July 2023 arXiv papers — page 96

Chee Sheng Fong, Anish Ghoshal, Abhishek Naskar, Moinul Hossain Rahat

We present the possibility that the seesaw mechanism and nonthermal leptogenesis can be {investigated} via primordial non-Gaussianities in the context of a majoron curvaton model. Originating as a massless Nambu-Goldstone boson from the spontaneous breaking of the global baryon ($B$) minus lepton ($L$) number symmetry at a scale $v_{B-L}$, majoron becomes ma

Haley Bowden, Peter Behroozi, Andrew Hearin

The stellar mass - halo mass relation provides a strong basis for connecting galaxies to their host dark matter halos in both simulations and observations. Other observable information, such as the density of the local environment, can place further constraints on a given halo's properties. In this paper, we test how the peak masses of dark matter halos and

Sylvain Rossi, Alessandro Tarantola

A Dirac operator on the plane with constant (positive) mass is a Chern insulator, sitting in class D of the Kitaev table. Despite its simplicity, this system is topologically ill-behaved: the non-compact Brillouin zone prevents definition of a bulk invariant, and naively placing the model on a manifold with boundary results in violations of the bulk-edge cor

Lakshya Bhardwaj, Lea E. Bottini, Ludovic Fraser-Taliente, Liam Gladden

These are a set of lecture notes on generalized global symmetries in quantum field theory. The focus is on invertible symmetries with a few comments regarding non-invertible symmetries. The main topics covered are the basics of higher-form symmetries and their properties including 't Hooft anomalies, gauging and spontaneous symmetry breaking. We also introdu

Evgeni Grishin, Shmuel Gilbaum, Nicholas C. Stone

Accretion discs in active galactic nuclei (AGN) foster black hole (BH) formation, growth, and mergers. Stellar mass BHs migrate inwards under the influence of hydrodynamical torques unless they encounter a region where the torque flips sign. At these migration traps, BHs accumulate and merge via dynamical or gas-assisted interactions, producing high-frequenc

Michal P. Heller, Aleksas Mazeliauskas, Thimo Preis

We study how isotropic and homogeneous far-from-equilibrium quantum systems relax to nonthermal attractors, which are of interest for cold atoms and nuclear collisions. We demonstrate that a first-order ordinary differential equation governs the self-similar approach to nonthermal attractors, i.e., the prescaling. We also show that certain natural scaling-br

Ouail Kitouni, Niklas Nolte, Michael Williams

The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This is especially important for interpretability and fairness considerations. In a broader context, scenarios in which monotonicity is important can be found in finance, medicine, phys

Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu

We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object, which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion

Wen Chen, Jun Gao, Yibei Li, Zhen Xu

The projected energy correlator measures the energy deposited in multiple detectors as a function of the largest angular distance $x_L = (1 - \cos\chi_L)/2$ between detectors. The collinear limit $x_L\to 0$ of the projected energy correlator is particularly interesting for understanding the jet-substructures, while the large logarithms of $x_L$ could potenti

Qi-Wei Wang, Hongyu Lu, Yu Chen, Da-Wei Zhou

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to

Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan

Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" models through finetuning. This has led to an ecosystem where models are related to each other, sharing structure and often even parameter values. However, it is hard to manage these mo

Cheuk Ting Li

Information diagram and the I-measure are useful mnemonics where random variables are treated as sets, and entropy and mutual information are treated as a signed measure. Although the I-measure has been successful in machine proofs of entropy inequalities, the theoretical underpinning of the ``random variables as sets'' analogy has been unclear until the rec

Enrico Formenti, Luca Mariot

We consider the problem of exhaustively visiting all pairs of linear cellular automata which give rise to orthogonal Latin squares, i.e., linear Orthogonal Cellular Automata (OCA). The problem is equivalent to enumerating all pairs of coprime polynomials over a finite field having the same degree and a nonzero constant term. While previous research showed ho

Rafael Aoude, Alexander Ochirov

We study gravitational absorption effects using effective on-shell scattering amplitudes. We develop an in-in probability-based framework involving plane- and partial-wave coherent states for the incoming wave to describe the interaction of the wave with a black hole or another compact object. We connect this framework to a simplified single-quantum analysis

