May 2022 arXiv papers — page 99

Joel Janek Dabrowski, Ashfaqur Rahman, Andrew Hellicar, Mashud Rana

We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and re

Joel Kuttruff, Marco Romanelli, Esteban Pedrueza-Villalmanzo, Jonas Allerbeck

Molecular polaritons are hybrid light-matter states that emerge when a molecular transition strongly interacts with photons in a resonator. At optical frequencies, this interaction unlocks a way to explore and control new chemical phenomena at the nanoscale. Achieving such a control at ultrafast timescales, however, is an outstanding challenge, as it require

Mohammed El Ansari, Youssef Akdim, Soumia Lalaoui Rhali

In this paper, we interest on some class of Stefan type problems. We prove the existence and uniqueness of renormalized solution in anisotropic Sobolev spaces with data belongs to $L^1- data,$ based on the properties of the renormalized trunctions and the generalized monotonicity method in the functional spaces.

Kabir Ahuja, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury

Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity. We argue that this makes the existing practices in multilingual evaluation unreliable and does not provide a f

Matej Grobelnik, Joaquin Vanschoren

Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a significant step forward in the field of automating the design of deep neural networks, thus further reducing the need for human expertise. However, most research is done targeting a single speci

F. J. Galindo-Guil, D. Barrado, H. Bouy, J. Olivares

Stellar ages are key to improving our understanding of different astrophysical phenomena. However, many techniques to estimate stellar ages are highly model-dependent. The lithium depletion boundary (LDB), based on the presence or absence of lithium in low-mass stars, can be used to derive ages in stellar associations of between 20 and 500~Ma. The purpose of

Raouf Barboza, Stefano Marni, Fabrizio Ciciulla, Farooq Ali Mir

We investigated the electrostatic behavior of ferroelectric liquid droplets exposed to the pyroelectric field of a lithium niobate ferroelectric crystal substrate. The ferroelectric liquid is a nematic liquid crystal in which almost complete polar ordering of the molecular dipoles generates an internal macroscopic polarization locally collinear to the mean m

Michaela Greiler, Margaret-Anne Storey, Abi Noda

Developer experience is an important concern for software organizations as enhancing developer experience improves productivity, satisfaction, engagement and retention. We set out to understand what affects developer experience through semi-structured interviews with 21 developers from industry, which we transcribed and iteratively coded. Our findings elucid

Charles Anderson, Jason Stock, David Anderson

Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods to interpret deep neural networks and to reduce their complexity. An approach is described here that starts with linear models an

Kabir Ahuja, Monojit Choudhury, Sandipan Dandapat

Borrowing ideas from {\em Production functions} in micro-economics, in this paper we introduce a framework to systematically evaluate the performance and cost trade-offs between machine-translated and manually-created labelled data for task-specific fine-tuning of massively multilingual language models. We illustrate the effectiveness of our framework throug

Jack W. Roddy, George T. Lesica, Travis J. Wheeler

We present SODA, a lightweight and open-source visualization library for biological sequence annotations that enables straightforward development of flexible, dynamic, and interactive web graphics. SODA is implemented in TypeScript and can be used as a library within TypeScript and JavaScript.

Mark D. Leising

We illustrate methods for deriving properties of thermonuclear, or Type Ia, supernovae, including synthesized $^{56}$Ni mass, total ejecta mass, ejecta kinetic energy, and $^{56}$Ni distribution in velocity, from gamma-ray line observations. We simulate data from a small number of published SN Ia models for a simple gamma-ray instrument, and measure their un

Timothy Carleton, Rogier A. Windhorst, Rosalia O'Brien, Seth H. Cohen

We present the first results from the HST Archival Legacy project "SKYSURF." As described in Windhorst et al. 2022, SKYSURF utilizes the large HST archive to study the diffuse UV, optical, and near-IR backgrounds and foregrounds in detail. Here we utilize SKYSURF's first sky-surface brightness measurements to constrain the level of near-IR diffuse Extragalac

