November 2022 arXiv papers — page 97

Gnyanesh Bangaru, Lalith Bharadwaj Baru, Kiran Chakravarthula

Neural networks trained on standard image classification data sets are shown to be less resistant to data set bias. It is necessary to comprehend the behavior objective function that might correspond to superior performance for data with biases. However, there is little research on the selection of the objective function and its representational structure wh

Vsevolod Ivanov, Ella Banyas, Liang Z. Tan

Power generation through the anomalous Nernst effect in topological Weyl materials has several advantages over conventional thermoelectrics due to the transverse geometry. However, the magnitude of the anomalous Nernst conductivity (ANC) in most known materials is too small to be of practical use, and there exist few guiding principles for finding materials

Charles Corbière

The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving. Yet, safety remains a major concern when it comes to deploying such systems in high-risk environments. The objective of this thesis is to develop methodological tools which provide reliable uncertainty estimates for deep neural networks. First

Daniel Rika, Dror Sholomon, Eli David, Nathan S. Netanyahu

Pairwise compatibility measure (CM) is a key component in solving the jigsaw puzzle problem (JPP) and many of its recently proposed variants. With the rapid rise of deep neural networks (DNNs), a trade-off between performance (i.e., accuracy) and computational efficiency has become a very significant issue. Whereas an end-to-end DNN-based CM model exhibits h

P. Judge, P. Bryans, R. Casini, L. Kleint

This paper identifies spectral lines from X-ray to infrared wavelengths which are optimally suited to measuring vector magnetic fields as high as possible in the solar atmosphere. Instrumental and Earth's atmospheric properties, as well as solar abundances, atmospheric properties and elementary atomic physics are considered without bias towards particular wa

Renee Lu, Mostafa Shahin, Beena Ahmed

Children's speech recognition is a vital, yet largely overlooked domain when building inclusive speech technologies. The major challenge impeding progress in this domain is the lack of adequate child speech corpora; however, recent advances in self-supervised learning have created a new opportunity for overcoming this problem of data scarcity. In this paper,

Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman

Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected from the actual system under consideration, despite the likely existence of operational data from similar systems which have previously been deployed in the field. In this paper, w

Hanjun Dai, Yuan Xue, Niao He, Bethany Wang

In real-world decision-making, uncertainty is important yet difficult to handle. Stochastic dominance provides a theoretically sound approach for comparing uncertain quantities, but optimization with stochastic dominance constraints is often computationally expensive, which limits practical applicability. In this paper, we develop a simple yet efficient appr

Luis Chahua, Juan Gutiérrez

In 1981, Tuza conjectured that the cardinality of a minimum set of edges that intersects every triangle of a graph is at most twice the cardinality of a maximum set of edge-disjoint triangles. This conjecture have been proved for several important graph classes, as planar graphs, tripartite graphs, among others. However, it remains open on other important cl

Alan Needleman

The focus is on discrete defects that can be modeled by continuum mechanics, but where the discreteness of the carriers of plastic deformation plays a significant role. The formulations are restricted to small deformation kinematics and the defects considered, dislocations and discrete shear transformation zones (STZs), are described by their linear elastic

Minghui Ding, Craig D. Roberts, Sebastian M. Schmidt

Visible matter is characterised by a single mass scale; namely, the proton mass. The proton's existence and structure are supposed to be described by quantum chromodynamics (QCD); yet, absent Higgs boson couplings, chromodynamics is scale invariant. Thus, if the Standard Model is truly a part of the theory of Nature, then the proton mass is an emergent featu

Davide Barilari, Andrea Mondino, Luca Rizzi

Recent advances in the theory of metric measures spaces on the one hand, and of sub-Riemannian ones on the other hand, suggest the possibility of a "great unification" of Riemannian and sub-Riemannian geometries in a comprehensive framework of synthetic Ricci curvature lower bounds. With the aim of achieving such a unification program, in this paper we initi

Mohamed Sadek Bouanane, Dalila Cherifi, Elisabetta Chicca, Lyes Khacef

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge. However, multiple spiking neuron models have been proposed in the literature with different levels of biological plausibility and different computational features and complexities. Consequently

Jennifer N. Jones-Baro

We characterize the stabilized automorphism group for odometers and Toeplitz subshifts and then prove an invariance property of the stabilized automorphism group of these dynamical systems. A particular case of interest is that for torsion free odometers the stabilized automorphism group is a full isomorphism invariant.

