May 2022 arXiv papers — page 47

Anastasia Razdaibiedina, Ashish Khetan, Zohar Karnin, Daniel Khashabi

Fine-tuning contextualized representations learned by pre-trained language models remains a prevalent practice in NLP. However, fine-tuning can lead to representation degradation (also known as representation collapse), which may result in instability, sub-optimal performance, and weak generalization. In this paper, we propose Representation Projection Invar

Anish Saha, Amith Ananthram, Emily Allaway, Heng Ji

Practitioners from many disciplines (e.g., political science) use expert-crafted taxonomies to make sense of large, unlabeled corpora. In this work, we study Seeded Hierarchical Clustering (SHC): the task of automatically fitting unlabeled data to such taxonomies using only a small set of labeled examples. We propose HierSeed, a novel weakly supervised algor

Afra Feyza Akyürek, Muhammed Yusuf Kocyigit, Sejin Paik, Derry Wijaya

Researchers have devised numerous ways to quantify social biases vested in pretrained language models. As some language models are capable of generating coherent completions given a set of textual prompts, several prompting datasets have been proposed to measure biases between social groups -- posing language generation as a way of identifying biases. In thi

Boyi Guo, Nengjun Yi

Cox proportional hazards model is one of the most popular models in biomedical data analysis. There have been continuing efforts to improve the flexibility of such models for complex signal detection, for example, via additive functions. Nevertheless, the task to extend Cox additive models to accommodate high-dimensional data is nontrivial. When estimating a

Samuel Kilian, Johannes Krisam, Meinhard Kieser

The primary endpoint in oncology is usually overall survival, where differences between therapies may only be observable after many years. To avoid withholding of a promising therapy, preliminary approval based on a surrogate endpoint is possible. The approval can be confirmed later by assessing overall survival within the same study. In these trials, the co

Huang Fang, Botond Tyukodi, W. Benjamin Rogers, Michael F. Hagan

In contrast to most self-assembling synthetic materials, which undergo unbounded growth, many biological self-assembly processes are self-limited. That is, the assembled structures have one or more finite dimensions that are much larger than the size scale of the individual monomers. In many such cases, the finite dimension is selected by a preferred curvatu

Samarth Tiwari, Michelle Yeo, Zeta Avarikioti, Iosif Salem

Payment channel networks (PCNs) are one of the most prominent solutions to the limited transaction throughput of blockchains. Nevertheless, PCNs suffer themselves from a throughput limitation due to the capital constraints of their channels. A similar dependence on high capital is also found in inter-bank payment settlements, where the so-called netting tech

Lukas Pieronek, Andreas Kleefeld

This paper investigates properties of complex-valued eigenvalue trajectories for the interior transmission problem parametrized by the index of refraction for homogeneous media. Our theoretical analysis for the unit disk shows that the only intersection points with the real axis, as well as the unique trajectorial limit points as the refractive index tends t

Oriol Artime, Manlio De Domenico

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This nontrivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems -- from physical to biological and social ones -- and is often rela

Wallace M. Bessa

From autonomous vacuum cleaners to self-driving cars, intelligent mechanical systems are becoming an intrinsic part of our daily lives. In this work, a framework for the development of intelligent mechanical systems is presented.Considering that in this scenario the adopted control approach plays an essential role, I show that the proposed scheme should be a

D. Adhikari, H. Albataineh, D. Androic, K. A. Aniol

We report a precise measurement of the parity-violating asymmetry $A_{\rm PV}$ in the elastic scattering of longitudinally polarized electrons from $^{48}{\rm Ca}$. We measure $A_{\rm PV} =2668\pm 106\ {\rm (stat)}\pm 40\ {\rm (syst)}$ parts per billion, leading to an extraction of the neutral weak form factor $F_{\rm W} (q=0.8733$ fm$^{-1}) = 0.1304 \pm 0.0

Prosenjit Kundu, Neil G. MacLaren, Hiroshi Kori, Naoki Masuda

Many complex dynamical systems in the real world, including ecological, climate, financial, and power-grid systems, often show critical transitions, or tipping points, in which the system's dynamics suddenly transit into a qualitatively different state. In mathematical models, tipping points happen as a control parameter gradually changes and crosses a certa

Pawan Kumar Gupta

The detection of gravitational waves from binary black holes sources has opened the possibility to search for electric charges and "dark" charges on black holes, the latter being candidates for dark matter. This requires theoretical predictions about the effect of charges on the inspiral of binary black holes in order to place constraint on charges. The effe

