October 2022 arXiv papers — page 121

Michal Krompiec, David Muñoz Ramo

We introduce QRDM-NEVPT2: a hybrid quantum-classical implementation of strongly-contracted N-electron Valence State $2^{nd}$-order Perturbation Theory (SC-NEVPT2), in which the Complete Active Space Configuration Interaction (CASCI) step, capturing static correlation effects, is replaced by a simulation performed on a quantum computer. Subsequently, n-partic

Nicola Tomassetti, Bruna Bertucci, Emanuele Fiandrini

The observed variability of the cosmic-ray intensity in the interplanetary space is driven by the evolution of the Sun's magnetic activity over its 11-year quasiperiodical cycle. Investigating the relationship between solar activity indices and cosmic-ray intensity measurements is then essential for understanding the fundamental processes of particle transpo

Jonas Hatzenbühler, Erik Jenelius, Győző Gidófalvi, Oded Cats

Recent developments in modular transport vehicles allow deploying multi-purpose vehicles which can alternately transport different kinds of flows. In this study, we propose a novel variant of the pickup and delivery problem, the multi-purpose pickup and delivery problem, where multi-purpose vehicles are assigned to serve a multi-commodity flow. We solve a se

Nicola Tomassetti, Bruna Bertucci, Federico Donnini, Emanuele Fiandrini

Galactic cosmic rays (GCRs) inside the heliosphere are affected by magnetic turbulence and Solar wind disturbances which result in the so-called solar modulation effect. To investigate this phenomenon, we have performed a data-driven analysis of the temporal dependence of the GCR flux over the solar cycle. With a global statistical inference of GCR data coll

Tanmoy Modak, Lennart Röver, Björn Malte Schäfer, Benedikt Schosser

Starobinsky inflation is an attractive, fundamental model to explain the Planck measurements, and its higher-order extension may allow us to probe quantum gravity effects. We show that future CMB data combined with the 21cm intensity map from SKA will meaningfully probe such an extended Starobinsky model. A combined analysis will provide a precise measuremen

Stefan Schreieder, Ruijie Yang

We show that a conjecture of Kotschick about one-forms without zeros on compact K\"ahler manifolds follows in the case of simple Albanese torus from a conjecture of Bobadilla and Koll\'ar about homologically trivial fibrations. As an application, we prove Kotschick's conjecture for compact K\"ahler manifolds X whose first betti number is at least 2dim(X)-2 a

David Pelosi, Nicola Tomassetti, Matteo Duranti

The flux of cosmic rays in the heliosphere is subjected to variations that are related to the Sun's magnetic activity. To study this effect, updated time series of multichannel observations are needed. Here we present a web application that collects real-time data on solar activity proxies, interplanetary plasma parameters, and charged cosmic-ray data. The d

Alicia Rouco Escorial, Wen-fai Fong, Edo Berger, Tanmoy Laskar

We present a comprehensive study of 29 short gamma-ray bursts (SGRBs) observed $\approx 0.8-60$ days post-burst using $Chandra$ and $XMM-Newton$. We provide the inferred distributions of SGRB jet opening angles and true event rates to compare against neutron star merger rates. We perform uniform analysis and modeling of their afterglows, obtaining 10 opening

E. Dalessandro, C. Crociati, M. Cignoni, F. R. Ferraro

Liller 1 and Terzan 5 are two massive systems in the Milky-Way bulge hosting populations characterized by significantly different ages ($\Delta t>7-8$ Gyr) and metallicities ($\Delta$[Fe/H]$\sim1$ dex). Their origin is still strongly debated in the literature and all formation scenarios proposed so far require some level of fine-tuning. The detailed star for

Nicola Tomassetti, Bruna Bertucci, Emanuele Fiandrini

When traveling in the heliosphere, Galactic cosmic rays (GCRs) are subjected to the solar modulation effect, a quasiperiodical change of their intensity caused by the 11-year cycle of solar activity. Here we investigate the association of solar activity and cosmic radiation over five solar cycles, from 1965 to 2020, using a collection of multichannel data fr

Héctor Maeso-García, José Polo-Gómez, Eduardo Martín-Martínez

We analyzed how entanglement harvesting is affected by the performance of a measurement on the quantum field. The measurement on the field is modelled as the coupling of a particle detector to the field, followed by a projective measurement performed on the detector. In our analysis, we considered different arrangements for two detectors harvesting entanglem

