May 2022 arXiv papers — page 133

Sanghyun Yoo, Inchul Song, Yoshua Bengio

Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to perform well in general, they are not easy to maintain when dialect-specific data is scarce and the number of dialects for each language is large. Therefore, a single unified acoustic

Azam Imomov, Misliddin Murtazaev

The paper considers the well-known Galton-Watson stochastic branching process. We are dealing with a non-critical case. In the subcritical case, when the mean of the direct descendants of one particle per generation of the time step is less than 1, the population mean of the number of particles on the positive trajectories of the process stabilizes and appro

Carlo Ferrigno, Enrico Bozzo, Patrizia Romano

Wind-fed supergiant X-ray binaries are precious laboratories not only to study accretion under extreme gravity and magnetic field conditions, but also to probe still highly debated properties of massive star winds. These includes the so-called clumps, originated from the inherent instability of line driven winds, and larger structures. In this paper, we repo

Takuya Midooka, Ken Ebisawa, Misaki Mizumoto, Yasuharu Sugawara

NGC 5548 is a very well-studied Seyfert 1 galaxy in broad wavelengths. Previous multiwavelength observation campaigns have indicated that its multiple absorbers are highly variable and complex. A previous study applied a two-zone partial covering model with different covering fractions to explain the complex X-ray spectral variation and reported a correlatio

Kim JuSong, Ri IlYong

Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. Fingerprint recognition technology has been studied for a long time, and its recognition rate has recently risen to a high level. In particular, with the introduct

Shihshu Walter Wei

We introduce \emph{normalized exponential Yang-Mills energy functional} $\mathcal{YM}_e^0$, stress-energy tensor $S_{e,\mathcal{YM}^0 }$ associated with the normalized \emph{exponential Yang-Mills energy functional} $\mathcal{YM}_e ^0 $, $e$-conservation law. We also introduce the notion of the {\it $e$-degree} $d_e$ which connects two separate parts in the

Farid Kalhor, Noah F. Opondo, Shoaib Mahmud, Leif Bauer

Generation of local magnetic field at the nanoscale is desired for many applications such as spin-qubit-based quantum memories. However, this is a challenge due to the slow decay of static magnetic fields. Here, we demonstrate photonic spin density (PSD) induced effective static magnetic field for an ensemble of nitrogen-vacancy (NV) centers in bulk diamond.

Ben Burgess, Avi Ginsberg, Edward W. Felten, Shaanan Cohney

Educators are rapidly switching to remote proctoring and examination software for their testing needs, both due to the COVID-19 pandemic and the expanding virtualization of the education sector. State boards are increasingly utilizing these software for high stakes legal and medical licensing exams. Three key concerns arise with the use of these complex soft

Terrence Richard Blackman, Zachary Stier

An algorithm of Ross and Selinger for the factorization of diagonal elements of PU(2) to within distance $\varepsilon$ was adapted by Parzanchevski and Sarnak into an efficient probabilistic algorithm for any element of PU(2) using at most effective $3\log_p\frac{1}{\varepsilon^{3}}$ factors from certain well-chosen sets associated to a number field and a pr

Lyle Regenwetter, Faez Ahmed

Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse design. We

Jiong Qiu, Jianxia Cheng

In this article, we measure the mean magnetic shear from the morphological evolution of flare ribbons, and examine the evolution of flare thermal and non-thermal X-ray emissions during the progress of flare reconnection. We analyze three eruptive flares and three confined flares ranging from GOES class C8.0 to M7.0. They exhibit well-defined two ribbons alon

Dong Li, Peipei Shen, Jinpeng Tian, Ge He

The magneto-transport properties are systematically measured under c-direction fields up to 33 T for a series of single-crystal films of intercalated iron-selenide superconductor (Li,Fe)OHFeSe. The film samples with varying degree of disorder are grown hydrothermally. We observe a magnetic-field-enhanced shoulder-like feature in the mixed state of the high-T