David Vallet, Anna C. Childs, Rebecca G. Martin, Mario Livio

While giant planet occurrence rates increase with stellar mass, occurrence rates of close-in super-Earths decrease. This is in contradiction to the expectation that the total mass of the planets in a system scale with the protoplanetary disc mass and hence the stellar mass. Since the snow line plays an important role in the planet formation process we examin

A. Cimmino, I. Ambrožová, S. Motta, R. Versaci

UHDpulse - Metrology for Advanced Radiotherapy using beams with Ultra-High Pulse Dose Rates is a European project aimed at developing novel dosimetry standards, as well as improving existing ones, for FLASH radiotherapy, very high energy electrons radiotherapy, and laser-driven medical accelerators. Within the scope of this project, Thermoluminescence (TL) a

Ryan Wilkinson, Marcus Roper

Comparing how different populations have suffered under COVID-19 is a core part of ongoing investigations into how public policy and social inequalities influence the number of and severity of COVID-19 cases. But COVID-19 incidence can vary multifold from one subpopulation to another, including between neighborhoods of the same city, making comparisons of ca

Evangelos S. Papaefthymiou, Ioannis Michos, Orestis Pavlou, Vicky Papadopoulou Lesta

We present a new diagnostic diagram for local ultraluminous infrared galaxies (ULIRGs) and quasars, analysing particularly the Spitzer Space Telescope's Infrared Spectrograph (IRS) spectra of 102 local ULIRGs and 37 Palomar Green quasars. Our diagram is based on a special non-linear mapping of these data, employing the Kernel Principal Component Analysis met

Gecheng Chen, Yu Zhou, Xudong Zhang, Rui Tuo

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). In this work, we propose a novel transfe

Chinmaya Bhargava, Matthew Buican, Hongliang Jiang

We study ultraviolet (UV) incarnations of deep infrared (IR) physics on the Coulomb branch of the simplest interacting 4D $\mathcal{N}=2$ superconformal field theory: the minimal Argyres-Douglas (MAD) theory. One of the most basic properties of the Coulomb branch is an emergent infinite-dimensional higher-spin symmetry. While the MAD theory is interacting an

Laiachi El Kaoutit, Aryan Ghobadi, Paolo Saracco, Joost Vercruysse

We provide a correspondence between one-sided coideal subrings and one-sided ideal two-sided coideals in an arbitrary bialgebroid. We prove that, under some expected additional conditions, this correspondence becomes bijective for Hopf algebroids. As an application, we investigate normal Hopf ideals in commutative Hopf algebroids (affine groupoid schemes) in

Paola Testa, Juan Martinez-Sykora, Bart De Pontieu

The element abundances in the solar corona and solar wind are often different from those of the solar photosphere, typically with a relative enrichment of elements with low first ionization potential (FIP effect). Here we study the spatial distribution and temporal evolution of the coronal chemical composition in an active region (AR) over about 10 days, usi

João G. F. Campos, Azadeh Mohammadi, Jose M. Queiruga, Andrzej Wereszczynski

We show that a spectral wall, i.e., an obstacle in the dynamics of a bosonic soliton, which arises due to the transition of a normal mode into the continuum spectrum, exists after coupling the original bosonic model to fermions. This spectral wall can be experienced if the boson or fermion field is in an excited state. Furthermore, while passing through a sp

Athul Augustine, P. Shankar

Let $n>1$ and let $\{U_{ij}\}_{1\leq i<j\leq n}$ be $n\choose 2$ commuting unitaries on a Hilbert space $\mathcal{H}$. Suppose $U_{ji}:=U^*_{ij}$, $1\leq i<j\leq n$. An n-tuple of power partial isometries $(V_1,...,V_n)$ on Hilbert space $\mathcal{H}$ is called $\mathcal{U}_n$-twisted power partial isometry with respect to $\{U_{ij}\}_{i<j}$ (or simply $\mat

Steve Macenski, Tully Foote, Brian Gerkey, Chris Lalancette

The next chapter of the robotics revolution is well underway with the deployment of robots for a broad range of commercial use-cases. Even in a myriad of applications and environments, there exists a common vocabulary of components that robots share - the need for a modular, scalable, and reliable architecture; sensing; planning; mobility; and autonomy. The

