May 2022 arXiv papers — page 150

M. S. Rumenskikh, I. F. Shaikhislamov, M. L. Khodachenko, H. Lammer

A 3D fully self-consistent multi-fluid hydrodynamic aeronomy model is applied to simulate the hydrogen-helium expanding upper atmosphere of the hot Jupiter HD189733b, and related absorption in the Lya line and the 10830 A line of metastable helium. We studied the influence of a high-energy stellar flux, stellar wind, and Lya cooling to reproduce the availabl

Erik Burman, Peter Hansbo, Mats G. Larson

In this note, we develop a new stabilization mechanism for cut finite element methods that generalizes previous approaches of ghost penalty type in two ways: (1) The quantity that is stabilized and (2) The choice of elements that are connected in the stabilization. In particular, we can stabilize functionals of the discrete function such as finite element de

Bo Berndtsson

We give some remarks on geodesics in the space of Kähler metrics that are defined for all time. Such curves are conjecturally induced by holomorphic vector fields, and we show that this is indeed so for regular geodesics, whereas the question for generalized geodesics is still open (as far as we know). We also give a result about the derivative of such geode

Julian Fuhrer, Alejandro Blenkmann, Tor Endestad, Anne-Kristin Solbakk

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Spec

Masahiro Ibe, Satoshi Shirai, Motoo Suzuki, Keiichi Watanabe

It is remarkable that the matter fields in the Standard Model (SM) are apparently unified into the $\mathrm{SU}(5)$ representations. A straightforward explanation of this fact is to embed all the SM gauge groups into a simple group containing $\mathrm{SU}(5)$, i.e., the grand unified theory (GUT). Recently, however, a new framework "fake GUT" has bee

Alexander Trautsch, Steffen Herbold

Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type prediction. Our model dominates the baseline fas

Marco Winckler, Philippe Palanque, Jean-Luc Hak, Eric Barboni

Along the design process of interactive system many intermediate artefacts (such as user interface prototypes, task models describing user work and activities, dialog models specifying system behavior, interaction models describing user interactions {\ldots}) are created, tested, revised and improved until the development team produces a validated version of

Tobias K. S. Ritschel, Asbjørn Thode Reenberg, John Bagterp Jørgensen

We propose a virtual clinical trial for assessing the safety and efficacy of closed-loop diabetes treatments prior to an actual clinical trial. Such virtual trials enable rapid and risk-free pretrial testing of algorithms, and they can be used to compare different treatment variations for large and diverse populations. The participants are represented by mul

Renaud Fabre, Otmane Azeroual, Patrice Bellot, Joachim Schöpfel

The cognitive manifold of published content is currently expanding in all areas of science. However, Scientific Knowledge Graphs (SKGs) only provide poor pictures of the adversarial directions and scientific controversies that feed the production of knowledge. In this Article, we tackle the understanding of the design of the information space of a cognitive

Jian Ding, Haoyu Liu

We study the shotgun assembly problem for the lattice labeling model, where i.i.d. uniform labels are assigned to each vertex in a $d$-dimensional box of side length $n$. We wish to recover the labeling configuration on the whole box given empirical profile of labeling configurations on all boxes of side length $r$. We determine the threshold around which th

Andrew E Chubykalo, Augusto Espinoza, B P Kosyakov

We outline a regular way for solving Maxwell's equations. We take, as the starting point, the notion of vector potentials. The rationale for introducing this notion in electrodynamics is that the set of Maxwell's equations is seemingly overdetermined. We demonstrate the existence of two fundamental solutions to Maxwell's equations whose linear co

Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah

Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration,

Alon Berliner, Guy Rotman, Yossi Adi, Roi Reichart

Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through the relevant structures often requires enumerating over an exponentially large latent space. Recently, various approache

Bishnupada Ghosh, Mrinmay Sahu, Debabrata Samanta, Pinku Saha

A detailed high pressure investigation is carried out using x-ray diffraction, Raman spectroscopy and low temperature resistivity measurements on hexagonal ZrSe$_{2}$ having an excess of 3 at.\% Zr. Structural studies show that the sample goes through a gradual structural transition from hexagonal to monoclinic phase, with a mixed phase in the pressure range