Benjamin Irwin, Antonio Rago, Francesca Toni

We introduce Forecasting Argumentation Frameworks (FAFs), a novel argumentation-based methodology for forecasting informed by recent judgmental forecasting research. FAFs comprise update frameworks which empower (human or artificial) agents to argue over time about the probability of outcomes, e.g. the winner of a political election or a fluctuation in infla

Antonio Rago, Pietro Baroni, Francesca Toni

Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models' outputs. The conceptualisation is based on reinterpreting desirable properties of s

Tao Lei, Ran Tian, Jasmijn Bastings, Ankur P. Parikh

In this work, we explore whether modeling recurrence into the Transformer architecture can both be beneficial and efficient, by building an extremely simple recurrent module into the Transformer. We compare our model to baselines following the training and evaluation recipe of BERT. Our results confirm that recurrence can indeed improve Transformer models by

Daniela Grandón, Elena Sellentin

Neural nets have become popular to accelerate parameter inferences, especially for the upcoming generation of galaxy surveys in cosmology. As neural nets are approximative by nature, a recurrent question has been how to propagate the neural net's approximation error, in order to avoid biases in the parameter inference. We present a Bayesian solution to propa

Babhrubahan Bose, Saikat Roy, Debmalya Sain

We study Birkhoff-James orthogonality and its local symmetry in some sequence spaces namely $\ell_p,$ for $1\leq p\leq\infty$, $p\neq2$, $c$, $c_0$ and $c_{00}$. Using the characterization of the local symmetry of Birkhoff-James orthogonality, we characterize isometries of each of these spaces onto itself and obtain the Banach-Lamperti theorem for onto opera

Josiane Maria de Macedo Fernande, Marcelo Costa Tanaka, Wallace Moreira Bessa, Edwin Kreuzer

This paper presents an intelligent controller for uncertain underactuated nonlinear systems. The adopted approach is based on sliding mode control and enhanced by an artificial neural network to cope with modeling inaccuracies and external disturbances that can arise. The sliding surfaces are defined as a linear combination of both actuated and unactuated va

Sikha Pentyala, Nicola Neophytou, Anderson Nascimento, Martine De Cock

Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity. Achieving group fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires using the sensitive attribute values of all

Ezra Lesser

Jet substructure observables have been used by experiments at the Large Hadron Collider (LHC) as instruments to search for new physics as well as to study perturbative and nonperturbative processes in quantum chromodynamics (QCD). Some observables are infrared and collinear safe and thus easily comparable to first-principles calculations, while others offer

Thomas van Loo, Anshul Jindal, Shajulin Benedict, Mohak Chadha

In the recent past, characterizing workloads has been attempted to gain a foothold in the emerging serverless cloud market, especially in the large production cloud clusters of Google, AWS, and so forth. While analyzing and characterizing real workloads from a large production cloud cluster benefits cloud providers, researchers, and daily users, analyzing th

Kevin Alabarta, Mariano Méndez, Federico García, Valentina Peirano

We study the properties of the type-C quasi-periodic oscillation (type-C QPO) of MAXI J1348-630 during its 2019 outburst and reflare with NICER. This is the first time that the evolution of the properties of type-C QPOs is studied during an outburst reflare. We found that the properties of the type-C QPO during the reflare are similar to those of type-C QPOs

Peter Sentz, Jehanzeb Hameed Chaudhry, Luke N. Olson

In this work, we investigate the convergence of numerical approximations to coercivity constants of variational problems. These constants are essential components of rigorous error bounds for reduced-order modeling; extension of these bounds to the error with respect to exact solutions requires an understanding of convergence rates for discrete coercivity co

James Kirk, Leonardo A. Dos Santos, Mercedes López-Morales, Munazza K. Alam

We present the detection of neutral helium at 10833A in the atmosphere of WASP-52b and tentative evidence of helium in the atmosphere of the grazing WASP-177b, using high-resolution observations acquired with the NIRSPEC instrument on the Keck II telescope. We detect excess absorption by helium in WASP-52b's atmosphere of $3.44 \pm 0.31$% ($11\sigma$), or eq