Xun Xu, Jingyi Liao, Lile Cai, Manh Cuong Nguyen

Semi-supervised learning (SSL) addresses the lack of labeled data by exploiting large unlabeled data through pseudolabeling. However, in the extremely low-label regime, pseudo labels could be incorrect, a.k.a. the confirmation bias, and the pseudo labels will in turn harm the network training. Recent studies combined finetuning (FT) from pretrained weights w

Jian Dang, Zaichen Zhang, Yewei Li, Liang Wu

Reconfigurable intelligent surface (RIS) can assist terahertz wireless communication to restore the fragile line-of-sight links and facilitate beam steering. Arbitrary reflection beam patterns are desired to meet diverse requirements in different applications. This paper establishes relationship between RIS beam pattern design with two-dimensional finite imp

Javier de la Cruz, Edgar Martínez-Moro, Ricardo Villanueva-Polanco

In this paper, we propose to use a skew dihedral group ring given by the group $D_{2n}$ and the finite field $\mathbb{F}_{q^2}$ for public-key cryptography. Using the ambient space $\mathbb{F}_{q^{2}}^θ D_{2n}$ and a group homomorphism $θ: D_{2n} \rightarrow \mathrm{Aut}(\mathbb{F}_{q^2})$, we introduce a key exchange protocol and present an analysis of its

Yueyao Li, Wenxun Xing

It is known that the set of perturbed data is key in robust optimization (RO) modelling. Distributionally robust optimization (DRO) is a methodology used for optimization problems affected by random parameters with uncertain probability distribution. In terms of the information of the perturbed data, it is essential to estimate an appropriate support set of

Xin Liu

The Steiner $k$-eccentricity of a vertex in graph $G$ is the maximum Steiner distance over all $k$-subsets containing the vertex. %Some general properties of the Steiner 3-eccentricity of trees are given. Let $\mathbb{T}_n$ be the set of all $n$-vertex trees, $\mathbb{T}_{n,Δ}$ be the set of $n$-vertex trees with given maximum degree equal to $Δ$, $\mathbb{T

Hongmei Cao, Wei-Xi Li, Chao-Jiang Xu

We study the spatially inhomogeneous Landau equations with hard potential in the perturbation setting, and establish the analytic smoothing effect in both spatial and velocity variables for a class of low-regularity weak solutions. This shows the Landau equations behave essentially as the hypoelliptic Fokker-Planck operators. The spatial analyticity relies o

Takashi Aoki, Takao Suzuki, Shofu Uchida

The Voros connection formula for WKB solutions to the Airy equation with a large parameter is proved by using cubic equations. Some parts of the results are generalized to the Pearcey system, which is a two-variable version of the Airy equation, is given.

George J. Cancro, Shimei Pan, James Foulds

When a human receives a prediction or recommended course of action from an intelligent agent, what additional information, beyond the prediction or recommendation itself, does the human require from the agent to decide whether to trust or reject the prediction or recommendation? In this paper we survey literature in the area of trust between a single human s

Boming Chi, Akira Terui

We propose a modification of the GPGCD algorithm, which has been presented in our previous research, for calculating approximate greatest common divisor (GCD) of more than 2 univariate polynomials with real coefficients and a given degree. In transferring the approximate GCD problem to a constrained minimization problem, different from the original GPGCD alg

Shaokai Hu, Hao Huang, Guan Gui, Hikmet Sari

This paper analyzes the power imbalance factor on the uplink of a 2-user Power-domain NOMA system and reveals that the minimum value of the average error probability is achieved when the user signals are perfectly balanced in terms of power as in Multi-User MIMO with power control. The analytic result is obtained by analyzing the pairwise error probability a

Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar

Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consuming and resource intensive. Thus, a lot of current research is focused on using transformers for multi-task learning (Raffel et al.,2020) and how to group the tasks to

Jien-De Sui, Tian-Sheuan Chang

Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit calibrations and design assumptions. Current deep learning methods suffer from few labeled data problem. To solve above problems,