Zhihong Pan, Xin Zhou, Hao Tian

Diffusion-based text-to-image generation models like GLIDE and DALLE-2 have gained wide success recently for their superior performance in turning complex text inputs into images of high quality and wide diversity. In particular, they are proven to be very powerful in creating graphic arts of various formats and styles. Although current models supported spec

Yijun Chen, Guodong Shi

The most widely used integrated assessment model for studying the economics of climate change is the dynamic/regional integrated model of climate and economy (DICE/RICE). In this document, we first represent the RICE-2011 model as a dynamic game, termed the RICE game. Then, both cooperative and non-cooperative solutions to the RICE game are considered. Next,

Christopher Roth, Attila Szabó, Allan MacDonald

We show that neural quantum states based on very deep (4--16-layered) neural networks can outperform state-of-the-art variational approaches on highly frustrated quantum magnets, including quantum-spin-liquid candidates. We focus on group convolutional neural networks (GCNNs) that allow us to impose space-group symmetries on our ans\"atze. We achieve state-o

Harry Freeman, Eric Schneider, Chung Hee Kim, Moonyoung Lee

In this paper, we present a method for creating high-quality 3D models of sorghum panicles for phenotyping in breeding experiments. This is achieved with a novel reconstruction approach that uses seeds as semantic landmarks in both 2D and 3D. To evaluate the performance, we develop a new metric for assessing the quality of reconstructed point clouds without

Oren Elishav, Roy Podgaetsky, Olga Meikler, Barak Hirshberg

Controlling polymorphism in molecular crystals is crucial in the pharmaceutical, dye, and pesticide industries. However, its theoretical description is extremely challenging, due to the associated long timescales ($ > 1 \, \mu s$). We present an efficient procedure for identifying collective variables that promote transitions between conformational polymorph

Puhua Wan, Oleksandr Zheliuk, Noah F. Q. Yuan, Xiaoli Peng

The conventional Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state relies on the Zeeman effect of an external magnetic field to break time-reversal symmetry, forming a state of finite-momentum Cooper pairing. In superconductors with broken inversion symmetries, the Rashba or Ising-type spin-orbit coupling (SOC) can interact with either the Zeeman or the orbital

Dingbin Huang, Delin Zhang, Yun Kim, Jian-Ping Wang

Understanding the rich physics of magnetization dynamics in perpendicular synthetic antiferromagnets (p-SAFs) is crucial for developing next-generation spintronic devices. In this work, we systematically investigate the magnetization dynamics in p-SAFs combining time-resolved magneto-optical Kerr effect (TR-MOKE) measurements with theoretical modeling. These

Joseph J. Peper, Lu Wang

Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this wor

Kiana Walter, Kobi Hall, David C. Latimer

The sole static electromagnetic property of a spin-$\frac{1}{2}$ Majorana fermion is its anapole moment. Though they cannot couple to single real photons, these particles can interact with electric currents through virtual photons. If a Majorana fermion is immersed in a background current, there is an energy difference between the spin states of the fermion;

C. D. Pruitt, J. E. Escher, R. Rahman

Optical-model potentials (OMPs) continue to play a key role in nuclear reaction calculations. However, the uncertainty of phenomenological OMPs in widespread use -- inherent to any parametric model trained on data -- has not been fully characterized, and its impact on downstream users of OMPs remains unclear. Here we assign well-calibrated uncertainties for

Mark S. Graham, Walter H. L. Pinaya, Petru-Daniel Tudosiu, Parashkev Nachev

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measur

Alina Ostafe, Igor E. Shparlinski, José Felipe Voloch

We obtain new bounds on short Weil sums over small multiplicative subgroups of prime finite fields which remain nontrivial in the range the classical Weil bound is already trivial. The method we use is a blend of techniques coming from algebraic geometry and additive combinatorics.

Amanda Calatrava, Hernán Asorey, Jan Astalos, Alberto Azevedo

Open Science is a paradigm in which scientific data, procedures, tools and results are shared transparently and reused by society as a whole. The initiative known as the European Open Science Cloud (EOSC) is an effort in Europe to provide an open, trusted, virtual and federated computing environment to execute scientific applications, and to store, share and

Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh, Huaming Wang

Emotions lie on a broad continuum and treating emotions as a discrete number of classes limits the ability of a model to capture the nuances in the continuum. The challenge is how to describe the nuances of emotions and how to enable a model to learn the descriptions. In this work, we devise a method to automatically create a description (or prompt) for a gi

