October 2023 arXiv papers — page 72

Can Li, Dejian Lai, Xiaoqian Jiang, Kai Zhang

Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes such as age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. Therefore, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predict

Bowen Fu, Sek Kun Leong, Yan Di, Jiwen Tang

Comprehending natural language instructions is a critical skill for robots to cooperate effectively with humans. In this paper, we aim to learn 6D poses for roboticassembly by natural language instructions. For this purpose, Language-Instructed 6D Pose Regression Network (LanPose) is proposed to jointly predict the 6D poses of the observed object and the cor

Dongyu Zhang, Liang Wang, Xin Dai, Shubham Jain

Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, sequential tabular data contains rich contextual and sequential information, where some fields are dynamically changing over time and others are static. Existing transformer-based app

Malek Alduhaymi, Ravindra Singh, Firdous Ul Nazir, Bikash C. Pal

Operating an active distribution network (ADN) in the absence of enough measurements, the presence of distributed energy resources, and poor knowledge of responsive demand behaviour is a huge challenge. This paper introduces systematic modelling of demand response behaviour which is then included in Forecasting Aided State Estimation (FASE) for better contro

Wouter Jongeneel

We characterize when a compact, invariant, asymptotically stable attractor on a locally compact Hausdorff space is a strong deformation retract of its domain of attraction.

Kawthar Al Rasbi, Lewis A. Clark, Almut Beige

Recent experimental tests of Bell inequalities confirm that entangled quantum systems cannot be described by local classical theories but still do not answer the question whether or not quantum systems could in principle be modelled by linear hidden variable theories. In this paper, we study the quantum trajectories of a single qubit that experiences a seque

Krystian Gajdzica

This paper deals with both the higher order Tur\'an inequalities and the Laguerre inequalities for quasi-polynomial-like functions -- that are expressions of the form $f(n)=c_l(n)n^l+\cdots+c_d(n)n^d+o(n^d)$, where $d,l\in\mathbb{N}$ and $d\leqslant l$. A natural example of such a function is the $A$-partition function $p_{A}(n)$, which enumerates the number

Andrew O'Hara, Benjamin Plotkin-Swing, Niklas Dellby, Juan Carlos Idrobo

Vibrations in materials and nanostructures at sufficiently high temperatures result in anharmonic atomic displacements, which leads to new phenomena such as thermal expansion and multiphonon scattering processes, with a profound impact on temperature-dependent material properties including thermal conductivity, phonon lifetimes, nonradiative electronic trans

Ajinkya Kulkarni, Hanan Aldarmaki

In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-su

Jungang Li, Guozhen Lu, Jianxiong Wang

The classification of solutions to semilinear partial differential equations, as well as the classification of critical points of the corresponding functionals, have wide applications in the study of partial differential equations and differential geometry. The classical moving plane method and the method of moving sphere on the Euclidean space $\mathbb{R}^n

Xabi Azagirre, Akshay Balwally, Guillaume Candeli, Nicholas Chamandy

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching alg

Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling, Jair Augusto Bottega

In this study, we present two distinct approaches within the realm of Deep Reinforcement Learning (Deep-RL) aimed at enhancing mapless navigation for a ground-based mobile robot. The research methodology primarily involves a comparative analysis between a Deep-RL strategy grounded in the foundational Deep Q-Network (DQN) algorithm, and an alternative approac

Fariba Islam, Chang-Min Lee, Samuel Harper, Mohammad Habibur Rahaman

Silicon T centers present the promising possibility to generate optically active spin qubits in an all-silicon device. However, these color centers exhibit long excited state lifetimes and a low Debye-Waller factor, making them dim emitters with low efficiency into the zero-phonon line. Nanophotonic cavities can solve this problem by enhancing radiative emis

Yu Sun, Xinhao Li, Karan Dalal, Chloe Hsu

We reformulate the problem of supervised learning as learning to learn with two nested loops (i.e. learning problems). The inner loop learns on each individual instance with self-supervision before final prediction. The outer loop learns the self-supervised task used by the inner loop, such that its final prediction improves. Our inner loop turns out to be e

