May 2022 arXiv papers — page 28

Rakhi Singh, John Stufken

Screening experiments are useful for screening out a small number of truly important factors from a large number of potentially important factors. The Gauss-Dantzig Selector (GDS) is often the preferred analysis method for screening experiments. Just considering main-effects models can result in erroneous conclusions, but including interaction terms, even if

Tim Pearce, Jong-Hyeon Jeong, Yichen Jia, Jun Zhu

This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, using a flexible function approximator. We begin by showing how an algorithm popular in linear models can

D. Bhetuwal, J. Matter, H. Szumila-Vance, C. Ayerbe Gayoso

Quasi-elastic scattering on $^{12}$C$(e,e'p)$ was measured in Hall C at Jefferson Lab for space-like 4-momentum transfer squared $Q^2$ in the range of 8--14.2\,(GeV/$c$)$^2$ with proton momenta up to 8.3\,GeV/$c$. The experiment was carried out in the upgraded Hall C at Jefferson Lab. It used the existing high momentum spectrometer and the new super high mom

Damon Bayer, Michael Fay, Barry Graubard

We present several related methods for creating confidence intervals to assess disease prevalence in variety of survey sampling settings. These include simple random samples with imperfect tests, weighted sampling with perfect tests, and weighted sampling with imperfect tests, with the first two settings considered special cases of the third. Our methods use

Shuqi Wang, Valentin Schmutz, Guillaume Bellec, Wulfram Gerstner

Can we use spiking neural networks (SNN) as generative models of multi-neuronal recordings, while taking into account that most neurons are unobserved? Modeling the unobserved neurons with large pools of hidden spiking neurons leads to severely underconstrained problems that are hard to tackle with maximum likelihood estimation. In this work, we use coarse-g

Andrea Cini, Daniele Zambon, Cesare Alippi

Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the practitioner is left with the problem of

I. O. Nimyi, V. Könye, S. G. Sharapov, V. P. Gusynin

It is known that in two-dimensional relativistic Dirac systems placed in orthogonal uniform magnetic and electric fields, the Landau levels collapse as the applied in-plane electric field reaches a critical value $\pm E_c$. We study this phenomenon for a distinct field configuration with in-plane constant radial electric field. The Dirac equation for this co

Ziyi Wang, Yongming Rao, Xumin Yu, Jie Zhou

Conventional point cloud semantic segmentation methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on these class-agnostic local geometric representations may raise confusion between local parts from different categories that are similar in appear

Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophysical data of homogeneous color patches or a limited number of simplistic natural photographic images. It is thus questionable whether existing CD measures generalize in the age of

Florian Hartig, Frédéric Barraquand

In a recent opinion article, Muff et al. recapitulate well-known objections to the Neyman-Pearson Null-Hypothesis Significance Testing (NHST) framework and call for reforming our practices in statistical reporting. We agree with them on several important points: the significance threshold P<0.05 is only a convention, chosen as a compromise between type I and

Tomasz Krajewski, Krzysztof Turzyński

We study geometrical destabilization of inflation with the aim of determining the fate of excited unstable modes. We use numerical lattice simulations to track the dynamics of both the inflaton and the spectator field. We find that geometrical destabilization is a short-lived phenomenon and that a negative feedback loop prevents field fluctuations from growi

Tianxiao Wang, Jiongmin Yong

Spike variation technique plays a crucial role in deriving Pontryagin's type maximum principle of optimal controls for differential equations of several types, including ordinary differential equations (ODEs), partial differential equations (PDEs), and stochastic differentia equations (SDEs), when the control domains are not assumed to be convex. This techni

Mritunjay Musale, Vaibhav Vasani

The rapidly advancing field of Fluid Mechanics has recently employed Deep Learning to solve various problems within that field. In that same spirit we try to perform Direct Numerical Simulation(DNS) which is one of the tasks in Computational Fluid Dynamics, using three fundamental architectures in the field of Deep Learning that were each used to solve vario

