October 2022 arXiv papers — page 105

Esther Banaian, Archan Sen

We explore a generalization of the Markov numbers that is motivated by a specific generalized cluster algebra arising from an orbifold, in the sense of Chekhov and Shapiro. We give an explicit algorithm for computing these generalized Markov numbers and exhibit several patterns analogous to those that appear within the ordinary Markov numbers. Along the way,

Giuseppe Attanasio, Debora Nozza, Federico Bianchi, Dirk Hovy

Language is constantly changing and evolving, leaving language models to become quickly outdated. Consequently, we should continuously update our models with new data to expose them to new events and facts. However, that requires additional computing, which means new carbon emissions. Do any measurable benefits justify this cost? This paper looks for empiric

Guillem Ramírez-Miranda, Daniel Mira, Eduardo J. Pérez-Sánchez, Anurag Surapaneni

This paper presents a load balancing strategy for reaction rate evaluation and chemistry integration in reacting flow simulations. The large disparity in scales during combustion introduces stiffness in the numerical integration of the PDEs and generates load imbalance during the parallel execution. The strategy is based on the use of the DLB library to redi

Aleksander B G Christiansen

Recent papers [Ber'2022], [GP'2020], [DHZ'2019] have addressed different variants of the (\Delta + 1)-edge colouring problem by concatenating or gluing together many Vizing chains to form what Bernshteyn [Ber'2022] coined \emph{multi-step Vizing chains}. In this paper, we propose a slightly more general definition of this term. We then apply multi-step Vizin

Chia-Chien Hung, Anne Lauscher, Dirk Hovy, Simone Paolo Ponzetto

Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks with traditional NLP models. In this work, we investigate whether these previous findings still hold with state-of-the-art pretrained Transformer-based language models (PLMs). We

Ezequiel Alvarez

We study the application of a Bayesian method to extract relevant information from data for the case of a signal consisting of two or more decaying particles and its background. The method takes advantage of the dependence that exists in the distributions of the decaying products at the event-by-event level and processes the information for the whole sample

Krzysztof Grochot, Piotr Ogrodnik, Jakub Mojsiejuk, Piotr Mazalski

This paper investigates the relationship among interlayer exchange coupling (IEC), Dzyaloshinskii-Moriya interaction (DMI), and multilevel magnetization switching within a Co/Pt/Co heterostructure, where varying Pt thicknesses enable control over the coupling strength. Employing Brillouin Light Scattering to quantify the effective DMI, we explore its potenti

Haiyu Wu, Grace Bezold, Manuel Günther, Terrance Boult

We report the first systematic analysis of the experimental foundations of facial attribute classification. Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate

Alexander Shurinov, Ivan Dyakonov, Sergei Kulik, Stanislav Straupe

We present a heuristic mathematical model of the relation between the geometry of a photonic crystal waveguide and the Purcell enhancement factor at a particular wavelength of interest. We use this model to propose approaches to the design of a photonic crystal waveguide maximizing the Purcell enhancement at a target wavelength. Numerical simulations indicat

Daniel Scarafoni, Irfan Essa, Thomas Ploetz

We address dense action forecasting: the problem of predicting future action sequence over long durations based on partial observation. Our key insight is that future action sequences are more accurately modeled with variable, rather than one, levels of abstraction, and that the optimal level of abstraction can be dynamically selected during the prediction p

Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna, Yu Zhang

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous works, we explore joint training with both modalities, rather than pre-training and fine-tuning. In addition, we explore JOIST using a streaming E2E model with an order of magnitude

Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz

Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has received increasing attention as being a lightweight method for interpreting the intrinsic mechanisms of large NLP models. In probing, post-hoc classifiers are trained on "out-of-domain"

Yan Gong, Peng Zhong, Thomas Opitz, Raphaël Huser

We propose a novel extremal dependence measure called the partial tail-correlation coefficient (PTCC), in analogy to the partial correlation coefficient in classical multivariate analysis. The construction of our new coefficient is based on the framework of multivariate regular variation and transformed-linear algebra operations. We show how this coefficient

