May 2022 arXiv papers — page 48

Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, Xavier Martinet

We propose an online training procedure for a transformer-based automated theorem prover. Our approach leverages a new search algorithm, HyperTree Proof Search (HTPS), inspired by the recent success of AlphaZero. Our model learns from previous proof searches through online training, allowing it to generalize to domains far from the training distribution. We

Makesh Narsimhan Sreedhar, Xiangpeng Wan, Yu Cheng, Junjie Hu

Subword tokenization schemes are the dominant technique used in current NLP models. However, such schemes can be rigid and tokenizers built on one corpus do not adapt well to other parallel corpora. It has also been observed that in multilingual corpora, subword tokenization schemes over-segment low-resource languages leading to a drop in translation perform

Mirko Mauri, Luca Migliorini

We study the summands of the decomposition theorem for the Hitchin system for $\mathrm{GL}_n$, in arbitrary degree, over the locus of reduced spectral curves. A key ingredient is a new correspondence between these summands and the topology of hypertoric quiver varieties. In contrast to the case of meromorphic Higgs fields, the intersection cohomology groups

Johannes Carmesin, Jan Kurkofka

Robertson and Seymour constructed for every graph $G$ a tree-decomposition that efficiently distinguishes all the tangles in $G$. While all previous constructions of these decompositions are either iterative in nature or not canonical, we give an explicit one-step construction that is canonical. The key ingredient is an axiomatisation of 'local properties' o

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li

We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language mod

Nathan Kallus, Miruna Oprescu

The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) average, and can overlook risks and tail events, which are important to treatment choice. In aggregate analyses, this is usually addressed by measuring the distributional treatment effec

Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang

Contrastive representation learning has gained much attention due to its superior performance in learning representations from both image and sequential data. However, the learned representations could potentially lead to performance disparities in downstream tasks, such as increased silencing of underrepresented groups in toxicity comment classification. In

Masato Mita, Keisuke Sakaguchi, Masato Hagiwara, Tomoya Mizumoto

Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges. To go beyond sentence-level automated grammatical error correction to NLP-based document-level revision assistant, there are two major obstacles: (1) there are few

K. D. Olumoyin

Differential equations are used to model problems that originate in disciplines such as physics, biology, chemistry, and engineering. In recent times, due to the abundance of data, there is an active search for data-driven methods to learn Differential equation models from data. However, many numerical methods often fall short. Advancements in neural network

Ivan Horváth, Peter Markoš, Robert Mendris

Fractal-like structures of varying complexity are common in nature, and measure-based dimensions (Minkowski, Hausdorff) supply their basic geometric characterization. However, at the level of fundamental dynamics, which is quantum, structure does not enter via geometry of fixed sets but is encoded in probability distributions on associated spaces. The questi

Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong

Language models (LMs) have been shown to memorize a great deal of factual knowledge contained in their training data. But when an LM generates an assertion, it is often difficult to determine where it learned this information and whether it is true. In this paper, we propose the problem of fact tracing: identifying which training examples taught an LM to gen

Anthony Rizzo, Utsav Dave, Asher Novick, Alexandre Freitas

Perturbations to the effective refractive index from nanometer-scale fabrication variations in waveguide geometry plague high index-contrast photonic platforms including the ubiquitous sub-micron silicon-on-insulator (SOI) process. Such variations are particularly troublesome for phase-sensitive devices such as interferometers and resonators, which exhibit d

Sanha Cheong, Josef C. Frisch, Sean Gasiorowski, Jason M. Hogan

We present a light field imaging system that captures multiple views of an object with a single shot. The system is designed to maximize the total light collection by accepting a larger solid angle of light than a conventional lens with equivalent depth of field. This is achieved by populating a plane of virtual objects using mirrors and fully utilizing the

M. V. Boev, V. M. Kovalev, O. V. Kibis

We developed the theory of elastic electron tunneling through a potential barrier driven by a strong high-frequency electromagnetic field. It is demonstrated that the driven barrier can be considered as a stationary two-barrier potential which contains the quasi-stationary electron states confined between these two barriers. When the energy of an incident el

