October 2022 arXiv papers — page 128

Matin Macktoobian, Denis Gillet, Jean-Paul Kneib

In this paper, we solve the complete coordination problem of robotic fiber positioners using supervisory control theory. In particular, we model positioners and their behavioral specifications as discrete-event systems by the discretization of their motion spaces. We synthesize a coordination supervisor associated with a specific set of positioners. In parti

Matteo Bugli, Jérôme Guilet, Thierry Foglizzo, Martin Obergaulinger

The gravitational collapse of rapidly rotating massive stars can lead to the onset of the low $T/\|W\|$ instability within the central proto-neutron star (PNS), which leaves strong signatures in both the gravitational wave (GW) and neutrino emission. Strong large-scale magnetic fields are usually invoked to explain outstanding stellar explosions of rapidly r

Julio D. da Fonseca, Edson D. Leonel, Rene O. Medrano-T

We obtain a formula for the statistical distribution of instantaneous frequencies in the Kuramoto-Sakaguchi model. This work is based on the Kuramoto-Sakaguchi's theory of globally coupled phase oscillators, which we review in full detail by discussing its assumptions and showing all steps behind the derivation of its main results. Our formula is a stationar

Ali Kanj, Paolo F. Ferrari, Arend M. van der Zande, Alexander F. Vakakis

Nonlinear micro-electro-mechanical systems (MEMS) resonators open new opportunities in sensing and signal manipulation compared to their linear counterparts by enabling frequency tuning and increased bandwidth. Here, we design, fabricate and study drumhead resonators exhibiting strongly nonlinear dynamics and develop a reduced order model (ROM) to capture th

Max Boleininger, Daniel R. Mason, Andrea E. Sand, Sergei L. Dudarev

At temperatures below the onset of vacancy migration, metals exposed to energetic ions develop dynamically fluctuating steady-state microstructures. Statistical properties of these microstructures in the asymptotic high exposure limit are not universal and vary depending on the energy and mass of the incident ions. We develop a model for the microstructure o

Vittorino Pata, Sergii Siryk, Nataliya Vasylyeva

For $0<\nu_2<\nu_1\leq 1$, we analyze a linear integro-differential equation on the space-time cylinder $\Omega\times(0,T)$ in the unknown $u=u(x,t)$ $$\mathbf{D}_{t}^{\nu_1}(\varrho_{1}u)-\mathbf{D}_{t}^{\nu_2}(\varrho_2 u)-\mathcal{L}_{1}u-\mathcal{K}*\mathcal{L}_{2}u =f$$ where $\mathbf{D}_{t}^{\nu_i}$ are the Caputo fractional derivatives, $\varrho_i=\va

Yihang She, Goutam Bhat, Martin Danelljan, Fisher Yu

Transfer learning based approaches have recently achieved promising results on the few-shot detection task. These approaches however suffer from ``catastrophic forgetting'' issue due to finetuning of base detector, leading to sub-optimal performance on the base classes. Furthermore, the slow convergence rate of stochastic gradient descent (SGD) results in hi

Hemant K. Mishra, Samad Khabbazi Oskouei, Mark M. Wilde

Continuous-variable (CV) teleportation is a foundational protocol in quantum information science. A number of experiments have been designed to simulate ideal teleportation under realistic conditions. In this paper, we detail an analytical approach for determining optimal input states for quantifying the performance of CV unidirectional and bidirectional tel

Martin Lopez-Corredoira

Statistical analyses of the measurements of the Hubble-Lema\^itre constant $H_0$ (163 measurements between 1976 and 2019) show that the statistical error bars associated with the observed parameter measurements have been underestimated -- or the systematic errors were not properly taken into account -- in at least 15-20\% of the measurements. The fact that t

Peyton Chandarana, Mohammadreza Mohammadi, James Seekings, Ramtin Zand

As the technology industry is moving towards implementing tasks such as natural language processing, path planning, image classification, and more on smaller edge computing devices, the demand for more efficient implementations of algorithms and hardware accelerators has become a significant area of research. In recent years, several edge deep learning hardw

Jacob H. Davidson, Antariksha Das, Nir Alfasi, Rufus L. Cone

The creation of well understood structures using spectral hole burning is an important task in the use of technologies based on rare earth ion doped crystals. We apply a series of different techniques to model and improve the frequency dependent population change in the atomic level structure of Thulium Yttrium Gallium Garnet (Tm:YGG). In particular we demon

