May 2022 arXiv papers — page 91

Zongyao Lyu, Nolan B. Gutierrez, William J. Beksi

Despite achieving enormous success in predictive accuracy for visual classification problems, deep neural networks (DNNs) suffer from providing overconfident probabilities on out-of-distribution (OOD) data. Yet, accurate uncertainty estimation is crucial for safe and reliable robot autonomy. In this paper, we evaluate popular calibration techniques for open-

Hashim A. Hashim

There is a great demand for vision-based robotics solutions that can operate using Global Positioning Systems (GPS), but are also robust against GPS signal loss and gyroscope failure. This paper investigates the estimation and tracking control in application to a Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) in six degrees of freedom (6

Yubo Tong

In this paper, we study the eigensubspace of the space of the holomorphic differentials of nodal curves over the algebracally closed field under the action of finite automorphism groups. We compute the Chevalley- Weil formula with some additional contidions of the quotient curve and give some examples.

Peter Bradshaw

The DP-coloring problem is a generalization of the list-coloring problem in which the goal is to find an independent transversal in a certain topological cover of a graph $G$. In the online DP-coloring problem, the cover of $G$ is revealed one component at a time, and the independent transversal of the cover must be constructed in parts based on incomplete i

Sara Kacmoli, Deborah L. Sivco, Claire F. Gmachl

Ideal ring resonators are characterized by travelling-wave counterpropagating modes, but in practice travelling waves can only be realized under unidirectional operation, which has proved elusive. Here, we have designed and fabricated a monolithic quantum cascade ring laser coupled to an active waveguide that allows for robust, deterministic and controllable

Jia-Li, Hu, Tian-Yu Ye

In this paper, a three-party secure semiquantum summation protocol, which can calculate the modulo 2 addition of the private bits from one quantum participant and two classical participants, is constructed by only using single qubits as the initial quantum resource. This protocol needs none of quantum entanglement swapping, the unitary operation or a pre-sha

Tian-Yu Ye, Tian-Jie Xu, Mao-Jie Geng, Ying Chen

In this paper, we propose a two-party semiquantum summation protocol, where two classical users can accomplish the summation of their private binary sequences with the assistance of a quantum semi-honest third party (TP). The term 'semi-honest' implies that TP cannot conspire with others but is able to implement all kinds oof attacks. This protocol employs l

Thibaud Taillefumier, Phillip Whitman

Idealized networks of integrate-and-fire neurons with impulse-like interactions obey McKean-Vlasov diffusion equations in the mean-field limit. These equations are prone to blowups: for a strong enough interaction coupling, the mean-field rate of interaction diverges in finite time with a finite fraction of neurons spiking simultaneously, thereby marking a m

Abhijit Suprem, Calton Pu

The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously; this can lead to performance degradation of fine-tuned models due to concept drift. Degredation can be mitigated if models generalize well-enough to capture s

Saurya Das, Sourav Sur

We show that Newton's gravitational potential, augmented by a logarithmic term, partly or wholly mitigates the need for dark matter. As a bonus, it also explains why MOND seems to work at galactic scales. We speculate on the origin of such a potential.

Tian-Yu Ye, Mao-Jie Geng, Tian-Jie Xu, Ying Chen

In this paper, a novel information leakage resistant quantum dialogue (QD) protocol with single photons in both polarization and spatial-mode degrees of freedom is proposed, which utilizes quantum encryption technology to overcome the information leakage problem. In the proposed QD protocol, during the transmission process, the single photons in both polariz

Gene Abrams, Efren Ruiz, Mark Tomforde

The classical Morita Theorem for rings established the equivalence of three statements, involving categorical equivalences, isomorphisms between corners of finite matrix rings, and bimodule homomorphisms. A fourth equivalent statement (established later) involves an isomorphism between infinite matrix rings. In our main result, we establish the equivalence o

A. C. Flores-Ortega, J. R. Nicolás-Carlock, J. L. Carrillo-Estrada

The functional features of spatial networks depend upon a non-trivial relationship between the topological and physical structure. Here, we explore that relationship for spatial networks with radial symmetry and disordered fractal morphology. Under a geometric graphs approach, we quantify the effectiveness of the exchange of information in the system from ce