Yuan Xu

We establish weighted Bernstein inequalities in $L^p$ space for the doubling weight on the conic surface $\mathbb{V}_0^{d+1} = \{(x,t): \|x\| = t, x \in \mathbb{R}^d, t\in [0,1]\}$ as well as on the solid cone bounded by the conic surface and the hyperplane $t =1$, which becomes a triangle on the plane when $d=1$. While the inequalities for the derivatives i

Vishnu Baburajan, João de Abreu e Silva, Francisco Camara Pereira

To improve the traveling experience, researchers have been analyzing the role of attitudes in travel behavior modeling. Although most researchers use closed-ended surveys, the appropriate method to measure attitudes is debatable. Topic Modeling could significantly reduce the time to extract information from open-ended responses and eliminate subjective bias,

Ranjan Maitra

Functional Magnetic Resonance Imaging~(fMRI) is widely used to study activation in the human brain. In most cases, data are commonly used to construct activation maps corresponding to a given paradigm. Results can be very variable, hence quantifying certainty of identified activation and inactivation over studies is important. This paper provides a model-bas

Lele Luan, Yang Liu, Hao Sun

Distilling interpretable physical laws from videos has led to expanded interest in the computer vision community recently thanks to the advances in deep learning, but still remains a great challenge. This paper introduces an end-to-end unsupervised deep learning framework to uncover the explicit governing equations of dynamics presented by moving object(s),

Ting-ting Ma, Dmitri B. Horoshko, Chang-shui Yu, Serge Ya. Kilin

We study photon/phonon statistics of a qubit-plasmon-phonon hybrid system in the ultrastrong coupling regime. The introduced qubit coupling causes parity conserving and non-conserving situations. We employ an analytic approximation approach for the parity conserving case to reveal the statistical behaviors of photons and phonons. It indicates that both photo

Yiren Jian, Chongyang Gao, Soroush Vosoughi

The impressive performance of GPT-3 using natural language prompts and in-context learning has inspired work on better fine-tuning of moderately-sized models under this paradigm. Following this line of work, we present a contrastive learning framework that clusters inputs from the same class for better generality of models trained with only limited examples.

Yiren Jian, Chongyang Gao, Soroush Vosoughi

Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language model can cause severe over-fitting. In this paper, we propose an Embedding Hallucination (EmbedHalluc) method, which generates auxiliary embedding-label pairs to expand the fine-tun

Federico Bobbio, Margarida Carvalho, Andrea Lodi, Alfredo Torrico

The many-to-one stable matching problem provides the fundamental abstraction of several real-world matching markets such as school choice and hospital-resident allocation. The agents on both sides are often referred to as residents and hospitals. The classical setup assumes that the agents rank the opposite side and that the capacities of the hospitals are f

Song Jiang, Xiangxiang Su, Feng Xie

We are concerned with the sharp interface limit for an incompressible Navier-Stokes and Allen-Cahn coupled system in this paper. When the thickness of the diffuse interfacial zone, which is parameterized by $\varepsilon$, goes to zero, we prove that a solution of the incompressible Navier-Stokes and Allen-Cahn coupled system converges to a solution of a shar

Sajal Saha, Anwar Haque, Greg Sidebottom

Prediction of network traffic behavior is significant for the effective management of modern telecommunication networks. However, the intuitive approach of predicting network traffic using administrative experience and market analysis data is inadequate for an efficient forecast framework. As a result, many different mathematical models have been studied to

Joy Morris, Adrian Skelton

A number of authors have studied the question of when a graph can be represented as a Cayley graph on more than one nonisomorphic group. In this paper we give conditions for when a Cayley graph on an abelian group can be represented as a Cayley graph on a generalized dihedral group, and conditions for when the converse is true.