Aupam Hamran, Marzieh Vaeztourshizi, Amirhossein Esmaili, Massoud Pedram

In this paper, we present different architectures of Convolutional Neural Networks (CNN) to analyze and classify the brain tumors into benign and malignant types using the Magnetic Resonance Imaging (MRI) technique. Different CNN architecture optimization techniques such as widening and deepening of the network and adding skip connections are applied to impr

P. Jishnu Sai, Rajeev Kumar Jain

Light spectator fields may not be dynamically relevant for the inflationary phase of the early universe, but they can still induce interesting imprints on cosmological observables. In this paper, we compute the cross-correlations of the inflationary perturbations, both scalar and tensor, with the fluctuations of a non-minimally interacting spectator field us

Farnaz Fazelpour, Vrinda D. Desai, Karen E. Daniels

The stability of a granular material is a collective phenomenon controlled by individual particles through their interactions. Forecasting when granular materials will undergo an abrupt failure is an ongoing challenge due to the intricate interactions between particles. Here, we report experiments on photoelastic disks undergoing intermittent stick-slip dyna

F. J. T. Salazara, A. F. B. A. Prado

To collect additional solar energy during the hours of darkness and to overcome the limited Terrestrial solar power due to the diurnal day night cycle, the concept of a Geostationary Tethered Collecting Solar Power Satellite System has been proposed by several authors in the last years. This tethered system consists of a long tether used to link two bodies:

Muhammad Faryad, Akhlesh Lakhtakia

Polymer banknotes are being increasingly adopted to replace older banknotes. Since banknotes are forensically important substrates for fingermark detection and identification, we present a single-step process to enhance the visualization of fingermarks on banknotes using columnar thin films (CTFs) of nickel. This single-step vacuum technique enhances the qua

Antonio F. B. A. Prado

Gravity-Assisted maneuvers have been used as a technique to reduce fuel consumption in deep space missions for several decades now. It opened the doors of the exterior solar system. The literature shows those results, as well as new versions of this maneuver, which includes: the use of propulsion combined with the close approach, both high or low thrust; the

Xiaoyi Cui

While $L_\infty$ algebras are fundamental structures in differential geometry and mathematical physics, the geometric information encoded in such structures is often implicit. We address the following question: What constitutes a geometrically meaningful deformation of an $L_\infty$ algebra arising from vector bundles, and how can such deformations classify

Becky Lin, Donald Witt, Jeff F. Young, Lukas Chrostowski

We present the required techniques for the successful low loss packaging of integrated photonic devices capable of operating down to 970 mK utilizing photonic wire bonds. This scalable technique is shown to have an insertion loss of less than 2 dB per connection between a SMF-28 single mode fibre and a silicon photonic chip at these temperatures. This techni

Ben Berger, Michal Feldman, Vasilis Gkatzelis, Xizhi Tan

In the metric distortion problem there is a set of candidates $C$ and voters $V$ in the same metric space. The goal is to select a candidate minimizing the social cost: the sum of distances of the selected candidate from all the voters, and the challenge arises from the algorithm receiving only ordinaL input: each voter's ranking of candidate, while the obje

Donald Witt, Jeff Young, Lukas Chrostowski

We present a new fab-in-the-loop reinforcement learning algorithm for the design of nano-photonic components that accounts for the imperfections present in nanofabrication processes. As a demonstration of the potential of this technique, we apply it to the design of photonic crystal grating couplers fabricated on an air clad 220 nm silicon on insulator singl

Yuting Xu, Jian Liang, Gengyun Jia, Ziming Yang

The growing threats of deepfakes to society and cybersecurity have raised enormous public concerns, and increasing efforts have been devoted to this critical topic of deepfake video detection. Existing video methods achieve good performance but are computationally intensive. This paper introduces a simple yet effective strategy named Thumbnail Layout (TALL),

Mingfei Cheng, Yuan Zhou, Xiaofei Xie

Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety violations while the diversity of the generated test cases is ignored, which may generate many redundant test cases and failures. Such redundant failures can reduce testing performa

Gregory A. Hamilton, Felix Leditzky

We propose a study of multipartite entanglement through persistent homology, a tool used in topological data analysis. In persistent homology, a 1-parameter filtration of simplicial complexes called persistence complex is used to reveal persistent topological features of the underlying data set. This is achieved via the computation of homological invariants

