October 2022 arXiv papers — page 122

Ahmad Abuaish, Mohit Srinivasan, Patricio A. Vela

Barrier function-based inequality constraints are a means to enforce safety specifications for control systems. When used in conjunction with a convex optimization program, they provide a computationally efficient method to enforce safety for the general class of control-affine systems. One of the main assumptions when taking this approach is the a priori kn

Andre Izidoro, Hilke E. Schlichting, Andrea Isella, Rajdeep Dasgupta

The size frequency distribution of exoplanet radii between 1 and 4$R_{\oplus}$ is bimodal with peaks at $\sim$1.4 $R_{\oplus}$ and $\sim$2.4 $R_{\oplus}$, and a valley at $\sim$1.8$R_{\oplus}$. This radius valley separates two classes of planets -- usually referred to as "super-Earths" and "mini-Neptunes" -- and its origin remains debated. One model proposes

Michael Feffer, Martin Hirzel, Samuel C. Hoffman, Kiran Kate

Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits. A popular approach to train more stable models is ensemble learning, but unfortunately, it is unclear how to combine ensembles with mitigators to best navigate trade-offs between fairness and predictive performance

Marco Antonio Rodríguez-Andrade, Gerardo Aragón-González, José Luis Aragón Vera

Coincidence site lattices of oblique planar lattices are algebraically characterized using as basic tool the Cartan-Dieudonn\'e theorem, that is, the decomposition of an orthogonal transformation as a product of reflections. The case of rectangular lattices is worked out in detail. We use the rectangular lattices for obtain the characterization of the corres

Shizhen Zhao, Xiaojuan Qi

Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This paper considers few-shot 3D point cloud object detection, where only a few annotated samples of novel classes are needed with abundant samples of base classes. To this end, we propos

Ioannis Avgerinos, Ioannis Mourtos, Georgios Zois

Intermodal logistics typically include the successive stages of intermodal shipment and last-mile delivery. We investigate this problem under a novel Logic-Based Benders Decomposition, which exploits the staged nature of the problem to minimise the sum of transport costs and delivery penalties. We establish the validity of our decomposition and apply effecti

Charis Anastopoulos, Maria-Electra Plakitsi

We analyze time-of-arrival probability distributions for relativistic particles in the context of quantum field theory (QFT). We show that QFT leads to a unique prediction, modulo post-selection that incorporates properties of the apparatus into the initial state. We also show that an experimental distinction of different probability assigments is possible e

Clarisse Fournier, Sébastien Roux, Kenji Watanabe, Takashi Taniguchi

Recently discovered quantum emitters in two-dimensional (2D) materials have opened new perspectives of integrated photonic devices for quantum information. Most of these applications require the emitted photons to be indistinguishable, which has remained elusive in 2D materials. Here, we investigate two-photon interference of a quantum emitter generated in h

Zaid Abdullah, George C. Alexandropoulos, Steven Kisseleff, Symeon Chatzinotas

Hybrid relaying networks (HRNs) combining both a relay and an intelligent reflective surface (IRS) can lead to enhanced rate performance compared to non-hybrid relaying schemes, where only either an IRS or a relay is utilized. However, utilizing both the relay and the IRS simultaneously results in higher power consumption for the HRNs compared to their count

Mohammad Ghomi, Joel Spruck

We show that a compact Riemannian $3$-manifold $M$ with strictly convex simply connected boundary and sectional curvature $K\leq a\leq 0$ is isometric to a convex domain in a complete simply connected space of constant curvature $a$, provided that $K\equiv a$ on planes tangent to the boundary of $M$. This yields a characterization of strictly convex surfaces

Kalun Bedingfield, Eoin Elliott, Arsenios Gisdakis, Nuttawut Kongsuwan

Plasmonic nanocavities form very robust sub-nanometer gaps between nanometallic structures and confine light in deep subwavelength volumes to enable unprecedented control on light-matter interactions. However, spherical nanoparticles acquire various polyhedral shapes during their synthesis, which has defining impact on controlling many light-matter interacti

Raymond Isichei

A review of the path integral approach to quantum cosmology and its relation to canonical quantisation. The initial derivation of the Hartle-Hawking and Vilenkin wavefunctions from the Euclidean Einstein-Hilbert action, and later, from the Lorentzian path integral using Picard-Lefshets Theory is discussed. Path integral quantisation of the Einstein-Cartan ac