Hasan Atakan Bedel, Irmak Şıvgın, Onat Dalmaz, Salman Ul Hassan Dar

Deep-learning models have enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for contextual representations across diverse time scales. Here, we present BolT, a blood-oxygen-level-dependent transformer model, for analyzing multi-variate fMRI time series. BolT leverages a

Krzysztof M. Czajkowski, Tomasz J. Antosiewicz

Large optical chirality in the vicinity of achiral high index dielectric nanostructures has been recently demonstrated as useful means of enhancing molecular circular dichroism. We theoretically study the spatial dependence of optical chirality enhancement in the vicinity of high index dielectric nanodisks and highlight its importance for the design of nanop

J. C. Cortissoz, J. Torres Orozco

In this paper we use a natural iteration technique to prove existence of solutions to nonlinear Dirichlet problems. Among the examples included is the prescribed mean curvature equation. The nature of the technique allows applications to unbounded domains, as we show with domains of the form $\mathbb{R}^{n-1}\times\left(-d/2,d/2\right)$.

Divas Singh Dagur, Chandana Mondal, Saikat Roy

In this work, we systematically investigate for the first time the nature of stress correlations in soft colloidal gel materials which support tensile and compressive forces as well as finite rolling torque, as a function of system pressure. Similar to previous studies on frictional granular matter with only compressive forces and without any rolling torque,

Thomas Strauszer-Caussade, Luiz M. Faria, Agustín Fernandez-Lado, Carlos Pérez-Arancibia

This paper introduces a novel boundary integral equation (BIE) method for the numerical solution of problems of planewave scattering by periodic line arrays of two-dimensional penetrable obstacles. Our approach is built upon a direct BIE formulation that leverages the simplicity of the free-space Green function but in turn entails evaluation of integrals ove

Linquan Ma, Hyunseung Kang, Lan Liu

Recently, Su and Cook proposed a dimension reduction technique called the inner envelope which can be substantially more efficient than the original envelope or existing dimension reduction techniques for multivariate regression. However, their technique relied on a linear model with normally distributed error, which may be violated in practice. In this work

Michael Skeide

Bringing forward the concept of convergence in moments from classical random variables to quantum random variables is what leads to what can be called algebraic central limit theorem for (classical and) quantum random variables. I reflect in a very personal way how such an idea is typical for the spirit of doing research in mathematics as I learned it in Wil

Tomáš Bzdušek, Joseph Maciejko

Motivated by the recent experimental realizations of hyperbolic lattices in circuit quantum electrodynamics and in classical electric-circuit networks, we study flat bands and band-touching phenomena in such lattices. We analyze noninteracting nearest-neighbor hopping models on hyperbolic analogs of the kagome and dice lattices with heptagonal and octagonal

F. C. E. Lima, C. A. S. Almeida

The topological structures that arise from two-dimensional models are relevant physically and the first step towards understanding more complex systems. In this work, one studies the kink-like solutions of the matter field that emerge in a two-dimensional dilaton gravity scenario. Considering this scenario, we examine the linear stability of the matter field

Katerina P. Hilleke, Eva Zurek

The high critical superconducting temperatures ($T_c$s) of metal hydride phases with clathrate-like hydrogen networks have generated great interest. Herein, we employ the Density Functional Theory-Chemical Pressure (DFT-CP) method to explain why certain electropositive elements adopt these structure types, whereas others distort the hydrogenic lattice, there

Martin Magris, Mostafa Shabani, Alexandros Iosifidis

We develop an optimization algorithm suitable for Bayesian learning in complex models. Our approach relies on natural gradient updates within a general black-box framework for efficient training with limited model-specific derivations. It applies within the class of exponential-family variational posterior distributions, for which we extensively discuss the

Michael Schleiss, Fahmi Rouatbi, Daniel Cremers

Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios. Recent work has focused on ground or close to ground applications such as self-driving cars or indoor-scenarios and low-altitude drone flights. However, applications such as Urban Air Mobi

Andrea J. Allen, Cristopher Moore, Laurent Hébert-Dufresne

Studies of dynamics on temporal networks often represent the network as a series of "snapshots," static networks active for short durations of time. We argue that successive snapshots can be aggregated if doing so has little effect on the overlying dynamics. We propose a method to compress network chronologies by progressively combining pairs of snapshots wh

M. E. Ismagambetov, P. M. Ostrovsky

Weyl semimetal is a solid material with isolated touching points between conduction and valence bands in its Brillouin zone -- Weyl points. Low energy excitations near these points exhibit a linear dispersion and act as relativistic massless particles. Weyl points are stable topological objects robust with respect to most perturbations. We study effects of w