Howard E. Bond, Jessica Mink, Allison Doane, Sarah Lavallee

Nova Sagittarii 1943 (V1148 Sgr) was an 8th-mag optical transient that was unusual in having a late-type spectrum during its outburst, in striking contrast to the normal high-excitation spectra seen in classical novae. Unfortunately, only an approximate position was given in the discovery announcement, hampering follow-up attempts to observe its remnant. We

Lei Su, Ivar Martin

We show that the integrable Lindblad superoperators found recently can be used to build integrable nonunitary quantum circuits with two-site gates by demonstrating that the $\check{R}$-matrices are completely positive and trace preserving. Using the bond-site transformation, we obtain the corresponding integrable nonunitary quantum circuits with three-site g

Ryoki Hamano, Shota Saito, Masahiro Nomura, Shinichi Shirakawa

This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in this study, is a population-based stochastic search method that samples solution candidates from a multivariate Gaussian distribution (MGD), which shows excellent performance in conti

Vincent Jeanselme, Glen Martin, Niels Peek, Matthew Sperrin

Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself constitutes an informative process. When using such data to develop prediction models, this phenomenon is often ignored, leading to sub-optimal performance and generalisability of mo

Roberto Salazar, Jakub Czartowski, A. de Oliveira Junior

A crucial goal of quantum information is to find new ways to exploit the properties of quantum devices as resources. One of the prominent properties of quantum devices of particular interest is their negativity in quasi-probability representations, intensively studied in foundational and practical investigations. In this article, we introduce the concept of

Ivan Marisca, Andrea Cini, Cesare Alippi

Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an effective representational framework that allows for developing models for time series analysis. In fact, discrete sequences of graphs can be processed by autoregressive graph neural networks to recursively learn representations at each discrete point in time and spac

Lili Manzo, Matthew R. Edwards, Yuan Shi

Strongly magnetizing a plasma adds a range of waves that do not exist in unmagnetized plasmas and enlarges the laser-plasma interaction (LPI) landscape. In this paper, we use particle-in-cell (PIC) simulations to investigate strongly magnetized LPI in one dimension under conditions relevant for magneto-inertial fusion experiments, focusing on a regime where

The LHCb VELO group

The thermal properties of the LHCb Vertex Locator (VELO) are studied using the real-time detector alignment procedure. The variation of the position and orientation of the detector elements as a function of the operating temperature of the VELO is presented. This study uses a dataset collected by the LHCb experiment during a VELO temperature scan performed a

Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang

Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges. (i) It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon. (ii) The observation and state spaces are often continuous, which induces a sample complexity that scales exponentially w

Yuxiang Guo, Xueye Hu, Thomas Schwarz, Bing Zhou

New front-end electronics including ASICs and FPGA boards are under development for the ATLAS Monitored Drift Tube (MDT) detector to handle the large data rates and harsh environment expected at high-luminosity LHC runs. A mobile Data Acquisition (miniDAQ) system is designed to perform integration tests of these front-end electronics. In addition, it will be

Mireille El-Assal, Pierre Tirilly, Ioan Marius Bilasco

Current advances in technology have highlighted the importance of video analysis in the domain of computer vision. However, video analysis has considerably high computational costs with traditional artificial neural networks (ANNs). Spiking neural networks (SNNs) are third generation biologically plausible models that process the information in the form of s

Alejandro M. Yacomotti, Zakari Denis, Alberto Biella, Cristiano Ciuti

Despite the enormous technological interest in micro and nanolasers, surprisingly, no class-B quantum density-matrix model is available to date, capable of accurately describing coherence and photon correlations within a unified theory. In class-B lasers $-$applicable for most solid-state lasers at room temperature$-$, the macroscopic polarization decay rate

Géza Ódor, Shengfeng Deng, Bálint Hartmann, Jeffrey Kelling

Dynamical simulation of the cascade failures on the EU and USA high-voltage power grids has been done via solving the second-order Kuramoto equation. We show that synchronization transition happens by increasing the global coupling parameter $K$ with metasatble states depending on the initial conditions so that hysteresis loops occur. We provide analytic res

