November 2022 arXiv papers — page 167

Saiyad Ashanujjaman, Kirtiman Ghosh, Rameswar Sahu

Search for light (within the mass range 84-200 GeV) doubly-charged Higgs bosons decaying into a pair of W-bosons has been deemed challenging using the conventional LHC searches with leptons, jets and missing transverse momentum in the final state. Such Higgses together with slightly heavier singly-charged and neutral Higgses, when arranged in an $SU(2)_L$ tr

Fergus Imrie, Alexander Norcliffe, Pietro Lio, Mihaela van der Schaar

In many real world problems, features do not act alone but in combination with each other. For example, in genomics, diseases might not be caused by any single mutation but require the presence of multiple mutations. Prior work on feature selection either seeks to identify individual features or can only determine relevant groups from a predefined set. We in

Daniel A. Cruz, Jack Toppen, Eunbi Park, Melissa L. Kemp

An agent-based model (ABM) is a computational model in which the local interactions of autonomous agents with each other and with their environment give rise to global properties within a given domain. As the detail and complexity of these models has grown, so too has the computational expense of running several simulations to perform sensitivity analysis an

Kirsten J. Casey, Johnny P. Greco, Annika H. G. Peter, A. Bianca Davis

Understanding quenching mechanisms in low-mass galaxies is essential for understanding galaxy evolution overall. In particular, isolated galaxies are important tools to help disentangle the complex internal and external processes that impact star formation. Comparisons between quenched field and satellite galaxies in the low mass regime offer a substantial o

H. Jayatissa, M. L. Avila, K. E. Rehm, P. Mohr

The $^{22}$Mg($\alpha$,$p$)$^{25}$Al reaction rate has been identified as a major source of uncertainty for understanding the nucleosynthesis flow in Type-I x-ray bursts (XRBs). We report a direct measurement of the energy- and angle-integrated cross sections of this reaction in a 3.3-6.9 MeV center-of-mass energy range using the MUlti-Sampling Ionization Ch

George G. Vega Yon, Mary Jo Pugh, Thomas W. Valente

Studies that collect multi-outcome data such as tobacco and alcohol use are becoming increasingly common. In principle, multi-outcomes studies investigate the correlations between outcomes, including, causal links and/or joint distributions. Although there are many methods for studying multivariate outcomes, significant limitations regarding scale and interp

Sayan Mondal, George Biswas, Ahana Ghoshal, Anindya Biswas

We show that the nearest neighbour entanglement in a mixture of ground and first excited states - a subjacent state - of the J1-J2 Heisenberg quantum spin chain can be used as an order parameter to detect the phase transition of the chain from a gapless spin fluid to a gapped dimer phase. We study the effectiveness of the order parameter for varying relative

Morteza Hasanvand

Kostochka and Woodall (2001) conjectured that the square of every graph has the same chromatic number and list chromatic number. In 2015 Kim and Park disproved this conjecture for non-bipartite and bipartite graphs. It was asked by several authors whether this conjecture holds for bipartite graphs with small degrees, claw-free graphs, or line graphs. In this

Lars Hummelgren, John Wikman, Oscar Eriksson, Philipp Haller

Efficient parallelization of algorithms on general-purpose GPUs is essential in many areas today. However, it is a non-trivial task for software engineers to utilize GPUs to improve the performance of high-level programs in general. Although many domain-specific approaches are available for GPU acceleration, it is difficult to accelerate existing high-level

Gleb Smirnov

Every Maslov-zero Lagrangian torus in a K3 surface has non-trivial homology class. This note aims to extend this result to Lagrangian tori with Maslov indices congruent to zero modulo 4. Conversely, we show that every homologically non-trivial Lagrangian torus is necessarily Maslov-zero.

