November 2022 arXiv papers — page 155

Gloria Montaña, Angels Ramos, Laura Tolos, Juan M. Torres-Rincon

The properties of the $X(3872)$ and its spin partner, the $X(4014)$, are studied both in vacuum and at finite temperature. Using an effective hadron theory based on the hidden-gauge Lagrangian, the $X(3872)$ is dynamically generated from the $s$-wave rescattering of a pair of pseudoscalar and vector charm mesons. By incorporating the thermal spectral functio

Harsh Varshney, Kamal Das, Pankaj Bhalla, Amit Agarwal

We investigate the second-order nonlinear electronic thermal transport induced by temperature gradient. We develop the quantum kinetic theory framework to describe thermal transport in presence of a temperature gradient. Using this, we predict an intrinsic scattering time independent nonlinear thermal current in addition to the known extrinsic nonlinear Drud

P. Peñil, M. Ajello, S. Buson, A. Domínguez

Blazars are known to exhibit variability across a broad range of timescales. This behavior can include periodicity in their $\gamma$-ray emission, whose clear detection remains an ongoing challenge, partly due to the inherent stochasticity of the processes involved and also the lack of adequately well-sampled light curves. In this study, we perform a systema

Bela Bajnok

In this survey paper we discuss some recent results and related open questions in additive combinatorics, in particular, questions about sumsets in finite abelian groups.

Michal Byra, Piotr Karwat, Ivan Ryzhankow, Piotr Komorowski

Standard classification methods based on handcrafted morphological and texture features have achieved good performance in breast mass differentiation in ultrasound (US). In comparison to deep neural networks, commonly perceived as "black-box" models, classical techniques are based on features that have well-understood medical and physical interpretation. How

Ugo Lebreuilly, Valentin Vallucci-Goy, Vincent Guillet, Maxime Lombart

We model the coagulation and fragmentation of dust grains during the protostellar collapse with our newly developed shark code. It solves the gas-dust hydrodynamics in a spherical geometry and the coagulation/fragmentation equation. It also computes the ionization state of the cloud and the Ohmic, ambipolar and Hall resistivities. We find that the dust size

Roman Cherniha, Vasyl' Davydovych

New Q-conditional (nonclassical) symmetries and exact solutions of the hunter-gatherer-farmer population model proposed by Aoki, Shida and Shigesada (Theor. Popul. Biol. 1996;50:1-17) are constructed. The main method used for the aforementioned purposes is an extension of the nonclassical method for system of partial differential equations. An analysis of pr

Bela Bajnok

We embark on a tour that takes us through four closely related topics: the dual concepts of independence and spanning in finite abelian groups and the analogous dual concepts of designs and distance sets on spheres. We review some of the main known results in each area, mention several open questions, and discuss some connections among these four interesting

Khalid Alnajjar, Mika Hämäläinen, Jörg Tiedemann, Jorma Laaksonen

Prerecorded laughter accompanying dialog in comedy TV shows encourages the audience to laugh by clearly marking humorous moments in the show. We present an approach for automatically detecting humor in the Friends TV show using multimodal data. Our model is capable of recognizing whether an utterance is humorous or not and assess the intensity of it. We use

Andrea Antonelli

Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, several compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron star

Merab Malishava

We devote our studies to the subject of weakly nonintegrable dynamics of systems with a macroscopic number of degrees of freedom. Our main points of interest are the relations between the timescales of thermalization and the timescales of chaotization; the choice of appropriate observables and the structure of equations coupling them; identifying the classes

Margherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro, Daniel Coelho de Castro

Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. However, training these models requires access to large amounts of man

Jason Walsh, Alice Othmani, Mayank Jain, Soumyabrata Dev

Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net.