Zakariae Aznay, Abdelmalek Ouahab, Hassan Zariouh

We prove that a bounded linear operator $T$ is a direct sum of an invertible operator and an operator with at most countable spectrum iff $0\notin\mbox{acc}^{\omega_{1}}\,\sigma(T),$ where $\omega_{1}$ is the smallest uncountable ordinal and $\mbox{acc}^{\omega_{1}}\,\sigma(T)$ is the $\omega_{1}$-th Cantor-Bendixson derivative of $\sigma(T).$

Moran Barenboim, Moshe Shienman, Vadim Indelman

Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online Partially Observable Markov Decision Processes (POMDPs) solvers do not support hybrid beliefs directly. In particular, these solvers do not

Cameron W. Johnson, Andreas K. Schmid, Marian Mankos, Robin Röpke

Modern electron microscopy and spectroscopy is a key technology for studying the structure and composition of quantum and biological materials in fundamental and applied sciences. High-resolution spectroscopic techniques and aberration-corrected microscopes are often limited by the relatively large energy distribution of currently available beam sources. Thi

Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski, Jindřich Libovický

Pre-trained multilingual language models (PMLMs) are commonly used when dealing with data from multiple languages and cross-lingual transfer. However, PMLMs are trained on varying amounts of data for each language. In practice this means their performance is often much better on English than many other languages. We explore to what extent this also applies t

Shoham Letzter

A separating path system for a graph $G$ is a collection $\mathcal{P}$ of paths in $G$ such that for every two edges $e$ and $f$ in $G$, there is a path in $\mathcal{P}$ that contains $e$ but not $f$. We show that every $n$-vertex graph has a separating path system of size $O(n \log^* n)$. This improves upon the previous best upper bound of $O(n \log n)$, an

Zhen Tao, Tian Qiu, Joseph E. Subotnik

We investigate how the Berry force (i.e. the pseudo-magnetic force operating on nuclei as induced by electronic degeneracy and spin-orbit coupling (SOC)) might modify a post-transition state bifurcation (PTSB) reaction path and affect product selectivity for situations when multiple products share the same transition state. To estimate the magnitude of this

Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities. We present a novel query-based framework, QueryForm, that extracts entity values from form-like documents in a zero-shot fashion. QueryForm contains a dual prompting mechanism that composes

Saša Brdnik, Boštjan Šumak, Vili Podgorelec

Learning analytics (LA) is data collection, analysis, and representation of data about learners in order to improve their learning and performance. Furthermore, LA opens the door to opportunities for self-regulated learning in higher education, a circular process in which learners activate and sustain behaviours that are oriented toward their personal learni

Pa Chia Thao, Andrew W. Mann, Peter Gao, Dylan A. Owens

Although all-sky surveys have led to the discovery of dozens of young planets, little is known about their atmospheres. Here, we present multi-wavelength transit data for the super Neptune-sized exoplanet, K2-33b -- the youngest (~10 Myr) transiting exoplanet to-date. We combined photometric observations of K2-33 covering a total of 33 transits spanning >2 y

Ippei Fujisawa, Ryota Kanai

Logical reasoning is essential in a variety of human activities. A representative example of a logical task is mathematics. Recent large-scale models trained on large datasets have been successful in various fields, but their reasoning ability in arithmetic tasks is limited, which we reproduce experimentally. Here, we recast this limitation as not unique to

Qimeng Yu, Simge Küçükyavuz

DR-submodular functions encompass a broad class of functions which are generally non-convex and non-concave. We study the problem of minimizing any DR-submodular function, with continuous and general integer variables, under box constraints and possibly additional monotonicity constraints. We propose valid linear inequalities for the epigraph of any DR-submo

Heinke Hihn, Daniel A. Braun

One notable weakness of current machine learning algorithms is the poor ability of models to solve new problems without forgetting previously acquired knowledge. The Continual Learning paradigm has emerged as a protocol to systematically investigate settings where the model sequentially observes samples generated by a series of tasks. In this work, we take a

Tanmay C. Shidhore, Aaron A. Cohen-Gadol, Vitaliy L. Rayz, Ivan C. Christov

Cerebral aneurysm progression is a result of a complex interplay of the biomechanical and clinical risk factors that drive aneurysmal growth and rupture. Subjects with multiple aneurysms are unique cases wherein clinical risk factors are expected to affect each aneurysm equally, thus allowing for disentangling the effect of biomechanical factors on aneurysma