Jiang Cao, Guido Gandus, Tarun Agarwal, Mathieu Luisier

A van der Waals (vdW) charge qubit, electrostatically confined within two-dimensional (2D) vdW materials, is proposed as building block of future quantum computers. Its characteristics are systematically evaluated with respect to its two-level anti-crossing energy difference ($\Delta$). Bilayer graphene ($\Delta$ $\approx$ 0) and a vdW heterostructure ($\Del

Zhiang Chen, Chelsea Scott, Devin Keating, Amanda Clarke

We apply a deep learning model to segment and identify rock characteristics based on a Structure-from-Motion orthomap and digital elevation model of a rocky fault scarp in the Volcanic Tablelands, eastern California. By post-processing the deep learning results, we build a semantic rock map and analyze rock trait distributions. The resulting semantic map con

Rosalia O'Brien, Timothy Carleton, Rogier A. Windhorst, Rolf A. Jansen

The diffuse, unresolved sky provides most of the photons that the Hubble Space Telescope (HST) receives, yet remains poorly understood. HST Archival Legacy program SKYSURF aims to measure the 0.2-1.6 $\mu$m sky surface brightness (sky-SB) from over 140,000 HST images. We describe a sky-SB measurement algorithm designed for SKYSURF that is able to recover the

Liran Shaul

Given a non-positive DG-ring $A$, associated to it are the reduction and coreduction functors $F(-) = \mathrm{H}^0(A)\otimes^{\mathrm{L}}_A -$ and $G(-) = \mathrm{R}\operatorname{Hom}_A(\mathrm{H}^0(A),-)$, considered as functors $\operatorname{\mathsf{D}}(A) \to \operatorname{\mathsf{D}}(\mathrm{H}^0(A))$, as well as the forgetful functor $S:\operatorname{\

Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent

A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is one promising direction aimed at aligning a model's representation with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumpt

Changjiang Li, Ren Pang, Zhaohan Xi, Tianyu Du

As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to eliminating the need for labeled data, research has found that SSL improves the adversarial robustness over supervised learning since lacking labels makes it more challenging for a

Megan Chambers, Natalie Johnston, Ian Livengood, Miya Spinelli

Pulmonary hypertension (PH), defined by a mean pulmonary arterial blood pressure above 20 mmHg, is a cardiovascular disease impacting the pulmonary vasculature. PH is accompanied by vascular remodeling, wherein vessels become stiffer, large vessels dilate, and smaller vessels constrict. Some types of PH, including hypoxia-induced PH (HPH), lead to microvascu

Ali Hirsa, Massoud Heidari

Individual trade orders are often bunched into a block order for processing efficiency, where in post execution, they are allocated into individual accounts. Since Regulators have not mandated any specific post trade allocation practice or methodology, entities try to rigorously follow internal policies and procedures to meet the minimum Regulatory ask of be

Anudeep K. Arora, Svetlana Roudenko

We consider the focusing $5$d Hartree equation, which is $L^2$-supercritical, with finite energy initial data, and investigate the solutions at the mass-energy threshold. We establish the existence of special solutions following the work of Duyckaerts-Roudenko [11] for the $3$d focusing cubic nonlinear Schr\"odinger equation (NLS). In particular, apart from

Ilker Turker, Serhat Orkun Tan

Applying graph-based approaches in deep learning receives more attention over time. This study presents statistical analysis on the use of graph-based approaches in deep learning and examines the scientific impact of the related articles. Processing the data obtained from the Web of Science database, metrics such as the type of the articles, funding availabi

Gustavo Pinto, Alberto de Souza

Software design techniques are undoubtedly crucial in the process of designing good software. Over the years, a large number of design techniques have been proposed by both researchers and practitioners. Unfortunately, despite their uniqueness, it is not uncommon to find software products that make subpar design decisions, leading to design degradation chall

Mufhumudzi Muthivhi, Terence L. van Zyl, Hairong Wang

Context-aware recommendation systems improve upon classical recommender systems by including, in the modelling, a user's behaviour. Research into context-aware recommendation systems has previously only considered the sequential ordering of items as contextual information. However, there is a wealth of unexploited additional multi-modal information available