Louis Felix Feiner, Andrzej M. Oleś

We demonstrate that the three-dimensional $e_g$ orbital Hubbard model can be generalized to arbitrary dimension $d$, and that the form of the result is determined uniquely by the requirements that (i) the two-fold degeneracy of the $e_g$ orbital be retained, and (ii) the cubic lattice be turned into a hypercubic lattice. While the local Coulomb interaction $

Nathaniel Xu

Training energy-based models (EBMs) with noise-contrastive estimation (NCE) is theoretically feasible but practically challenging. Effective learning requires the noise distribution to be approximately similar to the target distribution, especially in high-dimensional domains. Previous works have explored modelling the noise distribution as a separate genera

Devesh Shah, Anirudh Suresh, Alemayehu Admasu, Devesh Upadhyay

The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. Typical state of the art methods in evaluating synthetic images from generative models have relied on the Fr\'echet Inception Distance. However, this and other similar methods, are limited in the materials domain

Feihu Huang

Federated Learning is a popular distributed learning paradigm in machine learning. Meanwhile, composition optimization is an effective hierarchical learning model, which appears in many machine learning applications such as meta learning and robust learning. More recently, although a few federated composition optimization algorithms have been proposed, they

Marta Bilkova, Sabine Frittella, Daniil Kozhemiachenko

In this paper, we provide a Hilbert-style axiomatisation for the crisp bi-G\"{o}del modal logic $\KbiG$. We prove its completeness w.r.t.\ crisp Kripke models where formulas at each state are evaluated over the standard bi-G\"{o}del algebra on $[0,1]$. We also consider a paraconsistent expansion of $\KbiG$ with a De Morgan negation $\neg$ which we dub $\KGsq

Liangchen Li, Chong Li, Rong Luo, C. -Q Zhang

Bouchet conjectured in 1983 that every flow-admissible signed graph admits a nowhere-zero 6-flow which is equivalent to the restriction to cubic signed graphs. In this paper, we proved that every flow-admissible $3$-edge-colorable cubic signed graph admits a nowhere-zero $10$-flow. This together with the 4-color theorem implies that every flow-admissible bri

Junjie Luo, Jie Feng, Hong-Hao Zhang, Weipeng Lin

Since the first detection of gravitational waves by the LIGO/VIRGO team, the related research field has attracted more attention. The spinning compact binaries system, as one of the gravitational-wave sources for broadband laser interferometers, has been widely studied by related researchers. In order to analyze the gravitational wave signals using matched f

A. Jhingan, C. Schmitt, A. Lemasson, S. Biswas

Fission at low excitation energy is an ideal playground to probe the impact of nuclear structure on nuclear dynamics. While the importance of structural effects in the nascent fragments is well-established in the (trans-)actinide region, the observation of asymmetric fission in several neutron-deficient pre-actinides can be explained by various mechanisms. T

N. N. Arsenyev, A. P. Severyukhin

The properties of the isoscalar giant monopole resonance (ISGMR) for the double magic $^{48}$Ca are analyzed in the framework of a microscopic model based on Skyrme-type interactions. A method for simultaneously taking into account the coupling between one-, two-, and three-phonon terms in the wave functions of $0^{+}$ states has been developed. The inclusio

Daniel J. W. Touw, Patrick J. F. Groenen, Yoshikazu Terada

Convex clustering is a modern method with both hierarchical and $k$-means clustering characteristics. Although convex clustering can capture complex clustering structures hidden in data, the existing convex clustering algorithms are not scalable to large data sets with sample sizes greater than several thousands. Moreover, it is known that convex clustering

Bernard Derrida

The Bramson logarithmic shift of the position of pulled fronts is a universal feature common to a large class of monostable traveling wave equations. As one varies the non-linearities it so happens that one can observe, at some critical non linearity, a transition from pulled fronts to pushed fronts. At this transition the Bramson shift is modified. In the l

Breno Cecchi, Nathan Cruz, Marcelo Knobel, Kleber Roberto Pirota

Artificial spin ices (ASIs) are designable arrays of interacting nanomagnets that span a wide range of magnetic phases associated with a number of spin lattice models. Here, we demonstrate that the phase of an artificial kagome spin ice can be determined from its initial magnetization curve. As a proof of concept, micromagnetic simulations of these curves we