Michele Cotrufo, Andrea Cordaro, Dimitrios L. Sounas, Albert Polman

Nonreciprocal devices - in which light is transmitted with different efficiencies along opposite directions - are key technologies for modern photonic applications, yet their compact and miniaturized implementation remains an open challenge. Among different avenues, nonlinearity-induced nonreciprocity has attracted significant attention due to the absence of

Wenhui Feng, Heng Zhu, Bing Tang

An analytical work on intrinsic localized modes in a two-dimensional Heisenberg ferromagnet on the checkerboard lattice is presented. Taking advantage of an asymptotic method, the governing lattice dynamical equations are reduced to one (2+1) -dimensional nonlinear Schr\"odinger. In our work, we obtain two types of nonlinear localized mode solutions, namely,

Yining Wang

In this paper we study the non-stationary stochastic optimization question with bandit feedback and dynamic regret measures. The seminal work of Besbes et al. (2015) shows that, when aggregated function changes is known a priori, a simple re-starting algorithm attains the optimal dynamic regret. In this work, we designed a stochastic optimization algorithm w

Raymond Isichei, João Magueijo

We derive the fixed-$\Lambda$ and unimodular propagators using the path integral formalism as applied to the Einstein-Cartan action. The simplicity of the action (which is linear in the lapse function) allows for an exact integration starting from the lapse function and the enforcement of the Hamiltonian constraint, leading to a product of Chern-Simons state

Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system,

Juntao Yu, Silviu Paun, Maris Camilleri, Paloma Carretero Garcia

Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included. Yet, the approaches proposed to scale up anaphoric annotation haven't so far resulted in datasets overcoming these limitations. In t

Dominic Stone, Sergey Zelik

We give a comprehensive study of the 3D Navier-Stokes-Brinkman-Forchheimer equations in a bounded domain endowed with the Dirichlet boundary conditions and non-autonomous external forces. This study includes the questions related with the regularity of weak solutions, their dissipativity in higher energy spaces and the existence of the corresponding uniform

Tian Qin, Fengxiang He, Dingfeng Shi, Wenbing Huang

Designing an incentive-compatible auction mechanism that maximizes the auctioneer's revenue while minimizes the bidders' ex-post regret is an important yet intricate problem in economics. Remarkable progress has been achieved through learning the optimal auction mechanism by neural networks. In this paper, we consider the popular additive valuation and symme

Léonard Pille-Schneider

Using a construction by Yamamoto of tropical contractions, we construct a non-archimedean SYZ fibration on the Berkovich analytification of a class of maximally degenerate hypersurfaces in projective space. We furthermore prove that under a discrete symmetry assumption, the potential for the non-archimedean Calabi-Yau metric is constant along the fibers of t

Nikolaos Tsilivis, Julia Kempe

The adversarial vulnerability of neural nets, and subsequent techniques to create robust models have attracted significant attention; yet we still lack a full understanding of this phenomenon. Here, we study adversarial examples of trained neural networks through analytical tools afforded by recent theory advances connecting neural networks and kernel method

Alberto Alvarez, Jose Font, Julian Togelius

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure to

Stephen E. Kuenstner, Elizabeth C. van Assendelft, Saptarshi Chaudhuri, Hsiao-Mei Cho

We present the RF Quantum Upconverter (RQU) and describe its application to quantum metrology of electromagnetic modes between dc and the Very High Frequency band (VHF) ($\lesssim$300MHz). The RQU uses a Josephson interferometer made up of superconducting loops and Josephson junctions to implement a parametric interaction between a low-frequency electromagne

Iago Oliveira, Philip Cardiff, Carlos E. Baccin, José Gasche

Numerical simulations have been extensively used in the past two decades for the study of intracranial aneurysms (IAs), a dangerous disease that occurs in the arteries that reach the brain. They may affect up to 10 % of the world's population, with up to 50 % mortality rate, in case of rupture. Physically, the blood flow inside IAs should be modeled as a flu

