October 2022 arXiv papers — page 85

Han Xu, Menghai Pan, Zhimeng Jiang, Huiyuan Chen

The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detection, and anomaly detection, the data samples are usually mixed-type, which contain plenty of numerical and categorical features at the same

Rachit Bansal, Danish Pruthi, Yonatan Belinkov

Neural networks are known to exploit spurious artifacts (or shortcuts) that co-occur with a target label, exhibiting heuristic memorization. On the other hand, networks have been shown to memorize training examples, resulting in example-level memorization. These kinds of memorization impede generalization of networks beyond their training distributions. Dete

Gargi Desai, Nelly Elsayed, Zag Elsayed, Murat Ozer

The world is still overwhelmed by the spread of the COVID-19 virus. With over 250 Million infected cases as of November 2021 and affecting 219 countries and territories, the world remains in the pandemic period. Detecting COVID-19 using the deep learning method on CT scan images can play a vital role in assisting medical professionals and decision authoritie

E. Karamehmetoglu, J. Sollerman, F. Taddia, C. Barbarino

If high-mass stars (>20-25 Msun) are the progenitors of stripped-envelope (SE) supernovae (SNe), their massive ejecta should lead to broad, long-duration lightcurves (LCs). Instead, literature samples of SE~SNe have reported relatively narrow LCs with ejecta masses between 1-4 Msun that favor progenitors <20-25 Msun. Working with the untargeted sample of (i)

Farhad Arpanaei, M. Ranjbar Zefreh, Jose A. Hernandez, Andrea Carena

An unprecedented comparison of closed-form incoherent GN (InGN) models is presented with heterogeneous spans and partially loaded links in elastic optical networks. Results reveal that with accumulated dispersion correction and modulation format terms, the InGN shows higher accuracy.

J. M. Bauer, Y. R. Fernández, S. Protopapa, L. M. Woodney

We summarize the comet science provided by surveys. This includes surveys where the detections of comets are an advantageous benefit but were not part of the survey\'s original intent, as well as some pointed surveys where comet science was the goal. Many of the surveys are made using astrophysical and heliophysics assets. The surveys in our scope include th

Philipp Giese

Load forecasts have become an integral part of energy security. Due to the various influencing factors that can be considered in such a forecast, there is also a wide range of models that attempt to integrate these parameters into a system in various ways. Due to the growing importance of probabilistic load forecast models, different approaches are presented

Xiaowei Yang, Xinqiao Liu, Haoyu Wei

The Maximum Likelihood Estimator (MLE) serves an important role in statistics and machine learning. In this article, for i.i.d. variables, we obtain constant-specified and sharp concentration inequalities and oracle inequalities for the MLE only under exponential moment conditions. Furthermore, in a robust setting, the sub-Gaussian type oracle inequalities o

Stefano Catani, Leandro Cieri, Dimitri Colferai, Francesco Coradeschi

We consider the radiation of a soft gluon ($g$) and a soft quark-antiquark ($q{\bar q}$) pair in QCD hard scattering. In the soft limit the scattering amplitude has a singular behaviour that is factorized and controlled by a soft current, which has a process-independent structure in colour space. We evaluate the soft $gq{\bar q}$ current at the tree level fo

Sky CH-Wang, Evan Li, Oliver Li, Smaranda Muresan

Affective responses to music are highly personal. Despite consensus that idiosyncratic factors play a key role in regulating how listeners emotionally respond to music, precisely measuring the marginal effects of these variables has proved challenging. To address this gap, we develop computational methods to measure affective responses to music from over 403

Shatrughna Kumar, Pengfei Li, Boris A. Malomed

Currently, much interest is drawn to the analysis of optical and matter-wave modes supported by the fractional diffraction in nonlinear media. We predict a new type of such states, in the form of domain walls (DWs) in the two-component system of immiscible fields. Numerical study of the underlying system of fractional nonlinear Schroedinger equations demonst

Jaesung Yoo, Sunghyuk Choi, Ye Seul Yang, Suhyeon Kim

When a deep learning model is trained sequentially on different datasets, it often forgets the knowledge learned from previous data, a problem known as catastrophic forgetting. This damages the model's performance on diverse datasets, which is critical in privacy-preserving deep learning (PPDL) applications based on transfer learning (TL). To overcome this,

