arXiv papers from March 2023

Zihao Wang, Eugene Agichtein, Jinho Choi

Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual information from multiple channels. Specif

Guanzhou Zhu, Peng Liang, Cheng-Liang Huang, Shu-Chi Wu

Advancing new ideas of rechargeable batteries represents an important path to meeting the ever increasing energy storage needs. Recently we showed rechargeable sodium/chlorine (Na/Cl$_2$) (or lithium/chlorine Li/Cl$_2$) batteries that used a Na (or Li) metal negative electrode, a microporous amorphous carbon nanosphere (aCNS) positive electrode and an electr

Leandro Galo-Mendoza, Marcos López-García

We prove the null controllability of a one-dimensional degenerate parabolic equation with drift and a singular potential. Here, we consider a weighted Neumann boundary control at the left endpoint, where the potential arises. We use a spectral decomposition of a suitable operator, defined in a weighted Sobolev space, and the moment method by Fattorini and Ru

Lhuqita Fazry, Asep Haryono, Nuzulul Khairu Nissa, Sunarno

The left ventricular of ejection fraction is one of the most important metric of cardiac function. It is used by cardiologist to identify patients who are eligible for lifeprolonging therapies. However, the assessment of ejection fraction suffers from inter-observer variability. To overcome this challenge, we propose a deep learning approach, based on hierar

Romain Lacombe, Hannah Grossman, Lucas Hendren, David Lüdeke

To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data. Our primary focus is on tropical

Koondanibha Mitra, Stefanie Sonner

We analyze nonlinear degenerate coupled PDE-PDE and PDE-ODE systems that arise, for example, in the modelling of biofilm growth. One of the equations, describing the evolution of a biomass density, exhibits degenerate and singular diffusion. The other equations are either of advection-reaction-diffusion type or ordinary differential equations. Under very gen

Emily Frede, Hadi Zadeh-Haghighi, Christoph Simon

In neuroscience, it is of interest to consider all possible modes of information transfer between neurons in order to fully understand processing in the brain. It has been suggested that photonic communication may be possible along axonal connections, especially through the myelin sheath as a waveguide, due to its high refractive index. There is already a go

Rami Botros, Rohit Prabhavalkar, Johan Schalkwyk, Ciprian Chelba

In end-to-end (E2E) speech recognition models, a representational tight-coupling inevitably emerges between the encoder and the decoder. We build upon recent work that has begun to explore building encoders with modular encoded representations, such that encoders and decoders from different models can be stitched together in a zero-shot manner without furthe

Kangda Zhi, Cunhua Pan, Hong Ren, Kok Keong Chai

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete

Rami Botros, Anmol Gulati, Tara N. Sainath, Krzysztof Choromanski

Conformer models maintain a large number of internal states, the vast majority of which are associated with self-attention layers. With limited memory bandwidth, reading these from memory at each inference step can slow down inference. In this paper, we design an optimized conformer that is small enough to meet on-device restrictions and has fast inference o

Jnanajyoti Bhaumik, Naoki Masuda

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of natural selection, and those that suppress the spreading of fitter mutants are called suppressors. Research in the past two decades has found various families of amplifiers while suppressors still r

Liangjie Chen, John W. Simpson-Porco

Current approaches to data-driven control are geared towards optimal performance, and often integrate aspects of machine learning and large-scale convex optimization, leading to complex implementations. In many applications, it may be preferable to sacrifice performance to obtain significantly simpler controller designs. We focus here on the problem of outpu

Gal Orenstein, Ryan A. Duncan, Gilberto A. de la Pena Munoz, Yijing Huang

Nonequilibrium states of quantum materials can exhibit exotic properties and enable unprecedented functionality and applications. These transient states are inherently inhomogeneous, characterized by the formation of topologically protected structures, requiring nanometer spatial resolution on femtosecond timescales to resolve their evolution. Using ultrafas

Wesley Hanwen Deng, Motahhare Eslami, Kenneth Holstein

Data-driven algorithmic and AI systems are increasingly being deployed to automate or augment decision processes across a wide range of public service settings. Yet community members are often unaware of the presence, operation, and impacts of these systems on their lives. With the shift towards algorithmic decision-making in public services, technology deve