Jacques Carette, Gerardo Ortiz, Amr Sabry

Quantum models of computation are widely believed to be more powerful than classical ones. Efforts center on proving that, for a given problem, quantum algorithms are more resource efficient than any classical one. All this, however, assumes a standard predictive paradigm of reasoning where, given initial conditions, the future holds the answer. How about br

Ronen Tamari, Daniel Friedman, William Fischer, Lauren Hebert

The web has become a dominant epistemic environment, influencing people's beliefs at a global scale. However, online epistemic environments are increasingly polluted, impairing societies' ability to coordinate effectively in the face of global crises. We argue that centralized platforms are a main source of epistemic pollution, and that healthier environment

Seyed Mohammad Hosseiny, Milad Norouzi, Jamileh Seyed-Yazdi, Mohammad Hossein Ghamat

Josephson parametric amplifier (JPA) engineering is a significant component in the quantum two-mode squeezed radar (QTMS), to enhance, for instance, radar performance and the detection range or bandwidth. In this study, we apply quantum theory to a research domain focusing the design of QTMS radar. We apply engineered JPA (EJPA) to enhance the performance of

Lu Wei

As an alternative to entanglement entropies, the capacity of entanglement becomes a promising candidate to probe and estimate the degree of entanglement of quantum bipartite systems. In this work, we study the typical behavior of entanglement capacity over major models of random states. In particular, the exact and asymptotic formulas of average capacity hav

Veit D. Wild, Robert Hu, Dino Sejdinovic

We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). GWI leverages the Wasserstein distance between Gaussian measures on the Hilbert space of square-integrable functions in order to determine a variational posterior using a tractable

Mehmet Akif Erdal

Given a graph $G=(V,E)$, a set of vertices $D\subseteq V $ is called a dominating set if every vertex in $V\backslash D$ is adjacent to a vertex in $D$, and a subset $B\subseteq V $ is called a nonblocking set if $V-B$ is a dominating set. In this paper, we introduce a graph dynamical systems detecting vertex sets that are simultaneously dominating and nonbl

Bojan Novakovic, David Vokrouhlicky, Federica Spoto, David Nesvorny

Collisions are one of the key processes shaping planetary systems. Asteroid families are outcomes of such collisions still identifiable across our solar system. The families provide a unique view of catastrophic disruption phenomena and have been in the focus of planetary scientists for more than a century. Most of them are located in the main belt, a ring o

Ekta Chaubey, Mandeep Kaur, Ambresh Shivaji

We present analytic results for all the Feynman integrals relevant for ${\mathcal O}(\alpha \alpha_s)$ virtual corrections to $H \rightarrow ZZ^*$ decay. We use the method of differential equations to solve the master integrals while keeping the full dependence on the masses of all the particles including internal propagators. Due to the presence of four mas

Connor Oxenhorn

Stablecoins, digital assets pegged to a specific currency or commodity value, are heavily involved in transactions of major cryptocurrencies. The effects of deviations from their desired fixed values (depeggings) on the cryptocurrencies for which they are frequently used in transactions are therefore of interest to study. We propose a model for this phenomen

K. R. Labe

The muon magnetic anomaly, $a_{\mu}$, is a powerful test of the Standard Model of particle physics. A new experiment at Fermilab has recently measured $a_{\mu}$ with unprecedented precision, confirming the results of the earlier Brookhaven experiment and strengthening the tension with the prediction of the Standard Model as determined by dispersive methods.