Alessandra Belfiore, Angelo Salatino, Francesco Osborne

Interest in Artificial Intelligence (AI) continues to grow rapidly, hence it is crucial to support researchers and organisations in understanding where AI research is heading. In this study, we conducted a bibliometric analysis on 257K articles in AI, retrieved from OpenAlex. We identified the main conceptual themes by performing clustering analysis on the c

David Gelbwaser-Klimovsky, Noah Graham, Mehran Kardar, Matthias Krüger

We discuss and analyze the properties of Casimir forces acting between nonreciprocal objects in thermal equilibrium. By starting from the fluctuation-dissipation theorem and splitting the force into those arising from individual sources, we show that if all temperatures are equal, the resulting force is reciprocal and is derivable as the gradient of a Casimi

Cormac O'Sullivan

Here we examine the number of ways to partition an integer $n$ into $k$th powers when $n$ is large. Simplified proofs of some asymptotic results of Wright are given using the saddle-point method, including exact formulas for the expansion coefficients. The convexity and log-concavity of these partitions is shown for large $n$, and the stronger conjectures of

Ankit Kumar

One of the unitary forms of the quantum mechanical time evolution operator is given by Cayley's approximation. A numerical implementation of the same involves the replacement of second derivatives in Hamiltonian with the three-point formula, which leads to a tridiagonal system of linear equations. In this work, we invoke the highly accurate five-point stenci

Friederike Dittberner

We consider closed, embedded, smooth curves in the plane and study their behaviour under curve flows with a global forcing term. We prove an analogue to Huisken's distance comparison principle for curve shortening flow for initial curves whose local total curvature does not lie below -pi and arbitrary global forcing terms.

Namrata Anand, Tudor Achim

Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properties which enable targeted functions. To this end, we introduce a generative model of both protein structure and sequence that can operate at sig

Dustin D. Nguyen, Todd A. Thompson

Galactic outflows from local starburst galaxies typically exhibit a layered geometry, with cool $10^4\,$K flow sheathing a hotter $10^7\,$K, cylindrically-collimated, X-ray emitting plasma. Here, we argue that winds driven by energy-injection in a ring-like geometry can produce this distinctive large-scale multi-phase morphology. The ring configuration is mo

Nigel P. Byott

A question of interest both in Hopf-Galois theory and in the theory of skew braces is whether the holomorph $\mathrm{Hol(N)}$ of a finite soluble group $N$ can contain an insoluble regular subgroup. We investigate the more general problem of finding an insoluble transitive subgroup $G$ in $\mathrm{Hol}(N)$ with soluble point stabilisers. We call such a pair

Alexander Sakhnovich

In this paper, we consider matrix Schr\"odinger equation, dynamical Schr\"odinger equation and matrix KdV. We construct their explicit solutions using our GBDT version of B\"acklund--Darboux transformation and square roots of the generalised matrix eigenvalues. A separate section is dedicated to several examples including the case of strongly singular potent

Yongxin Guo, Xiaoying Tang, Tao Lin

Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which helps to improve communication efficiency. However, such a scheme is currently constrained by slow and unstable convergence due to the variety of data on different clients' devices. I

Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius

We study a communication game between a sender and a receiver. The sender chooses one of her signals about the state of the world (i.e., anecdotes) and communicates to the receiver who takes an action affecting both players. The sender and the receiver both care about the state of the world but are also influenced by personal preferences, so their ideal acti

Brenna Knaebe, Claudia C. Weiss, Jan Zimmermann, Benjamin Y. Hayden

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfar

Stefano Fiorini, Stefano Coniglio, Michele Ciavotta, Enza Messina

This paper introduces SigMaNet, a generalized Graph Convolutional Network (GCN) capable of handling both undirected and directed graphs with weights not restricted in sign nor magnitude. The cornerstone of SigMaNet is the Sign-Magnetic Laplacian ($L^{\sigma}$), a new Laplacian matrix that we introduce ex novo in this work. $L^{\sigma}$ allows us to bridge a

Giuseppe Gaetano Luciano, Yang Liu

We consider a correspondence between the tachyon dark energy model and Barrow holographic dark energy (BHDE). The latter is a modified scenario based on the application of the holographic principle with Barrow entropy instead of the usual Bekenstein-Hawking one. We reconstruct the dynamics of the tachyon scalar field $T$ in a curved Friedmann-Robertson-Walke