F. T. Brandt, J. Frenkel, D. G. C. McKeon, G. S. S. Sakoda

We calculate in a general background gauge, to one-loop order, the leading logarithmic contribution from the graviton self-energy at finite temperature $T$, extending a previous analysis done at $T=0$. The result, which has a transverse structure, is applied to evaluate the leading quantum correction of the gravitational vacuum polarization to the Newtonian

Yujie Quan, Yubi Chen, Bolin Liao

A thorough understanding of electrical and thermal transport properties of group-III nitride semiconductors is essential for their electronic and thermoelectric applications. Despite extensive previous studies, these transport properties were typically calculated without considering the nonequilibrium coupling effect between electrons and phonons, which can

Jeison Alfonso, Alejandro García-Varela

Context. Near open clusters as Pleiades, Praesepe and Blanco 1 have been extensively studied due to their proximity to the Sun. The Gaia data brings the opportunity to investigate these clusters, since it contains valuable astrometric and photometric information which can be used to update their kinematic and stellar properties. Aims. Our goal is to carry ou

Shenghui Chen, Yue Yu, David Fridovich-Keil, Ufuk Topcu

Markov games model interactions among multiple players in a stochastic, dynamic environment. Each player in a Markov game maximizes its expected total discounted reward, which depends upon the policies of the other players. We formulate a class of Markov games, termed affine Markov games, where an affine reward function couples the players' actions. We intro

Shuyi Liang, Kai-Tai Fang, Xin-Wei Huang, Yijing Xin

In clinical trials studying paired parts of a subject with binary outcomes, it is expected to collect measurements bilaterally. However, there are cases where subjects contribute measurements for only one part. By utilizing combined data, it is possible to gain additional information compared to using bilateral or unilateral data alone. With the combined dat

Thomas Barthel, Qiang Miao

Vanishing gradients can pose substantial obstacles for high-dimensional optimization problems. Here we consider energy minimization problems for quantum many-body systems with extensive Hamiltonians and finite-range interactions, which can be studied on classical computers or in the form of variational quantum eigensolvers on quantum computers. Barren platea

Duygu Nur Yaldiz, Tuo Zhang, Salman Avestimehr

Given the distributed nature, detecting and defending against the backdoor attack under federated learning (FL) systems is challenging. In this paper, we observe that the cosine similarity of the last layer's weight between the global model and each local update could be used effectively as an indicator of malicious model updates. Therefore, we propose CosDe

Mary Wilkerson

Every expanding Thurston map $f$ without periodic critical points is known to have an iterate $f^n$ which is the topological mating of two polynomials. This has been examined by Kameyama and Meyer; the latter who has offered an explicit construction for finding two polynomials in the unmating of the iterate. Initializing this algorithm depends on an invarian

Pavan R. Hebbar, Craig O. Heinke

Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems - detailed X-ray spectroscopy of individual sources is too time-consuming, while hardness ratios often lack accuracy, and can be difficult to use effectively. These me

Jiaqi Jiang, Guanqun Cao, Jiankang Deng, Thanh-Toan Do

Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher levels of autonomy, unlocking new applications in various industries such as healthcare, services and manufacturing. Despite numerous datasets and perception methods being proposed in

Yuri Abuchaim, Hélio D. Perottoni, Silvia Rossi, Guilherme Limberg

We present a chemodynamical study of the Triangulum-Andromeda overdensity (TriAnd) employing a sample of 31 candidate stars observed with the GRACES high-resolution ($R$=40,000) spectrograph at the Gemini North (8.1 m) telescope. TriAnd is a stellar substructure found toward the outer disk of the Milky Way, located at $R_{\rm GC}\sim 18$ kpc from the Sun, to

Vincent Leon, S. Rasoul Etesami

We consider online reinforcement learning in episodic Markov decision process (MDP) with unknown transition function and stochastic rewards drawn from some fixed but unknown distribution. The learner aims to learn the optimal policy and minimize their regret over a finite time horizon through interacting with the environment. We devise a simple and efficient