Onur Bilge, Burak Kaya

In this paper, we show that Frucht's theorem holds in Borel setting. More specifically, we prove that any standard Borel group can be realized as the Borel automorphism group of a Borel graph. A slight modification of our construction also yields the following result in topological setting: Any Polish group can be realized as the homeomorphic automorphism gr

Yicheng Bao, Scarlett S. Yu, Loïc Anderegg, Sean Burchesky

Optically trapped laser-cooled polar molecules hold promise for new science and technology in quantum information and quantum simulation. Large numerical aperture optical access and long trap lifetimes are needed for many studies, but these requirements are challenging to achieve in a magneto-optical trap (MOT) vacuum chamber that is connected to a cryogenic

Negin Heravi, Ayzaan Wahid, Corey Lynch, Pete Florence

Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for this, but most of the current methodologies learn task specific representations that do not necessarily transfer well to other tasks. Further

P. L. Cástulo, J. J. Toscano, E. S. Tututi

A bound on the CPT-odd four vector coefficient $k^\mu_\phi$ that appears in Higgs sector of the Minimal Standard Model Extended (MSME) is presented. The analysis is based on the contributions arising from the sector in question to the anomalous dipole moment for leptons calculated at the one loop level, for which an analytical expression is obtained. The lar

Ahmadreza Moradipari, Mohammad Ghavamzadeh, Mahnoosh Alizadeh

We study a collaborative multi-agent stochastic linear bandit setting, where $N$ agents that form a network communicate locally to minimize their overall regret. In this setting, each agent has its own linear bandit problem (its own reward parameter) and the goal is to select the best global action w.r.t. the average of their reward parameters. At each round

Alexander Thomasian

Large disk arrays are organized into storage nodes -- SNs or bricks with their own cashed RAID controller for multiple disks. Erasure coding at SN level is attained via parity or Reed-Solomon codes. Hierarchical RAID -- HRAID -- provides an additional level of coding across SNs, e.g., check strips P, Q at intra-SN level and R at the inter-SN level. Failed di

Carlos M. Reyes, Marco Schreck, Alex Soto

We consider diffeomorphism violation, which is parameterized by nondynamical background fields of the gravitational Standard-Model Extension (SME), and study its effects on the time evolution of the Universe. Our goal is to identify background field configurations that imply stages of accelerated expansion without exotic forms of matter and radiation present

Jianhang Xu, Ruiyi Zhou, Zhen Tao, Christopher Malbon

The nuclear-electronic orbital (NEO) method is a well-established approach for treating nuclei quantum mechanically in molecular systems beyond the usual Born-Oppenheimer approximation. In this work, we present a strategy to implement the NEO method for periodic electronic structure calculations, particularly focused on multicomponent density functional theo

Xiao Wang, Aristeidis Tsaris, Debangshu Mukherjee, Mohamed Wahib

Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution for ptychographic reconstruction requires significant amount of memory and computations, forcing many applications to compromise their image resolution in exchange for a smaller memo

Paul Debevec, Chloe LeGendre

We present a technique to reduce the dynamic range of an HDRI lighting environment map in an efficient, energy-preserving manner by spreading out the light of concentrated light sources. This allows us to display a reasonable approximation of the illumination of an HDRI map in a lighting reproduction system with limited dynamic range such as virtual producti

Ahmadreza Moradipari, Mohammad Ghavamzadeh, Taha Rajabzadeh, Christos Thrampoulidis

In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired by the work of [1] on meta-learning in a sequence of linear bandit problems whose parameters are sampled from a single distribution (i.e., a single environment), here we consider t

Rajam Elancheliyan, Giovanni Del Monte, Edouard Chauveau, Simona Sennato

Poly(N-isopropylacrylamide)-based microgels are soft colloids undergoing a Volume Phase Transition (VPT) close to ambient temperature. Although widely employed for fundamental research and application purposes, the modifications of the microgel internal structure occurring at the VPT are not yet completely understood, especially concerning the role of electr

Darren C. Pagan, Calvin R. Pash, Austin R. Benson, Matthew P. Kasemer

Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed

Armando Castañeda, Miguel Piña

A modular version of the baskets queue of Hoffman, Shalev and Shavit is presented. It manipulates the head and tail using a novel object called load-link/increment-conditional, which can be implemented using only READ/WRITE instructions, and admits implementations that spread contention. This suggests that there might be an alternative to the seemingly inher

Christopher Wagner, Nouraldin Jaber, Roopsha Samanta

The ubiquity of distributed agreement protocols, such as consensus, has galvanized interest in verification of such protocols as well as applications built on top of them. The complexity and unboundedness of such systems, however, makes their verification onerous in general, and, particularly prohibitive for full automation. An exciting, recent breakthrough