Akash Nagaraj, Mukund Sood, Gowri Srinivasa

In recent years, the role of big data analytics has exponentially grown and is now slowly making its way into the education industry. Several attempts are being made in this sphere in order to improve the quality of education being provided to students and while many collaborations have been carried out before, automated scoring of answers has been explored

Stephan Ehlen, Yingkun Li, Markus Schwagenscheidt

In this paper, we use a regularized theta lifting to construct harmonic Maass forms corresponding to binary theta functions of weight $k \ge 2$ under the $\xi$-operator. As a result, we show that their holomorphic parts have algebraic Fourier coefficients, with compatible Galois action. As an application, we prove rationality properties of coefficients of ha

Han Yu, Huiyuan Yang, Akane Sano

Contrastive learning has been utilized as a promising self-supervised learning approach to extract meaningful representations from unlabeled data. The majority of these methods take advantage of data-augmentation techniques to create diverse views from the original input. However, optimizing augmentations and their parameters for generating more effective vi

Sina Sanjari, Naci Saldi, Serdar Yüksel

We study stochastic mean-field games among finite number of teams with large finite as well as infinite number of decision makers. For this class of games within static and dynamic settings, we establish the existence of a Nash equilibrium, and show that a Nash equilibrium exhibits exchangeability in the finite decision maker regime and symmetry in the infin

Anna Winnicki, R. Srikant

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two

Payam Abdisarabshali, Minghui Liwang, Amir Rajabzadeh, Mahmood Ahmadi

Vehicular cloud (VC) is a promising technology for processing computation-intensive applications (CI-Apps) on smart vehicles. Implementing VCs over the network edge faces two key challenges: (C1) On-board computing resources of a single vehicle are often insufficient to process a CI-App; (C2) The dynamics of available resources, caused by vehicles' mobility,

H. Moritz Mangold, Helmut Karl, Hubert J. Krenner

Cadmium selenide nanocrystal quantum dots (NC-QDs) are site-selectively synthesized by sequential ion beam implantation of selenium and cadmium ions in a SiO2 matrix through sub-micron apertures followed by a rapid thermal annealing step. The size, areal density and optical emission energy of the NC-QDs are controlled by the ion fluence during implantation a

Raj Patel

Task competition by robots is still off from being completely dependable and usable. One way a robot may decipher information given to it and accomplish tasks is by utilizing FOON, which stands for functional object-oriented network. The network first needs to be created by having a human creates action nodes as well as input and output nodes in a .txt file.

M. Kozhevnikova, Yu. B. Ivanov

We present results of simulations of light-nuclei production in relativistic heavy-ion collisions within updated Three-fluid Hydrodynamics-based Event Simulator Extended by UrQMD (Ultra-relativistic Quantum Molecular Dynamics) final State interactions (THESEUS). The simulations were performed for Pb+Pb and Au+Au collisions in the collision energy range of $\

MohammadTaghi Hajiaghayi, MohammadReza Khani, Debmalya Panigrahi, Max Springer

The Santa Claus problem is a fundamental problem in fair division: the goal is to partition a set of heterogeneous items among heterogeneous agents so as to maximize the minimum value of items received by any agent. In this paper, we study the online version of this problem where the items are not known in advance and have to be assigned to agents as they ar

Mayana Pereira, Sikha Pentyala, Anderson Nascimento, Rafael T. de Sousa

Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holders supply their raw data to a trusted cur

R. F. Santos, A. C. Amaro Faria, L. G. Almeida

The objective of this work is to revisit fundamental aspects of relativistic hydrodynamics, aiming at the construction of a first course in relativistic hydrodynamics and its applications to astrophysics at the level of end of undergraduate course and beginning of graduate course. We aim to introduce more basic concepts of basic hydrodynamics, going through

