August 2022 arXiv papers — page 140

Xinmin Fu, Jie Yang, Jiafu Wang, Yajuan Han

Metasurfaces have provided unprecedented degree of freedom (DOF) in manipulating electromagnetic (EM) waves. Geometric phase can be readily obtained by rotating the meta-atom of metasurfaces. Nevertheless, such geometric phases are usually spin-coupled, with the same magnitude but opposite signs for left_ and right_handed circularly polarized (LCP,RCP) waves

Clara Javaherian, Chris Ferrie

Optimal transport is one of the primary goals for designing efficient quantum networks. In this work, the maximum transport is investigated for three-dimensional quantum networks with Platonic geometries affected by dephasing and dissipative Markovian noise. The network and the environmental characteristics corresponding the optimal design are obtained and i

Zhongyi Zhou, Koji Yatani

Interactive Machine Teaching (IMT) systems allow non-experts to easily create Machine Learning (ML) models. However, existing vision-based IMT systems either ignore annotations on the objects of interest or require users to annotate in a post-hoc manner. Without the annotations on objects, the model may misinterpret the objects using unrelated features. Post

Bei-Chuan Wang, Xiao-Hong Yang, De-Fu Bu, Shu-Su Huang

Ultra-fast outflows (UFOs) with mildly relativistic velocities are measured using the X-ray spectra of radio-quiet and -loud active galactic nuclei (AGNs). In general, UFOs are believed to be generated from the accretion disk around a black hole (BH). A line-force driving model is suggested to be the mechanism to drive UFOs from the accretion disk. In this p

Alexander V. Mamonov, Liliana Borcea, Josselin Garnier, Jörn Zimmerling

A novel approach to full waveform inversion (FWI), based on a data driven reduced order model (ROM) of the wave equation operator is introduced. The unknown medium is probed with pulses and the time domain pressure waveform data is recorded on an active array of sensors. The ROM, a projection of the wave equation operator is constructed from the data via a n

Nathaniel Josephs, Sida Peng, Forrest W. Crawford

Most businesses impose a supervisory hierarchy on employees to facilitate management, decision-making, and collaboration, yet routine inter-employee communication patterns within workplaces tend to emerge more naturally as a consequence of both supervisory relationships and the needs of the organization. What then is the relationship between a formal organiz

Konstantin Asteriadis, Alejandro Quiroga Triviño, Martin Spinrath

In this paper we discuss bremsstrahlung induced by neutrino scattering. This process should exist since neutrinos are expected to couple to photons via magnetic dipole and transition moments. These moments are loop-induced and tiny in the Standard Model with neutrino masses but could be significantly enhanced in extended theories. As concrete example we stud

Joseph A. Gallego, Juan F. Osorio, Fabio A. González

Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsuitable for most current big data applications. Several strategies, such as tree-based or hashing-based estimators, have been proposed to imp

Maricarmen A. Winkler, Camilo Vásquez-Wilson, Felipe A. Asenjo

The different forms of propagation of relativistic electron plasma wavepackets in terms of Airy functions are studied. It is shown that exact solutions can be constructed showing accelerated propagations along coordinates transverse to the thermal speed cone coordinate. Similarly, Airy propagation is a solution for relativistic electron plasma waves in the p

Peter G. Stratton, Andrew Wabnitz, Chip Essam, Allen Cheung

The surge in interest in Artificial Intelligence (AI) over the past decade has been driven almost exclusively by advances in Artificial Neural Networks (ANNs). While ANNs set state-of-the-art performance for many previously intractable problems, the use of global gradient descent necessitates large datasets and computational resources for training, potential

Oleksandr Kyriienko, Einar B. Magnusson

We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and unsupervised machine learning methods, where average precision is chosen as a robust metric for detecting anomalous data. We focus on kernel-based approaches for ease of direct com