Craig D. Roberts

The vast bulk of visible mass emerges from nonperturbative dynamics within quantum chromodynamics (QCD) -- the strong interaction sector of the Standard Model. The past decade has revealed the three pillars that support this emergent hadron mass (EHM); namely, a nonzero gluon mass-scale, a process-independent effective charge, and dressed-quarks with constit

Pablo Miranda, Daniel Parra

We study the continuum limit for Dirac-Hodge operators defined on the $n$ dimensional square lattice $h\mathbb{Z}^n$ as $h$ goes to $0$. This result extends to a first order discrete differential operator the known convergence of discrete Schr\"odinger operators to their continuous counterpart. To be able to define such a discrete analog, we start by definin

Liyan Chen, Weihan Wang, Philippos Mordohai

We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the di

Ursula Laa, German Valencia

We describe the applications of clustering and visualization tools using the so-called neutral B anomalies as an example. Clustering permits parameter space partitioning into regions that can be separated with some given measurements. It provides a visualization of the collective dependence of all the observables on the parameters of the problem. These metho

Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami

We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts. We benchmark two well-studied fluid flow systems, namely the 3D decaying Taylor-Green vortex and the 3D reve

Gian Carlo Diluvi, Sonja Isberg, Bruce Dunham, Nancy Heckman

Educational resources, such as web apps and self-directed tutorials, have become popular tools for teaching and active learning. Ideally, students - the intended users of these resources - should be involved in the resource development stage. However, in practice students often only interact with fully developed resources, when it might be too late to incorp

S. J Yaga, F. W. O Saporu

A compartmental deterministic model that allows (1) immunity from two stages of infection and carriage, and (2) disease induced death, is used in studying the dynamics of meningitis epidemic process in a closed population. It allows for difference in the transmission rate of infection to a susceptible by a carrier and an infective. It is generalized to allow

Yijun Xu, Marcos Netto, Lamine Mili

We propose a Koopman operator-based surrogate model for propagating parameter uncertainties in power system nonlinear dynamic simulations. First, we augment the a priori known state-space model by reformulating parameters deemed uncertain as pseudo-state variables. Then, we apply the Koopman operator theory to the resulting state-space model and obtain a lin

Artur P. Toshev, Ludger Paehler, Andrea Panizza, Nikolaus A. Adams

Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In this survey, we begin by providing an example of this with the parallels between the development trajectories of graph neural network acceleration for physical simulations and parti

Ziyun Yang, Sina Farsiu

Anatomical consistency in biomarker segmentation is crucial for many medical image analysis tasks. A promising paradigm for achieving anatomically consistent segmentation via deep networks is incorporating pixel connectivity, a basic concept in digital topology, to model inter-pixel relationships. However, previous works on connectivity modeling have ignored

Sebastien Boucksom, Mattias Jonsson

We study the non-Archimedean Monge-Amp\`ere equation on a smooth projective variety over a discretely or trivially valued field. First, we give an example of a Green's function, associated to a divisorial valuation, which is not Q-PL (i.e. not a model function in the discretely valued case). Second, we produce an example of a function whose Monge-Amp\`ere me

Jing Ma, Paizhe Xie, Kristyn Pantoja, David E. Jones

Compositional data, where only relative abundances are available, are common in microbiome and other high-throughput sequencing studies. Log ratios between groups of variables serve as key biomarkers in these settings. However, selecting predictive log ratios is a combinatorial challenge, and existing greedy search-based methods are computationally expensive

Gabriel Rondón, Paulo R. da Silva, Luiz F. S. Gouveia

In this paper, we are concerned with studying the existence of invariant complex manifolds of two-dimensional holomorphic systems. From the geometric singular perturbation theory we know that if a slow-fast system has associated a normally hyperbolic compact critical manifold, then there exists a smooth locally invariant manifold. However, this smooth manifo