Lei Yu, Yang Xu

Humans can flexibly extend word usages across different grammatical classes, a phenomenon known as word class conversion. Noun-to-verb conversion, or denominal verb (e.g., to Google a cheap flight), is one of the most prevalent forms of word class conversion. However, existing natural language processing systems are impoverished in interpreting and generatin

Soumen Dey, Ehsan M. Moqanaki, Cyril Milleret, Pierre Dupont

Spatial capture-recapture (SCR) models are now widely used for estimating density from repeated individual spatial encounters. SCR accounts for the inherent spatial autocorrelation in individual detections by modelling detection probabilities as a function of distance between the detectors and individual activity centres. However, additional spatial heteroge

Elliot Lu, B. Shanker, Carlo Piermarocchi

We introduce a computational Maxwell-Bloch framework for investigating out-of-equilibrium optical emitters in open systems. To do so, we compute the pulse-induced dynamics of each emitter from fundamental light-matter interactions and self-consistently calculate their radiative coupling, including phase inhomogeneity from propagation effects. This semiclassi

Atila Poro, Ehsan Paki, Mark G. Blackford, Fatemeh Davoudi

We present the photometric analysis of six short-period systems (EI Oct, V336 TrA, NX Boo, V356 Boo, PS Boo, and V2282 Cyg). This is the first photometric analysis of these systems except for V336 TrA. Observations were conducted for 27 nights at three observatories in the northern and southern hemispheres. We calculated a new ephemeris for each of the syste

Yuzki M. Oey, Farnaz Kaboudvand, Brenden R. Ortiz, Ram Seshadri

The family of AV$_3$Sb$_5$ (A = K, Rb, Cs) kagome metals exhibit charge density wave (CDW) order, proposed to be chiral, followed by a lower temperature superconducting state. Recent studies have proposed the importance of band structure saddle points proximal to the Fermi energy in governing these two transitions. Here we show the effects of hole-doping ach

Kevin Linka, Amelie Schafer, Xuhui Meng, Zongren Zou

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with

Evgenii Reznichenko

Groups with a topology that is in consistent one way or another with the algebraic structure are considered. Classical groups with a topology are topological, paratopological, semitopological, and quasitopological groups. We also study other ways of matching topology and algebraic structure. The minimum requirement in this paper is that the group is a right

Chen Liang, Alessandro Zocca, Steven H. Low, Adam Wierman

Transmission power systems usually consist of interconnected sub-grids that are operated relatively independently. When a failure happens, it is desirable to localize its impact within the sub-grid where the failure occurs. This paper introduces three interface networks to connect sub-grids, achieving better failure localization while maintaining robust netw

Andreas Fackler, Samuel Schlesinger, Matthew Doty

We present a novel and simple solution to Atomic Broadcast (AB). We reduce AB to two subproblems. One of them is Reliable Broadcast (RB). We also introduce a subproblem we call Weakly-terminating Binary Agreement (WBA). WBA relaxes Binary Agreement (BA) protocols by not always terminating. WBA admits much simpler solutions than BA. We discuss concrete soluti

William Poole, Thomas Ouldridge, Manoj Gopalkrishnan, Erik Winfree

Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computation is the intrinsic noise due to chemica

Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi, Teo Bloch

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically dependent society. Hence, computational models to forecast GICs globally with large forecast horizon, high spatial resolut

Ramandeep Gill, Jonathan Granot

Gamma-ray bursts (GRBs) are the most luminous explosions in the Universe and are powered by ultra-relativistic jets. Their prompt $\gamma$-ray emission briefly outshines the rest of the $\gamma$-ray sky making them detectable from cosmological distances. It is followed by, and sometimes partially overlaps with, a similarly energetic but very broadband and lo

Jakob Thumm, Matthias Althoff

Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipulators in real-world human environments. Therefore, we propos