Manish Shetty, Chetan Bansal, Sai Pramod Upadhyayula, Arjun Radhakrishna

Incident management is a key aspect of operating large-scale cloud services. To aid with faster and efficient resolution of incidents, engineering teams document frequent troubleshooting steps in the form of Troubleshooting Guides (TSGs), to be used by on-call engineers (OCEs). However, TSGs are siloed, unstructured, and often incomplete, requiring developer

Julian Klein, Benjamin Pingault, Matthias Florian, Marie-Christin Heißenbüttel

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional

Andrzej Grzesik, Ervin Győri, Nika Salia, Casey Tompkins

In this paper we disprove a conjecture of Lidick\'y and Murphy about the number of copies of a given graph in a $K_r$-free graph and give an alternative general conjecture. We also prove an asymptotically tight bound on the number of copies of any bipartite graph of radius at most $2$ in a triangle-free graph.

Daniel Bultrini, Samson Wang, Piotr Czarnik, Max Hunter Gordon

When error correction becomes possible it will be necessary to dedicate a large number of physical qubits to each logical qubit. Error correction allows for deeper circuits to be run, but each additional physical qubit can potentially contribute an exponential increase in computational space, so there is a trade-off between using qubits for error correction

Paulraj Gnanasekar, Karthik Peramaiy, Huafan Zhang, Tien Khee Ng

Photoelectrocatalytic (PEC) reduction of N2 to ammonia (NH3) is emerging as the potential alternative to overcome the standard Haber-Bosch approach. In this communication, solar N2 reduction was demonstrated with molybdenum carbide (Mo2C) co-catalyst assisted indium gallium nitride (InGaN) nanowires. The effect of aiding Mo2C on InGaN NWs arrests the dark cu

Matthias De Lange, Gido van de Ven, Tinne Tuytelaars

Time-dependent data-generating distributions have proven to be difficult for gradient-based training of neural networks, as the greedy updates result in catastrophic forgetting of previously learned knowledge. Despite the progress in the field of continual learning to overcome this forgetting, we show that a set of common state-of-the-art methods still suffe

Yan Dai, Haipeng Luo, Liyu Chen

We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visited state-action pairs (i.e., bandit feedback). While there has been a surge of studies on this problem using Online-Mirror-Descent (OMD) methods, ver

Yan Dai, Ruosong Wang, Simon S. Du

It is well-known that for sparse linear bandits, when ignoring the dependency on sparsity which is much smaller than the ambient dimension, the worst-case minimax regret is $\widetilde{\Theta}\left(\sqrt{dT}\right)$ where $d$ is the ambient dimension and $T$ is the number of rounds. On the other hand, in the benign setting where there is no noise and the act

K. S. Abdulkhaev, D. S. Shirokov

In this paper, we discuss characteristic polynomials in (Clifford) geometric algebras ${\mathcal {G}}_{p,q}$ of vector space of dimension $n=p+q$. We present basis-free formulas for all characteristic polynomial coefficients in the cases $n\leq 6$, alongside with a method to obtain general form of these formulas. The formulas involve only the operations of g

Antonio Fernández Anta, Chryssis Georgiou, Nicolas Nicolaou, Antonio Russo

Guaranteeing the validity of concurrent operations on distributed objects is a key property for ensuring reliability and consistency in distributed systems. Usually, the methods for validating these operations, if present, are wired in the object implementation. In this work, we formalize the notion of a {\em validated object}, decoupling the object operatio

Y. Liu, J. Li, X. H. Tang

In the literature, most of the known high-rate $(n,k)$ MDS array codes with the optimal repair property only support a single repair degree (i.e., the number of helper nodes contacted during a repair process) $d$, where $k\le d\le n-1$. However, in practical storage systems, the number of available nodes changes frequently. Thus, it is preferred to construct