Arthur Anthony da Cunha Romão E Silva, Francisco Márcio Barboza

In the present paper, the solution of the seismic data inversion problem through multi-objective optimization with NSGA II is addressed. The seismic inversion consists of estimating the slowness of rocks in the subsurface from the travel times of the waves through the transmitters and receivers of the seismic waves. The inversion process uses the multi-objec

Kofi Odame, Maria Nyamukuru, Mohsen Shahghasemi, Shengjie Bi

We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 um CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of p

Zihao Zhao, Yuzhu Mao, Yang Liu, Linqi Song

In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure wit

Li Yang, Shah Saad Alam, Han Pu

Quantum many-body systems in one dimension (1D) exhibit some peculiar properties. In this article, we review some of our work on strongly interacting 1D spinor quantum gas. First, we discuss a generalized Bose-Fermi mapping that maps the charge degrees of freedom to a spinless Fermi gas and the spin degrees of freedom to a spin chain model. This also maps th

Siwei Wang, Xinwang Liu, En Zhu

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in various applications, most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering, which could cause over-comp

Talya Eden, Jakob Bæk Tejs Houen, Shyam Narayanan, Will Rosenbaum

In classical statistics and distribution testing, it is often assumed that elements can be sampled from some distribution $P$, and that when an element $x$ is sampled, the probability $P$ of sampling $x$ is also known. Recent work in distribution testing has shown that many algorithms are robust in the sense that they still produce correct output if the elem

Bat-Od Battseren

We show that $M_d$-approximation-property, $M_d$-weak-amenability, and $M_d$-weak-Haagerup-property are stable under von Neumann equivalence (hence also Measure equivalence and W*-equivalence). We also show that these properties are inherited from lattices.

Wenxuan Ma, Jinming Zhang, Shuang Li, Chi Harold Liu

Extensive studies on Unsupervised Domain Adaptation (UDA) have propelled the deployment of deep learning from limited experimental datasets into real-world unconstrained domains. Most UDA approaches align features within a common embedding space and apply a shared classifier for target prediction. However, since a perfectly aligned feature space may not exis

Yuanyuan Lian, Kai Zhang

In this paper, we prove boundary pointwise $C^{k,\alpha}$ regularity for any $k\geq 1$ for fully nonlinear parabolic equations. As an application, we give a direct and short proof of the higher regularity of the free boundaries in obstacle-type problems.

Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas

Directed self-assembly (DSA) of block-copolymers (BCPs) is one of the most promising developments in the cost-effective production of nanoscale devices. The process makes use of the natural tendency for BCP mixtures to form nanoscale structures upon phase separation. The phase separation can be directed through the use of chemically patterned substrates to p

Antoine Jouin, Stefania Cherubini, Jean-Christophe Robinet

In some linearly unstable flows, secondary instability is found to have a much larger wavelength than that of the primary unstable modes, so that it cannot be recovered with a classical Floquet analysis. In this work, we apply a new formulation for capturing secondary instabilities coupling multiple length scales of the primary mode. This formulation, based

Yunfan Zhao, Qingkai Pan, Krzysztof Choromanski, Deepali Jain

We present a new class of structured reinforcement learning policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are chosen based on the attention scores of their learnable latent representations with those of the input states. By explicitly disentangling action from state processing in the policy stack, we achieve two main goals: subst

Xiaohu You, Yongming Huang, Shengheng Liu, Dongming Wang

Sixth-generation (6G) networks are evolving towards new features and order-of-magnitude enhancement of systematic performance metrics compared to the current 5G. In particular, the 6G networks are expected to achieve extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency, and $\mu$s-scale latency. To this end, an origin

Na Young Ahn, Dong Hoon Lee

NAND flash memory-based IoT device can potentially still leave behind original personal data in an invalid area even if the data has been deleted. In this paper, we raise the forensic issue of original data remaining in unmanaged blocks caused by NAND flash memory and introduce methods for secure deletion of such data in the invalid area. We also propose a v