Jorge A. Vila

One of the main concerns of Anfinsen was to reveal the connection between the amino acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, at a given milieu a pro

Sukanya Chakrabarti, Joshua D. Simon, Peter A. Craig, Henrique Reggiani

We describe the discovery of a solar neighborhood (d=468 pc) binary system with a main-sequence sunlike star and a massive non-interacting black hole candidate. The spectral energy distribution (SED) of the visible star is described by a single stellar model. We derive stellar parameters from a high signal-to-noise Magellan/MIKE spectrum, classifying the sta

Kalyani Kansal

Let $p$ be a fixed odd prime, and let $K$ be a finite extension of $\mathbb{Q}_p$ with ring of integers $\mathcal{O}_K$. The Emerton-Gee stack for $\mathrm{GL}_2$ is a stack of $(\varphi, \Gamma)$-modules. The stack, denoted $\mathcal{X}_2$, can be interpreted as a moduli stack of representations of the absolute Galois group of $K$ with $p$-adic coefficients

Pavithiran G, Sharan Padmanabhan, Ashwin Kumar BR, Vetriselvi A

Social media apps have become very promising and omnipresent in daily life. Most social media apps are used to deliver vital information to those nearby and far away. As our lives become more hectic, many of us strive to limit our usage of social media apps because they are too addictive, and the majority of us have gotten preoccupied with our daily lives. B

Kelin Luo, Alexandre M. Florio, Syamantak Das, Xiangyu Guo

Ride-sharing is an essential aspect of modern urban mobility. In this paper, we consider a classical problem in ride-sharing - the Multi-Vehicle Dial-a-Ride Problem (Multi-Vehicle DaRP). Given a fleet of vehicles with a fixed capacity stationed at various locations and a set of ride requests specified by origins and destinations, the goal is to serve all req

Ron Nissim, Ruixuan Zhang

We consider the Kardar-Parisi-Zhang (KPZ) fixed point $\mathrm{H}(x,\tau)$ with the narrow-wedge initial condition and investigate the distribution of $\mathrm{H}(x,\tau)$ conditioned on a large height at an earlier space-time point $\mathrm{H}(x',\tau')$. As $\mathrm{H}(x',\tau')$ tends to infinity, we prove that the conditional one-point distribution of $\

Charlotte Fowler, Xiaoxuan Cai, Justin T. Baker, Jukka-Pekka Onnela

The use of digital devices to collect data in mobile health (mHealth) studies introduces a novel application of time series methods, with the constraint of potential data missing at random (MAR) or missing not at random (MNAR). In time series analysis, testing for stationarity is an important preliminary step to inform appropriate later analyses. The augment

Arunima Ajay, S R Rajesh, Nishant K. Singh

We present here a simple hydrodynamic model based on a sequence of steady states of the inner sub-Keplerian accretion disc to model its spectral states. Correlations between different hydrodynamic steady states are studied with a goal to understand the origin of, e.g., the aperiodic variabilities. The plausible source of corona/outflow close to the central c

Nikita Dvornik, Isma Hadji, Hai Pham, Dhaivat Bhatt

In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute a procedure successfully, by following the set of steps in slightly varying orders. Thus, for successful local

Shubham Sharma, Jette Henderson, Joydeep Ghosh

Three key properties that are desired of trustworthy machine learning models deployed in high-stakes environments are fairness, explainability, and an ability to account for various kinds of "drift". While drifts in model accuracy, for example due to covariate shift, have been widely investigated, drifts in fairness metrics over time remain largely unexplore

Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick

Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small networks, small output spaces and small

Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan

Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to recognize ${\tt dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$ often introduces refinement to ontologies, such as

Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner

In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the event trigger bias and the frequent label

Nam Vu, Grace M. McLeod, Kenneth Hanson, A. Eugene DePrince

The enantiopurification of racemic mixtures of chiral molecules is important for a range of applications. Recent work has shown that chiral group-directed photoisomerization is a promising approach to enantioenrich racemic mixtures of BINOL, but increased control of the diasteriomeric excess (de) is necessary for its broad utility. Here we develop a cavity q