Fengjiao Liu, George Rapakoulias, Panagiotis Tsiotras

In this paper, we study the optimal control problem for steering the state covariance of a discrete-time linear stochastic system over a finite time horizon. First, we establish the existence and uniqueness of the optimal control law for a quadratic cost function. Then, we show the separation of the optimal mean and the covariance steering problems. We also

Michael Giegrich, Christoph Reisinger, Yufei Zhang

We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the

Peter Craig, Sukanya Chakrabarti, Robyn E. Sanderson, Farnik Nikakhtar

We analyze stellar streams in action-angle coordinates combined with recent local direct acceleration measurements to provide joint constraints on the potential of our Galaxy. Our stream analysis uses the Kullback-Leibler divergence with a likelihood analysis based on the two-point correlation function. We provide joint constraints from pulsar accelerations

Hamid Afshar, Narges Aghamir

We study holographic aspects of 2D dilaton-supergravity in flat space-time using gauge theoretic BF formulation. The asymptotic symmetries in Bondi gauge and at finite temperature span a supersymmetric extension of the warped Virasoro algebra at level zero. The boundary action is determined such that the bulk variational principle is ensured and turns out to

Junde Wu, Rao Fu, Huihui Fang, Yu Zhang

Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also found it is useful in many other vision tas

Prasanna Sriganesh, Namya Bagree, Bhaskar Vundurthy, Matthew Travers

Robotic systems need advanced mobility capabilities to operate in complex, three-dimensional environments designed for human use, e.g., multi-level buildings. Incorporating some level of autonomy enables robots to operate robustly, reliably, and efficiently in such complex environments, e.g., automatically "returning home" if communication between an operato

Taha Entesari, Sina Sharifi, Mahyar Fazlyab

We propose a novel Branch-and-Bound method for reachability analysis of neural networks in both open-loop and closed-loop settings. Our idea is to first compute accurate bounds on the Lipschitz constant of the neural network in certain directions of interest offline using a convex program. We then use these bounds to obtain an instantaneous but conservative

Tomilola M. Obadiya, Daniel M. Sussman

Data-driven approaches to inferring the local structures responsible for plasticity in amorphous materials have made substantial contributions to our understanding of the failure, flow, and rearrangement dynamics of supercooled fluids. Some of these methods, such as the ``softness'' approach based on linear support vector machines, have identified combinatio

Tobias König, Paul Laurain

For a bounded set $\Omega \subset \mathbb R^N$ and a perturbation $V \in C^1(\overline{\Omega})$, we analyze the concentration behavior of a blow-up sequence of positive solutions to \[ -\Delta u_\epsilon + \epsilon V = N(N-2) u_\epsilon^\frac{N+2}{N-2} \] for dimensions $N \geq 4$, which are non-critical in the sense of the Brezis--Nirenberg problem. For th

Avik Kumar Das, Raj Prince, Alok C. Gupta, Pankaj Kushwaha

The continuous monitoring capability of Fermi-LAT has enabled the exploration of Quasi-Periodic Oscillations (QPOs) in the $\gamma$-ray light curve of blazar that has given a new perspective to probe these sources over a wide range of time scales. We report the presence of transient QPOs in the long-term $\gamma$-ray light curve of blazars PKS 0244-470 and 4

Govindam Sharma, Akhlesh Lakhtakia, Pradip Kumar Jain

The planewave response of a bicontrollable metasurface absorber with graphene-patched pixels was simulated in the X band using commercial software. Each square meta-atom is a 4x4 array of 16 pixels, some patched with graphene and the others unpatched. The pixels are arranged on a PVC skin which is placed on a copper-backed YIG substrate. Graphene provides el

Tetsuhiro S. Hatakeyama, Ryudo Ohbayashi

The preferred conditions for evolutionary innovation is a fundamental question, but little is known, in part because the question involves rare events. We focused on the potential role of polyploidy in the evolution of novel traits. There are two hypotheses regarding the effects of polyploidy on evolution: Polyploidy reduces the effect of a single mutation a

Jianlong Liu

Given a two-dimensional substitution tiling space, we show that, under some reasonable assumptions, the $K$-theory of the groupoid $C^\ast$-algebra of its unstable groupoid can be explicitly reconstructed from the $K$-theory of the $AF$-algebras of the substitution rule and its analogue on the $1$-skeleton. We prove this by generalizing the calculations done

Phung Lai, NhatHai Phan, Tong Sun, Rajiv Jain

In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models (NLMs). To preserve UeDP, we developed a novel algorithm, called UeDP-Alg, optimizing the trade-off between privacy loss and m

Rahul Kumar Thakur, Harish Kumar, Shashikant Gupta, Dinkar Verma

Recent observations of Type Ia supernovae (SNe) by SH0ES collaboration (R11 and R16) diverge from the value reported by recent CMBR observations utilising the Planck satellite and application of the $\Lambda CDM$ cosmological model by at least $3 \sigma$. It is among the most challenging problems in contemporary cosmology and is known as the Hubble tension.