Xin Lin, Changxing Ding, Jing Zhang, Yibing Zhan

Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1) ambiguous object representations, as graph neural network-based message passing (GMP) modules are typically sensitive to spurious inter-node correlations, and 2) low diversity in

Ruoting Wu, Yuxin Zhang, Qibiao Peng, Liang Chen

In recent years, the rise of deep learning and automation requirements in the software industry has elevated Intelligent Software Engineering to new heights. The number of approaches and applications in code understanding is growing, with deep learning techniques being used in many of them to better capture the information in code data. In this survey, we pr

Mengzhe He, Yali Wang, Jiaxi Wu, Yiru Wang

Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target domain. Existing approaches mainly focus on either of these two difficulties, even though they are closely coupled in cross domain object detection

Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in structured and unstructured EHRs is not

Yu Tian, Jianxin Chang, Yannan Niu, Yang Song

Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage attention mechanisms and RNNs, recent efforts mainly follow two directions for improvement: multi-interest learning and graph convolutional aggregation. Specifically, multi-interes

Jianli Bai, Xiangfu Song, Shujie Cui, Ee-Chien Chang

Private decision tree evaluation (PDTE) allows a decision tree holder to run a secure protocol with a feature provider. By running the protocol, the feature provider will learn a classification result. Nothing more is revealed to either party. In most existing PDTE protocols, the required communication grows exponentially with the tree's depth $d$, which

Eugene Wu

Comparison is a core task in visual analysis. Although there are numerous guidelines to help users design effective visualizations to aid known comparison tasks, there are few formalisms that define the semantics of comparison operations in a way that can serve as the basis for a grammar of comparison interactions. Recent work proposed a formalism called Vie

N. A. Cruz, O. O. Melo, C. A. Martinez

In this study, we propose a family of correlation structures for crossover designs with repeated measures for both, Gaussian and non-Gaussian responses using generalized estimating equations (GEE). The structure considers two matrices: one that models between-period correlation and another one that models within-period correlation. The overall correlation ma

Xinmeng Xu, Rongzhi Gu, Yuexian Zou

Hand-crafted spatial features, such as inter-channel intensity difference (IID) and inter-channel phase difference (IPD), play a fundamental role in recent deep learning based dual-microphone speech enhancement (DMSE) systems. However, learning the mutual relationship between artificially designed spatial and spectral features is hard in the end-to-end DMSE.

Mohammadreza Hashemi, Adrian Ricardo Archilla

This paper presents a new procedure for evaluating the goodness of fit of Generalized Linear Models (GLM) estimated with Roadway Departure (RwD) crash frequency data for the State of Hawaii on two-lane two-way (TLTW) state roads. The procedure is analyzed using ten years of RwD crash data (including all severity levels) and roadway characteristics (e.g., tra

Yu Wang, Daniel Stoller, Rachel M. Bittner, Juan Pablo Bello

Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen instruments. To address this, we propose a few-shot musical source separation paradigm. We condition a generic U-Net source separation model using few audio examples of the target inst

Yuma Ichikawa, Koji Hukushima

Deep learning methods relying on multi-layered networks have been actively studied in a wide range of fields in recent years, and deep Boltzmann machines(DBMs) is one of them. In this study, a model of DBMs with some properites of weight parameters obtained by learning is studied theoretically by a statistical-mechanical approach based on the replica method.

Mario I. Molina

We examine the interplay between disorder and fractionality in a one-dimensional tight-binding Anderson model. In the absence of disorder, we observe that the two lowest energy eigenvalues detach themselves from the bottom of the band, as fractionality $s$ is decreased, becoming completely degenerate at $s=0$, with a common energy equal to a half bandwidth,

Lillian Clark, Jeffrey A. Edlund, Marc Sanchez Net, Tiago Stegun Vaquero

Multi-robot exploration of complex, unknown environments benefits from the collaboration and cooperation offered by inter-robot communication. Accurate radio signal strength prediction enables communication-aware exploration. Models which ignore the effect of the environment on signal propagation or rely on a priori maps suffer in unknown, communication-rest