Neal Patwari, Di Huang, Kiki Bonetta-Misteli

Studies have shown pulse oximeter measurements of blood oxygenation have statistical bias that is a function of race, which results in higher rates of occult hypoxemia, i.e., missed detection of dangerously low oxygenation, in patients of color. This paper further characterizes the statistical distribution of pulse ox measurements, showing they also have a h

Jose Paolo Talusan, Ayan Mukhopadhyay, Dan Freudberg, Abhishek Dubey

The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. Ho

Robert Chuchro

Coverage Path Planning involves visiting every unoccupied state in an environment with obstacles. In this paper, we explore this problem in environments which are initially unknown to the agent, for purposes of simulating the task of a vacuum cleaning robot. A survey of prior work reveals sparse effort in applying learning to solve this problem. In this pape

Peimeng Guan, Jihui Jin, Justin Romberg, Mark A. Davenport

In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements. Classical optimization-based techniques proceed by optimizing a data consistency metric together with a regularizer. Current state-of-the-art machine learning approaches draw inspiration from such techniques by unrolling t

Yong Rui Poh, Sindhana Pannir-Sivajothi, Joel Yuen-Zhou

The rate of non-radiative decay between two molecular electronic states is succinctly described by the energy gap law, which suggests an approximately-exponential dependence of the rate on the electronic energy gap. Here, we inquire whether this rate is modified under vibrational strong coupling, a regime whereby the molecular vibrations are strongly coupled

Danilo F. Rocha, Leonardo A. Almeida, Augusto Damineli, Felipe Navarete

Westerlund 1 (Wd 1) is one of the most relevant star clusters in the Milky Way to study massive star formation, although it is still poorly known. Here, we used photometric and spectroscopic data to model the eclipsing binary W36, showing that its spectral type is O6.5 III + O9.5 IV, hotter and more luminous than thought before. Its distance $d_{\rm W36}$ $=

Matthias Hübler, Wolfgang Belzig

The scattering picture of electron transport in mesoscopic conductors shows that fluctuations of the current reveal additional information on the scattering mechanism not available through the conductance alone. The electronic fluctuations are coupled to the electromagnetic field and a junction at finite bias or temperature will emit radiation. The nonsymmet

George Bakewell-Smith, Federico Girotti, Mădălin Guţă, Juan P. Garrahan

Thermodynamic uncertainty relations (TURs) are general lower bounds on the size of fluctutations of dynamical observables. They have important consequences, one being that the precision of estimation of a current is limited by the amount of entropy production. Here we prove the existence of general upper bounds on the size of fluctuations of any linear combi

Hanjie Chen, Faeze Brahman, Xiang Ren, Yangfeng Ji

Generating free-text rationales is a promising step towards explainable NLP, yet evaluating such rationales remains a challenge. Existing metrics have mostly focused on measuring the association between the rationale and a given label. We argue that an ideal metric should focus on the new information uniquely provided in the rationale that is otherwise not p

Yu Wei Tan, Nicholas Chua, Nathan Biette, Anand Bhojan

Depth of Field (DoF) in games is usually achieved as a post-process effect by blurring pixels in the sharp rasterized image based on the defined focus plane. This paper describes a novel real-time DoF technique that uses ray tracing with image filtering to achieve more accurate partial occlusion semi-transparencies on edges of blurry foreground geometry. Thi

Soumojit Das, Partha Lahiri

We propose an approximate hierarchical Bayes approach that uses the Natural Exponential Family with Quadratic Variance Function in combining information from multiple sources to improve traditional survey estimates of finite population means for small areas. Unlike other Bayesian approaches in finite population sampling, we do not assume a model for all unit

Danielle L. Ferreira, Connor Lau, Zaynaf Salaymang, Rima Arnaout

Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches require the same laborious manual annotations. We built a pipeline for self-supervised (no manual labels) segmentation combining computer vision, clinical domain knowledge, and deep

Vitalij A. Chatyrko, Alexandre Karassev

It is shown that a connected non-compact metrizable manifold of dimension $\ge 2$ is strongly discrete homogeneous if and only if it has one end (in the sense of Freudenthal compactification).