Juliana M. da Silva, Fernando A. F. Santana, Jorge G. G. S. Ramos, Anderson L. R. Barbosa

We investigate the spin Hall effect in a single-layer graphene device with disorder and interface-induced spin-orbit coupling. Our graphene device is connected to four semi-infinite leads that are embedded in a {Landauer-B\"uttiker} setup for quantum transport. We show that the spin Hall angle of graphene devices exhibits mesoscopic fluctuations that are sim

Shih-Yu Chang

The introduction of Schur multipliers into the context of Double Operator Integrals (DOIs) was proposed by V. V. Peller in 1985. This work extends theorem on Schur multipliers from measurable functions to their closure space and generalizes the definition of DOIs to Multiple Operator Integrals (MOIs) for integrand functions as Schur multipliersconstructible

O. A. Akindele, A. Bernstein, M. Bergevin, S. A. Dazeley

To date, antineutrino experiments built for the purpose of demonstrating a nonproliferation capability have typically employed organic scintillators, were situated as close to the core as possible -typically a few meters to tens of meters distant and have not exceeded a few tons in size. One problem with this approach is that proximity to the reactor core re

Federico A. Fogantini, Federico García, Jorge A. Combi, Sylvain Chaty

SS433 is a Galactic microquasar with powerful outflows (double jet, accretion disk and winds) with well known orbital, precessional and nutational period. In this work we characterise different outflow parameters throughout the precessional cycle of the system. We analyse 10 NuSTAR ($3-70$ keV) observations of $\sim$30~ks that span $\sim$1.5 precessional cyc

Istiak Ahmad, Fahad AlQurashi, Rashid Mehmood

Knowledge is central to human and scientific developments. Natural Language Processing (NLP) allows automated analysis and creation of knowledge. Data is a crucial NLP and machine learning ingredient. The scarcity of open datasets is a well-known problem in machine and deep learning research. This is very much the case for textual NLP datasets in English and

Anderson L. R. Barbosa, Jonas R. F. Lima, Luiz Felipe C. Pereira

We investigate the propagation of waves in one-dimensional systems with L\'evy-type disorder. We perform a complete analysis of non-relativistic and relativistic wave transmission submitted to potential barriers whose width, separation or both follow L\'evy distributions characterized by an exponent $0 < \alpha <1$. For the first two cases, where one of the

Pavel Svetlichnyy, T. A. B. Kennedy

The connection between quantum state recovery and quantum conditional mutual information (QCMI) is studied for the class of purely generated finitely correlated states (pgFCS) of one-dimensional quantum spin chains. For a tripartition of the chain into two subsystems separated by a buffer region, it is shown that a pgFCS is an approximate quantum Markov chai

Yiqi Xie, Deep Chatterjee, Gilbert Holder, Daniel E. Holz

The distance-inclination degeneracy limits gravitational-wave parameter estimation of compact binary mergers. Although the degeneracy can be partially broken by including higher-order modes or precession, these effects are suppressed in binary neutron stars. In this work, we implement a new parametrization of the tidal effects in the binary neutron-star wave

Guanghui Wang, Zihao Hu, Vidya Muthukumar, Jacob Abernethy

The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitations when implemented at scale. More recent sophisticated techniques, which we refer to as oracle-efficient methods, address this problem by dispatching to an offline optimization o

Jacob Beyer, Jonas B. Profe, Lennart Klebl, Tilman Schwemmer

The Rashba-Hubbard model on the square lattice is the paradigmatic case for studying the effect of spin-orbit coupling, which breaks spin and inversion symmetry, in a correlated electron system. We employ a truncated-unity variant of the functional renormalization group which allows us to analyze magnetic and superconducting instabilities on equal footing. W

P. Kamphuis, E. Jütte, G. H. Heald, N. Herrera Ruiz

Galaxies in the local Universe are thought to require ongoing replenishment of their gas reservoir in order to maintain the observed star formation rates. Cosmological simulations predict that such accretion can occur in both a dynamically hot and cold mode. However, until now observational evidence of the accretion required to match the observed star format

Pouria Mahdavinia, Yuyang Deng, Haochuan Li, Mehrdad Mahdavi

Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known about the theoretical guarantees of these methods in nonconvex settings. To bridge this gap, for the first time, this paper establishes the convergence of OGDA and EG methods under the