Zhilin Wang, Qin Hu, Xukai Zou

Is it secure to measure the reliability of local models by similarity in federated learning (FL)? This paper delves into an unexplored security threat concerning applying similarity metrics, such as the L_2 norm, Euclidean distance, and cosine similarity, in protecting FL. We first uncover the deficiencies of similarity metrics that high-dimensional local mo

Sarah Coffland, Katie Christensen, Filip Jagodzinski, Brian Hutchinson

An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequentl

Xuechun Li, Paula M. Burgi, Wei Ma, Hae Young Noh

Onsite disasters like earthquakes can trigger cascading hazards and impacts, such as landslides and infrastructure damage, leading to catastrophic losses; thus, rapid and accurate estimates are crucial for timely and effective post-disaster responses. Interferometric Synthetic aperture radar (InSAR) data is important in providing high-resolution onsite infor

Otávio M. L. Gomide, Pedro G. Mattos, Régis Varão

In this work we develop a well-defined theory of orbit spaces for piecewise smooth vector fields (PSVFs). This approach is inspired by the techniques already used in the study of endomorphisms, namely inverse limit analysis, and has been used before for PSVFs. We then apply the construction of our theory to understanding transitivity in PSVFs. Our results pr

Teresa W. Lo, Han James Choi, Dean Huang, Paul A. Wiggins

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal p

Eduardo Soares, Akihiro Kishimoto, Emilio Vital Brazil, Seiji Takeda

Pre-trained Language Models have emerged as promising tools for predicting molecular properties, yet their development is in its early stages, necessitating further research to enhance their efficacy and address challenges such as generalization and sample efficiency. In this paper, we present a multi-view approach that combines latent spaces derived from st

Marco Centini, Chiyu Yang, Maria Cristina Larciprete, Mauro Antezza

We investigate a bi-layer scheme for circularly polarized infrared thermal radiation. Our approach takes advantage of the strong anisotropy of low-symmetry materials such as $\beta$-Ga$_2$O$_3$ and $\alpha$-MoO$_3$. We numerically report narrow-band, high degree of circular polarization (over 0.85), thermal radiation at two typical emission frequencies relat

Andrea Sottana, Bin Liang, Kai Zou, Zheng Yuan

Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the pace of development of generative models. We aim to improve the understanding of current models' performance by providing a preliminary and hybrid evaluation on a range of open an

William Kyle Barker

This paper is concerned with traveling waves to an diffusive SIR model with delay placed in the diffusion terms as well as nonlinear incidence rate with delay. Using a cross iteration scheme and partial monotone conditions it will be shown that the existence of quasi upper and lower solutions is a sufficient condition for the existence of a traveling wave fr

Sandipan Kundu, Yuntao Bai, Saurav Kadavath, Amanda Askell

Human feedback can prevent overtly harmful utterances in conversational models, but may not automatically mitigate subtle problematic behaviors such as a stated desire for self-preservation or power. Constitutional AI offers an alternative, replacing human feedback with feedback from AI models conditioned only on a list of written principles. We find this ap

Manuel Hasenbichler, Benjamin Joseph, Gregoire Loeper, Jan Obloj

We develop a numerical method for the martingale analogue of the Benamou--Brenier optimal transport problem, which seeks a martingale interpolating two prescribed marginals which is closest to the Brownian motion. Recent contributions have established existence of the optimal martingale under finite second moment assumptions on the marginals, but numerical m

Søren Wengel Mogensen

Graphical models use graphs to represent conditional independence structure in the distribution of a random vector. In stochastic processes, graphs may represent so-called local independence or conditional Granger causality. Under some regularity conditions, a local independence graph implies a set of independences using a graphical criterion known as $\delt

Teale W Masrani, Helen Ai He, Geoffrey Messier

On any night in Canada, at least 35,000 individuals experience homelessness. These individuals use emergency shelters to transition out of homelessness and into permanent housing. We designed and deployed a technology to support front-line staff at the largest emergency housing shelter in Calgary, Canada. Over a period of five months in 2022, we worked close