Sahand Mosharafian, Shirin Afzali, Yajie Bao, Javad Mohammadpour Velni

Presence of model uncertainties creates challenges for model-based control design, and complexity of the control design is further exacerbated when coping with nonlinear systems. This paper presents a sliding mode control (SMC) design approach for nonlinear systems with partially known dynamics by blending data-driven and model-based approaches. First, an SM

Wooil Yang, Bo Gyu Jang, Young-Woo Son, Seung-Hoon Jhi

We study the lattice dynamics of antiferromagnetic transition-metal oxides by using self-consistent Hubbard functionals. We calculate the ground states of the oxides with the on-site and intersite Hubbard interactions determined self-consistently within the framework of density functional theory. The on-site and intersite Hubbard terms fix the errors associa

Daniel Engel, Yingjie Xue

An option is a financial agreement between two parties to trade two assets. One party is given the right, but not the obligation, to complete the swap before a specified termination time. In todays financial markets, an option is considered an asset which can itself be transferred: while an option is active, one party can sell its rights (or obligations) to

Keisuke Oshimi, Yushi Nishimura, Tsutomu Matsubara, Masuaki Tanaka

We report a notch-shaped coplanar microwave waveguide antenna on a glass plate designed for on-chip detection of optically detected magnetic resonance (ODMR) of fluorescent nanodiamonds (NDs). A lithographically patterned thin wire at the center of the notch area in the coplanar waveguide realizes a millimeter-scale ODMR detection area (1.5 x 2.0 mm^2) and g

Delphin Sénizergues, Sigurdur Örn Stefánsson, Benedikt Stufler

We define decorated $α$-stable trees which are informally obtained from an $α$-stable tree by blowing up its branchpoints into random metric spaces. This generalizes the $α$-stable looptrees of Curien and Kortchemski, where those metric spaces are just deterministic circles. We provide different constructions for these objects, which allows us to understand

Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik

Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doome

Sepehr Mosadegh, Sina Kheirkhah

The droplet and flame chemiluminescence dynamics as well as their coupling during atomization events of graphene oxide doped diesel are investigated experimentally. The tested doping concentrations are 0, 0.001, 0.005, 0.01, and 0.02% by weight. To minimize heat transfer between the droplet and its suspension mechanism, small diameter fibers are used for the

Vishal Gajjar, Dominic LeDuc, Jiani Chen, Andrew P. V. Siemion

The search for extraterrestrial intelligence at radio frequencies has largely been focused on continuous-wave narrowband signals. We demonstrate that broadband pulsed beacons are energetically efficient compared to narrowband beacons over longer operational timescales. Here, we report the first extensive survey searching for such broadband pulsed beacons tow

Sangwon Seo, Vaibhav V. Unhelkar

We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning techniques, BTIL explicitly models and infers the time-varying mental states of team members, thereby enabling learning of decentralized team

Shengqi Yang, Xiaolong Du, Zhichao Carton Zeng, Andrew Benson

The scale-free gravothermal fluid formalism has long proved effective in describing the evolution of self-interacting dark matter halos with a constant dark matter particle cross section. However, whether the gravothermal fluid solutions match numerical simulations for velocity-dependent cross-section scenarios remains untested. In this work, we provide a fa

David Yun, Nathan A. Malarich, Ryan K. Cole, Scott C. Egbert

Supersonic engine development requires accurate and detailed measurements of fluidic and thermodynamic parameters to optimize engine designs and benchmark computational fluid dynamic (CFD) simulations. Here, we demonstrate that dual frequency comb spectroscopy (DCS) with mode-locked frequency combs can provide simultaneous absolute measurements of several fl

Nadav Dym, Steven J. Gortler

This paper studies separating invariants: mappings on $D$ dimensional domains which are invariant to an appropriate group action, and which separate orbits. The motivation for this study comes from the usefulness of separating invariants in proving universality of equivariant neural network architectures. We observe that in several cases the cardinality of s

Paul Menker, Andrew J. Benson

We describe a semi-analytic model to predict the triaxial shapes of dark matter halos utilizing the sequences of random merging events captured in merger trees to follow the evolution of each halo's energy tensor. When coupled with a simple model for relaxation toward a spherical shape, we find that this model predicts distributions of halo axis length ratio