D. V. Khveshchenko

This note discusses examples of 0+1-dimensional Liouvillean dynamics instigated by the various deformations of the Sachdev-Ye-Kitaev (SYK) model. In reference to such deformations the main focus is on the regions of parameter space where the competing SYK couplings are of comparable strength and can not be treated as each others perturbations in the vicinity

Laura Donnay, Kevin Nguyen, Romain Ruzziconi

We demonstrate that the one-loop exact subleading soft graviton theorem automatically follows from conservation of the BMS charges, provided that the hard and soft fluxes separately represent the extended BMS algebra at null infinity. This confirms that superrotations are genuine symmetries of the gravitational $\mathcal{S}$-matrix beyond the semiclassical r

Behzad Ataei, Ali Ayatollah Rafsanjani, Alireza Bahrampour, Mohammad Taeibi Rahni

Recent researches suggest an analogy between the theory of general relativity (GR) and fluid dynamics. As a result of this analogy, the Navier-Stokes equations and Einstein field equations are the same, and it is possible to study the properties of space-time by using fluid mechanics. In this paper, we present a new model to describe gravitational phenomena

Pedro Bicudo, Nuno Cardoso, Lasse Mueller, Marc Wagner

In this paper we study $I = 0$ bottomonium in $S$, $P$, $D$ and $F$ waves considering five coupled channels, one confined quarkonium and four open $B^{(*)} \bar B^{(*)}$ and $B_s^{(*)} \bar B_s^{(*)}$ meson-meson channels. To this end we use and extend a recently developed novel approach utilizing lattice QCD string breaking potentials for the study of quark

Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks

Due to the intractability of characterizing everything that looks unlike the normal data, anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only normal samples. However, it has recently been found that unsupervised image AD can be drastically improved through the utilization of huge corpora of random images to represent ano

Shreya Shankar, Bernease Herman, Aditya G. Parameswaran

While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly. In this position paper, we discuss how the nature of streaming ML problems introduces

Benjamin Schiller, Johannes Daxenberger, Andreas Waldis, Iryna Gurevych

The task of Argument Mining, that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if

Amare Abebe

Integrability conditions arising from general irrotational fluid-flow considerations of a universe dominated by cosmic dark fluids will be investigated under special assumptions on the nature of the spacetime shear. Special emphasis will be placed on linearized perturbations of quasi-Newtonian and anti-Newtonian spacetimes, whereby the conditions for the exi

Zakaria Mhammedi

In this paper, we develop new efficient projection-free algorithms for Online Convex Optimization (OCO). Online Gradient Descent (OGD) is an example of a classical OCO algorithm that guarantees the optimal $O(\sqrt{T})$ regret bound. However, OGD and other projection-based OCO algorithms need to perform a Euclidean projection onto the feasible set $\mathcal{

Edward Chen, Linyu Lin, Nam T. Dinh

The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the complex and non-linear nature of nuclear reactor transients as well as the large range of tasks required, it is infeasible for a single ML model to generalize across all tasks. In thi

Yifan Gao, Xinyi Li, Wei Qian

For a Brownian loop soup with intensity $c\in(0,1]$ in the unit disk, we show that almost surely, the set of simple (resp. double) points on any portion of boundary of any of its clusters has Hausdorff dimension $2-\xi_c(2)$ (resp. $2-\xi_c(4)$), where $\xi_c(k)$ is the generalized disconnection exponent computed in arxiv:1901.05436. As a consequence, when t

Md Mosharaf Hossain, Luke Holman, Anusha Kakileti, Tiffany Iris Kao

This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1% of them convey that an event actually occurred. Annotators generate and answer 7,277 questions for the

Benoît Legat, Chenyang Yuan, Pablo A. Parrilo

We study the problem of decomposing a polynomial $p$ into a sum of $r$ squares by minimizing a quadratically penalized objective $f_p(\mathbf{u}) = \left\lVert \sum_{i=1}^r u_i^2 - p\right\lVert^2$. This objective is nonconvex and is equivalent to the rank-$r$ Burer-Monteiro factorization of a semidefinite program (SDP) encoding the sum of squares decomposit

Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang

Summarization datasets are often assembled either by scraping naturally occurring public-domain summaries -- which are nearly always in difficult-to-work-with technical domains -- or by using approximate heuristics to extract them from everyday text -- which frequently yields unfaithful summaries. In this work, we turn to a slower but more straightforward ap

Gabriela B. Correa, Jessica C. S. L. Maciel, Frederico W. Tavares, Charlles R. A. Abreu

Alchemical free energy calculations via molecular dynamics have been widely used to obtain thermodynamic properties related to protein-ligand binding and solute-solvent interactions. Although soft-core modeling is the most common approach, the linear basis function (LBF) methodology [Naden, L. N.; et al. J. Chem. Theory Comput. 2014, 10 (3), 1128; 2015, 11 (

Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard, Kentaro Inui

Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading. This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities

Shunye Gao, Shuai Zhang, Cuixiang Wang, Shaohua Yan

The Mott insulator provides an excellent foundation for exploring a wide range of strongly correlated physical phenomena, such as high-temperature superconductivity, quantum spin liquid, and colossal magnetoresistance. A Mott insulator with the simplest degree of freedom is an ideal and highly desirable system for studying the fundamental physics of Mottness

Cheuk Ting Li

We resolve three long-standing open problems, namely the (algorithmic) decidability of network coding, the decidability of conditional information inequalities, and the decidability of conditional independence implication among random variables, by showing that these problems are undecidable. The proof utilizes a construction inspired by Herrmann's arguments

Arpan Kusari, Wenbo Sun

Low dimensional primitive feature extraction from LiDAR point clouds (such as planes) forms the basis of majority of LiDAR data processing tasks. A major challenge in LiDAR data analysis arises from the irregular nature of LiDAR data that forces practitioners to either regularize the data using some form of gridding or utilize a triangular mesh such as trian

Talha Ongun, Simona Boboila, Alina Oprea, Tina Eliassi-Rad

The cyber-threat landscape has evolved tremendously in recent years, with new threat variants emerging daily, and large-scale coordinated campaigns becoming more prevalent. In this study, we propose CELEST (CollaborativE LEarning for Scalable Threat detection, a federated machine learning framework for global threat detection over HTTP, which is one of the m

Mohamed Alzayat, Jonathan Mace, Peter Druschel, Deepak Garg

Security is a core responsibility for Function-as-a-Service (FaaS) providers. The prevailing approach has each function execute in its own container to isolate concurrent executions of different functions. However, successive invocations of the same function commonly reuse the runtime state of a previous invocation in order to avoid container cold-start dela

Rui Loja Fernandes, Ioan Marcut

We construct a first order local model for Poisson manifolds around a large class of Poisson submanifolds and we give conditions under which this model is a local normal form. The resulting linearization theorem includes as special cases all the known linearization theorems for fixed points and symplectic leaves. The symplectic groupoid version of these resu

Luis Espinosa-Anke, Alexander Shvets, Alireza Mohammadshahi, James Henderson

Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to the varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture

Olesia Dmytruk, Mircea Trif

We study a topological superconducting nanowire that hosts gliding Majorana zero modes in the presence of a microwave cavity field. We show that the cavity decay rate depends on both the parity encoded by the Majorana zero modes and their motion, in the absence of any direct overlap of their wavefunctions. That is because the extended bulk states that overla

John Kirchenbauer, Jacob Oaks, Eric Heim

Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observation that modern neural network classifiers are poorly calibrated. Much of this focus has been towards the goal of learning classifiers such that their output with largest magnitude

Justin Forlano, Leonardo Tolomeo

We consider the Cauchy problem for the fractional nonlinear Schr\"{o}dinger equation (FNLS) on the one-dimensional torus with cubic nonlinearity and high dispersion parameter $\alpha > 1$, subject to a Gaussian random initial data of negative Sobolev regularity $\sigma<s-\frac{1}{2}$, for $s \le \frac 12$. We show that for all $s_{*}(\alpha) <s\leq \frac{1}{