Petros Androvitsaneas, Rachel N. Clark, Matthew Jordan, Tomas Peach

We have developed a process to mass-produce quantum dot micropillar cavities using direct-write lithography. This technique allows us to achieve high volume patterning of high aspect ratio pillars with vertical, smooth sidewalls maintaining a high quality factor for diameters below 2.0 $\mu$m. Encapsulating the cavities in a thin layer of oxide (Ta$_2$O$_5$)

Yu Jiao, Steffen J. Schmidt, Nikolaus A. Adams

We present an efficient, fully conservative numerical scheme valid in the entire range of highly to weakly compressible flows using a single-fluid four equation approach together with multi-component thermodynamic models. The approach can easily be included into existing finite volume methods on compact stencils and enables handling of compressibility of all

Aida Manzano Kharman, Pietro Ferraro, Anthony Quinn, Robert Shorten

We present a decentralised class of algorithms called Tree-Proof-of-Position (T-PoP). T-PoP algorithms rely on the web of interconnected devices in a smart city to establish how likely it is that an agent is in the position they claim to be. T-PoP operates under adversarial assumptions, by which some agents are incentivised to be dishonest. We present a theo

Shaun Allison

In recent years, much work has been done to measure and compare the complexity of orbit equivalence relations, especially for certain classes of Polish groups. We start by introducing some language to organize this previous work, namely the notion of \textbf{classification strength} of Polish groups. Broadly speaking, a Polish group $G$ has stronger classifi

Jiapeng Xu, Ying Tan, Xiang Chen

This paper presents a controller design and optimization framework for nonlinear dynamic systems to track a given reference signal in the presence of disturbances when the task is repeated over a finite-time interval. This novel framework mainly consists of two steps. The first step is to design a robust linear quadratic tracking controller based on the exis

DAMPE Collaboration, F. Alemanno, C. Altomare, Q. An

Recent observations of the light component of the cosmic-ray spectrum have revealed unexpected features that motivate further and more precise measurements up to the highest energies. The Dark Matter Particle Explorer is a satellite-based cosmic-ray experiment that has been operational since December 2015, continuously collecting data on high-energy cosmic p

Ryan Budney

In this note we describe a family of arguments that link the homotopy-type of a) the diffeomorphism group of the disc $D^n$, b) the space of co-dimension one embedded spheres in a sphere and c) the homotopy-type of the space of co-dimension two trivial knots in a sphere. We also describe some natural extensions to these arguments. We begin with Cerf's `upgra

Antoine Chance, Barbara Dalena, Tatiana Da Silva, Ahmad Mashal

One of the major upcoming challenges in particle physics is achieving precise measurements of the Z, W, and H bosons, as well as the top quark. To meet these targets, the next e\textsuperscript{+}e\textsuperscript{-} collider complex, FCC-ee, will need to achieve unprecedented luminosities. The FCC-IS European Study is investigating the feasibility of these

Jianfeng Wu, Yi Su, Yanxi Chen, Wenhui Zhu

Background: Alzheimer's Disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary ta

Angelos Chatzimparmpas, Rafael M. Martins, Alexandru C. Telea, Andreas Kerren

As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models-such as rule sets and decision trees-that sufficiently approximate the original one

Shubham Atreja, Shruthi Srinath, Mohit Jain, Joyojeet Pal

With the increasing dominance of the internet as a source of news consumption, there has been a rise in the production and popularity of email newsletters compiled by individual journalists. However, there is little research on the processes of aggregation, and how these differ between expert journalists and trained machines. In this paper, we interviewed jo

Andrey A. Vyshnevyy, Georgy A. Ermolaev, Dmitriy V. Grudinin, Kirill V. Voronin

With the advance of on-chip nanophotonics, there is a high demand for high refractive index, low-loss materials. Currently, this technology is dominated by silicon, but van der Waals (vdW) materials with high refractive index can offer a very advanced alternative. Still, up to now it was not clear if the optical anisotropy perpendicular to the layers might b