Yi-Hsien Du, Ho Tat Lam, Leo Radzihovsky

We study an effective field theory of a vortex lattice in a two-dimensional neutral rotating superfluid. Utilizing particle-vortex dualities, we explore its formulation in terms of a $U(1)$ gauge theory coupled to elasticity, that at low energies reduces to a compact Lifshitz theory augmented with a Berry phase term encoding the vortex dynamics in the presen

Yunmo Chen, William Gantt, Tongfei Chen, Aaron Steven White

We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e.g. event and relation extraction, syntactic and semantic parsing). Our framework requires representing the outputs of these tasks as objects of certain data types, and derives metrics through matching of common substructures, possibly f

Yufan Huang, David F. Gleich, Nate Veldt

Dense subgraph discovery is a fundamental primitive in graph and hypergraph analysis which among other applications has been used for real-time story detection on social media and improving access to data stores of social networking systems. We present several contributions for localized densest subgraph discovery, which seeks dense subgraphs located nearby

Saman Soleymani, Shima Mohammadzadeh

The increasing global demand for clean and environmentally friendly energy resources has caused increased interest in harnessing solar power through photovoltaic (PV) systems for smart grids and homes. However, the inherent unpredictability of PV generation poses problems associated with smart grid planning and management, energy trading and market participa

Nasr Ahmed, Anirudh Pradhan

The existence of Schwarzchild black holes in the structure of Swiss-cheese brane-world led to the conclusion that this specific brane-world scenario is more realistic than the FLRW branes. In this paper, we show that a Logamediate inflation on the Swiss-cheese brane with time-dependent cosmological constant $\Lambda(H)$ leads to a positive kinetic term and a

Michel Gros, Bernard Le Stum, Adolfo Quirós

Let $p$ be a prime, $W$ the ring of Witt vectors of a perfect field $k$ of characteristic $p$ and $ζ$ a primitive $p$th root of unity. We introduce a new notion of calculus over $W$ that we call absolute calculus. It may be seen as a singular version of the $q$-calculus used in previous work, in the sense that the role of the coordinate is now played by $q$

Rémi Cocou Avohou, Reiko Toriumi, Matthias Vancraeynest

The authors studied in [Ann. Inst. Henri Poincar\'e D 9, 367-433, (2022)], a complex multi-matrix model with $\mathrm{U}(N)^2 \times \mathrm{O}(D)$ symmetry, and whose double scaling limit where simultaneously the large-$N$ and large-$D$ limits were taken while keeping the ratio $N/\sqrt{D}=M$ finite and fixed. In this double scaling limit, the complete recu

D. Gribanov, D. Malyshev, P. Pardalos, N. Zolotykh

In this paper, we consider the counting function $E_P(y) = |P_{y} \cap Z^{n_x}|$ for a parametric polyhedron $P_{y} = \{x \in R^{n_x} \colon A x \leq b + B y\}$, where $y \in R^{n_y}$. We give a new representation of $E_P(y)$, called a \emph{piece-wise step-polynomial with periodic coefficients}, which is a generalization of piece-wise step-polynomials and i

Miguel AP Oliveira, Stephane Manara, Bruno Molé, Thomas Muller

Individuals and organizations cope with an always-growing amount of data, which is heterogeneous in its contents and formats. An adequate data management process yielding data quality and control over its lifecycle is a prerequisite to getting value out of this data and minimizing inherent risks related to multiple usages. Common data governance frameworks r

Jack Nicholls, Aditya Kuppa, Nhien-An Le-Khac

Financial cybercrime prevention is an increasing issue with many organisations and governments. As deep learning models have progressed to identify illicit activity on various financial and social networks, the explainability behind the model decisions has been lacklustre with the investigative analyst at the heart of any deep learning platform. In our paper

Eric Aubinais, Elisabeth Gassiat, Pablo Piantanida

Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensitive information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models at large. More precisely, we first deriv

Hyungsik Roger Moon, Frank Schorfheide, Boyuan Zhang

We incorporate a version of a spike and slab prior, comprising a pointmass at zero ("spike") and a Normal distribution around zero ("slab") into a dynamic panel data framework to model coefficient heterogeneity. In addition to homogeneity and full heterogeneity, our specification can also capture sparse heterogeneity, that is, there is a core group of units

Stefan Hoffelner, Paul Larson, Ralf Schindler, Liuzhen Wu

We produce, relative to a ${\sf ZFC}$ model with a supercompact cardinal, a ${\sf ZFC}$ model of the Proper Forcing Axiom in which the nonstationary ideal on $\omega_1$ is $\Pi_1$-definable in a parameter from $H_{\aleph_2}$.

