arXiv papers from December 2022

Yiran Luo, Li-Ta Hsu, Yang Jiang, Baoyu Liu

Many multi-sensor navigation systems urgently demand accurate positioning initialization from global navigation satellite systems (GNSSs) in challenging static scenarios. However, ground blockages against line-of-sight (LOS) signal reception make it difficult for GNSS users. Steering local codes in GNSS basebands is a desiring way to correct instantaneous si

Pedro A. Santos-Florez, Shinnosuke Hattori, Qiang Zhu

In recent years, certain molecular crystals have been reported to possess surprising flexibility by undergoing significant elastic or plastic deformation in response to mechanical loads. However, despite this experimental evidence, there currently exists no atomistic mechanism to explain the physical origin of this phenomenon from numerical simulations. In t

Rohit Kannan, Harsha Nagarajan, Deepjyoti Deka

We learn optimal instance-specific heuristics for the global minimization of nonconvex quadratically-constrained quadratic programs (QCQPs). Specifically, we consider partitioning-based convex mixed-integer programming relaxations for nonconvex QCQPs and propose the novel problem of strong partitioning to optimally partition variable domains without sacrific

Benjamin MacAdam

Ehresmann's introduction of differentiable groupoids in the 1950s may be seen as a starting point for two diverging lines of research, many-object Lie theory (the study of Lie algebroids and Lie groupoids) and sketch theory. This thesis uses tangent categories to build a bridge between these two lines of research, providing a structural account of Lie algebr

Rajesh Kumar Singh, Jie Bao, Chao Wang, Yucheng Fu

Computational countercurrent flow investigation in the structured packed column is a multiscale problem. Multiphase flow studies using volume of fluid (VOF) method in the representative elementary unit (REU) of the packed column can insight into the local hydrodynamics such as interfacial area, film thickness, etc. The interfacial area dictates the mass tran

Georgios Paraskevopoulos, Theodoros Kouzelis, Georgios Rouvalis, Athanasios Katsamanis

Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited. In this work we propose M2DS2, a simple and sample-efficient finetuning strategy for large pretrained speech models, based on mixe

Arash Ahmadian, Louis S. P. Liu, Yue Fei, Konstantinos N. Plataniotis

Differentiable Architecture Search (DARTS) has attracted considerable attention as a gradient-based neural architecture search method. Since the introduction of DARTS, there has been little work done on adapting the action space based on state-of-art architecture design principles for CNNs. In this work, we aim to address this gap by incrementally augmenting

Hangfeng He, Hongming Zhang, Dan Roth

Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional

Sebastian Czerwiński

A harmonious coloring of a $k$-uniform hypergraph $H$ is a vertex coloring such that no two vertices in the same edge have the same color, and each $k$-element subset of colors appears on at most one edge. The harmonious number $h(H)$ is the least number of colors needed for such a coloring. The paper contains a new proof of the upper bound $h(H)=O(\sqrt[k]{

Rachel Redberg, Yuqing Zhu, Yu-Xiang Wang

The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond

Sivaguru S. Sritharan, Saba Mudaliar

This paper identifies certain interesting mathematical problems of stochastic quantization type in the modeling of Laser propagation through turbulent media. In some of the typical physical contexts the problem reduces to stochastic Schrodinger equation with space-time white noise of Gaussian, Poisson and Levy type. We identify their mathematical resolution

Jenna M. Reinen, Carla Agurto, Guillermo Cecchi, Jeffrey L. Rogers

The technical capacity to monitor patients with a mobile device has drastically expanded, but data produced from this approach are often difficult to interpret. We present a solution to produce a meaningful representation of patient status from large, complex data streams, leveraging both a data-driven approach, and use clinical knowledge to validate results

T. Wakhare, C. Vignat

An unpublished identity of Gosper restates a hypergeometric identity for odd zeta values in terms of an infinite product of matrices. We show that this correspondence runs much deeper, and show that many examples of WZ-accelerated series for zeta values lift to infinite matrix products. We also introduce a new matrix subgroup, the Gosper group, which all of

