October 2022 arXiv papers — page 14

Chanyoung Sung, Yuya Takeuchi

The CR Yamabe constant is an invariant of a compact strongly pseudoconvex CR manifold and plays an important role in CR geometry. We show some integral formulae of the CR Yamabe constant. We also construct an infinite-dimensional family of strongly pseudoconvex CR structures with varying CR Yamabe constants and a compact simply-connected manifold admitting t

Benedito Leandro, Róbson Lousa

We prove that three-dimensional electrostatic manifolds with divergence-free Bach tensor are locally conformally flat, provide that the electric field and the gradient of the lapse function are linearly dependent. Consequently, a three-dimensional electrostatic manifold admits a local warped product structure with a one-dimensional base and a constant curvat

Christopher J. Vogl, Ilon Joseph, Milan Holec

Highly accurate simulation of plasma transport is needed for the successful design and operation of magnetically confined fusion reactors. Unfortunately, the extreme anisotropy present in magnetized plasmas results in thin boundary layers that are expensive to resolve. This work investigates how mesh refinement strategies might reduce that expense to allow f

Aitor Belenguer, Jose A. Pascual, Javier Navaridas

Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or

Dorian Cazeneuve, Franck Marchis, Guillaume Blaclard, Paul A. Dalba

We propose to design and build an algorithm that will use a Convolutional Neural Network (CNN) and observations from the Unistellar network to reliably detect asteroid occultations. The Unistellar Network, made of more than 10,000 digital telescopes owned by citizen scientists, and is regularly used to record asteroid occultations. In order to process the in

Bekir Baytas, Nelson Yokomizo

We introduce a class of states characterized by proposed conditions of homogeneity and isotropy in loop quantum gravity and construct concrete examples given by Bell-network states on a special class of homogeneous graphs. Such states provide new representations of cosmological spaces that can be explored for the formulation of cosmological models in the con

Jack S. Baker, Haim Horowitz, Santosh Kumar Radha, Stenio Fernandes

Electron dynamics, financial markets and nuclear fission reactors, though seemingly unrelated, all produce observable characteristics evolving with time. Within this broad scope, departures from normal temporal behavior range from academically interesting to potentially catastrophic. New algorithms for time series anomaly detection (TAD) are therefore certai

Ethan Patrick White

Let $\mathcal{F}$ denote the set of functions $f \colon [-1/2,1/2] \to \mathbb{R}$ such that $\int f = 1$. We determine the value of $\inf_{f \in \mathcal{F}} \| f \ast f \|_2$ up to a 0.0014\% error, thereby making progress on a problem asked by Ben Green. Furthermore, we prove that a unique minimizer exists. As a corollary, we obtain improvements on the ma

Xiyuan Lu, Feng Zhou, Yi Sun, Mingkang Wang

Micro-/nanocavities that combine high quality factor ($Q$) and small mode volume ($V$) have been used to enhance light-matter interactions for cavity quantum electrodynamics (cQED). Whispering gallery mode (WGM) geometries such as microdisks and microrings support high-$Q$ and are design- and fabrication-friendly, but $V$ is often limited to tens of cubic wa

Ji Wang, Ding Lu, Ian Davidson, Zhaojun Bai

There are synergies of research interests and industrial efforts in modeling fairness and correcting algorithmic bias in machine learning. In this paper, we present a scalable algorithm for spectral clustering (SC) with group fairness constraints. Group fairness is also known as statistical parity where in each cluster, each protected group is represented wi

Hongxia Lin, Jiahong Wu, Yi Zhu

The small data global well-posedness of the 3D incompressible Navier-Stokes equations in $\mathbb R^3$ with only one-directional dissipation remains an outstanding open problem. The dissipation in just one direction, say $\partial_1^2 u$ is simply insufficient in controlling the nonlinearity in the whole space $\mathbb R^3$. The beautiful work of Paicu and Z

Xiaoman Pan, Wenlin Yao, Hongming Zhang, Dian Yu

Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot settings. In addition, it is hard to adapt to the evolving world knowledge without the costly model re-training. In this paper, we develop a novel semi-parametric language model arc