Francesco Knechtli, Tomasz Korzec, Michael Peardon, Juan Andrés Urrea-Niño

Smearing the bare quantum fields in lattice calculations before applying composite hadron creation operators has a long record of substantially improving overlaps onto low-lying energy eigenstates. A technique called distillation which defines smearing for quark fields as a low-rank linear projection operator into a small vector space of smooth gauge-covaria

Niels Sayez, Christophe De Vleeschouwer

Collecting image annotations remains a significant burden when deploying CNN in a specific applicative context. This is especially the case when the annotation consists in binary masks covering object instances. Our work proposes to delineate instances in three steps, based on a semi-automatic approach: (1) the extreme points of an object (left-most, right-m

Chengyang Bao

In this paper, we classify all continuous Galois representations $\rho:\mathrm{Gal}(\overline{\mathbf{Q}}/\mathbf{Q})\to \mathrm{GL}_2(\overline{\mathbf{Q}}_p)$ which are unramified outside $\{p,\infty\}$ and locally induced at $p$, under the assumption that $\overline{\rho}$ is exceptional, that is, has image of order prime to $p$. We prove two results. If

Yash Deshpande, Elchanan Mossel, Youngtak Sohn

Bayesian models of group learning are studied in Economics since the 1970s. and more recently in computational linguistics. The models from Economics postulate that agents maximize utility in their communication and actions. The Economics models do not explain the ``probability matching" phenomena that are observed in many experimental studies. To address th

V. Ravindran, Aparna Sankar, Surabhi Tiwari

We present the resumed predictions consisting of both soft-virtual(SV) as well as next-to-SV(NSV) threshold logarithms to all orders in perturbative QCD for the rapidity distribution of Higgs Boson till $\rm NNLO + \overline{NNLL}$ accuracy at LHC. Using our recent formalism\cite{Ajjath:2020lwb}, the resummation is carried out in the double Mellin space by r

Takashi Hiramatsu

We put constraints on the degenerate higher-order scalar-tensor (DHOST) theories using the Planck 2018 likelihoods. In our previous paper, we developed a Boltzmann solver incorporating the effective field theory parameterised by the six time-dependent functions, $\alpha_i$ $(i={\rm B},{\rm K},{\rm T},{\rm M},{\rm H})$ and $\beta_1$, which can describe the DH

Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh

Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural

F. F. Karpeshin, M. B. Trzhaskovskaya

Traditionally double neutrinoless electronic capture is considered as a resonance process. We have fulfilled shake-off probability calculations, leading to ionization of the electron shell, in the case of $^{164}$Er. Allowance for the shake-off removes the requirement of resonance, leading to an increase of the capture rate. The contribution of the new mecha

Shrawan Kumar

Lusztig defined an abelian category ${\mathscr{C}}_{k}$ of a class of representations of a multi-loop algebra and asked various questions connecting it to the modular representation theory of simple algebraic groups in char. p>0. The aim of this paper is to show that some of these questions have negative answer.

G. De Filippis, A. de Candia, G. Di Bello, C. A. Perroni

By using worldline Monte Carlo technique, matrix product state and a variational approach \`a la Feynman, we investigate the equilibrium properties and relaxation features of the dissipative quantum Rabi model, where a two level system is coupled to a linear harmonic oscillator embedded in a viscous fluid. We show that, in the Ohmic regime, a Beretzinski-Kos

Ali Golestani, Alex O. C. Davis, Filip Sośnicki, Michał Mikołajczyk

The power spectrum of an optical field can be acquired without a spectrally resolving detector by means of Fourier-transform spectrometry, based on measuring the temporal autocorrelation of the optical field. Analogously, we here perform temporal envelope measurements of ultrashort optical pulses without time resolved detection. We introduce the technique of

Peter Constantin, Mihaela Ignatova, Fizay-Noah Lee

We consider the Nernst-Planck-Navier-Stokes system in a bounded domain of ${\mathbb {R}}^d$, $d=2,3$ with general nonequilibrium Dirichlet boundary conditions for the ionic concentrations. We prove the existence of smooth steady state solutions and present a sufficient condition in terms of only the boundary data that guarantees that these solutions have non