Souvik Kundu, Sharath Nittur Sridhar, Maciej Szankin, Sairam Sundaresan

Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question answering, requiring only few epochs of fine-tuning. However, their large model sizes often prohibit their applications on resource-constrained edge devices. Existing solutions of yie

Daniele Dorigoni, Rudolfs Treilis

Generalised Eisenstein series are non-holomorphic modular invariant functions of a complex variable, $\tau$, subject to a particular inhomogeneous Laplace eigenvalue equation on the hyperbolic upper-half $\tau$-plane. Two infinite classes of such functions arise quite naturally within different string theory contexts. A first class can be found by studying t

Yong Li, Sven Schewe, Qiyi Tang

Families of DFAs (FDFAs) have recently been introduced as a new representation of $\omega$-regular languages. They target ultimately periodic words, with acceptors revolving around accepting some representation $u\cdot v^\omega$. Three canonical FDFAs have been suggested, called periodic, syntactic, and recurrent. We propose a fourth one, limit FDFAs, which

Dapeng Hu, Jian Liang, Xinchao Wang, Chuan-Sheng Foo

Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive uncertainty in the target domain, a crucial aspect of the safe deployment of UDA models, has received limited attention. The conventional in-domain calibration method, \textit{temp

Adam Gleichman, Kindred Griffis, Sergey V. Baryshev

The electron fluid model in plasmonic field effect transistor (FET) operation is related to the behavior of a radio-frequency (RF) cavity. This new understanding led to finding the relationships between physical device parameters and equivalent circuit components in traditional parallel resistor, inductor, and capacitor (RLC) and transmission models for cavi

Daiqing Li, Huan Ling, Amlan Kar, David Acuna

In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific perception tasks. We investigate two types

Dongjin Lee, Elle Lavichant, Boris Kramer

In uncertainty quantification, variance-based global sensitivity analysis quantitatively determines the effect of each input random variable on the output by partitioning the total output variance into contributions from each input. However, computing conditional expectations can be prohibitively costly when working with expensive-to-evaluate models. Surroga

Manas Kulkarni, Satya N. Majumdar

We consider a closed quantum system subjected to stochastic Poissonian resetting with rate $r$ to its initial state. Resetting drives the system to a nonequilibrium stationary state (NESS) with a mixed density matrix which has both classical and quantum correlations. We provide a general framework to study these NESS correlations for a closed quantum system

Aditya Mitra, Anisha Ghosh, Sibi Chakkaravarthy Sethuraman

Passwordless authentication was first tested for seamless and secure merchant payments without the use of passwords or pins. It opened a whole new world of authentications giving up the former reliance on traditional passwords. It relied on the W3C Web Authentication (WebAuthn) and Client to Authenticator Protocol (CTAP) standards to use the public key crypt

Gorjan Radevski, Dusan Grujicic, Marie-Francine Moens, Matthew Blaschko

The focal point of egocentric video understanding is modelling hand-object interactions. Standard models, e.g. CNNs or Vision Transformers, which receive RGB frames as input perform well. However, their performance improves further by employing additional input modalities that provide complementary cues, such as object detections, optical flow, audio, etc. T

Simon Holdenried-Krafft, Peter Somers, Ivonne A. Montes-Majarro, Diana Silimon

Whole slide image (WSI) assessment is a challenging and crucial step in cancer diagnosis and treatment planning. WSIs require high magnifications to facilitate sub-cellular analysis. Precise annotations for patch- or even pixel-level classifications in the context of gigapixel WSIs are tedious to acquire and require domain experts. Coarse-grained labels, on

Klaudia Maj

Relativistic heavy-ion beams at the LHC are accompanied by a large flux of equivalent photons, leading to multiple photon-induced processes. This proceeding presents searches for physics beyond the Standard Model enabled by photon-photon processes in both di-tau and diphoton final states. The tau-pair production measurements can constrain the tau lepton's an