Alireza Aghelan, Modjtaba Rouhani

In the field of medical image analysis, there is a substantial need for high-resolution (HR) images to improve diagnostic accuracy. However, it is a challenging task to obtain HR medical images, as it requires advanced instruments and significant time. Deep learning-based super-resolution methods can help to improve the resolution and perceptual quality of l

Varun Kompella, Thomas J. Walsh, Samuel Barrett, Peter Wurman

Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper introduces Stratified Sampling from Event Tables (SSET), which partitions an ER buffer into Event Tables, each capturing important subsequences o

David Nukrai, Ron Mokady, Amir Globerson

We consider the task of image-captioning using only the CLIP model and additional text data at training time, and no additional captioned images. Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar. Therefore, we only need to learn how to translate CLIP textual embeddings back into text, and we can learn how to

Denys Bulavka, Eran Nevo, Yuval Peled

We study the generic volume rigidity of $(d-1)$-dimensional simplicial complexes in $\mathbb R^{d-1}$, and show that the volume rigidity of a complex can be identified in terms of its exterior shifting. In addition, we establish the volume rigidity of triangulations of several $2$-dimensional surfaces and prove that, in all dimensions $>1$, volume rigidity i

Subhyal Bin Iqbal, Salman Nadaf, Ahmad Awada, Umur Karabulut

Although frequency range 2 (FR2) systems are an essential part of 5G-Advanced and future 3GPP releases, the mobility performance of multi-panel user equipment (MPUE) with hand blockage is still an area open for research and standardization. In this article, a comprehensive study on the mobility performance of MPUE with hand blockage is performed for conditio

Chang Liu, Yuwen Yang, Zhe Xie, Hongtao Lu

Data-efficient learning on graphs (GEL) is essential in real-world applications. Existing GEL methods focus on learning useful representations for nodes, edges, or entire graphs with ``small'' labeled data. But the problem of data-efficient learning for subgraph prediction has not been explored. The challenges of this problem lie in the following aspects: 1)

Chanakya Ekbote, Moksh Jain, Payel Das, Yoshua Bengio

Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects $x$ given a reward function $R(x)$, indicating the utility of the object and trained independently from the GFlowNet by supervised learning to predict a desirable property $y$ given $x$. We hypothesize that this can lead to inco

Ashish Tiwari, Sresth Tosniwal, Shanmuganathan Raman

Graph convolution networks (GCNs) have been enormously successful in learning representations over several graph-based machine learning tasks. Specific to learning rich node representations, most of the methods have solely relied on the homophily assumption and have shown limited performance on the heterophilous graphs. While several methods have been develo

G. Angloher, S. Banik, D. Bartolot, G. Benato

The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose

Marco Biroli, Hernan Larralde, Satya N. Majumdar, Gregory Schehr

We study a one-dimensional gas of $N$ Brownian particles that diffuse independently, but are {\it simultaneously} reset to the origin at a constant rate $r$. The system approaches a non-equilibrium stationary state (NESS) with long-range interactions induced by the simultaneous resetting. Despite the presence of strong correlations, we show that several obse

John C. Baez, Xiaoyan Li, Sophie Libkind, Nathaniel D. Osgood

Stock and flow diagrams are already an important tool in epidemiology, but category theory lets us go further and treat these diagrams as mathematical entities in their own right. In this chapter we use communicable disease models created with our software, StockFlow.jl, to explain the benefits of the categorical approach. We first explain the category of st