Xiaoan Lang, Jeremy Rouse

We study the problem of determining, given an integer $k$, the rational solutions to $C_{k} : x^{3}z + x^{2} y^{2} + y^{3}z = kz^{4}$. For $k \ne 0$, the curve $C_{k}$ has genus $3$ and there are maps from $C_{k}$ to three elliptic curves $E_{1,k}$, $E_{2,k}$, $E_{3,k}$. We explicitly determine the rational points on $C_{k}$ under the assumption that one of

Lingfeng Tao, Jiucai Zhang, Xiaoli Zhang

Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task. Varying priority makes a robot hardly or even failed to learn an optimal policy with a deep reinforcement learning (DRL) method. To solve this problem, we develop a novel Adaptive Hierarchical Reward Mecha

Daniela di Serafino, Monica Pragliola

We address the image restoration problem under Poisson noise corruption. The Kullback-Leibler divergence, which is typically adopted in the variational framework as data fidelity term in this case, is coupled with the second-order Total Generalized Variation (TGV$^2$). The TGV$^2$ regularizer is known to be capable of preserving both smooth and piece-wise co

Anthony Mezzacappa

Motivated by their role as the direct or indirect source of many of the elements in the Universe, numerical modeling of core collapse supernovae began more than five decades ago. Progress toward ascertaining the explosion mechanism(s) has been realized through increasingly sophisticated models, as physics and dimensionality have been added, as physics and nu

Kai Hugtenburg

This paper considers the (negative) cyclic open-closed map $\mathcal{OC}^{-}$, which maps the cyclic homology of the Fukaya category of a symplectic manifold to its $S^1$-equivariant quantum cohomology. We prove (under simplifying technical hypotheses) that this map respects the respective natural connections in the direction of the equivariant parameter. In

Nguyen Hong Son, Hieu M. Vu, Tuan-Anh D. Nguyen, Minh-Tien Nguyen

This paper introduces a new information extraction model for business documents. Different from prior studies which only base on span extraction or sequence labeling, the model takes into account advantage of both span extraction and sequence labeling. The combination allows the model to deal with long documents with sparse information (the small amount of e

Pablo A. Arrutia Sota, Philip N. Burrows, Matthew A. Fraser, Francesco M. Velotti

FLASH radiation therapy calls for the delivery of fast bursted spills of particles with dose delivery times of the order of milliseconds. The requirements overlap with fundamental physics experimental requests that are being studied at CERN, albeit at very different energy scales. In this contribution, a scheme for extracting millisecond bursts from synchrot

Bohao Yao, Robin J. Evans

In this paper, we introduce a new identifiability criteria for linear structural equation models, which we call regression identifiability. We provide necessary and sufficient graphical conditions for a directed edge to be regression identifiable. Suppose $\Sigma^*$ corresponds to the covariance matrix of the graphical model $G^*$ obtained by performing an e

Ben Grinboim, Itay Shrem, Ofer Amrani

Increasing network utilization is often considered as the holy grail of communications. In this article, the concept of sub-rate coding and decoding in the framework of linear network coding (LNC) is discussed for single-source multiple-sinks finite acyclic networks. Sub-rate coding offers an add-on to existing LNC. It allows sinks whose max-flow is smaller

Ian Frederick Vigogne Goodbody Hunter

GNOLL ("GNOLL's Not *OLL") is a software library for dice notation. Unlike previous papers, GNOLL's dice notation syntax is focused on parsing a language that tabletop role-players and board gamers are already used to for specifying dice rolls in many popular software applications. Existing implementations of such a syntax are either incomplete, fragile, or

Francesco Giovanni Celiberto, Michael Fucilla, Mohammed M. A. Mohammed, Alessandro Papa

We investigate the inclusive emission in unpolarized proton collisions of a charm-flavored hadron in association with a Higgs boson, featuring large transverse momenta and produced with a large rapidity distance. Taking advantage of a narrow timing coincidence between the ATLAS detector and the future FPF ones, we study the behavior of cross sections and azi

Meena Mahajan, Gaurav Sood

The Merge Resolution proof system (M-Res) for QBFs, proposed by Beyersdorff et al. in 2019, explicitly builds partial strategies inside refutations. The original motivation for this approach was to overcome the limitations encountered in long-distance Q-Resolution proof system (LD-Q-Res), where the syntactic side-conditions, while prohibiting all unsound res

Po Hu, Igor Kriz, Petr Somberg, Foling Zou

We completely calculate the $RO(\mathbb{Z}/p)$-graded coefficients $H\underline{\mathbb{Z}/p}_\star H\underline{\mathbb{Z}/p}$ for the constant Mackey functor $\underline{\mathbb{Z}/p}$.