Negar Heidari, Alexandros Iosifidis

Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability and accordingly its performance in different learning tasks. Facial expression recognition in the wild has attracted interest in recent years due to the challenges existing in this area for extracting discriminative and informative features

Tamás Pető, Ferenc Iglói, István A. Kovács

Motivated by the compound ${\rm LiHo}_x{\rm Y}_{1-x}{\rm F}_4$, we consider the Ising chain with random couplings and in the presence of simultaneous random transverse and longitudinal fields, and study its low-energy properties at zero temperature by the strong disorder renormalization group approach. In the absence of longitudinal fields, the system exhibi

Miquel López-Suárez, Claudio Melis, Luciano Colombo, Walter Tarantino

Metallic nanogranular films display a complex dynamical response to a constant bias, showing up as atypical resistive switching mechanism which could be used to create electrical components for neuromorphic applications. To model such a phenomenon we use a multiscale approach blending together an ab initio treatment of the electric current at the nanoscale,

Chenning Yu, Hongzhan Yu, Sicun Gao

Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices in each step. In multi-agent navigation problems, the optimal actions depend heavily on the agents' density. Their interaction patterns grow exponentially with respect to such den

S. Gkelios, A. Kastellos, S. Chatzichristofis

This paper presents the 6th place solution to the Google Universal Image Embedding competition on Kaggle. Our approach is based on the CLIP architecture, a powerful pre-trained model used to learn visual representation from natural language supervision. We also utilized the SubCenter ArcFace loss with dynamic margins to improve the distinctive power of class

Youssef Fahmy, Joseph Guinness

We describe our implementation of the multivariate Mat\'ern model for multivariate spatial datasets, using Vecchia's approximation and a Fisher scoring optimization algorithm. We consider various pararameterizations for the multivariate Mat\'ern that have been proposed in the literature for ensuring model validity, as well as an unconstrained model. A streng

Erik Buchholz, Alsharif Abuadbba, Shuo Wang, Surya Nepal

Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due

Yi Zhang, Juniyali Nauriyal, Meiting Song, Marissa Granados Baez

The lack of a bulk second-order nonlinearity (\c{hi}(2)) in silicon nitride (Si3N4) keeps this low-loss, CMOS-compatible platform from key active functions such as Pockels electro-optic (EO) modulation and efficient second harmonic generation (SHG). We demonstrate a successful induction of \c{hi}(2) in Si3N4 through electrical poling with an externally-appli

Sanam Ghorbani Lyastani, Michael Backes, Sven Bugiel

Heuristics for user experience state that users will transfer their expectations from one product to another. A lack of consistency between products can increase users' cognitive friction, leading to frustration and rejection. This paper presents the first systematic study of the external, functional consistency of two-factor authentication user journeys on

Hui Zhou, Yansha Deng, Xiaonan Liu, Nikolaos Pappas

Upon the arrival of emerging devices, including Extended Reality (XR) and Unmanned Aerial Vehicles (UAVs), the traditional communication framework is approaching Shannon's physical capacity limit and fails to guarantee the massive amount of transmission within latency requirements. By jointly exploiting the context of data and its importance to the task, an

Guanghui Wang, Rafael Hanashiro, Etash Guha, Jacob Abernethy

The classical Perceptron algorithm of Rosenblatt can be used to find a linear threshold function to correctly classify $n$ linearly separable data points, assuming the classes are separated by some margin $\gamma > 0$. A foundational result is that Perceptron converges after $\Omega(1/\gamma^{2})$ iterations. There have been several recent works that managed

Won Kyu Calvin Sun, Paola Cappellaro

Characterizing and understanding the environment affecting quantum systems is critical to elucidate its physical properties and engineer better quantum devices. We develop an approach to reduce the quantum environment causing single-qubit dephasing to a simple yet predictive noise model. Our approach, inspired by quantum noise spectroscopy, is to define a "s

Andre Lukas, Fabian Ruehle

Calabi-Yau threefolds with infinitely many flops to isomorphic manifolds have an extended Kahler cone made up from an infinite number of individual Kahler cones. These cones are related by reflection symmetries across flop walls. We study the implications of this cone structure for mirror symmetry, by considering the instanton part of the prepotential in Cal

