July 2022 arXiv papers — page 132

Qian Huang, Minghao Hu, David Jones Brady

We demonstrate a physics-aware transformer for feature-based data fusion from cameras with diverse resolution, color spaces, focal planes, focal lengths, and exposure. We also demonstrate a scalable solution for synthetic training data generation for the transformer using open-source computer graphics software. We demonstrate image synthesis on arrays with d

Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano V. Albrecht

Successful deployment of multi-agent reinforcement learning often requires agents to adapt their behaviour. In this work, we discuss the problem of teamwork adaptation in which a team of agents needs to adapt their policies to solve novel tasks with limited fine-tuning. Motivated by the intuition that agents need to be able to identify and distinguish tasks

Thomas Siegert, Deirdre Horan, Gottfried Kanbach

This chapter outlines the general principles for the detection and characterisation of high-energy $\gamma$-ray photons in the energy range from MeV to GeV. Applications of these fundamental photon-matter interaction processes to the construction of instruments for $\gamma$-ray astronomy are described, including a short review of past and present realisation

Mona Fathollahi, Mohammad Hasan Sarhan, Ramon Pena, Lela DiMonte

Mastering the technical skills required to perform surgery is an extremely challenging task. Video-based assessment allows surgeons to receive feedback on their technical skills to facilitate learning and development. Currently, this feedback comes primarily from manual video review, which is time-intensive and limits the feasibility of tracking a surgeon's

Qi Zhang, Xi Chen, David Guéry-Odelin

With the advent of quantum technologies, control issues are becoming increasingly important. In this article, we address the control in phase space under a global constraint provided by a minimal energy-like cost function and a local (in Fourier space) constraint resulting from a robustness criterion. The inverse engineering Lagrangian formalism developed he

N. R. Eggen, C. Scarlata, E. Skillman, A. Jaskot

Pox 186 is an exceptionally small dwarf starburst galaxy hosting a stellar mass of $\sim10^5$ M$_{\odot}$. Undetected in HI (M $ < 10^6$ M$_{\odot}$) from deep 21 cm observations and with an [OIII]/[OII]\ (5007/3727) ratio of 18.3 $\pm$ 0.11, Pox~186 is a promising candidate Lyman continuum emitter. It may be a possible analog of low-mass reionization-era ga

Martin J. Renner, Armin Tavakoli, Marco Túlio Quintino

We consider general prepare-and-measure scenarios in which Alice can transmit qubit states to Bob, who can perform general measurements in the form of positive operator-valued measures (POVMs). We show that the statistics obtained in any such quantum protocol can be simulated by the purely classical means of shared randomness and two bits of communication. F

Ghanashyam Date

These are lecture notes of the QFT-I course I gave in an online mode at Chennai Mathematical Institute. The course focussed on the free relativistic quantum fields, their interactions in the perturbative scattering framework, standard computations of QED processes, radiative corrections at 1-loop with renormalization and an introduction to the toolbox of pat

Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro

We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented algorithm for solving the aforementioned radio resource management (RRM) problems, where, alongside the

Tyson Brooks

The rapid growth of the Internet of Things (IoT), cloud computing, Fog computing, mobile edge computing and wireless grids has resulted in the widespread deployment of relatively immature technology. These technologies, which will primarily use 5G wireless communication networks, are becoming popular because they can be deployed quickly with little infrastru

Justin Dulay, Sonia Poltoratski, Till S. Hartmann, Samuel E. Anthony

{G}{ustav} Fechner's 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science. In psychophysics, a researcher parametrically varies some aspects of a stimulus, and measures the resulting changes in a human subject's experience of that stimulus; doing so

Liuke Lyu, William Witczak-Krempa

The thermal Hall effect, which arises when heat flows transverse to an applied thermal gradient, has become an important observable in the study of quantum materials. Recent experiments found a large thermal Hall conductivity $\kappa_{xy}$ in many high-temperature cuprate superconductors, including deep inside the Mott insulator, but the underlying mechanism

Hui-Ke Jin, W. M. H. Natori, Johannes Knolle

By combining the density matrix renormalization group (DMRG) method with Gutzwiller projected wave functions, we study the SU(4) symmetric spin-orbital model on the honeycomb lattice. We find that the ground states can be well described by a Gutzwiller projected $\pi$-flux state with Dirac-type gapless excitations at one quarter filling. Although these Dirac