Ahmed Farid, Ahmed Samy, Ahmed Shalaby, Ahmed Tarek

Realizing functional space systems using flight-tested components is problematic in developing economies, as such components are costly for most institutions to sponsor. The B.Sc. project, Subsystems for 2nd Iteration Cairo University Cube-Satellite, addresses technology demonstration using commercially available electronics and low cost computing platforms,

Yanran Wang, James O'Keeffe, Qiuchen Qian, David Boyle

This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforcement Learning (RL) estimator for unknown aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for trajectory tracking. Aerodynamic effects derived from drag for

Zishen Wan, Ashwin Lele, Bo Yu, Shaoshan Liu

Robotic computing has reached a tipping point, with a myriad of robots (e.g., drones, self-driving cars, logistic robots) being widely applied in diverse scenarios. The continuous proliferation of robotics, however, critically depends on efficient computing substrates, driven by real-time requirements, robotic size-weight-and-power constraints, cybersecurity

L. Ake Hau, Saul Burgos, Didier A. Solis

In this work we revisit the notion of the (future) causal completion of a globally hyperbolic spacetime and endow it with the structure of a Lorentzian pre-length space. We further carry out this construction for a certain class of generalized Robertson-Walker spacetimes.

Sarah Chasins, Alvin Cheung, Natacha Crooks, Ali Ghodsi

Technology ecosystems often undergo significant transformations as they mature. For example, telephony, the Internet, and PCs all started with a single provider, but in the United States each is now served by a competitive market that uses comprehensive and universal technology standards to provide compatibility. This white paper presents our view on how the

Lénaïc Chizat, Stephen Zhang, Matthieu Heitz, Geoffrey Schiebinger

Trajectory inference aims at recovering the dynamics of a population from snapshots of its temporal marginals. To solve this task, a min-entropy estimator relative to the Wiener measure in path space was introduced by Lavenant et al. arXiv:2102.09204, and shown to consistently recover the dynamics of a large class of drift-diffusion processes from the soluti

Daiki Kido, Hiromitsu Uehara, Satoru Takakusagi, Jun-ya Hasegawa

We have determined the local structure of photoexcited metastable WO$_3$ created 150 ps after 400 nm laser irradiation by the pulse pump--probe L$_3$-edge extended X-ray absorption fine structure spectroscopy and the constrained thorough search analysis. We have found a highly distorted octahedral local structure with one of the shortest W=O bonds being furt

Mengchu Li, Thomas B. Berrett, Yi Yu

Network data are ubiquitous in our daily life, containing rich but often sensitive information. In this paper, we expand the current static analysis of privatised networks to a dynamic framework by considering a sequence of networks with potential change points. We investigate the fundamental limits in consistently localising change points under both node an

Arthur Vesperini, Ghofrane Bel-Hadj-Aissa, Roberto Franzosi

Entanglement, and quantum correlation, are precious resources for quantum technologies implementation based on quantum information science, such as, for instance, quantum communication, quantum computing, and quantum interferometry. Nevertheless, to our best knowledge, a directly computable measure for the entanglement of multipartite mixed-states is still l

Flavio Galvao Calhau, Alysson Pezzutti, Joberto S. B. Martins

This paper presents the power network reconfiguration algorithm HATSGA with a "R" modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modeling of the power distribution network with the language "R" is used to represent the network and support the computation of distinc

Zeki Hayran, Francesco Monticone

Time-varying systems open intriguing opportunities to explore novel approaches in the design of efficient electromagnetic devices. While such explorations date back to more than half a century ago, recent years have experienced a renewed and increased interest into the design of dynamic electromagnetic systems. This resurgence has been partly fueled by the d

Wenzhe Guo, Mohammed E Fouda, Ahmed M. Eltawil, Khaled N. Salama

Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost and prohibiting model parallelization. Existing local training methods aim to resolve the training obstacle by com