Cameron McNamee, Renee Reijo Pera

Studying systems where many individual bodies in motion interact with one another is a complex and interesting area. Simple mechanisms that may be determined for biological, chemical, or physical reasons can lead to astonishingly complex results that require a further understanding of the moving bodies. With the increasing interaction between computation and

Ranjan Sapkota, John Stenger, Michael Ostlie, Paulo Flores

Currently, weed control in commercial corn production is performed without considering weed distribution information in the field. This kind of weed management practice leads to excessive amounts of chemical herbicides being applied in a given field. The objective of this study was to perform site-specific weed control (SSWC) in a corn field by 1) using an u

J. L. Iguain, L. Padilla

We study single random walks and the electrical resistance for fractals obtained as the limit of a sequence of periodic structures. In the long-scale regime, power laws describe both the mean-square displacement of a random walk as a function of time and the electrical resistance as a function of length. We show that the corresponding power-law exponents sat

Michael H. Freedman

This note initiates an investigation of packing links into a region of Euclidean space to achieve a maximal density subject to geometric constraints. The upper bounds obtained apply only to the class of homotopically essential links and even there seem extravagantly large, leaving much working room for the interested reader.

W. W. Koczkodaj, M. Mazurek, W. Pedrycz, E. Rogalska

Harmful Internet use (HIU) is a term coined for the unintended use of the Internet. In this study, we propose a more accurate HIU measuring method based on the peer assessment and differential evolution approach. The sample data comprises a juvenile population in Poland; 267 subjects assessed 1,513 peers. In addition to classic statistical analysis, differen

Kingsley Nweye, Siva Sankaranarayanan, Zoltan Nagy

The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response.

Stephen Tapsak, Brielle Hunt, J. H. Huckans

The decay of luminescence emitted by an activated natural calcite sample after being excited by longwave ultraviolet light (370 nm) has been measured and analyzed. The time evolution of the light intensity did not follow a single exponential decay. Rather, a distribution of decay-times was inferred via the extraction of a fit parameter characterizing the nat

Marina Huerta, Guido van der Velde

We focus our attention on the one dimensional scalar theories that result from dimensionally reducing the free scalar field theory in arbitrary d dimensions. As is well known, after integrating out the angular coordinates, the free scalar theory can be expressed as an infinite sum of theories living in the semi-infinite line, labeled by the angular modes $\{

Alan P. Boss

Protoplanets formed in a marginally gravitationally unstable (MGU) disk by either core accretion or disk instability will be subject to dynamical interactions with massive spiral arms, possibly resulting in inward or outward orbital migration, mergers with each other, or even outright ejection from the protoplanetary system. The latter process has been hypot

Markus Pelger, Jiacheng Zou

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a n

Benjamin Colburn, Luis G. Sanchez Giraldo, Jose C. Principe

This paper presents a close form solution in Reproducing Kernel Hilbert Space (RKHS) for the famed Wiener filter, which we called the functional Wiener filter(FWF). Instead of using the Wiener-Hopf factorization theory, here we define a new lagged RKHS that embeds signal statistics based on the correntropy function. In essence, we extend Parzen$'$s work on t

Mohammadhossein Askarihemmat, Sean Wagner, Olexa Bilaniuk, Yassine Hariri

We present a DNN accelerator that allows inference at arbitrary precision with dedicated processing elements that are configurable at the bit level. Our DNN accelerator has 8 Processing Elements controlled by a RISC-V controller with a combined 8.2 TMACs of computational power when implemented with the recent Alveo U250 FPGA platform. We develop a code gener

Dean Wang

In this paper, we present a formal Fourier analysis (FA) of Picard iteration for the coupled neutronics/thermal hydraulics (N/TH) problem and derive theoretical predictions for the spectral radius of Picard iteration for such coupled calculations as a function of the temperature difference between the fuel and coolant, temperature coefficients of cross secti

Alexandru D. Ionescu, Sameer Iyer, Hao Jia

We give a proof of linear inviscid damping and vorticity depletion for non-monotonic shear flows with one critical point in a bounded periodic channel. In particular, we obtain quantitative depletion rates for the vorticity function without any symmetry assumptions.