Ayush K. Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness

The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in te

Julian Braun, Christoph Ortner, Yangshuai Wang, Lei Zhang

Crystalline materials exhibit long-range elastic fields due to the presence of defects, leading to significant domain size effects in atomistic simulations. A rigorous far-field expansion of these long-range fields identifies low-rank structure in the form of a sum of discrete multipole terms and continuum correctors. We propose a novel numerical scheme that

Zhenbang Wu, Huaxiu Yao, Zhe Su, David M Liebovitz

Drug recommendation assists doctors in prescribing personalized medications to patients based on their health conditions. Existing drug recommendation solutions adopt the supervised multi-label classification setup and only work with existing drugs with sufficient prescription data from many patients. However, newly approved drugs do not have much historical

Zhaoqiang Liu, Xinshao Wang, Jiulong Liu

In this paper, we study phase retrieval under model misspecification and generative priors. In particular, we aim to estimate an $n$-dimensional signal $\mathbf{x}$ from $m$ i.i.d.~realizations of the single index model $y = f(\mathbf{a}^T\mathbf{x})$, where $f$ is an unknown and possibly random nonlinear link function and $\mathbf{a} \in \mathbb{R}^n$ is a

Geumsook Park, Malcolm J. Currie, Holly S. Thomas, Erik Rosolowsky

We present the full data release of 12CO (3-2) High-Resolution Survey (COHRS), which has mapped the inner Galactic plane over the range of 9.5$^{\circ}$ $\le$ l $\le$ 62.3$^{\circ}$ and $|b| \le 0.5^{\circ}$. The COHRS has been carried out using the Heterodyne Array Receiver Program (HARP) on the 15 m James Clerk Maxwell Telescope (JCMT) in Hawaii. The relea

Ryuichiro Kitano, Hiromasa Takaura

We compute the electron $g$ factor to the $\mathcal{O}(\alpha^5)$ order on the lattice in quenched QED. We first study finite volume corrections in various IR regularization methods to discuss which regularization is optimal for our purpose. We find that in QED$_L$ the finite volume correction to the effective mass can have different parametric dependences d

Bo Li, Yongqiang Yao, Jingru Tan, Xin Lu

Data in real-world object detection often exhibits the long-tailed distribution. Existing solutions tackle this problem by mitigating the competition between the head and tail categories. However, due to the scarcity of training samples, tail categories are still unable to learn discriminative representations. Bringing more data into the training may allevia

Yong Liu, Ran Yu, Jiahao Wang, Xinyuan Zhao

This paper studies semi-supervised video object segmentation through boosting intra-frame interaction. Recent memory network-based methods focus on exploiting inter-frame temporal reference while paying little attention to intra-frame spatial dependency. Specifically, these segmentation model tends to be susceptible to interference from unrelated nontarget o

Jingru Tan, Bo Li, Xin Lu, Yongqiang Yao

Long-tail distribution is widely spread in real-world applications. Due to the extremely small ratio of instances, tail categories often show inferior accuracy. In this paper, we find such performance bottleneck is mainly caused by the imbalanced gradients, which can be categorized into two parts: (1) positive part, deriving from the samples of the same cate

J. Enrique Vázquez-Lozano, Iñigo Liberal

Regarded as a promising alternative to spatially shaping matter, time-varying media can be seized to control and manipulate wave phenomena, including thermal radiation. Here, based upon the framework of macroscopic quantum electrodynamics, we elaborate a comprehensive quantum theoretical formulation that lies the basis for investigating thermal emission effe

Jhony H. Giraldo, Vincenzo Scarrica, Antonino Staiano, Francesco Camastra

Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this problem, we propose a new algorithm that uses HyperGraph Convolutional Networks for Weakly-supervised Semantic Se

Hao Li, Bo-Qiang Ma

Lorentz invariance violation~(LIV) can change the threshold behavior predicted by special relativity and cause threshold anomalies which affect the propagation of cosmic photons. In this work, we focus on the threshold anomaly effect on cosmic photon attenuations by extragalactic background light~(EBL) and discuss how to identify LIV from observations of ver

Boshuai Zhao, Roel Leus

Truck platooning is a promising transportation mode in which several trucks drive together and thus save fuel consumption by suffering less air resistance. In this paper, we consider a truck platooning system for which we jointly optimize the truck routes and schedules from the perspective of a central platform. We improve an existing decomposition-based heu