Nan Chen, Shubin Fu

Lagrangian data assimilation exploits the trajectories of moving tracers as observations to recover the underlying flow field. One major challenge in Lagrangian data assimilation is the intrinsic nonlinearity that impedes using exact Bayesian formulae for the state estimation of high-dimensional systems. In this paper, an analytically tractable mathematical

Fenglin Liu, Xian Wu, Shen Ge, Xuancheng Ren

Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, various pre-trained V-L models are proposed to learn V-L representations and achieve improved results in many task

Michele Giuliano Carlino, Elena Gaburro

In this work, we propose a second-order accurate scheme for shallow water equations in general covariant coordinates over manifolds. In particular, the covariant parametrization in general covariant coordinates is induced by the metric tensor associated to the manifold. The model is then re-written in a hyperbolic form with a tuple of conserved variables com

Rahul Rajkumar, David Weisbart

The fundamental solution of a pseudo-differential equation for functions defined on the $d$-fold product of the $p$-adic numbers, $\mathbb{Q}_p$, induces an analogue of the Wiener process in $\mathbb{Q}_p^d$. As in the real setting, the components are $1$-dimensional $p$-adic Brownian motions with the same diffusion constant and exponent as the original proc

Xubo Liu, Qiushi Huang, Xinhao Mei, Haohe Liu

Audio captioning aims to generate text descriptions of audio clips. In the real world, many objects produce similar sounds. How to accurately recognize ambiguous sounds is a major challenge for audio captioning. In this work, inspired by inherent human multimodal perception, we propose visually-aware audio captioning, which makes use of visual information to

Julia Guskind, Walter O. Krawec

Semi-quantum cryptography involves at least one user who is semi-quantum or "classical" in nature. Such a user can only interact with the quantum channel in a very restricted way. Many semi-quantum key distribution protocols have been developed, some with rigorous proofs of security. Here we show for the first time, to our knowledge, that quantum random numb

Tamunonye Cheetham-West

We give a criterion for distinguishing a prime knot $K$ in $S^3$ from every other knot in $S^3$ using the finite quotients of $\pi_1(S^3\setminus K)$. Using recent work of Baldwin-Sivek, we apply this criterion to the hyperbolic knots $5_2$, $15n_{43522}$, and the three-strand pretzel knots $P(-3,3,2n+1)$ for every integer $n$.

Nikolai Yampolsky, Sandra Biedron, Bjorn Manuel Hegelich, Scott Luedtke

We access the possibility of using the conventional RF accelerator as an injector for the plasma driven wakefield accelerator. Conventional accelerators deliver high quality beams with low emittance and low energy spread. Once injected into the plasma wake, the emittance may be preserved upon proper beam matching while the energy spread may not due to long b

Chao Pan, Jin Sima, Saurav Prakash, Vishal Rana

Federated clustering (FC) is an unsupervised learning problem that arises in a number of practical applications, including personalized recommender and healthcare systems. With the adoption of recent laws ensuring the "right to be forgotten", the problem of machine unlearning for FC methods has become of significant importance. We introduce, for the first ti

Matthew Stanley, Yunsik Jung, Michael Bowman, Lingfeng Tao

Although data-driven motion mapping methods are promising to allow intuitive robot control and teleoperation that generate human-like robot movement, they normally require tedious pair-wise training for each specific human and robot pair. This paper proposes a transferability-based mapping scheme to allow new robot and human input systems to leverage the map

Sangwoo Cho, Kaiqiang Song, Xiaoyang Wang, Fei Liu

Text segmentation is important for signaling a document's structure. Without segmenting a long document into topically coherent sections, it is difficult for readers to comprehend the text, let alone find important information. The problem is only exacerbated by a lack of segmentation in transcripts of audio/video recordings. In this paper, we explore the ro

Honggeun Kim, Bang D. Nhan, Jacqueline N. Hewitt, Nicholas S. Kern

Detecting cosmological signals from the Epoch of Reionization (EoR) requires high-precision calibration to isolate the cosmological signals from foreground emission. In radio interferometery, perturbed primary beams of antenna elements can disrupt the precise calibration, which results in contaminating the foreground-free region, or the EoR window, in the cy