V. Borka Jovanović, D. Borka, P. Jovanović

Here we investigate the giant radio galaxy DA 240, which is a FR II source. Specifically, we investigate its flux density, as well as the spectral index distribution. For that purpose, we used publicly available data for the source: Leahy's atlas of double radio-sources and NASA/IPAC Extragalactic Database (NED). We used observations at 326 MHz (92 cm) and a

Logan Frank, Jim Davis

Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the original training dataset is always available. However, this is not always the case due to privacy concerns and more. In recent years, "data-free" KD has emerged as a growing research topic which focuses on the scenario of per

Jacob K. Johnson, Ana Marasović

Contrast set consistency is a robustness measurement that evaluates the rate at which a model correctly responds to all instances in a bundle of minimally different examples relying on the same knowledge. To draw additional insights, we propose to complement consistency with relative consistency -- the probability that an equally accurate model would surpass

Aaron L. Brown, Matteo Salvador, Lei Shi, Martin R. Pfaller

In numerical simulations of cardiac mechanics, coupling the heart to a model of the circulatory system is essential for capturing physiological cardiac behavior. A popular and efficient technique is to use an electrical circuit analogy, known as a lumped parameter network or zero-dimensional (0D) fluid model, to represent blood flow throughout the cardiovasc

J. A. Kropotina, A. A. Petrukovich, O. M. Chugunova, A. M. Bykov

We directly compare hybrid kinetic simulations and in situ observations of a high Mach number high-$\beta$ shock in the Solar wind. We launch virtual probes to demonstrate that the model quantitatively reproduces the observations. The observed wave properties are caused by the ion Weibel instability in the shock foot. Parameters of reflected ions in the shoc

Rajarshi Roy, Daniel Neider

We consider the problem of automatically inferring specifications in the branching-time logic, Computation Tree Logic (CTL), from a given system. Designing functional and usable specifications has always been one of the biggest challenges of formal methods. While in recent years, works have focused on automatically designing specifications in linear-time log

Áron Jánosik, Csenge Miklós, Dániel G. Simon, Kristóf Zólomy

We investigate a discrete search game called the Multiple Caching Game where the searcher's aim is to find all of a set of $d$ treasures hidden in $n$ locations. Allowed queries are sets of locations of size $k$, and the searcher wins if in all $d$ queries, at least one treasure is hidden in one of the $k$ picked locations. P\'alv\"olgyi showed that the valu

Paul Apisa

We introduce a construction of affine invariant subvarieties in strata of translation surfaces whose input is purely combinatorial. We then show that this construction can be used to construct the Bouw-Moeller Teichmueller curves and the seven Eskin-McMullen-Mukamel-Wright rank two orbit closures. The construction is based on the theory of Hurwitz spaces and

Wenzheng Zhang, Sam Wiseman, Karl Stratos

Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance. In this work, we present compelling evidence that such models are not necessary. We finetune a pretrained seq2seq transformer to map an input document to a tagged sequence encoding the coreference annotation. Despite the extreme s

Simone Daniotti, Vito D. P. Servedio, Johannes Kager, Aad Robben-Baldauf

In public railway systems, minor disruptions can trigger cascading events that lead to delays in the entire system. Typically, delays originate and propagate because the equipment is blocking ways, operational units are unavailable, or at the wrong place at the needed time. The specific understanding of the origins and processes involved in delay-spreading i

Tianshi Cao, Karsten Kreis, Sanja Fidler, Nicholas Sharp

We present TexFusion (Texture Diffusion), a new method to synthesize textures for given 3D geometries, using large-scale text-guided image diffusion models. In contrast to recent works that leverage 2D text-to-image diffusion models to distill 3D objects using a slow and fragile optimization process, TexFusion introduces a new 3D-consistent generation techni