Ross Anderson

Ian Levy and Crispin Robinson's position paper "Thoughts on child safety on commodity platforms" is to be welcomed for extending the scope of the debate about the extent to which child safety concerns justify legal limits to online privacy. Their paper's context is the laws proposed in both the UK and the EU to give the authorities the power to undermine end

Ruohan Wang, Marco Ciccone, Giulia Luise, Andrew Yapp

A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most current methods make strong assumptions on the schedule and have unpredictable performance when such requirements are not met. A key challenge in CL is thus to design methods robu

Junsoo Kim, Dongwoo Kim, Yongsoo Song, Hyungbo Shim

Encrypted control has been introduced to protect controller data by encryption at the stage of computation and communication, by performing the computation directly on encrypted data. In this article, we first review and categorize recent relevant studies on encrypted control. Approaches based on homomorphic encryption, multi-party computation, and secret sh

Chen Henry Wu, Fernando De la Torre

Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper provides an alternative, Gaussian formulation of the late

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser, James M. Robins

It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse view: that missing data problems are a f

Chengkun Wang, Wenzhao Zheng, Zheng Zhu, Jie Zhou

The pretrain-finetune paradigm in modern computer vision facilitates the success of self-supervised learning, which tends to achieve better transferability than supervised learning. However, with the availability of massive labeled data, a natural question emerges: how to train a better model with both self and full supervision signals? In this paper, we pro

Terry Yue Zhuo, Yaqing Liao, Yuecheng Lei, Lizhen Qu

We introduce ViLPAct, a novel vision-language benchmark for human activity planning. It is designed for a task where embodied AI agents can reason and forecast future actions of humans based on video clips about their initial activities and intents in text. The dataset consists of 2.9k videos from \charades extended with intents via crowdsourcing, a multi-ch

Valery E. Lyubovitskij, Alexey S. Zhevlakov, Aliaksei Kachanovich, Serguei Kuleshov

We study the non-abelian $SU(2)_D$ extension of the $U(1)_D$ Stueckelberg portal, which plays a role of the mediator between the Standard Model (SM) and Dark Sector (DS). This portal is specified by the Stueckelberg mechanism for generation of dark gauge boson masses. Proposed $U(1)_D\times SU(2)_D$ Stueckelberg portal has a connection with SM matter fields

Sampat Sharma

In this article, we prove that if $R$ is an affine algebra of dimension $d\geq 4$ over $\overline{\mathbb{F}}_{p}$ and $1/(d-1)! \in R,$ then any unimodular row over $R$ of length $d$ can be mapped to a factorial row by elementary transformations.

Adria Marcos-Morales, Matan Leibovich, Sreyas Mohan, Joshua Lawrence Vincent

Unsupervised denoising is a crucial challenge in real-world imaging applications. Unsupervised deep-learning methods have demonstrated impressive performance on benchmarks based on synthetic noise. However, no metrics are available to evaluate these methods in an unsupervised fashion. This is highly problematic for the many practical applications where groun

Michael Amir, Yigal Koifman, Yakov Bloch, Ariel Barel

We consider the general problem of geometric task allocation, wherein a large, decentralised swarm of simple mobile agents must detect the locations of tasks in the plane and position themselves nearby. The tasks are represented by an a priori unknown demand profile $\Phi(x,y)$ that determines how many agents are needed in each location. The agents are auton

Meng Cao, Jing Yang

Let $\mathrm{SLAut}(\mathbb{F}_{q}^{n})$ denote the group of all semilinear isometries on $\mathbb{F}_{q}^{n}$, where $q=p^{e}$ is a prime power. In this paper, we investigate general properties of linear codes associated with $\sigma$ duals for $\sigma\in\mathrm{SLAut}(\mathbb{F}_{q}^{n})$. We show that the dimension of the intersection of two linear codes

Lachlan P. Lindoy, Arkajit Mandal, David R. Reichman

We employ an exact quantum mechanical simulation technique to investigate a model of cavity-modified chemical reactions in the condensed phase. The model contains the coupling of the reaction coordinate to a generic solvent, cavity coupling to either the reaction coordinate or a non-reactive mode, and the coupling of the cavity to lossy modes. Thus, many of