Tianyi Chen, Charalampos E. Tsourakakis

Benford's law describes the distribution of the first digit of numbers appearing in a wide variety of numerical data, including tax records, and election outcomes, and has been used to raise "red flags" about potential anomalies in the data such as tax evasion. In this work, we ask the following novel question: given a large transaction or financial graph, h

Dazhao Du, Bing Su, Yu Li, Zhongang Qi

Action classification has made great progress, but segmenting and recognizing actions from long untrimmed videos remains a challenging problem. Most state-of-the-art methods focus on designing temporal convolution-based models, but the inflexibility of temporal convolutions and the difficulties in modeling long-term temporal dependencies restrict the potenti

José F. Alves, Wael Bahsoun, Marks Ruziboev, Paulo Varandas

In this article we study random tower maps driven by an ergodic automorphism. We prove quenched exponential correlations decay for tower maps admitting exponential tails. Our technique is based on constructing suitable cones of functions, defined on the random towers, which contract with respect to the Hilbert metric under the action of appropriate transfer

Fengpei Li, Vitalii Ihnatiuk, Ryan Kinnear, Anderson Schneider

Market impact is an important problem faced by large institutional investor and active market participant. In this paper, we rigorously investigate whether price trajectory data from the metaorder increases the efficiency of estimation, from an asymptotic view of statistical estimation. We show that, for popular market impact models, estimation methods based

Kiril Danilchenko, Michael Segal, Dan Vilenchik

E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automati

Rongguang Wang, Pratik Chaudhari, Christos Davatzikos

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evide

Yiyue Zhao, Xinyu Yun, Chen Chai, Zhiyu Liu

The complex driving environment brings great challenges to the visual perception of autonomous vehicles. It's essential to extract clear and explainable information from the complex road and traffic scenarios and offer clues to decision and control. However, the previous scene explanation had been implemented as a separate model. The black box model makes it

Armin Khamoshi, Guo P. Chen, Francesco A. Evangelista, Gustavo E. Scuseria

Electronic structure methods typically benefit from symmetry breaking and restoration, specially in the strong correlation regime. The same goes for Ans\"atze on a quantum computer. We develop a unitary coupled cluster method on the antisymmetrized geminal power (AGP) -- a state formally equivalent to the number-projected Bardeen--Cooper--Schrieffer wavefunc

Erika Alves dos Santos, Silvio Peroni, Marcos Luiz Mucheroni

Current citation practices observed in articles are very noisy, confusing, and not standardised, making identifying the cited works problematic for hu-mans and any reference extraction software. In this work, we want to investigate such citation practices for referencing different types of entities and, in particular, to understand the most used metadata in

Lucas Friedrich, Jonas Maziero

Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. Such algorithms are constructed using a parameterization U($\pmb{\theta}$) with a classical optimizer that updates the parameters $\pmb{\theta}$ in order to minimize a cost function $C$. For this task, in general the gradient

Y. H. Song, Xin Wang, H. D. Liu, X. X. Yi

In this work, we study the counterdiabatic driving scheme in pseudo- and antipseudo- Hermitian systems. By discussing the adiabatic condition for non-Hermitian system, we show that the adiabatic evolution of state can only be realized in the non-Hermitian system which possesses real energy spectrum. Therefore, the counterdiabatic driving scheme to reproduce

Yaqi Sun, Shijing Si, Jianzong Wang, Yuhan Dong

Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally stored. However, heterogeneous data distributions over different clients remain a challenge to mainstream FL algorithms, which may cause slow convergence, overall performance degradation an

Liang Li, Min Hu, Changlong Hu, Bowen Li

The mass storage and removal in solid conductors always played vital role on the technological applications such as modern batteries, permeation membranes and neuronal computations, which were seriously lying on the ion diffusion and kinetics in bulk lattice. However, the ions transport was kinetically limited by the low diffusional process, which made it a