Emmanuel Gottlob, Ulrich Schneider

Quasicrystals are long-range ordered, yet not periodic, and thereby present a fascinating challenge for condensed matter physics, as one cannot resort to the usual toolbox based on Bloch's theorem. Here, we present a numerical method for constructing the Hubbard Hamiltonian of non-periodic potentials without making use of Bloch's theorem and apply it to the

Abhishek Banerjee, Joshua Eby, Gilad Perez

We highlight general issues associated with quality and naturalness problems in theories of light QCD-axions, axion-like particles, and relaxions. We show the presence of Planck-suppressed operators generically lead to scalar coupling of axions with the Standard model. We present a new class of $\mathbb Z_{N}$ QCD relaxion models that can address both the QC

Run Zhou, Ryan J. Marshman, Sougato Bose, Anupam Mazumdar

Placing a large mass in a large spatial superposition, such as a Schr\"odinger Cat state is a significant and important challenge. In particular, the large spatial superposition (${\cal O}(10-100)$ $\mu$m) of mesoscopic masses ($m\sim {\cal O}(10^{-14} -10^{-15})$ kg) makes it possible to test the quantum nature of gravity via entanglement in the laboratory.

R. Forsberg, R. M. Rich, N. Nieuwmunster, H. Jönsson

Aims. Stars with strong enhancements of r-process elements are rare and tend to be metal-poor, with generally [Fe/H] <-2 dex and found in the halo. In this work we aim to investigate a candidate r-process enriched bulge star with a relatively high metallicity of -0.65 dex, and compare it with a previously published r-rich candidate star in the bulge. Methods

Daniel R. Mayerson

The Geroch-Hansen and Thorne (ACMC) formalisms give rigorous and equivalent definitions for gravitational multipoles in stationary vacuum spacetimes. However, despite their ubiquitous use in gravitational physics, it has not been shown that these formalisms can be generalized to non-vacuum stationary solutions, except in a few special cases. This paper shows

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer

We combine amortized neural posterior estimation with importance sampling for fast and accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian posterior using neural networks, and then attach importance weights based on the underlying likelihood and prior. This provides (1) a corrected posterior free from network inaccuraci

Antonio Ambrosone, Marco Chianese, Damiano F. G. Fiorillo, Antonio Marinelli

Starburst galaxies are well-motivated astrophysical emitters of high-energy gamma-rays. They are well-known cosmic-ray "reservoirs", thanks to their large magnetic fields which confine high-energy protons for $\sim 10^5$ years. Over such long times, cosmic-ray transport can be significantly affected by scatterings with sub-GeV dark matter. Here we point out

Keck Collaboration, P. A. R. Ade, Z. Ahmed, M. Amiri

We characterize Galactic dust filaments by correlating BICEP/Keck and Planck data with polarization templates based on neutral hydrogen (H I) observations. Dust polarization is important for both our understanding of astrophysical processes in the interstellar medium (ISM) and the search for primordial gravitational waves in the cosmic microwave background (

Dirk Scholte, Amélie Saintonge

Gas plays an important role in many processes in galaxy formation and evolution, but quantifying the importance of gas has been hindered by the challenge to measure gas masses for large samples of galaxies. Datasets of direct atomic and molecular gas measurements are sufficient to establish simple scaling relations, but often not large enough to quantify thr

Alessandro Lupi

The detection of gravitational waves has opened a new era for astronomy, allowing for the combined use of gravitational wave and electromagnetic emissions to directly probe the physics of compact objects, still poorly understood. So far, the theoretical modelling of these sources has mainly relied on standard numerical techniques as grid-based methods or smo

Flavia Gesualdi, Alberto Daniel Supanitsky

We present a general method for estimating the number of particles impinging on a segmented counter or, in general, on a counter with sub-units. We account for unresolved particles, i.e., the effect of two or more particles hitting the same sub-unit almost simultaneously. To achieve full time resolution we account for the dead time that occurs after the firs