Ina Heckelmann, Zifei Lu, Joseph C. A. Prentice, Florian Auras

Small molecule organic semiconductors hold great promise for efficient, printable, and flexible optoelectronic applications like solar cells and displays. However, strong excited-state quenching due to uncontrolled aggregation currently limits their performance and employability in devices. Here, we report on the self-assembly of a supramolecular pseudo-cube

Troy J. Sewell, Christopher David White, Brian Swingle

Many simulation tasks require that one first prepare a system's Gibbs state. We present a family of quantum circuits for variational preparation of thermal Gibbs states on a quantum computer; we call them the thermal multi-scale entanglement renormalization ansatz (TMERA). TMERA circuits transform input qubits to wavepacket modes localized to varying length

F. Chierchie, C. R. Chavez, M. Sofo Haro, G. Fernandez Moroni

We present a new instrument composed of a large number of sub-electron noise Skipper-CCDs operated with a two stage analog multiplexed readout scheme suitable for scaling to thousands of channels. New, thick, $1.35$ Mpix sensors, from a new foundry, are glued into a Multi-Chip Module (MCM) printed circuit board on a ceramic substrate which has 16 sensors eac

Giulia Ceriotti, Fiona H M Tang, Federico Maggi

The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aim to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by

Jacob DeLange, Kinjol Barua, Val Zwiller, Stephan Steinhauer

Cuprous oxide (Cu${}_2$O) has recently been proposed as a promising solid-state host for excitonic Rydberg states with large principal quantum numbers ($n$), whose exaggerated wavefunction sizes ($\propto n^2)$ facilitate gigantic dipole-dipole ($\propto n^4$) and van der Waals ($\propto n^{11}$) interactions, making them an ideal basis for solid-state quant

Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie

The conclusions of randomized controlled trials may be biased when the outcome of one unit depends on the treatment status of other units, a problem known as interference. In this work, we study interference in the setting of one-sided bipartite experiments in which the experimental units - where treatments are randomized and outcomes are measured - do not i

Navid Shervani-Tabar, Robert Rosenbaum

Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedba

Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana

Mixup is a regularization technique that artificially produces new samples using convex combinations of original training points. This simple technique has shown strong empirical performance, and has been heavily used as part of semi-supervised learning techniques such as mixmatch~\citep{berthelot2019mixmatch} and interpolation consistent training (ICT)~\cit

Yiğit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro

We consider a radio resource management (RRM) problem in a multi-user wireless network, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We propose a state-augmented parameterization for the RRM policy, where alongside the instantaneous network states, the RRM policy takes as in

John Burge, Matthew R. Bonanni, R. Lily Hu, Matthias Ihme

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the int

Victor Lopez-Richard, Rafael Schio Wengenroth Silva, Ovidiu Lipan, Fabian Hartmann

The inertia of trapping and detrapping of nonequilibrium charge carriers affects the electrochemical and transport properties of both bulk and nanoscopic structures in a very peculiar way. An emerging memory response with a hysteresis in the current-voltage response and its eventual multiple crossing, produced by this universally available ingredient, are si

O. El Atwani, H. T. Vo, M. Tunes, C. Lee

In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission reactors, fusion devices, space applications, etc), design, prediction and control of advanced materials beyond current material designs become a paramount goal. Here, though a combined experimental and simulation methodology, t

Rafael P. Greminger

When designing product rankings, online retailers and platforms choose which outcome to maximize: revenues from commissions or markups, the number of transactions, or consumer welfare. These objectives need not align, creating potential trade-offs. This paper studies how rankings differ between objectives and quantifies the resulting trade-offs. I provide de

Yuling Gu, Yao Fu, Valentina Pyatkin, Ian Magnusson

Figurative language (e.g., "he flew like the wind") is challenging to understand, as it is hard to tell what implicit information is being conveyed from the surface form alone. We hypothesize that to perform this task well, the reader needs to mentally elaborate the scene being described to identify a sensible meaning of the language. We present DREAM-FLUTE,