Antonia Karamolegkou, Jiaang Li, Li Zhou, Anders Søgaard

Language models may memorize more than just facts, including entire chunks of texts seen during training. Fair use exemptions to copyright laws typically allow for limited use of copyrighted material without permission from the copyright holder, but typically for extraction of information from copyrighted materials, rather than {\em verbatim} reproduction. T

S. Jon Chapman, M. Kavousanakis, E. G. Charalampidis, I. G. Kevrekidis

In the present work we revisit the problem of the generalized Korteweg-de Vries equation parametrically, as a function of the relevant nonlinearity exponent, to examine the emergence of blow-up solutions, as traveling waveforms lose their stability past a critical point of the relevant parameter $p$, here at $p=5$. We provide a {\it normal form} of the assoc

Benson Chen, Mohammad M. Sultan, Theofanis Karaletsos

DNA-Encoded Library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly-efficient screening assays. These selection experiments, involving multiple stages of washing, elution, and identification of potent binders via unique DNA barcodes, often generate complex data. This complexity can potenti

Muhammed Kocabas, Ye Yuan, Pavlo Molchanov, Yunrong Guo

We present a method to estimate human motion in a global scene from moving cameras. This is a highly challenging task due to the coupling of human and camera motions in the video. To address this problem, we propose a joint optimization framework that disentangles human and camera motions using both foreground human motion priors and background scene feature

Ruth Johnson, Michelle M. Li, Ayush Noori, Owen Queen

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks (GNNs), stands out for its capability to capture intricate relationships within structured clinical datasets. With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by thei

Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger, Dengxin Dai

Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird's-Eye-View (BEV) segmentation allow for accurate estimation of local scene appearance and in turn, can benefit the relocalization of the vehicle. However, one downside of BEV methods is the heavy comput

Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O'Malley, James M. Hyman

Limiting the injection rate to restrict the pressure below a threshold at a critical location can be an important goal of simulations that model the subsurface pressure between injection and extraction wells. The pressure is approximated by the solution of Darcy's partial differential equation (PDE) for a given permeability field. The subsurface permeability

Leonardo V. Santoro, Victor M. Panaretos

We develop a statistical framework for conducting inference on collections of time-varying covariance operators (covariance flows) over a general, possibly infinite dimensional, Hilbert space. We model the intrinsically non-linear structure of covariances by means of the Bures-Wasserstein metric geometry. We make use of the Riemmanian-like structure induced

Tejas Prasanna, Todd A. Thompson, Christopher Hirata

After a successful supernova, a proto-neutron star (PNS) cools by emitting neutrinos on $\sim 1-100$ s timescales. Provided that there are neutrino emission `hot-spots' or `cold-spots' on the surface of the rotating PNS, we can expect a periodic modulation in the number of neutrinos observable by detectors. We show that Fourier transform techniques can be us

P. Makaremi-Esfarjani, A. Najafi-Yazdi

In the present study, a characteristic-based boundary condition scheme is developed for the compressible magnetohydrodynamic (MHD) equations in the general curvilinear coordinate system, which is an extension of the characteristic boundary scheme for the Navier-Stokes equations. The eigenstructure and the complete set of characteristic waves are derived for

Tomáš Matys Grygar, Una Radojičić, Ivana Pavlů, Sonja Greven

Geochemical mapping of risk element concentrations in soils is performed in countries around the world. It results in large datasets of high analytical quality, which can be used to identify soils that violate individual legislative limits for safe food production. However, there is a lack of advanced data mining tools that would be suitable for sensitive ex

Hwanjun Song, Igor Shalyminov, Hang Su, Siffi Singh

Sequence-level knowledge distillation reduces the size of Seq2Seq models for more efficient abstractive summarization. However, it often leads to a loss of abstractiveness in summarization. In this paper, we propose a novel approach named DisCal to enhance the level of abstractiveness (measured by n-gram overlap) without sacrificing the informativeness (meas

Sripathi Sridhar, Mark Cartwright

Current audio classification models have small class vocabularies relative to the large number of sound event classes of interest in the real world. Thus, they provide a limited view of the world that may miss important yet unexpected or unknown sound events. To address this issue, open-set audio classification techniques have been developed to detect sound