Domitille Gérard, Michael Rosticher, Kenji Watanabe, Takashi Taniguchi

Integrated quantum photonics, with potential applications in quantum information processing, relies on the integration of quantum emitters into on-chip photonic circuits. Hexagonal boron nitride (hBN) is recognized as a material that is compatible with such implementations, owing to its relatively high refractive index and low losses in the visible range, to

David Froelicher, Hyunghoon Cho, Manaswitha Edupalli, Joao Sa Sousa

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confidentiality. Our solution, SF-PCA, is an end-to-end secure system that preserves the confidentiality of b

Doğanalp Ergenç, Cornelia Brülhart, Mathias Fischer

Mission-critical systems (MCSs) have embraced new design paradigms such as service-oriented architecture (SOA) and IEEE 802.1 Time-sensitive Networking (TSN). These approaches tackle the static and closed-loop design and configuration of MCSs to address their strict performance and resilience requirements. While SOA enables the dynamic placement of critical

Sajad Meisami, Sadaf Meisami, Melina Yousefi, Mohammad Reza Aref

The emergence of the Internet of Things (IoT) has resulted in a significant increase in research on e-health. As the amount of patient data grows, it has become increasingly challenging to protect patients' privacy. Patient data is commonly stored in the cloud, making it difficult for users to control and protect their information. Moreover, the recent rise

Osoro B. Ogutu

In this paper, we apply the ITU-R P.618-8 model with data from the ITU-R P.837-7, Tropical Rain Measuring Mission (TRMM) and Global Precipitation Mission (GPM) satellite to determine the level of attenuation and available link margin for a LEO system such as Telesat. The specific and predicted attenuation for chosen six candidate ground stations (Abuja, Hart

Yu. Kordyukov, V. Manuilov

Recently, M. Ludewig and G. C. Thiang introduced a notion of a uniformly localized Wannier basis with localization centers in an arbitrary uniformly discrete subset $D$ in a complete Riemannian manifold $X$. They show that, under certain geometric conditions on $X$, the class of the orthogonal projection onto the span of such a Wannier basis in the $K$-theor

Rowan Killip, Thierry Laurens, Monica Visan

The Benjamin--Ono equation is shown to be well-posed, both on the line and on the circle, in the Sobolev spaces $H^s$ for $s>-\tfrac12$. The proof rests on a new gauge transformation and benefits from our introduction of a modified Lax pair representation of the full hierarchy. As we will show, these developments yield important additional dividends beyond w

Rory Conboye

Discrete forms of the mean and directed curvature are constructed on piecewise flat manifolds, providing local curvature approximations for smooth manifolds embedded in both Euclidean and non-Euclidean spaces. The resulting expressions take the particularly simple form of a weighted scalar sum of hinge angles, the angles between the normals of neighbouring p

Harish Karunakaran, Gopeshh Raaj Subbaraj

Mobile manipulator systems are comprised of a mobile platform with one or more manipulators and are of great interest in a number of applications such as indoor warehouses, mining, construction, forestry etc. We present an approach for computing actuator commands for such systems so that they can follow desired end-effector and platform trajectories without

Ryan Koo, Anna Martin, Linghe Wang, Dongyeop Kang

Scholarly writing presents a complex space that generally follows a methodical procedure to plan and produce both rationally sound and creative compositions. Recent works involving large language models (LLM) demonstrate considerable success in text generation and revision tasks; however, LLMs still struggle to provide structural and creative feedback on the

Iskander Aliev, Martin Henk

In this short survey we want to present some of the impact of Minkowski's successive minima within Convex and Discrete Geometry. Originally related to the volume of an $o$-symmetric convex body, we point out relations of the successive minima to other functionals, as e.g., the lattice point enumerator or the intrinsic volumes and we present some old and new

Mehran Ghafarian Tamizi, Homayoun Honari, Aleksey Nozdryn-Plotnicki, Homayoun Najjaran

Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient

Zhengjiang Lin

Given a nonnegative integrable function $J$ on $\mathbb{R}^n$, we relate the asymptotic properties of the nonlocal energy functional \begin{equation*} \int_{\Omega} \int_{\Omega^c} J \bigg(\frac{x-y}{t}\bigg) \ dx dy \end{equation*} as $t \to 0^+$ with the boundary properties of a given domain $\Omega \subset \mathbb{R}^n$. Then, we use these asymptotic prop