Siddharth Muthukrishnan

To what extent does the black hole information paradox lead to violations of quantum mechanics? I explain how black hole complementarity provides a framework to articulate how quantum characterizations of black holes can remain consistent despite the information paradox. I point out that there are two ways to cash out the notion of consistency in play here:

Hua Wang, Andrew Zhang

The famous Gallai's Conjecture states that any connected graph with n vertices has a path decomposition containing at most (n+1)/2 paths. In this note, we explore graphs generated from removing edges from complete graphs. We first provide an explicit construction for a path decomposition of complete graphs that satisfies Gallai's Conjecture. We then use that

Florence Regol, Anja Kroon, Mark Coates

The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets can be a powerful tool to provide a better understanding of a model's strengths, weaknesses, and overall capabilities. Gaining these insights can be particularly important for genera

Gianluca Cena, Stefano Scanzio, Adriano Valenzano

Seamless redundancy can be profitably exploited to improve predictability of wireless networks in general and, in particular, IEEE 802.11. According to this approach, packets are transmitted by senders on two (or more) channels at the same time and duplicate copies are discarded by receivers. As long as the behavior of physical channels is uncorrelated, comm

R. Bučík, G. M. Mason, R. Gómez-Herrero, V. Krupar

The origin of 3He abundance enhancements in coronal mass ejection (CME)-driven shock gradual solar energetic particle (SEP) events remains largely unexplained. Two mechanisms have been suggested - the re-acceleration of remnant flare material in interplanetary space and concomitant activity in the corona. We explore the first gradual SEP event with enhanced

Artavazd Maranjyan, Mher Safaryan, Peter Richtárik

We study a class of distributed optimization algorithms that aim to alleviate high communication costs by allowing clients to perform multiple local gradient-type training steps before communication. In a recent breakthrough, Mishchenko et al. (2022) proved that local training, when properly executed, leads to provable communication acceleration, and this ho

Henrique K. Miyamoto, Fábio C. C. Meneghetti, Sueli I. R. Costa

Choosing a suitable loss function is essential when learning by empirical risk minimisation. In many practical cases, the datasets used for training a classifier may contain incorrect labels, which prompts the interest for using loss functions that are inherently robust to label noise. In this paper, we study the Fisher-Rao loss function, which emerges from

Aditya Cowsik, Tankut Can, Paolo Glorioso

Commonly used optimization algorithms often show a trade-off between good generalization and fast training times. For instance, stochastic gradient descent (SGD) tends to have good generalization; however, adaptive gradient methods have superior training times. Momentum can help accelerate training with SGD, but so far there has been no principled way to sel

Pooya Mohammadi Kazaj, MohammadHossein Koosheshi, Ali Shahedi, Alireza Vafaei Sadr

According to WHO[1], since the 1970s, diagnosis of melanoma skin cancer has been more frequent. However, if detected early, the 5-year survival rate for melanoma can increase to 99 percent. In this regard, skin lesion segmentation can be pivotal in monitoring and treatment planning. In this work, ten models and four augmentation configurations are trained on

Marie Grace, Xajavion "Jay" Seabrum, Dananjay Srinivas, Alexis Palmer

The automatic detection of offensive language is a pressing societal need. Many systems perform well on explicit offensive language but struggle to detect more complex, nuanced, or implicit cases of offensive and hateful language. OLEA is an open-source Python library that provides easy-to-use tools for error analysis in the context of detecting offensive la

Paul R. Anderson, Shohreh Gholizadeh Siahmazgi, Zachary P. Scofield

Detailed behaviors of the modes of quantized scalar fields in the Unruh state for various eternal black holes in two dimensions are investigated. It is shown that the late-time behaviors of some of the modes of the quantum fields and of the symmetric two-point function are determined by infrared effects. The nature of these effects depends upon whether there

Gianluca Cena, Stefano Scanzio, Adriano Valenzano

Lifetime of motes in wireless sensor networks can be enlarged by decreasing the energy spent for communication. Approaches like time slotted channel hopping pursue this goal by performing frame exchanges according to a predefined schedule, which helps reducing the duty cycle. Unfortunately, whenever the receiving radio interface is active but nobody in the n