Linshan Hou, Zhongyun Hua, Yuhong Li, Yifeng Zheng

Deep neural networks (DNNs) are vulnerable to backdoor attacks, where a backdoored model behaves normally with clean inputs but exhibits attacker-specified behaviors upon the inputs containing triggers. Most previous backdoor attacks mainly focus on either the all-to-one or all-to-all paradigm, allowing attackers to manipulate an input to attack a single tar

Tilman Beck, Andreas Waldis, Iryna Gurevych

Stance detection deals with identifying an author's stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly. Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-tr

Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto

We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy, non-negative entropy production), we modify accordingly the port-Hamiltonian formalism so as to achieve a port-metriplectic one. W

Sam Spiro, Erlang Surya

Let $\mathrm{pm}(G)$ denote the number of perfect matchings of a graph $G$, and let $K_{r\times 2n/r}$ denote the complete $r$-partite graph where each part has size $2n/r$. Johnson, Kayll, and Palmer conjectured that for any perfect matching $M$ of $K_{r\times 2n/r}$, we have for $2n$ divisible by $r$ \[\frac{\mathrm{pm}(K_{r\times 2n/r}-M)}{\mathrm{pm}(K_{

Emmett Wise, Qilong Cheng, Jonathan Kelly

Autonomous vehicles (AVs) fuse data from multiple sensors and sensing modalities to impart a measure of robustness when operating in adverse conditions. Radars and cameras are popular choices for use in sensor fusion; although radar measurements are sparse in comparison to camera images, radar scans penetrate fog, rain, and snow. However, accurate sensor fus

Jakub Janarek, Jakub Zakrzewski, Dominique Delande

We study numerically the impact of many-body interactions on the quantum boomerang effect. We consider various cases: weakly interacting bosons, the Tonks-Girardeau gas, and strongly interacting bosons (which may be mapped onto weakly interacting fermions). Numerical simulations are performed using the time-evolving block decimation algorithm, a quasi-exact

Oriol Artime

The state of many physical, biological and socio-technical systems evolves by combining smooth local transitions and abrupt resetting events to a set of reference values. The inclusion of the resetting mechanism not only provides the possibility of modeling a wide variety of realistic systems but also leads to interesting novel phenomenology not present in r

P. Chayer, C. Mendoza, M. Meléndez, J. Deprince

We report the first detection of cesium (Z = 55) in the atmosphere of a white dwarf. Around a dozen absorption lines of Cs IV, Cs V, and Cs VI have been identified in the Far Ultraviolet Spectroscopic Explorer spectrum of the He-rich white dwarf HD 149499B (Teff = 49,500 K, log g = 7.97). The lines have equivalent widths ranging from 2.3 to 26.9 m\r{A}. We p

Ishraq U. Ahmed, Helen M. Byrne, Mary R. Myerscough

Atherosclerosis is an inflammatory disease characterised by the formation of plaques, which are deposits of lipids and cholesterol-laden macrophages that form in the artery wall. The inflammation is often non-resolving, due in large part to changes in normal macrophage anti-inflammatory behaviour that are induced by the toxic plaque microenvironment. These c

Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov

Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce

James Marshall Reber

Let $M$ be a closed surface and let $\{g_s \ | \ s \in (-\epsilon, \epsilon)\}$ be a smooth one-parameter family of Riemannian metrics on $M$. Also let $\{\kappa_s : M \rightarrow \mathbb{R} \ | \ s \in (-\epsilon, \epsilon)\}$ be a smooth one-parameter family of functions on $M$. Then the family $\{(g_s, \kappa_s) \ | \ s \in (-\epsilon, \epsilon)\}$ gives

K. Riisager

Beta decays of mirror nuclei differ in Q-value, but will otherwise proceed with transitions of similar strength. The current status is reviewed: Fermi transitions are all very similar, whereas Gamow-Teller transitions can differ in strength by more than a factor two. The main cause of the asymmetries appears to be binding energy differences between the mirro

Thong Vo, Pranjal Dave, Gaurav Bajpai, Rasha Kashef

With the rapid growth of the Internet of Things (IoT) and a wide range of mobile devices, the conventional cloud computing paradigm faces significant challenges (high latency, bandwidth cost, etc.). Motivated by those constraints and concerns for the future of the IoT, modern architectures are gearing toward distributing the cloud computational resources to