Xiaoxuan Liu, Shuxian Wang, Mengzhu Sun, Sicheng Pan

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present ConstrOpt, the first tool that identifies data relationships by analyzing database-bac

Jonathan Francis, Bingqing Chen, Siddha Ganju, Sidharth Kathpal

We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envision autonomo

Francesco Maggi, Michael Novack

We obtain a full resolution result for minimizers in the exterior isoperimetric problem with respect to a compact obstacle in the large volume regime $v\to\infty$. This is achieved by the study of a Plateau-type problem with free boundary (both on the compact obstacle and at infinity) which is used to identify the first obstacle-dependent term (called {\it i

Stephen Salerno, Yi Li

In the era of precision medicine, time-to-event outcomes such as time to death or progression are routinely collected, along with high-throughput covariates. These high-dimensional data defy classical survival regression models, which are either infeasible to fit or likely to incur low predictability due to over-fitting. To overcome this, recent emphasis has

Augusto T. Chantada, Susana J. Landau, Pavlos Protopapas, Claudia G. Scóccola

The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to c

Mingyu Lu, Yifang Chen, Su-In Lee

Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclinical/clinical studies for modeling multiple treatment efficacies is often an expensive, time-con

Brice Flamencourt

A locally conformally product (LCP) structure on compact manifold $M$ is a conformal structure $c$ together with a closed, non-exact and non-flat Weyl connection $D$ with reducible holonomy. Equivalently, an LCP structure on $M$ is defined by a reducible, non-flat, incomplete Riemannian metric $h_D$ on the universal cover $\tilde M$ of $M$, with respect to w

Yang Li, Frank Neffke

A growing body of research documents that the size and growth of an industry in a place depends on how much related activity is found there. This fact is commonly referred to as the "principle of relatedness". However, there is no consensus on why we observe the principle of relatedness, how best to determine which industries are related or how this empirica

Ray Treinen

We consider capillary surfaces that are constructed by bounded generating curves. This class of surfaces includes radially symmetric and lower dimensional fluid-fluid interfaces. We use the arc-length representation of the differential equations for these surfaces to allow for vertical points and inflection points along the generating curve. These considerat

Giovanni Brigati, Ivailo Hartarsky

We analyse the class of convex functionals $\mathcal E$ over $\mathrm{L}^2(X,m)$ for a measure space $(X,m)$ introduced by Cipriani and Grillo and generalising the classic bilinear Dirichlet forms. We investigate whether such non-bilinear forms verify the normal contraction property, i.e., if $\mathcal E(\phi \circ f) \leq \mathcal E(f)$ for all $f \in \math

R. Panico, P. Comaron, M. Matuszewski, A. S. Lanotte

Turbulent phenomena are among the most striking effects that both classical and quantum fluids can exhibit. While classical turbulence is ubiquitous in nature, the observation of quantum turbulence requires the precise manipulation of quantum fluids such as superfluid helium or atomic Bose-Einstein condensates. In this work we demonstrate the turbulent dynam

Stefano Scopa, Dávid X. Horváth

We investigate the non-equilibrium dynamics of the symmetry-resolved Rényi entropies in a one-dimensional gas of non-interacting spinless fermions by means of quantum generalised hydrodynamics, which recently allowed to obtain very accurate results for the total entanglement in inhomogeneous quench settings. Although our discussion is valid for any quench se

Chandrakant Bothe, Stefan Wermter

Many socio-linguistic cues are used in conversational analysis, such as emotion, sentiment, and dialogue acts. One of the fundamental cues is politeness, which linguistically possesses properties such as social manners useful in conversational analysis. This article presents findings of polite emotional dialogue act associations, where we can correlate the r

Camilo Sarmiento, Gauvain Bourgne, Katsumi Inoue, Daniele Cavalli

Although moral responsibility is not circumscribed by causality, they are both closely intermixed. Furthermore, rationally understanding the evolution of the physical world is inherently linked with the idea of causality. Thus, the decision-making applications based on automated planning inevitably have to deal with causality, especially if they consider imp