Akash Nagaraj, Akhil K, Akshay Venkatesh, Srikanth HR

In this paper, we present a characteristic extraction algorithm and the Multi-domain Image Characteristics Dataset of characteristic-tagged images to simulate the way a human brain classifies cross-domain information and generates insight. The intent was to identify prominent characteristics in data and use this identification mechanism to auto-generate insi

Jessica Burns, Owen Root, Hui Jing, Imran M. Mirza

We theoretically study the spectral properties of a pump-probe driven hybrid spinning optomechanical ring resonator optically coupled with a two-level quantum emitter (QE or qubit). Recently we have shown [arXiv:1810.03709] that in the absence of the emitter the coupled cavity version of this setup is not only capable of nonreciprocal light propagation but c

Jee-eun Lee, Jaemin Lee, Tirthankar Bandyopadhyay, Luis Sentis

Model generalization of the underlying dynamics is critical for achieving data efficiency when learning for robot control. This paper proposes a novel approach for learning dynamics leveraging the symmetry in the underlying robotic system, which allows for robust extrapolation from fewer samples. Existing frameworks that represent all data in vector space fa

Tom Coates, Liana Heuberger, Alexander M. Kasprzyk

We describe recent progress in a program to understand the classification of three-dimensional Fano varieties with $\mathbb{Q}$-factorial terminal singularities using mirror symmetry. As part of this we give an improved and more conceptual understanding of Laurent inversion, a technique that sometimes allows one to construct a Fano variety $X$ directly from

Ilker Turker, Serhat Orkun Tan

Introduction of fifth generation (5G) wireless network technology has matched the crucial need for high capacity and speed needs of the new generation mobile applications. Recent advances in Artificial Intelligence (AI) also empowered 5G cellular networks with two mainstreams as machine learning (ML) and deep learning (DL) techniques. Our study aims to uncov

Samy Jelassi, Michael E. Sander, Yuanzhi Li

Vision Transformers (ViTs) have achieved comparable or superior performance than Convolutional Neural Networks (CNNs) in computer vision. This empirical breakthrough is even more remarkable since, in contrast to CNNs, ViTs do not embed any visual inductive bias of spatial locality. Yet, recent works have shown that while minimizing their training loss, ViTs

Neelam Choudhary, Nicolas Gillis, Punit Sharma

In this paper, we provide a dissipative Hamiltonian (DH) characterization for the set of matrices whose eigenvalues belong to a given LMI region. This characterization is a generalization of that of Choudhary et al. (Numer. Linear Algebra Appl., 2020) to any LMI region. It can be used in various contexts, which we illustrate on the nearest $\Omega$-stable ma

Vanderléa R. Bazao, César R. de Oliveira, Pablo A. Diaz

It is shown that if $X$ is a unitary operator so that a singular subspace of~$U$ is unitarily equivalent to a singular subspace of~$UX$ (or $XU$), for each unitary operator~$U$, then $X$ is the identity operator. In other words, there is no nontrivial generalization of Birman-Krein Theorem that includes the preservation of a singular spectral subspace in thi

Bingkai Wang, Chan Park, Dylan S. Small, Fan Li

Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands a

Ali Safaya, Engin Erzin

While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this report, we present our findings on Turkish ASR with speech representation learning using HUBERT. We investigate pre-training HUBERT for Turkish with large-scale data curated from online resources. We pre-train

Anuradha M. Annaswamy, Vineet Jagadeesan Nair

Several examples of Cyber-physical human systems (CPHS) include real-time decisions from humans as a necessary building block for the successful performance of the overall system. Many of these decision-making problems necessitate an appropriate model of human behavior. Tools from Utility Theory have been used successfully in several problems in transportati

Evan Crothers, Nathalie Japkowicz, Herna Viktor

Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of

Daniel Štěrba, Jaroslav Novotný, Igor Jex

The paper concerns spontaneous asymptotic phase-locking and synchronization in two-qubit systems undergoing continuous Markovian evolution described by Lindbladian dynamics with normal Lindblad operators. Using analytic methods, all phase-locking-enforcing mechanisms within the given framework are obtained and classified. Detailed structures of their respect