M. Barbisan, B. Zaniol, M. Cavenago, R. Pasqualotto

Consorzio RFX and INFN-LNL are building a flexible small ion source (NIO1) capable of producing about 130 mA of H- ions accelerated at 60 KeV. Aim of the experiment is to test and develop the instrumentation for SPIDER and MITICA, the prototypes respectively of the negative ion sources and of the whole neutral beam injectors which will operate in the ITER ex

António Girão, David Munhá Correia

We show that there is a constant $C$ such that for every $\varepsilon>0$ any $2$-coloured $K_n$ with minimum degree at least $n/4+\varepsilon n$ in both colours contains a complete subgraph on $2t$ vertices where one colour class forms a $K_{t,t}$, provided that $n\geq \varepsilon^{-Ct}$. Also, we prove that if $K_n$ is $2$-coloured with minimum degree at le

Zhenwei LI, Xuefei Chen, Hongwei Ge, Hai-Liang Chen

Mass transfer stability is an essential issue in binary evolution. Ge et al. studied critical mass ratios for dynamically stable mass transfer by establishing adiabatic mass loss model and found that the donor stars on the giant branches tend to be more stable than that based on the composite polytropic stellar model. We would investigate the influence of ma

Filippo Airaldi, Bart De Schutter, Azita Dabiri

We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a parametric MPC scheme that is employed as model-based controller with approximate knowledge on the real system's dynamics, 2) an episodic RL algorithm tasked with adjusting the MPC p

Ramtin Gharleghi, Dona Adikari, Katy Ellenberger, Mark Webster

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images,

Solveig Klepper, Ulrike von Luxburg

Graph auto-encoders are widely used to construct graph representations in Euclidean vector spaces. However, it has already been pointed out empirically that linear models on many tasks can outperform graph auto-encoders. In our work, we prove that the solution space induced by graph auto-encoders is a subset of the solution space of a linear map. This demons

A. A. Eremko, L. S. Brizhik, V. M. Loktev

It is shown that the Dirac equation with the Coulomb potential can be solved using the algebra of the three spinor invariants of the Dirac equation without the involvement of the methods of supersymmetric quantum mechanics. The Dirac Hamiltonian is invariant with respect to the rotation transformation, which indicates the dynamical (hidden) symmetry $ SU(2)

Shihan Ma, Alexander Kenneth Clarke, Kostiantyn Maksymenko, Samuel Deslauriers-Gauthier

Numerical models of electromyographic (EMG) signals have provided a huge contribution to our fundamental understanding of human neurophysiology and remain a central pillar of motor neuroscience and the development of human-machine interfaces. However, whilst modern biophysical simulations based on finite element methods are highly accurate, they are extremel

Martin Palmer, Arthur Soulié

We construct a 3-variable enrichment of the Lawrence-Krammer-Bigelow (LKB) representation of the braid groups, which is the limit of a pro-nilpotent tower of representations having the original LKB representation as its bottom layer. We also construct analogous pro-nilpotent towers of representations of surface braid groups and loop braid groups.

Leonardo Badurina, Valerie Gibson, Christopher McCabe, Jeremiah Mitchell

Single-photon atom gradiometry is a powerful experimental technique that can be employed to search for the oscillation of atomic transition energies induced by ultralight scalar dark matter (ULDM). In the sub-Hz regime the background is expected to be dominated by gravity gradient noise (GGN), which arises as a result of mass fluctuations around the experime

Rinaldo M. Colombo, Mauro Garavello, Matthew Tandy

Consider the coupling of $2$ evolution equations, each generating a global process. We prove that the resulting system generates a new global process. This statement can be applied to differential equations of various kinds. In particular, it also yields the well posedness of a predator-prey model, where the coupling is in the differential terms, and of an e