Zixuan Ke, Haowei Lin, Yijia Shao, Hu Xu

Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unla

Felix Hummel

Many methods for computing electronic correlation effects at finite temperature are related to many-body perturbation theory in the grand-canonical ensemble. In most applications, however, the average number of electrons is known rather than the chemical potential, requiring that expensive correlation calculations must be repeated iteratively in search for t

Athanasios T. Margaritis, Clément Scherding, Olaf Marxen, Peter J. Schmid

In this paper, we present a methodology to achieve high-fidelity simulations of chemically reacting hypersonic flows and demonstrate our numerical solver's capabilities on a selection of configurations. The numerical tools are developed based on previous in-house codes for high-speed simulations with improvements in both numerical and physical modeling. Addi

Stanislav Fort, Ekin Dogus Cubuk, Surya Ganguli, Samuel S. Schoenholz

Deep neural network classifiers partition input space into high confidence regions for each class. The geometry of these class manifolds (CMs) is widely studied and intimately related to model performance; for example, the margin depends on CM boundaries. We exploit the notions of Gaussian width and Gordon's escape theorem to tractably estimate the effective

Christophe Fluhr, Benoît Dubois, Guillaume Le Tetu, Valerie Soumann

The cryogenic sapphire oscillator (CSO) is a highly specialized machine, which delivers a reference signal exhibiting the lowest frequency fluctuations. For the best units, the Allan deviation (ADEV) is <1e-15 for integration time between 1 and 10,000 s, with a drift <1e-14 in one day.The oscillator is based on a sapphire monocrystal resonating at 10 GHz in

Farid Bozorgnia, Dohyun Kwon, Son N. T. Tu

We study the additive eigenvalues on changing domains, along with the associated vanishing discount problems. We consider the convergence of the vanishing discount problem on changing domains for a general scaling type $\Omega_\lambda = (1+r(\lambda))\Omega$ with a continuous function $r$ and a positive constant $\lambda$. We characterize all solutions to th

Leah Epstein

We consider online preemptive scheduling of jobs arriving one by one, to be assigned to two identical machines, with the goal of makespan minimization. We study the effect of selecting the best solution out of two independent solutions constructed in parallel in an online fashion. Two cases are analyzed, where one case is purely online, and in the other one

Inbar Zohar, Ben Haylock, Yoav Romach, Muhammad Junaid Arshad

Quantum sensors can potentially achieve the Heisenberg limit of sensitivity over a large dynamic range using quantum algorithms. The adaptive phase estimation algorithm (PEA) is one example that was proven to achieve such high sensitivities with single-shot readout (SSR) sensors. However, using the adaptive PEA on a non-SSR sensor is not trivial due to the l

Christoph Dlapa, Gregor Kälin, Zhengwen Liu, Jakob Neef

We obtain the total impulse in the scattering of non-spinning binaries in general relativity at fourth Post-Minkowskian order, i.e. ${\cal O}(G^4)$, including linear, nonlinear, and hereditary radiation-reaction effects. We derive the total radiated spacetime momentum as well as the associated energy flux. The latter can be used to compute gravitational-wave

E. Linnér, A. I. Lichtenstein, S. Biermann, E. A. Stepanov

Systems with strong electronic Coulomb correlations often display rich phase diagrams exhibiting different ordered phases involving spin, charge, or orbital degrees of freedom. The theoretical description of the interplay of the corresponding collective fluctuations giving rise to this phenomenology remains however a tremendous challenge. Here, we introduce

Nienke van der Marel

Protoplanetary disks surrounding young stars are the birth place of planets. Of particular interest are the transition disks with large inner dust cavities of tens of au, hinting at the presence of massive companions. These cavities were first recognized by a deficit in their Spectral Energy Distribution (SED), later confirmed by millimeter interferometry ob

Junwen Xia, Zishu Zhan, Jingxiao Zhang

In survival contexts, substantial literature exists on estimating optimal treatment regimes, where treatments are assigned based on personal characteristics to maximize the survival probability. These methods assume that a set of covariates is sufficient to deconfound the treatment-outcome relationship. However, this assumption can be limited in observationa