Chinmayee Athalye, Rima Arnaout

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Here, we test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found tha

Fadlullah Raji, Lei Miao

Future wireless networks require high throughput and energy efficiency. This paper studies using Reinforcement Learning (RL) to do transmission rate and power control for maximizing a joint reward function consisting of both throughput and energy consumption. We design the system state to include factors that reflect packet queue length, interference from ot

Vijja Wichitwechkarn, Charles Fox

The Modular Automated Crop Array Online System (MACARONS) is an extensible, scalable, open hardware system for plant transport in automated horticulture systems such as vertical farms. It is specified to move trays of plants up to 1060mm x 630mm and 12.5kg at a rate of 100mm/s along the guide rails and 41.7mm/s up the lifts, such as between stations for moni

Jeremiah Birrell, Yannis Pantazis, Paul Dupuis, Markos A. Katsoulakis

We propose a new family of regularized R\'enyi divergences parametrized not only by the order $\alpha$ but also by a variational function space. These new objects are defined by taking the infimal convolution of the standard R\'enyi divergence with the integral probability metric (IPM) associated with the chosen function space. We derive a novel dual variati

Lara C. Ortmanns, Maarten R. Wegewijs, Janine Splettstoesser

We analyze the time-dependent solution of master equations by exploiting fermionic duality, a dissipative symmetry applicable to a large class of open systems describing quantum transport. Whereas previous studies mostly exploited duality relations after partially solving the evolution equations, we here systematically exploit the invariance under the fermio

Dries Van De Putte, Stefan I. B. Cartledge, Karl D. Gordon, Geoffrey C. Clayton

We aim to compare variations in the full-UV dust extinction curve (912-3000 Angstrom), with the HI/H$_2$/total H content along diffuse Milky Way sightlines, to investigate possible connections between ISM conditions and dust properties. We combine an existing sample of 75 UV extinction curves based on IUE and FUSE data, with atomic and molecular column densi

Lucio M. Dery, Abram L. Friesen, Nando De Freitas, Marc'Aurelio Ranzato

As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has never been more pressing. Multi-step planning based approaches to hyperparameter optimization promise improved efficiency over myopic alternatives by more effectively balancing out

Mohammad Roueentan, Roghaieh Khosravi

The main purpose of the present work is an investigation of the notions Hopfian (co-Hopfian) acts whose their surjective (injective) endomorphisms are isomorphisms. While we investigate conditions that are relevant to these classes of acts, their interrelationship with some other concepts for example quasi-injective and Dedekind-finite acts is studied. Using

Yi-Jie Wang, Qi-Xin Xie, Shuang-Yong Zhou

Two types of excited oscillons are investigated. We first focus on spherical symmetry and find that there are a tower of spherical oscillons with higher energies. Despite having multiple approximate "nodes" in their energy density profiles, these oscillons are long-lived. We find that during the lifetime of a highly excited oscillon it will cascade down all

Gustavo O. de Carvalho, Fábio P. Machado

The frog model is a system of interacting random walks. Initially, there is one particle at each vertex of a connected graph $\mathcal{G}$. All particles are inactive at time zero, except for the one which is placed at the root of $\mathcal{G}$, which is active. At each instant of time, each active particle may die with probability $1-p$. Once an active part

Yaser Rowshan, Ali Taherkhani

Let $K_p$ be a complete graph of order $p\geq 2$. A $K_p$-free $k$-coloring of a graph $H$ is a partition of $V(H)$ into $V_1, V_2\ldots,V_k$ such that $H[V_i]$ does not contain $K_p$ for each $i\leq k $. In 1977 Borodin and Kostochka conjectured that any graph $H$ with maximum degree $\Delta(H)\geq 9$ and without $K_{\Delta(H)}$ as a subgraph has chromatic

Alex Rodrigo dos Santos Sousa

The present work describes simulation studies to compare the performances of bayesian wavelet shrinkage methods in estimating component curves from aggregated functional data. To do so, five methods were considered: the bayesian shrinkage rule under logistic prior by Sousa (2020), bayesian shrinkage rule under beta prior by Sousa et al. (2020), Large Posteri

Anthony N. Ciavarella, Stephan Caspar, Marc Illa, Martin J. Savage

An adiabatic state preparation technique, called the adiabatic spiral, is proposed for the Heisenberg model. This technique is suitable for implementation on a number of quantum simulation platforms such as Rydberg atoms, trapped ions, or superconducting qubits. Classical simulations of small systems suggest that it can be successfully implemented in the nea

