January 2022 arXiv papers — page 25

Andrei Stefan Nedelcu, Fabian Steiner, Gerhard Kramer

Downlink precoding is considered for multi-path multi-input single-output channels where the base station uses orthogonal frequency-division multiplexing and low-resolution signaling. A quantized coordinate minimization (QCM) algorithm is proposed and its performance is compared to other precoding algorithms including squared infinity-norm relaxation (SQUID)

Konstantin Todorov, Giovanni Colavizza

Neural language models are the backbone of modern-day natural language processing applications. Their use on textual heritage collections which have undergone Optical Character Recognition (OCR) is therefore also increasing. Nevertheless, our understanding of the impact OCR noise could have on language models is still limited. We perform an assessment of the

Grace H. Zhang, David R. Nelson

Conical surfaces, with a delta function of Gaussian curvature at the apex, are perhaps the simplest example of geometric frustration. We study two-dimensional liquid crystals with $p$-fold rotational symmetry ($p$-atics) on the surfaces of cones. For free boundary conditions at the base, we find both the ground state(s) and a discrete ladder of metastable st

Wesley B. J. Fonseca, Flavio Garcia, Francesco Caravelli, Clodoaldo I. L de Araujo

We study memristive effects in Kagom\'{e} nanopatterned permalloy. We observe that at low frequencies a thermistor effect is present, a phenomenon arising due to the lithography and absent in similar experiments for thin films. However, we also show via an independent anisotropic magnetoresistive study that a small hysteresis accounting for 1% of the effect

Prateek Singh, Ambalika Sharma, Shreesha Maiya

The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide. For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks. For achieving better classification, it is mandatory to have good pre-processing of physiological signals. Keeping this in mind, we have proposed

Roland Axel Richter, Vladimir Kalashnikov, Irina T. Sorokina

The pronounced dependence of the nonlinear parameters of both dielectric and semiconductor materials on the wavelength, and the nonlinear interaction between the ultra-short laser pulse and the material requires precise control of the wavelength of the pulse, in addition to the precise control of the pulse energy, pulse duration and focusing optics. This bec

Michael Sinner, Vlaho Petrović, Lucy Y. Pao

Based on a series of two experimental campaigns testing advanced controllers on a scaled wind turbine operating in a wind tunnel, this contribution describes the overall experimental method, challenges faced, lessons learned, and opportunities for future work. The two campaigns, run in Fall 2018 and Fall 2019, tested unconstrained and constrained optimal bla

Peter Washington, Cezmi Onur Mutlu, Aaron Kline, Kelley Paskov

Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences. While CV classifiers for traditional and structured classification tasks can be developed with standard machine learning pipelines for supervised learning consi

Eldon Schoop, Ben Wedin, Andrei Kapishnikov, Tolga Bolukbasi

Developing a suitable Deep Neural Network (DNN) often requires significant iteration, where different model versions are evaluated and compared. While metrics such as accuracy are a powerful means to succinctly describe a model's performance across a dataset or to directly compare model versions, practitioners often wish to gain a deeper understanding of the

Sonja Kraiczy, Edith Elkind

In some preference aggregation scenarios, voters' preferences are highly structured: e.g., the set of candidates may have one-dimensional structure (so that voters' preferences are single-peaked) or be described by a binary decision tree (so that voters' preferences are group-separable). However, sometimes a single axis or a decision tree is insufficient to

Arjun R Akula, Song-Chun Zhu

Attention based explanations (viz. saliency maps), by providing interpretability to black box models such as deep neural networks, are assumed to improve human trust and reliance in the underlying models. Recently, it has been shown that attention weights are frequently uncorrelated with gradient-based measures of feature importance. Motivated by this, we as

Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer

Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost-intensive and have been shown to be subjective. They c

Yuzhi Li, Bhuvana Srinivasan, Yanzeng Zhang, Xian-Zhu Tang

The plasma exit flow speed at the sheath entrance is constrained by the Bohm criterion. The so-called Bohm speed regulates the plasma particle and power exhaust fluxes to the wall, and it is commonly deployed as a boundary condition to exclude the sheath region in quasi-neutral plasma modeling. Here the Bohm criterion analysis is performed in the intermediat