Michael Albert, Mathilde Bouvel, Valentin Féray, Marc Noy

We prove that the class of 231-avoiding permutations satisfies a logical limit law, i.e. that for any first-order sentence $\Psi$, in the language of two total orders, the probability $p_{n,\Psi}$ that a uniform random 231-avoiding permutation of size $n$ satisfies $\Psi$ admits a limit as $n$ is large. Moreover, we establish two further results about the be

Piao Chen, Xun Xiao

In this paper, novel closed-form point estimators of the beta distribution are proposed and investigated. The first estimators are a modified version of Pearson's method of moments. The underlying idea is to involve the sufficient statistics, i.e., log-moments in the moment estimation equations and solve the mixed type of moment equations simultaneously. The

Luís Carvalho, Cristina Diogo, Sérgio Mendes

We study the numerical range of bounded linear operators on quaternionic Hilbert spaces and its relation with the S-spectrum. The class of complex operators on quaternionic Hilbert spaces is introduced and the upper bild of normal complex operators is completely characterized in this setting.

Linghua Tang, Le Hui, Jin Xie

Due to the few annotated labels of 3D point clouds, how to learn discriminative features of point clouds to segment object instances is a challenging problem. In this paper, we propose a simple yet effective 3D instance segmentation framework that can achieve good performance by annotating only one point for each instance. Specifically, to tackle extremely f

Jonghwa Yim, Minjae Kim

Transformer is eminently suitable for auto-regressive image synthesis which predicts discrete value from the past values recursively to make up full image. Especially, combined with vector quantised latent representation, the state-of-the-art auto-regressive transformer displays realistic high-resolution images. However, sampling the latent code from discret

Michael Borinsky, Zeno Capatti, Eric Laenen, Alexandre Salas-Bernárdez

We introduce a new diagrammatic approach to perturbative quantum field theory, which we call flow-oriented perturbation theory (FOPT). Within it, Feynman graphs are replaced by strongly connected directed graphs (digraphs). FOPT is a coordinate space analogue of time-ordered perturbation theory and loop-tree duality, but it has the advantage of having combin

Lauren H. Li, Mehran Kardar

As a population grows, spreading to new environments may favor specialization. In this paper, we introduce and explore a model for specialization at the front of a colony expanding synchronously into new territory. We show through numerical simulations that, by gaining fitness through accumulating mutations, progeny of the initial seed population can differe

Kai Shinbrough, Virginia O. Lorenz

The storage and retrieval of photonic quantum states, quantum memory, is a key resource for a wide range of quantum applications. Here we investigate the sensitivity of $\Lambda$-type quantum memory to experimental fluctuations and drift. We use a variance-based approach, focusing on the effects of fluctuations and drift on memory efficiency. We consider sho

Ilias Chalkidis, Xiang Dai, Manos Fergadiotis, Prodromos Malakasiotis

Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents. There are clear benefits to these approaches compared to the original Transformer in terms of efficiency, but Hierarchical Attention Transformer (HAT) models are a vastly understudied alternative. We develop and

Neeraj Varshney, Chitta Baral

Do all instances need inference through the big models for a correct prediction? Perhaps not; some instances are easy and can be answered correctly by even small capacity models. This provides opportunities for improving the computational efficiency of systems. In this work, we present an explorative study on 'model cascading', a simple technique that utiliz

Matteo Barucco

We compute rational $T$-equivariant elliptic cohomology of CP(V), where $T$ is the circle group, and CP(V) is the $T$-space of complex lines for a finite dimensional complex $T$-representation V. Starting from an elliptic curve C over the complex numbers and a coordinate data around the identity, we achieve this computation by proving that the $T$-equivarian

Hasan B. Al Ba'ba'a, Carson L. Willey, Vincent W. Chen, Abigail T. Juhl

Phononic crystals exhibit Bragg bandgaps, frequency regions within which wave propagation is forbidden. In solid continua, bandgaps are the outcome of destructive interferences resulting from periodically alternating layers. Under certain conditions, natural frequencies emerge within these bandgaps in the form of high-amplitude localized vibrations near a st

Sebastian Leontica, David Amaro

We embed 1-layer QAOA circuits into the larger class of parameterized Instantaneous Quantum Polynomial circuits to produce an improved variational quantum algorithm for solving combinatorial optimization problems. The use of analytic expressions to find optimal parameters classically makes our protocol robust against barren plateaus and hardware noise. The a