Houyi Yu

We give a combinatorial description for the weak order on the hyperoctahedral group. This characterization is then used to analyze the order-theoretic properties of the shifted products of hyperoctahedral groups. It is shown that each shifted product is a disjoint union of some intervals, which can be convex embedded into a hyperoctahedral group. As an appli

Juefan Wang, Abhishek A. Panchal, Gopalakrishnan Sai Gautam, Pieremanuele Canepa

A crucial ingredient in lithium (Li) and sodium (Na)-ion batteries (LIBs and NIBs) is the electrolytes. The use of Li-metal (Na-metal) as anode in liquid electrolyte LIBs (NIBs) is constrained by several issues including thermal runway and flammability, electrolyte leakage, and limited chemical stability. Considerable effort has been devoted toward the devel

Shichao Lv, Kai Peng, Hongxia Wang, Huanshui Zhang

This paper is concerned with mismatched disturbance rejection control for the second-order discrete-time systems.Different from previous work, the controllability of the system is applied to design the disturbance compensation gain, which does not require any coordinate transformations. Via this new idea, it is shown that disturbance in the regulated output

Yoshihiro Ohashi

This study investigates the prevention of market manipulation using a price-impact model of financial market trading as a linear system. First, I define a trading game between speculators such that they implement a manipulation trading strategy that exploits momentum traders. Second, I identify market intervention by a controller (e.g., a central bank) with

Yanwei Han, Zijian Zhang

The flywheel ball and hexagonal structures in the design of the classical centrifugal governor systems lead to both modeling and analytical difficulties. In the present paper, a new trigonal centrifugal governor is proposed in an attempt to overcome both of these difficulties by introducing the radical nonlinearity and nonsmooth control strategy in the simpl

Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Ming Ding

We study the privacy-utility trade-off in the context of metric differential privacy. Ghosh et al. introduced the idea of universal optimality to characterise the best mechanism for a certain query that simultaneously satisfies (a fixed) $ε$-differential privacy constraint whilst at the same time providing better utility compared to any other $ε$-differentia

Spencer, Gales, Sunder Sethuraman, Kwang-Sung Jun

Linear bandits have a wide variety of applications including recommendation systems yet they make one strong assumption: the algorithms must know an upper bound $S$ on the norm of the unknown parameter $θ^*$ that governs the reward generation. Such an assumption forces the practitioner to guess $S$ involved in the confidence bound, leaving no choice but to w

Qiwei Feng, Bin Han, Peter Minev

For elliptic interface problems with discontinuous coefficients, the maximum accuracy order for compact 9-point finite difference scheme in irregular points is three [7]. The discontinuous coefficients usually have abrupt jumps across the interface curve in the porous medium of realistic problems, causing the pollution effect of numerical methods. So, to obt

Timothy Ludlow, Jonas Fooken, Christiern Rose, Kam Tang

Housing expenditure tends to be sticky and costly to adjust, and makes up a large proportion of household expenditure. Additionally, the loss of housing can have catastrophic consequences. These specific features of housing expenditure imply that housing stress could cause negative mental health impacts. This research investigates the effects of housing stre

James Martin, Jin L. C. Guo

Understanding the correct API usage sequences is one of the most important tasks for programmers when they work with unfamiliar libraries. However, programmers often encounter obstacles to finding the appropriate information due to either poor quality of API documentation or ineffective query-based searching strategy. To help solve this issue, researchers ha

Takahiro Miura, Ichiro Sakata

In the Cumulative Advantage(CA) model, which is one of the most fundamental approaches to understand the mechanism of citation dynamics, papers receive citations depending on how much they have been already cited. On the other hand, a substantial effect not included in CA is that some surprising discoveries suddenly acquire citations after a long time from p

Tian-Yu Ye, Li-Zhen Jiang

A quantum steganography protocol with large payload is proposed, based on dense coding and entanglement swapping of GHZ states. Its super quantum channel is formed by building up the hidden channel within the original quantum secure direct communication (QSDC) scheme. Based on the original QSDC, secrets messages are transmitted by integrating dense coding of