Kara E. Rudolph, Nicholas T. Williams, Elizabeth A. Stuart, Ivan Diaz

We develop flexible, semiparametric estimators of the average treatment effect (ATE) transported to a new population ("target population") that offer potential efficiency gains. Transport may be of value when the ATE may differ across populations. We consider the setting where differences in the ATE are due to differences in the distribution of baseline cova

Mathias Kraus, Julia Anna Bingler, Markus Leippold, Tobias Schimanski

Large language models (LLMs) have significantly transformed the landscape of artificial intelligence by demonstrating their ability in generating human-like text across diverse topics. However, despite their impressive capabilities, LLMs lack recent information and often employ imprecise language, which can be detrimental in domains where accuracy is crucial

Aman Pathak, Zehao Yu, Daniel Paredes, Elio Paul Monsour

The ultrasound characteristics of thyroid nodules guide the evaluation of thyroid cancer in patients with thyroid nodules. However, the characteristics of thyroid nodules are often documented in clinical narratives such as ultrasound reports. Previous studies have examined natural language processing (NLP) methods in extracting a limited number of characteri

Daniel Campos, ChengXiang Zhai

Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to leverage contextual representations for documents and queries. As these vector-based systems rely on contextual language models, their usage commonly requires GPUs, which can be e

Simone Biondini, Nora Brambilla, Gramos Qerimi, Antonio Vairo

In order to predict the cosmological abundance of dark matter, an estimation of particle rates in an expanding thermal environment is needed. For thermal dark matter, the non-relativistic regime sets the stage for the freeze-out of the dark matter energy density. We compute transition widths and annihilation, bound-state formation, and dissociation cross sec

Georg Börner, Fabio Schittler Neves, Marc Timme

Spiking neural network models characterize the emergent collective dynamics of circuits of biological neurons and help engineer neuro-inspired solutions across fields. Most dynamical systems' models of spiking neural networks typically exhibit one of two major types of interactions: First, the response of a neuron's state variable to incoming pulse signals (

Aokun Chen, Daniel Paredes, Zehao Yu, Xiwei Lou

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that

Sheetal Jain, Christopher J. S. Heath, Dixshant Shree Shreemal, Blair W. Lebert

We carried out X-ray diffraction and Extended X-ray Absorption Fine Structure (EXAFS) studies to investigate the origin of the low lattice thermal conductivity in BiCuSeO, and the role of silver (Ag) doping in doped samples. BiCuSeO is a promising thermoelectric material with high thermoelectric efficiency, which is significantly enhanced by doping either si

Leonardo Tinti

The collisionless Boltzmann equation, also called free-streaming, is a convenient approximation. It is rather simple to implement numerically, and and it is effective at reducing the irregularities of rough initial conditions. It can be obtained as a small $\hbar$ limit from a free scalar quantum field. Namely, by neglecting the $\hbar^2$ term in the dynamic

Yawen Feng, Mikko Parviainen, Saara Sarsa

We study a general form of a degenerate or singular parabolic equation $$ u_t-|Du|^{\gamma}\big(\Delta u+(p-2)\Delta_\infty^Nu\big)=0 $$ that generalizes both the standard parabolic $p$-Laplace equation and the normalized version that arises from stochastic game theory. We develop a systematic approach to study second order Sobolev regularity and show that $

Justin Diamond

OpenAI's GPT-4 is a Large Language Model (LLM) that can generate coherent constructed languages, or "conlangs," which we propose be called "genlangs" when generated by Artificial Intelligence (AI). The genlangs created by ChatGPT for this research (Voxphera, Vivenzia, and Lumivoxa) each have unique features, appear facially coherent, and plausibly "translate

T. J. Volkoff, Andrew T. Sornborger

We analyze the energy and training data requirements for supervised learning of an $M$-mode linear optical circuit by minimizing an empirical risk defined solely from the action of the circuit on coherent states. When the linear optical circuit acts non-trivially only on $k<M$ unknown modes (i.e., a linear optical $k$-junta), we provide an energy-efficient,