Vadim Khrapai

A quantum Hall interferometer containing a grounded superconducting terminal is proposed. This geometry allows to control the Andreev and normal scattering amplitudes of sub-gap Bogoliubov quasiparticles with the Aharonov-Bohm phase, as well as with the constrictions defining the interferometer loop. The conductance matrix of such a three-terminal NSN interf

Bukun Son, Hyelim Choi, Jaemin Yoon, Dongjun Lee

We present a two-stage framework that integrates a learning-based estimator and a controller, designed to address contact-intensive tasks. The estimator leverages a Bayesian particle filter with a mixture density network (MDN) structure, effectively handling multi-modal issues arising from contact information. The controller combines a self-supervised and re

Wei-Fan Hu, Te-Sheng Lin, Yu-Hau Tseng, Ming-Chih Lai

A new and efficient neural-network and finite-difference hybrid method is developed for solving Poisson equation in a regular domain with jump discontinuities on embedded irregular interfaces. Since the solution has low regularity across the interface, when applying finite difference discretization to this problem, an additional treatment accounting for the

Rémi Bardenet, Alexandre Feller, Jérémie Bouttier, Pascal Degiovanni

Some fifty years ago, in her seminal PhD thesis, Odile Macchi introduced permanental and determinantal point processes. Her initial motivation was to provide models for the set of detection times in fundamental bosonic or fermionic optical experiments, respectively. After two rather quiet decades, these point processes have quickly become standard examples o

Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou

Inverted file structure is a common technique for accelerating dense retrieval. It clusters documents based on their embeddings; during searching, it probes nearby clusters w.r.t. an input query and only evaluates documents within them by subsequent codecs, thus avoiding the expensive cost of exhaustive traversal. However, the clustering is always lossy, whi

Luís Carvalho, Cristina Diogo, Sérgio Mendes, Helena Soares

The numerical range in the quaternionic setting is, in general, a non convex subset of the quaternions. The essential numerical range is a refinement of the numerical range that only keeps the elements that have, in a certain sense, infinite multiplicity. We prove that the essential numerical range of a bounded linear operator on a quaternionic Hilbert space

Ruixiang Zhang, Tong Che, Boris Ivanovic, Renhao Wang

Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Accordingly, it is a crucial step for machine learning models to be capable of inferring ob

Kaitlin Dennison, Nathan Stacey, Simone D'Amico

This paper first defines a class of estimation problem called simultaneous navigation and characterization (SNAC), which is a superset of simultaneous localization and mapping (SLAM). A SNAC framework is then developed for the Autonomous Nanosatellite Swarming (ANS) mission concept to autonomously navigate about and characterize an asteroid including the ast

Yuxi Xiao, Li Li, Xiaodi Li, Jian Yao

Two-view structure from motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM (vSLAM). Many existing end-to-end learning-based methods usually formulate it as a brute regression problem. However, the inadequate utilization of traditional geometry model makes the model not robust in unseen environments. To improve the generalization capability

Makoto Maejima, Noriyoshi Sakuma

We study the free central limit theorem for not necessarily identically distributed free random variables where the limiting distribution is the semicircle distribution. Starting from an estimate for the Kolmogorov distance between the measure of suitably normalized sums of free random variables and the semicircle distribution without any moment condition, w

Domantas Burba, Mantas Račiūnas, Ian B. Spielman, Gediminas Juzeliūnas

Recent experiments demonstrated deeply subwavelength lattices using atoms with $N$ internal states Raman-coupled with lasers of wavelength $\lambda$. The resulting unit cell was $\lambda/2N$ in extent, an $N$-fold reduction compared to the usual $\lambda/2$ periodicity of an optical lattice. For resonant Raman coupling, this lattice consists of $N$ independe

Jonathan Körber, Maximilian Pallmann, Julia Heupel, Rainer Stöhr

Spin-bearing color centers in the solid state are promising candidates for the realization of quantum networks and distributed quantum computing. A remaining key challenge is their efficient and reliable interfacing to photons. Incorporating minimally processed membranes into open-access microcavities represents a promising route for Purcellenhanced spin-pho