Prashant Kumar, R. N. Bhatt

We study the phase diagram of $\nu_e=7/3$ state in the $N=1$ Landau level in the presence of band mass anisotropy. Using density matrix renormalization group on an infinite cylinder geometry, we find a continuous transition from the topologically ordered Laughlin fractional quantum Hall state to a stripe phase with a period of approximately five and a half m

Jonas Richter, Oliver Lunt, Arijeet Pal

Conservation laws can constrain entanglement dynamics in isolated quantum systems, manifest in a slowdown of higher R\'enyi entropies. Here, we explore this phenomenon in a class of long-range random Clifford circuits with U$(1)$ symmetry where transport can be tuned from diffusive to superdiffusive. We unveil that the different hydrodynamic regimes reflect

Yue M. Lu, Horng-Tzer Yau

We investigate random matrices whose entries are obtained by applying a nonlinear kernel function to pairwise inner products between $n$ independent data vectors, drawn uniformly from the unit sphere in $\mathbb{R}^d$. This study is motivated by applications in machine learning and statistics, where these kernel random matrices and their spectral properties

Felipe Ortiz-Huerta, Karina Garay-Palmett

The emission of light of an efficient single-photon emitter (SPE) should have a high-count rate into a well-defined spatio-temporal mode along with an accessible numerical aperture (NA) to increase the light extraction efficiency that is required for effective coupling into optical waveguides. Based on a previously developed experimental approach to fabricat

Zheng Zhao, Simo Särkkä, Jens Sjölund, Thomas B. Schön

We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible. Our model represents these functions by non-linearly cascaded Gaussian processes represented as non-linear stochastic differential equations. The posterior distribution of the

Robin Kips, Ruowei Jiang, Sileye Ba, Brendan Duke

Augmented reality applications have rapidly spread across online platforms, allowing consumers to virtually try-on a variety of products, such as makeup, hair dying, or shoes. However, parametrizing a renderer to synthesize realistic images of a given product remains a challenging task that requires expert knowledge. While recent work has introduced neural r

Yohan Poirier-Ginter, Alexandre Lessard, Ryan Smith, Jean-François Lalonde

Deep generative models like StyleGAN hold the promise of semantic image editing: modifying images by their content, rather than their pixel values. Unfortunately, working with arbitrary images requires inverting the StyleGAN generator, which has remained challenging so far. Existing inversion approaches obtain promising yet imperfect results, having to trade

Sarah Alnegheimish, Alicia Guo, Yi Sun

Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need for a prompt-style dataset to trigger specific behaviors of language models. In this paper, we address this gap by creating a prompt dataset with respect to occupations collected from real-world natural sentences present in Wi

Arun Kenath, Christoph Schiller, C. Sivaram

There are at least two ways to deduce Einstein's field equations from the principle of maximum force $c^4/4G$ or from the equivalent principle of maximum power $c^5/4G$. Tests in gravitational wave astronomy, cosmology, and numerical gravitation confirm the two principles. Apparent paradoxes about the limits can all be resolved. Several related bounds arise.

Mariliza Tzes, Vasileios Vasilopoulos, Yiannis Kantaros, George J. Pappas

In this paper we address mobile manipulation planning problems in the presence of sensing and environmental uncertainty. In particular, we consider mobile sensing manipulators operating in environments with unknown geometry and uncertain movable objects, while being responsible for accomplishing tasks requiring grasping and releasing objects in a logical fas

Aparimit Chandra, Wenhan Dai, Don Towsley

Quantum teleportation channels can overcome the effects of photonic loss, a major challenge in the implementation of a quantum network over fiber. Teleportation channels are created by distributing an entangled state between two nodes which is a probabilistic process requiring classical communication. This causes critical delays that can cause information lo

Yuxuan Zhang, Shahin Jahanbani, Daoheng Niu, Reza Haghshenas

We present a holographic quantum simulation algorithm to variationally prepare thermal states of $d$-dimensional interacting quantum many-body systems, using only enough hardware qubits to represent a ($d$-1)-dimensional cross-section. This technique implements the thermal state by approximately unraveling the quantum matrix-product density operator (qMPDO)

Jon Aycock, Andrew Kobin

The Dedekind zeta function of a quadratic number field factors as a product of the Riemann zeta function and the $L$-function of a quadratic Dirichlet character. We categorify this formula using objective linear algebra in the abstract incidence algebra of the division poset.