Wahei Hara, Michael Wemyss

This paper classifies spherical objects in various geometric settings in dimensions two and three, including both minimal and partial crepant resolutions of Kleinian singularities, as well as arbitrary flopping 3-fold contractions with only Gorenstein terminal singularities. The main result is much more general: in each such setting, we prove that all object

Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton, Chenhao Tan

Despite the strong performance of current NLP models, they can be brittle against adversarial attacks. To enable effective learning against adversarial inputs, we introduce the use of rationale models that can explicitly learn to ignore attack tokens. We find that the rationale models can successfully ignore over 90% of attack tokens. This approach leads to

Gizem Sengor, Constantinos Skordis

We demonstrate how free massive scalar fields in the set up that usually appears in early universe inflationary studies, correspond to the principal series and complementary series representations of the group SO(d+1,1) by introducing late-time operators and computing their two-point functions.

Matteo Gori, Philip Kurian, Alexandre Tkatchenko

The many-body dispersion (MBD) framework is a successful approach for modeling the long-range electronic correlation energy and optical response of systems with thousands of atoms. Inspired by field theory, here we develop a second-quantized MBD formalism (SQ-MBD) that recasts a system of atomic quantum Drude oscillators in a Fock-space representation. SQ-MB

Gang Yang, Jialun Ping, Jorge Segovia

The fully-charm and -bottom pentaquarks, \emph{i.e.} $cccc\bar{c}$ and $bbbb\bar{b}$, with spin-parity quantum numbers $J^P=\frac{1}{2}^-$, $\frac{3}{2}^-$ and $\frac{5}{2}^-$, are investigated within a Lattice-QCD inspired quark model, which has already successfully described the recently announced fully-charm tetraquark candidate $X(6900)$, and has also pr

Flavio Del Santo, Nicolas Gisin

Discussions on indeterminism in physics focus on the possibility of an open future, i.e. the possibility of having potential alternative future events, the realisation of one of which is not fully determined by the present state of affairs. Yet, can indeterminism affect also the past, making it open as well? We show that by upholding principles of finiteness

Wei-Hao Wang, Sebastien Foucaud, Bau-Ching Hsieh, Hung-Yu Jian

The Subaru Hyper Suprime-Cam (HSC) Strategic Survey is the latest-generation multi-band optical imaging survey for galaxy evolution and structure formation. The "Ultra-Deep" component of the HSC survey provides $grizy$ broad-band images over $\sim3.4$ deg$^2$ to detection limits of $\sim26$-28 AB, along with narrow-band images, in the COSMOS and the SXDS fie

George N. Wojcik

The vector portal/kinetic mixing simplified model of dark matter, in which thermal dark matter of a mass ranging from a few MeV to a few GeV can be realized with a dark-sector $U(1)$, relies on a small kinetic mixing term between this dark $U(1)$ and the Standard Model (SM) hypercharge. It is well-known that kinetic mixing of the right magnitude can be gener

Adam Nahum, Sthitadhi Roy, Sagar Vijay, Tianci Zhou

We study real-time local correlators $\langle\mathcal{O}(\mathbf{x},t)\mathcal{O}(0,0)\rangle$ in chaotic quantum many-body systems. These correlators show universal structure at late times, determined by the dominant operator-space Feynman trajectories for the evolving operator $\mathcal{O}(\mathbf{x},t)$. The relevant trajectories involve the operator cont

Maxwell J. D. Ramstead, Dalton A. R. Sakthivadivel, Conor Heins, Magnus Koudahl

The aim of this paper is to introduce a field of study that has emerged over the last decade called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e., into particles), where the internal states (or the trajectories of internal states) of a particular

Han Ma

We study the critical bosonic O(N) vector model with quenched random mass disorder in the large N limit. Due to the replicated action which is sometimes not bounded from below, we avoid the replica trick and adopt a traditional approach to directly compute the disorder averaged physical observables. At $N=\infty$, we can exactly solve the disordered model. T

João Luís Rosa, Paulo Garcia, Frédéric H. Vincent, Vitor Cardoso

Pushed by a number of advances, electromagnetic observatories have now reached the horizon scale of supermassive black holes. The existence and properties of horizons in our universe is one of the outstanding fundamental issues that can now be addressed. Here we investigate the ability to discriminate between black holes and compact, horizonless objects, foc