Maitrey Gramopadhye, Daniel Szafir

Large Language Models (LLMs) trained using massive text datasets have recently shown promise in generating action plans for robotic agents from high level text queries. However, these models typically do not consider the robot's environment, resulting in generated plans that may not actually be executable, due to ambiguities in the planned actions or environ

Yosef Ashkenazy, Eli Tziperman, Francis Nimmo

It has been suggested that the ice shell of Jupiter's moon Europa may drift non-synchronously due to tidal torques. Here we argue that torques applied by the underlying ocean are also important and can result in non-synchronous rotation (NSR). The resulting spin rate can be slightly slower than the synchronous angular rate that would have kept the same point

Samuel Greess, Jan Egedal, Adam Stanier, Joseph Olson

The rate of reconnection characterizes how quickly flux and mass can move into and out of the reconnection region. In the Terrestrial Reconnection EXperiment (TREX), the rate at which antiparallel asymmetric reconnection occurs is modulated by the presence of a shock and a region of flux pileup in the high-density inflow. Simulations utilizing a generalized

Nicolás Firbas, Òscar Garibo-i-Orts, Miguel Ángel Garcia-March, J. Alberto Conejero

The results of the Anomalous Diffusion Challenge (AnDi Challenge) have shown that machine learning methods can outperform classical statistical methodology at the characterization of anomalous diffusion in both the inference of the anomalous diffusion exponent alpha associated with each trajectory (Task 1), and the determination of the underlying diffusive r

Fan Wu, Sanghyun Hong, Donsub Rim, Noseong Park

Continuous-time dynamics models, such as neural ordinary differential equations, have enabled the modeling of underlying dynamics in time-series data and accurate forecasting. However, parameterization of dynamics using a neural network makes it difficult for humans to identify causal structures in the data. In consequence, this opaqueness hinders the use of

Mohammadhossein Firouznia, Spencer H. Bryngelson, David Saintillan

A weakly conducting liquid droplet immersed in another leaky dielectric liquid can exhibit rich dynamical behaviors under the effect of an applied electric field. Depending on material properties and field strength, the nonlinear coupling of interfacial charge transport and fluid flow can trigger electrohydrodynamic instabilities that lead to shape deformati

Christophe Gomez, Olivier Pinaud

We propose in this work a Monte Carlo method for three dimensional scalar radiative transfer equations with non-integrable, space-dependent scattering kernels. Such kernels typically account for long-range statistical features, and arise for instance in the context of wave propagation in turbulent atmosphere, geophysics, and medical imaging in the peaked-for

Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Angel Bautista

Diffusion models (DMs) have recently emerged as SoTA tools for generative modeling in various domains. Standard DMs can be viewed as an instantiation of hierarchical variational autoencoders (VAEs) where the latent variables are inferred from input-centered Gaussian distributions with fixed scales and variances. Unlike VAEs, this formulation limits DMs from

Saleh Kalantari, Tong Bill Xu, Armin Mostafavi, Andrew Dilanchian

Research examining older adults interactions with Virtual Reality (VR) and the impact of social VR experiences on outcomes such as social engagement has been limited, especially among older adults. This multi-site pilot study evaluated the feasibility and acceptability of a novel social virtual reality (VR) program that paired older adults from different geo

Ghazaleh Ardeshiri, Azadeh Vosoughi

We consider a network, tasked with solving binary distributed detection, consisting of N sensors, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC

Xuting Yang, Meryem Benelajla, Jennifer T. Choy

Atomic magnetometry is one of the most sensitive field-measurement techniques for biological, geo-surveying, and navigation applications. An essential process in atomic magnetometry is measurement of optical polarization rotation of a near-resonant beam due to its interaction with atomic spins under an external magnetic field. In this work, we present the de

Danilo S. Carvalho, Edoardo Manino, Julia Rozanova, Lucas Cordeiro

This work proposes a novel methodology for measuring compositional behavior in contemporary language embedding models. Specifically, we focus on adjectival modifier phenomena in adjective-noun phrases. In recent years, distributional language representation models have demonstrated great practical success. At the same time, the need for interpretability has