Rafael S. González D'León, Joshua Hallam, Yeison A. Quiceno D

We say that a pair of nonnegative integer sequences $(\{a_k\}_{k\geq 0},\{b_k\}_{k\geq 0})$ is Whitney-realizable if there exists a poset $P$ for which (the absolute values) of the Whitney numbers of the first and second kind are given by the numbers $a_k$ and $b_k$ respectively. The pair is said to be Whitney-dualizable if, in addition, there exists another

Peter Zalom

The Anderson impurity model (AIM) has long served as a cornerstone in the study of correlated electron systems. While numerical renormalization group (RG) offers great flexibility for metallic reservoirs, it becomes impossible in an unbiased way when a spectral gap $\Delta$ opens up in the tunneling density of states. The only known exception is provided by

Yasunori Lee, Keita Kanno

Natural frequencies and normal modes are basic properties of a structure which play important roles in analyses of its vibrational characteristics. As their computation reduces to solving eigenvalue problems, it is a natural arena for application of quantum phase estimation algorithms, in particular for large systems. In this note, we take up some simple exa

Tatsuki Koga, Congzheng Song, Martin Pelikan, Mona Chitnis

Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large population of devices, FL with DP produces a performant model in a timely manner. However, for applications with a smaller population, not only does the model utility degrade as the D

Alessandro Granelli, Silvia Pascoli, Serguey T. Petcov

We study the freeze-in scenario of leptogenesis via oscillations within the type-I seesaw model with two quasi-degenerate heavy Majorana neutrinos $N_{1,\,2}$ having masses $M_2 > M_1 \sim (0.1-100)\,\text{GeV}$, $(M_2-M_1)/M_1 \ll 1$, focusing on the role of the CP-violation provided by the Dirac phase $\delta$ of the Pontecorvo-Maki-Nakagawa-Sakata lepton

Jamie Donald-McCann, Rafaela Gsponer, Ruiyang Zhao, Kazuya Koyama

We present a series of full-shape analyses of galaxy power spectrum multipole measurements from the 6dFGS, BOSS, and eBOSS galaxy surveys. We use an emulated effective field theory of large-scale structure (EFTofLSS) model to conduct these analyses. We exploit the accelerated prediction speed of the neural-network-based emulator to explore various analysis s

Susi Lehtola, Miguel A. L. Marques

Density functional theory is the workhorse of chemistry and materials science, and novel density functional approximations (DFAs) are published every year. To become available in program packages, the novel DFAs need to be (re)implemented. However, according to our experience as developers of Libxc [Lehtola et al, SoftwareX 7, 1 (2018)], a constant problem i

A. Esteban-Gutiérrez, E. Mediavilla, J. Jiménez-Vicente, J. A. Muñoz

We use X-ray observations of quasar microlensing (sensitive to smaller compact objects than in the optical) to study the possible presence of a population of low mass black holes (from $\sim$ $10^{-3}M_{\odot}$ to $10^{-1}M_{\odot}$) in lens galaxies. We compare these observations with microlensing magnification simulations of a mixed population of stars and

Martin Hairer, Tommaso Rosati

This work studies the angular component $ \pi_{t} = u_{t} / \| u_{t} \| $ associated to the solution $ u $ of a vector-valued linear hyperviscous SPDE on a $d$-dimensional torus $$\mathrm{d} u^{\alpha} =- \nu^{\alpha} (- \Delta)^{\mathbf{a} } u^{\alpha} \mathrm{d} t + (u \cdot \mathrm{d} W)^{\alpha} \;,\quad \alpha \in \{ 1, \dots, m \} $$ for $ u \colon \ma

Mostafa Honari-Latifpour, Jiajie Ding, Igor Belykh, Mohammad-Ali Miri

Network synchronization of lasers is critical for reaching high-power levels and for effective optical computing. Yet, the role of network topology for the frequency synchronization of lasers is not well understood. Here, we report our significant progress toward solving this critical problem for networks of heterogeneous laser model oscillators with repulsi

Eric Braaten, Roberto Bruschini, Li-Ping He, Kevin Ingles

We study the time evolution of the numbers of charm mesons after the kinetic freezeout of the expanding hadron gas produced by the hadronization of the quark-gluon plasma from a central heavy-ion collision. The $\pi D$ reaction rates have contributions from a $D^\ast$ resonance in the $s$ channel. The $\pi D^\ast$ reaction rates are enhanced by $t$-channel s