Paul Dobson, Joris Bierkens

In this paper we aim to construct infinite dimensional versions of well established Piecewise Deterministic Monte Carlo methods, such as the Bouncy Particle Sampler, the Zig-Zag Sampler and the Boomerang Sampler. In order to do so we provide an abstract infinite-dimensional framework for Piecewise Deterministic Markov Processes (PDMPs) with unbounded event i

Bharti Bhoy, Praveen C. Srivastava

In the present work, we have done a systematic shell model study of $N=82$ and $N=126$ isotones. For the $N=82$ isotones, we have performed calculations using SN100PN interaction, while for $N=126$ isotones, we have used KHPE interaction. Similarities between these two isotonic chains have been reported, using the strong resemblance between the high-$j$ orbi

Ralf Hofmann, Janning Meinert, Shyam Sunder Balaji

A review of the spatially flat cosmological model SU(2)$_{\rm CMB}$, minimally induced by the postulate that the Cosmic Microwave Background (CMB) is subject to an SU(2) rather than a U(1) gauge principle, is given. Cosmological parameter values, which are determined from the Planck CMB power spectra at small angular scales, are compared to their values in s

Raffaele Zippo, Giovanni Stea

This paper describes Nancy, a Network Calculus (NC) library that allows users to perform complex min-plus and max-plus algebra operations efficiently. To the best of our knowledge, Nancy is the only open-source library that implements operations working on arbitrary piecewise affine functions, as well as to implement some of them (e.g. sub-additive closure a

Alexandre Galashov, Josh Merel, Nicolas Heess

We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what we call the policy cloning setting, in which we use online or offline queries of an expert or expert policy to inform the behavior of a student policy. This setting arises naturally

Luisa Fernanda Rodríguez Díaz, Lionel Bigot, Víctor Aguirre Børsen-Koch, Mikkel N. Lund

High-precision photometric data from space missions have improved our understanding of stellar granulation. These observations have shown with precision the stochastic brightness fluctuations of stars across the HR diagram, allowing us to better understand how stellar surface convection reacts to a change in stellar parameters. These fluctuations need to be

Masahiro Kobayashi, Kouhei Nakaji, Naoki Yamamoto

The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniqu

Fotios K. Anagnostopoulos, Viktor Gakis, Emmanuel N. Saridakis, Spyros Basilakos

The $f(Q)$ theories of modified gravity arise from the consideration of non-metricity as the basic geometric quantity, and have been proven to be very efficient in describing the late-time Universe. We use the Big Bang Nucleosynthesis (BBN) formalism and observations in order to extract constraints on various classes of f(Q) models. In particular, we calcula

Zhubing Jia, Ye Wang, Bichen Zhang, Jacob Whitlow

Trapped atomic ions are a versatile platform for studying interactions between spins and bosons by coupling the internal states of the ions to their motion. Measurement of complex motional states with multiple modes is challenging, because all motional state populations can only be measured indirectly through the spin state of ions. Here we present a general

Anton Kolonin, Vignav Ramesh

In the presented study, we discover that the so-called "transition freedom" metric appears superior for unsupervised tokenization purposes in comparison to statistical metrics such as mutual information and conditional probability, providing F-measure scores in range from 0.71 to 1.0 across explored multilingual corpora. We find that different languages requ

Danny Calegari

A wiggle is an embedded curve in the plane that is the attractor of an iterated function system associated to a complex parameter z. We show the space of wiggles is disconnected -- i.e. there is a wiggle island.