Abel Souza, Noman Bashir, Jorge Murillo, Walid Hanafy

Cloud platforms' rapid growth is raising significant concerns about their carbon emissions. To reduce emissions, future cloud platforms will need to increase their reliance on renewable energy sources, such as solar and wind, which have zero emissions but are highly unreliable. Unfortunately, today's energy systems effectively mask this unreliability in hard

Ziqi Yan

We study the renormalization of an N = 1 supersymmetric Lifshitz sigma model in three dimensions. The sigma model exhibits worldvolume anisotropy in space and time around the high-energy z = 2 Lifshitz point, such that the worldvolume is endowed with a foliation structure along a preferred time direction. In curved backgrounds, the target-space geometry is e

Kleanthis Malialis, Manuel Roveri, Cesare Alippi, Christos G. Panayiotou

In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time,

Boris Sauterey, Benjamin Charnay, Antonin Affholder, Stephane Mazevet

During the Noachian, Mars' crust may have provided a favorable environment for microbial life. The porous brine-saturated regolith would have created a physical space sheltered from UV and cosmic radiations and provided a solvent, while the below-ground temperature and diffusion of a dense reduced atmosphere may have supported simple microbial organisms that

Fatma Tokmak Fen, Mehmet Onur Fen

The existence, uniqueness, and asymptotic stability of modulo periodic Poisson stable solutions of dynamic equations on a periodic time scale are investigated. The model under investigation involves a term which is constructed via a Poisson stable sequence. Novel definitions for Poisson stable as well as modulo periodic Poisson stable functions on time scale

Liyu Chen, Andrea Tirinzoni, Matteo Pirotta, Alessandro Lazaric

We study the sample complexity of learning an $\epsilon$-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bounds when the learner has access to a generative model. We show that there exists a worst-case SSP instance with $S$ states, $A$ actions, minimum cost $c_{\min}$, and maximum expected cost of the optimal p

Yifan Wang, Shize Yang, Peter A. Crozier

Photonic modes in dielectric nanostructures, e.g., wide gap semiconductor like CeO2 (ceria), has potential for various applications such as light harvesting and information transmission. To fully understand the properties of such phenomenon in nanoscale, we applied electron energy-loss spectroscopy (EELS) in scanning transmission electron microscope (STEM) t

Dayang Wang, Yongshun Xu, Shuo Han, Hengyong Yu

Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical a

Cristian Vay

The copointed liftings of the Fomin-Kirillov algebra $\mathcal{FK}_3$ over the algebra of functions on the symmetric group $\mathbb{S}_3$ were classified by Andruskiewitsch and the author. We demonstrate here that those associated to a generic parameter are Morita equivalent to the non-simple blocks of well-known Hopf algebras: the Drinfeld doubles of the Ta

Yuanyuan Yuan, Qi Pang, Shuai Wang

Contemporary DNN testing works are frequently conducted using metamorphic testing (MT). In general, de facto MT frameworks mutate DNN input images using semantics-preserving mutations and determine if DNNs can yield consistent predictions. Nevertheless, we find that DNNs may rely on erroneous decisions (certain components on the DNN inputs) to make predictio

Haoming Yang, Steven Winter, Zhengwu Zhang, David Dunson

One of the central problems in neuroscience is understanding how brain structure relates to function. Naively one can relate the direct connections of white matter fiber tracts between brain regions of interest (ROIs) to the increased co-activation in the same pair of ROIs, but the link between structural and functional connectomes (SCs and FCs) has proven t

Nathaniel Hanson, Wesley Lewis, Kavya Puthuveetil, Donelle Furline

Liquids and granular media are pervasive throughout human environments. Their free-flowing nature causes people to constrain them into containers. We do so with thousands of different types of containers made out of different materials with varying sizes, shapes, and colors. In this work, we present a state-of-the-art sensing technique for robots to perceive

G. Nandakumar, N. Ryde, M. Montelius, B. Thorsbro

Phosphorus (P) is considered to be one of the key elements for life, making it an important element to look for in the abundance analysis of spectra of stellar systems. Yet, there exists only a handful of spectroscopic studies to estimate the P abundances and investigate its trend across a range of metallicities. We have observed full HK band spectra at a sp

Simon Telen

Solving polynomial equations is a subtask of polynomial optimization. This article introduces systems of such equations and the main approaches for solving them. We discuss critical point equations, algebraic varieties, and solution counts. The theory is illustrated by many examples using different software packages.