Lukas Weber, Antoine Yves Dimitri Fache, Frédéric Mila, Stefan Wessel

We examine the ground-state phase diagram and thermal phase transitions in a plaquettized fully frustrated bilayer spin-1/2 Heisenberg model. Based on a combined analysis from sign-problem free quantum Monte Carlo simulations, perturbation theory and free-energy arguments, we identify a first-order quantum phase transition line that separates two competing q

Wil Thomason, Marlin P. Strub, Jonathan D. Gammell

High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous geometric realization (i.e., motion plans)

Ananta Nair, Farnoush Banaei-Kashani

The field of artificial intelligence has seen explosive growth and exponential success. The last phase of development showcased deep learnings ability to solve a variety of difficult problems across a multitude of domains. Many of these networks met and exceeded human benchmarks by becoming experts in the domains in which they are trained. Though the success

J. Martin Laming

The first critical fast Mach number is defined for a magnetohydrodynamic shock as the Mach number where the shock transitions from subcritical, laminar, behavior to supercritical behavior, characterized by incident ion reflection from the shock front. The ensuing upstream waves and turbulence are convected downstream leading to a turbulent shock structure. F

Han Xu, Pengfei He, Jie Ren, Yuxuan Wan

The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration. Previously established methods leverage greedy search method, which can be very time-consuming to con

Daiki Suenaga, Masakiyo Kitazawa

We investigate the phase diagram and thermodynamics of $SU(N)$ pure Yang-Mills theory on a manifold $\mathbb{T}^2\times \mathbb{R}^2$ with an effective model that includes two Polyakov loops along two compactified directions. We find that a rich phase structure can appear owing to the spontaneous breaking of two center symmetries for $N=2$ and $3$. Thermodyn

Jaeyoung Park, Muxuan Liang, Ying-Qi Zhao, Xiang Zhong

Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. The capability to predict such measures enables patient-provider shared decision making and the delivery of patient-centered care. However, due to their voluntary nature, PRO measures often suffer from a high missing rate, and the missingn

A. Tajmir Riahi, G. Woollard, F. Poitevin, A. Condon

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational opti

Elettra L. Piacentino, Karin I. Öberg

The distribution of molecules between the gas and solid phase during star and planet formation, determines the trajectory of gas and grain surface chemistry, as well as the delivery of elements to nascent planets. This distribution is primarily set by the binding energies of different molecules to water ice surfaces. We computationally estimated the binding

Sven Smolka, Jan Laufer, Zoltán Ádám Mann, Klaus Pohl

Edge computing enables the processing of data - frequently personal data - at the edge of the network. For personal data, legislation such as the European General Data Protection Regulation requires data protection by design. Hence, data protection has to be accounted for in the design of edge computing systems whenever personal data is involved. This leads

Kyriakos Destounis, Arun Kulathingal, Kostas D. Kokkotas, Georgios O. Papadopoulos

Circumambient and galactic-scale environments are intermittently present around black holes that reside in active galactic nuclei. As supermassive black holes impart energy on their host galaxy, so the galactic environment affects the dynamics of solar-mass objects around black holes and the gravitational waves emitted from non-vacuum asymmetric binaries. On

Andreas Deuchert, Christian Hainzl, Marcel Maier

We consider the Bardeen-Cooper-Schrieffer (BCS) free energy functional with weak and macroscopic external electric and magnetic fields and derive the Ginzburg-Landau functional. We also provide an asymptotic formula for the BCS critical temperature as a function of the external fields. This extends our previous results in arXiv:2105.05623 for the constant ma

Smahane El-Halouy, Silvia Noschese, Lothar Reichel

Complex systems that consist of different kinds of entities that interact in different ways can be modeled by multilayer networks. This paper uses the tensor formalism with the Einstein tensor product to model this type of networks. Several centrality measures, that are well known for single-layer networks, are extended to multilayer networks using tensors a

Cesar S. Eschenazi, Wanderson J. Lambert, Marlon M. López-Flores, Dan Marchesin

We utilize a three-dimensional manifold to solve Riemann Problems that arise from a system of two conservation laws with quadratic flux functions. Points in this manifold represent potential shock waves, hence its name wave manifold. This manifold is subdivided into regions according to the Lax admissibility inequalities for shocks. Finally, we present solut

Girish M. Duvvuri, J. Sebastian Pineda, Zachory K. Berta-Thompson, Kevin France

We obtained ultraviolet and optical spectra for 9 M~dwarfs across a range of rotation periods to determine whether they showed stochastic intrinsic variability distinguishable from flares. The ultraviolet spectra were observed during the Far Ultraviolet M~Dwarf Evolution Survey \emph{Hubble~Space~Telescope} program using the Space Telescope Imaging Spectrogr