Youlong Ding, Xueyang Wu

Hyperparameter tuning is a common practice in the application of machine learning but is a typically ignored aspect in the literature on privacy-preserving machine learning due to its negative effect on the overall privacy parameter. In this paper, we aim to tackle this fundamental yet challenging problem by providing an effective hyperparameter tuning frame

Ali Kavis, Stratis Skoulakis, Kimon Antonakopoulos, Leello Tadesse Dadi

We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $L$-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider combines an AdaGrad-inspired [Duchi et al., 2011, McMahan & Streeter, 2010], but a fairly distinct, adaptive step-size schedule with the recursive stochastic path integrated estimator prop

Vernon Cooray, Gerald Cooray, Marcos Rubinstein, Farhad Rachidi

The electromagnetic fields of a long dipole working without dispersive and dissipative losses are analyzed in the frequency domains. The dipole produces radiation in bursts of duration T/2 where T is the period of oscillation. The parameter studied in this paper is the energy, U, dissipated in a single burst of radiation of duration T/2. We have studied how

Franz Weißer, Michael Baur, Wolfgang Utschick

In this work, we consider the use of a model-based decoder in combination with an unsupervised learning strategy for direction-of-arrival (DoA) estimation. Relying only on unlabeled training data we show in our analysis that we can outperform existing unsupervised machine learning methods and classical methods. The proposed approach consists of introducing a

Gábor Melis

Just because some purely recurrent models suffer from being hard to optimize and inefficient on today's hardware, they are not necessarily bad models of language. We demonstrate this by the extent to which these models can still be improved by a combination of a slightly better recurrent cell, architecture, objective, as well as optimization. In the process,

Hongrui Jin

From face recognition in smartphones to automatic routing on self-driving cars, machine vision algorithms lie in the core of these features. These systems solve image based tasks by identifying and understanding objects, subsequently making decisions from these information. However, errors in datasets are usually induced or even magnified in algorithms, at t

Tian-Chen Wu, Li-Sheng Geng

Searching for exotic multiquark states and elucidating their nature remains a central topic in understanding quantum chromodynamics--the underlying theory of the strong interaction. Two of the most studied such states are the charm-strange states $D_{s0}^*(2317)$ and $D_{s1}(2460)$. In this letter, we show for the first time that their prompt production yiel

Hansjoerg Albrecher, Oscar Peralta

Hybrid stochastic differential equations are a useful tool to model continuously varying stochastic systems which are modulated by a random environment that may depend on the system state itself. In this paper, we establish the pathwise convergence of the solutions to hybrid stochastic differential equations via space-grid discretizations. While time-grid di

Gandhar Joshi, Reem Yassawi

In this article we define the semigroup associated to a substitution. We use it to construct a minimal automaton which generates a substitution sequence u in reverse reading. We show, in the case where the substitution has a coincidence, that this automaton completely describes the semicocycle discontinuities of u.

Simon Schrodi, Danny Stoll, Binxin Ru, Rhea Sukthanker

The discovery of neural architectures from simple building blocks is a long-standing goal of Neural Architecture Search (NAS). Hierarchical search spaces are a promising step towards this goal but lack a unifying search space design framework and typically only search over some limited aspect of architectures. In this work, we introduce a unifying search spa

Jérémie Koenig

I present a formal connection between algebraic effects and game semantics, two important lines of work in programming languages semantics with applications in compositional software verification. Specifically, the algebraic signature enumerating the possible side-effects of a computation can be read as a game, and strategies for this game constitute the fre

Ioannis Mavromatis, Adrian Sanchez-Mompo, Francesco Raimondo, James Pope

Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift esti

Filip Szatkowski, Karol J. Piczak, Przemysław Spurek, Jacek Tabor

Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals. Recent applications of INRs include image super-resolution, compression of high-dimensional signals, or 3D rendering. However, these solutions usually focus on visual data, and adapting them to the audio domain is not

Xerxes D. Arsiwalla, David Chester, Louis H. Kauffman

We investigate operator algebraic origins of the classical Koopman-von Neumann wave function $\psi_{KvN}$ as well as the quantum mechanical one $\psi_{QM}$. We introduce a formalism of Operator Mechanics (OM) based on a noncommutative Poisson, symplectic and noncommutative differential structures. OM serves as a pre-quantum algebra from which algebraic struc