Richard H. J. Kim, Joong-Mok Park, Samuel J. Haeuser, Liang Luo

We have developed a versatile near-field microscopy platform that can operate at high magnetic fields and below liquid-helium temperatures. We use this platform to demonstrate an extreme terahertz (THz) nanoscope operation and to obtain the first cryogenic magneto-THz time-domain nano-spectroscopy/imaging at temperatures as low as 1.8 K and magnetic fields o

Yuan Le, Yicheng Zhang, Sarang Gopalakrishnan, Marcos Rigol

Hydrodynamics accurately describes relativistic heavy-ion collision experiments well before local thermal equilibrium is established. This unexpectedly rapid onset of hydrodynamics -- which takes place on the fastest available timescale -- is called hydrodynamization. It occurs when an interacting quantum system is quenched with an energy density that is muc

Hong Yang, Travis Desell

This paper presents the largest publicly available, non-simulated, fleet-wide aircraft flight recording and maintenance log data for use in predicting part failure and maintenance need. We present 31,177 hours of flight data across 28,935 flights, which occur relative to 2,111 unplanned maintenance events clustered into 36 types of maintenance issues. Flight

Niklas Muennighoff, Nouamane Tazi, Loïc Magne, Nils Reimers

Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as va

Pushyami Kaveti, Shankara Narayanan Vaidyanathan, Arvind Thamilchelvan, Hanumant Singh

Multi-camera systems have been shown to improve the accuracy and robustness of SLAM estimates, yet state-of-the-art SLAM systems predominantly support monocular or stereo setups. This paper presents a generic sparse visual SLAM framework capable of running on any number of cameras and in any arrangement. Our SLAM system uses the generalized camera model, whi

Michael Patrick Sheehan, Julian Tachella, Mike E. Davies

Photon counting lidar has become an invaluable tool for 3D depth imaging due to the fine-precision it can achieve over long ranges. However, high frame rate, high resolution lidar devices produce an enormous amount of time-of-flight (ToF) data which can cause a severe data processing bottleneck hindering the deployment of real-time systems. In this paper, an

Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta, Shachi Dave

Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models. However, for many low-resource languages, the availability of a reliable translation service entails signifi

Md Masudur Rahman, Yexiang Xue

This paper proposes an advantage estimation approach based on data augmentation for policy optimization. Unlike using data augmentation on the input to learn value and policy function as existing methods use, our method uses data augmentation to compute a bootstrap advantage estimation. This Bootstrap Advantage Estimation (BAE) is then used for learning and

A. Libal, P. Forgacs, A. Neda, C. Reichhardt

The Susceptible-Infected (SI) and Susceptible-Infected-Recovered (SIR) models provide two distinct representations of epidemic evolution, distinguished by the lack of spontaneous recovery in the SI model. Here we introduce a new active matter epidemic model, the ``Susceptible-Cleric-Zombie-Recovered'' (SCZR) model, in which spontaneous recovery is absent but

Yuan Luo

Hypergraph neural networks can model multi-way connections among nodes of the graphs, which are common in real-world applications such as genetic medicine. In particular, genetic pathways or gene sets encode molecular functions driven by multiple genes, naturally represented as hyperedges. Thus, hypergraph-guided embedding can capture functional relations in

M. Ahumada, A. Ledesma-Araujo, Leonardo Gordillo, Juan F. Marín

The crisis caused by the COVID-19 outbreak around the globe raised an increasing concern about the ongoing emergence of variants of SARS-CoV-2 that may evade the immune response provided by vaccines. New variants appear due to mutation, and as the cases accumulate, the probability of the emergence of a variant of concern increases. In this article, we propos

Poly H. da Silva, Arash Jamshidpey, Simon Tavaré

Models of counts-of-counts data have been extensively used in the biological sciences, for example in cancer, population genetics, sampling theory and ecology. In this paper we explore properties of one model that is embedded into a continuous-time process and can describe the appearance of certain biological data such as covid DNA sequences in a database. M