Maya Prabhakar, K. P Raju

Coronal active regions are studied using Hinode/EIS observations in the EUV line Fe XII {\lambda}195.12 by analyzing their line profiles from 2006 December to 2019 December. The period covers the last 2 yr of solar cycle 23 and solar cycle 24 fully. Active regions are the main source of magnetic field in the solar atmosphere, important in its heating and dyn

Yaya Etiabi, Marwa Chafii, El Mehdi Amhoud

Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propos

Michał Narajewski

The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalanc

Benjamin Hsu, Graham Horwood

Identifying related entities and events within and across documents is fundamental to natural language understanding. We present an approach to entity and event coreference resolution utilizing contrastive representation learning. Earlier state-of-the-art methods have formulated this problem as a binary classification problem and leveraged large transformers

Miguel Grados, Anna-Maria von Pippich

Let $N\geq 3$ be a composite, odd, and square-free integer and let $\Gamma$ be the principal congruence subgroup of level $N$. Let $X(N)$ be the modular curve of genus $g_{\Gamma}$ associated to $\Gamma$. In this article, we study the Arakelov invariant $e(\Gamma)=\bar{\omega}^2/\varphi(N)$, with $\bar{\omega}^2$ denoting the self-intersection of the relativ

D. Goderidze, A. Friesen, Yu. Kalinovsky

The temperature behaviour of the pion width in the hadronic phase is investigated in the framework of the NJL model. The contribution to the width from the pion-pion collision is considered with a scalar sigma-meson as an intermediate state. It is shown that the pion width significantly broadens at $T>0.1$ GeV. Using the two-step iteration method, suggested

Gabriella Zsidi, Eleonora Fiorellino, Ágnes Kóspál, Péter Ábrahám

Classical T Tauri stars are low-mass objects, which are still accreting material from the surrounding circumstellar disk. The accretion process is essential in the formation of Sun-like stars and in setting the properties of the disk at the time when planet formation occurs. We constructed a complex dataset in order to examine the accretion process of VW Cha

Qiuliang Ye, Li-Wen Wang, Daniel P. K. Lun

With the success of deep learning methods in many image processing tasks, deep learning approaches have also been introduced to the phase retrieval problem recently. These approaches are different from the traditional iterative optimization methods in that they usually require only one intensity measurement and can reconstruct phase images in real-time. Howe

Laura von Rueden, Sebastian Houben, Kostadin Cvejoski, Christian Bauckhage

When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that have been pre-trained on other large data sets, pre-training on more concise forms of knowledge has rather been overlooked. In this paper, we propose a novel informed machine learnin

Joe Stacey, Pasquale Minervini, Haim Dubossarsky, Marek Rei

Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models are known to learn from annotation artefacts and dataset biases, it is unclear to what extent the models are learning the task of NLI instead of learning from shallow heuristics in t

James Leake, Mark Linton, Spiro Antiochos

Solar eruptive events such as coronal mass ejections and eruptive flares are frequently associated with the emergence of magnetic flux from the convection zone into the corona. We use three dimensional magnetohydrodynamic numerical simulations to study the interaction of coronal magnetic fields with emerging flux and determine the conditions that lead to eru

Cristina Flaut, Dana Piciu

In this paper using the connections between some subvarieties of residuated lattices, we investigated some properties of the lattice of ideals in commutative and unitary rings. We give new characterizations for commutative rings $A$ in which $Id(A)$ is an MV-algebra, a Heyting algebra or a Boolean algebra and we establish connections between these types of r

Vladimir Chernov, Ryan Maguire

A theorem of Kronheimer and Mrowka states that Khovanov homology is able to detect the unknot. That is, if a knot has the Khovanov homology of the unknot, then it is equivalent to it. Similar results hold for the trefoils and the figure-eight knot. We conjecture that Khovanov homology is able to distinguish all torus and twist knots. Numerical evidence has b

Anthony T. Chieco, James P. Sethna, Douglas J. Durian

Two-dimensional dry foams coarsen according to the von Neumann law as $dA/dt \propto (n-6)$ where $n$ is the number of sides of a bubble with area $A$. Such foams reach a self-similar scaling state where area and side-number distributions are stationary. Combining self-similarity with the von Neumann law, we derive time derivatives of moments of the bubble a

Yaya Etiabi, Mohammed JOUHARI, Andreas Burg, El Mehdi Amhoud

Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map, leading to inaccurate position estimates. As such, in this work, we propose a novel LoRa RSSI fingerprinting approach that ta