Yubo Zhang, Xingxing Zhang, Xun Wang, Si-qing Chen

Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties of generated summaries and potentially improve summarization quality (since more information are given). Unfortunately, control signals are not already available during inference

Aditya Joglekar, John H. L. Hansen

The Fearless Steps Challenge 2019 Phase-1 (FSC-P1) is the inaugural Challenge of the Fearless Steps Initiative hosted by the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas. The goal of this Challenge is to evaluate the performance of state-of-the-art speech and language systems for large task-oriented teams with naturalistic aud

Y. G. Zheng, S. J. Kang, K. R. Zhu, C. Y. Yang

There are still some {significant and} unanswered questions about the {incredible} {very high energy (VHE)} $\gamma$-ray signatures. To help understand the mechanism, focusing on the linear and quadratic perturbation mode for the subluminal regime, the present paper revisited the expected signature for the Lorentz invariance violation effects on $\gamma-\gam

Jean-Simon Pacaud Lemay

Cartesian differential categories come equipped with a differential combinator that formalizes the directional derivative from multivariable calculus. Cartesian differential categories provide a categorical semantics of the differential lambda-calculus and have also found applications in causal computation, incremental computation, game theory, differentiabl

Yue Zhao, Sean Zhang, Leman Akoglu

Today there exists no shortage of outlier detection algorithms in the literature, yet the complementary and critical problem of unsupervised outlier model selection (UOMS) is vastly understudied. In this work we propose ELECT, a new approach to select an effective candidate model, i.e. an outlier detection algorithm and its hyperparameter(s), to employ on a

Kimon Antonakopoulos, Ali Kavis, Volkan Cevher

This work proposes a universal and adaptive second-order method for minimizing second-order smooth, convex functions. Our algorithm achieves $O(\sigma / \sqrt{T})$ convergence when the oracle feedback is stochastic with variance $\sigma^2$, and improves its convergence to $O( 1 / T^3)$ with deterministic oracles, where $T$ is the number of iterations. Our me

Toby St Clere Smithe

We extend our earlier work on the compositional structure of cybernetic systems in order to account for the embodiment of such systems. All their interactions proceed through their bodies' boundaries: sensations impinge on their surfaces, and actions correspond to changes in their configurations. We formalize this morphological perspective using polynomial f

Nicolas Curien

We introduce a variant of the Erd\"os--R\'enyi random graph where the number of vertices is random and follows a Poisson law. A very simple Markov property of the model entails that the Lukasiewicz exploration is made of \textit{independent} Poisson increments. Using a vanilla Poisson counting process, this enables us to give very short proofs of classical r

Ioannis Mavromatis, Aftab Khan

This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new

Matthew Di Meglio

Lenses encode protocols for synchronising systems. We continue the work begun by Chollet et al. at the Applied Category Theory Adjoint School in 2020 to study the properties of the category of small categories and asymmetric delta lenses. The forgetful functor from the category of lenses to the category of functors is already known to reflect monos and epis

Jiangjun Peng, Hailin Wang, Xiangyong Cao, Xinlin Liu

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an

Ramesh Manuvinakurike, Sovan Biswas, Giuseppe Raffa, Richard Beckwith

Development of task guidance systems for aiding humans in a situated task remains a challenging problem. The role of search (information retrieval) and conversational systems for task guidance has immense potential to help the task performers achieve various goals. However, there are several technical challenges that need to be addressed to deliver such conv

Rui Ge, Xiangmin Liu, Xiongshuo Yan, Xianfeng Chen

In this work, a doubly resonant photonic crystal (PhC) cavity using the merged bound states in the continuum (BICs) is proposed to obtain a higher second harmonic generation (SHG) efficiency. Firstly by scanning geometry parameters the accidental BICs and a band-edge mode outside the light cone can be obtained. Then as the lattice constant or the thickness o