Jinyoung Kim, Hongjun An, Kaya Mori

We investigated a wealth of X-ray and gamma-ray spectral energy distribution (SED) and multi-band light curve (LC) data of the gamma-ray binary HESS J0632+057 using a phenomenological intrabinary shock (IBS) model. Our baseline model assumes that the IBS is formed by colliding winds from a putative pulsar and its Be companion, and particles accelerated in th

Jie Hong, Pengfei Fang, Weihao Li, Junlin Han

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer represe

F. Lomoc, A. P. Boette, N. Canosa, R. Rossignoli

We analyze the application of the history state formalism to quantum walks. The formalism allows one to describe the whole walk through a pure quantum history state, which can be derived from a timeless eigenvalue equation. It naturally leads to the notion of system-time entanglement of the walk, which can be considered as a measure of the number of orthogon

Li-Yuan Chiang, Chun-Ta Wang, Steve Pappert, Paul K. L. Yu

We propose and demonstrate a passively biased 2 by 2 thermo-optic switch with high power efficiency and fast response time. The device benefits from the highly concentrated optical field of a slot waveguide mode and the strong thermo-optic effect of a nematic liquid crystal (NLC) cladding. The NLC fills the nano-slot region and is aligned by the subwavelengt

Tijana Zrnic, Eric Mazumdar

We construct a zeroth-order gradient estimator for a smooth function defined on the probability simplex. The proposed estimator queries the simplex only. We prove that projected gradient descent and the exponential weights algorithm, when run with this estimator instead of exact gradients, converge at a $\mathcal O(T^{-1/4})$ rate.

J. C. T. Thangaraj, R. C. Dhuley

Fermilab has developed a novel concept for an industrial electron linac using Nb3Sn coating technology and conduction cooling. These conduction-cooled linacs can generate electron beam energies up to 10 MeV in continuous-wave operation and reach higher power (>=1 MW) by combing several modules. Compact and light enough to mount on mobile platforms, our machi

Tomer Ravid, Tom Banks

We derive multidimensional bosonization directly from the electron gas in a low-energy, low momentum regime where $\omega\gg \frac{k^2}{k_F}$, such that the dispersion can be linearized. To reach this limit, the Fermi momentum and the number of patches are scaled simultaneously keeping the width of each patch finite. We apply this to obtain an exact low-ener

Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura Cruz

Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a methodology for predicting student performance from their online learning activities that optimizes inference accuracy over different demographic groups such as race and gender. Buildin

Alexandre Conlon, Jason Crann, David W. Kribs, Rupert H. Levene

We introduce a notion of teleportation scheme between subalgebras of semi-finite von Neumann algebras in the commuting operator model of locality. Using techniques from subfactor theory, we present unbiased teleportation schemes for relative commutants $N'\cap M$ of a large class of finite-index inclusions $N\subseteq M$ of tracial von Neumann algebras, wher

Martin Jankowiak

Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty. However, wider adoption of Bayesian variable selection has been hampered by computational challenges, especially in difficult regimes with a large number of covariates P or non-conjugat

Anastasia A. Golubtsova, Marina K. Usova

We study holographic RG flows in a 3d supergravity model from the side of the dynamical system theory. The gravity equations of motion are reduced to an autonomous dynamical system. Then we find equilibrium points of the system and analyze them for stability. We also restore asymptotic solutions near the critical points. We find two types of solutions: with

Christopher Chamberland, Luis Goncalves, Prasahnt Sivarajah, Eric Peterson

Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requirements. We then introduce the construction of local neural network (NN) decoders using three-dimensional con

Newton Solórzano, Víctor León

We study the cylindrical symmetric Finsler metrics. We obtain the system of differential equations of such metrics which are projectively flat. We give a family of solutions of this system. Examples are included.