Ran Tamir, Neri Merhav

We derive various error exponents for communication channels with random states, which are available non-causally at the encoder only. For both the finite-alphabet Gel'fand-Pinsker channel and its Gaussian counterpart, the dirty-paper channel, we derive random coding exponents, error exponents of the typical random codes (TRCs), and error exponents of expurg

Constantin Seibold, Simon Reiß, M. Saquib Sarfraz, Rainer Stiefelhagen

When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies

Dimitar Grantcharov, Ivan Penkov, Vera Serganova

We classify simple bounded weight modules over the complex simple Lie superalgebras $\mathfrak{sl}(\infty |\infty)$ and $\mathfrak{osp} (m | 2n)$, when at least one of $m$ and $n$ equals $\infty$. For $\mathfrak{osp} (m | 2n)$ such modules are of spinor-oscillator type, i.e., they combine into one the known classes of spinor $\mathfrak{o} (m)$-modules and os

Xiaolei Zhang

In this paper, we introduce and study the notions of uniformly $S$-finitely presented modules and uniformly $S$-coherent rings (modules) which are "uniform" versions of ($c$-)$S$-finitely presented modules and ($c$-)$S$-coherent rings (modules) introduced by Bennis and Hajoui \cite{bh18}. Among the results, uniformly $S$-versions of Chase's result, Chase The

Tim Gould, Derk P. Kooi, Paola Gori-Giorgi, Stefano Pittalis

Density functional theory (DFT) has greatly expanded our ability to affordably compute and understand electronic ground states, by replacing intractable {\em ab initio} calculations by models based on paradigmatic physics from high- and low-density limits. But, a comparable treatment of excited states lags behind. Here, we solve this outstanding problem by e

Benjamin Kadoch, Diego del-Castillo-Negrete, Wouter J. T. Bos, Kai Schneider

Lagrangian statistics and particle transport in edge plasma turbulence are investigated using the Hasegawa-Wakatani model and its modified version. The latter shows the emergence of pronounced zonal flows. Different values of the adiabaticity parameter are considered. The main goal is to characterize the role of coherent structures, i.e., vortices and zonal

Shuming Liu, Mengmeng Xu, Chen Zhao, Xu Zhao

Temporal action detection (TAD) with end-to-end training often suffers from the pain of huge demand for computing resources due to long video duration. In this work, we propose an efficient temporal action detector (ETAD) that can train directly from video frames with extremely low GPU memory consumption. Our main idea is to minimize and balance the heavy co

Peter V. Gordon, Fedor Nazarov, Yuval Peres

We consider a boundary value problem for the $p$-Laplacian, posed in the exterior of small cavities that all have the same $p$-capacity and are anchored to the unit sphere in $\mathbb{R}^d$, where $1<p<d.$ We assume that the distance between anchoring points is at least $\varepsilon$ and the characteristic diameter of cavities is $\alpha \varepsilon$, where

Tuncer Kaya

Recently we have performed large-scale average magnetization studies of the Ising model for the square, honeycomb, triangular, and simple cubic lattice. We want to complement those studies with the structurally more complicated body-centered cubic lattice Ising model in this paper. We have relevantly calculated the order parameter or average magnetization ex

Xin Du

The heterogeneous edge-cloud computing paradigm can provide an optimal solution to deploy scientific workflows compared to cloud computing or other traditional distributed computing environments. Owing to the different sizes of scientific datasets and the privacy issue concerning some of these datasets, it is essential to find a data placement strategy that

J. L. Gutiérrez-Santiago, G. López-Castro

We evaluate the contributions of $(\pi^0\pi^0, \pi^0\eta, \eta\eta)\gamma$ exclusive channels to the dispersion integral of the leading order HVP of the muon anomalous magnetic moment. These channels are included in some way in previous evaluations of the $\pi^0\omega, \eta\omega$ and $\eta\phi$ contributions to $a_{\mu}^{\rm had, LO}$, where the vector reso

Alice Tarzariol, Martin Gebser, Mark Law, Konstantin Schekotihin

Many industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily-solvable instances is