Joseph Merritt, Lukasz Fidkowski

We use Majorana operators to study entanglement dynamics under random free fermion unitary evolution and projective measurements in one dimension. For certain choices of unitary evolution, namely those which swap neighboring Majorana operators, and measurements of neighboring Majorana bilinears, one can map the evolution to the statistical model of completel

Jae Hyeok Chang, María Olalla Olea-Romacho, Erwin H. Tanin

Finite temperature effects in the Standard Model tend to restore the electroweak symmetry in the early universe, but new fields coupled to the higgs field may as well reverse this tendency, leading to the so-called electroweak symmetry non-restoration (EW SNR) scenario. Previous works on EW SNR often assume that the reversal is due to the thermal fluctuation

Elliot Y. Davies, Eugene Vasiliev, Vasily Belokurov, N. Wyn Evans

Recent work uncovered features in the phase space of the Milky Way's stellar halo which may be attributed to the last major merger. When stellar material from a satellite is accreted onto its host, it phase mixes and appears finely substructured in phase space. For a high-eccentricity merger, this substructure most clearly manifests as numerous wrapping chev

Mason Ng, Ronald A. Remillard, James F. Steiner, Deepto Chakrabarty

We report on results from a one-year soft X-ray observing campaign of the ultraluminous X-ray pulsar NGC 300 ULX-1 by the Neutron star Interior Composition Explorer (NICER) during 2018--2019. Our analysis also made use of data from Swift/XRT and XMM-Newton in order to model and remove contamination from the nearby eclipsing X-ray binary NGC 300 X-1. We const

Francois Foucart, Matthew D. Duez, Roland Haas, Lawrence E. Kidder

Recent gravitational wave observations of neutron star-neutron star and neutron star-black hole binaries appear to indicate that massive neutron stars may not be too uncommon in merging systems. In this manuscript, we present a first set of evolution of massive neutron star binaries using Monte-Carlo radiation transport for the evolution of neutrinos. We stu

Saeed Saadatnejad, Ali Rasekh, Mohammadreza Mofayezi, Yasamin Medghalchi

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach that can predict given noisy observations. We frame the prediction task as a denoising problem, where both

Yang Li, Xiaoxue Chen, Hao Zhao, Jiangtao Gong

We study embodied reference understanding, the task of locating referents using embodied gestural signals and language references. Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line. However, existing human pose representations fa

Nataniel Ruiz, Miriam Bellver, Timo Bolkart, Ambuj Arora

We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes. ABS is based on the skinned multiperson li

Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu

As a pioneering work exploring transformer architecture for 3D point cloud understanding, Point Transformer achieves impressive results on multiple highly competitive benchmarks. In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations

Yue Jiang, Marc Habermann, Vladislav Golyanik, Christian Theobalt

Monocular 3D human performance capture is indispensable for many applications in computer graphics and vision for enabling immersive experiences. However, detailed capture of humans requires tracking of multiple aspects, including the skeletal pose, the dynamic surface, which includes clothing, hand gestures as well as facial expressions. No existing monocul

Kushal Chawla, Weiyan Shi, Jingwen Zhang, Gale Lucas

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. Th

Nur Muhammad Mahi Shafiullah, Chris Paxton, Lerrel Pinto, Soumith Chintala

We propose CLIP-Fields, an implicit scene model that can be used for a variety of tasks, such as segmentation, instance identification, semantic search over space, and view localization. CLIP-Fields learns a mapping from spatial locations to semantic embedding vectors. Importantly, we show that this mapping can be trained with supervision coming only from we

Zhengbang Zhu, Rongjun Qin, Junjie Huang, Xinyi Dai

Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have achieved better performance in terms of user engagement metrics such as clicks and browsing time. The increase in the measu

Alvaro E. Chavarria

We report on recent progress in the search for dark matter particles with masses from 1 MeV to 1 GeV. Several dark matter candidates in this mass range are expected to generate measurable electronic-recoil signals in direct-detection experiments. We focus on dark matter particles scattering with electrons in semiconductor detectors since they have fundamenta

Lin Fan, Peter W. Glynn

We establish strong laws of large numbers and central limit theorems for the regret of two of the most popular bandit algorithms: Thompson sampling and UCB. Here, our characterizations of the regret distribution complement the characterizations of the tail of the regret distribution recently developed by Fan and Glynn (2021) (arXiv:2109.13595). The tail char