Weibo Fu

We prove a degree-one saving bound for the dimension of the space of cohomological automorphic forms of fixed level and growing weight on $\mathrm{SL}_2$ over any number field that is not totally real. In particular, we establish a sharp bound on the growth of cuspidal Bianchi modular forms. We transfer our problem into a question over the completed universa

Ioannis Giannakis, Divyanshi Sar, Joel Friedman, Chang-Jong Kang

Kondo hybridization in partially filled f-electron systems conveys significant amount of electronic states sharply near the Fermi energy leading to various instabilities from superconductivity to exotic electronic orders. UGe2 is a 5f heavy fermion system, where the Kondo hybridization is interrupted by the formation of two ferromagnetic phases below a 2nd o

Minoru Kusaba, Chang Liu, Ryo Yoshida

The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy surface, which in turn can be evaluated using first-principles calculations. However, performing the iterative gradient

Ashar Ali, Upal Mahbub, Gokce Dane, Gerhard Reitmayr

In this paper, we present DIREG3D, a holistic framework for 3D Hand Tracking. The proposed framework is capable of utilizing camera intrinsic parameters, 3D geometry, intermediate 2D cues, and visual information to regress parameters for accurately representing a Hand Mesh model. Our experiments show that information like the size of the 2D hand, its distanc

Maarten Solleveld

Let A be a \C-algebra with an action of a finite group G, let $\natural$ be a 2-cocycle on $G$ and consider the twisted crossed product $A \rtimes \C [G,\natural]$. We determine the Hochschild homology of $A \rtimes \C [G,\natural]$ for two classes of algebras A: - rings of regular functions on nonsingular affine varieties, - graded Hecke algebras. The resul

Michael Dougherty

The tree complex is a simplicial complex defined in recent work of Belk, Lanier, Margalit, and Winarski with natural applications to mapping class groups and complex dynamics. In this article, we connect this setting with the study of certain convex polytopes: associahedra and cyclohedra. Specifically, we describe a characterization of these polytopes using

LAT collaboration, Soheila Abdollahi, Fabio Acero, Luca Baldini

We present an incremental version (4FGL-DR3, for Data Release 3) of the fourth Fermi-LAT catalog of gamma-ray sources. Based on the first twelve years of science data in the energy range from 50 MeV to 1 TeV, it contains 6658 sources. The analysis improves on that used for the 4FGL catalog over eight years of data: more sources are fit with curved spectra, w

Zhenan Fan, Huang Fang, Michael P. Friedlander

We study the federated optimization problem from a dual perspective and propose a new algorithm termed federated dual coordinate descent (FedDCD), which is based on a type of coordinate descent method developed by Necora et al.[Journal of Optimization Theory and Applications, 2017]. Additionally, we enhance the FedDCD method with inexact gradient oracles and

Mariam Kiran, Melis Ozyildirim

Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a wide variety of applications from controlling simple pendulums to complex data centers. However, setting the right hyperparameters can have a huge impact on the deployed solution performa

Arthur Câmara, David Maxwell, Claudia Hauff

Complex search tasks - such as those from the Search as Learning (SAL) domain - often result in users developing an information need composed of several aspects. However, current models of searcher behaviour assume that individuals have an atomic need, regardless of the task. While these models generally work well for simpler informational needs, we argue th

Ethan G. Arnault, Sara Idris, Aeron McConnell, Lingfei Zhao

The dynamical properties of multi-terminal Josephson junctions have recently attracted interest, driven by the promise of new insights into synthetic topological phases of matter and Floquet states. This effort has culminated in the discovery of Cooper multiplets, in which the splitting of a Cooper pair is enabled via a series of Andreev reflections that ent

Letian Chen

We show that any integral Brakke flow coming out of a rotationally symmetric double cone with entropy at most two must stay rotationally symmetric for all time, provided the flow is smooth for a short time. We also show the existence of a non-self-similar flow coming out of a double cone with entropy at most two, and give an example of such a flow with a fin