Yaguang Li, Timothy R. Bedding, Dennis Stello, Daniel Huber

In asteroseismology, the surface effect refers to a disparity between the observed and the modelled frequencies in stars with solar-like oscillations. It originates from improper modelling of the surface layers. Correcting the surface effect usually requires using functions with free parameters, which are conventionally fitted to the observed frequencies. On

Pavel Galashin, Thomas Lam

We study relations between cluster algebra invariants and link invariants. First, we show that several constructions of positroid links (permutation links, Richardson links, grid diagram links, plabic graph links) give rise to isotopic links. For a subclass of permutations arising from concave curves, we also provide isotopies with the corresponding Coxeter

Jiaye Wu, Xuanyi Liu, Boris A. Malomed, Kuan-Chang Chang

Establishing relations between fundamental effects in far-flung areas of physics is a subject of great interest in the current research. We here report realization of a novel photonic system akin to the radio-frequency superconducting quantum interference device (RF-SQUID), in a fiber laser cavity with epsilon-near-zero (ENZ) nanolayers as intra-cavity compo

Harold R. Parks, Dean C. Wills

We give a combinatorial proof of a formula giving the partial sums of the $k$-bonacci sequence as alternating sums of powers of two multiplied by binomial coefficients. As a corollary we obtain a formula for the $k$-bonacci numbers.

Yen-Tsung Chen

Let $L$ be a finite extension of the rational function field over a finite field $\mathbb{F}_q$ and $E$ be a Drinfeld module defined over $L$. Given finitely many elements in $E(L)$, this paper aims to prove that linear relations among these points can be characterized by solutions of an explicitly constructed system of homogeneous linear equations over $\ma

Peter A. Jansen, Marc-Alexandre Côté

Text-based games offer a challenging test bed to evaluate virtual agents at language understanding, multi-step problem-solving, and common-sense reasoning. However, speed is a major limitation of current text-based games, capping at 300 steps per second, mainly due to the use of legacy tooling. In this work we present TextWorldExpress, a high-performance sim

Yin Zhong

In this paper we introduce a solvable two-orbital/band model with infinite-range Hatsugai-Kohmoto interaction, which serves as a modified periodic Anderson model. Its solvability results from strict locality in momentum space, and is valid for arbitrary lattice geometry and electron filling. Case study on a one-dimension ($1D$) chain shows that the ground-st

Fabian Duffhauss, Tobias Demmler, Gerhard Neumann

Estimating 6D poses of objects is an essential computer vision task. However, most conventional approaches rely on camera data from a single perspective and therefore suffer from occlusions. We overcome this issue with our novel multi-view 6D pose estimation method called MV6D which accurately predicts the 6D poses of all objects in a cluttered scene based o

Grigor Aslanyan, Ian Wetherbee

There are many general purpose benchmark datasets for Semantic Textual Similarity but none of them are focused on technical concepts found in patents and scientific publications. This work aims to fill this gap by presenting a new human rated contextual phrase to phrase matching dataset. The entire dataset contains close to $50,000$ rated phrase pairs, each

Nicolas Vidal-Silva, Roberto E. Troncoso

Collinear magnets in honeycomb lattices under the action of time-dependent strains are investigated. Given the limits of high-frequency periodically varying deformations, we derive an effective Floquet theory for spin system that results in the emergence of a spin chirality. We find that the coupling between magnons and spin chirality depends on the details

Haoran Hu, Connor M Kennedy, Panayotis G. Kevrekidis, Hongkun Zhang

A variety of approaches using compartmental models have been used to study the COVID-19 pandemic and the usage of machine learning methods with these models has had particularly notable success. We present here an approach toward analyzing accessible data on Covid-19's U.S. development using a variation of the "Physics Informed Neural Networks" (PINN) which

Lingjing Jiang, Michael Rosenblum, Yu Du

The primary analysis of clinical trials in diabetes therapeutic area often involves a mixed-model repeated measure (MMRM) approach to estimate the average treatment effect for longitudinal continuous outcome, and a generalized linear mixed model (GLMM) approach for longitudinal binary outcome. In this paper, we considered another estimator of the average tre