Siddhartha Paul Tiwari

Among the essential elements of knowledge management is the use of information and data, as well as the knowledge, skills, and abilities inherent within communities, as well as their ideas, commitments, and motivations for making good decisions as emerging technologies become more prevalent. Numerous leading social scientists in this field have asserted that

Saranga Kingkor Mahanta, Darsh Kaushik, Shubham Jain, Hoang Van Truong

With the periodic rise and fall of COVID-19 and countries being inflicted by its waves, an efficient, economic, and effortless diagnosis procedure for the virus has been the utmost need of the hour. COVID-19 positive individuals may even be asymptomatic making the diagnosis difficult, but amongst the infected subjects, the asymptomatic ones need not be entir

Eric Balkanski, Tingting Ou, Clifford Stein, Hao-Ting Wei

Algorithms with predictions is a recent framework that has been used to overcome pessimistic worst-case bounds in incomplete information settings. In the context of scheduling, very recent work has leveraged machine-learned predictions to design algorithms that achieve improved approximation ratios in settings where the processing times of the jobs are initi

Eric Auerbach, Yong Cai

We are interested in the distribution of treatment effects for an experiment where units are randomized to a treatment but outcomes are measured for pairs of units. For example, we might measure risk sharing links between households enrolled in a microfinance program, employment relationships between workers and firms exposed to a trade shock, or bids from b

O. I. Kartavtsev, A. V. Malykh

Binding of two heavy fermions interacting with a light particle via the contact interaction is possible only for sufficiently large heavy-light mass ratio. In this work, the two-variable inequality is derived to determine a specific value $ μ^* $ providing that there are no three-body bound states for the mass ratio smaller than $ μ^* $. The value $ μ^* = 5.

Guangyan Cai, Kai Yan, Zhao Dong, Ioannis Gkioulekas

Mathematically representing the shape of an object is a key ingredient for solving inverse rendering problems. Explicit representations like meshes are efficient to render in a differentiable fashion but have difficulties handling topology changes. Implicit representations like signed-distance functions, on the other hand, offer better support of topology ch

Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly configure and periodically update. Security negli

Taro Kimura, Souradeep Purkayastha

We study generalizations of the Gross--Witten--Wadia unitary matrix model for the special orthogonal and symplectic groups. We show using a standard Coulomb gas treatment -- employing a path integral formalism for the ungapped phase and resolvent techniques for the gapped phase with one coupling constant -- that in the large $N$ limit, the free energy normal

C. Donoso-Oliva, I. Becker, P. Protopapas, G. Cabrera-Vives

Taking inspiration from natural language embeddings, we present ASTROMER, a transformer-based model to create representations of light curves. ASTROMER was pre-trained in a self-supervised manner, requiring no human-labeled data. We used millions of R-band light sequences to adjust the ASTROMER weights. The learned representation can be easily adapted to oth

Kevin Zhao, Prateesh Goyal, Mohammad Alizadeh, Thomas E. Anderson

In this paper, we consider how to provide fast estimates of flow-level tail latency performance for very large scale data center networks. Network tail latency is often a crucial metric for cloud application performance that can be affected by a wide variety of factors, including network load, inter-rack traffic skew, traffic burstiness, flow size distributi

Luca Di Liello, Siddhant Garg, Luca Soldaini, Alessandro Moschitti

Inference tasks such as answer sentence selection (AS2) or fact verification are typically solved by fine-tuning transformer-based models as individual sentence-pair classifiers. Recent studies show that these tasks benefit from modeling dependencies across multiple candidate sentences jointly. In this paper, we first show that popular pre-trained transforme

Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi

Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present a

Miller Eaton, Amr Hossameldin, Richard J. Birrittella, Paul M. Alsing

Macroscopic quantum phenomena, such as observed in superfluids and superconductors, have led to promising technological advancements and some of the most important tests of fundamental physics. At present, quantum detection of light is mostly relegated to the microscale, where avalanche photodiodes are very sensitive to distinguishing single-photon events fr