Sebastian Cardoch, Fabian Trost, Howard A. Scott, Henry N. Chapman

Saturable absorption is a nonlinear effect where a material's ability to absorb light is frustrated due to a high influx of photons and the creation of electron vacancies. Experimentally induced saturable absorption in copper revealed a reduction in the temporal duration of transmitted X-ray laser pulses, but a complete understanding of this process is still

D. B. Karki, Edouard Boulat, Winston Pouse, David Goldhaber-Gordon

Quantum impurity models with frustrated Kondo interactions can support quantum critical points with fractionalized excitations. Recent experiments [arXiv:2108.12691] on a circuit containing two coupled metal-semiconductor islands exhibit transport signatures of such a critical point. Here we show using bosonization that the double charge-Kondo model describi

Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi

Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing number of satellites in orbit, an increasing number of datasets with diverse sensors and research domains are being published to facilitate the research of the remote sensing commun

Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh

Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and fine-grained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich su

Shuo Kong, David Whitworth, Rowan J. Smith, Erika T. Hamden

A collision-induced magnetic reconnection (CMR) mechanism was recently proposed to explain the formation of a filament in the Orion A molecular cloud. In this mechanism, a collision between two clouds with antiparallel magnetic fields produces a dense filament due to the magnetic tension of the reconnected fields. The filament contains fiber-like sub-structu

Kiyoon Kim, Davide Moltisanti, Oisin Mac Aodha, Laura Sevilla-Lara

Precisely naming the action depicted in a video can be a challenging and oftentimes ambiguous task. In contrast to object instances represented as nouns (e.g. dog, cat, chair, etc.), in the case of actions, human annotators typically lack a consensus as to what constitutes a specific action (e.g. jogging versus running). In practice, a given video can contai

Arunkumar Byravan, Jan Humplik, Leonard Hasenclever, Arthur Brussee

We present a system for applying sim2real approaches to "in the wild" scenes with realistic visuals, and to policies which rely on active perception using RGB cameras. Given a short video of a static scene collected using a generic phone, we learn the scene's contact geometry and a function for novel view synthesis using a Neural Radiance Field (NeRF). We au

Manuel Alberto M. Ferreira

Some important results on the variance of the $M|G|\infty$ queue busy period are presented. Often, this parameter depends on the whole structure of the service time distribution. So, the importance of the bounds presented, depending only on some parameters. Also, some bounds for service time distributions important in reliability theory, with technological a

R. Abbasi, M. Ackermann, J. Adams, N. Aggarwal

Beginning in 2016, the IceCube Neutrino Observatory has sent out alerts in real time containing the information of high-energy ($E \gtrsim 100$~TeV) neutrino candidate events with moderate-to-high ($\gtrsim 30$\%) probability of astrophysical origin. In this work, we use a recent catalog of such alert events, which, in addition to events announced in real-ti

Geoffrey Ramseyer, Mohak Goyal, Ashish Goel, David Mazières

Batch auctions are a classical market microstructure, acclaimed for their fairness properties, and have received renewed interest in the context of blockchain-based financial systems. Constant function market makers (CFMMs) are another market design innovation praised for their computational simplicity and applicability to liquidity provision via smart contr

Tao Xie, A. A. Eberharter, Jie Xing, S. Nishimoto

Fifty years after Anderson's resonating valence-bond proposal, the spin-1/2 triangular-lattice Heisenberg antiferromagnet (TLHAF) remains the ultimate platform to explore highly entangled quantum spin states in proximity to magnetic order. Yb-based delafossites are ideal candidate TLHAF materials, which allow experimental access to the full range of applied

Leonardo Chataignier, Alexander Yu. Kamenshchik, Alessandro Tronconi, Giovanni Venturi

In the context of canonical quantum gravity, we consider the effects of a non-standard expression for the gravitational wave function on the evolution of inflationary perturbations. Such an expression and its effects may be generated by a sudden variation in the (nearly constant) inflaton potential. The resulting primordial spectra, up to the leading order,

Phrudth Jaroenjittichai, Koichiro Sugiyama, Busaba H. Kramer, Boonrucksar Soonthornthum