Matteo Escudé, Paula Onuchic, Ludvig Sinander, Quitzé Valenzuela-Stookey

We study the link between Phelps-Aigner-Cain-type statistical discrimination and familiar notions of statistical informativeness. Our central insight is that Blackwell's Theorem, suitably relabeled, characterizes statistical discrimination in terms of statistical informativeness. This delivers one-half of Chambers and Echenique's (2021) characterization of s

Tuncer Kaya

In this work, the susceptibility of the square lattice Ising model is investigated using the recently obtained average magnetization interrelation, which is given by $\langle\sigma_{0, i}\rangle= \langle\tanh[K(\sigma_{1,i}+\sigma_{2,i}+\dots +\sigma_{z,i})+H]\rangle $. Here, $z$ is the number of nearest neighbors, $\sigma_{0,i}$ denotes the central spin at

Mohammed Gharib, Fatemeh Afghah, Elizabeth Serena Bentley

Cooperative ad-hoc UAV networks have been turning into the primary solution set for situations where establishing a communication infrastructure is not feasible. Search-and-rescue after a disaster and intelligence, surveillance, and reconnaissance (ISR) are two examples where the UAV nodes need to send their collected data cooperatively into a central decisi

Xin Du

Brain simulation, as one of the latest advances in artificial intelligence, facilitates better understanding about how information is represented and processed in the brain. The extreme complexity of human brain makes brain simulations only feasible upon high-performance computing platforms. Supercomputers with a large number of interconnected graphical proc

Sabeesh Ethiraj, Bharath Kumar Bolla

The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transfer learning to classify the SDSS-IV dataset, we further extended our research in the fine tuning of these architectures to study the effect in the cla

Natalia Tomashenko, Brij Mohan Lal Srivastava, Xin Wang, Emmanuel Vincent

The VoicePrivacy Challenge aims to promote the development of privacy preservation tools for speech technology by gathering a new community to define the tasks of interest and the evaluation methodology, and benchmarking solutions through a series of challenges. In this document, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Ch

B. Mirbach, M. Boguslawski

In this paper we investigate the question of how much combined measurements can increase the accuracy of additive quantities. Therefore, we consider a set of measurements from a selection of all possible combinations of the $n$ labeled masses and then estimate the individual weights of the $n$ masses by a linear regression approach. We present experimental r

L. A. Serra Filho, R. Felix dos Santos, G. G. A. de Souza, M. M. M. Paulino

The Helium-3 shortage and the growing interest in neutron science constitute a driving factor in developing new neutron detection technologies. In this work, we report the development of a double-GEM detector prototype that uses a $^{10}$B$_4$C layer as a neutron converter material. GEANT4 simulations were performed predicting an efficiency of 3.14(10) %, ag

Prathima Dileep, Bharath Kumar Bolla, Sabeesh Ethiraj

Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eyes center, the eye's inner and outer corners, the mouth center, and the nose tip from which human emotions and intent can be explained. The focus

Sergey Agievich

We suggest an upper bound on binomial coefficients that holds over the entire parameter range and whose form repeats the form of the de Moivre-Laplace approximation of the symmetric binomial distribution. Using the bound, we estimate the number of continuations of a given Boolean function to bent functions, investigate dependencies into the Walsh-Hadamard sp

Özge Sürer, Filomena M. Nunes, Matthew Plumlee, Stefan M. Wild

Breakup reactions are one of the favored probes to study loosely bound nuclei, particularly those in the limit of stability forming a halo. In order to interpret such breakup experiments, the continuum discretized coupled channel method is typically used. In this study, the first Bayesian analysis of a breakup reaction model is performed. We use a combinatio

Bharath Kumar Bolla, Mohan Kingam, Sabeesh Ethiraj

Quality inspection has become crucial in any large-scale manufacturing industry recently. In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defective products. In this paper, we have compared and contrasted various pre-trained and custom-built architectures using model size, perform

Anna Romanova, Konstantin Katamadze, Grant Avosopiants, Leon Biguaa

We study temporal correlations of interfering quasi-thermal fields, obtained by scattering laser radiation on a rotating ground glass disk. We show that the Doppler effect causes oscillations in temporal cross-correlation function. Furthermore, we propose how to use Hong-Ou-Mandel interference of quasi-thermal fields in the time domain to characterize linear

Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa

Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is possible in real-world use cases due to the diversity of sample characteristics. That is, a set of samples is a mixture of critical ones which should not be missed and less importa

Ping Liu, Habib Ammari

In this paper, we develop a new technique to obtain nearly optimal estimates of the computational resolution limits introduced in Appl. Comput. Harmon. Anal. 56 (2022) 402-446; IEEE Trans. Inf. Theory 67(7) (2021) 4812-4827; Inverse Probl. 37(10) (2021) 104001 for two-dimensional super-resolution problems. Our main contributions are fivefold: (i) Our work im

Serban T. Belinschi, Hari Bercovici, Ching-Wei Ho

It is shown that the free multiplicative convolution of two nondegenerate probability measures on the unit circle has no continuous singular part relative to arclength measure. Analogous results have long been known for free additive convolutions on the line and free multiplicative convolution on the positive half-line.

Zachary Pierce Bansingh, Tzu-Ching Yen, Peter D. Johnson, Artur F. Izmaylov

Measuring quantum observables by grouping terms that can be rotated to sums of only products of Pauli $\hat z$ operators (Ising form) is proven to be efficient in near term quantum computing algorithms. This approach requires extra unitary transformations to rotate the state of interest so that the measurement of a fragment's Ising form would be equivalent t

Kassandra Friedrichs, Philipp Kellmeyer

Over the last three decades, the human brain, and its role in determining behavior have been receiving a growing amount of attention in academia as well as in society more generally. Neuroscientific explanations of human behavior or other phenomena are often especially appealing to lay people. Therefore, neuroscientific explanations that can affect individua

Attila A. Yavuz, Rouzbeh Behnia

Forward-secure signatures guarantee that the signatures generated before the compromise of private key remain secure, and therefore offer an enhanced compromise-resiliency for real-life applications such as digital forensics, audit logs, and financial systems. However, the vast majority of state-of-the-art forward-secure signatures rely on conventional intra

Vibhuti Arora, Shankey Kumar, Saminathan Ponnusamy

This article determines the exact asymptotic value of the Bohr radii and the arithmetic Bohr radii for the holomorphic functions defined on the unit ball of the $\ell_p^n$ space and having values in the simply connected domain of $\mathbb{C}$. Moreover, we investigate sharp Bohr radius for four distinct categories of holomorphic functions. These functions ma

Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson, Robert A. Barton

Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The rising abundance of single-cell genomic data from human tumors and tissue offers a new opportunity to optimize model systems by their similarity to targeted human cell typ

Paul Irofti, Andrei Pătraşcu, Andrei Iulian Hîji

Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive adversarial attacks. A malicious intrusion, defined as an invasive action intending to illegally exploit private resources, manifes

Frank Sicong Chen, Amith K. Belman, Vir V. Phoha

Accelerometer signals generated through gait present a new frontier of human interface with mobile devices. Gait cycle detection based on these signals has applications in various areas, including authentication, health monitoring, and activity detection. Template-based studies focus on how the entire gait cycle represents walking patterns, but these are com

Sam Mugiraneza, Alannah M. Hallas

Magnetic susceptibility measurements are often the first characterization tool that researchers turn to when beginning to assess the magnetic nature of a newly discovered material. Breakthroughs in instrumentation have made the collection of high quality magnetic susceptibility data more accessible than ever before. However, the analysis of susceptibility da

Hsin-Hsiung Huang, Feng Yu, Xing Fan, Teng Zhang

While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors. To address these issues, this paper proposes a framework of matrix variate regression models base

A. Karassev, E. Shchepin, V. Valov

It is shown that any homeomorphism between two compact subsets of $\mathbb N^\tau$ can be extended to an autohomeomorphism of $\mathbb N^\tau$.