Mohammadhossein Mohammadisiahroudi, Ramin Fakhimi, Tamás Terlaky

Quantum computing has attracted significant interest in the optimization community because it potentially can solve classes of optimization problems faster than conventional supercomputers. Several researchers proposed quantum computing methods, especially Quantum Interior Point Methods (QIPMs), to solve convex optimization problems, such as Linear Optimizat

Jasper R. Stroud, David F. Plusquellic

An electro-optic dual comb system based on chirped-pulse waveforms is used to simultaneously acquire temporally magnified rapid passage signals and normal spectral line shapes from the back-transformation to the time domain. Multi-heterodyne THz wave generation and detection is performed with the difference frequency mixing of two free-running lasers. The me

Victoria Lagerquist, Sebastian E. Kuhn, Nobuo Sato

In this paper, we present for the first time comprehensive and detailed results on the correspondence between the extrapolated deep inelastic structure function $g_1$ of both the proton and the neutron with the same quantity measured in the nucleon resonance region. We use a QCD parameterization of the world data on DIS spin structure functions, extrapolated

Indira Sen, Daniele Quercia, Licia Capra, Matteo Montecchi

It is hard to establish whether a company supports internal sustainability efforts (ISEs) like gender equality, diversity, and general staff welfare, not least because of lack of methodologies operationalizing these internal sustainability practices, and of data honestly documenting such efforts. We developed and validated a six-dimension framework reflectin

Or Abramovich, Hadas Pizem, Jan Van Eijgen, Ilan Oren

Objective: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the

Alessio Franchi, Davide Rossini, Ettore Vicari

We study the post-quench unitary dynamics of a quantum sunburst spin model, composed of a transverse-field quantum Ising ring which is suddenly coupled to a set of independent external qubits along the longitudinal direction, in a way to respect a residual translation invariance and the Ising $\mathbb{Z}_2$ symmetry. Starting from the different equilibrium q

Dinh-Liem Nguyen, Kale Stahl, Trung Truong

This paper is concerned with the inverse problem of determining the shape of penetrable periodic scatterers from scattered field data. We propose a sampling method with a novel indicator function for solving this inverse problem. This indicator functionl is very simple to implement and robust against noise in the data. The resolution and stability analysis o

Shriram Chennakesavalu, Grant M. Rotskoff

Controlling thermodynamic cycles to minimize the dissipated heat is a longstanding goal in thermodynamics, and more recently, a central challenge in stochastic thermodynamics for nanoscale systems. Here, we introduce a theoretical and computational framework for optimizing nonequilibrium control protocols that can transform a system between two distributions

Jingxing Qian, Veronica Chatrath, Jun Yang, James Servos

Maintaining an up-to-date map to reflect recent changes in the scene is very important, particularly in situations involving repeated traversals by a robot operating in an environment over an extended period. Undetected changes may cause a deterioration in map quality, leading to poor localization, inefficient operations, and lost robots. Volumetric methods,

William Gustafson

We introduce posets of simple vertex labeled minors of graphs and a generalization to the level of polymatroids, collectively termed minor posets. We show that any minor poset is isomorphic to the face poset of a regular CW sphere, and in particular, is Eulerian. We establish cd-index inequalities induced by strong maps, a tight upper bound for cd-indices of

Martin Brokate, Constantin Christof

We prove strong stationarity conditions for optimal control problems that are governed by a prototypical rate-independent evolution variational inequality, i.e., first-order necessary optimality conditions in the form of a primal-dual multiplier system that are equivalent to the purely primal notion of Bouligand stationarity. Our analysis relies on recent re

Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala

Quadruped locomotion is rapidly maturing to a degree where robots are able to realise highly dynamic manoeuvres. However, current planners are unable to vary key gait parameters of the in-swing feet midair. In this work we address this limitation and show that it is pivotal in increasing controller robustness by learning a latent space capturing the key stan