Yanjie Li, Yiquan Li, Xuelong Dai, Songtao Guo

2D face recognition has been proven insecure for physical adversarial attacks. However, few studies have investigated the possibility of attacking real-world 3D face recognition systems. 3D-printed attacks recently proposed cannot generate adversarial points in the air. In this paper, we attack 3D face recognition systems through elaborate optical noises. We

Suraj Pawar, Omer San

There is a growing interest in developing data-driven reduced-order models for atmospheric and oceanic flows that are trained on data obtained either from high-resolution simulations or satellite observations. The data-driven models are non-intrusive in nature and offer significant computational savings compared to large-scale numerical models. These low-dim

Pavel Osinenko, Grigory Yaremenko, Georgiy Malaniya

Control Lyapunov function is a central tool in stabilization. It generalizes an abstract energy function -- a Lyapunov function -- to the case of controlled systems. It is a known fact that most control Lyapunov functions are non-smooth -- so is the case in non-holonomic systems, like wheeled robots and cars. Frameworks for stabilization using non-smooth con

Magdalena Larfors, Andre Lukas, Fabian Ruehle, Robin Schneider

We introduce neural networks to compute numerical Ricci-flat CY metrics for complete intersection and Kreuzer-Skarke Calabi-Yau manifolds at any point in K\"ahler and complex structure moduli space, and introduce the package cymetric which provides computation realizations of these techniques. In particular, we develop and computationally realize methods for

Hussam Al Daas, Grey Ballard, Laura Grigori, Suraj Kumar

Communication lower bounds have long been established for matrix multiplication algorithms. However, most methods of asymptotic analysis have either ignored the constant factors or not obtained the tightest possible values. Recent work has demonstrated that more careful analysis improves the best known constants for some classical matrix multiplication lower

Calvin Hawkins, Matthew Hale

Privacy in multi-agent control is receiving increased attention, though often a networked system and privacy protections are designed separately, which can harm performance. Therefore, this paper presents a co-design framework for networks and private controllers, and we apply it to private formation control. Agents' state trajectories are protected using di

Luis A. Zapata, Laurent Loinard, Manuel Fernández-López, Jesús A. Toalá

The nature and origin of the molecular gas component located in the circumstellar vicinity of $\eta$ Carinae are still far from being completely understood. Here, we present Atacama Large Millimeter/Submillimeter Array (ALMA) CO(3$-$2) observations with a high angular resolution ($\sim$0.15$''$), and a great sensitivity that are employed to reveal the origin

Yuzhou Tao, Benrong Mu, Siyuan Hui, Jun Tao

Doubly special relativity (DSR) is an effective model for encoding quantum gravity in flat spacetime. To incorporate DSR into general relativity, one could use gravity's rainbow, where the spacetime background felt by a test particle would depend on its energy. In this paper, we investigate the thermodynamics of rainbow Kerr black hole in the scenario with t

Chenchen Mou, Jianfeng Zhang

The theory of Mean Field Game of Controls considers a class of mean field games where the interaction is through the joint distribution of the state and control. It is well known that, for standard mean field games, certain monotonicity condition is crucial to guarantee the uniqueness of mean field equilibria and then the global wellposedness for master equa

Kuan Zhang, Xiangdong Ji, Yi-Bo Yang, Fei Yao

To calculate the transverse-momentum-dependent parton distribution functions (TMDPDFs) from lattice QCD, an important goal yet to be realized, it is crucial to establish a viable non-perturbative renormalization approach for linear divergences in the corresponding Euclidean quasi-TMDPDF correlators in large-momentum effective theory. We perform a first syste

Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu

Relative Positional Encoding (RPE), which encodes the relative distance between any pair of tokens, is one of the most successful modifications to the original Transformer. As far as we know, theoretical understanding of the RPE-based Transformers is largely unexplored. In this work, we mathematically analyze the power of RPE-based Transformers regarding whe