Charles Lu, Anastasios N. Angelopoulos, Stuart Pomerantz

The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent developments in distribution-free uncertainty quant

A. de Oliveira Junior, Jakub Czartowski, Karol Życzkowski, Kamil Korzekwa

The second law of thermodynamics imposes a fundamental asymmetry in the flow of events. The so-called thermodynamic arrow of time introduces an ordering that divides the system's state space into past, future and incomparable regions. In this work, we analyse the structure of the resulting thermal cones, i.e., sets of states that a given state can thermodyna

Arthur D. Bosman, Edwin A. Bergin, Jenny K. Calahan, Sara E. Duval

Carbon Dioxide is an important tracer of the chemistry and physics in the terrestrial planet forming zone. Using a thermo-chemical model that has been tested against the mid-infrared water emission we re-interpret the CO2 emission as observed with Spitzer. We find that both water UV-shielding and extra chemical heating significantly reduce the total CO2 colu

Igor Pesando

We discuss how the perturbative particle paradigm fails in certain background with space-like singularity but asymptotically flat which should admit a S-matrix. The Feynman approach relies on the interaction picture. This approach means that we can interpret interactions as exchanges of particles. Particles are the modes of the quadratic part of the Lagrangi

Tomáš Husek

The branching ratio of the $K^+\to\pi^+e^+e^-e^+e^-$ ($K^+\to\pi^+4e$) decay is calculated at leading order in the Standard Model. The dominance of the neutral-pion pole determines the overall branching ratio to be $B(K^+\to\pi^+4e)=B(K^+\to\pi^+\pi^0)B(\pi^0\to4e)\approx7.0(3)\times10^{-6}$. The significance of this contribution is very much concentrated in

Steven L. Finkelstein, Micaela B. Bagley

Studies of the high-redshift rest-frame ultraviolet (UV) luminosity functions (LFs) have typically treated the star-forming galaxy and active galactic nuclei (AGN) populations separately, as they have different survey depth and area requirements. However, the recent advent of wide-area deep ground-based imaging surveys now probe volumes large enough to disco

Rogério Monteiro-Oliveira

We present the weak lensing mass distribution of a triple merging cluster candidate at $z_{\rm photo}\sim 0.36$ belonging to a supercluster recently discovered during the eROSITA Performance Verification phase. Our analysis solved a previous tension in the merger classification by confirming that the cluster pair eFEDS J093513.3+004746 and eFEDS J093510.7+00

Tianshu Wang, David Vartanyan, Adam Burrows, Matthew S. B. Coleman

Calibrating with detailed 2D core-collapse supernova simulations, we derive a simple core-collapse supernova explosion condition based solely upon the terminal density profiles of state-of-the-art stellar evolution calculations of the progenitor massive stars. This condition captures the vast majority of the behavior of the one hundred 2D state-of-the-art mo

Aleksandr Azatov, Giulio Barni, Sabyasachi Chakraborty, Miguel Vanvlasselaer

We analyze the phase transitions in the minimal extension of the SM with a real singlet scalar field. The novelty of our study is that we identify and analyze in details the region of parameter space where the first order phase transition can occur and in particular when the bubbles with true vacuum can reach relativistic velocities. This region is interesti

Isabel M. E. Santos-Santos, Julio F. Navarro, Alan McConnachie

We use the APOSTLE Local Group (LG) cosmological hydro-simulations to examine the properties of "backsplash" galaxies, i.e, dwarfs which were within the virial boundaries of the Milky Way (MW) or M31 in the past, but are today outside their virial radius ($r_{200}$). More than half of all dwarfs between $1-2\,r_{200}$ of each primary are backsplash. More dis

Penghao Zhu, Xiao-Qi Sun, Taylor L. Hughes, Gaurav Bahl

While chirality imbalances are forbidden in conventional lattice systems, non-Hermiticity can effectively avoid the chiral-doubling theorem to facilitate 1D chiral dynamics. Indeed, such systems support unbalanced unidirectional flows that can lead to the localization of an extensive number of states at the boundary, known as the non-Hermitian skin effect (N

Francisco Nogueras-Lara, Rainer Schödel, Nadine Neumayer

The Milky Way's center is the closest galaxy nucleus and the most extreme environment of the Galaxy. Although its volume is less than 1% of that of the Galactic disk, up to 10% of all new-born stars in the Galaxy in the past 100 Myr formed there. Therefore, it constitutes a perfect laboratory to understand star formation under extreme conditions, similar to