Yuhang Wen, Zixuan Tang, Yunsheng Pang, Beichen Ding

Recognizing interactive action plays an important role in human-robot interaction and collaboration. Previous methods use late fusion and co-attention mechanism to capture interactive relations, which have limited learning capability or inefficiency to adapt to more interacting entities. With assumption that priors of each entity are already known, they also

Dohyun Kim, Yeseung Kim, Jaehwi Jang, Minjae Song

The spoken language serves as an accessible and efficient interface, enabling non-experts and disabled users to interact with complex assistant robots. However, accurately grounding language utterances gives a significant challenge due to the acoustic variability in speakers' voices and environmental noise. In this work, we propose a novel speech-scene graph

Claudio Iuliano, Jochen Zahn

Electromagnetic and gravitational perturbations on Kerr spacetime can be reconstructed from solutions to the Teukolsky equations. We study the canonical quantization of solutions to these equations for any integer spin. Our quantization scheme involves the analysis of the Hertz potential and one of the Newman-Penrose scalars, which must be related via the Te

Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen, Maren Mahsereci

Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of quantifying the uncertainties of predicted function values. For the quantified uncertainties to be well-calibrated, however, the kernel of the GP prior has to be carefully selected. In this paper, we theoretically compare two met

Shinji Koshida

This paper studies planar algebras of Jones' style associated with the Young graph. We first see that, given a positive real valued function on the Young graph, we may obtain a planar algebra whose structure is defined in terms of a state sum over the ways of filling planar tangles with Young diagrams. We delve into the case that the function is harmonic and

Mykhaylo Balynskyy, Alexander Khitun

In this work, we consider a type of magnetic memory where information is encoded into the mutual arrangements of magnets. The device is an active ring circuit comprising magnetic and electronic parts connected in series. The electric part includes a broad-band amplifier, phase shifters, and attenuators. The magnetic part is a mesh of magnonic waveguides with

Stefano Cusumano, Gabriele De Chiara

Quantum collision models normally consist of a system interacting with a set of ancillary units representing the environment. While these ancillary systems are usually assumed to be either two level systems (TLS) or harmonic oscillators, in this work we move further and represent each ancillary system as a structured system, i.e., a system made out of two or

Tamal K. Dey, Tao Hou

Vines and vineyard connecting a stack of persistence diagrams have been introduced in the non-zigzag setting by Cohen-Steiner et al. We consider computing these vines over changing filtrations for zigzag persistence while incorporating two more operations: expansions and contractions in addition to the transpositions considered in the non-zigzag setting. Alt

David Gamarnik, Aukosh Jagannath, Eren C. Kızıldağ

We study the Ising pure $p$-spin model for large $p$. We investigate the landscape of the Hamiltonian of this model. We show that for any $\gamma>0$ and any large enough $p$, the model exhibits an intricate geometrical property known as the multi Overlap Gap Property above the energy value $\gamma\sqrt{2\ln 2}$. We then show that for any inverse temperature

Ashwani Anand, Georg Zetzsche

When a system sends messages through a lossy channel, then the language encoding all sequences of messages can be abstracted by its downward closure, i.e. the set of all (not necessarily contiguous) subwords. This is useful because even if the system has infinitely many states, its downward closure is a regular language. However, if the channel has congestio

Toktarkhan Komesh, Guido Garay, Christian Henkel, Aruzhan Omar

We report high angular resolution observations, made with the Atacama Large Millimeter Array in band 6, of high excitation molecular lines of $\rm CH_3CN$ and $\rm SO_2$ and of the H29$\alpha$ radio recombination line towards the G345.0061+01.794 B HC H II region, in order to investigate the physical and kinematical characteristics of its surroundings. Emiss

Conrado da Costa, Mikhail Menshikov, Andrew Wade

We consider a random walk on the first quadrant of the square lattice, whose increment law is, roughly speaking, homogeneous along a finite number of half-lines near each of the two boundaries, and hence essentially specified by finitely-many transition laws near each boundary, together with an interior transition law that applies at sufficient distance from