David I. Dunsky, Lawrence J. Hall, Keisuke Harigaya

The naturalness problem of PQ symmetry motivates study of the heavy QCD axion, with masses $m_a >$ 1 MeV generated at scales above the QCD scale, and low values of the PQ symmetry breaking scale, $f_a$. We compute the abundance of such axions in a model-independent way, assuming only that they freeze-out after reheating from inflation, and are not subsequent

Aditya A. Wagh, Priyanka Garg, Kingshuk Mallick, Suja Elizabeth

We present spin transport studies on a low-field, room-temperature magnetoelectric multiferroic polycrystalline $\text{Sr}_{3}\text{Co}_{2}\text{Fe}_{24}\text{O}_{41}$ (SCFO)|Pt heterostructure wherein a highly tunable transverse conical magnetic phase is responsible for static and dynamic magnetoelectric coupling. We measured angular dependence of spin Hall

Walter Winter, Cecilia Lunardini

Three Tidal Disruption Event (TDE) candidates (AT2019dsg, AT2019fdr, AT2019aalc) have been associated with high energy astrophysical neutrinos in multi-messenger follow-ups. In all cases, the neutrino observation occurred O(100) days after the maximum of the optical-ultraviolet (OUV) luminosity. We discuss unified fully time-dependent interpretations of the

Ana-Roxana Pop, Lars Hernquist, Daisuke Nagai, Rahul Kannan

One of the main limitations in precision cluster cosmology arises from systematic errors and uncertainties in estimating cluster masses. Using the Mock-X pipeline, we produce synthetic X-ray images and derive cluster and galaxy group X-ray properties for a sample of over 30,000 simulated galaxy groups and clusters with $M_{\rm 500crit}$ between $10^{12}$ and

Albert Sneppen, Charles L. Steinhardt, Hagan Hensley, Adam S. Jermyn

A universal stellar initial mass function (IMF) should not be expected from theoretical models of star formation, but little conclusive observational evidence for a variable IMF has been uncovered. In this paper, a parameterization of the IMF is introduced into photometric template fitting of the COSMOS2015 catalog. The resulting best-fit templates suggest s

Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky

Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several techniques. They also have a wide range of applications in the field of spintronics, particularly in developing new technologi

Harley Katz, Thibault Garel, Joakim Rosdahl, Valentin Mauerhofer

Limited constraints on the evolution of the Lyman Continuum (LyC) escape fraction represent one of the primary uncertainties in the theoretical determination of the reionization history. Due to the intervening intergalactic medium (IGM), the possibility of observing LyC photons directly in the epoch of reionization is highly unlikely. For this reason, multip

Mathias Garny, Petter Taule

We compute the density and velocity power spectra at next-to-next-to-leading order taking into account the effect of time- and scale-dependent growth of massive neutrino perturbations as well as the departure from Einstein--de-Sitter (EdS) dynamics at late times non-linearly. We determine the impact of these effects by comparing to the commonly adopted appro

Yohei Ema, Ting Gao, Maxim Pospelov

We derive indirect limits on the charm and bottom quark electric dipole moments (EDMs) from paramagnetic AMO and neutron EDM experiments. The charm and bottom quark EDMs generate $CP$-odd photon-gluon operators and light quark EDMs at the $c$- and $b$-quark mass thresholds. These $CP$-odd operators induce the $CP$-odd semi-leptonic operator $C_S$ and the neu

Stefan Antusch, Kevin Hinze, Shaikh Saad

We revisit mixing sum rule relations in the lepton and quark sectors under the assumption that the 1-3 elements of the flavour mixing matrices ($V^u_L,V^d_L,V^e_L,V^\nu_L$) are zero in the flavour basis. We consider the exact relations resulting from the validity of this "zero 1-3 flavour mixing hypothesis" and analyse their implications based on the current

James T. Liu, Ruben Minasian, Raffaele Savelli, Andreas Schachner

We study the non-linear structure of Type IIB eight-derivative couplings involving the metric and the complexified three-form $G_3$. We show that, at the level of five-point string amplitudes, the kinematics in the maximally R-symmetry-violating sector is fully matched by standard superspace integrals and by superparticle amplitudes in M-theory on a two-toru

Yongcheng Wu, Ke-Pan Xie, Ye-Ling Zhou

We discuss domain walls from spontaneous breaking of Abelian discrete symmetries $Z_N$. A series of different domain wall structures are predicted, depending on the symmetry and charge assignments of scalars leading to the spontaneous symmetry breaking (SSB). A widely-existing type of domain walls are those separating degenerate vacua which are adjacent in t