Fedor Kuyanov, Alexey Slizkov

We study Feynman checkers, an elementary model of electron motion introduced by R. Feynman. In this model, a checker moves on a checkerboard, and we count the turns. Feynman checkers are also known as a one-dimensional quantum walk. We prove some new number-theoretic results in this model, for example, sign alternation of the real and imaginary parts of the

Riccardo Catena, Daniel Cole, Timon Emken, Marek Matas

The search for sub-GeV dark matter (DM) particles via electronic transitions in underground detectors attracted much theoretical and experimental interest in the past few years. A still open question in this field is whether experimental results can in general be interpreted in a framework where the response of detector materials to an external DM probe is d

Agne Semenaite, Ariel G. Sánchez, Andrea Pezzotta, Jiamin Hou

We analyse the full shape of anisotropic clustering measurements from the extended Baryon Oscillation Spectroscopic survey (eBOSS) quasar sample together with the combined galaxy sample from the Baryon Oscillation Spectroscopic Survey (BOSS). We obtain constraints on the cosmological parameters independent of the Hubble parameter $h$ for the extensions of th

Gino Biondini, Xu-Dan Luo, Jeffrey Oregero, Alexander Tovbis

We present an explicit two-parameter family of finite-band Jacobi elliptic potentials for a non-self-adjoint Dirac operator which connects two previously known limiting cases in which the elliptic parameter is zero or one. A full characterization of the spectrum is obtained by relating the periodic and antiperiodic eigenvalue problems for the Dirac operator

Nikolaos Papadis, Leandros Tassiulas

Payment channel networks (PCNs) are a layer-2 blockchain scalability solution, with its main entity, the payment channel, enabling transactions between pairs of nodes "off-chain," thus reducing the burden on the layer-1 network. Nodes with multiple channels can serve as relays for multihop payments by providing their liquidity and withholding part of the pay

Jaehoon Ko, Kyusun Cho, Daewon Choi, Kwangrok Ryoo

With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the e

Julius Dicke, Lukas Rammelmüller, Fabian Grusdt, Lode Pollet

We study the phase diagram of two different mixed-dimensional $t-J_z-J_{\perp}$-models on the square lattice, in which the hopping amplitude $t$ is only nonzero along the $x$-direction. In the first, bosonic, model, the spin exchange amplitude $J_{\perp}$ is negative and isotropic along the $x$ and $y$ directions of the lattice, and $J_z$ is isotropic and po

Tibério Pereira, Riccardo Sturani

Numerical Relativity has been of fundamental importance for studying compact binary coalescence dynamics, waveform modelling, and eventually for gravitational waves observations. As the sensitivity of the detector network improves, more precise template modelling will be necessary to guarantee a more accurate estimation of astrophysical parameters. To help i

Robin J. Boyd, Gary D. Powney, Oliver L. Pescott

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of 'big data', however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spur

Dacheng Li, Hongyi Wang, Eric Xing, Hao Zhang

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups. In this paper, we develop AMP, a framework that automatically derives suc

Frans van der Sluis

Rather than using (proxies of) end user or expert judgment to decide on the ranking of information, this paper asks whether conversations about information quality might offer a feasible and valuable addition for ranking information. We introduce a theoretical framework for information quality, outlining how information interaction should be perceived as a c

Mayank Mishra, Danish Contractor, Dinesh Raghu

Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses. However, relevant information required to generate responses may also reside in unstructured sources, such as documents. Recent state of the art models such as HyKnow and SeKnow aimed at overcoming these challenges make

Thanh Le, Ha Tran, Suresh Singh

Terahertz frequencies are an untapped resource for providing high-speed short-range communications. As a result, it is of interest to study the propagation characteristics of terahertz waves and to develop channel models. In previous work we used a measurement-based approach to develop an accurate channel model for line of sight (LoS) links. In this paper we

C. Hebborn, F. M. Nunes, G. Potel, W. H. Dickhoff

We review recent progress and motivate the need for further developments in nuclear optical potentials that are widely used in the theoretical analysis of nucleon elastic scattering and reaction cross sections. In regions of the nuclear chart away from stability, which represent a frontier in nuclear science over the coming decade and which will be probed at