Matteo Cacciola, Antonio Frangioni, Andrea Lodi

In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Progr

Fedor V. Fomin, Petr A. Golovach, Danil Sagunov, Kirill Simonov

The fundamental theorem of Tur\'{a}n from Extremal Graph Theory determines the exact bound on the number of edges $t_r(n)$ in an $n$-vertex graph that does not contain a clique of size $r+1$. We establish an interesting link between Extremal Graph Theory and Algorithms by providing a simple compression algorithm that in linear time reduces the problem of fin

Jan Friso Groote, Tim A. C. Willemse

We introduce the notion of a Real Equation System (RES), which lifts Boolean Equation Systems (BESs) to the domain of extended real numbers. Our RESs allow arbitrary nesting of least and greatest fixed-point operators. We show that each RES can be rewritten into an equivalent RES in normal form. These normal forms provide the basis for a complete procedure t

Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad

Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed appr

Shubhangi Bansude, Reza Sheikhi

A filtered density function (FDF) model based on deep neural network (DNN), termed DNN-FDF, is introduced for large eddy simulation (LES) of turbulent flows involving conserved scalar transport. The primary objectives of this study are to develop the DNN-FDF models and evaluate their predictive capability in accounting for various filtered moments, including

Stephen Kerby, Abraham D. Falcone, Paul S. Ray

Following the discovery of radio pulsars at the position of Fermi-LAT unassociated sources by the TRAPUM group, we conduct Swift-XRT observations of six of those 4FGL sources to determine if any pulsar-like X-ray sources are present and to confirm the reported detection of an X-ray counterpart via eROSITA at 4FGL J1803.1-6708. At two of the six targets, we d

Benjamin Doyon, Friedrich Hübner

We present steps towards an ab initio derivation of generalised hydrodynamics in quantum integrable models, starting from the Bethe wave functions, and explained on the example of the repulsive Lieb-Liniger model. This includes an identification of the generalised hydrodynamics quasi-particles as wave packets in the quantum model. These wave packets evolve a

Vittoria Vecchiotti, Francesco L. Villante, Giulia Pagliaroli

IceCube collaboration reported the first high-significance observation of the neutrino emission from the Galactic disk. The observed signal can be due to diffuse emission produced by cosmic rays interacting with interstellar gas but can also arise from a population of sources. In this paper, we evaluate both the diffuse and source contribution by taking adva

Maria Elovenkova, Alexander Pechen

Quantum systems with dynamical symmetries have conserved quantities which are preserved under coherent controls. Therefore such systems can not be completely controlled by means of only coherent control. In particular, for such systems maximal transition probability between some pair of states over all coherent controls can be less than one. However, incoher

Alessandro Epasto, Tamalika Mukherjee, Peilin Zhong

Clustering problems (such as $k$-means and $k$-median) are fundamental unsupervised machine learning primitives, and streaming clustering algorithms have been extensively studied in the past. However, since data privacy becomes a central concern in many real-world applications, non-private clustering algorithms may not be as applicable in many scenarios. In

I. M. Flór, A. Donís-Vela, C. W. J. Beenakker, G. Lemut

The chiral edge modes of a topological superconductor can transport fermionic quasiparticles, with Abelian exchange statistics, but they can also transport non-Abelian anyons: Edge-vortices bound to a $\pi$-phase domain wall that propagates along the boundary. A pair of such edge-vortices is injected by the application of an $h/2e$ flux bias over a Josephson

Kelly Pohland

Let $p$ be an odd prime, and let $C_p$ denote the cyclic group of order $p$. We use equivariant surgery methods to classify all closed, connected $2$-manifolds with an action of $C_p$. We additionally provide a way to construct representatives of each isomorphism class using a series of equivariant surgery operations. The results in this paper serve as an od

Yuji Hirono, Ankit Gupta, Mustafa Khammash

Perfect adaptation is a phenomenon whereby the output variables of a system can maintain certain values despite external disturbances. Robust perfect adaptation (RPA) refers to an adaptation property that does not require fine-tuning of system parameters. RPA plays a vital role for the survival of living systems in unpredictable environments. However, comple

Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang

Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines. Conventionally, a molecule graph can be represented either as a graph-structured data or a SMILES text. Recently, the rapid development of Large Language Models (LLMs) has revolutionized the field of NLP. Although it is natur

