/F_4$. Combined with earlier results, this resolves the stability problem for irreducible symmetric spaces of compact type.

Staal A. Vinterbo

The standard closed form lower bound on $\sigma$ for providing $(\epsilon, \delta)$-differential privacy by adding zero mean Gaussian noise with variance $\sigma^2$ is $\sigma > \Delta\sqrt {2}(\epsilon^{-1}) \sqrt {\log \left( 5/4\delta^{-1} \right)}$ for $\epsilon \in (0,1)$. We present a similar closed form bound $\sigma \geq \Delta (\epsilon\sqrt{2})^{-1

Anush Tserunyan, Jenna Zomback

We prove a new pointwise ergodic theorem for probability-measure-preserving (pmp) actions of free groups, where the ergodic averages are taken over arbitrary finite subtrees of the standard Cayley graph rooted at the identity. This result is a significant strengthening of a theorem of Grigorchuk (1987) and Nevo and Stein (1994), and a version of it was conje

Padi Fuster Aguilera, Vincent R. Martinez, Kyle Kun Zhao

In this paper, we study the initial-boundary value problem and its asymptotic behavior for a repulsive chemotaxis model with logarithmic sensitivity and logistic growth. We establish global well-posedness of strong solutions for large initial data with Neumann boundary conditions and, moreover, establish the qualitative result that both the population densit