Adrien DeLazzer Meunier, Christoph Schweigert, Matthias Traube

We develop a string-net construction for the (2,1)-dimensional part of a $G$-equivariant three-dimensional topological field theory based on a $G$-graded spherical fusion category. In this construction, a $G$-equivariant generalization of the Ptolemy groupoid enters. We compute the associated cylinder categories and show that, as expected, the model is close

James P. Sethna, David Hathcock, Jaron Kent-Dobias, Archishman Raju

Our community has a deep and sophisticated understanding of phase transitions and their universal scaling functions. We outline and advocate an ambitious program to use this understanding as an anchor for describing the surrounding phases. We explain how to use normal form theory to write universal scaling functions in systems where the renormalization-group

Sierra Casten, Tod Strohmayer, Peter Bult

We present a study of weak, thermonuclear X-ray bursts from the accreting millisecond X-ray pulsar SAX J1808.4-3658. We focus on a burst observed with the Neutron Star Interior Composition Explorer on 2019 August 9, and describe a similar burst observed with the Rossi X-ray Timing Explorer in 2005 June. These bursts occurred soon after outburst onset, $2.9$

James H Adler, Xiaozhe Hu, Yuwen Li, Ludmil T. Zikatanov

It is well known that via the augmented Lagrangian method, one can solve Stokes' system by solving the nearly incompressible linear elasticity equation. In this paper, we show that the converse holds, and approximate the inverse of the linear elasticity operator with a convex linear combination of parameter-free operators. In such a way, we construct a unifo

Yan Chen, James H. Holmes, Curtis Corum, Vincent Magnotta

Recent quantitative parameter mapping methods including MR fingerprinting (MRF) collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as Deep Factor Model(DFM), which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the re

Yingjun Du, Jiayi Shen, Xiantong Zhen, Cees G. M. Snoek

Modern image classifiers perform well on populated classes, while degrading considerably on tail classes with only a few instances. Humans, by contrast, effortlessly handle the long-tailed recognition challenge, since they can learn the tail representation based on different levels of semantic abstraction, making the learned tail features more discriminative

Zihao Liang, Wenjian Hao, Shaoshuai Mou

This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a challenging situation when the system dynamics is unknown. The key idea of the proposed approach comes from the deep Koo

Arti Joshi, Nikita Rawat, Axel Schwope, J. C. Pandey

We present analyses of an Intermediate Polar, IGR J15094-6649, based on the archival optical data obtained from the Transiting Exoplanet Survey Satellite (TESS) and X-ray data obtained from the Suzaku, NuSTAR, and Neil Gehrels Swift Observatory (Swift). Present analysis confirms and refines the previously reported spin period of IGR J15094-6649 as 809.49584$

Rita F. Sonka, Shannon M. Duff, Daniel Dutcher, Suzanne T. Staggs

The Simons Observatory aims to field 70,000 Transition-Edge Sensor (TES) bolometers to measure the Cosmic Microwave Background. With so many detectors, rapid but accurate validation of their properties prior to their integration into telescopes is of particular importance. This paper describes an exploration of a new method to improve the simultaneous charac

Tao Li, Quanyan Zhu

Transparency of information disclosure has always been considered an instrumental component of effective governance, accountability, and ethical behavior in any organization or system. However, a natural question follows: \emph{what is the cost or benefit of being transparent}, as one may suspect that transparency imposes additional constraints on the inform

Daniele Boffi, Ramon Codina, Önder Türk

We consider nodal-based Lagrangian interpolations for the finite element approximation of the Maxwell eigenvalue problem. The first approach introduced is a standard Galerkin method on Powell-Sabin meshes, which has recently been shown to yield convergent approximations in two dimensions, whereas the other two are stabilized formulations that can be motivate