Jonathan Johnson, Henry Segerman

Results of Perron and Rolfsen imply that untwisted hyperbolic once-punctured torus bundles over the circle have bi-orderable fundamental groups. They do this by showing that the action of the monodromy preserves a "standard" bi-ordering formed using the lower central series of the free group. Here we investigate other bi-orderings that punctured torus bundle

Christopher F. Kane, Niladri Gomes, Michael Kreshchuk

We present several improvements to the recently developed ground state preparation algorithm based on the Quantum Eigenvalue Transformation for Unitary Matrices (QETU), apply this algorithm to a lattice formulation of U(1) gauge theory in 2+1D, as well as propose a novel application of QETU, a highly efficient preparation of Gaussian distributions. The QETU

Xiang Fu, Albert Musaelian, Anders Johansson, Tommi Jaakkola

The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a multiple-time-step (MTS) integrator that evaluates certain potential energy terms that vary more slowly than others less frequently. This approach is enabled by the simple but limi

Diego Garcia Lambas, Frode K. Hansen, Facundo Toscano, Heliana E. Luparello

The non-Gaussian Cold Spot (CS) surrounded by its hot ring is one of the most striking features of the CMB. It has been speculated that either new physics or ISW effect induced by the presence of a cosmic void at high redshift can account for the observations. Here we investigate if the systematic decrease in CMB temperature in the neighbourhood of nearby ga

Eugenia Moris, Ignacio Larrabide

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using Wavelets for feature extraction and Random Forest for classification, an automatic sleep-stage classification method was sought and as

Yu Ge, Maximilian Stark, Musa Furkan Keskin, Frank Hofmann

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first deriv

Nicholas P. Bauman

Downfolding coupled cluster (CC) techniques are powerful tools for reducing the dimensionality of many-body quantum problems. This work investigates how ground-state downfolding formalisms can target excited states using non-Aufbau reference determinants, paving the way for applications of quantum computing in excited-state chemistry. This study focuses on d

M. S. Oey, Amit N. Sawant, Ashkbiz Danehkar, Sergiy Silich

We use the Hubble Space Telescope ACS camera to obtain the first spatially resolved, nebular imaging in the light of C IV 1548,1551 by using the F150LP and F165LP filters. These observations of the local starburst Mrk 71 in NGC 2366 show emission apparently originating within the interior cavity around the dominant super star cluster (SSC), Knot A. Together

Rainer Mandel

We prove weighted versions of the 2D Restriction Conjecture for the unit sphere in $\mathbb{R}^2$. Our results involve the weight functions $(1+|x|)^\alpha(1+|y|)^\beta$ and $(1+|x|+|y|)^\gamma$ with $\alpha,\beta,\gamma\geq 0$.

José Moreira de Sousa

In Physics, we are generally interested in real solutions involving natural phenomena, where knowledge of real functions of real variables is sufficient to obtain physically relevant results. However, the complexity of phenomena associated with nature lead us to situations where it is necessary to extend mathematical knowledge to complex values of complex va

Rafał Masełek

This thesis summarises four years of research aiming at revealing the prospects for detection of long-lived particles at the LHC. It contains results of four projects, which have been published as independent articles. Each of the projects investigated prospects for the detection of long-lived particles predicted by one of the many BSM models, e.g. supersymm

Amr Keleg, Sharon Goldwater, Walid Magdy

Transcribed speech and user-generated text in Arabic typically contain a mixture of Modern Standard Arabic (MSA), the standardized language taught in schools, and Dialectal Arabic (DA), used in daily communications. To handle this variation, previous work in Arabic NLP has focused on Dialect Identification (DI) on the sentence or the token level. However, DI

Arjun Roy, Christos Koutlis, Symeon Papadopoulos, Eirini Ntoutsi

The generalisation capacity of Multi-Task Learning (MTL) suffers when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients. This is known as negative transfer and leads to a drop in MTL accuracy compared to single-task learning (STL). Lately, there has been a growing focus on the fairness of MTL models, requir