Giorgio Galanti, Lara Nava, Marco Roncadelli, Fabrizio Tavecchio

The LHAASO Collaboration detected the gamma ray burst GRB 221009A at energies above $500 \, {\rm GeV}$ with a tail extending up to $18 \, \rm TeV$, whose spectral analysis has presently been performed up to $7 \, \rm TeV$ for the lower energy instrument LHAASO-WCDA only, with no indication of a cutoff. Soon thereafter, Carpet-2 at Baksan Neutrino Observatory

Giovanni Tambalo, Miguel Zumalacárregui, Liang Dai, Mark Ho-Yeuk Cheung

Gravitational wave (GW) astronomy offers the potential to probe the wave-optics regime of gravitational lensing. Wave optics (WO) effects are relevant at low frequencies, when the wavelength is comparable to the characteristic lensing time delay multiplied by the speed of light, and are thus often negligible for electromagnetic signals. Accurate predictions

Peter Kocsis, Peter Súkeník, Guillem Brasó, Matthias Nießner

Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive performance. These architectures typically have a vast number of weights and need to be trained on massive datasets; hence, they are not suitable for their use in low-data regimes

Monica Marras, Stella Vernier-Piro, Tomomi Yokota

In this paper we consider radially symmetric solutions of the following parabolic--elliptic cross-diffusion system \begin{equation*} \begin{cases} u_t = \Delta u - \nabla \cdot (u f(|\nabla v|^2 )\nabla v) + g(u), & \\[2mm] 0= \Delta v -m(t)+ u , \quad \int_{\Omega}v \,dx=0, & \\[2mm] u(x,0)= u_0(x), & \end{cases} \end{equation*} in $\Omega \times (0,\infty)

Peng Sun, Thomas Van Vaerenbergh, Sean Hooten, Raymond Beausoleil

We have designed a polarization-splitting grating coupler (PSGC) in silicon-oninsulator (SOI) that has 1.2 dB peak loss in numerical simulations, which is the best simulated performance of PSGCs without a bottom reflector to the best of our knowledge. Adjoint method-based shape optimization enables us to explore complex geometries that are intractable with c

Ian Geiger, Jian Luo, Enrique J. Lavernia, Penghui Cao

Refractory multi-principal element alloys exhibiting promising mechanical properties such as excellent strength retention at elevated temperatures have been attracting increasing attention. Although their inherent chemical complexity is considered a defining feature, a challenge arises in predicting local chemical ordering, particularly in grain boundary reg

Ivan Gonzales Gargate, Thiago Castilho de Mello

In this paper we consider images of (ordinary) noncommutative polynomials on matrix algebras endowed with a graded structure. We give necessary and sufficient conditions to verify that some multilinear polynomial is a central polynomial, or a trace zero polynomial, and we use this approach to present an equivalent statement to the Lvov-Kaplansky conjecture.

Riley W. Chien, Joel Klassen

In this work we present a method for generating a fermionic encoding tailored to a set of target fermionic operators and to a target hardware connectivity. Our method uses brute force search, over the space of all encodings which map from Majorana monomials to Pauli operators, to find an encoding which optimizes a target cost function. In contrast to earlier

I. Arnquist, N. Avalos, P. Bailly, D. Baxter

The DAMIC-M (DArk Matter In CCDs at Modane) experiment employs thick, fully depleted silicon charged-coupled devices (CCDs) to search for dark matter particles with a target exposure of 1 kg-year. A novel skipper readout implemented in the CCDs provides single electron resolution through multiple non-destructive measurements of the individual pixel charge, p

M. N. Chernodub

We suggest the existence of systems in which the statistics of a particle changes with the quantum level it occupies. The occupation numbers in thermal equilibrium depend on a continuous statistical parameter that interpolates between bosonic or fermionic and ghost-like statistical distributions. We call such particle states ``ninions'': they are different f

O. Bastani, Y. J. Ma, E. Shen, W. Xu

In safety-critical applications of reinforcement learning such as healthcare and robotics, it is often desirable to optimize risk-sensitive objectives that account for tail outcomes rather than expected reward. We prove the first regret bounds for reinforcement learning under a general class of risk-sensitive objectives including the popular CVaR objective.