Matt Landreman, Stefan Buller, Michael Drevlak

Quasisymmetry can greatly improve the confinement of energetic particles and thermal plasma in a stellarator. The magnetic field of a quasisymmetric stellarator at high plasma pressure is significantly affected by the bootstrap current, but the computational cost of accurate stellarator bootstrap calculations has precluded use inside optimization. Here, a ne

Anton Glushchenko, Konstantin Lastochkin

A novel method of an adaptive linear quadratic (LQ) regulation of uncertain continuous linear time-invariant systems is proposed. Such an approach is based on the direct self-tuning regulators design framework and the exponentially stable adaptive control technique developed earlier by the authors. Unlike the known solutions, a procedure is proposed to obtai

A. Senol, S. Spor, E. Gurkanli, V. Cetinkaya

In this study, we investigate the potential of $μ^{-} μ^{+}\to Zγ\to ν\barνγ$ process at the future muon collider with a center-of-mass energy of 3 TeV to examine the anomalous $ZZγ$ and $Zγγ$ neutral triple gauge couplings defining $CP$-conserving $C_{\widetilde{B}W}/{Λ^4}$ coupling and three $CP$-violating $C_{BB}/{Λ^4}$, $C_{BW}/{Λ^4}$, $C_{WW}/{Λ^4}$ cou

Rodrigo Queiroz, Divit Sharma, Ricardo Caldas, Krzysztof Czarnecki

Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required micro-simulation, while also supporting behavior reuse and test

Yu-Jui Huang, Yuchong Zhang

This paper approaches the unsupervised learning problem by gradient descent in the space of probability density functions. A main result shows that along the gradient flow induced by a distribution-dependent ordinary differential equation (ODE), the unknown data distribution emerges as the long-time limit. That is, one can uncover the data distribution by si

Sebastian Jäger, Jessica Greene, Max Jakob, Ruben Korenke

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by ac

Jong-Hyun Yoon

We discuss a minimal extension of the Standard Model (SM) where a single real scalar field serves as both inflaton and dark matter. The corresponding Lagrangian contains the renormalizable interactions of the inflaton field. Quantum effects generally induce a non-minimal coupling to gravity which facilitates inflation consistent with the PLANCK constraints.

CMS Collaboration

New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the PYTHIA 8 event generator, are obtained based on the default CMS PYTHIA 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies $\sqrt{s}$ = 7

Brian Tran, Melvin Leok

Adjoint systems are widely used to inform control, optimization, and design in systems described by ordinary differential equations or differential-algebraic equations. In this paper, we explore the geometric properties and develop methods for such adjoint systems. In particular, we utilize symplectic and presymplectic geometry to investigate the properties

Hao Zhang, Neil Jethani, Aahlad Puli, Leonid Garber

Diabetes has a long asymptomatic period which can often remain undiagnosed for multiple years. In this study, we trained a deep learning model to detect new-onset diabetes using 12-lead ECG and readily available demographic information. To do so, we used retrospective data where patients have both a hemoglobin A1c and ECG measured. However, such patients may

Evangelos Matsinos

Over twenty-five years ago, two analyses of the pion-nucleon ($πN$) data at low energy (i.e., for pion laboratory kinetic energy $T \leq 100$ MeV) reported on the departure of the extracted scattering amplitudes, corresponding to the two elastic-scattering reactions $π^\pm p \to π^\pm p$ and to the $π^- p$ charge-exchange reaction $π^- p \to π^0 n$, from the

Ahmet-Serdar Karakaya, Konstantin Köhler, Julian Heinovski, Falko Dressler

Increasing the modal share of bicycle traffic to reduce carbon emissions, reduce urban car traffic, and to improve the health of citizens, requires a shift away from car-centric city planning. For this, traffic planners often rely on simulation tools such as SUMO which allow them to study the effects of construction changes before implementing them. Similarl