Melanie Kircheis, Daniel Potts

An inverse nonequispaced fast Fourier transform (iNFFT) is a fast algorithm to compute the Fourier coefficients of a trigonometric polynomial from nonequispaced sampling data. However, various applications such as magnetic resonance imaging (MRI) are concerned with the analogous problem for bandlimited functions, i.e., the reconstruction of point evaluations

Stuart J. Masson, Jacob P. Covey, Sebastian Will, Ana Asenjo-Garcia

In inverted atomic ensembles, photon-mediated interactions give rise to Dicke superradiance, a form of many-body decay that results in a rapid release of energy as a photon burst. While originally studied in pointlike ensembles, this phenomenon persists in extended ordered systems if the inter-particle distance is below a certain bound. Here, we investigate

Divyanshi Dwivedi, Pradeep Kumar Yemula, Mayukha Pal

The expansion in technology and attainability of a large number of sensors has led to a huge amount of real-time streaming data. The real-time data in the electrical distribution system is collected through distribution-level phasor measurement units referred to as $\mu$PMU which report high-resolution phasor measurements comprising various event signatures

A. Kogut, Dale Fixsen, Nabila Aghanim, Jens Chluba

The Primordial Inflation Explorer (PIXIE) is an Explorer-class mission concept to measure the spectrum and polarization of the cosmic microwave background. Cosmological signals are small compared to the instantaneous instrument noise, requiring strict control of instrumental signals. The instrument design provides multiple levels of null operation, signal mo

Fabian O. von Rohr

The concept of topological superconductivity has attracted immense interest in the physics community recently for several reasons: First, topological superconductors represent new phases of matter, topologically distinct from any other known phase of matter. Second, their discovery would present the first realization of Majorana zero modes. Third, intrinsic

Holger Theisel, Anke Friederici, Tobias Günther

We analyze two recently-introduced flow measured that are based on a single trajectory only: trajectory stretching exponent (TSE) to detect hyperbolic (stretching) behavior, and trajectory angular velocity (TRA) to detect elliptic (rotation) behavior. Haller et al. [2021] and Haller et al. [2022] introduced TSE, TRA as well as the concept of quasi-objectivit

Arad Nasiri

One of the major tasks in discrete theories of gravity, including causal set theory, is to discover how the combinatorics of the underlying discrete structure recovers various geometric aspects of the emergent spacetime manifold. In this paper, I develop a new covariant approach to connect the combinatorics of a Poisson sprinkled causal set to the geometry o

L. González-Cuesta, S. Mathur, R. A. García, F. Pérez Hernández

The NASA K2 mission that succeeded the nominal Kepler mission observed several hundreds of thousands of stars during its operations. While most of the stars were observed in single campaigns of 80 days, some of them were targeted for more than one campaign. We perform an asteroseismic study of a sample of eight solar-like stars observed during K2 Campaigns 6

Ajit Desai

This article provides a curated review of selected papers published in prominent economics journals that use machine learning (ML) tools for research and policy analysis. The review focuses on three key questions: (1) when ML is used in economics, (2) what ML models are commonly preferred, and (3) how they are used for economic applications. The review highl

Galatia Cleanthous, Athanasios G. Georgiadis, Philip A. White

We are studying the problem of estimating density in a wide range of metric spaces, including the Euclidean space, the sphere, the ball, and various Riemannian manifolds. Our framework involves a metric space with a doubling measure and a self-adjoint operator, whose heat kernel exhibits Gaussian behaviour. We begin by reviewing the construction of kernel de

R. Fioresi, A. Marraffa, J. Petkovic

We present a review of known models and a new simple mathematical modelling for border completion in the visual cortex V1 highlighting the striking analogies with bicycle rear wheel motions in the plane.

Abhinav Kumar, Miguel A. Guirao Aguilera, Reza Tourani, Satyajayant Misra

The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical--a facet that has not received much attentio

Nicolas Garrel

In order to study certain algebraic objects, and notably algebraic groups, Serre introduced the notion on invariants, in particular cohomological invariants. The construction of non-trivial cohomological invariants of algebraic groups is an active area of modern research, and very few invariants are known in degree greater than 3. In the first chapter, we gi