Yongqiang Wang

We address differential privacy for fully distributed aggregative games with shared coupling constraints. By co-designing the generalized Nash equilibrium (GNE) seeking mechanism and the differential-privacy noise injection mechanism, we propose the first GNE seeking algorithm that can ensure both provable convergence to the GNE and rigorous epsilon-differen

Reza Yousefi Mashhoor, Ahmad Ayatollahi

Cardiovascular disease is one of the leading causes of death according to WHO. Phonocardiography (PCG) is a costeffective, non-invasive method suitable for heart monitoring. The main aim of this work is to classify heart sounds into normal/abnormal categories. Heart sounds are recorded using different stethoscopes, thus varying in the domain. Based on recent

Xi Chen, Klaus Zollner, Christian Moulsdale, Vladimir I. Fal'ko

We theoretically investigate 3D layered crystals of alternating graphene and hBN layers with different symmetries. Depending on the hopping parameters between the graphene layers, we find that these synthetic 3D materials can feature semimetallic, gapped, or Weyl semimetal phases. Our results demonstrate that 3D crystals stacked from individual 2D materials

Shuo Zhang, Yang Liu, Lei Xie

Biological functions of RNAs are determined by their three-dimensional (3D) structures. Thus, given the limited number of experimentally determined RNA structures, the prediction of RNA structures will facilitate elucidating RNA functions and RNA-targeted drug discovery, but remains a challenging task. In this work, we propose a Graph Neural Network (GNN)-ba

Yichao Zhou, James B. Wendt, Navneet Potti, Jing Xie

A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy. We propose Selective Labeling to simplify the labeling task to provide "yes/no" labels for candidate extractions predicted by

James L Hart, Lopa Bhatt, Yanbing Zhu, Myung-Geun Han

The layer stacking order in 2D materials strongly affects functional properties and holds promise for next generation electronic devices. In bulk, octahedral MoTe$_2$ possesses two stacking arrangements, the Weyl semimetal T$_d$ phase, and the higher-order topological insulator 1T' phase; however, it remains unclear if thin exfoliated flakes of MoTe$_2$ foll

Nathaniel Johnston, Benjamin Lovitz, Aravindan Vijayaraghavan

We introduce a hierarchy of linear systems for showing that a given subspace of pure quantum states is entangled (i.e., contains no product states). This hierarchy outperforms known methods already at the first level, and it is complete in the sense that every entangled subspace is shown to be so at some finite level of the hierarchy. It generalizes straight

Ana María Cetto, Luis de la Peña

Abstract This paper provides elements in support of the random zero-point radiation field (zpf) as an essential ontological ingredient needed to explain distinctive properties of quantum-mechanical systems. We show that when an otherwise classical particle is connected to the zpf, a drastic, qualitative change in the dynamics takes place, leading eventually

Tiff Brydges, Arslan S. Raja, Angelo Gelmini, Grigorii Lihachev

This work demonstrates the capabilities of an entangled photon-pair source at telecom wavelengths, based on a photonic integrated Si$_3$N$_4$ microresonator with monolithically integrated piezoelectric frequency tuning. Previously, frequency tuning of photon-pairs generated by microresonators has only been demonstrated using thermal control, however these ha

Qinyi Chen, Negin Golrezaei, Djallel Bouneffouf

Traditional multi-armed bandit (MAB) frameworks, predominantly examined under stochastic or adversarial settings, often overlook the temporal dynamics inherent in many real-world applications such as recommendation systems and online advertising. This paper introduces a novel non-stationary MAB framework that captures the temporal structure of these real-wor

Mo Sodwatana, Saif R. Kazi, Kaarthik Sundar, Anatoly Zlotnik

We present an economic optimization problem for allocating the flow of natural gas and hydrogen blends through a large-scale transportation pipeline network. Physical flow of the gas mixture is modeled using a steady-state relation between pressure decrease and flow rate, which depends on mass concentration of the constituents as it varies by location in the

Alvaro Arias, Vladimir Kovalchuk

We show that there are uncountably many geodesics between any two non-isometric $n$-dimensional normed spaces. We construct two explicit geodesics that can be used to describe all the points of the other geodesics.