Kastriot Kadriu

A naive approach for finding similar audio items would be to compare each entry from the feature vector of the test example with each feature vector of the candidates in a k-nearest neighbors fashion. There are already two problems with this approach: audio signals are represented by high dimensional vectors and the number of candidates can be very large - t

Almog Yalinewich, Paz Beniamini

In this work we consider the eruption of a tenuous relativistic hydrodynamic jet from a dense baryonic envelope. As the jet moves out and away, it carries along and continues to accelerate a layer of baryonic material which we refer to as the plug. We solve the relativistic equations of motion for the trajectory of the plug, and verify it using a relativisti

Wei Zhao, Michael Strube, Steffen Eger

Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation syste

Michele Bolognesi, Robert Laterveer

Gushel-Mukai sixfolds are an important class of so-called Fano-K3 varieties. In this paper we show that they admit a multiplicative Chow-K\"unneth decomposition modulo algebraic equivalence and that they have the Franchetta property. As side results, we show that double EPW sextics and cubes have the Franchetta property, modulo algebraic equivalence, and som

Marcos Isai Morales Inostroza

We apply general methods to generate upper and lower bounds for the essential minimum of a specific family of height functions. In particular, the results shown in this article apply to the case of the Zhang-Zagier height. Furthermore, we can find intervals, where the images of these heights are dense. Our main tool to find upper bounds and intervals of dens

Trevor McCourt, Charles Neill, Kenny Lee, Chris Quintana

Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom. This noise poses a challenge to quantum computing, where two-qubit gate performance is critical. Here, we develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates. In ou

Tu Anh Dinh, Danni Liu, Jan Niehues

Recently, end-to-end speech translation (ST) has gained significant attention as it avoids error propagation. However, the approach suffers from data scarcity. It heavily depends on direct ST data and is less efficient in making use of speech transcription and text translation data, which is often more easily available. In the related field of multilingual t

Zhenyu Wei, Raymond K. W. Wong, Thomas C. M. Lee

In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression, for which the corresponding MDL solutions often enjoy consistent properties and produce very promising empirical results.

Benjamin M. Coughenour, John A. Tomsick, Aarran W. Shaw, Koji Mukai

Many new and unidentified Galactic sources have recently been revealed by ongoing hard X-ray surveys. A significant fraction of these have been shown to be the type of accreting white dwarfs known as cataclysmic variables (CVs). Follow-up observations are often required to categorize and classify these sources, and may also identify potentially unique or int

Stefano Montaldo, Cezar Oniciuc, Alvaro Pampano

We characterise the profile curves of non-CMC biconservative rotational hypersurfaces of space forms $N^n(\rho)$ as $p$-elastic curves, for a suitable rational number $p\in[1/4,1)$ which depends on the dimension $n$ of the ambient space. Analysing the closure conditions of these $p$-elastic curves, we prove the existence of a discrete biparametric family of

Sanket Kadulkar, Zachary M. Sherman, Venkat Ganesan, Thomas M. Truskett

Designing functional materials requires a deep search through multidimensional spaces for system parameters that yield desirable material properties. For cases where conventional parameter sweeps or trial-and-error sampling are impractical, inverse methods that frame design as a constrained optimization problem present an attractive alternative. However, eve

Michael F. Howland, Jesus Bas Quesada, Juan Jose Pena Martinez, Felipe Palou Larranaga

Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of comput

Hojjat Abdollahi, Mohammad H. Mahoor, Rohola Zandie, Jarid Siewierski

This paper presents our recent research on integrating artificial emotional intelligence in a social robot (Ryan) and studies the robot's effectiveness in engaging older adults. Ryan is a socially assistive robot designed to provide companionship for older adults with depression and dementia through conversation. We used two versions of Ryan for our study, e

Silas Richelson, Sourya Roy

Random walks in expander graphs and their various derandomizations (e.g., replacement/zigzag product) are invaluable tools from pseudorandomness. Recently, Ta-Shma used s-wide replacement walks in his breakthrough construction of a binary linear code almost matching the Gilbert-Varshamov bound (STOC 2017). Ta-Shma's original analysis was entirely linear alge