Lucas Schneider, Philip Beck, Levente Rózsa, Thore Posske

Edge states forming at the boundaries of topologically non-trivial phases of matter are promising candidates for future device applications because of their stability against local perturbations. Magnetically ordered spin chains proximitized by an s-wave superconductor are predicted to enter a topologically non-trivial mini-gapped phase with zero-energy Majo

Tong Su, Ching Hua Lee, San-Dong Guo, Guangzhao Wang

Two-dimensional (2D) niobium oxydihalide NbOI$_2$ has been recently demonstrated as an excellent in-plane piezoelectric and nonlinear optical materials. Here we show that Janus niobium oxydihalide, NbO$XY$ (X, Y = Cl, Br, I and X$\neq$Y), is a multifunctional anisotropic semiconductor family with exceptional piezoelectric, electronic, photocatalytic and opti

Shashwat Bhattacharya, Thomas Boeck, Dmitry Krasnov, Jörg Schumacher

We study the influence of fringing magnetic fields on turbulent thermal convection in a horizontally extended rectangular domain. The magnetic field is created in the gap between two semi-infinite planar magnetic poles, with the convection layer located near the edge of the gap. We employ direct numerical simulations in this setup for fixed Rayleigh and smal

Roberto Gorrieri

Step net bisimulation is a coinductive behavioral relation for finite Petri nets, which is a smooth generalization of the definition of standard step bisimulation \cite{NT84} on finite Petri nets. Its induced equivalence offers an alternative, much simpler characterization of causal-net bisimilarity \cite{G15,Gor22}, as it does not resort to any causality st

Yong Chen, Yuanyuan Gong

We study the stochastic Leray-{\alpha} model of Euler equations with transport noise. We first use weak convergence approach to show the large deviations of the stochastic Leray-{\alpha} model of Euler equations in a suitable scaling limit. Then, we establish the quantitative convergence rate by semigroup approach. Moreover, we obtain a central limit theorem

Kartik P. Iyer

The asymptotic energy dissipation is connected to the third-order scaling of the longitudinal velocity increment magnitude in three-dimensional turbulence via the Kolmogorov $4/5$ law. It is shown that the third-order longitudinal absolute velocity increment scaling should not exceed unity for anomalous dissipation to occur, that is for non-vanishing average

Jonte R. Hance, Sabine Hossenfelder

A recent Nature Physics editorial (Nat. Phys. (2022) 18, 961) falsely claims ``any theory that uses hidden variables still requires non-local physics.'' We correct this claim and explain why it is important to get this right.

Roberto Paroni, Paolo Podio-Guidugli, Brian Seguin

In the literature various notions of nonlocal curvature can be found. Here we propose a notion of nonlocal curvature tensor. This we do by generalizing an appropriate representation of the classical curvature tensor and by exploiting some analogies with certain fractional differential operators.

Marios Papachristou, Rishab Goel, Frank Portman, Matthew Miller

In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged by GCNs and GATs. Such architectures, however, cannot easily scale to large real-world graphs. On the other hand, shallow (or node-level) m

Kristin Courtney, Anna Duwenig, Magdalena C. Georgescu, Astrid an Huef

We consider a twist $E$ over an \'etale groupoid $G$. When $G$ is principal, we prove that the nuclear dimension of the reduced twisted groupoid $\mathrm{C}^*$-algebra is bounded by a number depending on the dynamic asymptotic dimension of $G$ and the topological covering dimension of its unit space. This generalizes an analogous theorem by Guentner, Willett

J. Sitnicka, M. Konczykowski, K. Sobczak, P. Skupiński

The magnetic coupling mechanisms underlying ferromagnetism and magnetotransport phenomena in magnetically doped topological insulators have been a central issue to gain controlled access to the magneto-topological phenomena such as quantum anomalous Hall effect and topological axion insulating state. Here, we focus on the role of bulk carriers in magnetism o

Junling Zheng

We introduce Igsua-Todorov distances of Artin algebra, prove its invariance under derived equivalence, present its application to exterior algebra, and establish the link between the dimension of the singularity category and this distance.

Eric Hambro, Roberta Raileanu, Danielle Rothermel, Vegard Mella

Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets. However, progress on this research has been hindered by the scarcity of open-sourced datasets and the prohibitive computational cost to work with them.