S. Nojiri, S. D. Odintsov, V. K. Oikonomou, A. Constantini

Motivated by the fact that the pre-inflationary era may evolve in an exotic way, in this work we formalize anisotropic evolution in the context of modified gravity, focusing on pre-inflationary and near the vicinity of the inflationary epochs. We specialize on specific metrics like Bianchi and Taub and we formalize the inflationary theory in vacuum $F(R)$ gr

Nathan Ginis, Timothy Marchok

We present a new method for creating a model consensus to improve real-time hurricane track prediction. The method is based on the statistical fitting of historic numerical model track forecasts to the observed storm positions and learning from their historical errors and biases. Our method is closest to the HFIP Corrected Consensus Approach (HCCA) methodolo

Srishti Gupta, Chun-Hua Tsai, John M. Carroll

This study looks at water quality monitoring and management as a new form of community engagement. Through a series of a unique research method called `design hackathons', we engaged with a hyperlocal community of citizens who are actively involved in monitoring and management of their local watershed. These design hackathons sought to understand the motivat

Graham West, Matthew I. Swindall, Ben Keener, Timothy Player

Performing classification on noisy, crowdsourced image datasets can prove challenging even for the best neural networks. Two issues which complicate the problem on such datasets are class imbalance and ground-truth uncertainty in labeling. The AL-ALL and AL-PUB datasets - consisting of tightly cropped, individual characters from images of ancient Greek papyr

Federico Carlini, Nicolas Cherroret

It has been recently shown that the coherent component of light propagating in transversally disordered media, the so-called coherent mode, exhibits an optical spin Hall effect (SHE). In non-resonant materials, however, this phenomenon shows up at a spatial scale much larger than the mean free path, making its observation challenging due to the exponential a

Juan Ospina, David M. Fobes, Russell Bent, Andreas Wächter

Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between trans

Thomas Führer, Michael Karkulik

We provide a framework for the numerical approximation of distributed optimal control problems, based on least-squares finite element methods. Our proposed method simultaneously solves the state and adjoint equations and is $\inf$--$\sup$ stable for any choice of conforming discretization spaces. A reliable and efficient a posteriori error estimator is deriv

Matias G. Delgadino, Daniel Weser

In this paper we analyze the shape of a droplet inside a smooth container. To characterize their shape in the capillarity regime, we obtain a new form of the Heintze-Karcher inequality for mean convex hypersurfaces with boundary lying on curved substrates.

Antonio R. Linero

Bayesian additive regression tree (BART) models have seen increased attention in recent years as a general-purpose nonparametric modeling technique. BART combines the flexibility of modern machine learning techniques with the principled uncertainty quantification of Bayesian inference, and it has been shown to be uniquely appropriate for addressing the high-

Kyungtae Kim, Alexander Aeppli, Tobias Bothwell, Jun Ye

A Wannier-Stark optical lattice clock has demonstrated unprecedented measurement precision for optical atomic clocks. We present a systematic evaluation of the lattice light shift, a necessary next step for establishing this system as an accurate atomic clock. With precise control of the atomic motional states in the lattice, we report accurate measurements

Alex Deng, Michelle Du, Anna Matlin

Online experimentation platforms collect user feedback at low cost and large scale. Some systems even support real-time or near real-time data processing, and can update metrics and statistics continuously. Many commonly used metrics, such as clicks and page views, can be observed without much delay. However, many important signals can only be observed after

Giovani Peruzzo, Silvio Paolo Sorella

We present an investigation of the $CHSH$ inequality within a relativistic quantum field theory model built up with a pair of free massive scalar fields $(\varphi_A, \varphi_B)$ where, as it is customary, the indices $(A,B)$ refer to Alice and Bob, respectively. A set of bounded Hermitian operators is introduced by making use of the Weyl operators. A $CHSH$

Han Wu, Sareh Rowlands, Johan Wahlstrom

As cloud computing becomes pervasive, deep learning models are deployed on cloud servers and then provided as APIs to end users. However, black-box adversarial attacks can fool image classification models without access to model structure and weights. Recent studies have reported attack success rates of over 95% with fewer than 1,000 queries. Then the questi