Ana-Roxana Pop, Lars Hernquist, Daisuke Nagai, Rahul Kannan

Observable thermodynamical properties of the intracluster medium (ICM) reflect the complex interplay between AGN feedback and the gravitational collapse of haloes. Using the large volume TNG300 simulation of the IllustrisTNG project we provide predictions for X-ray and Sunyaev-Zel'dovich (SZ) scaling relations for a sample of over 30,000 haloes that cover a

Jae Hyeok Chang, David E. Kaplan, Surjeet Rajendran, Harikrishnan Ramani

We study the solar emission of light dark sector particles that self-interact strongly enough to self-thermalize. The resulting outflow behaves like a fluid which accelerates under its own thermal pressure to highly relativistic bulk velocities in the solar system. Compared to the ordinary non-interacting scenario, the local outflow has at least $\sim 10^3$

R. J. Bouwens, G. D. Illingworth, R. S. Ellis, P. A. Oesch

We present new determinations of the rest-UV luminosity functions (LFs) at z=2-9 to extremely low luminosities (>-14 mag) from a sample of >2500 lensed galaxies found behind the HFF clusters. For the first time, we present faint-end slope results from lensed samples that are fully consistent with blank-field results over the redshift range z=2-9, while reach

S. Contarini, G. Verza, A. Pisani, N. Hamaus

The Euclid mission $-$ with its spectroscopic galaxy survey covering a sky area over $15\,000 \ \mathrm{deg}^2$ in the redshift range $0.9<z<1.8\ -$ will provide a sample of tens of thousands of cosmic voids. This paper explores for the first time the constraining power of the void size function on the properties of dark energy (DE) from a survey mock catalo

Justin Khoury, Sam S. C. Wong

Probabilities in eternal inflation are traditionally defined as limiting frequency distributions, but a unique and unambiguous probability measure remains elusive. In this paper, we present a different approach, based on Bayesian reasoning. Our starting point is the master equation governing vacuum dynamics, which describes a random walk on the network of va

Sunghoon Kim, Adhip Agarwala, Debanjan Chowdhury

Band-topology is traditionally analyzed in terms of gauge-invariant observables associated with crystalline Bloch wavefunctions. Recent work has demonstrated that many of the free fermion topological characteristics survive even in an amorphous setting. In this work, we extend these studies to incorporate the effect of strong repulsive interactions on the fa

Nicolás Bernal, Yuber F. Perez-Gonzalez, Yong Xu

Rotating black holes (BHs) can efficiently transfer energy to the surrounding environment via superradiance. In particular, when the Compton length of a particle is comparable to the gravitational radius of a BH, the particle's occupation number can be exponentially amplified. In this work, we investigate the effect of the primordial-black-hole (PBH) superra

Udith Haputhanthri, Kithmini Herath, Ramith Hettiarachchi, Hasindu Kariyawasam

With applications ranging from metabolomics to histopathology, quantitative phase microscopy (QPM) is a powerful label-free imaging modality. Despite significant advances in fast multiplexed imaging sensors and deep-learning-based inverse solvers, the throughput of QPM is currently limited by the speed of electronic hardware. Complementarily, to improve thro

Randall Balestriero, Yann LeCun

Self-Supervised Learning (SSL) surmises that inputs and pairwise positive relationships are enough to learn meaningful representations. Although SSL has recently reached a milestone: outperforming supervised methods in many modalities\dots the theoretical foundations are limited, method-specific, and fail to provide principled design guidelines to practition

Dongruo Zhou, Quanquan Gu

Recent studies have shown that episodic reinforcement learning (RL) is not more difficult than contextual bandits, even with a long planning horizon and unknown state transitions. However, these results are limited to either tabular Markov decision processes (MDPs) or computationally inefficient algorithms for linear mixture MDPs. In this paper, we propose t

Ekdeep Singh Lubana, Chi Ian Tang, Fahim Kawsar, Robert P. Dick

Federated learning is generally used in tasks where labels are readily available (e.g., next word prediction). Relaxing this constraint requires design of unsupervised learning techniques that can support desirable properties for federated training: robustness to statistical/systems heterogeneity, scalability with number of participants, and communication ef

Moniba Keymanesh, Adrian Benton, Mark Dredze

Expressing natural language descriptions of structured facts or relations -- data-to-text generation (D2T) -- increases the accessibility of structured knowledge repositories. Previous work shows that pre-trained language models(PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data. On the other h