This White Paper summarises potential key science topics to be achieved with Thai National Radio Telescope (TNRT). The commissioning phase has started in mid 2022. The key science topics consist of "Pulsars and Fast Radio Bursts (FRBs)", "Star Forming Regions (SFRs)", "Galaxy and Active Galactic Nuclei (AGNs)", "Evolved Stars", "Radio Emission of Chemically

Loïc Herviou, Frédéric Mila

Motivated by the experimental observation of a quantized 5/2 thermal conductance at filling $\nu=5/2$, a result incompatible with both the Pfaffian and the Antipfaffian states, we have pushed the expansion of the effective Hamiltonian of the $5/2$ quantized Hall state to third-order in the parameter $\kappa=E_c/\hbar \omega_c \propto 1/\sqrt{B}$ controlling

Scott Hagen, Chris Done

Continuum reverberation mapping of AGN can provide new insight into the nature and geometry of the accretion flow. Some of the X-rays from the central corona irradiating the disc are absorbed, increasing the local disc temperature. This gives an additional re-processed contribution to the spectral energy distribution (SED) which is lagged and smeared relativ

Carlos Bercini, Alexandre Homrich, Pedro Vieira

We propose a new framework for computing three-point functions in planar $\mathcal{N}=4$ super Yang-Mills where these correlators take the form of multiple integrals of Separation of Variables type. We test this formalism at weak coupling at leading and next-to-leading orders in a non-compact SL(2) sector of the theory and all the way to next-to-next-to-lead

David J. Whitworth, Rowan J. Smith, Ralf S. Klessen, Mordecai-Mark Mac Low

Many studies concluded that magnetic fields suppress star formation in molecular clouds and Milky Way like galaxies. However, most of these studies are based on fully developed fields that have reached the saturation level, with little work on investigating how an initial weak primordial field affects star formation in low metallicity environments. In this p

Anindya Dey

We propose a family of IR dualities for 3d $\mathcal{N}=4$ $U(N)$ SQCD with $N_f$ fundamental flavors and $P$ Abelian hypermultiplets i.e. $P$ hypermultiplets in the determinant representation of the gauge group. These theories are good in the Gaiotto-Witten sense if the number of fundamental flavors obeys the constraint $N_f \geq 2N-1$ with generic $P \geq

Matthew R. Buckley, Andrew Mastbaum, Gopolang Mohlabeng

We investigate the sensitivity of a large, underground LArTPC-based neutrino detector to dark matter in the Galactic Center annihilating into neutrinos. Such a detector could have the ability to resolve the direction of the electron in a neutrino scattering event, and thus to infer information about the source direction for individual neutrino events. We con

Diogo Cruz, Duarte Magano

Quantum computers, using efficient Hamiltonian evolution routines, have the potential to simulate Green's functions of classically-intractable quantum systems. However, the decoherence errors of near-term quantum processors prohibit large evolution times, posing limits to the spectrum resolution. In this work, we show that Atomic Norm Minimization, a well-kn

Jing-Yu Zhao, Shuai A. Chen, Rong-Yang Sun, Zheng-Yu Weng

We examine a wavefunction ansatz in which a doped hole can experience a quantum transition from a charge $+e$ Landau quasiparticle to a neutral spinon as a function of the underlying spin-spin correlation. As shown variationally, such a wavefunction accurately captures all the essential features revealed by exact diagonalization and density matrix renormaliz

Duncan Adams, Daniel Baxter, Hannah Day, Rouven Essig

The Migdal effect has received much attention from the dark matter direct detection community, in particular due to its power in setting limits on sub-GeV particle dark matter. Currently, there is no experimental confirmation of the Migdal effect through nuclear scattering using Standard Model probes. In this work, we extend existing calculations of the Migd

Rosa A. González-Lópezlira, Luis Lomelí-Núñez, Yasna Ordenes-Briceño, Laurent Loinard

We continue to explore the relationship between globular cluster total number, $N_{\rm GC}$, and central black hole mass, $M_\bullet$, in spiral galaxies. We present here results for the Sab galaxies NGC 3368, NGC 4736 (M 94) and NGC 4826 (M 64), and the Sm galaxy NGC 4395. The globular cluster (GC) candidate selection is based on the ($u^*$ - $i^\prime$) ve