Edwin Bedolla, Luis Carlos Padierna, Ramón Castañeda-Priego

The Ornstein-Zernike equation is solved for the hard-sphere and square-well fluids using a diverse selection of closure relations; the attraction range of the square-well is chosen to be $\lambda=1.5.$ In particular, for both fluids we mainly focus on the solution based on a three-parameter version of the Verlet closure relation [Mol. Phys. 42, 1291-1302 (19

Narayanaswamy Balakrishnan, María Jaenada, Leandro Pardo

One-shot devices data represent an extreme case of interval censoring.Some kind of one-shot units do not get destroyed when tested, and so, survival units can continue within the test providing extra information about their lifetime. Moreover, one-shot devices may last for long times under normal operating conditions, and so accelerated life tests (ALTs) may

Jananan Arulseelan, Isaac Goldbring, Bradd Hart

We show that neither the class of C*-algebras with Kirchberg's QWEP property nor the class of W*-probability spaces with the QWEP property are effectively axiomatizable (in the appropriate languages). The latter result follows from a more general result, namely that the hyperfinite III$_1$ factor does not have a computable universal theory in the language of

Meixin Cheng, Shazhou Zhong, Nicolás Rivas, Tina Dekker

Laser excitation has emerged as a means to expose hidden states of matter and promote phase transitions on demand. Such laser induced transformations are often rendered possible owing to the delivery of spatially and/or temporally manipulated light, carrying energy quanta well above the thermal background. Here, we report time-resolved broadband femtosecond

Philipp Ratz

Artificial Neural Networks (ANN) have been employed for a range of modelling and prediction tasks using financial data. However, evidence on their predictive performance, especially for time-series data, has been mixed. Whereas some applications find that ANNs provide better forecasts than more traditional estimation techniques, others find that they barely

Gerard Sant, Gerard I. Gállego, Belen Alastruey, Marta R. Costa-Jussà

Transformer-based models have been achieving state-of-the-art results in several fields of Natural Language Processing. However, its direct application to speech tasks is not trivial. The nature of this sequences carries problems such as long sequence lengths and redundancy between adjacent tokens. Therefore, we believe that regular self-attention mechanism

Shilei Fu, Feng Xu

Forward modeling of wave scattering and radar imaging mechanisms is the key to information extraction from synthetic aperture radar (SAR) images. Like inverse graphics in optical domain, an inherently-integrated forward-inverse approach would be promising for SAR advanced information retrieval and target reconstruction. This paper presents such an attempt to

Sandipan Das, Navid Mahabadi, Addi Djikic, Cesar Nassir

We demonstrate a multi-lidar calibration framework for large mobile platforms that jointly calibrate the extrinsic parameters of non-overlapping Field-of-View (FoV) lidar sensors, without the need for any external calibration aid. The method starts by estimating the pose of each lidar in its corresponding sensor frame in between subsequent timestamps. Since

Takashi Tsuchimochi, Masaki Taii, Taisei Nishimaki, Seiichiro L. Ten-no

Quantum computing is a promising approach to harnessing strong correlation in molecular systems; however, current devices only allow for hybrid quantum-classical algorithms with a shallow circuit depth, such as the variational quantum eigensolver (VQE). In this study, we report the importance of the Hamiltonian symmetry in constructing VQE circuits adaptivel

Sandipan Das, Navid Mahabadi, Saikat Chatterjee, Maurice Fallon

Reliable knowledge of road boundaries is critical for autonomous vehicle navigation. We propose a robust curb detection and filtering technique based on the fusion of camera semantics and dense lidar point clouds. The lidar point clouds are collected by fusing multiple lidars for robust feature detection. The camera semantics are based on a modified Efficien

A. L. Rebenko

The infinite set of coupled integral nonlinear equations for correlation functions in the case of classical canonical ensemble is considered. Some kind of graph expansions of correlation functions in the density parameter are constructed. Existing of unique solutions for small value of density and high temperature is discussed.