Nitesh Kumar, Ondrej Kuzelka, Luc De Raedt

Statistical relational AI and probabilistic logic programming have so far mostly focused on discrete probabilistic models. The reasons for this is that one needs to provide constructs to succinctly model the independencies in such models, and also provide efficient inference. Three types of independencies are important to represent and exploit for scalable i

Garrett Garner, Daniel Streetman, Joshua Fricker, Neal Patel

Focal cortical dysplasias (FCDs) are the most common cause of treatment resistant epilepsy affecting the pediatric population. Most individuals with FCD have seizure onset during the first five years of life and the majority will have seizures by the age of sixteen. Many cases of FCD are postulated to be the result of abnormal brain development in utero by g

Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki

We develop an efficient Bayesian sequential inference framework for factor analysis models observed via various data types, such as continuous, binary and ordinal data. In the continuous data case, where it is possible to marginalise over the latent factors, the proposed methodology tailors the Iterated Batch Importance Sampling (IBIS) of Chopin (2002) to ha

Bastian Boll, Alexander Zeilmann, Stefania Petra, Christoph Schnörr

We propose a novel class of deep stochastic predictors for classifying metric data on graphs within the PAC-Bayes risk certification paradigm. Classifiers are realized as linearly parametrized deep assignment flows with random initial conditions. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables (i) to use risk boun

Shivani Grover, Keith T. Butler, Umesh V Waghmare, Ricardo Grau-Crespo

Bismuth ferrite, BiFeO3, is a multiferroic solid that is attracting increasing attention as a potential photocatalytic material, because the ferroelectric polarisation enhances the separation of photogenerated carriers. With the motivation of finding routes to engineer the band gap and the band alignment, while conserving or enhancing the ferroelectric prope

J. Peña-Rodríguez, A. Vesga-Ramírez, A. Vásquez-Ramírez, M. Suárez-Durán

We present the Colombo-Argentinian Muography Program for studying inland Latin-American volcanoes. It describes the implementation of a simulation framework covering various factors with different spatial and time scales: the geomagnetic effects at a particular geographic point, the development of extensive air showers in the atmosphere, the propagation thro

Stanley Rabinowitz, Ercole Suppa

The incircle of a triangle touches the sides of the triangle in three points. It is well known that the lines from these points to the opposite vertices meet at a point known as the Gergonne point of the triangle. We use a computer to discover and catalog properties of the Gergonne point.

Dengjun Guo

We consider the two-dimensional incompressible Euler equation \[\begin{cases} \partial_t \omega + u\cdot \nabla \omega=0 \\ \omega(0,x)=\omega_0(x). \end{cases}\] We are interested in the cases when the initial vorticity has the form $\omega_0=\omega_{0,\epsilon}+\omega_{0p,\epsilon}$, where $\omega_{0,\epsilon}$ is concentrated near $M$ disjoint points $p_m

Shahriar Talebi, Mehran Mesbahi

In this paper, we study linearly constrained policy optimization over the manifold of Schur stabilizing controllers, equipped with a Riemannian metric that emerges naturally in the context of optimal control problems. We provide extrinsic analysis of a generic constrained smooth cost function, that subsequently facilitates subsuming any such constrained prob

Ayden Higgins, Koen Jochmans

The maximum-likelihood estimator of nonlinear panel data models with fixed effects is consistent but asymptotically-biased under rectangular-array asymptotics. The literature has thus far concentrated its effort on devising methods to correct the maximum-likelihood estimator for its bias as a means to salvage standard inferential procedures. Instead, we show

Ion Stagkos Efstathiadis, Guilherme Paulino-Passos, Francesca Toni

The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on concrete narratives presented by users. Existing analysis of the forum focuses on its comments, and does not make the underlying data publicly available. In this paper we build a new dataset of comments and also investigate the classification of the posts in the forum. Further, we

Sergey A. Matveev, Stanislav Budzinskiy

We propose new approximate alternating projection methods, based on randomized sketching, for the low-rank nonnegative matrix approximation problem: find a low-rank approximation of a nonnegative matrix that is nonnegative, but whose factors can be arbitrary. We calculate the computational complexities of the proposed methods and evaluate their performance i