Carmen Chan-Zheng, Pablo Borja, Jacquelien M. A Scherpen

This manuscript introduces a passivity-based control methodology for fully-actuated mechanical systems with symmetric or asymmetric dead-zones. To this end, we find a smooth approximation of the inverse of the function that describes such a nonlinearity. Then, we propose an energy and damping injection approach - based on the PI-PBC technique - that compensa

A. Boselli, M. Fossati, J. Roediger, M. Boquien

Using a compilation of Halpha fluxes for 384 star forming galaxies detected during the VESTIGE survey, we study several important scaling relations for a complete sample of galaxies in a rich environment. The extraordinary sensitivity of the data allows us to sample the whole dynamic range of the Halpha luminosity function, from massive (M*~10^11 Mo) to dwar

Camille Lagoin, Stephan Suffit, Kirk Baldwin, Loren Pfeiffer

Strongly correlated quantum particles in lattice potentials are the building blocks for a large variety of quantum insulators, for instance Mott phases and density waves breaking the lattice symmetry. Such collective states are accessible to bosonic and fermionic systems. To expand further the spectrum of accessible quantum matter phases, mixing both species

Junjie Wang, Fude Li, X. X. Yi

A notable feature of non-Hermitian systems with skin effects is the sensitivity of their spectra and eigenstates to the boundary conditions. In the literature, three types of boundary conditions-periodic boundary condition,open boundary condition and a defect in the system as a boundary, are explored. In this work we introduce the other type of boundary cond

Tyler J. Slade, Ranuri S. Dissanayaka Mudiyanselage, Nao Furukawa, Tanner R. Smith

We report the growth and characterization of MnPd$_5$P, a ferromagnet with T$_C$ $\approx$ 295 K, and conduct a substitutional study with its antiferromagnetic analogue MnPt$_5$P. We grow single crystals of MnPd$_5$P and Mn(Pt$_{1-x}$Pd$_x$)$_5$P by adding Mn into (Pt$_{1-x}$Pd$_{x}$)-P based melts. All compounds in the family adopt the layered anti-CeCoIn$_

David Fernández Llorca, Vicky Charisi, Ronan Hamon, Ignacio Sánchez

New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing

Dinesh Kumar Singha, Monojit Ghosh, Rudra Majhi, Rukmani Mohanta

In this paper, we study the capability of different long-baseline experiment options at the KM3NeT facility i.e., P2O, Upgraded P2O and P2SO to probe the light sterile neutrino and compare their sensitivities with DUNE. The P2O option will have neutrinos from a 90 KW beam at Protvino to be detected at the ORCA detector, the Upgraded P2O will have neutrinos f

Michele Delvecchio, Teodora Kirova, Ennio Arimondo, Donatella Ciampini

We propose quantum control protocols for the high-fidelity preparation of target states in systems with Autler-Townes splitting. We investigate an approximated three-level system obtained from a four-level one by adiabatically eliminating a state that does not participate in the evolution. In our work we use linear, arctan, and Roland-Cerf functions for tran

S Rakshith, Jayesh Rajkumar Vachhani, Sourabh Vasant Gothe, Rishabh Khurana

In recent years, most of the deep learning solutions are targeted to be deployed in mobile devices. This makes the need for development of lightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) com

Sevag Tafnakaji, Hadi Hajieghrary, Quentin Teixeira, Yasemin Bekiroglu

There are various trajectory planners for mobile manipulators. It is often challenging to compare their performance under similar circumstances due to differences in hardware, dissimilarity of tasks and objectives, as well as uncertainties in measurements and operating environments. In this paper, we propose a simulation framework to evaluate the performance

Yue Zhai, Xiaodong Yang, Kai Tang, Xinyue Long

Quantum metrology is supposed to significantly improve the precision of parameter estimation by utilizing suitable quantum resources. However, the predicted precision can be severely distorted by realistic noises. Here, we propose a control-enhanced quantum metrology scheme to defend against these noises for improving the metrology performance. Our scheme ca

Patrick Charbonneau

Among the various remarkable contributions of Giorgio Parisi to physics, his formulation of the replica symmetry breaking solution for the Sherrington-Kirkpatrick model stands out. In this article, different historical sources are used to reconstruct the scientific and professional contexts of this prodigious advance.