Omer Ronen, Theo Saarinen, Yan Shuo Tan, James Duncan

Bayesian Additive Regression Trees (BART) is a popular Bayesian non-parametric regression algorithm. The posterior is a distribution over sums of decision trees, and predictions are made by averaging approximate samples from the posterior. The combination of strong predictive performance and the ability to provide uncertainty measures has led BART to be comm

Melvin Hochster, Jack Jeffries, Vaibhav Pandey, Anurag K. Singh

Consider a reductive linear algebraic group $G$ acting linearly on a polynomial ring $S$ over an infinite field; key examples are the general linear group, the symplectic group, the orthogonal group, and the special linear group, with the classical representations as in Weyl's book: for the general linear group, consider a direct sum of copies of the standar

Anatolij Dvurečenskij, Omid Zahiri

The paper provides a study of pseudo MV-algebras with square roots. We introduce different notions of a square root on a pseudo MV-algebra, and present their main properties. We show that the class of pseudo-MV-algebras with square roots is a proper subvariety of the variety of pseudo MV-algebras. Then, we define a strict square root to classify the class of

Alper Canberk, Cheng Chi, Huy Ha, Benjamin Burchfiel

Automating garment manipulation is challenging due to extremely high variability in object configurations. To reduce this intrinsic variation, we introduce the task of "canonicalized-alignment" that simplifies downstream applications by reducing the possible garment configurations. This task can be considered as "cloth state funnel" that manipulates arbitrar

Pavan Hosur, Daniel Palacios

We theoretically investigate the consequences of proximity-induced conventional superconductivity in metals that break time-reversal and inversion symmetries through their energy dispersion. We discover behaviors impossible in an isolated superconductor such as an equilibrium supercurrent that apparently violates a no-go theorem and, at suitable topological

Elisa Bassignana, Barbara Plank

Relation Extraction (RE) has attracted increasing attention, but current RE evaluation is limited to in-domain evaluation setups. Little is known on how well a RE system fares in challenging, but realistic out-of-distribution evaluation setups. To address this gap, we propose CrossRE, a new, freely-available cross-domain benchmark for RE, which comprises six

Tiago Cruz

In this paper, we develop two new homological invariants called relative dominant dimension with respect to a module and relative codominant dimension with respect to a module. These are used to establish precise connections between Ringel duality, split quasi-hereditary covers and double centraliser properties. These homological invariants are studied over

Shara Balakrishnan, Aqib Hasnain, Robert Egbert, Enoch Yeung

When complex systems with nonlinear dynamics achieve an output performance objective, only a fraction of the state dynamics significantly impacts that output. Those minimal state dynamics can be identified using the differential geometric approach to the observability of nonlinear systems, but the theory is limited to only analytical systems. In this paper,

Gallot Thomas, Sedofeito Camila, Ginares Alejandro, Tancredi Gonzalo

Observational data of asteroids can be explained by considering them as an agglomerate of granular material. Understanding the mechanical properties of these objects is relevant for many scientific reasons: space missions design, evaluation of impact threats to our planet, and understanding the nature of asteroids and their implication in the origin of the s

Giulia Tozzi, Elisabeta Lusso, Lapo Casetti, Marco Romoli

We report on the changing-look nature of the active galactic nucleus (AGN) in the galaxy NGC 4156, as serendipitously discovered thanks to data acquired in 2019 at the Telescopio Nazionale Galileo (TNG) during a students' observing programme. Previous optical spectra had never shown any signatures of broad-line emission, and evidence of the AGN had come only

Tulika Bose, Irina Illina, Dominique Fohr

The concerning rise of hateful content on online platforms has increased the attention towards automatic hate speech detection, commonly formulated as a supervised classification task. State-of-the-art deep learning-based approaches usually require a substantial amount of labeled resources for training. However, annotating hate speech resources is expensive,

Mingjun Gang, Xin Wang, Zhonglei Wang, Wei Zhong

Regression analysis is commonly conducted in survey sampling. However, existing methods fail when the relationships vary across different areas or domains. In this paper, we propose a unified framework to study the group-wise covariate effect under complex survey sampling based on pairwise penalties, and the associated objective function is solved by the alt

Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang

Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning. However, these works are not pretrained to learn a deep fusion of the two modalities at scale, limiting t

Jung Yeon Park, Lawson L. S. Wong

Behavior cloning of expert demonstrations can speed up learning optimal policies in a more sample-efficient way over reinforcement learning. However, the policy cannot extrapolate well to unseen states outside of the demonstration data, creating covariate shift (agent drifting away from demonstrations) and compounding errors. In this work, we tackle this iss

Martin Beneke, Patrick Hager, Robert Szafron

This chapter reviews the construction of ``soft-collinear gravity'', the effective field theory which describes the interaction of collinear and soft gravitons with matter (and themselves), to all orders in the soft-collinear power expansion, focusing on the essential concepts. Among them are an emergent soft background gauge symmetry, which lives on the lig

Jia Liu, Xiaolin Ma, Lian-Tao Wang, Xiao-Ping Wang

Models with an Axion Like Particle (ALP) can provide an explanation for the discrepancy between experimental measurement of the muon anomalous magnetic moment $(g-2)_\mu$ and the Standard Model prediction. This explanation relies on the couplings of the ALP to the muon and the photon. We also include more general couplings to the electroweak gauge bosons and

Sean Kinahan, Julie Liss, Visar Berisha

The DIVA model is a computational model of speech motor control that combines a simulation of the brain regions responsible for speech production with a model of the human vocal tract. The model is currently implemented in Matlab Simulink; however, this is less than ideal as most of the development in speech technology research is done in Python. This means

Koichi Hamaguchi, Shihwen Hor, Natsumi Nagata

We revisit the minimal Nambu-Goldstone (NG) Higgs supersymmetric (SUSY) SU(5) grand unified model and study its phenomenological implications. The Higgs sector of the model possesses a global SU(6) symmetry, which is spontaneously broken and results in the Higgs doublets of the minimal SUSY Standard Model (MSSM) as NG chiral superfields. Therefore, the model

Henning Bahl, Thomas Biekötter, Sven Heinemeyer, Cheng Li

The codes HiggsBounds and HiggsSignals compare model predictions of BSM models with extended scalar sectors to searches for additional scalars and to measurements of the detected Higgs boson at 125 GeV. We present a unification and extension of the functionalities provided by both codes into the new HiggsTools framework. The codes have been re-written in mod

Christa Cuchiero, Luca Di Persio, Francesco Guida, Sara Svaluto-Ferro

We introduce a framework that allows to employ (non-negative) measure-valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath-Jarrow-Morton approach can be translated to this framework, thus guaranteeing arbitrage free modeling in infini

Claudio Severi, Eleni Vryonidou

We present the first analysis of top spin polarizations, $t \bar t$ spin correlations, and $t \bar t$ spin entanglement at the LHC in the context of the Standard Model Effective Field Theory, that goes beyond Leading Order QCD accuracy. The complete set of independent dimension-6 operators entering $t \bar t$ production is identified, and their effects on al

Jianhang Chen, R. J. Ivison, Martin A. Zwaan, Ian Smail

Wide, deep, blind continuum surveys at submillimetre/millimetre (submm/mm) wavelengths are required to provide a full inventory of the dusty, distant Universe. However, conducting such surveys to the necessary depth, with sub-arcsec angular resolution, is prohibitively time-consuming, even for the most advanced submm/mm telescopes. Here, we report the most r

Alessandro Valenti, Luca Vecchi

We argue that in general renormalizable field theories the topological angles may develop an additive beta function starting no earlier than 2-loop order. The leading expression is uniquely determined by a single model-independent coefficient. The associated divergent diagrams are identified and a few independent methods for extracting the beta function in d

Jens Chluba, Thomas Kite, Andrea Ravenni

Spectral distortions of the cosmic microwave background (CMB) have been recognized as an important future probe of the early Universe. Existing theoretical studies primarily focused on describing the evolution and creation of average distortions, ignoring spatial perturbations in the plasma. One of the main reasons for this choice is that a treatment of the

Vladimir Calvera, Steven A. Kivelson, Erez Berg

We study multi-valley electron gases in the low density ($r_s \gg 1$) limit. Here the ground-state is always a Wigner crystal (WC), with additional pseudo-spin order where the pseudo-spins are related to valley occupancies. Depending on the symmetries of the host semiconductor and the values of the parameters such as the anisotropy of the effective mass tens