Alon Albalak, Sharon Levy, William Yang Wang

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no

Alexey Lokhov, Benedikt Bieringer, Guido Drexlin, Stephan Dyba

The KATRIN experiment aims at measuring the electron neutrino mass with a sensitivity of 0.2 eV$/c^2$ after five years of data taking. Recently a new upper limit for the neutrino mass of 0.8 eV$/c^2$ (90% CL) was obtained. To reach the design sensitivity, a reduction of the background rate by one order of magnitude is required. The shifted analysing plane (S

Alexander Kott, Maureen S. Golan, Benjamin D. Trump, Igor Linkov

The term "cyber resilience by design" is growing in popularity. Here, by cyber resilience we refer to the ability of the system to resist, minimize and mitigate a degradation caused by a successful cyber-attack on a system or network of computing and communicating devices. Some use the term "by design" when arguing that systems must be designed and implement

G. Diaz-Porto, I. S. Gutierrez, A. Torres-Grandisson

In this paper, we introduce the t-graphs defined on finitely-generate groups. We study some general aspects of the t-graphs on 2-generator groups, emphasising establishing necessary conditions for their connectedness. In particular, we investigate the properties of t-graphs defined on finite dihedral groups.

Muhammad Imran, Jinsheng Sun, Fatima Sehar Zaidi, Zameer Abbas

Control charts, one of the main tools in Statistical Process Control (SPC), have been widely adopted in manufacturing sectors as an effective strategy for malfunction detection throughout the previous decades. Measurement errors (M.E's) are involved in the quality characteristic of interest. The authors explored the impact of a linear covariate error model o

Daniella Bar-Lev, Sagi Marcovich, Eitan Yaakobi, Yonatan Yehezkeally

We study the adversarial torn-paper channel. This problem is motivated by applications in DNA data storage where the DNA strands that carry information may break into smaller pieces which are received out of order. Our model extends the previously researched probabilistic setting to the worst-case. We develop code constructions for any parameters of the chan

Mycal Tucker, William Kuhl, Khizer Shahid, Seth Karten

Neural nets are powerful function approximators, but the behavior of a given neural net, once trained, cannot be easily modified. We wish, however, for people to be able to influence neural agents' actions despite the agents never training with humans, which we formalize as a human-assisted decision-making problem. Inspired by prior art initially developed f

Yue Bi, Michèle Wigger, Philippe Ciblat, Yue Wu

We consider a cooperative X-channel with $\sf K$ transmitters (TXs) and $\sf K$ receivers (Rxs) where Txs and Rxs are gathered into groups of size $\sf r$ respectively. Txs belonging to the same group cooperate to jointly transmit a message to each of the $\sf K- \sf r$ Rxs in all other groups, and each Rx individually decodes all its intended messages. By i

Alexandre K. Ligo, Alexander Kott, Igor Linkov

From denial-of-service attacks to spreading of ransomware or other malware across an organization's network, it is possible that manually operated defenses are not able to respond in real time at the scale required, and when a breach is detected and remediated the damage is already made. Autonomous cyber defenses therefore become essential to mitigate the ri

Xiao Xu, Marta D'Elia, Christian Glusa, John T. Foster

Anomalous behavior is ubiquitous in subsurface solute transport due to the presence of high degrees of heterogeneity at different scales in the media. Although fractional models have been extensively used to describe the anomalous transport in various subsurface applications, their application is hindered by computational challenges. Simpler nonlocal models

J. A. Aguilar-Saavedra

We address the modeling dependence of jet taggers built using the method of Mass Unspecific Supervised Tagging, by using two different parton showering and hadronisation schemes. We find that the modeling dependence of the results - estimated by using different schemes in the design of the taggers and applying them to the same type of data - is rather small,

A. Möller, M. Smith, M. Sako, M. Sullivan

As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multi-band light-curves and host galaxy redshifts. For this analysis, we use the photometric classification framework SuperNNova (SNN; M\"oller et al. 2019) trained on realistic DES-like simulations. Fo