Seonmi Choi, Sam Nelson

We consider the notion of mosaic diagrams for surface-links using marked graph diagrams. We establish bounds, in some cases tight, on the mosaic numbers for the surface-links with ch-index up to 10. As an application, we use mosaic diagrams to enhance the kei counting invariant for unoriented surface-links as well as classical knots and links.

Juvenal F. Barajas, Timothy Sun

We show that the complete graphs on $24s+21$ vertices have decompositions into two edge-disjoint subgraphs, each of which triangulates an orientable surface. The special case where the two surfaces are homeomorphic solves a generalized Earth-Moon problem for that surface. Unlike previous constructions, these pairs of triangular embeddings are derived from in

Dean Wang

In this paper, we deal with the differential properties of the scalar flux defined over a two-dimensional bounded convex domain, as a solution to the integral radiation transfer equation. Estimates for the derivatives of the scalar flux near the boundary of the domain are given based on Vainikko's regularity theorem. A numerical example is presented to demon

Emmanuel Hartman, Martin Bauer, Eric Klassen

The Square Root Normal Field (SRNF) framework is a method in the area of shape analysis that defines a (pseudo) distance between unparametrized surfaces. For piecewise linear (PL) surfaces it was recently proved that the SRNF distance between unparametrized surfaces is equivalent to the Wasserstein Fisher Rao (WFR) metric on the space of finitely supported m

Yusuke Ide

In this paper, we consider Szegedy's walk, a type of discrete time quantum walk, and corresponding continuous time quantum walk related to the birth and death chain. We show that the scaling limit of time averaged distribution for the continuous time quantum walk induces that of Szegedy's walk if there exists the spectral gap on so-called the corresponding J

G. Gandus, A. Jayaraj, D. Passerone, R. Stadler

Strongly correlated physics arises from electron-electron scattering within partially filled orbitals. Organic molecules in open-shell configurations are therefore good candidates to exhibit many-body effects. We focus on electron transport in a two-terminal single-molecule junction setup, in which the molecular bridge consists of an organic radical with a m

D. Damian

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a sel

Mariam Zomorodi, Ismail Ghodsollahee, Jennifer H. Martin, Nicholas J. Talley

A comprehensive pharmaceutical recommendation system was designed based on the patients and drugs features extracted from Drugs.com and Druglib.com. First, data from these databases were combined, and a dataset of patients and drug information was built. Secondly, the patients and drugs were clustered, and then the recommendation was performed using differen

Refat Abdelmawla Khaled Assaad

Let $R$ be a ring. An $R$-module $M$ is said to be a weak $w$-projective module if ${\rm Ext}_R^1(M,N)=0$ for all $N \in \mathcal{P}_{w}^{\dagger_\infty}$ (see, \cite{FLQ}). In this paper, we introduce and study some properties of weak $w$-projective modules. And we use these modules to characterize some classical rings, for example, we will prove that a rin

Dibyayan Chakraborty, Jérémie Chalopin, Florent Foucaud, Yann Vaxès

A set $S$ of isometric paths of a graph $G$ is ``$v$-rooted'', where $v$ is a vertex of $G$, if $v$ is one of the endpoints of all the isometric paths in $S$. The isometric path complexity of a graph $G$, denoted by $ipco{G}$, is the minimum integer $k$ such that there exists a vertex $v\in V(G)$ satisfying the following property: the vertices of any single

Matias D. Cattaneo, Max H. Farrell, Michael Jansson, Ricardo Masini

The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory for kernel-based DWAD estimators relies on tuning parameter restrictions and model assumptions that imply an asymptotic linear representation of the point estimator. These conditions