Ding Xu, Arkajit Mandal, James M. Baxter, Shan-Wen Cheng

Semiconductor excitations can hybridize with cavity photons to form exciton-polaritons (EPs) with remarkable properties, including light-like energy flow combined with matter-like interactions. To fully harness these properties, EPs must retain ballistic, coherent transport despite matter-mediated interactions with lattice phonons. Here we develop a nonlinea

Emanuel V. Chimanski, Ronaldo V. Lobato, Andre R. Goncalves, Carlos A. Bertulani

The description of stellar interior remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter $ρ_{0} = 2.8\times 10^{14} {\rm\ g\ cm^{-3}}$, regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme

A. Deur, V. Burkert, J. P. Chen, W. Korsch

The QCD effective charge $α_{g_1}(Q)$ is an observable that characterizes the magnitude of the strong interaction. At high momentum $Q$, it coincides with the QCD running coupling $α_{\rm s}(Q)$. At low $Q$, it offers a nonperturbative definition of the running coupling. We have extracted $α_{g_1}(Q)$ from measurements carried out at Jefferson Lab that span

Debabrata Chandra, Supratik Pal

In this article, we do a thorough investigation of the competency of the forthcoming Cosmic Microwave Background (CMB) and Galaxy surveys in probing the features in the primordial power spectrum. Primordial features are specific model-dependent corrections on top of the standard power-law inflationary power spectrum; the functional form being given by differ

Paul Ralph, Sebastian Baltes

Confusion over different kinds of secondary research, and their divergent purposes, is undermining the effectiveness and usefulness of secondary studies in software engineering. This short paper therefore explains the differences between ad hoc review, case survey, critical review, meta-analysis (aka systematic literature review), meta-synthesis (aka themati

Renan Lira, Peter Riseborough, Jereson Silva-Valencia, Marcos Figueira

We study the single-band Hubbard model under the action of an external magnetic field using the cumulant Green's functions method (CGFM). The starting point of the method is to diagonalize a cluster containing N correlated sites (seed) and employ the cumulants calculated from the cluster solution to obtain the full Green's functions for the lattice.

Monika Henzinger, Charlotte Peale, Omer Reingold, Judy Hanwen Shen

Finding a representative cohort from a broad pool of candidates is a goal that arises in many contexts such as choosing governing committees and consumer panels. While there are many ways to define the degree to which a cohort represents a population, a very appealing solution concept is lexicographic maximality (leximax) which offers a natural (pareto-optim

Takumi Murayama

We give short, closure-theoretic proofs for uniform bounds on the growth of symbolic powers of ideals in regular rings. The author recently proved these bounds in mixed characteristic using various versions of perfectoid/big Cohen-Macaulay test ideals, with special cases obtained earlier by Ma and Schwede. In mixed characteristic, we instead use Heitmann&#39

Sam F. Sweere, Ivan Valtchanov, Maggie Lieu, Antonia Vojtekova

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work we present the first application of Machine Learning based super-resolution (SR) and de-noising (DN) to enhance X-ray images from the European Space Agency's XMM-

Miru Lee, Niklas Weber, Cynthia A. Volkert, Matthias Krüger

We theoretically study the frictional damping of a small probe object on a coated planar surface, analyzing the resulting phonon modes via a theory of viscoelasticity. Three different types of excitations are found to contribute to friction in distinct ways: traveling (3D) spherical waves, traveling (2D) surface waves, and evanescent waves. While traveling w

Tobias Schlagenhauf, Tim Scheurenbrand, Dennis Hofmann, Oleg Krasnikow

The actual progression of pitting on ball screw drive spindles is not well known since previous studies have only relied on the investigation of indirect wear effects (e. g. temperature, motor current, structure-borne noise). Using images from a camera system for ball screw drives, this paper elaborates on the visual analysis of pitting itself. Due to its di

Yi Lai

For any 3D steady gradient Ricci soliton with positive curvature, we prove that it must be isometric to the Bryant soliton if it is asymptotic to a ray. Otherwise, it is asymptotic to a sector and hence a flying wing. We show that all 3D flying wings are O(2)-symmetric. Therefore, all 3D steady gradient Ricci solitons are O(2)-symmetric.