Pavel Jáchym, Ming Sun, Masafumi Yagi, Chong Ge

We report the detection of CO emission in the recently discovered multiphase isolated gas cloud in the nearby galaxy cluster Abell 1367. The cloud is located about 800 kpc in projection from the center of the cluster and at a projected distance of > 80 kpc from any galaxy. It is the first and the only known isolated intra-cluster cloud detected in X-ray, H$\

Shahaf Asban, Vladimir Y. Chernyak, Shaul Mukamel

We develop closed expressions for a time-resolved photon counting signal induced by an entangled photon pair in an interferometric spectroscopy setup. Superoperator expressions in Liouville-space are derived that can account for relaxation and dephasing induced by coupling to a bath. Interferometric setups mix matter and light variables non-trivially, which

Cassandra L. Barlow-Hall, Jack Delaney, James Aird, Philip A. Evans

X-ray luminosity functions (XLFs) of Active Galactic Nuclei (AGN) trace the growth and evolution of supermassive black hole populations across cosmic time, however, current XLF models are poorly constrained at redshifts of z>6, with a lack of spectroscopic constraints at these high redshifts. In this work we \redit{place limits} on the bright-end of the XLF

Zhan-Feng Mai, Run-Qiu Yang, H. Lü

It was recently shown that the extremal charged black holes in STU supergravity suffer from superradiant instability owing to the existence of the unstable (low-frequency) quasibound states associated with a charged massive scalar field. In this paper, we show numerically that for some charge configurations, these black holes can also excite the (higher-freq

G. Bruno De Luca, Eva Silverstein

We introduce a novel framework for optimization based on energy-conserving Hamiltonian dynamics in a strongly mixing (chaotic) regime and establish its key properties analytically and numerically. The prototype is a discretization of Born-Infeld dynamics, with a squared relativistic speed limit depending on the objective function. This class of frictionless,

Hagai B. Perets

Observations of pulsars in globular clusters (GCs) give evidence that more >10-20% of neutron stars (NSs) ever formed in GCs were retained there. However, the velocity distribution of field pulsars peaks at 5-10 times the escape velocities of GCs. Consequently, only a small fraction of GC-NSs should have been retained, even accounting for low-velocity NSs fo

Yiping Shu, Raoul Cañameras, Stefan Schuldt, Sherry H. Suyu

We carry out a search for strong-lens systems containing high-redshift lens galaxies with the goal of extending strong-lensing-assisted galaxy evolutionary studies to earlier cosmic time. Two strong-lens classifiers are constructed from a deep residual network and trained with datasets of different lens-redshift and brightness distributions. We classify a sa

John R. Weaver, Keith Horne

We present a study of 9242 spectroscopically-confirmed quasars with multi-epoch ugriz photometry from the SDSS Southern Survey. By fitting a separable linear model to each quasar's spectral variations, we decompose their five-band spectral energy distributions into variable (disc) and non-variable (host galaxy) components. In modelling the disc spectra, we i

Pranshu Chaturvedi, Asad Khan, Minyang Tian, E. A. Huerta

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaG

Raphael Bousso, Arvin Shahbazi-Moghaddam

Assuming the Bousso bound, we prove a singularity theorem: if the light rays entering a hyperentropic region contract, then at least one light ray must be incomplete. "Hyperentropic" means that the entropy of the region exceeds the Bekenstein-Hawking entropy of its spatial boundary. Our theorem provides a direct link between singularities and quantum informa

T. Jayasinghe, Todd A. Thompson, C. S. Kochanek, K. Z. Stanek

We report the discovery of a stripped giant + lower giant binary, 2M04123153+6738486 (2M0412), identified during a search for non-interacting compact object-star binaries. 2M0412 is an evolved ($T_{\rm eff, giant}\simeq4000$ K), luminous ($L_{\rm giant}\simeq150~L_\odot$) red giant in a circular $P=81.2$ day binary. 2M0412 is a known variable star previously

Laura J. Henderson, Su Yu Ding, Robert B. Mann

One consequence of the cosmic censorship conjecture is that any topological structure will ultimately collapse to within the horizons of a set of black holes, and as a result, an external classical observer will be unable to probe it. However a single two-level quantum system (UDW detector) that remains outside of the horizon has been shown to distinguish be