A. Mahmoodzadeh, B. Malakolkalami, K. Ghaderi

Regular Schwarzschild Anti-de-Sitter (RSch-AdS) spacetime, which is a maximally symmetric and Lorentzian manifold of negative curvature, is a solution of Einstein's field equations with a negative cosmological constant and a matter source with a Gaussian distribution. Studying of black hole (BH) in such a spacetime, known as RSch-AdS black hole, can be instr

Lu Shen, Casey Papovich, Jasleen Matharu, Nor Pirzkal

We study the H$\alpha$ equivalent width, EW(H$\alpha$), maps of 19 galaxies at $0.6 < z < 2.2$ in the Hubble Ultra Deep Field (HUDF) derived from NIRISS slitless spectroscopy as part of the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey. Our galaxies mostly lie on the star-formation main sequence with a stellar mass range of $\mathrm{1

Aitijhya Saha, Debraj Rakshit

In this work, we discuss a non-Hermitian system described via a one-dimensional single-particle tight-binding model, where the non-Hermiticity is governed by random nearest-neighbour tunnellings, such that the left-to-right and right-to-left hopping strengths are unequal. A physical situation of completely real eigenspectrum arises owing to the Hamiltonian's

Pierre A. Pantaleon, Hector Sainz-Cruz, Francisco Guinea

We analyze the elastic properties, structural effects, and low-energy physics of a sheared nanoribbon placed on top of graphene, which creates a gradually changing moire pattern. By means of a classical elastic model we derive the strains in the ribbon and we obtain its electronic energy spectrum with a scaled tight-binding model. The size of the sheared reg

Michael Kreshchuk, James P. Vary, Peter J. Love

We develop a non-perturbative approach to simulating scattering on classical and quantum computers, in which the initial and final states contain a fixed number of composite particles. The construction is designed to mimic a particle collision, wherein two composite particles are brought in contact. The initial states are assembled via consecutive applicatio

Dung Xuan Nguyen, Sergej Moroz

Despite a long history of studies of vortex crystals in rotating superfluids, their melting due to quantum fluctuations is poorly understood. Here we develop a fracton-elasticity duality to investigate a two-dimensional vortex lattice within the fast rotation regime, where the Lifshitz model of the collective Tkachenko mode serves as the leading-order low-en

D. J. Watts, U. Fuskeland, R. Aurlien, A. Basyrov

We present the first model of full-sky polarized synchrotron emission that is derived from all WMAP and Planck LFI frequency maps. The basis of this analysis is the set of end-to-end reprocessed Cosmoglobe Data Release 1 sky maps presented in a companion paper, which have significantly lower instrumental systematics than the legacy products from each experim

Stuart L. Shapiro

We consider the steady-state density and velocity dispersion profiles of collisionless matter around a Schwarzschild black hole (BH) and its associated rate of accretion onto the BH. We treat matter, which could be stars or dark matter particles, whose orbits are {\it unbound} to the BH, but still governed by its gravitational field. We consider two opposite

Giuliano Chiriacò

In recent years there has been great interest towards optical cavities as a tool to manipulate the properties and phases of embedded quantum materials. Due to the Purcell effect, a cavity changes the photon phase space and thus the rate of electromagnetic transitions within the material, modifying the exchange rate of heat radiation with the photon environme

Adomas Baliuka, Markus Stöcker, Michael Auer, Peter Freiwang

Quantum key distribution (QKD) protocols are proven secure based on fundamental physical laws, however, the proofs consider a well-defined setting and encoding of the sent quantum signals only. Side channels, where the encoded quantum state is correlated with properties of other degrees of freedom of the quantum channel, allow an eavesdropper to obtain infor

Marston Copeland, Sean Fleming, Rohit Gupta, Reed Hodges

We compare the relative importance of different mechanisms for polarized $J/\psi$ production in semi-inclusive deep inelastic scattering processes at large $Q^2$. The transverse momentum dependent (TMD) factorization framework and nonrelativistic quantum chromodynamics are used to study the leading contributions from light quark fragmentation to polarized $J

Shoy Ouseph, Keiichiro Furuya, Nima Lashkari, Kwing Lam Leung

The local two-dimensional Poincar\'e algebra near the horizon of an eternal AdS black hole, or in proximity to any bifurcate Killing horizon, is generated by the Killing flow and outward null translations on the horizon. In holography, this local Poincar\'e algebra is reflected as a pair of unitary flows in the boundary Hilbert space whose generators under m