Nicholas Meegan, Hansi Liu, Bryan Bo Cao, Abrar Alali

We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian data collected from RGB-D camera footage and WiFi Fine Time Measurements (FTM) from a user's smartphone device. Depth data from RGB-D (vision domain) is inherently linked with an ob

Amin Abolghasemi, Arian Askari, Suzan Verberne

Term-based ranking with pre-trained transformer-based language models has recently gained attention as they bring the contextualization power of transformer models into the highly efficient term-based retrieval. In this work, we examine the generalizability of two of these deep contextualized term-based models in the context of query-by-example (QBE) retriev

Eloi Descamps, Nicolas Fabre, Arne Keller, Perola Milman

We study the role of the electromagnetic field's frequency in time precision measurements using single photons as a paradigmatic system. For such, we independently identify the contributions of intensity and spectral resources and show that both can play a role on the scaling of the precision of parameter estimation with the number of probes. We show in part

Moumita Naskar, Muktish Acharyya, Erol Vatansever, Nikolaos G. Fytas

In the present work, we investigate the effects of disorder on the reversal time ($\tau$) of classical anisotropic Heisenberg ferromagnets in three dimensions by means of Monte Carlo simulations. Starting from the pure system, our analysis suggests that $\tau$ increases with increasing anisotropy strength. On the other hand, for the case of randomly distribu

Jaewoong Choi, Geonho Hwang, Hyunsoo Cho, Myungjoo Kang

The ideally disentangled latent space in GAN involves the global representation of latent space with semantic attribute coordinates. In other words, considering that this disentangled latent space is a vector space, there exists the global semantic basis where each basis component describes one attribute of generated images. In this paper, we propose an unsu

Meghdad Kurmanji, Peter Triantafillou

Machine Learning (ML) is changing DBs as many DB components are being replaced by ML models. One open problem in this setting is how to update such ML models in the presence of data updates. We start this investigation focusing on data insertions (dominating updates in analytical DBs). We study how to update neural network (NN) models when new data follows a

Marc Barroso Mancha, Guy D. Moore

We develop a new method to determine thermal activation rates, such as for bubble nucleation, topology change, \textsl{etc.}, using 4-dimensional Euclidean methods. This allows nonperturbative study on the lattice. We then investigate the strong sphaleron rate in pure-glue QCD at temperatures between 1.3 $\Tc$ and 1000 $T_\mathrm{c}$, making contact with pre

Matteo Paltenghi, Rahul Pandita, Austin Z. Henley, Albert Ziegler

Recent neural models of code, such as OpenAI Codex and AlphaCode, have demonstrated remarkable proficiency at code generation due to the underlying attention mechanism. However, it often remains unclear how the models actually process code, and to what extent their reasoning and the way their attention mechanism scans the code matches the patterns of develop

Arvind C. N. Hughes, Coryn A. L. Bailer-Jones, Sara Jamal

In this work, we assess the combined use of Gaia photometry and astrometry with infrared data from CatWISE in improving the identification of extragalactic sources compared to the classification obtained using Gaia data. We evaluate different input feature configurations and prior functions, with the aim of presenting a classification methodology integrating

Margaret E. Carrington, Gabor Kunstatter, Christopher Phillips, Marcelo E. Rubio

We present numerical simulations for the evolution of an expanding system of massless scalar fields with quartic coupling. By setting a rotating, non-isotropic initial configuration, we compute the energy density, the transverse and longitudinal pressures and the angular momentum of the system. We compare the time scales associated with the isotropization an

Monoranjan Guchait, Arnab Roy

We study the top-quark production along with a Higgs boson and a jet (tHq) at the LHC experiment within the framework of the Standard Model Effective Field Theory (SMEFT). A strategy is developed to constrain the Wilson Coefficients (WC) corresponding to the associated SMEFT operators using the latest LHC measurements. The best-fit values of these WCs are pr

Tim Suchan, Kathrin Welker, Winnifried Wollner

Within this work, we present a novel approach to fracture simulations based on shape optimization techniques. Contrary to widely-used phase-field approaches in literature the proposed method does not require a specified 'length-scale' parameter defining the diffused interface region of the phase-field. We provide the formulation and discuss the used solution