Mohammed Ayman Shalaby, Charles Champagne Cossette, James Richard Forbes, Jerome Le Ny

Time-of-flight-based ranging among transceivers with different clocks requires protocols that accommodate varying rates of the clocks. Double-sided two-way ranging (DS-TWR) is widely adopted as a standard protocol due to its accuracy; however, the precision of DS-TWR has not been clearly addressed. In this paper, an analytical model of the variance of DS-TWR

Erixhen Sula, Lizhong Zheng

The Expectation Maximization (EM) algorithm is widely used as an iterative modification to maximum likelihood estimation when the data is incomplete. We focus on a semi-supervised case to learn the model from labeled and unlabeled samples. Existing work in the semi-supervised case has focused mainly on performance rather than convergence guarantee, however w

Ioannis Prapas, Akanksha Ahuja, Spyros Kondylatos, Ilektra Karasante

Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas

Christoph Czichowsky, Raphael Huwyler

We consider robust utility maximisation in continuous-time financial markets with proportional transaction costs under model uncertainty. For this purpose, we work in the framework of Chau and R\'asonyi (2019), where robustness is achieved by maximising the worst-case expected utility over a possibly uncountable class of models that are all given on the same

O. Melchert, S. Bose, S. Willms, I. Babushkin

We study incoherently coupled two-frequency pulse compounds in waveguides with single zero-dispersion and zero-nonlinearity points. In such waveguides, supported by a negative nonlinearity, soliton dynamics can be obtained even in domains of normal dispersion. We demonstrate trapping of weak pulses by solitary-wave wells, forming nonlinear-photonics meta-ato

Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov

Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires prior knowledge on the unknown image. We present DOLPH as a new deep model-based architecture for phase retrieval that integrates an image prior specified using a diffusion model wit

Hai-Jun Li, Wei Chao

We investigate the photon-axionlike particle (ALP) oscillation effects on TeV gamma-ray spectral irregularities from the uncertain redshift active galactic nuclei (AGN) VER J0521+211. The gamma-ray spectra are measured by Fermi-LAT and VERITAS with the three flux states in 2013 and 2014. We set the combined constraints on the ALP parameter ($m_a, g_{a\gamma}

Xingchen Song, Di Wu, Zhiyong Wu, Binbin Zhang

In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models. The core idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames, see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not require any alignment. We demonstrate that TrimTail i

Y. Senyk, J. J. Abraham, Y. Shemerliuk, S. Selter

In this work we report a detailed ESR spectroscopic study of the single-crystalline samples of the van der Waals compounds $M_{\text{2}}$P$_{\text{2}}$S$_{\text{6}}$ ($M_{\text{2}}$ = Mn$_{\text{2}}$, MnNi, Ni$_{\text{2}}$), performed at an excitation frequency of 9.56 GHz, in a broad range of temperatures above the magnetic order, and at different orientati

Shuo Gong, Yijun Hu, Linxiao Wei

Model uncertainty has been one prominent issue both in the theory of risk measures and in practice such as financial risk management and regulation. Motivated by this observation, in this paper, we take a new perspective to describe the model uncertainty, and thus propose a new class of risk measures under model uncertainty. More precisely, we use an auxilia

Yanran Guan, Andrei Chubarau, Ruby Rao, Derek Nowrouzezahrai

Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations co

Hyun-Sik Jeong, Wen-Bin Pan, Ya-Wen Sun, Yuan-Tai Wang

We study the $(d+2)$-dimensional Hyperscaling Violating (HV) geometries in the presence of both a finite temperature $T$ and a UV cutoff $r_c$. This gravitational system is conjectured to be dual to $T\bar{T}$ like deformed HV QFTs. We consider the representative quantum entanglement quantity in holography, i.e. the entanglement entropy $S(A)$, and perform a

Ding Jia

Quantum cosmology based on Lorentzian path integrals is a promising avenue. However, many previous works allow non-Lorentzian configurations by integrating the squared scale factor over the whole real line. Here we show that restricting the minisuperspace path integral to Lorentzian configurations with positive squared scale factor can significantly change t