Conor Mc Keever, Michael Lubasch

Hamiltonian simulation is a promising application for quantum computers to achieve a quantum advantage. We present classical algorithms based on tensor network methods to optimize quantum circuits for this task. We show that, compared to Trotter product formulas, the classically optimized circuits can be orders of magnitude more accurate and significantly ex

Xuan Chen, Thomas Gehrmann, Nigel Glover, Alexander Huss

Charged gauge boson production at hadron colliders is a fundamental benchmark for the extraction of electroweak parameters and the understanding of the proton structure. To enable precision phenomenology for this process, we compute the third-order (N$^3$LO) QCD corrections to the rapidity distribution and charge asymmetry in W boson production and to the tr

D. Bockelée-Morvan, N. Biver, C. A. Schambeau, J. Crovisier

29P/Schwassmann-Wachmann 1 is a distant Centaur/comet, showing persistent CO-driven activity and frequent outbursts. We used the Herschel space observatory in 2010, 2011, and 2013 to observe H$_2$O and NH$_3$ and to image the dust coma. Observations with the IRAM 30 m were undertaken in 2007, 2010, 2011, and 2021 to monitor the CO production rate and to sear

Mateo Casariego, Emmanuel Zambrini Cruzeiro, Stefano Gherardini, Tasio Gonzalez-Raya

The field of propagating quantum microwaves has started to receive considerable attention in the past few years. Motivated at first by the lack of an efficient microwave-to-optical platform that could solve the issue of secure communication between remote superconducting chips, current efforts are starting to reach other areas, from quantum communications to

Emmanuel Brempong Asiedu, Simon Kornblith, Ting Chen, Niki Parmar

Semantic segmentation labels are expensive and time consuming to acquire. Hence, pretraining is commonly used to improve the label-efficiency of segmentation models. Typically, the encoder of a segmentation model is pretrained as a classifier and the decoder is randomly initialized. Here, we argue that random initialization of the decoder can be suboptimal,

Ruben Fedriani, Jonathan C. Tan, Zoie Telkamp, Yichen Zhang

We present $\sim10-40\,\mu$m SOFIA-FORCAST images of 11 isolated protostars as part of the SOFIA Massive (SOMA) Star Formation Survey, with this morphological classification based on 37 $\mu$m imaging. We develop an automated method to define source aperture size using the gradient of its background-subtracted enclosed flux and apply this to build spectral e

Kalina Petrova, Miloš Trujić

A loose Hamilton cycle in a hypergraph is a cyclic sequence of edges covering all vertices in which only every two consecutive edges intersect and do so in exactly one vertex. With Dirac's theorem in mind, it is natural to ask what minimum $d$-degree condition guarantees the existence of a loose Hamilton cycle in a $k$-uniform hypergraph. For $k=3$ and each

Md. Ismail Hossain, Mohammed Rakib, Sabbir Mollah, Fuad Rahman

Word-level handwritten optical character recognition (OCR) remains a challenge for morphologically rich languages like Bangla. The complexity arises from the existence of a large number of alphabets, the presence of several diacritic forms, and the appearance of complex conjuncts. The difficulty is exacerbated by the fact that some graphemes occur infrequent

Eojindl Yi, Juyoung Yang, Junmo Kim

Despite its importance, unsupervised domain adaptation (UDA) on LiDAR semantic segmentation is a task that has not received much attention from the research community. Only recently, a completion-based 3D method has been proposed to tackle the problem and formally set up the adaptive scenarios. However, the proposed pipeline is complex, voxel-based and requi

Jaeseong Jeong, Namhun Koo, Soonhak Kwon

Recently, a new concept called the $c$-differential uniformity was proposed by Ellingsen et al. (2020), which allows to simplify some types of differential cryptanalysis. Since then, finding functions having low $c$-differential uniformity has attracted the attention of many researchers. However it seems that, at this moment, there are not many non-monomial