W. F. Bergan, M. Blaskiewicz, G. Stupakov

Coherent electron cooling is a promising technique to cool high-intensity hadron bunches by imprinting the noise in the hadron beam on a beam of electrons, amplifying the electron density modulations, and using them to apply cooling kicks to the hadrons. The typical size for these perturbations can be on the $μ$m scale, allowing us to extend the reach of cla

Nathan Smith, Jennifer E. Andrews, Alexei V. Filippenko, Ori D. Fox

We present new HST imaging photometry for the site of the Type IIn supernova (SN) 2009ip taken almost a decade after explosion. The optical source has continued to fade steadily since the SN-like event in 2012. In the F606W filter, which was also used to detect its luminous blue variable (LBV) progenitor 13~yr before the SN, the source at the position of SN2

Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz Thamsen

The growing electricity demand of cloud and edge computing increases operational costs and will soon have a considerable impact on the environment. A possible countermeasure is equipping IT infrastructure directly with on-site renewable energy sources. Yet, particularly smaller data centers may not be able to use all generated power directly at all times, wh

Ido Tishby, Ofer Biham, Eytan Katzav

We present analytical results for the distribution of first-passage (FP) times of random walks (RWs) on random regular graphs that consist of $N$ nodes of degree $c \ge 3$. Starting from a random initial node at time $t=0$, at each time step $t \ge 1$ an RW hops into a random neighbor of its previous node. In some of the time steps the RW may hop into a yet-

Piotr Sowinski, Katarzyna Wasielewska-Michniewska, Maria Ganzha, Marcin Paprzycki

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the p

Brian Doolittle, Tom Bromley, Nathan Killoran, Eric Chitambar

The inherent noise and complexity of quantum communication networks leads to challenges in designing quantum network protocols using classical methods. To address this issue, we develop a variational quantum optimization framework that simulates quantum networks on quantum hardware and optimizes the network using differential programming techniques. We use o

Aleksandra Łopion, Aleksander Bogucki, Wiktor Kraśnicki, Karolina E. Połczyńska

Time-resolved optically detected magnetic resonance (ODMR) is a valuable technique to study the local deformation of the crystal lattice around magnetic ion as well as the ion spin relaxation time. Here we utilize selective Mn-doping to additionally enhance the inherent locality of the ODMR technique. We present the time-resolved ODMR studies of single {(Cd,

Peter Mitrano, Dmitry Berenson

The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem. Within computer vision, a common approach to

Matthew Leming, Sudeshna Das, Hyungsoon Im

Automated disease detection in neuroimaging holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that cause data to both differ between sites and be systematically associated with the disease of interest, thus negatively affecting the robustnes

Nikita Geldhauser, Maksim Zhykhovich

We extend the notion of the $J$-invariant to arbitrary semisimple linear algebraic groups and provide complete decompositions for the normed Chow motives of all generically quasi-split twisted flag varieties. Besides, we establish some combinatorial patterns for normed Chow groups and motives and provide some explicit formulae for values of the $J$-invariant

N. Razzaghi, S. M. M. Rasouli, P. Parada, P. V. Moniz

The overall characteristics of the solar and atmospheric neutrino oscillations are approximately consistent with a tribimaximal form of the mixing matrix $U$ of the lepton sector. Exact tribimaximal mixing leads to $θ_{13}=0$. However, the results from the Daya Bay and RENO experiments have established, such that in comparison to the other neutrino mixing an

Mikael Kurula, Jukka-Pekka Humaloja, Stevan Dubljevic

We extend discrete-time explicit model predictive control (MPC) rigorously to linear distributed parameter systems. After formulating an MPC framework and giving a relevant KKT theorem, we realize fast regionless explicit MPC by using the dual active set method QPKWIK. A Timoshenko beam with input and state constraints is used to demonstrate the efficacy of

Farima Fatahi Bayat, Nikita Bhutani, H. V. Jagadish

A major drawback of modern neural OpenIE systems and benchmarks is that they prioritize high coverage of information in extractions over compactness of their constituents. This severely limits the usefulness of OpenIE extractions in many downstream tasks. The utility of extractions can be improved if extractions are compact and share constituents. To this en