R. Calabrese, M. Fiorini, E. Luppi, L. Minzoni

The performance of the ring-imaging Cherenkov detectors at the LHCb experiment is determined during the LHC Run 2 period between 2015 and 2018. The stability of the Cherenkov angle resolution and number of detected photons with time and running conditions is measured. The particle identification performance is evaluated with data and found to satisfy the req

Mayowa Ayodele, Richard Allmendinger, Manuel López-Ibáñez, Matthieu Parizy

Quantum and quantum-inspired optimisation algorithms are designed to solve problems represented in binary, quadratic and unconstrained form. Combinatorial optimisation problems are therefore often formulated as Quadratic Unconstrained Binary Optimisation Problems (QUBO) to solve them with these algorithms. Moreover, these QUBO solvers are often implemented u

Dimitris Spathis, Stephanie L. Hyland

Clinical machine learning models show a significant performance drop when tested in settings not seen during training. Domain generalisation models promise to alleviate this problem, however, there is still scepticism about whether they improve over traditional training. In this work, we take a principled approach to identifying Out of Distribution (OoD) env

Bingrui Peng, Hongming Weng, Chen Fang

We present a unified framework to construct and classify topological crystalline superconductors (TCSCs). The building blocks are one-dimensional topological superconductors (TSCs) protected solely by onsite symmetries, which are arranged and glued by crystalline symmetries in real space. We call this real-space scheme "wire construction", and we show its pr

Muhammad Nadeem, Xiaolin Wang

The existence of chiral edge states, corresponding to the nontrivial bulk-band topology characterized by a non-vanishing topological invariant, and the manipulation of topological transport via chiral edge states promise topological electronic/spintronic device applications. Here we predict the existence, practical realization, topological protection, and to

D. M. Gerontogiannis, Michael F. Whittaker, Joachim Zacharias

A fundamental ingredient in the noncommutative geometry program is the notion of KK-duality, often called K-theoretic Poincar\'{e} duality, that generalises Spanier-Whitehead duality. In this paper we construct a $\theta$-summable Fredholm module that represents the fundamental class in KK-duality between the stable and unstable Ruelle algebras of a Smale sp

Marios Papachristou, Siddhartha Banerjee, Jon Kleinberg

We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities and use this to study the design of external intervention policies. Our controller has a fixed resource budget in each round and

Amritpal Singh

Robotic surgery has increased the domain of surgeries possible. Several examples of partial surgical automation have been seen in the past decade. We break down the path of automation tasks into features required and provide a checklist that can help reach higher levels of surgical automation. Finally, we discuss the current challenges and advances required

Dandan Fan, Xueyi Huang, Huiqiu Lin

Over the past half century, the rigidity of graphs in $R^2$ has aroused a great deal of interest. Lov\'{a}sz and Yemini (1982) proved that every $6$-connected graph is rigid in $R^2$. Jackson and Jord\'{a}n (2005) provided a similar vertex-connectivity condition for the globally rigidity of graphs in $R^2$. These results imply that a graph $G$ with algebraic

Michael D. Himes, Joseph Harrington

The atmospheric structure of WASP-12b has been hotly contested for years, with disagreements on the presence of a thermal inversion as well as the carbon-to-oxygen ratio, C/O, due to retrieved abundances of H2O, CO2, and other included species such as HCN and C2H2. Previously, these difficult-to-diagnose discrepancies have been attributed to model difference

Caludio Mannini, Tommaso Massai, Enrico Panettieri, Niccolò Barni

The paper concerns an experimental study on the wind pressures over the surface of a worldwide known Gothic Cathedral: Notre Dame of Paris. The experimental tests have been conducted in the CRIACIV wind tunnel, Prato (Italy), on a model of the Cathedral at the scale 1:200 reproducing the atmospheric boundary layer. Two types of tests have been conducted: wit

Mario Ferraro, Fabio Mangini, Yann Leventoux, Alessandro Tonello

We introduce and experimentally demonstrate the concept of all-optical beam switching in graded-index multimode optical fibers. Nonlinear coupling between orthogonally polarized seed and signal beams permits to control the spatial beam quality at the fiber output. Remarkably, we show that even a weak few-mode control beam may substantially enhance the qualit