Lingyuan Ji, Marc Kamionkowski, Jose Luis Bernal

We present a formulation of cosmological perturbation theory where the Boltzmann hierarchies that evolve the neutrino phase-space distributions are replaced by integrals that can be evaluated easily with fast Fourier transforms. The simultaneous evaluation of these integrals combined with the differential equations for the rest of the system (dark matter, ph

Ioanna Kourkoulou, Alberto Nicolis, Guanhao Sun

We exploit an ambiguity somewhat hidden in Noether's theorem to derive systematically, for relativistic field theories, the stress-energy tensor's improvement terms that are associated with additional spacetime symmetries beyond translations. We work out explicitly the cases of Lorentz invariance, scale invariance, and full conformal invariance. The main ide

Hayata Yamasaki, Sathyawageeswar Subramanian

Fault-tolerant measurement-based quantum computation (MBQC) with recent progress on quantum technologies leads to a promising scalable platform for realizing quantum computation, conducted by preparing a large-scale graph state over many qubits and performing single-qubit measurements on the state. With fault-tolerant MBQC, even if the graph-state preparatio

Matías Suazo, Erik Zackrisson, Jason T. Wright, Andreas Korn

Dyson spheres are hypothetical megastructures built by advanced extraterrestrial civilizations to harvest radiation energy from stars. Here, we combine optical data from Gaia DR2 with mid-infrared data from AllWISE to set the strongest upper limits to date on the prevalence of partial Dyson spheres within the Milky Way, based on their expected waste-heat sig

Mitali Damle, Martin Sparre, Philipp Richter, Maan H. Hani

Recent observations have revealed remarkable insights into the gas reservoir in the circumgalactic medium (CGM) of galaxy haloes. In this paper, we characterise the gas in the vicinity of Milky Way and Andromeda analogues in the HESTIA (High resolution Environmental Simulations of The Immediate Area) suite of constrained Local Group (LG) simulations. The HES

J. Orell-Miquel, F. Murgas, E. Pallé, M. Lampón

The $\ion{He}{I}$ 10833\,$\AA$ triplet is a powerful tool for characterising the upper atmosphere of exoplanets and tracing possible mass loss. Here, we analysed one transit of GJ\,1214\,b observed with the CARMENES high-resolution spectrograph to study its atmosphere via transmission spectroscopy around the $\ion{He}{I}$ triplet. Although previous studies u

B. Dias, M. C. Parisi, M. Angelo, F. Maia

The structure of the Small Magellanic Cloud (SMC) is very complex, in particular in the periphery that suffers more from the interactions with the Large Magellanic Cloud (LMC). A wealth of observational evidence has been accumulated revealing tidal tails and bridges made up of gas, stars and star clusters. Nevertheless, a full picture of the SMC outskirts is

E. Stadnichuk, E. Svechnikova

Relativistic runaway electron avalanches (RREA) accelerated by thunderstorm large-scale electric fields are one of the sources of atmospheric gamma radiation. In strong electric fields, RREAs can multiply by the relativistic feedback. Infinite relativistic feedback makes avalanches self-sustainable and hypothetically can cause a terrestrial gamma-ray flash (

Ingemar Nåsell

Errors of approximations of the quasi-stationary distribution (the QSD) of the logistic SIS model are evaluated numerically. The results are used to derive asymptotic approximations of the approximation errors for large populations. We show in particular that there are two approximations above threshold for which the approximation errors are exponentially sm

Stephanie Galaitsi, Benjamin D. Trump, Jeffrey M. Keisler, Igor Linkov

To benefit from AI advances, users and operators of AI systems must have reason to trust it. Trust arises from multiple interactions, where predictable and desirable behavior is reinforced over time. Providing the system's users with some understanding of AI operations can support predictability, but forcing AI to explain itself risks constraining AI capabil

Sourya Dey, Walt Woods

This paper presents LAGOON -- an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced by these communities, and help analysts identify bad actors who might compromise an OSS project's security. LAGOON provi

Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks historical data. Most of these existing approaches have focused

S. Kirschenmann, M. Bezak, S. Bharthuar, E. Brücken

Cadmium Telluride (CdTe) is a high-Z material with excellent photon radiation absorption properties, making it a promising material to include in radiation detection technologies. However, the brittleness of CdTe crystals as well as their varying concentration of defects necessitate a thorough quality assessment before the complex detector processing procedu

Herbert Ullrich, Jan Drchal, Martin Rýpar, Hana Vincourová

In this paper, we examine several methods of acquiring Czech data for automated fact-checking, which is a task commonly modeled as a classification of textual claim veracity w.r.t. a corpus of trusted ground truths. We attempt to collect sets of data in form of a factual claim, evidence within the ground truth corpus, and its veracity label (supported, refut

Evan Hernandez, Sarah Schwettmann, David Bau, Teona Bagashvili

Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying neurons that respond to individual concept categories like colors, textures, and object classes. But these techniques are limited in scope, labeling only a small subset of neurons

Jinjie Zhang, Yixuan Zhou, Rayan Saab

While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research. By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained. To th

Julia Lieb, Simran Tinani

LDPC codes constructed from permutation matrices have recently attracted the interest of many researchers. A crucial point when dealing with such codes is trying to avoid cycles of short length in the associated Tanner graph, i.e. obtaining a possibly large girth. In this paper, we provide a framework to obtain constructions of such codes. We relate criteria

Alexander Kott, Paul Theron

Today's cyber defense tools are mostly watchers. They are not active doers. To be sure, watching too is a demanding affair. These tools monitor the traffic and events; they detect malicious signatures, patterns and anomalies; they might classify and characterize what they observe; they issue alerts, and they might even learn while doing all this. But they do

Raisa Fabiha, Jonathan Lundquist, Sudip Majumder, Erdem Topsakal

We investigate tripartite coupling between phonons, magnons and photons in a periodic array of elliptical magnetostrictive nanomagnets delineated on a piezoelectric substrate to form a two-dimensional two-phase multiferroic crystal. A surface acoustic wave (phonons) of 5 - 35 GHz frequency launched into the substrate causes the magnetizations of the nanomagn

Christopher Warner, Kiersten Ruda, Friedrich T. Sommer

We introduce a novel, probabilistic binary latent variable model to detect noisy or approximate repeats of patterns in sparse binary data. The model is based on the "Noisy-OR model" (Heckerman, 1990), used previously for disease and topic modelling. The model's capability is demonstrated by extracting structure in recordings from retinal neurons, but it can

Unchittha Prasatar, Thawatchai Mayteevarunyoo, Boris A. Malomed

We address the dynamics of two-dimensional (2D) truncated Airy waves and three-component solitons in the system of two fundamental-frequency and second-harmonic fields, coupled by quadratic (chi^(2)) terms. The system models second-harmonic-generating optical media and atomic-molecular mixtures in Bose-Einstein condensates. In addition to stable solitons, th

Kai Trepte, Johannes Voss

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Here, we present an empirical approach to simultaneously optimize semi-local exchange and non-local correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk,

Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter

Finding optimal paths in connected graphs requires determining the smallest total cost for traveling along the graph's edges. This problem can be solved by several classical algorithms where, usually, costs are predefined for all edges. Conventional planning methods can, thus, normally not be used when wanting to change costs in an adaptive way following the

Konstantin Ditschuneit, Johannes S. Otterbach

State-of-the-art semantic segmentation models are characterized by high parameter counts and slow inference times, making them unsuitable for deployment in resource-constrained environments. To address this challenge, we propose \textsc{Auto-Compressing Subset Pruning}, \acosp, as a new online compression method. The core of \acosp consists of learning a cha

Daniel Blixt, Rafael Ferraro, Alexey Golovnev, María-José Guzmán

General relativity dynamics can be derived from different actions -- which depart from the Einstein-Hilbert action in boundary terms -- and for different choices of the dynamical variables. Among them, the teleparallel equivalent of general relativity is a torsion-based theory for the tetrad field. More general torsion-based theories have been built in the l