Vojtěch Rödl, Marcelo Sales, Yi Zhao

A well-known result of Ajtai et al. from 1982 states that every $k$-graph $H$ on $n$ vertices, with girth at least five, and average degree $t^{k-1}$ contains an independent set of size $c n (\log t)^{1/(k-1)}/t$ for some $c>0$. In this paper we show that an independent set of the same size can be found under weaker conditions allowing certain cycles of leng

Benjamin Guiot

Recently, the definitions of the Kimber-Martin-Ryskin-Watt (KMRW) unintegrated parton densities (UPDFs) have been discussed by several groups. In the first part of this manuscript, we remind the issues encountered with these definitions and discuss the proposed solutions. In our opinion, none of these solutions is fully satisfactory. We observe that these is

CMS Collaboration

A measurement is presented of the production of Z bosons that decay into two electrons or muons in association with jets, in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data were recorded by the CMS Collaboration at the LHC with an integrated luminosity of 35.9 fb$^{-1}$. The differential cross sections are measured as a function of th

Euclid Collaboration, S. E. van Mierlo, K. I. Caputi, M. Ashby

(Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift

Simon Lupart, Thibault Formal, Stéphane Clinchant

Pre-trained Language Models have recently emerged in Information Retrieval as providing the backbone of a new generation of neural systems that outperform traditional methods on a variety of tasks. However, it is still unclear to what extent such approaches generalize in zero-shot conditions. The recent BEIR benchmark provides partial answers to this questio

Kenny Choo, Bastien Lapierre, Clemens Kuhlenkamp, Apoorv Tiwari

We study the dissipative dynamics of a periodically driven inhomogeneous critical lattice model in one dimension. The closed system dynamics starting from pure initial states is well-described by a driven Conformal Field Theory (CFT), which predicts the existence of both heating and non-heating phases in such systems. Heating is inhomogeneous and is manifest

Adrian Lewis, Tonghua Tian

Identifiability, and the closely related idea of partial smoothness, unify classical active set methods and more general notions of solution structure. Diverse optimization algorithms generate iterates in discrete time that are eventually confined to identifiable sets. We present two fresh perspectives on identifiability. The first distills the notion to a s

Klaus Richter, Juan Diego Urbina, Steven Tomsovic

Quantum chaos of many-body systems has been swiftly developing into a vibrant research area at the interface between various disciplines, ranging from statistical physics to condensed matter to quantum information and to cosmology. In quantum systems with a classical limit, advanced semiclassical methods provide the crucial link between classically chaotic d

Hongyu Li, Shanpu Shen, Bruno Clerckx

Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective

Wynn C. G. Ho, Lucien Kuiper, Cristobal M. Espinoza, Sebastien Guillot

Measuring a pulsar's rotational evolution is crucial to understanding the nature of the pulsar. Here we provide updated timing models for the rotational evolution of six pulsars, five of which are rotation phase-connected using primarily NICER X-ray data. For the newly-discovered fast energetic young pulsar, PSR J0058-7218, we increase the baseline of it

Nilmani Mathur, M. Padmanath, Debsubhra Chakraborty

We report the first lattice QCD study of the heavy dibaryons in which all six quarks have the bottom (beauty) flavor. Performing a state-of-the-art lattice QCD calculation we find clear evidence for a deeply bound $\Omega_{bbb}$-$\Omega_{bbb}$ dibaryon in the $^1S_0$ channel, as a pole singularity in the $S$-wave $\Omega_{bbb}$-$\Omega_{bbb}$ scattering ampl

Rok Medves, Alba Soto-Ontoso, Gregory Soyez

We revisit the calculation of the average jet multiplicity in high-energy collisions. First, we introduce a new definition of (sub)jet multiplicity based on Lund declusterings obtained using the Cambridge jet algorithm. We develop a new systematic resummation approach. This allows us to compute both the Lund and the Cambridge average multiplicities to next-t