Suman Bhowmick, Igor M. Sokolov, Hartmut H. K. Lentz

After the detection of coronavirus disease 2019 (Covid-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, Hubei Province, China in late December, the cases of Covid-19 have spiralled out around the globe. Due to the clinical similarity of Covid-19 with other flulike syndromes, patients are assayed for other pathogens of

Luigi Procopio, Simone Conia, Edoardo Barba, Roberto Navigli

Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. Although certainly effective, this strategy pres

Dennis Dobler, Kathrin Möllenhoff

A very classical problem in statistics is to test the stochastic superiority of one distribution to another. However, many existing approaches are developed for independent samples and, moreover, do not take censored data into account. We develop a new estimand-driven method to compare the effectiveness of two treatments in the context of right-censored surv

Nirmal Raj

Greek mythology supplies fundamental physics with the names of numerous (100+) experiments, machines, codes, and phenomena. I present the central narrative of Greek mythos via these names. Hyperlinks are provided for their physics counterparts, and the names are collected in myth- and physics-themed indices.

Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen

It has become standard to solve NLP tasks by fine-tuning pre-trained language models (LMs), especially in low-data settings. There is minimal theoretical understanding of empirical success, e.g., why fine-tuning a model with $10^8$ or more parameters on a couple dozen training points does not result in overfitting. We investigate whether the Neural Tangent K

Jacob van den Berg, Pierre Nolin

We study forest fire processes in two dimensions. On a given planar lattice, vertices independently switch from vacant to occupied at rate $1$ (initially they are all vacant), and any connected component "is burnt" (its vertices become instantaneously vacant) as soon as its cardinality crosses a (typically large) threshold $N$, the parameter of the model. Ou

M. J. Coak, K. Götze, T. Northam De La Fuente, C. Castelnovo

Rare-earth pyrochlore iridates host two interlocking magnetic sublattices of corner-sharing tetrahedra and can harbour a unique combination of frustrated moments, exotic excitations and highly correlated electrons. They are also the first systems predicted to display both topological Weyl semimetal and axion insulator phases. We have measured the transport a

Matthew Hogancamp, David E. V. Rose, Paul Wedrich

We construct a Kirby color in the setting of Khovanov homology: an ind-object of the annular Bar-Natan category that is equipped with a natural handle slide isomorphism. Using functoriality and cabling properties of Khovanov homology, we define a Kirby-colored Khovanov homology that is invariant under the handle slide Kirby move, up to isomorphism. Via the M

Chris Lu, Jakub Grudzien Kuba, Alistair Letcher, Luke Metz

Tremendous progress has been made in reinforcement learning (RL) over the past decade. Most of these advancements came through the continual development of new algorithms, which were designed using a combination of mathematical derivations, intuitions, and experimentation. Such an approach of creating algorithms manually is limited by human understanding and

Yang Ye, Xiulong Yang, Shihao Ji

Processing large point clouds is a challenging task. Therefore, the data is often downsampled to a smaller size such that it can be stored, transmitted and processed more efficiently without incurring significant performance degradation. Traditional task-agnostic sampling methods, such as farthest point sampling (FPS), do not consider downstream tasks when s

Charlotte Olsen, Eric Gawiser

Conformity denotes the correlation of properties between pairs of galaxies as a function of separation. Correlations between properties such as star formation rate (SFR), stellar mass, and specific star formation rate (sSFR) have implications for the impact of environment upon galaxy formation and evolution. Conformity between primary galaxies and satellites

Rakshit Jain, Max Stanley, Arnab Bose, Anthony R. Richardella

We complete measurements of interconversions among the full triad of thermal gradients, charge currents, and spin currents in the topological insulator Bi$_2$Se$_3$ by quantifying the efficiency with which thermal gradients can generate transverse spin currents. We accomplish this by comparing the spin Nernst magneto-thermopower to the spin Hall magnesistanc

Claudio Ravasio, Lyndon Da Cruz, Christos Bergeles

We present a robust theoretical framework for the characterisation and manipulation of optical flow, i.e 2D vector fields, in the context of their use in motion estimation algorithms and beyond. The definition of two frames of reference guides the mathematical derivation of flow field application, inversion, evaluation, and composition operations. This struc