Ryan C. Ng, Paul Nizet, Daniel Navarro-Urrios, Guillermo Arregui

Phonons offer the possibility to connect the microwave and optical domains while being efficiently transduced with electronic and optical signals. Here, we present a multimodal optomechanical platform, consisting of a mechanical-optical-mechanical resonator configuration. The mechanical modes, with frequencies at 265 MHz and 6.8 GHz, can be simultaneously ex

Eilon Solan, Omri Nisan Solan

A parametric version of Brouwer's Fixed Point Theorem, which is proven using the fixed-point index, states that for every continuous mapping $f : (X \times Y) \to Y$, where $X$ is nonempty, compact, and connected subset of a Hausdorff topological space and $Y$ is a nonempty, convex, and compact subset of a locally-convex topological vector space, the set of

Miguel Hoyuelos, Pablo Sisterna

Dark energy is an elusive concept, which has been introduced two decades ago in order to make the acceleration of the universe a comprehensible phenomenon. However, the nature of this energy is far from being understood, both from a fundamental as well as an observational way. In this work we study cosmological consequences of the existence of particles (whi

Seyedsina Nabavirazavi, S. Sitharama Iyengar

The rapid development of IoT networks has led to a research trend in designing effective security features for them. Due to the power-constrained nature of IoT devices, the security features should remain as lightweight as possible. Currently, most of the IoT network traffic is unencrypted. The leakage of smart devices' unencrypted data can come with the sig

Alejandra Gonzalez, Francesco Zappa, Matteo Breschi, Sebastiano Bernuzzi

We present the second data release of gravitational waveforms from binary neutron star merger simulations performed by the Computational Relativity (CoRe) collaboration. The current database consists of 254 different binary neutron star configurations and a total of 590 individual numerical-relativity simulations using various grid resolutions. The released

Andrius Ovsianas, Jason Ramapuram, Dan Busbridge, Eeshan Gunesh Dhekane

Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios, the downstream task might be biased with respect to the target label distribution. This in turn moves the learned fine-tuned model posterior away from the initial (label) bias-free

Blake R. Duschatko, Jonathan Vandermause, Nicola Molinari, Boris Kozinsky

Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body d

Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro

The deviation between chronological age and biological age is a well-recognized biomarker associated with cognitive decline and neurodegeneration. Age-related and pathology-driven changes to brain structure are captured by various neuroimaging modalities. These datasets are characterized by high dimensionality as well as collinearity, hence applications of g

Yunlong Zhao, Jia Guo, Markus Feifel, Hao-Tien Cheng

High quality n-type AlGaAs distributed Bragg reflectors (DBRs) and lnGaAs multiple quantum wells were successfully monolithically grown on 4-inch off-cut Ge (100) wafers. The grown structures have photoluminescence spectra and reflectance spectra comparable to those grown on conventional bulk GaAs wafers and have smooth morphology and reasonable uniformity.

Weizhu Bao, Yue Feng, Ying Ma

We present different regularizations and numerical methods for the nonlinear Schr\"odinger equation with singular nonlinearity (sNLSE) including the regularized Lie-Trotter time-splitting (LTTS) methods and regularized Lawson-type exponential integrator (LTEI) methods. Due to the blowup of the singular nonlinearity, i.e., $f(\rho)=\rho^{\alpha}$ with a fixed

Marius Dadarlat, Ulrich Pennig

We extend our previous results on generalized Dixmier-Douady theory to graded $C^*$-algebras, as means for explicit computations of the invariants arising for bundles of ungraded $C^*$-algebras. For a strongly self-absorbing $C^*$-algebra $D$ and complex Clifford algebras $\mathbb{C}\ell_{n}$ we show that the classifying spaces of the groups of graded automo

James Holland, Grigor Sargsyan

In the context of $\mathsf{ZF}+\mathsf{DC}$, we force $\mathsf{DC}_\kappa$ for relations on $\mathcal{P}(\kappa)$ for $\kappa{}<\aleph_\omega$ over the Chang model $\mathrm{L}(\mathrm{Ord}^\omega)$ making some assumptions on the thorn sequence defined by ${\it \unicode{xFE}}_0=\omega$, ${\it \unicode{xFE}}_{\alpha{}+1}$ as the least ordinal not a surjective