Abdelhamid Salem, Kai-Kit Wong, Chan-Byoung Chae

Reconfigurable intelligent surface (RIS) has been recognized as a promising technique for the sixth generation (6G) of mobile communication networks. The key feature of RIS is to reconfigure the propagation environment via smart signal reflections. In addition, active RIS schemes have been recently proposed to overcome the deep path loss attenuation inherent

Edward B. Baker

We show that a class of previously defined maps, called self-dual and causal morphisms, form classical symmetries of Yang-Mills fields in four complex dimensions. These maps generalize conformal transformations, and admit a nonlocal pullback connection that preserves the equations of the theory. First it is shown that self-dual morphisms form symmetries of t

Ana Laura Boscolo, Valdir Barbosa da Silva Junior, Luiz Antonio Barreiro

The problem of a bouncing ball on a non-planar surface is investigated. We discovered that surface undulation adds a horizontal component to the impact force, which acquires a random character. Some aspects of Brownian motion are found in the horizontal distribution of the particle. On the x-axis, normal and super diffusion are observed. For the probability

Carla Farsi, Frederic Latremoliere, Judith Packer

In the context of metric geometry, we introduce a new necessary and sufficient condition for the convergence of an inductive sequence of quantum compact metric spaces for the Gromov-Hausdorff propinquity, which is a noncommutative analogue of the Gromov-Hausdorff distance for compact metric spaces. This condition is easy to verify in many examples, such as q

Peter Bürgisser

Consider a system $f_1(x)=0,\ldots,f_n(x)=0$ of $n$ random real polynomials in $n$ variables, where each $f_i$ has a prescribed set of exponent vectors described by a set $A_i \subseteq \mathbb{Z}^n$ of cardinality $t_i$, whose convex hull is denoted $P_i$. Assuming that the coefficients of the $f_i$ are independent standard Gaussian, we prove that the expec

Vladimir Sauli

We extract the spectral function of quarks using the QCD functional formalism.The reliability of approximations is controlled by the masses and decays of pseudoscalar mesons. For light quarks, we select the pion; for charm quarks, we choose the ground and excited states of the $\eta_c$ meson. To this end, we solved the ladder-rainbow approximation of the spe

Ricardo Muñoz-Cancino, Cristián Bravo, Sebastián A. Ríos, Manuel Graña

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present

Alan J. Cain, Ricardo P. Guilherme, António Malheiro

The hypoplactic monoid was introduced by Krob and Thibon through a presentation and through quasi-ribbon tableaux and an insertion algorithm. Just as Kashiwara crystals enriched the structure of the plactic monoid and allowed its generalization, the first and third authors of this paper introduced a construction of the hypoplactic monoid by identifying verti

Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo, Christos Faloutsos

Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among node classes, and (c) exploit GNE efficiently to improve the performance on downstream tasks? The knowledge of GNE is valuable for various tasks like node classification, and targete

Ali Abedi, Haofan Lu, Alex Chen, Charlie Liu

WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and ke

Maxwell McManus, Yuqing Cui, Josh, Zhang

In existing wireless networks, the control programs have been designed manually and for certain predefined scenarios. This process is complicated and error-prone, and the resulting control programs are not resilient to disruptive changes. Data-driven control based on Artificial Intelligence and Machine Learning (AI/ML) has been envisioned as a key technique

Brad Rodgers, Harshith Sai Vallabhaneni

We find closed formulas for arbitrarily high mixed moments of characteristic polynomials of the Alternative Circular Unitary Ensemble (ACUE), as well as closed formulas for the averages of ratios of characteristic polynomials in this ensemble. A comparison is made to analogous results for the Circular Unitary Ensemble (CUE). Both moments and ratios are studi

David A. King, Randall D. Kamien

Not every particle that forms a nematic liquid crystal makes a smectic. The particle tip is critical for this behaviour. Ellipsoids do not make a smectic, but sphero-cylinders do. Similarly, only those N-CB alkylcyanobiphenyls with sufficiently long ($N\ge 8$ carbons) alkane tails form smectics. We understand the role of the particle tip in the smectic trans