Shameem A Puthiya Parambath, Christos Anagnostopoulos, Roderick Murray-Smith

We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequence learning approaches that exploit historical interaction data. Due to the supervision involved in the learning process, such a

Aristeidis Karras, Christos Karras

User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ease the sparsity issue and enhance the quality of suggestions. This work presents a deep model for concurrently learning item attributes and user behav

Tania Robens

I give an overview on models that extend the Standard Model scalar sector by additional gauge singlets or multiplets. I discuss current constraints on such models, as well as possible signatures and discovery prospects at current and future colliders.

Aristomenis Donos, Christiana Pantelidou

Second order phase transitions are universally driven by an order parameter which becomes trivial at the critical point. At the same time, collective excitations which involve the amplitude of the order parameter develop a gap which smoothly closes to zero at criticality. We develop analytical techniques to study this "Higgs" mode in holographic systems whic

A. A. Herasymchuk, P. O. Sukhachov, E. V. Gorbar

The electric and chiral current response to the time- and coordinate-dependent pseudoelectric field $\mathbf{E}_5$ in Weyl semimetals is studied. It is found that $\mathbf{E}_5$ leads to an electric current in the direction perpendicular to the field and the wave vector of the field-inducing perturbation. We dubbed this phenomenon the anomalous pseudo-Hall e

Pierre Jamet, Aurélien Drezet

In this work we develop a model based on the double solution theory of de Broglie in order to reproduce the famous Landau levels splitting in a constant magnetic field.

Ashish Alexander, Christopher G. Weddle, Christopher J. K. Richardson

Measuring the internal quality factor of coplanar waveguide superconducting resonators is an established method of determining small losses in superconducting devices. Traditionally, the resonator losses are only attributed to two-level system (TLS) defects using a power dependent model for the quality factor. However, excess non-equilibrium quasiparticles c

Jean-François Bisson

Phase conjugation with stimulated Brillouin scattering can be used to correct wavefront aberrations in high-power laser systems. Here, we consider such process with cylindrical vector lasers beams. By using a vectorial approach and a modal decomposition, we obtain a differential matrix equation that enables the calculation of the structure of Stokes vector e

Shijie Shang

We show that a bound of the Castelnuovo-Mumford regularity of any power of the ideal sheaf of a smooth projective complex variety $X\subseteq\mathbb{P}^r$ is sharp exactly for complete intersections, provided the variety $X$ is cut out scheme-theoretically by several hypersurfaces in $\mathbb{P}^r$. This generalizes a result of Bertram-Ein-Lazarsfeld.

Hezi Halawi

Let $G$ be a linear split algebraic group. The degenerate Eisenstein series associated to a maximal parabolic subgroup $E_{P}(f^{0},s,g)$ with the spherical section $f^{0}$ is studied in the first part of the thesis. In this part, we study the poles of $E_{P}(f^{0},s,g)$ in the region $\operatorname{Re} s >0$. We determine when the leading term in the Lauren

Ayon Basumallik, Darius Bunandar, Nicholas Dronen, Nicholas Harris

Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs on AMS devices with fixed-point numbers can incur an accuracy penalty because of precision loss. To mitigate this penalty, we present a novel AMS-compatible adaptive block floating

Xinyang Dong, Lorenzo Del Re, Alessandro Toschi, Emanuel Gull

We study the fluctuations responsible for pairing in the $d$-wave superconducting state of the two-dimensional Hubbard model at intermediate coupling within a cluster dynamical mean-field theory with a numerically exact quantum impurity solver. By analyzing how momentum and frequency dependent fluctuations generate the $d-$wave superconducting state in diffe