Hongguang Sun, Yajun Ma, Tony Q. S. Quek, Xijun Wang

As a promising architecture, Mobile Data Collector (MDC) enhanced Internet of Things (IoT) exhibits broad prospects in efficient data collection and data aggregation especially for sparse deployment scenarios. Combining the tools from queueing theory and stochastic geometry, we propose an analytical framework to study the network performance of an MDC enhanc

Tsuyoshi Kato, Daisuke Kishimoto, Mitsunobu Tsutaya

Let $M$ be a non-compact connected manifold with a cocompact and properly discontinuous action of a discrete group $G$. We establish a Poincar\'{e}-Hopf theorem for a bounded vector field on $M$ satisfying a mild condition on zeros. As an application, we show that such a vector field must have infinitely many zeros whenever $G$ is amenable and the Euler char

Mohan Shi, Jie Zhang, Zhihao Du, Fan Yu

Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint training of single-channel target-speaker sep

Guo-Liang Yu, Zhen-Yu Li, Zhi-Gang Wang, Jie Lu

In this work, we perform a systematic study of the mass spectra, the root mean square(r.m.s.) radii and the radial density distributions of the doubly charmed baryons $\Xi_{cc}$ and $\Omega_{cc}$. The calculations are carried out in the frame work of Godfrey-Isgur (GI) relativized quark model, where the baryon is regarded as a real three-body system of quark

Alireza Sheikh, Jac Romme, Jochem Govers, Amirashkan Farsaei

The growth in the number of low-cost narrow band radios such as Bluetooth low energy (BLE) enabled applications such as asset tracking, human behavior monitoring, and keyless entry. The accurate range estimation is a must in such applications. Phase-based ranging has recently gained momentum due to its high accuracy in multipath environment compared to tradi

Avy Soffer, Xiaoxu Wu

We give a proof of Local Decay Estimates for Schr\"odinger type equations, which is based on the knowledge of Asymptotic Completeness (AC). This approach extends to time dependent potential perturbations, as it does not rely on Resolvent Estimates or related methods. Global in time Strichartz estimates follow for quasi-periodic time-dependent potentials from

Fabio Nobile, Tommaso Vanzan

We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of Optimal Control Problems (OCPs) constrained by random partial differential equations. The method requires to solve the OCP for several low-fidelity spatial grids and quadrature formu

Marco Comunità, Christian J. Steinmetz, Huy Phan, Joshua D. Reiss

Deep learning approaches for black-box modelling of audio effects have shown promise, however, the majority of existing work focuses on nonlinear effects with behaviour on relatively short time-scales, such as guitar amplifiers and distortion. While recurrent and convolutional architectures can theoretically be extended to capture behaviour at longer time sc

Xinyi Gao, Wentao Zhang, Yingxia Shao, Quoc Viet Hung Nguyen

Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications under real-time inference scenarios. Although existing scalable GNNs leverage linear propagation to preprocess the features and accelerate the training and inference procedure, these

Patience Ablett, Stephen Coughlan

Recent work of Ablett arXiv:2112.03400 and Kapustka, Kapustka, Ranestad, Schenck, Stillman and Yuan arXiv:2111.05817 outlines a number of constructions for singular Gorenstein codimension four varieties. Earlier work of Coughlan, Go{\l}\c{e}biowski, Kapustka and Kapustka arXiv:1609.01195 details a series of nonsingular Gorenstein codimension four constructio

Kai Wang, Houjun Mo, Cheng Li, Yangyao Chen

We propose a general framework leveraging the halo-galaxy connection to link galaxies observed at different redshift in a statistical way, and use the link to infer the redshift evolution of the galaxy population. Our tests based on hydrodynamic simulations show that our method can accurately recover the stellar mass assembly histories up to $z\sim 3$ for pr

Hongrui Jin

Social media may create echo chambers that reaffirm users' beliefs and opinions through repeated exposure of similar notions. Whilst the formation and effect of echo chambers have been intensively examined in thread-based platforms such as Twitter, Facebook and Reddit, we shift our focus on product review discussions on YouTube. This paper examines YouTube c