Johannes Krah

A conjecture of Bondal-Polishchuk states that, in particular for the bounded derived category of coherent sheaves on a smooth projective variety, the action of the braid group on full exceptional collections is transitive up to shifts. We show that the braid group acts transitively on the set of maximal numerically exceptional collections on rational surface

Siavash Golkar, David Lipshutz, Tiberiu Tesileanu, Dmitri B. Chklovskii

Finding informative low-dimensional representations that can be computed efficiently in large datasets is an important problem in data analysis. Recently, contrastive Principal Component Analysis (cPCA) was proposed as a more informative generalization of PCA that takes advantage of contrastive learning. However, the performance of cPCA is sensitive to hyper

Chandra Kanth Nagesh, Abhishek Purushothama

Birds are vital parts of ecosystems across the world and are an excellent measure of the quality of life on earth. Many bird species are endangered while others are already extinct. Ecological efforts in understanding and monitoring bird populations are important to conserve their habitat and species, but this mostly relies on manual methods in rough terrain

Alex Cebrian, Wilson Forero

We introduce the notion of directed hereditary species and show that they have associated monoidal decomposition spaces, comodule bialgebras, and operadic categories. The notion subsumes Schmitt's hereditary species, G\'alvez--Kock--Tonks directed restrictions species, and a directed version of Carlier's construction of monoidal decomposition spaces and como

Burak Ahmet Ozden, Erdogan Aydin, Fatih Cogen

The demands for high data rate, reliability, high energy efficiency, high spectral efficiency, and low latency communication have been increasing rapidly. For this reason, communication models that use limited resources in the best way, allow fast data transmission, and increase performance has become very important. In this work, a novel high energy and spe

Sanjana Narayanan, Isaac Lage, Finale Doshi-Velez

Global explanations of a reinforcement learning (RL) agent's expected behavior can make it safer to deploy. However, such explanations are often difficult to understand because of the complicated nature of many RL policies. Effective human explanations are often contrastive, referencing a known contrast (policy) to reduce redundancy. At the same time, these

Karthik Siva, Gerwin Koolstra, John Steinmetz, William P. Livingston

Reconstructing the Hamiltonian of a quantum system is an essential task for characterizing and certifying quantum processors and simulators. Existing techniques either rely on projective measurements of the system before and after coherent time evolution and do not explicitly reconstruct the full time-dependent Hamiltonian or interrupt evolution for tomograp

Meziane Privat

To predict the behavior of a population game when time becomes very long, the process that characterizes the evolution of our game dynamics must be reversible. Known games satisfying this are 2 strategy games as well as potential games with an exponential protocol. We will try to extend the study of infinite horizons for what are called symetric strategy gam

Anatole Gaudin

In this paper, we propose an elementary construction of homogeneous Sobolev spaces of fractional order on $\mathbb{R}^n$ and $\mathbb{R}^n_+$. This construction completes the construction of homogeneous Besov spaces on $\mathcal{S}'_h(\mathbb{R}^n)$ started by Bahouri, Chemin and Danchin on $\mathbb{R}^n$. We will also extend the treatment done by Danchin an

Kazumasa Ohno, Pa Chia Thao, Andrew W. Mann, Jonathan J. Fortney

Young exoplanets are attractive targets for atmospheric characterization to explore the early phase of planetary evolution and the surrounding environment. Recent observations of the 10 Myr young Neptune-sized exoplanet K2-33b revealed that the planet's transit depth drastically decreases from the optical to near-infrared wavelengths. Thao et al. (2022) sugg

Adrien Merlini, Clément Henry, Davide Consoli, Lyes Rahmouni

Quasi-Helmholtz decompositions are fundamental tools in integral equation modeling of electromagnetic problems because of their ability of rescaling solenoidal and non-solenoidal components of solutions, operator matrices, and radiated fields. These tools are however incapable, per se, of modifying the refinement-dependent spectral behavior of the different

Takeshi Egami, Chae Woo Ryu

Elucidating the atomic structure of liquid and glass is one of the important open questions in condensed matter physics. In the conventional bottom-up approach one starts with focusing on an atom and the short-range order of its neighboring atoms, and the global structure is described in terms of overlapping local clusters of atoms as building units. However

Alexis Léculier, Nga Nguyen

In this paper, we introduce a control strategy for applying the Sterile Insect Technique (SIT) to eliminate the population of Aedes mosquitoes which are the vectors of various deadly diseases like dengue, zika, chikungunya... in a wide area. We use a system of reaction-diffusion equations to model the mosquito population and study the effect of releasing ste