Francisco Martín-Vega, Edwin Herrera, Beilun Wu, Víctor Barrena

The electronic bandstructure of Au$_2$Pb has a Dirac cone which gaps when undergoing a structural transition into a low temperature superconducting phase. This suggests that the superconducting phase ($T_c=1.1$ K) might hold topological properties at the surface. Here we make Scanning Tunneling Microscopy experiments on the surface of superconducting Au$_2$P

Davide Trotta, Matteo Spadetto, Valeria de Paiva

G\"odel's Dialectica interpretation was conceived as a tool to obtain the consistency of Peano arithmetic via a proof of consistency of Heyting arithmetic in the 40s. In recent years, several proof-theoretic transformations, based on G\"odel's Dialectica interpretation, have been used systematically to extract new content from classical proofs, following a s

Sajad Daei, Marios Kountouris

Emerging communication networks are envisioned to support massive wireless connectivity of heterogeneous devices with sporadic traffic and diverse requirements in terms of latency, reliability, and bandwidth. Providing multiple access to an increasing number of uncoordinated users and sharing the limited resources become essential in this context. In this wo

N. Yedukondalu, Tribhuwan Pandey, S. C. Rakesh Roshan

Dibismuth dioxychalcogenides, Bi$_2$O$_2$Ch (Ch = S, Se, Te) are emerging class of materials for next generation electronics and thermoelectrics with an ultrahigh carrier mobility and excellent air stability. Among these, Bi$_2$O$_2$S is fascinating because of stereochemically active 6$s^2$ lone pair of Bi$^{3+}$ cation, heterogeneous bonding and high mass c

Nikos I. Bosse, Hugo Gruson, Anne Cori, Edwin van Leeuwen

Evaluating forecasts is essential to understand and improve forecasting and make forecasts useful to decision makers. A variety of R packages provide a broad variety of scoring rules, visualisations and diagnostic tools. One particular challenge, which scoringutils aims to address, is handling the complexity of evaluating and comparing forecasts from several

V. Manuilov

Let $M\subset N$ be Hilbert $C^*$-modules over a $C^*$-algebra $A$ with $M^\perp=0$. It was shown recently by J. Kaad and M. Skeide that there exists a non-zero $A$-valued functional on $N$ such that its restriction onto $M$ is zero. Here we show that this may happen even if $A$ is monotone complete. On the other hand, we show that for certain type I $W^*$-a

Fabio Scardigli, Gaetano Lambiase

Scale dependence of fundamental physical parameters is a generic feature of ordinary quantum field theory. When applied to gravity, this idea produces effective actions generically containing a running Newtonian coupling constant, from which new (spherically symmetric) black hole spacetimes can be inferred. As a minimum useful requirement, of course, the new

Giuseppe Genovese

Restricted Boltzmann machines are energy models made of a visible and a hidden layer. We identify an effective energy function describing the zero-temperature landscape on the visible units and depending only on the tail behaviour of the hidden layer prior distribution. Studying the location of the local minima of such an energy function, we show that the ab

Joonas Kalda, Tanel Alumäe

In this paper, we present a novel training method for speaker change detection models. Speaker change detection is often viewed as a binary sequence labelling problem. The main challenges with this approach are the vagueness of annotated change points caused by the silences between speaker turns and imbalanced data due to the majority of frames not including

David Ahmedt-Aristizabal, Chuong Nguyen, Lachlan Tychsen-Smith, Ashley Stacey

Advanced artificial intelligence and machine learning have great potential to redefine how skin lesions are detected, mapped, tracked and documented. Here, We propose a 3D whole-body imaging system known as 3DSkin-mapper to enable automated detection, evaluation and mapping of skin lesions. A modular camera rig arranged in a cylindrical configuration was des

Carlo Rizza, Giuseppe Castaldi, Vincenzo Galdi

We study a class of temporal metamaterials characterized by time-varying dielectric permittivity waveforms of duration much smaller than the characteristic wave-dynamical timescale. In the analogy between spatial and temporal metamaterials, such a short-pulsed regime can be viewed as the temporal counterpart of metasurfaces. We introduce a general and compac

Tanel Alumäe, Kunnar Kukk

This paper investigates different pretraining approaches to spoken language identification. The paper is based on our submission to the Oriental Language Recognition 2021 Challenge. We participated in two tracks of the challenge: constrained and unconstrained language recognition. For the constrained track, we first trained a Conformer-based encoder-decoder