Yixin Fang, Pallavi Mishra-Kalyani, Xiang Zhang, Susan Gruber

The American Statistical Association Biopharmaceutical Section (ASA BIOP) scientific working group on real-world evidence (RWE) has been making continuous, extended efforts towards a goal of supporting and advancing regulatory science with respect to clinical studies intended to use real-world data for evidence generation for the purpose of medical product d

Ryan K. Cole, Connor Fredrick, Newton H. Nguyen, Scott A. Diddams

We report precision atmospheric spectroscopy of $CO_2$ using a laser heterodyne radiometer (LHR) calibrated with an optical frequency comb. Using the comb-calibrated LHR, we record spectra of atmospheric $CO_2$ near 1572.33 nm with a spectral resolution of 200 MHz using sunlight as a light source. The measured $CO_2$ spectra exhibit frequency shifts by appro

Emily Fox

We describe a simple deterministic near-linear time approximation scheme for uncapacitated minimum cost flow in undirected graphs with real edge weights, a problem also known as transshipment. Specifically, our algorithm takes as input a (connected) undirected graph $G = (V, E)$, vertex demands $b \in \mathbb{R}^V$ such that $\sum_{v \in V} b(v) = 0$, positi

Sophie Starck, Yadunandan Vivekanand Kini, Jessica Johanna Maria Ritter, Rickmer Braren

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between chronological and biological age. To improve understanding of age-related changes in various body parts, we investigate the ageing of the human body on a large scale by using whol

Scott Ahlgren, Nickolas Andersen, Robert Dicks

The Shimura correspondence is a fundamental tool in the study of half-integral weight modular forms. In this paper, we prove a Shimura-type correspondence for spaces of half-integral weight cusp forms which transform with a power of the Dedekind eta multiplier twisted by a Dirichlet character. We prove that the lift of a cusp form of weight $\lambda+1/2$ and

Ankit Agrawal, Jane Cleland-Huang

Safety-critical system's failure or malfunction can cause loss of human lives or damage to the physical environment; therefore, continuous safety assessment is crucial for such systems. In many domains this includes the use of Safety assurance cases (SACs) as a structured argument that the system is safe for use. SACs can be challenging to maintain during sy

Liam Lonergan, Mengjie Qian, Neasa Ní Chiaráin, Christer Gobl

The Irish language is rich in its diversity of dialects and accents. This compounds the difficulty of creating a speech recognition system for the low-resource language, as such a system must contend with a high degree of variability with limited corpora. A recent study investigating dialect bias in Irish ASR found that balanced training corpora gave rise to

M E Dieckmann, C Huete, F Cobos, A Bret

We study with particle-in-cell (PIC) simulations the stability of fast magnetosonic shocks. They expand across a collisionless plasma and an orthogonal magnetic field that is aligned with one of the directions resolved by the 2D simulations. The shock speed is 1.6 times the fast magnetosonic speed when it enters a layer with a reduced density of mobile ions,

W. Zhao, F. Yin, H. Ma, Q. Wu

The Leaf Area Index (LAI) is vital for predicting winter wheat yield. Acquisition of crop conditions via Sentinel-2 remote sensing images can be hindered by persistent clouds, affecting yield predictions. Synthetic Aperture Radar (SAR) provides all-weather imagery, and the ratio between its cross- and co-polarized channels (C-band) shows a high correlation w

Niklas Jost

The multi allocation p-hub median problem (MApHM), the multi allocation uncapacitated hub location problem (MAuHLP) and the multi allocation p-hub location problem (MApHLP) are common hub location problems with several practical applications. HLPs aim to construct a network for routing tasks between different locations. Specifically, a set of hubs must be ch

Volodymyr Riabov

We establish universal Gaussian fluctuations for the mesoscopic linear eigenvalue statistics in the vicinity of the cusp-like singularities of the limiting spectral density for Wigner-type random matrices. Prior to this work, the linear eigenvalue statistics at the cusp-like singularities were not studied in any ensemble. Our analysis covers not only the exa

Pablo A. Cano, Kwinten Fransen, Thomas Hertog, Simon Maenaut

We compute the spectrum of linearized gravitational excitations of black holes with substantial angular momentum in the presence of higher-derivative corrections to general relativity. We do so perturbatively to leading order in the higher-derivative couplings and up to order fourteen in the black hole angular momentum. This allows us to accurately predict q

Jean-Yves Thibon

The inner plethysm of symmetric functions corresponds to the $\lambda$-ring operations of the representation ring $R({\mathfrak S}_n)$ of the symmetric group. It is known since the work of Littlewood that this operation possesses stability properties w.r.t. $n$. These properties have been explained in terms of vertex operators [Scharf and Thibon, Adv. Math.