Chris. J. Oates

For two decades, reproducing kernels and their associated discrepancies have facilitated elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now receiving interest in statistics and related fields, as criteria that can be used to select an appropriate statistical model for a given dataset. The focus of this article is on mi

Dirk Hundertmark, Michal Jex, Markus Lange

We present a method to calculate the asymptotic behavior of eigenfunctions of Schr\"odinger operators that also works at the threshold of the essential spectrum. It can be viewed as a higher order correction to the well-known WKB method which does need a safety distance to the essential spectrum. We illustrate its usefulness on examples of quantum particles

S. A. Shah, Hao Li, Eric R. Bittner, Carlos Silva

Nonlinear Optical Spectroscopy is a well-developed field with theoretical and experimental advances that have aided multiple fields including chemistry, biology and physics. However, accurate quantum dynamical simulations based on model Hamiltonians are need to interpret the corresponding multi-dimensional spectral signals properly. In this article, we prese

Wesley C. Fraser, Luke Dones, Kathryn Volk, Maria Womack

Kuiper Belt Objects, or more generally Trans-Neptunian Objects (TNOs), are planetesimals found beyond the orbit of Neptune. Some TNOs evolve onto Neptune-crossing orbits and become Centaurs. Many Centaurs, in turn, reach Jupiter-crossing orbits and become Jupiter-family comets (JFCs). TNOs are the main source of the JFCs. TNOs offer a different window than t

Yoji Yamato

In order to make full use of heterogeneous hardware, it is necessary to have a technical skill of hardware such as OpenCL, and the current situation is that the barrier is high. Based on this background, I have proposed environment-adaptive software that enables high-performance operation by automatically converting application code written for normal CPUs b

Thomas Plagemann, Vera Goebel, Matthias Hollick, Boris Koldehofe

Big data applications offer smart solutions to many urgent societal challenges, such as health care, traffic coordination, energy management, etc. The basic premise for these applications is "the more data the better". The focus often lies on sensing infrastructures in the public realm that produce an ever-increasing amount of data. Yet, any smartphone and s

Václav Rozhoň, Bernhard Haeupler, Anders Martinsson, Christoph Grunau

We introduce stronger notions for approximate single-source shortest-path distances, show how to efficiently compute them from weaker standard notions, and demonstrate the algorithmic power of these new notions and transformations. One application is the first work-efficient parallel algorithm for computing exact single-source shortest paths graphs -- resolv

Siqi Zheng

In this paper, we replicated a Bayesian educational research project, which explores the association between broadband access and online course enrollment in the US. We summarized key findings from our replication and compared them with the original project. Based on my replication experience, we aim to demonstrate the challenges of research reproduction, ev

Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan, Osmar R. Zaïane

We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clinically

Katherine Baker, Lehel Banjai, Mariya Ptashnyk

We develop a numerical method for the Westervelt equation, an important equation in nonlinear acoustics, in the form where the attenuation is represented by a class of non-local in time operators. A semi-discretisation in time based on the trapezoidal rule and A-stable convolution quadrature is stated and analysed. Existence and regularity analysis of the co

Anton Pribytok

We address the novel structures arising in quantum and string integrable theories, as well as construct methods to obtain them and provide further analysis. Specifically, we implement the automorphic symmetries on periodic lattice systems to obtain integrable hierarchies, whose commutativity and integrable transformations induce a generating structure of int

Somasekhara Goud Sontti, Mohsen Sadeghi, Kaiyu Zhou, Enzu Zheng

Bitumen residues in the oil sand tailings can be a threat to the environment that separating them from tailings before disposal is crucial. However, low bitumen concentration in the tailings slurry and the complex transport characteristics of the four-phase mixture make the process difficult. This study establishes an Eulerian-Eulerian CFD model for an indus

Nicholas Soucy, Salimeh Yasaei Sekeh

Semantic segmentation models classifying hyperspectral images (HSI) are vulnerable to adversarial examples. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease in performance compared to networks trained individually on each