Rafael Pastrana

A variational autoencoder (VAE) is a probabilistic machine learning framework for posterior inference that projects an input set of high-dimensional data to a lower-dimensional, latent space. The latent space learned with a VAE offers exciting opportunities to develop new data-driven design processes in creative disciplines, in particular, to automate the ge

E. Thygesen, Y. Sun, J. Huang, K. C. Dage

Ultraluminous X-ray Sources (ULXs) in globular clusters are low mass X-ray binaries that achieve high X-ray luminosities through a currently uncertain accretion mechanism. Using archival Chandra and Hubble Space Telescope observations, we perform a volume-limited search ($\lesssim$ 70 Mpc) of 21 of the most massive ($>10^{11.5} M_\odot$) early-type galaxies

Yves Achdou, Jean-Michel Lasry, Pierre Louis Lions

The celebrated Krusel-Smith growth model is an important example of a Mean Field Game with a common noise. The Mean Field Game is encoded in the master equation, a partial differential equation satisfied by the value of the game which depends on the whole distribution of states. The latter equation is therefore posed in an infinite dimensional space. This ma

Sarah McGuire, Shane Jackson, Tegan Emerson, Henry Kvinge

There is a growing body of work that leverages features extracted via topological data analysis to train machine learning models. While this field, sometimes known as topological machine learning (TML), has seen some notable successes, an understanding of how the process of learning from topological features differs from the process of learning from raw data

Etienne Labbé, Thomas Pellegrini, Julien Pinquier

Automatic Audio Captioning (AAC) is the task that aims to describe an audio signal using natural language. AAC systems take as input an audio signal and output a free-form text sentence, called a caption. Evaluating such systems is not trivial, since there are many ways to express the same idea. For this reason, several complementary metrics, such as BLEU, C

Farid Alijani, Esa Rahtu

In this paper, we present a comprehensive study on the utility of deep convolutional neural networks with two state-of-the-art pooling layers which are placed after convolutional layers and fine-tuned in an end-to-end manner for visual place recognition task in challenging conditions, including seasonal and illumination variations. We compared extensively th

Michele Cicoli, Matteo Licheri, Anshuman Maharana, Kajal Singh

We obtain the joint distribution of the gravitino mass and the cosmological constant in KKLT and LVS models with anti-D3 brane uplifting described via the nilpotent goldstino formalism. Moduli stabilisation (of both complex structure and Kaehler moduli) is incorporated so that we sample only over points corresponding to vacua. Our key inputs are the distribu

Hamza Ennaji, Quentin Mérigot, Luca Nenna, Brendan Pass

We study the problem of maximizing a spectral risk measure of a given output function which depends on several underlying variables, whose individual distributions are known but whose joint distribution is not. We establish and exploit an equivalence between this problem and a multi-marginal optimal transport problem. We use this reformulation to establish e

Yelnar Yerdesh, Tangnur Amanzholov, Abdurashid Aliuly, Abzal Seitov

The ground source heat pump heating system is considered as one of the best solutions for the transition towards green heating under the continental climate conditions like Kazakhstan. In this paper, experimental and theoretical investigations were carried out to develop a ground source heat pump-based heating system under the weather conditions in Kazakhsta

Jin Li, Deepta Rajan, Chintan Shah, Dinkar Juyal

Histopathology images are gigapixel-sized and include features and information at different resolutions. Collecting annotations in histopathology requires highly specialized pathologists, making it expensive and time-consuming. Self-training can alleviate annotation constraints by learning from both labeled and unlabeled data, reducing the amount of annotati

Prashanth Amireddy, Ankit Garg, Neeraj Kayal, Chandan Saha

We prove super-polynomial lower bounds for low-depth arithmetic circuits using the shifted partials measure [Gupta-Kamath-Kayal-Saptharishi, CCC 2013], [Kayal, ECCC 2012] and the affine projections of partials measure [Garg-Kayal-Saha, FOCS 2020], [Kayal-Nair-Saha, STACS 2016]. The recent breakthrough work of Limaye, Srinivasan and Tavenas [FOCS 2021] proved

Aaron Grapentin, Arnold Sterle, Jörg Raisch, Christian A. Hans

In this paper, an approach for active power control of individual wind turbines is presented. State-of-the-art controllers typically employ separate control loops for torque and pitch control. In contrast, we use a multivariable control approach. In detail, active power control is achieved by using reference trajectories for generator speed, generator torque