Debbie Eeltink, Filippo Vicentini, Vincenzo Savona

We present a method to simulate the dynamics of large driven-dissipative many-body open quantum systems using a variational encoding of the Wigner or Husimi-Q quasi-probability distributions. The method relies on Monte-Carlo sampling to maintain a polynomial computational complexity while allowing for several quantities to be estimated efficiently. As a firs

Haijun Liu, Xi Su, Xiangfei Shen, Lihui Chen

Hyperspectral anomaly detection (HAD) aims to recognize a minority of anomalies that are spectrally different from their surrounding background without prior knowledge. Deep neural networks (DNNs), including autoencoders (AEs), convolutional neural networks (CNNs) and vision transformers (ViTs), have shown remarkable performance in this field due to their po

Andrés E. Piatti

The tangential velocity dispersion of stars belonging to the Milky Way globular cluster's tidal tails has recently been found from N-body simulations to be a parameter that distinguishes between cored and cuspy profiles of low-mass dwarf galaxy dark matter subhaloes where that globular cluster formed, and the in-situ formation scenario. In this context, we d

Sadan Saquib Khan, Sumit Kumar, Benish Jan, Laxmeesha Somappa

Usually, the current generated by the photodiode proportional to the oxygenated blood in the photoplethysmography (PPG) and functional infrared spectroscopy (fNIRS) based recording systems is small as compared to the offset-current. The offset current is the combination of the dark current of the photodiode, the current due to ambient light, and the current

Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao

This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first build an intuitive baseline that can be seamlessly incorporated into existing AL frameworks. Though effecti

Mohamed Elgaar, Hadi Amiri

We introduce the problem of curriculum discovery and describe a curriculum learning framework capable of discovering effective curricula in a curriculum space based on prior knowledge about sample difficulty. Using annotation entropy and loss as measures of difficulty, we show that (i): the top-performing discovered curricula for a given model and dataset ar

Edvin G. Idrisov, Eddwi H. Hasdeo, Byjesh N. Radhakrishnan, Thomas L. Schmidt

We study a two-dimensional (2D) electron system with a linear spectrum in the presence of Rashba spin-orbit (RSO) coupling in the hydrodynamic regime. We derive a semiclassical Boltzmann equation with a collision integral due to Coulomb interactions in the basis of the eigenstates of the system with RSO coupling. Using the local equilibrium distribution func

Antesh Upadhyay, Abolfazl Hashemi

We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink communications. Such imperfect communication scenarios arise in the practical deployment of FL in emerging communication systems and protocols. The analysis developed in this paper demo

Kyriakos Axiotis, Taisuke Yasuda

Despite widespread adoption in practice, guarantees for the LASSO and Group LASSO are strikingly lacking in settings beyond statistical problems, and these algorithms are usually considered to be a heuristic in the context of sparse convex optimization on deterministic inputs. We give the first recovery guarantees for the Group LASSO for sparse convex optimi

A. Lima, G. Alencar, Diego Sáez-Chillon Gómez

The present paper is devoted to a new black bounce solution that regularize the well-known rotating black string in $3+1$ dimensions. To do so, the procedure pointed out by Simpson-Visser is followed, which has been already applied successfully to other static cases of black strings, with and without electric charge. This method implies to force a bounce on

Elisa Todarello, Marco Regis, Javier Reynoso-Cordova, Marco Taoso

Nearby dwarf spheroidal galaxies are ideal targets in the search for indirect dark matter (DM) signals. In this work, we analyze MUSE spectroscopic observations of a sample of five galaxies, composed of both classical and ultra-faint dwarf spheroidals. The goal is to search for radiative decays of axion-like particles (ALPs) in the mass range of 2.7-5.3 eV.