Dillon Bowen

How accurately can behavioral scientists predict behavior? To answer this question, we analyzed data from five studies in which 640 professional behavioral scientists predicted the results of one or more behavioral science experiments. We compared the behavioral scientists' predictions to random chance, linear models, and simple heuristics like "behavioral i

Carlos A. Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino

Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and ra

Nejib Saadaoui

The main purpose of this paper is to provide a second cohomology group of a (metric) Hom-Jacobi algebra with coefficients in a given representation. Moreover, we show that second cohomology group classifies abelian extensions of a (metric) Hom-Jacobi algebra algebra by a representation.

J. G. Walsh

The T2K experiment is a long-baseline neutrino oscillation experiment which uses $\nu_{\mu}$ and $\bar{\nu}_{\mu}$ beams to constrain CP-violating effects in a 3-flavor PMNS neutrino mixing model. Through $\nu_{\mu}\to\nu_{e}$ and $\bar{\nu}_{\mu}\to\bar{\nu}_{e}$ appearance channels, T2K is sensitive to CP-violating effects in neutrino mixing. An excess of

Xuan Luo, Jian Pei, Zicun Cong, Cheng Xu

In many applications, an organization may want to acquire data from many data owners. Data marketplaces allow data owners to produce data assemblage needed by data buyers through coalition. To encourage coalitions to produce data, it is critical to allocate revenue to data owners in a fair manner according to their contributions. Although in literature Shapl

Khalil El Bourakadi

We investigate a new possible solution to the Hubble constant tension. we propose a simple resolution to the problem assuming that a first-order phase transition related to H0 transition occurred in the early Universe. The early evolution of the Universe is a result of hybrid inflation that has lasted for a specific period until symmetry breaking takes place

Jicheng Li, Anjana Bhat, Roghayeh Barmaki

Movement synchrony reflects the coordination of body movements between interacting dyads. The estimation of movement synchrony has been automated by powerful deep learning models such as transformer networks. However, instead of designing a specialized network for movement synchrony estimation, previous transformer-based works broadly adopted architectures f

Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the

Ye Yu, Jialin Yuan, Gaurav Mittal, Li Fuxin

Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing work faces challenges segmenting visually similar objects in close proximity of each other. In this paper, we propose a novel Bilateral Attention Transformer in Motion-Appearance N

Immanuel Ben Porat

We give a rigorous derivation of the incompressible 2D Euler equation from the von Neumann equation with magnetic field. The convergence is with respect to the modulated energy functional, and implies weak convergence in the sense of measures. This is the quantum counterpart of theorem 1.2 in [Key: 10]. Our proof is based on a Gronwall estimate for the modul

Alex DiChristofano, Henry Shuster, Shefali Chandra, Neal Patwari

Past research has identified discriminatory automatic speech recognition (ASR) performance as a function of the racial group and nationality of the speaker. In this paper, we expand the discussion beyond bias as a function of the individual national origin of the speaker to look for bias as a function of the geopolitical orientation of their nation of origin

Jaren N. Ashcraft, Heejoo Choi, Ewan S. Douglas, Kevin Derby

The development of spaceborne coronagraphic technology is of paramount importance to the detection of habitable exoplanets in visible light. In space, coronagraphs are able to bypass the limitations imposed by the atmosphere to reach deeper contrasts and detect faint companions close to their host star. To effectively test this technology in a flight-like en

Kyle Van Gorkom, Ewan S. Douglas, Jaren N. Ashcraft, Sebastiaan Haffert

The 2020 Decadal Survey on Astronomy and Astrophysics endorsed space-based high contrast imaging for the detection and characterization of habitable exoplanets as a key priority for the upcoming decade. To advance the maturity of starlight suppression techniques in a space-like environment, we are developing the Space Coronagraph Optical Bench (SCoOB) at the