Eduardo Bittencourt, Elliton O. S. Brandão, Érico Goulart

We scrutinize the geometrical properties of light propagation inside a nonlinear medium modeled by a fully covariant electromagnetic theory in $2+1$-dimensions. After setting the nonlinear constitutive relations, the phase velocity and the polarization of waves are derived and three special cases are analyzed in details. In spite of the dimensional reduction

Abdellah Rahmani, Arun Venkitaraman, Pascal Frossard

In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN

Yiqin He

Let $L$ be a finite extension of $\mathbf{Q}_p$. In this paper, we study the locally $\mathbf{Q}_p$-analytic generalized parabolic Steinberg representations of $\mathrm{GL}_n(L)$, and compute the $\mathrm{Ext}$-groups of locally $\mathbf{Q}_p$-analytic generalized parabolic Steinberg representations. They carry the Breuil's simple $\mathcal{L}$-invariants, w

Laure Marêché

In 2008, T\'oth and Vet\H{o} defined the self-repelling random walk with directed edges as a non-Markovian random walk on $\mathbb{Z}$: in this model, the probability that the walk moves from a point of $\mathbb{Z}$ to a given neighbor depends on the number of previous crossings of the directed edge from the initial point to the target, called the local time

Nina Dörnemann, Holger Dette

For a given $p\times n$ data matrix $\textbf{X}_n$ with i.i.d. centered entries and a population covariance matrix $\bf{\Sigma}$, the corresponding sample precision matrix $\hat{\bf\Sigma}^{-1}$ is defined as the inverse of the sample covariance matrix $\hat{\bf{\Sigma}} = (1/n) \bf{\Sigma}^{1/2} \textbf{X}_n\textbf{X}_n^\top \bf{\Sigma}^{1/2}$. We determine

Qi'an Guan, Zhitong Mi, Zheng Yuan

In this article, we present a concavity property of the minimal $L^{2}$ integrals related to multiplier ideal sheaves with Lebesgue measurable gain on weakly pseudoconvex K\"ahler manifolds. As applications, we give a necessary condition for the concavity degenerating to linearity, and a characterization for the holding of the equality in optimal jets $L^2$

Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers

Counterfactual reasoning -- envisioning hypothetical scenarios, or possible worlds, where some circumstances are different from what (f)actually occurred (counter-to-fact) -- is ubiquitous in human cognition. Conventionally, counterfactually-altered circumstances have been treated as "small miracles" that locally violate the laws of nature while sharing the

Adam Norton, Reza Ahmadzadeh, Kshitij Jerath, Paul Robinette

This handbook outlines all test methods developed under the Development and Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by the University of Massachusetts Lowell for evaluating small unmanned aerial systems (sUAS) performance in subterranean and constrained indoor environments, spanning communicat

Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan

Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing to the in

Shijie Bao, Qi'an Guan, Zheng Yuan

In this article, we present characterizations of the concavity property of minimal $L^2$ integrals degenerating to linearity in the case of fibrations over open Riemann surfaces. As applications, we obtain characterizations of the holding of equality in optimal jets $L^2$ extension problem from fibers over analytic subsets to fibrations over open Riemann sur

Like Huang

Ever science the invention of solar cells, thermodynamics has been used to assess their performance limits, guiding advances in materials science and photovoltaic technology to reduce the gap between the practical efficiencies and the thermodynamic limits to photovoltaic energy conversion. By systematically addressing the thermodynamic efficiency losses in c

I. A. Luchnikov, M. A. Gavreev, A. K. Fedorov

Quantum many-body control is among most challenging problems in quantum science, due to computational complexity of related underlying problems. We propose an efficient approach for solving a class of control problems for many-body quantum systems, where time-dependent controls are applied to a sufficiently small subsystem. The approach is based on a tensor-

Mehdi Rafiei, Dat Thanh Tran, Alexandros Iosifidis

Mineral wool production is a non-linear process that makes it hard to control the final quality. Therefore, having a non-destructive method to analyze the product quality and recognize defective products is critical. For this purpose, we developed a visual quality control system for mineral wool. X-ray images of wool specimens were collected to create a trai