Calum Ross, Muneto Nitta

Domain wall skyrmions are skyrmions trapped inside a domain wall. We investigate domain wall skyrmions in chiral magnets using a fully analytic approach. Treating the Dzyaloshinskii-Moriya (DM) interaction perturbatively, we construct the low-energy effective theory of a magnetic domain wall in an $O(3)$ sigma model with the DM interaction and an easy-axis p

Haoran Xu, Philipp Koehn, Kenton Murray

Recent model pruning methods have demonstrated the ability to remove redundant parameters without sacrificing model performance. Common methods remove redundant parameters according to the parameter sensitivity, a gradient-based measure reflecting the contribution of the parameters. In this paper, however, we argue that redundant parameters can be trained to

Mohammad Sadek, Tuğba Yesin

Let $C$ be a smooth genus one curve described by a quartic polynomial equation over the rational field $\mathbb Q$ with $P\in C(\mathbb Q)$. We give an explicit criterion for the divisibility-by-$2$ of a rational point on the elliptic curve $(C,P)$. This provides an analogue to the classical criterion of the divisibility-by-$2$ on elliptic curves described b

Julian Ahrens, Lia Ahrens, Michael Zentarra, Hans D. Schotten

In this paper, the task of channel sounding using software defined radios (SDRs) is considered. In contrast to classical channel sounding equipment, SDRs are general purpose devices and require additional steps to be implemented when employed for this task. On top of this, SDRs may exhibit quirks causing signal artefacts that obstruct the effective collectio

Ayal Klein, Eran Hirsch, Ron Eliav, Valentina Pyatkin

Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements. In this paper, we consider three QA-based semantic tasks - namely, QA-SRL, QANom and QADiscourse, each targeting a certain type of predication - and propose to regard them as

Carlo Cenedese, Michele Cucuzzella, Antonella Ferrara, John Lygeros

In this paper, we propose a novel model that describes how the traffic evolution on a highway stretch is affected by the presence of a service station. The presented model enhances the classical CTM dynamics by adding the dynamics associated with the service stations, where the vehicles may stop before merging back into the mainstream. We name it CTMs. We di

Jonathan Brophy, Daniel Lowd

Gradient-boosted regression trees (GBRTs) are hugely popular for solving tabular regression problems, but provide no estimate of uncertainty. We propose Instance-Based Uncertainty estimation for Gradient-boosted regression trees (IBUG), a simple method for extending any GBRT point predictor to produce probabilistic predictions. IBUG computes a non-parametric

Shane Dooley, Michael J. Kewming, Mark T. Mitchison, John Goold

In Phys. Rev. Lett. 128, 200501 (2022) the authors consider the thermodynamic cost of quantum metrology. One of the main results is $\mathcal{S} \geq \log(2) \| h_\lambda \|^{-2} F_Q [\psi_\lambda]$, which purports to relate the Shannon entropy $\mathcal{S}$ of an optimal measurement (i.e., in the basis of the symmetric logarithmic derivative) to the quantum

R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar

Gamma-ray bursts (GRBs) are considered as promising sources of ultra-high-energy cosmic rays (UHECRs) due to their large power output. Observing a neutrino flux from GRBs would offer evidence that GRBs are hadronic accelerators of UHECRs. Previous IceCube analyses, which primarily focused on neutrinos arriving in temporal coincidence with the prompt gamma ra

Yi Song, Yuxian Gu, Minlie Huang

In this work, we formulate \textbf{T}ext \textbf{C}lassification as a \textbf{M}atching problem between the text and the labels, and propose a simple yet effective framework named TCM. Compared with previous text classification approaches, TCM takes advantage of the fine-grained semantic information of the classification labels, which helps distinguish each

Yuqiu Fu

We prove that the dynamical zeta function $Z(s)$ associated to $z^2 + c$ with $c < -3.75$ has essential zero-free strips of size $1/2 +$, that is, for every $\epsilon > 0$, there exist only finitely many zeros in the strip $\mathrm{Re}(s) > 1/2 + \epsilon$. We also present some numerical plots of zeros of $Z(s)$ using the method proposed in Jenkinson-Pollico