Aamna Ahmed, Ajith Ramachandran, Ivan M. Khaymovich, Auditya Sharma

We study the effect of quasiperiodic Aubry-Andr\'e disorder on the energy spectrum and eigenstates of a one-dimensional all-bands-flat (ABF) diamond chain. The ABF diamond chain possesses three dispersionless flat bands with all the eigenstates compactly localized on two unit cells in the zero disorder limit. The fate of the compact localized states in the p

Oliver Schib, Christoph Mordasini, Ravit Helled

We aim to develop a simple prescription for migration and accretion in 1D disc models, calibrated with results of 3D hydrodynamic simulations. Our focus lies on non-self-gravitating discs, but we also discuss to what degree our prescription could be applied when the discs are self-gravitating. We study migration using torque densities. Our model for the torq

Hao Chen, Ian Moult, Jesse Thaler, Hua Xing Zhu

The microscopic dynamics of particle collisions is imprinted into the statistical properties of asymptotic energy flux, much like the dynamics of inflation is imprinted into the cosmic microwave background. This energy flux is characterized by correlation functions $\langle \mathcal{E}(\vec n_1)\cdots \mathcal{E}(\vec n_k) \rangle$ of energy flow operators $

Daniel Junghans

We elaborate on recent results regarding the self-consistency of de Sitter vacua in the LARGE-volume scenario of type IIB string theory. In particular, we analyze to what extent the control over warping, curvature and $g_s$ corrections depends on the topology and the orientifold/brane data of a compactification. We compute a general bound on the magnitude of

Emanuele Nardini, Dong-Woo Kim, Silvia Pellegrini

The interstellar medium (ISM) of galaxies very often contains a gas component that reaches the temperature of several million degrees, whose physical and chemical properties can be investigated through imaging and spectroscopy in the X-rays. We review the current knowledge on the origin and retention of the hot ISM in star-forming and early-type galaxies, fr

Dave Epstein, Taesung Park, Richard Zhang, Eli Shechtman

We propose an unsupervised, mid-level representation for a generative model of scenes. The representation is mid-level in that it is neither per-pixel nor per-image; rather, scenes are modeled as a collection of spatial, depth-ordered "blobs" of features. Blobs are differentiably placed onto a feature grid that is decoded into an image by a generativ

Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang

This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints in

Vladimir Guzov, Julian Chibane, Riccardo Marin, Yannan He

Our world is not static and humans naturally cause changes in their environments through interactions, e.g., opening doors or moving furniture. Modeling changes caused by humans is essential for building digital twins, e.g., in the context of shared physical-virtual spaces (metaverses) and robotics. In order for widespread adoption of such emerging applicati

Atabey Kaygun, Serkan Sütlü

Motivated by the fact that the Hopf-cyclic (co)homologies of function algebras over Lie groups and universal enveloping algebras over Lie algebras capture the Lie group and Lie algebra (co)homologies, we hereby upgrade the classical van Est isomorphism to ones between the Hopf-cyclic (co)homologies of quantized algebras of functions and quantized universal e

Peng-Shuai Wang, Yang Liu, Xin Tong

We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding. Our method encodes the volumetric field of a 3D shape with an adaptive feature volume organized by an octree and applies a compact multilayer perceptron network fo

C. L. Tschirhart, Evgeny Redekop, Lizhong Li, Tingxin Li

In spin torque magnetic memories, electrically actuated spin currents are used to switch a magnetic bit. Typically, these require a multilayer geometry including both a free ferromagnetic layer and a second layer providing spin injection. For example, spin may be injected by a nonmagnetic layer exhibiting a large spin Hall effect, a phenomenon known as spin-

Laeschkir Würthner, Andriy Goychuk, Erwin Frey

Intracellular protein patterns regulate a variety of vital cellular processes such as cell division and motility, which often involve dynamic changes of cell shape. These changes in cell shape may in turn affect the dynamics of pattern-forming proteins, hence leading to an intricate feedback loop between cell shape and chemical dynamics. While several comput