Wenbin Lu, Luis E. Peña, Pavel Shamis, Valentin Churavy

In this paper, we present a framework for moving compute and data between processing elements in a distributed heterogeneous system. The implementation of the framework is based on the LLVM compiler toolchain combined with the UCX communication framework. The framework can generate binary machine code or LLVM bitcode for multiple CPU architectures and move t

Eric Hopper

Deligne and Goncharov constructed a neutral tannakian category of mixed Tate motives unramified over $\mathbb{Z}[\mu_N,1/N]$. Brown and Hain--Matsumoto computed the depth 2 quadratic relations of the motivic Galois group of this category for $N = 1$. We take the first steps in generalizing their results to all $N \ge 1$ by realizing the generators of the mot

Ozan Ozyegen, Nicholas Prayogo, Mucahit Cevik, Ayse Basar

This study focuses on exploring the use of local interpretability methods for explaining time series clustering models. Many of the state-of-the-art clustering models are not directly explainable. To provide explanations for these clustering algorithms, we train classification models to estimate the cluster labels. Then, we use interpretability methods to ex

Khurram Usman Mazher, Amine Mezghani, Robert W Heath

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant

Matthew McDermott, Jason Rife

In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the Lidar beam, it is relatively easy to eliminate shadow edges which cause systematic errors in Lidar scan matching. As we show through simulation, our proposed algorithm prov

Shuang Chen, Amir Atapour-Abarghouei, Jane Kerby, Edmond S. L. Ho

A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in improving surgical outcomes. If AI can be used to predict what a repaired cleft lip would look like, surgeons

Ben London, Levi Lu, Ted Sandler, Thorsten Joachims

We propose the first boosting algorithm for off-policy learning from logged bandit feedback. Unlike existing boosting methods for supervised learning, our algorithm directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the excess empirical risk decreases (possibly exponentially fast) with each round of boost

Yi Dong, Yang Chen, Xingyu Zhao, Xiaowei Huang

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different

Yuhang Wu, Demetrios N. Christodoulides, Frank W. Wise

Spatiotemporal mode-locking in a laser with anomalous dispersion is investigated. Mode-locked states with varying modal content can be observed, but we find it difficult to observe highly multimode states. We describe the properties of these mode-locked states and compare them to the results of numerical simulations. Prospects for the generation of highly-mu

J. F. Bourhill, E. C. I. Paterson, M. Goryachev, M. E. Tobar

M\"obius-ring resonators stem from a well-studied and fascinating geometrical structure that features a one-sided topology; the M\"obius strip, and have been shown to exhibit fermion rotational symmetry with respect to a ring resonator with no twist (which exhibits boson rotational symmetry) (see PhysRevLett.101.247701). Here, we present a new type of resona

Narayan Poudel, Daniel J. Murray, Jason R. Jeffries, Krzysztof Gofryk

We have studied the effects of phonon-boundary scattering on the thermal transport in topological Kondo insulator, SmB$_6$. The studies have been performed by using the $3\omega$ method in the temperature range 300K - 3K. We show that the observed thermal conductivity of micro-size SmB$_6$ is of the order of magnitude smaller than for a bulk single-crystal.

Chieh Lin

The experimental status of the $K \to \pi \nu \bar{\nu}$ search is presented. The $K \to \pi \nu \bar{\nu}$ decay is sensitive to New Physics because it is theoretically pristine and highly suppressed. The $K_L^0 \to \pi^0 \nu \bar{\nu}$ search is performed by the KOTO experiment and a branching fraction limit of $\mathcal{B}(K_L^0 \to \pi^0 \nu \bar{\nu})$

David Damanik, Jake Fillman, Zhenghe Zhang

We consider two-sided Jacobi matrices whose coefficients are obtained by continuous sampling along the orbits of a homeomorphim of a compact metric space. Given an ergodic probability measure, we study the topological structure of the associated almost sure spectrum. We establish a gap labelling theorem in the spirit of Johnson and Schwartzman. That is, we s