Sebastian Perez-Salazar, Mohit Singh, Alejandro Toriello

In online sales, sellers usually offer each potential buyer a posted price in a take-it-or-leave fashion. Buyers can sometimes see posted prices faced by other buyers, and changing the price frequently could be considered unfair. The literature on posted price mechanisms and prophet inequality problems has studied the two extremes of pricing policies, the fi

Karmesh Yadav, Ram Ramrakhya, Santhosh Kumar Ramakrishnan, Theo Gervet

We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of objec

Zeng Yang, Linhai Zhang, Deyu Zhou

Few-shot named entity recognition (NER) aims at identifying named entities based on only few labeled instances. Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used. To tackle training-from-scratch setting, it is crucial to make

Andrea Pizzo, Angel Lozano

An alternative derivation is provided for the degrees of freedom (DOF) formula on line-of-sight (LOS) channels via Landau's eigenvalue theorem for bandlimited signals. Compared to other approaches, Landau's theorem provides a general framework to compute the DOF in arbitrary environments, this framework is herein specialized to LOS propagation. The developme

Ziyang Wang, Yuxuan Cosmi Lin, Kunyan Zhang, Wenjing Wu

Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are

Francesca von Braun-Bates

A particular open problem in cosmology is whether dark matter on small scales is clumpy, forming gravitationally-bound halos distributed within the Galaxy. The practical difficulties inherent in testing this hypothesis stem from the fact that, on astrophysical scales, dark matter is solely observable via its gravitational interaction with other objects. This

Yier Lin, Li-Cheng Tsai

We consider the variational problem associated with the Freidlin--Wentzell Large Deviation Principle of the Stochastic Heat Equation (SHE). The logarithm of the minimizer of the variational problem gives the most probable shape of the solution of the Kardar--Parisi--Zhang equation conditioned on achieving certain unlikely values. Taking the SHE with the delt

Marion Cromb, Sara Restuccia, Graham M. Gibson, Marko Toros

Understanding quantum mechanics within curved spacetime is a key stepping stone towards understanding the nature of spacetime itself. Whilst various theoretical models have been developed, it is significantly more challenging to carry out actual experiments that probe quantum mechanics in curved spacetime. By adding Sagnac interferometers into the arms of a

Charles S. do Amaral, Diogo C. dos Santos

We consider a random sequential adsorption process on the one-dimensional lattice with nearest-neighbor exclusion. In this model, each site $s \in \mathbb{Z}$ starts empty and we will try to occupy it in time $t_s$, where $(t_s)_{s\in\mathbb{Z}}$ is a sequence of independent random variables uniformly distributed on the interval $[0,1]$. The site will be occ

Abdulrahman Kerim, Felipe Chamone, Washington Ramos, Leandro Soriano Marcolino

Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source

Chen Liu, Rami Masri, Beatrice Riviere

This paper is devoted to the analysis of an energy-stable discontinuous Galerkin algorithm for solving the Cahn-Hilliard-Navier-Stokes equations within a decoupled splitting framework. We show that the proposed scheme is uniquely solvable and mass conservative. The energy dissipation and the $L^\infty$ stability of the order parameter are obtained under a CF

Rafael Wagner, Anita Camillini, Ernesto F. Galvão

We analyse nonclassical resources in interference phenomena using generalized noncontextuality inequalities and basis-independent coherence witnesses. We use recently proposed inequalities that witness both resources within the same framework. We also propose, in view of previous contextual advantage results, a systematic way of applying these tools to chara

A. Bhanpurawala, K. El-Fakih, I. Zualkernan

With the rapid growth in the number of IoT devices being added to the network, a major concern that arises is the security of these systems. As these devices are resource constrained, safety measures are difficult to implement on the edge. We propose a novel approach for the detection of IoT device attacks based on the use of formal modeling and mutation tes

Peter Imkeller, Rhoss Likibi Pellat, Olivier Menoukeu Pamen

In this paper, we consider quadratic forward-backward SDEs (QFBSDEs), for {which} the drift in the forward equation does not satisfy the standard globally Lipschitz condition and the driver of the backward system {possesses} nonlinearity of type $f(|y|)|z|^2,$ where $f$ is any locally integrable function. We prove both the Malliavin and classical derivative