Steven Hurder, Olga Lukina

A group action has essential holonomy if the set of points with non-trivial holonomy has positive measure. If such an action is topologically free, then having essential holonomy is equivalent to the action not being essentially free, which means that the set of points with non-trivial stabilizer has positive measure. In this paper, we investigate the relati

Ana Glidden, Sara Seager, Jingcheng Huang, Janusz J. Petkowski

The search for signs of life on other worlds has largely focused on terrestrial planets. Recent work, however, argues that life could exist in the atmospheres of temperate sub-Neptunes. Here, we evaluate the usefulness of carbon dioxide isotopologues as evidence of aerial life. Carbon isotopes are of particular interest as metabolic processes preferentially

Ken J. Shen, Eliot Quataert

Recent observations suggest our understanding of mass loss in classical novae is incomplete, motivating a new theoretical examination of the physical processes responsible for nova mass ejection. In this paper, we perform hydrodynamical simulations of classical nova outflows using the stellar evolution code MESA. We find that, when the binary companion is ne

Sherwood Richers, Huaiyu Duan, Meng-Ru Wu, Soumya Bhattacharyya

The fast flavor instability (FFI) is expected to be ubiquitous in core-collapse supernovae and neutron star mergers. It rapidly shuffles neutrino flavor in a way that could impact the explosion mechanism, neutrino signals, mass outflows, and nucleosynthesis. The variety of initial conditions and simulation methods employed in simulations of the FFI prevent a

Josefina Michea, Anna Pasquali, Rory Smith, Paula Calderón-Castillo

It has been well established that dwarf early-type galaxies (ETGs) can often exhibit a complex morphology, whereby faint spiral arms, bars, edge-on disks or clumps are embedded in their main, brighter diffuse body. In our first paper (Brought to Light I: Michea et al. 2021), we developed a new method for robustly identifying and extracting substructures in d

Louise Breuval, Adam G. Riess, Pierre Kervella, Richard I. Anderson

The Cepheid period-luminosity (PL) relation (or Leavitt law) has served as the first rung of the most widely used extragalactic distance ladder and is central to the determination of the local value of the Hubble constant ($H_0$). We investigate the influence of metallicity on Cepheid brightness, a term that significantly improves the overall fit of the dist

Daine L. Danielson, Gautam Satishchandran, Robert M. Wald

We show that if a massive body is put in a quantum superposition of spatially separated states, the mere presence of a black hole in the vicinity of the body will eventually destroy the coherence of the superposition. This occurs because, in effect, the gravitational field of the body radiates soft gravitons into the black hole, allowing the black hole to ac

Nikita Astrakhantsev, Guglielmo Mazzola, Ivano Tavernelli, Giuseppe Carleo

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver (VQE) algorithm aims to prepare the ground state of a Hamiltonian exploiting parametrized quantum circuits that may offer an advantage compared to classical trial states used, for

Luca Broggi, Elisa Bortolas, Matteo Bonetti, Alberto Sesana

In this paper we develop a computationally efficient, two-population, time-dependent Fokker-Plank approach in the two dimensions of energy and angular momentum to study the rates of tidal disruption events (TDEs), extreme mass ratio inspirals (EMRIs) and direct plunges occurring around massive black holes (MBHs) in galactic nuclei. We test our code by explor

Debmalya Chakraborty, Annica M. Black-Schaffer

Understanding the nature of Cooper pairs is essential to describe the properties of superconductors. The original proposal of Bardeen, Cooper, and Schrieffer (BCS) was based on electrons pairing with same energy and zero center-of-mass momentum. With the advent of new superconductors, different forms of pairing have been discussed. In particular, Cooper pair

Massimo Dotti, Matteo Bonetti, Fabio Rigamonti, Elisa Bortolas

The observation of a population of massive black hole binaries (MBHBs) is key for our complete understanding of galaxy mergers and for the characterization of the expected gravitational waves (GWs) signal. However, MBHBs still remain elusive with only a few candidates proposed to date. Among these, SDSSJ143016.05+230344.4 ('tick-tock' hereafter) is the only

Romuald A. Janik, Matti Jarvinen, Hesam Soltanpanahi, Jacob Sonnenschein

We show that for a range of strongly coupled theories with a first order phase transition, the domain wall or bubble velocity can be expressed in a simple way in terms of a perfect fluid hydrodynamic formula, and thus in terms of the equation of state. We test the predictions for the domain wall velocities using the gauge/gravity duality.