Yael Alush, Nicholas Chamberlain Stone

The "no-hair" theorem can, in principle, be tested at the center of the Milky Way by measuring the spin and the quadrupole moment of Sgr A$^*$ with the orbital precession of S-stars, measured over their full periods. Contrary to the original method, we show why it is possible to test the no-hair theorem using observations from only a single star, by measurin

Stefano Antonini, Petar Simidzija, Brian Swingle, Mark Van Raamsdonk

A large class of flat big bang - big crunch cosmologies with negative cosmological constant are related by analytic continuation to asymptotically AdS traversable wormholes with planar cross section. In recent works (arXiv: 2102.05057, 2203.11220) it was suggested that such wormhole geometries may be dual to a pair of 3D holographic CFTs coupled via auxiliar

Zachary Nasipak

We calculate the scalar self-force experienced by a scalar point-charge orbiting a Kerr black hole along $r\theta$-resonant geodesics. We use the self-force to calculate the averaged rate of change of the charge's orbital energy $\langle\dot{E}\rangle$, angular momentum $\langle\dot{L}_z\rangle$, and Carter constant $\langle\dot{Q}\rangle$, which together ca

Wooyoung Jang, Doojin Kim, Kyoungchul Kong, Youngjoon Kwon

We propose a novel experimental scheme, called DAMSA (Dump-produced Aboriginal Matter Searches at an Accelerator), for searching for dark-sector particles, using rare nuclear isotope accelerator facilities that provide high-flux proton beams to produce a large number of rare nuclear isotopes. The high-intensity nature of their beams enables the investigation

Guo-yuan Huang

In light of the exciting campaign of cosmogenic neutrino detection, we investigate the double and multiple tau bangs detectable at future tau neutrino telescopes. Such events are expected from the Standard Model (SM) higher-order processes, which can be easily identified with broad techniques anticipated at future tau neutrino telescopes. We find that SM per

John Coffey, David McKeen, David E. Morrissey, Nirmal Raj

Scattering interactions between dark matter and Standard Model states mediated by pseudoscalars are generically challenging to uncover at direct detection experiments due to rates suppressed by powers of the local dark matter velocity v ~ 0.001 c. However, they may be observed in the dark matter-induced heating of neutron stars, whose steep gravitational pot

Seongjin Ahn, Sankar Das Sarma

Using Ioffe-Regel-Mott (IRM) criterion for strong localization crossover in disordered doped 2D electron systems, we theoretically study the relationships among the three key experimentally determined localization quantities: critical density ($n_\mathrm{c}$), critical resistance ($\rho_\mathrm{c}$), and sample quality defined by the effective impurity densi

Svea Hernandez, Autumn Winch, Søren Larsen, Bethan L. James

We analyze spectroscopic observations of five young massive clusters (YMCs) in the barred spiral galaxy NGC 1313 to obtain detailed abundances from their integrated light. Our sample of YMCs was observed with the X-Shooter spectrograph on the Very Large Telescope (VLT). We make use of theoretical isochrones to generate synthetic integrated-light spectra, ite

Mohammadreza Ayromlou, Guinevere Kauffmann, Abhijeet Anand, Simon D. M. White

We employ several galaxy formation models, in particular, L-GALAXIES, IllustrisTNG, and EAGLE, as well as observational samples from SDSS and DESI, to investigate galactic conformity, the observed large-scale correlation between the star-formation properties of central (primary) galaxies and those of their neighbours. To analyse the models and observations u

Lachlan Lancaster, Sarah Pearson, Benjamin F. Williams, Kathryn V. Johnston

Studies of resolved stellar populations in the Milky Way and nearby galaxies reveal an amazingly detailed and clear picture of galaxy evolution. Within the Local Group, the ability to probe the stellar populations of small and large galaxies opens up the possibility of exploring key questions such as the nature of dark matter, the detailed formation history

Shlok Gilda, Tanvi Jain, Aashish Dhalla

Authentication and authorization of a user's identity are generally done by the service providers or identity providers. However, these centralized systems limit the user's control of their own identity and are prone to massive data leaks due to their centralized nature. We propose a blockchain-based identity management system to authenticate and authorize u