Eun Kyo Ko, Sungsoo Hahn, Changhee Sohn, Sangmin Lee

Hund's rule coupling ($\textit{J}$) has attracted much attention recently for its role in the description of the novel quantum phases of multi orbital materials. Depending on the orbital occupancy, $\textit{J}$ can lead to various intriguing phases. However, experimental confirmation of the orbital occupancy dependency has been difficult as controlling the o

Karthik V. Myilswamy, Suparna Seshadri, Hsuan-Hao Lu, Mohammed S. Alshaykh

Biphoton frequency combs (BFCs) are promising quantum sources for large-scale and high-dimensional quantum information and networking systems. In this context, the spectral purity of individual frequency bins will be critical for realizing quantum networking protocols like teleportation and entanglement swapping. Measurement of the temporal auto-correlation

Matthew Brendel, Chang Su, Zilong Bai, Hao Zhang

Single-cell RNA-sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during development of complex or

Isabel Papadimitriou, Kezia Lopez, Dan Jurafsky

While multilingual language models can improve NLP performance on low-resource languages by leveraging higher-resource languages, they also reduce average performance on all languages (the 'curse of multilinguality'). Here we show another problem with multilingual models: grammatical structures in higher-resource languages bleed into lower-resource languages

Elissa Mhanna, Mohamad Assaad

In this work, we consider a distributed multi-agent stochastic optimization problem, where each agent holds a local objective function that is smooth and convex, and that is subject to a stochastic process. The goal is for all agents to collaborate to find a common solution that optimizes the sum of these local functions. With the practical assumption that a

Elisabeth Larsson, Robert Schaback

This paper studies the influence of scaling on the behavior of Radial Basis Function interpolation. It focuses on certain central aspects, but does not try to be exhaustive. The most important questions are: How does the error of a kernel-based interpolant vary with the scale of the kernel chosen? How does the standard error bound vary? And since fixed funct

Jiapeng Tang, Lev Markhasin, Bi Wang, Justus Thies

We present Neural Shape Deformation Priors, a novel method for shape manipulation that predicts mesh deformations of non-rigid objects from user-provided handle movements. State-of-the-art methods cast this problem as an optimization task, where the input source mesh is iteratively deformed to minimize an objective function according to hand-crafted regulari

Jean-Marcel Tanoh Dje, Benoît F. Sehba

In this note, we provide various two-weight norm estimates of the multi-linear fractional maximal function and weighted maximal function between different Orlicz spaces. More precisely, we obtain Sawyer-type characterizations and norm estimates for these operators.

Meir Brand, Itay Naeh, Daniel Teitelman

In this paper, we present a proof of concept for adversarially attacking the image-based localization module of an autonomous vehicle. This attack aims to cause the vehicle to perform a wrong navigational decisions and prevent it from reaching a desired predefined destination in a simulated urban environment. A database of rendered images allowed us to train

Chao-Han Huck Yang, I-Fan Chen, Andreas Stolcke, Sabato Marco Siniscalchi

Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise perturbation often results in a severe performance degradation in automatic speech recognition (ASR) in order to meet a privacy budget $\varepsilon$. Private aggregation of teacher ense

Muhammad Khalifa, Yogarshi Vyas, Shuai Wang, Graham Horwood

We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured documents, as positional, layout, and style information play a vital role in interpreting such documents. The standard classification setting where categories are fixed during both t

Treanungkur Mal

I am going to provide a new technique of approximating area under the curve, using the Newton-Raphson Method. I am also going to provide a formula that would help us approximate any Definite Integral or help us find the area under the curve, under certain conditions. The relative error of this formula is very small, which makes it even more interesting.