Matija Bucić, Richard Montgomery

In the 1960's, Erd\H{o}s and Gallai conjectured that the edges of any $n$-vertex graph can be decomposed into $O(n)$ cycles and edges. We improve upon the previous best bound of $O(n\log\log n)$ cycles and edges due to Conlon, Fox and Sudakov, by showing an $n$-vertex graph can always be decomposed into $O(n\log^{*}n)$ cycles and edges, where $\log^{*}n$ is

Raffaele Salvia, Mohammad Mehboudi, Martí Perarnau-Llobet

We investigate critical quantum metrology,that is the estimation of parameters in many-body systems close to a quantum critical point, through the lens of Bayesian inference theory. We first derive a no-go result stating that any non-adaptive measurement strategy will fail to exploit quantum critical enhancement (i.e. precision beyond the shot-noise limit) f

Bechir Hamdaoui, Abdurrahman Elmaghbub

Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are collected under varying deployment doma

Elie Abdo, Fizay-Noah Lee, Weinan Wang

We consider the Nernst-Planck equations describing the nonlinear time evolution of multiple ionic concentrations in a two-dimensional incompressible fluid. The velocity of the fluid evolves according to either the Euler or Darcy's equations, both forced nonlinearly by the electric forces generated by the presence of charged ions. We address the global well-p

Peter Rakyta, Gregory Morse, Jakab Nádori, Zita Majnay-Takács

The formulation of quantum programs in terms of the fewest number of gate operations is crucial to retrieve meaningful results from the noisy quantum processors accessible these days. In this work, we demonstrate a use-case for Field Programmable Gate Array (FPGA) based data-flow engines (DFEs) to scale up variational quantum compilers to synthesize circuits

You Zuo, Yixuan Li, Alma Parias García, Kim Gerdes

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hy

Anthony N. Ciavarella, Stephan Caspar, Hersh Singh, Martin J. Savage

The 1+1D O(3) non-linear {\sigma}-model is a model system for future quantum lattice simulations of other asymptotically-free theories, such as non-Abelian gauge theories. We find that utilizing dimensional reduction can make efficient use of two-dimensional layouts presently available on cold atom quantum simulators. A new definition of the renormalized cou

R. Loganayagam, Mukund Rangamani, Julio Virrueta

We present a unified picture of open quantum systems, the theory of a system probing a noisy thermal environment, distilling lessons learnt from previous holographic analyses. Our treatment is applicable both when the system is coupled to short-lived (Markovian), and long-lived (non-Markovian) environmental degrees of freedom. The thermal environment is mode

Arief Ahmad, Bernd Freytag, Susanne Höfner

The characteristic variability of cool giants and supergiants is attributed to a combination of stellar pulsation and large-scale convective flows. Full 3D radiation-hydrodynamical modelling is an essential tool for understanding the nature of these dynamical processes. The parameter space in our 3D model grid of red giants has expanded in recent years. Thes

Laisi Chen, Amy X. Wu, Naol Tulu, Joshua Wang

Confining materials to two-dimensional forms changes the behavior of electrons and enables new devices. However, most materials are challenging to produce as uniform thin crystals. Here, we present a new synthesis approach where crystals are grown in a nanoscale mold defined by atomically-flat van der Waals (vdW) materials. By heating and compressing bismuth

Martijn de Vries, Adam B. Mantz, Steven W. Allen, R. Glenn Morris

We present a Chandra study of surface brightness fluctuations in the diffuse intracluster medium of the Perseus Cluster. Our study utilizes deep, archival imaging of the cluster core as well as a new mosaic of 29 short 5 ks observations extending in 8 different directions out to radii of r_500 ~ 2.2r_2500. Under the assumption that the distribution of densit

Christopher J. Agostino, Samir Salim, Sara L. Ellison, Robert W. Bickley

In this work, we use ~500 low-redshift (z ~ 0.1) X-ray AGNs observed by XMM-Newton and SDSS to investigate the prevalence and nature of AGNs that apparently lack optical emission lines (``optically dull AGNs''). Although 1/4 of spectra appear absorption-line dominated in visual assessment, line extraction with robust continuum subtraction from the MPA/JHU ca