Eirik Holm Fyhn, Arne Brataas, Alireza Qaiumzadeh, Jacob Linder

Antiferromagnets have no net spin-splitting on the scale of the superconducting coherence length. Despite this, antiferromagnets have been observed to suppress superconductivity in a similar way as ferromagnets, a phenomenon that still lacks a clear understanding. We find that this effect can be explained by the role of impurities in antiferromagnets. Using

Michael A. Fedderke, Anubhav Mathur

Gravitational-wave (GW) detectors that monitor fluctuations in the separation between inertial test masses (TMs) are sensitive to new forces acting on those TMs. Ultralight dark-photon dark matter (DPDM) coupled to $U(1)_B$ or $U(1)_{B-L}$ charges supplies one such force that oscillates with a frequency set by the DPDM mass. GW detectors operating in differe

P. Beniamini, Z. Wadiasingh, J. Hare, K. Rajwade

Two recent discoveries, namely PSR J0901-4046 and GLEAM-X J162759.5-523504.3 (hereafter GLEAM-X J1627), have corroborated an extant population of radio-loud periodic sources with long periods (76 s and 1091 s respectively) whose emission can hardly be explained by rotation losses. We argue that GLEAM-X J1627 is a highly-magnetized object consistent with a ma

M. A. Tucker, B. J. Shappee, M. E. Huber, A. V. Payne

We present the Spectroscopic Classification of Astronomical Transients (SCAT) survey, which is dedicated to spectrophotometric observations of transient objects such as supernovae and tidal disruption events. SCAT uses the SuperNova Integral-Field Spectrograph (SNIFS) on the University of Hawai'i 2.2-meter (UH2.2m) telescope. SNIFS was designed specifically

Tom Peham, Lukas Burgholzer, Robert Wille

Compiling a high-level quantum circuit down to a low-level description that can be executed on state-of-the-art quantum computers is a crucial part of the software stack for quantum computing. One step in compiling a quantum circuit to some device is quantum circuit mapping, where the circuit is transformed such that it complies with the architecture's limit

Lindsey Byrne, Claude-André Faucher-Giguère, Jonathan Stern, Daniel Anglés-Alcázar

Several recent simulations of galaxy formation predict two main phases of supermassive black hole (BH) accretion: an early, highly intermittent phase (during which BHs are under-massive relative to local scaling relations), followed by a phase of accelerated growth. We investigate physical factors that drive the transition in BH accretion in cosmological zoo

Anindya Dey

We show that a large subclass of 3d $\mathcal{N}=4$ quiver gauge theories consisting of unitary and special unitary gauge nodes with only fundamental/bifundamental matter have multiple Seiberg-like IR duals. A generic quiver $\mathcal{T}$ in this subclass has a non-zero number of balanced special unitary gauge nodes and it is a good theory in the Gaiotto-Wit

Nikolay Bobev, Junho Hong, Valentin Reys

We study the large $N$ limit of some supersymmetric partition functions of the $\mathrm{U}(N)_{k}\times \mathrm{U}(N)_{-k}$ ABJM theory computed by supersymmetric localization. We conjecture an explicit expression, valid to all orders in the large $N$ limit, for the partition function on the $\mathrm{U}(1)\times \mathrm{U}(1)$ invariant squashed sphere in th

Kuan-Chou Hou, Gregg Hallinan, Uri Keshet

Galaxy clusters accrete mass through large scale, strong, structure-formation shocks. Such a virial shock is thought to deposit fractions $\xi_e$ and $\xi_B$ of the thermal energy in cosmic-ray electrons (CREs) and magnetic fields, respectively, thus generating a leptonic virial ring. However, the expected synchrotron signal was not convincingly established

Camila S. Machado, Sophie Renner, Dave Sutherland

A powerful aspect of effective field theories is connecting scales through renormalisation group (RG) flow. The anomalous dimension matrix of the Standard Model Effective Field Theory (SMEFT) encodes clues to where to find relics of heavy new physics in data, but its unwieldy 2499-by-2499 size (at operator dimension 6) makes it difficult to draw general conc