Junyu Xie, Weidi Xie, Andrew Zisserman

The objective of this paper is a model that is able to discover, track and segment multiple moving objects in a video. We make four contributions: First, we introduce an object-centric segmentation model with a depth-ordered layer representation. This is implemented using a variant of the transformer architecture that ingests optical flow, where each query v

Rezvan Sherkati, James J. Clark

We present a new method for image salience prediction, Clustered Saliency Prediction. This method divides subjects into clusters based on their personal features and their known saliency maps, and generates an image salience model conditioned on the cluster label. We test our approach on a public dataset of personalized saliency maps and cluster the subjects

Rui Shao, Tianxing Wu, Ziwei Liu

Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However, existing methods only focus on detecting one-step facial manipulation. As the emergence of easy-accessible facial editing ap

Sourav Manna, Sanjib Kumar Das, Bitan Roy

Topological insulators, featuring bulk-boundary correspondence, have been realized on a large number of noncrystalline materials, among which amorphous network, quasicrystals and fractal lattices are the most prominent ones. By contrast, topological superconductors beyond the realm of quantum crystals are yet to be harnessed, as their nucleation takes place

Runsheng Xu, Zhengzhong Tu, Hao Xiang, Wei Shao

Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for autonomous driving. Although recent literature has made significant progress on BEV map understanding, they are all based on single-agent camera-based systems. These solutions sometimes have difficulty handling occlusions or detecting distant objects in complex traffic sc

Jiadai Sun, Yuchao Dai, Xianjing Zhang, Jintao Xu

Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path planning, and static map construction. How to effectively exploit the spatial-temporal information is a critical question for 3D LiDAR moving object segmentation (LiDAR-MOS). In thi

Dibya Ghosh, Anurag Ajay, Pulkit Agrawal, Sergey Levine

Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge is to employ pessimistic or conservative methods, which avoid behaviors that are too dissimilar from those in the training dataset. However, relying exclusively on conservatism has

Vitor S. Barroso, August Geelmuyden, Zack Fifer, Sebastian Erne

For the past two hundred years, parametric instabilities have been studied in various physical systems, such as fluids, mechanical devices and even inflationary cosmology. It was not until a few decades ago that this subharmonic unstable response arose as a central mechanism for the thermalisation of the Early Universe, in a theory known as preheating. Here

Ryan Requist

The Schr\"odinger-like equations for the marginal and conditional probability amplitudes resulting from the exact factorization of the wavefunction of a two-component system are derived in a form that is invariant to gauge and coordinate transformations. Coupled equations equivalent to the nonrelativistic Schr\"odinger equation of a molecule are derived as a

Carlos de Gois, Kiara Hansenne, Otfried Gühne

Quantum measurements are inherently probabilistic and quantum theory often forbids to precisely predict the outcomes of simultaneous measurements. This phenomenon is captured and quantified through uncertainty relations. Although studied since the inception of quantum theory, the problem of determining the possible expectation values of a collection of quant

Hengyuan Ma, Li Zhang, Xiatian Zhu, Jianfeng Feng

Score-based generative models (SGMs) have recently emerged as a promising class of generative models. However, a fundamental limitation is that their inference is very slow due to a need for many (e.g., 2000) iterations of sequential computations. An intuitive acceleration method is to reduce the sampling iterations which however causes severe performance de

Mikhail Charnotskii

Point Spread Function (PSF) for imaging through inhomogeneous refractive medium, such as atmospheric turbulence is bounded by three constraints [Charnotskii, Opt. Eng., 52, 04600, (2013)]. PSF is non-negative, band-limited, and the third constraint, related to the energy conservation principle, warrants the absence of fluctuations in the image of a uniformly

Guoxiang Grayson Tong, Daniele E. Schiavazzi

We propose a data-driven framework to increase the computational efficiency of the explicit finite element method in the structural analysis of soft tissue. An encoder-decoder long short-term memory deep neural network is trained based on the data produced by an explicit, distributed finite element solver. We leverage this network to predict synchronized dis

Yacine Aoun, Sébastien Ott, Yvan Velenik

We consider the Random-Cluster model on $\mathbb{Z}^d$ with interactions of infinite range of the form $J_x = \psi(x)\mathsf{e}^{-\rho(x)}$ with $\rho$ a norm on $\mathbb{Z}^d$ and $\psi$ a subexponential correction. We first provide an optimal criterion ensuring the existence of a nontrivial saturation regime (that is, the existence of $\beta_{\rm sat}(s)>0