Viorel Barbu, Michael Röckner

This work is concerned with the existence of mild solutions to non-linear Fokker-Planck equations with fractional Laplace operator $(-\Delta)^s$ for $s\in\left(\frac12,1\right)$. The uniqueness of Schwartz distributional solutions is also proved under suitable assumptions on diffusion and drift terms. As applications, weak existence and uniqueness of solutio

Chinh Ngo, Trieu H. Trinh, Long Phan, Hieu Tran

We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation. MTet consists of 4.2M high-quality training sentence pairs and a multi-domain test set refined by the Vietnamese research community. Combining with previous works on English-Vietnamese translation, we grow the existing parallel dataset to 6.2M sentence pairs

Ruiqi Wang, Diego Klabjan

Stochastic optimization algorithms using exponential moving averages of the past gradients, such as ADAM, RMSProp and AdaGrad, have been having great successes in many applications, especially in training deep neural networks. ADAM in particular stands out as efficient and robust. Despite of its outstanding performance, ADAM has been proved to be divergent f

Sneha Prakash Mudambi, S. B. Gudennavar, R. Misra, S. G. Bubbly

We report spectral analysis of the persistent black hole X-ray binary, 4U 1957+115, using AstroSat, Swift and NuSTAR observations carried out between 2016-2019. Modelling with a disk emission, thermal comptonization and blurred reflection components revealed that the source was in the high soft state with the disk flux $\sim 87$ % of the total and high energ

Gerd Grubb

Let $P$ be a symmetric $2a$-order classical strongly elliptic pseudodifferential operator with even symbol $p(x,\xi )$ on $R^n$ ($0<a<1$), for example a perturbation of $(-\Delta )^a$. Let $\Omega \subset R^n$ be bounded, and let $P_D$ be the Dirichlet realization in $L_2(\Omega )$ defined under the exterior condition $u=0$ in $R^n\setminus\Omega $. When $p(

Martin Lanthaler, Clemens Dlaska, Kilian Ender, Wolfgang Lechner

We present a scalable architecture for solving higher-order constrained binary optimization problems on current neutral-atom hardware operating in the Rydberg blockade regime. In particular, we formulate the recently developed parity encoding of arbitrary connected higher-order optimization problems as a maximum-weight independent set (\textsf{MWIS}) problem

M. N. Shneider, V. V. Semak

A theoretical model that describes a new mechanism of atomic and molecular ionization in a low intensity electro-magnetic wave (light or laser beam) with the energy of quanta that is lower than required for a single photon ionization is presented. The essence of the proposed physical mechanism is the step-like gain of energy of a bound electron that occurs e

Ana Shirley Monteiro, Regivan Santiago, Martin Papco, Radko Mesiar

In this paper, we develop the notions of weak/directional monotonicity (developed by Sesma-Sara et al. in terms of the \emph{Kulisch-Miranker order}) and the notion of $ G $-monotonicity (introduced by Santiago et al. for $ [0,\!1]$) for intervals endowed with admissible orders.

Sylvain D. Brechet

We consider the electrodynamics of electric charges and currents in vacuum and then generalise our results to the description of a dielectric and magnetic material medium : first in spatial algebra (SA) and then in space-time algebra (STA). Introducing a polarisation multivector $\tilde{P} = \boldsymbol{\tilde{p}} -\,\frac{1}{c}\,\boldsymbol{\tilde{M}}$ and

Yuanzheng He, Jiang Wang, Daobilige Su, Kazuhiro Nakadai

Multiple microphone arrays have many applications in robot audition, including sound source localization, audio scene perception and analysis, etc. However, accurate calibration of multiple microphone arrays remains a challenge because there are many unknown parameters to be identified, including the Euler angles, geometry, asynchronous factors between the m

Guangchun Ruan, Jianxiao Wang, Haiwang Zhong, Qing Xia

The superior performance of deep learning relies heavily on a large collection of sample data, but the data insufficiency problem turns out to be relatively common in global electricity markets. How to prevent overfitting in this case becomes a fundamental challenge when training deep learning models in different market applications. With this in mind, we pr

Long Phan, Tai Dang, Hieu Tran, Trieu H. Trinh

Biomedical data and benchmarks are highly valuable yet very limited in low-resource languages other than English such as Vietnamese. In this paper, we make use of a state-of-the-art translation model in English-Vietnamese to translate and produce both pretrained as well as supervised data in the biomedical domains. Thanks to such large-scale translation, we

M Quamer Nasim, Narendra Patwardhan, Tannistha Maiti, Tarry Singh

Raster well-log images are digital representations of well-logs data generated over the years. Raster digital well logs represent bitmaps of the log image in a rectangular array of black (zeros) and white dots (ones) called pixels. Experts study the raster logs manually or with software applications that still require a tremendous amount of manual input. Bes