Maryam Saadati, Morteza Oveisiha

In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition in a fuzzy form for approximate weakly robust efficient solutions and then establish necessary optimality theorem for approximate weakly robust quasi-efficient solutions of t

Anirban Biswas, Debasish Borah, Nayan Das, Dibyendu Nanda

We propose a scenario where dark matter (DM) can be generated non-thermally due to the presence of a light Dirac neutrino portal between the standard model (SM) and dark sector particles. The SM is minimally extended by three right handed neutrinos ($ν_R$), a Dirac fermion DM candidate ($ψ$) and a complex scalar ($ϕ$), transforming non-trivially under an unb

Boris Khesin, Gerard Misiolek, Alexander Shnirelman

Geometric Hydrodynamics has flourished ever since the celebrated 1966 paper of V. Arnold. In this paper we present a collection of open problems along with several new constructions in fluid dynamics and a concise survey of recent developments and achievements in this area. The topics discussed include variational settings for different types of fluids, mode

Xiaofang Wang, Kris M. Kitani

Considerable research effort has been devoted to LiDAR-based 3D object detection and empirical performance has been significantly improved. While progress has been encouraging, we observe an overlooked issue: it is not yet common practice to compare different 3D detectors under the same cost, e.g., inference latency. This makes it difficult to quantify the t

Dong An, Di Fang, Stephen Jordan, Jin-Peng Liu

Nonlinear differential equations exhibit rich phenomena in many fields but are notoriously challenging to solve. Recently, Liu et al. [1] demonstrated the first efficient quantum algorithm for dissipative quadratic differential equations under the condition $R < 1$, where $R$ measures the ratio of nonlinearity to dissipation using the $\ell_2$ norm. Here we

Jonathan Harper

Bit threads, a dual description of the Ryu-Takyanagi formula for holographic entanglement entropy (EE), can be interpreted as a distillation of the quantum information to a collection of Bell pairs between different boundary regions. In this article we discuss a generalization to hyperthreads which can connect more than two boundary regions leading to a rich

Takanori Anegawa, Norihiro Iizuka, Daniel Kabat

We previously proposed that entanglement across a planar surface can be obtained from the partition function on a Euclidean hourglass geometry. Here we extend the prescription to spherical entangling surfaces in conformal field theory. We use the prescription to evaluate log terms in the entropy of a CFT in two dimensions, a conformally-coupled scalar in fou

Nojan Bagheri-Sadeghi, Brian T. Helenbrook

Unsteady simulations of horizontal ribbon growth of silicon were performed that included both Marangoni and buoyancy effects. A chaotic flow was observed dominated by strong Marangoni-driven jets emerging near the local temperature minima on the free surface. This oscillatory flow caused the vertical position of the leading edge of the sheet to fluctuate, re

Idris Abdulmumin, Satya Ranjan Dash, Musa Abdullahi Dawud, Shantipriya Parida

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context information to decrease the ambiguity of input sentences. Despite the increasing popularity of such a technique, good and sizeable datasets are scarce, limiting the full extent of

Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus

Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics. These parameterizations are a well-known source of uncertainty in weather forecasts that can be quantified via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters. It is part

Mahaveer Prasad, Hari Kumar Yadalam, Manas Kulkarni, Camille Aron

Chaos sets a fundamental limit to quantum-information processing schemes. We study the onset of chaos in spatially extended quantum many-body systems that are relevant to quantum optical devices. We consider an extended version of the Tavis-Cummings model on a finite chain. By studying level-spacing statistics, adjacent gap ratios, and spectral form factors,

Mario De Marco, Andrea Sangiovanni, Roberto Valandro

We classify rank zero 5d SCFTs geometrically engineered from M-theory on quasi-homogeneous compound Du Val isolated threefold singularities. For all such theories, we characterize the Higgs Branch, by computing the dimension, the continuous and discrete symmetry groups, as well as more refined details such as the charges of the hypermultiplets under these gr