Ch. Sobhan Babu, Ravindra Guravannavar, Arvind Hulgeri

GANs have two competing modules: the generator module is trained to generate new examples, and the discriminator module is trained to discriminate real examples from generated examples. The training procedure of GAN is modeled as a finitely repeated simultaneous game. Each module tries to increase its performance at every repetition of the base game (at ever

Naser G. A. Mahfouz, Yi Ming, Kaleb Smith

Processes related to cloud physics constitute the largest remaining scientific uncertainty in climate models and projections. This uncertainty stems from the coarse nature of current climate models and relatedly the lack of understanding of detailed physics. We train a generative adversarial network to generate realistic cloud fields conditioned on meterolog

José A. Salmerón, Giulia Di Nunno, Bernardo D'Auria

Default risk calculus plays a crucial role in portfolio optimization when the risky asset is under threat of bankruptcy. However, traditional stochastic control techniques are not applicable in this scenario, and additional assumptions are required to obtain the optimal solution in a before-and-after default context. We propose an alternative approach using

Yassine Tissaoui, Simone Marras, Annalisa Quaini, Felipe A. V. De Braganca Alves

Numerical weather prediction is pushing the envelope of grid resolution at local and global scales alike. Aiming to model topography with higher precision, a handful of articles introduced unstructured vertical grids and tested them for dry atmospheres. The next step towards effective high-resolution unstructured grids for atmospheric modeling requires that

Jun-Kun Wang, Andre Wibisono

Hamiltonian Monte Carlo (HMC) is a popular method in sampling. While there are quite a few works of studying this method on various aspects, an interesting question is how to choose its integration time to achieve acceleration. In this work, we consider accelerating the process of sampling from a distribution $\pi(x) \propto \exp(-f(x))$ via HMC via time-var

Wang Yang, Alberto Nocera, Chao Xu, Hae-Young Kee

We study the nearest neighboring spin-1/2 Kitaev-Heisenberg-Gamma ($KJ\Gamma$) model on the honeycomb lattice in the parameter region of ferromagnetic (FM) Kitaev and antiferromagnetic (AFM) Heisenberg couplings relevant for honeycomb iridates, using a coupled-chain analysis. Starting from the gapless Luttinger liquid phase of a decoupled $KJ\Gamma$ chain, t

Felix Wagner, Daniel Bartolot, Damir Rizvanovic, Florian Reindl

Cryogenic solid state detectors are widely used in dark matter and neutrino experiments, and require a sensible raw data analysis. For this purpose, we present Cait, an open source Python package with all essential methods for the analysis of detector modules fully integrable with the Python ecosystem for scientific computing and machine learning. It comes w

Lei Xiao, Salah Nouri, Joel Hegland, Alberto Garcia Garcia

Virtual reality (VR) headsets provide an immersive, stereoscopic visual experience, but at the cost of blocking users from directly observing their physical environment. Passthrough techniques are intended to address this limitation by leveraging outward-facing cameras to reconstruct the images that would otherwise be seen by the user without the headset. Th

Jialu Li, Hao Tan, Mohit Bansal

Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. In this paper, we aim to solve two key challenges in this task: utilizing multilingual instructions for improved instruction-path grounding and navigating through new environments that are unseen during training. To address these ch

Sai K Popuri

Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in the variance of the forecast. In large scale applications of the model with millions of data points and hundreds of featur

S. Khrapak, A. Khrapak

A freezing density scaling of transport properties of the Lennard-Jones fluid is rationalized in terms of the Rosenfeld's excess entropy scaling and isomorph theory of Roskilde-simple systems. Then, it is demonstrated that the freezing density scaling operates reasonably well for viscosity and thermal conductivity coefficients of liquid argon, krypton, and x

Jae Soon Baik, In Young Yoon, Jun Won Choi

Modern deep learning has achieved great success in various fields. However, it requires the labeling of huge amounts of data, which is expensive and labor-intensive. Active learning (AL), which identifies the most informative samples to be labeled, is becoming increasingly important to maximize the efficiency of the training process. The existing AL methods