Lars Aalsma, Gary Shiu

The mild form of the Weak Gravity Conjecture (WGC) requires higher derivative corrections to extremal charged black holes to increase their charge-to-mass ratio. This allows decay via emission of a smaller extremal black hole. In this paper, we investigate if similar constraints hold for extremal rotating black holes. We do so by considering the leading high

Francesco Capozzi, Madhurima Chakraborty, Sovan Chakraborty, Manibrata Sen

In the dense supernova environment, neutrinos can undergo fast flavor conversions which depend on the large neutrino-neutrino interaction strength. It has been recently shown that both their presence and outcome can be affected when passing from the commonly used three neutrino species approach to the more general one with six species. Here, we build up on a

N. Winkel, B. Husemann, T. A. Davis, I. Smirnova-Pinchukova

The host galaxy conditions for rapid supermassive black hole growth are poorly understood. Narrow-line Seyfert 1 (NLS1) galaxies often exhibit high accretion rates and are hypothesized to be prototypes of active galactic nuclei (AGN) at an early stage of their evolution. We present VLT MUSE NFM-AO observations of Mrk 1044, the nearest super-Eddington accreti

Wenzer Qin, Katelin Schutz, Aaron Smith, Enrico Garaldi

A near-future detection of the 21cm signal from the epoch of reionization will provide unique opportunities to probe the underlying cosmology, provided that such cosmological information can be extracted with precision. To this end, we further develop effective field theory (EFT) inspired techniques for the 21cm brightness temperature field during the epoch

Shivam Duggal, Deepak Pathak

We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without

Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, Xian Li

Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant

Yue Zhao, Yantao Shen, Yuanjun Xiong, Shuo Yang

Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy by forcing a new model to imitate the old models, or use ensembles, which multiply inference cost prohibitively. We analyze the role of ensembles in reducing NF

Ang-Kun Wu, Daniel Bauernfeind, Xiaodong Cao, Sarang Gopalakrishnan

The Anderson model for a magnetic impurity in a one-dimensional quasicrystal is studied using the numerical renormalization group (NRG). The main focus is elucidating the physics at the critical point of the Aubry-Andre (AA) Hamiltonian, which exhibits a fractal spectrum with multifractal wave functions, leading to an AA Anderson (AAA) impurity model with an

D. Sarenac, M. E. Henderson, H. Ekinci, Charles W. Clark

Methods of preparation and analysis of structured waves of light, electrons, and atoms have been advancing rapidly. Despite the proven power of neutrons for material characterization and studies of fundamental physics, neutron science has not been able to fully integrate such techniques due to small transverse coherence lengths, the relatively poor resolutio

Alon Albalak, Yi-Lin Tuan, Pegah Jandaghi, Connor Pryor

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA:

Zane M. Rossi, Isaac L. Chuang

Recent work shows that quantum signal processing (QSP) and its multi-qubit lifted version, quantum singular value transformation (QSVT), unify and improve the presentation of most quantum algorithms. QSP/QSVT characterize the ability, by alternating ans\"atze, to obliviously transform the singular values of subsystems of unitary matrices by polynomial functi

Nilanjandev Bhaumik, Anish Ghoshal, Marek Lewicki

Hawking evaporation of primordial black holes (PBHs) can facilitate the generation of matter-antimatter asymmetry. We focus on ultra-low mass PBHs that briefly dominate the universe and evaporate before the big bang nucleosynthesis. We propose a novel test of this scenario by detecting its characteristic doubly peaked gravitational wave (GW) spectrum in futu