Lorenzo Arsini, Matteo Straccamore, Andrea Zaccaria

Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we devel

Anthony J. Brady, Xin Chen, Kewen Xiao, Yi Xia

The nature of dark matter is one of the most important open questions in modern physics. The search for dark matter is challenging since, besides gravitational interaction, it feebly interacts with ordinary matter. Mechanical sensors are one of the leading candidates for dark matter searches in the low frequency region. Here, we propose entanglement-enhanced

Berkay Kullukçu, Mohammad Bathaei, Muhammad Awais, Hadi Mirzajani

This research describes a piezoelectric Poly(vinylidene fluoride-co-trifluoroethylene)/ Polyethylene Terephthalate energy harvester for structural health monitoring of wind turbines. The piezoelectric energy harvester was made of a polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) layer. In addition, PET sheets, double-sided micron-thick tapes, and PVDF

Xi Wang, Tomas Geffner, Justin Domke

Black-box variational inference performance is sometimes hindered by the use of gradient estimators with high variance. This variance comes from two sources of randomness: Data subsampling and Monte Carlo sampling. While existing control variates only address Monte Carlo noise, and incremental gradient methods typically only address data subsampling, we prop

Cristian D. Panda, Matthew Tao, James Egelhoff, Miguel Ceja

In quantum metrology and quantum simulation, a coherent non-classical state must be manipulated before unwanted interactions with the environment lead to decoherence. In atom interferometry, the non-classical state is a spatial superposition, where each atom coexists in multiple locations as a collection of phase-coherent partial wavepackets. These states en

Suddhasattwa Brahma, Robert Brandenberger, Samuel Laliberte

We review a proposal to obtain an emergent metric space-time and an emergent early universe cosmology from the BFSS matrix model. Some challenges and directions for future research are outlined.

Khashayar Namdar, Matthias W. Wagner, Kareem Kudus, Cynthia Hawkins

Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, and identification of molecular markers for pLGG is crucial for successful treatment planning. Convolutional Neural Network (CNN) models for pLGG subtype identification rely on tumor segmentation. We hypothesize tumor segmentations are suboptimal and

Arnab Sen Sharma, Mohammad Ruhul Amin, Muztaba Fuad

Online remote learning has certain advantages, such as higher flexibility and greater inclusiveness. However, a caveat is the teachers' limited ability to monitor student interaction during an online class, especially while teachers are sharing their screens. We have taken feedback from 12 teachers experienced in teaching undergraduate-level online classes o

Nikolaos Stathoulopoulos, Anton Koval, George Nikolakopoulos

Current global re-localization algorithms are built on top of localization and mapping methods andheavily rely on scan matching and direct point cloud feature extraction and therefore are vulnerable infeatureless demanding environments like caves and tunnels. In this article, we propose a novel globalre-localization framework that: a) does not require an ini

X. Zhang, A. Simionescu, F. Gastaldello, D. Eckert

The footprint of LoTSS-DR2 covers 309 PSZ2 galaxy clusters, 83 of which host a radio halo and 26 host a radio relic(s). It provides us an excellent opportunity to statistically study the properties of extended cluster radio sources, especially their connection with merging activities. We aim to quantify cluster dynamic states to investigate their relation wi

Mihir Sheth

Let $F$ be a non-archimedean local field of residual characteristic $p>3$ and residue degree $f>1$. We study a certain type of diagram, called \emph{cyclic diagrams}, and use them to show that the universal supersingular modules of $\mathrm{GL}_{2}(F)$ admit infinitely many non-isomorphic irreducible admissible quotients.