Andra Stroe, Victoria Catlett, Jeremy J. Harwood, Tessa Vernstrom

Based on their differing radio morphologies, powerful radio galaxies can be separated into the Fanaroff-Riley I (FR-I) and II (FR-II) classes. Hybrid morphology radio sources (HyMoRS) contain morphologies consistent with each type of jet on either side: a powerful, highly relativistic FR-II-like jet terminating in a hotspot on one side and an FRI-like plume

Kevin Slagle

Although local Hamiltonians exhibit local time dynamics, this locality is not explicit in the Schr\"{o}dinger picture in the sense that the wavefunction amplitudes do not obey a local equation of motion. We show that geometric locality can be achieved explicitly in the equations of motion by "gauging" the global unitary invariance of quantum mechanics into a

Anirban Das, Noah Kurinsky, Rebecca K. Leane

We point out that power measurements of single quasiparticle devices open a new avenue to detect dark matter (DM). The threshold of these devices is set by the Cooper pair binding energy, and is therefore so low that they can detect DM as light as about an MeV incoming from the Galactic halo, as well as the low-velocity thermalized DM component potentially p

L. Sommovigo, A. Ferrara, S. Carniani, A. Pallottini

Recent ALMA large surveys unveiled the presence of significant dust continuum emission in star-forming galaxies at $z>4$. Unfortunately, such large programs -- i.e. ALPINE ($z\sim 5$) and REBELS ($z \sim 7$) -- only provide us with a single Far-Infrared (FIR) continuum data point for their individual targets. Therefore, high-$z$ galaxies FIR spectral energy

Evan Craft, Kyle Lee, Bianka Meçaj, Ian Moult

Understanding the detailed structure of energy flow within jets, a field known as jet substructure, plays a central role in searches for new physics, and precision studies of QCD. Many applications of jet substructure require an understanding of jets initiated by heavy quarks, whose description has lagged behind remarkable recent progress for massless jets.

Andrea Caputo, Michela Negro, Marco Regis, Marco Taoso

We study the prospects in the search of dark matter offered by the newly selected NASA MeV mission COSI (Compton Spectrometer and Imager). This instrument is designed and optimized to detect spectral lines, and we show it offers an exquisite possibility to detect dark matter directly decaying or annihilating into monochromatic gamma-rays. This is the case, f

Alex Mei, Anisha Kabir, Sharon Levy, Melanie Subbiah

An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threatening consequences. However, the degree of explicitness of a generated statement that can cause physical harm varies. In this paper, we distinguish types of text that can lead to

Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri

Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates. At the same time, the attack power of an individual user is limited because their updates are quickly drowned out by those of many other users. Existing attacks do not account for future

Jinghao Zhou, Li Dong, Zhe Gan, Lijuan Wang

Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align images and texts. Nonetheless, the loose correlation between images and texts of web-crawled data renders the contrastive objective data inefficient and craving for a large training batch size. In this work, we explore the validity of non-contrastive language-image pre-trai

Didam Duniya, Amare Abebe, Alvaro de la Cruz-Dombriz, Peter Dunsby

f(R) gravity is one of the simplest viable modifications to General Relativity: it passes local astrophysical tests, predicts both the early-time cosmic inflation and the late-time cosmic acceleration, and also describes dark matter. In this paper, we probe cosmic magnification on large scales in f(R) gravity, using the well-known Hu-Sawicki model as an exam

Maxime Markov, Vladimir Markov

We investigate the impact of big winner stocks on the performance of active and passive investment strategies using a combination of numerical and analytical techniques. Our analysis is based on historical stock price data from 2006 to 2021 for a large variety of global indexes. We show that the log-normal distribution provides a reasonable fit for total ret

Laia Casamiquela, Marwan Gebran, Marcel Agüeros, Hervé Bouy

The recently re-discovered open cluster Stock 2, located roughly 375 pc away and about 400 Myr old, has the potential to be an exciting new testbed for our understanding of stellar evolution. We present results from a spectroscopic campaign to characterize stars near the cluster's main-sequence turnoff; our goal is to identify candidate chemically peculiar s

Juan Manuel Salerno, Hernán Muriel, Valeria Coenda, Sofía A. Cora

We analyse the connection between the star formation quenching of galaxies and their location in theoutskirts of clusters in the redshift range $z=[0,2]$ by estimating the fraction of red galaxies. More specifically, we focus on galaxies that infall isotropically from those that are infalling alongside filaments. We use a sample of galaxies obtained from the