Chiara Guidi, Vittorio Martino, Giulio Tralli

In this paper we aim at identifying the level sets of the gauge norm in the Heisenberg group $\mathbb{H}^n$ via the prescription of their (non-constant) horizontal mean curvature. We establish a uniqueness result in $\mathbb{H}^1$ under an assumption on the location of the singular set, and in $\mathbb{H}^n$ for $n\geq 2$ in the proper class of horizontally

Aladdin Abdulhassan, Mahmood Ahmadi

Software-defined Networking is an approach that decouples the software-based control plane from the hardware-based data plane proposed for enterprise networks; OpenFlow is the most famous flexible protocol that can manage network traffic between the control and the data plane. Software-Defined Networking (SDN) requires up to 18 fields of the packets header t

Huawei Lin, Haozhe Liu, Qiufu Li, Linlin Shen

Can we construct an explainable face recognition network able to learn a facial part-based feature like eyes, nose, mouth and so forth, without any manual annotation or additionalsion datasets? In this paper, we propose a generic Explainable Channel Loss (ECLoss) to construct an explainable face recognition network. The explainable network trained with ECLos

Leonid Chindelevitch, Elita Jauneikaite, Nicole E. Wheeler, Kasim Allel

Antimicrobial resistance (AMR) is a growing public health threat, estimated to cause over 10 million deaths per year and cost the global economy 100 trillion USD by 2050 under status quo projections. These losses would mainly result from an increase in the morbidity and mortality from treatment failure, AMR infections during medical procedures, and a loss of

Kamal Das, Shibalik Lahiri, Rhonald Burgos Atencia, Dimitrie Culcer

The second-order nonlinear current originates from three physical mechanisms: extrinsic nonlinear Drude and Berry curvature dipole and intrinsic Berry connection polarizability. Here, we predict a new intrinsic contribution to the current related to the quantum metric, a quantum geometric property of the electronic wave function. This contribution manifests

Andrei A. Klishin, Nicolas H. Christianson, Cynthia S. Q. Siew, Dani S. Bassett

The structure of knowledge is commonly described as a network of key concepts and semantic relations between them. A learner of a particular domain can discover this network by navigating the nodes and edges presented by instructional material, such as a textbook, workbook, or other text. While over a long temporal period such exploration processes are certa

Emma D'Aniello, Anthony Gauvan, Laurent Moonens, Joseph M. Rosenblatt

In this paper, we study Lebesgue differentiation processes along rectangles $R_k$ shrinking to the origin in the Euclidean plane, and the question of their almost everywhere convergence in $L^p$ spaces. In particular, classes of examples of such processes failing to converge a.e. in $L^\infty$ are provided, for which $R_k$ is known to be oriented along the s

Giulia Tenasini, David Soler-Delgado, Zhe Wang, Fengrui Yao

We report experimental investigations of transport through bilayer graphene (BLG)/chromium trihalide (CrX$_3$; X=Cl, Br, I) van der Waals interfaces. In all cases, a large charge transfer from BLG to CrX$_3$ takes place (reaching densities in excess of $10^{13}$ cm$^{-2}$), and generates an electric field perpendicular to the interface that opens a band gap

Palak Arora

In this paper, we present a constructive proof of Popescu's non-commutative Fej\'er-Riesz theorem for non-commuting polynomials. We are considering non-commutating polynomial in left-creation and left-annihilation multi-Toeplitz operators.

Jae Soon Baik, In Young Yoon, Jun Won Choi

There is growing interest in the challenging visual perception task of learning from long-tailed class distributions. The extreme class imbalance in the training dataset biases the model to prefer recognizing majority class data over minority class data. Furthermore, the lack of diversity in minority class samples makes it difficult to find a good representa

Giuseppe Dibitetto, Nicolò Petri

Coleman-de Luccia transitions are spontaneous processes of nucleation of bubbles within metastable gravitational vacua including in their interior a true stable vacuum. From the perspective of lower-dimensional gauged supergravities obtained by truncating Type II and M-theory, these instantonic processes are represented by smooth domain walls featured by de

Karim Cherifi, Philipp Schulze, Volker Mehrmann, Leo Goßlau

Digital twins have become popular for their ability to monitor and optimize a process or a machine, ideally through its complete life cycle using simulations and sensor data. In this paper, we focus on the challenge of accurate and real-time simulations for digital twins in the context of electrical machines. To build such a digital twin involves not only co