Atish Dabholkar, Upamanyu Moitra

We consider $\mathbb{Z}_N$ orbifolds of Type-II compactifications to four and six dimensions on several Calabi-Yau manifolds in the orbifold limit with the aim to compute the entanglement entropy. The spectrum can contain tachyons in the doubly-twisted sectors which can lead to new infrared divergences for the partition function that are not present in the o

Ludwig M. Böss, Klaus Dolag, Ulrich P. Steinwandel, Elena Hernández-Martínez

Aims: Detecting diffuse synchrotron emission from the cosmic web is still a challenge for current radio telescopes. We aim to make predictions about the detectability of cosmic web filaments from simulations. Methods: We present the first cosmological magnetohydrodynamic simulation of a 500 $h^{-1} c$Mpc volume with an on-the-fly spectral cosmic ray (CR) mod

Elliott Gesteau

Various diagnostics of the emergence of an arrow of time in the bulk description of a holographic theory have been proposed, including the decay of some real time correlation functions and the appearance of type III$_1$ von Neumann algebras carrying half-sided modular inclusions. This note puts forward a close parallel between these diagnostics and a quantum

A. Afruni, S. Lopez, P. Anshul, N. Tejos

One of the biggest puzzles regarding the circumgalactic medium (CGM) is the structure of its cool ($T\sim10^4$ K) gas phase. While the kinematics of quasar absorption systems suggests the CGM is composed of a population of different clouds, constraining the clouds' extent and spatial distribution has proven challenging, both from the theoretical and observat

Austin Batz, Timothy Cohen, David Curtin, Caleb Gemmell

We study confining dark sectors where the lightest hadrons are glueballs. Such models can provide viable dark matter candidates and appear in some neutral naturalness scenarios. In this work, we introduce a new phenomenological model of dark glueball hadronization inspired by the Lund string model. This enables us to make realistic predictions for dark glueb

V. Alfradique, C. R. Bom, A. Palmese, G. Teixeira

The current and next observation seasons will detect hundreds of gravitational waves (GWs) from compact binary systems coalescence at cosmological distances. When combined with independent electromagnetic measurements, the source redshift will be known, and we will be able to obtain precise measurements of the Hubble constant $H_0$ via the distance-redshift

Andreagiovanni Reina, Thierry Njougouo, Elio Tuci, Timoteo Carletti

To succeed in their objectives, groups of individuals must be able to make quick and accurate collective decisions on the best option among a set of alternatives with different qualities. Group-living animals aim to do that all the time. Plants and fungi are thought to do so too. Swarms of autonomous robots can also be programmed to make best-of-n decisions

Ruoci Sun

This paper is dedicated to studying matrix solutions of the cubic Szeg\H{o} equation on the line in Pocovnicu [arXiv:1001.4037, arXiv:1012.2943] and G\'erard--Pushnitski [arXiv:2307.06734], leading to the following matrix Szeg\H{o} equation on $\mathbb{R}$, \begin{equation*} i \partial_t U = \Pi_{\geq 0} \left(U U ^* U \right), \quad \widehat{\left(\Pi_{\geq

Manan Bhatia

For Brownian surfaces with boundary and an interior marked point, a natural observable to consider is the distance profile, defined as the process of distances from the marked point to a variable point $x$ lying on the boundary. When the boundary is parametrized by the natural length measure on it, this distance profile turns out to be locally absolutely con

Jacob Galajda, Kien Hua

There are two aspects of machine learning and artificial intelligence: (1) interpreting information, and (2) inventing new useful information. Much advance has been made for (1) with a focus on pattern recognition techniques (e.g., interpreting visual data). This paper focuses on (2) with intelligent duplication (ID) for invention. We explore the possibility

Kan Chen, Mihkel Kama, Paola Pinilla, Luke Keyte

(Exo-)planets inherit their budget of chemical elements from a protoplanetary disk. The disk temperature determines the phase of each chemical species, which sets the composition of solids and gas available for planet formation. We investigate how gap structures, which are widely seen by recent disk observations, alter the thermal and chemical structure of a