Stefan Lattner

Modern digital music production typically involves combining numerous acoustic elements to compile a piece of music. Important types of such elements are drum samples, which determine the characteristics of the percussive components of the piece. Artists must use their aesthetic judgement to assess whether a given drum sample fits the current musical context

A. Mammadli, Z. Ahmadov, K. Isayev, N. Sadigova

Modern unmanned technologies make it possible to implement them in almost all industries, including radiation monitoring. Already in 2011, at the Fukushima nuclear power plant accident, the first drones were demonstrated to measure the radiation background of the territories. Improvement of such devices is one of the topical directions. Our research team has

Juan M. Burgos, Miguel Paternain

Consider a mechanical system with a real analytic potential. We prove that in dimension three, there is an open and dense subset of the set of non strict local minimums of the potential such that every one of its points is a Lyapunov unstable equilibrium point.

Hao Chen, Longjiang Qu, Chengju Li, Shanxiang Lyu

In this paper, we give upper bounds on the sizes of $(d, L)$ list-decodable codes in the Hamming metric space from covering codes with the covering radius smaller than or equal to $d$. When the list size $L$ is $1$, this gives many new Singleton type upper bounds on the sizes of codes with a given minimum Hamming distance. These upper bounds are stronger tha

Javad Ghofrani, Paria Heravi, Kambiz A. Babaei, Mohammad Soorati

Open source projects play a significant role in software production. Most of the software projects reuse and build upon the existing open source projects and libraries. While reusing is a time and cost-saving strategy, some of the key factors are often neglected that create vulnerability in the software system. We look beyond the static code analysis and dep

Fangjun Li, David C. Hogg, Anthony G. Cohn

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing thi

Andreas Bauer, Alexander Nietner

The content of this paper can be roughly organized into a three-level hierarchy of generality. At the first, most general level, we introduce a new language which allows us to express various categorical structures in a systematic and explicit manner in terms of so-called 2-schemes. Although 2-schemes can formalize categorical structures such as symmetric mo

David Peer, Bart Keulen, Sebastian Stabinger, Justus Piater

Training deep neural networks is a very demanding task, especially challenging is how to adapt architectures to improve the performance of trained models. We can find that sometimes, shallow networks generalize better than deep networks, and the addition of more layers results in higher training and test errors. The deep residual learning framework addresses

James R. A. Gray

We show that for a pointed protomodular category $\mathbb{C}$ satisfying a certain condition on those Huq commutators which exist, if $X$ is a perfect object in $\mathbb{C}$ such that the split extension classifier $[X]$ exists, then the centralizer of the \emph{conjugation} morphism $c_X : X\to [X]$ is trivial and hence $[X]$ has trivial center.

Markus J. Pflaum

Localization methods are ubiquitous in cyclic homology theory, but vary in detail and are used in different scenarios. In this paper we will elaborate on a common feature of localization methods in noncommutative geometry, namely sheafification of the algebra under consideration and reduction of the computation to the stalks of the sheaf. The novelty of our

Ivan Solonenko

We prove that every automorphism of the restricted root system of a real semisimple Lie algebra -- when defined properly -- can be lifted to an automorphism of that Lie algebra. In particular, this can be applied to automorphisms of the Dynkin diagram of the restricted root system. We also discuss some applications of this result to the theory of symmetric s

Catherine E. Grant, Eric D. Miller, Marshall W. Bautz, Richard Foster

A science goal of many future X-ray observatories is mapping the cosmic web through deep exposures of faint diffuse sources. Such observations require low background and the best possible knowledge of the remaining unrejected background. The dominant contribution to the background above 1-2 keV is from Galactic Cosmic Ray protons. Their flux and spectrum are

Byron C. Jaeger, Sawyer Welden, Kristin Lenoir, Jaime L. Speiser

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors to create branches, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles often have higher prediction accuracy than standard RSF ensembles. However