Oliver Janzer, Benny Sudakov, István Tomon

In this paper we study the fundamental problem of finding small dense subgraphs in a given graph. For a real number $s>2$, we prove that every graph on $n$ vertices with average degree at least $d$ contains a subgraph of average degree at least $s$ on at most $nd^{-\frac{s}{s-2}}(\log d)^{O_s(1)}$ vertices. This is optimal up to the polylogarithmic factor, a

Tamás Kriváchy, Krzysztof T. Kaczmarek, Mikael Afzelius, Jean Etesse

Achieving spin squeezing within solid-state devices is a long standing research goal, due to the promise of their particularities, for instance their long coherence times, the possibility of low-temperature experiments or integration of entanglement-assisted sensors on-chip. In this work, we investigate an interferometer-free four-color scheme to achieve spi

Daniel Ferguson, François G. Meyer

For graph-valued data sampled iid from a distribution $\mu$, the sample moments are computed with respect to a choice of metric. In this work, we equip the set of graphs with the pseudo-metric defined by the $\ell_2$ norm between the eigenvalues of the respective adjacency matrices. We use this pseudo metric and the respective sample moments of a graph value

Norbert Kaiser

The well known analytical formula for $SU(2)$ matrices $U = \exp(i \vec \tau \!\cdot\! \vec \varphi\,) = \cos|\vec \varphi\,| + i\vec \tau \!\cdot\! \hat\varphi \, \sin|\vec \varphi\,|$\\ is extended to the $SU(3)$ group with eight real parameters. The resulting analytical formula involves the sum over three real roots of a cubic equation, corresponding to t

George Barnes, Adrian Padellaro, Sanjaye Ramgoolam

We describe the implications of permutation symmetry for the state space and dynamics of quantum mechanical systems of matrices of general size $N$. We solve the general 11- parameter permutation invariant quantum matrix harmonic oscillator Hamiltonian and calculate the canonical partition function. The permutation invariant sector of the Hilbert space, for

Yiqiu Han, Xiao Chen

We study entanglement fluctuations and quantum error correction in the weakly monitored volume-law phase of quantum automaton circuits subject to repeated local measurements. We numerically observe that the entanglement entropy exhibits strong fluctuation with the exponent close to the ``growth exponent'' of the Kardar-Parisi-Zhang (KPZ) universality class,

Jānis Cīrulis

Sectional pseudocomplementation (sp-complementation) on a poset is a partial operation $*$ which associates with every pair $(x,y)$ of elements, where $x \ge y$, the pseudocomplement $x*y$ of $x$ in the upper section $[y)$. Any total extension $\to$ of $*$ is said to be an extended sp-complementation and is considered as an implication-like operation. Extend

Henning Reinken, Sabine H. L. Klapp, Michael Wilczek

Microswimmer suspensions self-organize into complex spatio-temporal flow patterns, including vortex lattices and mesoscale turbulence. Here we explore the consequences for the motion of passive tracers, based on a continuum model for the microswimmer velocity field. We observe two qualitatively different regimes distinguished via the dimensionless Kubo numbe

Ioannis N. Tzortzis, Ioannis Rallis, Konstantinos Makantasis, Anastasios Doulamis

In Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. In this paper, we propose a Rank-$R$ tensor-based learning model to identi

Paolo Maramotti, Alessandro Paolo Capasso, Giulio Bacchiani, Alberto Broggi

In the typical autonomous driving stack, planning and control systems represent two of the most crucial components in which data retrieved by sensors and processed by perception algorithms are used to implement a safe and comfortable self-driving behavior. In particular, the planning module predicts the path the autonomous car should follow taking the correc

Adam Falkowski, Antonio Rodríguez-Sánchez

Measurements of angular correlations in nuclear beta decay are important tests of the Standard Model (SM). Among those, the so-called D correlation parameter occupies a particular place because it is odd under time reversal, and because the experimental sensitivity is at the $10^{-4}$ level, with plans of further improvement in the near future. Using effecti

Israa Khalaf Salman Al-Tameemi, Mohammad-Reza Feizi-Derakhshi, Saeed Pashazadeh, Mohammad Asadpour

Social media networks have become a significant aspect of people's lives, serving as a platform for their ideas, opinions and emotions. Consequently, automated sentiment analysis (SA) is critical for recognising people's feelings in ways that other information sources cannot. The analysis of these feelings revealed various applications, including brand evalu