David Galvin, Gwen McKinley, Will Perkins, Michail Sarantis

We study the locations of complex zeroes of independence polynomials of bounded degree hypergraphs. For graphs, this is a long-studied subject with applications to statistical physics, algorithms, and combinatorics. Results on zero-free regions for bounded-degree graphs include Shearer's result on the optimal zero-free disk, along with several recent res

Mikhail Silaev

Ultrastrong light-matter coupling opens exciting possibilities to generate squeezed quantum states and entanglement. Here we propose a way to achieve this regime in superconducting hybrid nanostructures with ferromagnetic interlayers. Strong confinement of electromagnetic field between superconducting plates is found to result in the existence of magnon-pola

Jianqiang Yang

In this paper, we consider the rationally elliptic projective fourfolds that are holomorphically embedded into the complex projective eight-space $\mathbb{P}^8$. It is proved that a simply-connected $\mathbb Q$-homological projective four-space $X\subset\mathbb{P}^8$ is biholomorphic to $\mathbb P^4$ by using Euler characteristic and Chern numbers formulae o

Shulei Wang

Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effective

Danish Ali, Zhiqing Yang, Abid Hussain, Mohd Ibrahim Sheikh

An unknotting operation is a local move such that any knot diagram can be transformed into a diagram of the trivial knot by a finite sequence of these operations plus some Reidemeister moves. It is known that for all $n \geq 2$ the $H(n)$-move is an unknotting operation for classical knots and links. In this paper, we extend the classical unknotting operatio

Iryna Raievska, Maryna Raievska, Yaroslav Sysak

It is proved that the additive group of every semidistributive nearring $R$ with an identity is abelian and if R has no elements of order $2$, then the nearring $R$ actually is an associative ring.

D. Obric, B. Nikolic

In this paper we consider non-commutativity that arises from T-duality of bosonic coordinates of type II superstring in presence of coordinate dependent Ramond-Ramond field. Action with such choice of the background fields is not translational invariant. Consequently, we will employ generalization of Buscher procedure that can be applied to cases that have c

Alexander Bogatskiy, Timothy Hoffman, David W. Miller, Jan T. Offermann

Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limiting their applicability as scientific modeling tools. In this work, we present a machine learning architecture that uses a set of inputs maximally reduced with respect to the full

Aditya Agashe, Ethan Lee, A. Shadi Tahvildar-Zadeh

In the classical electrodynamics of point charges in vacuum, the electromagnetic field, and therefore the Lorentz force, is ill-defined at the locations of the charges. Kiessling resolved this problem by using the momentum balance between the field and the particles, extracting an equation for the force that is well-defined where the charges are located, so

Anastasia Doikou

We review the discrete evolution problem and the corresponding solution as a discrete Dyson series in order to rigorously derive a generalized discrete version of the Magnus expansion. We also systematically derive the discrete analogue of the pre-Lie Magnus expansion and express the elements of the discrete Dyson series in terms of a tridendriform algebra b

Yulong Lu, Dejan Slepčev, Lihan Wang

Motivated by the challenge of sampling Gibbs measures with nonconvex potentials, we study a continuum birth-death dynamics. We improve results in previous works [51,57] and provide weaker hypotheses under which the probability density of the birth-death governed by Kullback-Leibler divergence or by $\chi^2$ divergence converge exponentially fast to the Gibbs

Marius Bild, Matteo Fadel, Yu Yang, Uwe von Lüpke

The superposition principle is one of the most fundamental principles of quantum mechanics. According to the Schr\"odinger equation, a physical system can be in any linear combination of its possible states. While the validity of this principle is routinely validated for microscopic systems, it is still unclear why we do not observe macroscopic objects to be

Eduardo Abi Jaber, Eyal Neuman

We consider a class of optimal liquidation problems where the agent's transactions create transient price impact driven by a Volterra-type propagator along with temporary price impact. We formulate these problems as maximization of a revenue-risk functionals, where the agent also exploits available information on a progressively measurable price predicting s