Wenkai Fu, Steven R. Spurgeon, Chongmin Wang, Yuyan Shao

We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous fram

Atheer Abu Zaid, Manar H. Alalfi, Ali Miri

Software vulnerabilities in access control models can represent a serious threat in a system. In fact, OWASP lists broken access control as number 5 in severity among the top 10 vulnerabilities. In this paper, we study the permission model of an emerging Smart-Home platform, SmartThings, and explore an approach that detects privilege escalation in its permis

Christopher Popp, Beatrix C. Hiesmayr

With a probability of success of $95 \%$ we solve the separability problem for Bell diagonal qutrit states with positive partial transposition (PPT). The separability problem, i.e. distinguishing separable and entangled states, generally lacks an efficient solution due to the existence of bound entangled states. In contrast to free entangled states that can

Michael Prasthofer, Tim De Ryck, Siddhartha Mishra

Existing architectures for operator learning require that the number and locations of sensors (where the input functions are evaluated) remain the same across all training and test samples, significantly restricting the range of their applicability. We address this issue by proposing a novel operator learning framework, termed Variable-Input Deep Operator Ne

Sean Eberhard, Mikhail Muzychuk

This paper is a follow-up to (arXiv:2203.03687), in which the first author studied primitive association schemes lying between a tensor power $\mathcal{T}_m^d$ of the trivial association scheme and the Hamming scheme $\mathcal{H}(m,d)$. A question which arose naturally in that study was whether all primitive fusions of $\mathcal{T}_m^d$ lie between $\mathcal

Pedro Sanchez, Jeremy P. Voisey, Tian Xia, Hannah I. Watson

Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g.\ outcome given a treatment). Quantifying effects of interventions allows actionable decisions

Marc Geiller, Céline Zwikel

We present a detailed analysis of gravity in a partial Bondi gauge, where only the three conditions $g_{rr}=0=g_{rA}$ are fixed. We relax in particular the so-called determinant condition on the transverse metric, which is only assumed to admit a polyhomogeneous radial expansion. This is sufficient in order to build the solution space, which here includes a

Mario Rosenfelder, Henrik Ebel, Jasmin Krauspenhaar, Peter Eberhard

Non-holonomic vehicles are of immense practical value and increasingly subject to automation. However, controlling them accurately, e.g., when parking, is known to be challenging for automatic control methods, including model predictive control (MPC). Combining results from MPC theory and sub-Riemannian geometry in the form of homogeneous nilpotent system ap

Nélson Gonçalves, Rui Rua, Jácome Cunha, Rui Pereira

This paper presents an empirical study regarding the energy consumption of the most used web browsers on the Android ecosystem. In order to properly compare the web browsers in terms of energy consumption, we defined a set of typical usage scenarios to be replicated in the different browsers, executed in the same testing environment and conditions. The resul

Justin Kay, Catherine M. Foley, Tom Hart

Crowd-sourcing is an increasingly popular tool for image analysis in animal ecology. Computer vision methods that can utilize crowd-sourced annotations can help scale up analysis further. In this work we study the potential to do so on the challenging task of fine-grained counting. As opposed to the standard crowd counting task, fine-grained counting also in

Mingbao Lin, Mengzhao Chen, Yuxin Zhang, Chunhua Shen

We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision trans

Chen Chen, Clint Dawson, Eirik Valseth

The development of surrogate models to study uncertainties in hydrologic systems requires significant effort in the development of sampling strategies and forward model simulations. Furthermore, in applications where prediction time is critical, such as prediction of hurricane storm surge, the predictions of system response and uncertainties can be required

Meiqi Hu, Chen Wu, Bo Du

Hyperspectral anomalous change detection has been a challenging task for its emphasis on the dynamics of small and rare objects against the prevalent changes. In this paper, we have proposed a Multi-Temporal spatial-spectral Comparison Network for hyperspectral anomalous change detection (MTC-NET). The whole model is a deep siamese network, aiming at learnin