Madeline C. Schiappa, Shruti Vyas, Hamid Palangi, Yogesh S. Rawat

Joint visual and language modeling on large-scale datasets has recently shown good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In this work, we perform the first extensive robustness study of video-language models against various real-worl

Hongzhi Huang, Yu Wang, Qinghua Hu, Ming-Ming Cheng

Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods basing on auto-encoder (AE) and prototype learning show great potential in handling this challenging task. In this study, we propose a novel method, called Class-Specific

Tong Wei, Linlong Wu, Kumar Vijay Mishra, M. R. Bhavani Shankar

Intelligent reflecting surface (IRS) is recognized as an enabler of future dual-function radar-communications (DFRC) by improving spectral efficiency, coverage, parameter estimation, and interference suppression. Prior studies on IRS-aided DFRC focus either on narrowband processing, single-IRS deployment, static targets, non-clutter scenario, or on the under

James Michelson

Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. However, as machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different than those presently studied. The existing research paradigm in machine learning which develops metrics and de

Muriel Livernet, Sarah Whitehouse

Let $R$ be a commutative ring with unit. We consider the homotopy theory of the category of spectral sequences of $R$-modules with the class of weak equivalences given by those morphisms inducing a quasi-isomorphism at a certain fixed page. We show that this admits a structure close to that of a category of fibrant objects in the sense of Brown and in partic

Marie-Claude Arnaud, Anna Florio, Valentine Roos

Motivated by Mather theory of minimizing measures for symplectic twist dynamics, we study conformally symplectic flows on a cotangent bundle. These dynamics are the most general dynamics for which it makes sense to look at (asymptotic) dynamical Maslov index. Our main result is the existence of invariant measures with vanishing index without any convexity hy

Samuel Alvite, Nerea G. Barral, Javier Majadas

We show that a modification of the proof of a result of Gulliksen gives an elementary proof of the following important theorem by Avramov: if $(A,k) \to (B,l)$ is a homomorphism of noetherian local rings and $B$ is of finite flat dimension over $A$, then the homomorphism induced in Andr\'e-Quillen homology modules $H_2(A,l,l)\to H_2(B,l,l)$ is injective.

Hanbin Hong, Binghui Wang, Yuan Hong

We study certified robustness of machine learning classifiers against adversarial perturbations. In particular, we propose the first universally approximated certified robustness (UniCR) framework, which can approximate the robustness certification of any input on any classifier against any $\ell_p$ perturbations with noise generated by any continuous probab

D. E. King, D. C. Jones, C. Gal, D. Gaskell

The PREX-2 and CREX experiments in Hall A at Jefferson Lab are precision measurements of parity violating elastic electron scattering from complex nuclei. One requirement was that the incident electron beam polarization, typically $\approx$90\%, be known with 1\% precision. We commissioned and operated a M{\o}ller polarimeter on the beam line that exceeds th

Renye Zhang, Yimin Yin, Wanxia Deng, Chen Li

Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields. Because veins are buried beneath the skin tissue, finger vein image recognition has an unparalleled advantage, which is not easily disturbed by external factors. This review summarizes 46 papers about deep learning for f

Moaathe Belhaj Ahmed, David Kubiznak, Robert B. Mann

An isolated critical point is a peculiar thermodynamic critical point that occurs in the phase diagram of hyperbolic black holes in Kth-order Lovelock gravity in higher dimensions (with K odd) for special tuned Lovelock coupling constants. It corresponds to a "merger" of two swallowtails and is characterized by non-standard critical exponents. Upon employing

Tom Edinburgh, Ari Ercole, Stephen J. Eglen

Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expres

Florian Peißker, Andreas Eckart, Michal Zajaček, Silke Britzen

The ongoing monitoring of the Galactic center and Sgr A*, the central supermassive black hole, produces surprising and unexpected findings. This goes hand in hand with the technical evolution of ground- and space-based telescopes and instruments, but also with the progression of image filter techniques such as the Lucy Richardson algorithm. As we continue to

Maria Alberich-Carramiñana, Jordi Guàrdia, Enric Nart, Adrien Poteaux

Given a valued field $(K,v)$ and an irreducible polynomial $g\in K[x]$, we survey the ideas of Ore, Maclane, Okutsu, Montes, Vaqui\'e and Herrera-Olalla-Mahboub-Spivakovsky, leading (under certain conditions) to an algorithm to find the factorization of $g$ over a henselization of $(K,v)$.