Huagui Duan, Hui Liu, Yiming Long, Wei Wang

In this paper, we first generalize the common index jump theorem of Long-Zhu in 2002 and Duan-Long-Wang in 2016 to the case where the mean indices of symplectic paths are not required to be all positive. As applications, we study closed characteristics on compact star-shaped hypersurfaces in ${\bf R}^{2n}$, when both positive and negative mean indices may ap

Shreya Sharma, Jigyasa Gupta, Shreshth Tuli, Rohan Paul

Our goal is to enable a robot to learn how to sequence its actions to perform tasks specified as natural language instructions, given successful demonstrations from a human partner. The ability to plan high-level tasks can be factored as (i) inferring specific goal predicates that characterize the task implied by a language instruction for a given world stat

C. Y. Qiao, J. C. Pei

This work investigated the first-chance survival probabilities of highly excited compound superheavy nuclei in the prospect of synthesizing new superheavy elements. The main feature of our modelings is the adoption of microscopic temperature dependent fission barriers in calculations of fission rates. A simple derivation is demonstrated to elucidate the conn

Babak Rokh, Ali Azarpeyvand, Alireza Khanteymoori

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant c

Hatem A. Alharbi, Barzan A. Yosuf, Mohammad Aldossary, Jaber Almutairi

Unmanned Aerial Vehicles (UAVs) are poised to play a central role in revolutionizing future services offered by the envisioned smart cities, thanks to their agility, flexibility, and cost-efficiency. UAVs are being widely deployed in different verticals including surveillance, search and rescue missions, delivery of items, and as an infrastructure for aerial

Faxiang Qin, Mengyue Peng, Diana Estevez, Christian Brosseau

Electromagnetic (EM) composites have stimulated tremendous fundamental and practical interests owing to their flexible electromagnetic properties and extensive potential engineering applications. Hence, it is necessary to systematically understand the physical mechanisms and design principles controlling EM composites. In this tutorial, we first provide an o

Zachary Feinstein, Birgit Rudloff

In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater's condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated

Ramashish Gaurav, Bryan Tripp, Apurva Narayan

Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep SNNs, with Artificial Neural Network (ANN)-to-SNN conversion being highly successful. MaxPooling layers in Convolutional Neural Networks (CNNs) are an integral component to downsa

Dennis Haitz, Boris Jutzi, Patrick Huebner, Markus Ulrich

Corrosion is a form of damage that often appears on the surface of metal-made objects used in industrial applications. Those damages can be critical depending on the purpose of the used object. Optical-based testing systems provide a form of non-contact data acquisition, where the acquired data can then be used to analyse the surface of an object. In the fie

Suraka Bhattacharjee, Urbashi Satpathi, Supurna Sinha

We study the Quantum Brownian motion of a charged particle moving in a harmonic potential in the presence of an uniform external magnetic field and linearly coupled to an Ohmic bath through momentum variables. We analyse the growth of the mean square displacement of the particle in the classical high temperature domain and in the quantum low temperature doma

Jun Wang, Omran Alamayreh, Benedetta Tondi, Mauro Barni

In this paper, we address a new image forensics task, namely the detection of fake flood images generated by ClimateGAN architecture. We do so by proposing a hybrid deep learning architecture including both a detection and a localization branch, the latter being devoted to the identification of the image regions manipulated by ClimateGAN. Even if our goal is

Miguel Ottina

We introduce a new combinatorial invariant, which we call crosscut poset, that is finer than the crosscut complex. We exhibit many applications of the crosscut poset which include a generalization of Bj\"orner's crosscut theorem and two results concerning the fixed point property and the fixed simplex property.

Alan Coley, Robert van den Hoogen

In teleparallel geometries, symmetries are represented by affine frame symmetries which constrain both the (co)frame basis and the spin-connection (which are the primary geometric objects). In this paper we shall study teleparallel geometries with a single affine symmetry, utilizing the locally Lorentz covariant approach and adopting a complex null gauge. We

Atefeh Termehchi, Mehdi Rasti

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the pr