Rob J. Spaargaren, Haiyang S. Wang, Stephen J Mojzsis, Maxim D Ballmer

Rocky planet compositions regulate planetary evolution by affecting core sizes, mantle properties, and melting behaviours. Yet, quantitative treatments of this aspect of exoplanet studies remain generally under-explored. We attempt to constrain the range of potential bulk terrestrial exoplanet compositions in the solar neighbourhood (<200 pc). We circumscrib

Denis Belomestny, Ekaterina Morozova, Vladimir Panov

This paper deals with statistical inference for the scale mixture models. We study an estimation approach based on the Mellin -- Stieltjes transform that can be applied to both discrete and absolute continuous mixing distributions. The accuracy of the corresponding estimate is analysed in terms of its expected pointwise error. As an important technical resul

Qing Feng, Ruihao Zhu, Stefanus Jasin

Motivated by the prevalence of ``price protection guarantee", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchase date) in case the seller decides to lower the price, we study the impact of such policy on the

Zeqi Tan, Yongliang Shen, Xuming Hu, Wenqi Zhang

Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redund

Jelle Hartong, Emil Have

We further develop the string $1/c^2$ expansion of closed bosonic string theory, where $c$ is the speed of light. The expansion will be performed up to and including the next-to-next-to-leading order (NNLO). We show that the next-to-leading order (NLO) theory is equal to the Gomis--Ooguri string, generalised to a curved target space, provided the target spac

Jarah Evslin, Hui Liu, Baiyang Zhang

In a (1+1)-dimensional scalar quantum field theory, we calculate the leading-order probability of meson multiplication, which is the inelastic scattering process: kink + meson $\rightarrow$ kink + 2 mesons. We also calculate the differential probability with respect to the final meson momenta and the probability that one or two of the final mesons recoils ba

Rudi Coppola, Andrea Peruffo, Manuel Mazo

A common technique to verify complex logic specifications for dynamical systems is the construction of symbolic abstractions: simpler, finite-state models whose behaviour mimics the one of the systems of interest. Typically, abstractions are constructed exploiting an accurate knowledge of the underlying model: in real-life applications, this may be a costly

Song He, Yu-tin Huang, Chia-Kai Kuo, Zhenjie Li

In this paper, we present the two-loop correction to scattering amplitudes in three-dimensional $\mathcal{N}=6$ Chern-Simons matter theory. We use eight-point case as our main example, but the method generalizes to all multiplicities. The integrand is completely fixed by dual conformal symmetry, maximal cuts, constraints from soft-collinear behavior and from

A. Geyer, D. Trabert, M. Hofmann, N. Anders

We present experimental data on the strong field tunnel ionization of argon in a counter-rotating two-color (CRTC) laser field. We find that the initial momentum component along the tunneling direction changes sign comparing the rising and the falling edge of the CRTC field. If the initial momentum at the tunnel exit points in the direction of the ion at the

Marina S. Butuzova

The study of the polarization direction is crucial in the issue of restoring the spatial structure of the magnetic field in the active galaxy parsec-scale jets. But, due to relativistic effects, the magnetic field projected onto the celestial sphere in the source reference frame cannot be assumed to be orthogonal to the observed direction of the electric vec

Daiki Suenaga, Kotaro Murakami, Etsuko Itou, Kei Iida

We investigate modifications of hadron masses at finite quark chemical potential in two-flavor and two-color QCD, of which the data are available from lattice simulations, within a linear sigma model based on approximate Pauli-Gursey $SU(4)$ symmetry. The model describes not only ground-state scalar diquarks and pseudo-scalar mesons but also the excited pseu

Shifan Cui, Haoxiang Chen

The premelting of ice is well known, but little is known about how the premelted and solid surfaces convert to each other. In this work, the transition dynamics between two phases are revealed with large-scale molecular dynamics simulations. Supercooling and superheating states exist in the transition, and are overcome by nucleation-like processes. The natur