Pooja Siwach, Anna M. Suliga, A. Baha Balantekin

Extreme conditions present in the interiors of the core-collapse supernovae make neutrino-neutrino interactions not only feasible but dominant in specific regions, leading to the non-linear evolution of the neutrino flavor. Results obtained when such collective neutrino oscillations are treated in the mean-field approximation deviate from the results using t

Ivano Saccheo, Angela Bongiorno, Enrico Piconcelli, Manuela Bischetti

Hyper-luminous Quasi-Stellar Objects (QSOs) represent the ideal laboratory to investigate Active Galactic Nuclei (AGN) feedback mechanism since their formidable energy release causes powerful winds at all scales and thus the maximum feedback is expected. We aim at deriving the mean Spectral Energy Distribution (SED) of a sample of 85 WISE-SDSS Selected Hyper

Jun-Ang Wang, Mohamed Assili, Panagiotis Kotetes

We demonstrate that topological constraints do not only dictate the geometric part of the superfluid stiffness, but can also govern the total superfluid stiffness. By introducing a general adiabatic approach for superfluid responses, we showcase such a possibility by proving that the stiffness of a superconducting Dirac cone in two dimensions (2D) is proport

Mudit Gaur, Vaneet Aggarwal, Mridul Agarwal

Deep Q-learning based algorithms have been applied successfully in many decision making problems, while their theoretical foundations are not as well understood. In this paper, we study a Fitted Q-Iteration with two-layer ReLU neural network parameterization, and find the sample complexity guarantees for the algorithm. Our approach estimates the Q-function i

Amit Adhikary, Shankha Banerjee, Rahool Kumar Barman, Brian Batell

We study the prospects for observing exotic decays of the Standard Model Higgs boson $h$ into light beyond the Standard Model scalars $a$ with mass $m_{a} \lesssim m_{h}/2$ in the single Higgs and Higgs pair production channels at the high luminosity run of the Large Hadron Collider (HL-LHC). Discovery prospects for single Higgs production in the gluon-gluon

Samuel Beiler, Katelyn Allers, Michael Cushing, Jacqueline Faherty

We present a $L$-band (2.98--3.96$\mu$m) spectroscopic study of 8 young L dwarfs with spectral types ranging from L2 to L7. Our spectra (${\lambda}/{\Delta \lambda}\approx$ 250 to 600) were collected using the Gemini Near-InfraRed Spectrograph. We first examine the young $L$-band spectral sequence, most notably analyzing the evolution of the $Q$-branch of me

Sang Wook Kim, Mohamed Elsayed, Nathan S. Nichols, Taras Lakoba

Bosonic atoms deposited on atomically thin substrates represent a playground for exotic quantum many-body physics due to the highly-tunable, atomic-scale nature of the interaction potentials. The ability to engineer strong interparticle interactions can lead to the emergence of complex collective atomic states of matter, not possible in the context of dilute

Parul Jain, Niko Jokela, Matti Jarvinen, Subhash Mahapatra

The entanglement wedge cross sections (EWCSs) are postulated as dual gravity probes to certain measures for the entanglement of multiparty systems. We test various proposed inequalities for EWCSs. As it turns out, contrary to expectations, the EWCS is not clearly monogamous nor polygamous for tripartite systems but the results depend on the details and dimen

Camila de Sá-Freitas, Francesca Fragkoudi, Dimitri A. Gadotti, Jesús Falcón-Barroso

The epoch in which galactic discs settle is a major benchmark to test models of galaxy formation and evolution but is as yet largely unknown. Once discs settle and become self-gravitating enough, stellar bars are able to form; therefore, determining the ages of bars can shed light on the epoch of disc settling, and on the onset of secular evolution. Neverthe

Paz Beniamini, Pawan Kumar

We show that the 216.8$\pm$0.1 ms periodicity reported for the fast radio burst (FRB) 20191221A is very constraining for burst models. The high accuracy of burst periodicity (better than one part in 10$^3$), and the 2\% duty cycle (ratio of burst-duration and inter-burst interval), suggest a pulsar-like rotating beam model for the observed activity; the radi

Digvijay Wadekar, Zihui Wang

Cold interstellar gas systems have been used to constrain dark matter (DM) models by the condition that the heating rate from DM must be lower than the astrophysical cooling rate of the gas. Following the methodology of Wadekar and Farrar (2021), we use the interstellar medium of a gas-rich dwarf galaxy, Leo T, and a Milky Way-environment gas cloud, G33.4-8.