Shilong Liu, Yingwen Zhang, Boris A. Malomed, Ebrahim Karimi

The fractional Schr\"{o}dinger equation (FSE) -- a natural extension of the standard Schr\"{o}dinger equation -- is the basis of fractional quantum mechanics. It can be obtained by replacing the kinetic-energy operator with a fractional derivative. Here, we report the experimental realisation of an optical FSE for femtosecond laser pulses in the temporal dom

Trenton Chang, Michael W. Sjoding, Jenna Wiens

As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e.,

Hong Chang

Let $S_g$ denoting the genus $g$ closed orientable surface. An {\em origami} (or flat structure) on $S_g$ is obtained from a finite collection of unit Euclidean squares by gluing each right edge to a left one and each top edge to a bottom one. Coherent filling pairs of simple closed curves, $(\alpha,\beta)$ in $S_g$ are pairs for which their minimal intersec

Diego Dominici, Francisco Marcellán

We consider the family of polynomials $p_{n}\left( x;z\right) ,$ orthogonal with respect to the inner product \[ \left\langle f,g\right\rangle = \int_{-z}^{z} f\left( x\right) g\left( x\right) e^{-x^{2}} \,dx. \] We show some properties about the coefficients in their 3-term recurrence relation, connections between $p_{n}\left( x;z\right) $ and $p_{n}^{\prim

Anna Duwenig, Boyu Li

We introduce the notion of self-similar actions of grouopids on other groupoids and Fell bundles. This leads to a new imprimitivity theorem arising from such dynamics, generalizing many earlier imprimitivity theorems involving group and groupoid actions.

Muhammad Asif, Asim Ihsan, Wali Ullah Khan, Ali Ranjha

In this manuscript, we propose an alternating optimization framework to maximize the energy efficiency of a backscatter-enabled cooperative Non-orthogonal multiple access (NOMA) system by optimizing the transmit power of the source, power allocation coefficients (PAC), and power of the relay node under imperfect successive interference cancellation (SIC) dec

Martin Ehler, Karlheinz Gröchenig

We study Gauss quadrature for Freud weights and derive worst case error estimates for functions in a family of associated Sobolev spaces. For the Gaussian weight $e^{-\pi x^2}$ these spaces coincide with a class of modulation spaces which are well-known in (time-frequency) analysis and also appear under the name of Hermite spaces. Extensions are given to mor

Francesco Sinigaglia, Giulia Rodighiero, Ed Elson, Mattia Vaccari

We present the first measurements of HI galaxy scaling relations from a blind survey at $z>0.15$. We perform spectral stacking of 9023 spectra of star-forming galaxies undetected in HI at $0.23<z<0.49$, extracted from MIGHTEE-HI Early Science datacubes, acquired with the MeerKAT radio telescope. We stack galaxies in bins of galaxy properties ($M_*$, SFR, and

Mansoor Saburov

One could observe drastically different dynamics of zero-sum and non-zero-sum games under replicator equations. In zero-sum games, heteroclinic cycles naturally occur whenever the species of the population supersede each other in a cyclic fashion (like for the Rock-Paper-Scissors game). In this case, the highly erratic oscillations may cause the divergence o

Alex Meiburg

This note describes the development of an exact solver for Minimal Directed Feedback Vertex Set as part of the PACE 2022 competition. The solver is powered largely by aggressively trying to reduce the DFVS problem to a Minimal Cover problem, and applying reduction rules adapted from Vertex Cover literature. The resulting problem is solved as an Integer Linea

Laurynas Valantinas, Tom Vettenburg

Heterogeneous materials such as biological tissue scatter light in random, yet deterministic, ways. Wavefront shaping can reverse the effects of scattering to enable deep-tissue microscopy. Such methods require either invasive access to the internal field or the ability to numerically compute it. However, calculating the coherent field on a scale relevant to