Muhammed Murat Özbek, Süleyman Yıldırım, Muhammet Aksoy, Eric Kernin

The advent of deep learning (DL) gave rise to significant breakthroughs in Reinforcement Learning (RL) research. Deep Reinforcement Learning (DRL) algorithms have reached super-human level skills when applied to vision-based control problems as such in Atari 2600 games where environment states were extracted from pixel information. Unfortunately, these envir

Eknath Ghate, Daniel Le, Mihir Sheth

Let $p>3$ and $F$ be a non-archimedean local field with residue field a proper finite extension of $\mathbb{F}_p$. We construct smooth absolutely irreducible non-admissible representations of $\mathrm{GL}_2(F)$ defined over the residue field of $F$ extending the earlier results of the authors for $F$ unramified over $\mathbb{Q}_{p}$. This construction uses t

Frank Quinn

In "Object generators, relaxed sets, and a foundation for mathematics", we introduced ``object generators'', a logical environment much more general than set theory. Inside this we found a `relaxed' version of set theory. That paper is focused on construction of the universal Zermillo-Fraenkel-Choice theory, and the argument that it alone is consistent with

Hazem Daoud, Dhruv Sirohi, Endri Mjeku, John Feng

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper we present an attempt to tackle this problem using machine learning. While most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an

Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

Bayesian model comparison (BMC) offers a principled probabilistic approach to study and rank competing models. In standard BMC, we construct a discrete probability distribution over the set of possible models, conditional on the observed data of interest. These posterior model probabilities (PMPs) are measures of uncertainty, but -- when derived from a finit

Mahmoud Assran, Randall Balestriero, Quentin Duval, Florian Bordes

A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these methods is an overlooked prior to learn features that enable uniform clustering of the data. While this prior has led to remarkably semantic repre

Teodor Borislavov Vasilev, Mariam Bouhmadi-López, Prado Martín-Moruno

We revise the future fate of a group of scalar-tensor theories known as kinetic gravity braiding models. As it is well-known, these theories can safely drive the expansion of the universe towards a future de Sitter state if the corresponding Lagrangian is invariant under constant shifts in the scalar field. However, this is not the only possible future state

Jaime Santos-Rodriguez, Sergio Zamora

We obtain results about fundamental groups of $RCD^{\ast}(K,N)$ spaces previously known under additional conditions such as smoothness or lower sectional curvature bounds. For fixed $K \in \mathbb{R}$, $N \in [1,\infty )$, $D > 0 $, we show the following, $\bullet$ There is $C>0$ such that for each $RCD^{\ast}(K,N)$ space $X$ of diameter $\leq D$, its fundam

Pablo Barenbaum, Teodoro Freund

The lambda-PRK-calculus is a typed lambda-calculus that exploits the duality between the notions of proof and refutation to provide a computational interpretation for classical propositional logic. In this work, we extend lambda-PRK to encompass classical second-order logic, by incorporating parametric polymorphism and existential types. The system is shown

Zachary Gelles, Koushik Chatterjee, Michael Johnson, Bart Ripperda

Images of supermassive black holes produced using very long baseline interferometry provide a pathway to directly observing effects of a highly curved spacetime, such as a bright ``photon ring'' that arises from strongly lensed emission. In addition, the emission near supermassive black holes is highly variable, with bright high-energy flares regularly obser

Umang Mehta, Shiraz Minwalla, Chintan Patel, Shiroman Prakash

We present a conjecture for the crossing symmetry rules for Chern-Simons gauge theories interacting with massive matter in $2+1$ dimensions. Our crossing rules are given in terms of the expectation values of particular tangles of Wilson lines, and reduce to the standard rules at large Chern-Simons level. We present completely explicit results for the special

Thomas Chun Pong Chau, Łukasz Dudziak, Hongkai Wen, Nicholas Donald Lane

Neural architecture search (NAS) has been successfully used to design numerous high-performance neural networks. However, NAS is typically compute-intensive, so most existing approaches restrict the search to decide the operations and topological structure of a single block only, then the same block is stacked repeatedly to form an end-to-end model. Although

Aashish Arora, Harshitha Malireddi, Daniel Bauer, Asad Sayeed

We put forward an end-to-end multi-step machine learning model which jointly labels semantic roles and the proto-roles of Dowty (1991), given a sentence and the predicates therein. Our best architecture first learns argument spans followed by learning the argument's syntactic heads. This information is shared with the next steps for predicting the semantic r