Noah Sailer, Shi-Fan Chen, Martin White

The optical depth $\tau$ is the least well determined parameter in the standard model of cosmology, and one whose precise value is important for both understanding reionization and for inferring fundamental physics from cosmological measurements. We forecast how well future epoch of reionization experiments could constraint $\tau$ using a symmetries-based bi

Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky

We propose a method for arbitrary textual style transfer (TST)--the task of transforming a text into any given style--utilizing general-purpose pre-trained language models. Our method, Prompt-and-Rerank, is based on a mathematical formulation of the TST task, decomposing it into three constituent components: textual similarity, target style strength, and flu

Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang

Logical reasoning is needed in a wide range of NLP tasks. Can a BERT model be trained end-to-end to solve logical reasoning problems presented in natural language? We attempt to answer this question in a confined problem space where there exists a set of parameters that perfectly simulates logical reasoning. We make observations that seem to contradict each

Yanan Wang, Michihiro Yasunaga, Hongyu Ren, Shinya Wada

Visual question answering (VQA) requires systems to perform concept-level reasoning by unifying unstructured (e.g., the context in question and answer; "QA context") and structured (e.g., knowledge graph for the QA context and scene; "concept graph") multimodal knowledge. Existing works typically combine a scene graph and a concept graph of the scene by conn

Khalil Iskarous, Jennifer Mather, Jean Alupay

The skin of a cephalopod forms a dazzling array of patterns made by chromatophores, elastic sacs of pigment that can be expanded by muscles to reveal their color. Tens of thousands of these chromatophores can work together to generate a stable display of stripes, spots, mottled grainy camouflage, or dynamic oscillations and traveling waves of activation. How

Justin Janquart, Anupreeta More, Chris Van Den Broeck

When traveling from their source to the observer, gravitational waves can get deflected by massive objects along their travel path. When the lens is massive enough and the source aligns closely with the line-of-sight to the lens, the wave undergoes strong lensing, leading to several images with the same frequency evolution. These images are separated in time

Nandan Thakur, Nils Reimers, Jimmy Lin

Dense retrieval overcome the lexical gap and has shown great success in ad-hoc information retrieval (IR). Despite their success, dense retrievers are expensive to serve across practical use cases. For use cases requiring to search from millions of documents, the dense index becomes bulky and requires high memory usage for storing the index. More recently, l

Tristan Roy

Consider the focusing energy-critical Klein-Gordon equation in dimension d=3,4,5. We describe the global dynamics of real-valued solutions of which the energy is slightly larger than that of the ground states'. We classify the flows of the solutions that are ejected from a small neighborhood of the ground states or that are away from them.

Euan T. McGonigle, Haeran Cho

There exist several methods developed for the canonical change point problem of detecting multiple mean shifts, which search for changes over sections of the data at multiple scales. In such methods, estimation of the noise level is often required in order to distinguish genuine changes from random fluctuations due to the noise. When serial dependence is pre

William Harvey, Saeid Naderiparizi, Vaden Masrani, Christian Weilbach

We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset of video frames conditioned on any other subset and present an architecture adapted for this purpose

Xiao Han, Shahn Majid

We show that the Ehresmann-Schauenburg bialgebroid of a quantum principal bundle $P$ or Hopf Galois extension with structure quantum group $H$ is in fact a left Hopf algebroid $L(P,H)$. We show further that if $H$ is coquasitriangular then $L(P,H)$ has an antipode map $S$ obeying certain minimal axioms. Trivial quantum principal bundles or cleft Hopf Galois

Jakub Rembielinski, Jacek Ciborowski

Assuming that the interacting neutrino and the solar matter can be treated as an open quantum system we formulate a formalism of neutrino state evolution in terms of the Bloch vector, obtained by solving a quasilinear extension of the von Neumann equation. We broaden the classical Wolfenstein formalism by means of exploiting the strictly linear von Neumann e

M. Murra, D. Schulte, C. Huhmann, C. Weinheimer

A high-flow radon removal system based on cryogenic distillation was developed and constructed to reduce radon-induced backgrounds in liquid xenon detectors for rare event searches such as XENONnT. A continuous purification of the XENONnT liquid xenon inventory of 8.4 tonnes at process flows up to 71 kg/h (200 slpm) is required to achieve a radon reduction b