Eva Miranda, Arnau Planas

This monograph explores classification and perturbation problems for integrable systems on a class of Poisson manifolds called $b^m$-Poisson manifolds. Even if the class of $b^m$-Poisson manifolds is not ample enough to represent general Poisson manifolds, this investigation can be seen as a first step for the study of perturbation theory for general Poisson

Yuqi Fang, Pew-Thian Yap, Weili Lin, Hongtu Zhu

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different domains. Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and t

Piyush Batra, Gagan Raj Singh, Neeraj Goyal

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground

Philippe Cieutat

We give an extension of Bochner's criterion for the almost periodic functions. By using our main result, we extend two results of A. Haraux. The first is a generalization of Bochner's criterion which is useful for periodic dynamical systems. The second is a characterization of periodic functions in term of Bochner's criterion.

Nabeel Gillani, Rebecca Eynon, Catherine Chiabaut, Kelsey Finkel

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations-many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper

Kohei Suzuki

We construct a strongly local symmetric Dirichlet form on the configuration space $\Upsilon$ whose symmetrising (thus also invariant) measure is $\mathsf{sine}_\beta$, which is the law of the sine $\beta$ ensemble for every $\beta>0$. For every $\beta>0$, this Dirichlet form satisfies the Bakry-\'Emery gradient estimate $\mathsf{BE}(K, \infty)$ with $K=0$. T

A. Jain, P. K. Sharma, S. K. Jain, J. K. Deegwal

The possibility of cluster emission from trans-lead (86$\leq$Z$\leq$96) region of periodic chart has been explored comprehensively by employing few empirical formulas which are modified by adding angular momentum ($l$) or isospin-dependent ($I=(N-Z)/A$) or both terms for the calculation of cluster decay half-lives. These modified versions of the formulas are

Lang Liu, Zaid Harchaoui

This paper revisits a fundamental problem in statistical inference from a non-asymptotic theoretical viewpoint $\unicode{x2013}$ the construction of confidence sets. We establish a finite-sample bound for the estimator, characterizing its asymptotic behavior in a non-asymptotic fashion. An important feature of our bound is that its dimension dependency is ca

Hao-Tse Huang, Wenbin Lu

The rate of observable tidal disruption events (TDEs) by the most massive black holes (BHs) is suppressed due to direct capture of stars by the event horizon. This suppression effect depends on the shape of the horizon and holds the promise of probing the spin distribution of dormant BHs at the centers of galaxies. By extending the frozen-in approximation co

Narges Sereshti, Merve Bodur, James R. Luedtke

We consider a multi-stage stochastic lot-sizing problem with service level constraints and supplier-driven product substitution. A firm has multiple products and it has the option to meet demand from substitutable products at a cost. Considering the uncertainty in future demands, the firm wishes to make ordering decisions in every period such that the probab

Haytham Al Ewaidat, Youness El Brag

Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neura

Evan Deddo, Leopoldo A. Pando Zayas, Christoph F. Uhlemann

We quantitatively address the following question: for a QFT which is partially compactified, so as to realize an RG flow from a $D$-dimensional CFT in the UV to a $d$-dimensional CFT in the IR, how does the entanglement entropy of a small spherical region probing the UV physics evolve as the size of the region grows to increasingly probe IR physics? This ent

Onat Arısoy, Jen-Tsung Hsiang, Bei-Lok Hu

Entanglement being a foundational cornerstone of quantum sciences and the primary resource in quantum information processing, understanding its dynamical evolution in realistic conditions is essential. Unfortunately, numerous model studies show that degradation of entanglement from a quantum system's environment, especially thermal noise, is almost unavoidab

Parakh M. Gupta, Eric Pairet, Tiago Nascimento, Martin Saska

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to la

Chengbo Yuan, Qianhui Xu, Yong Luo

Depression is a leading cause of death worldwide, and the diagnosis of depression is nontrivial. Multimodal learning is a popular solution for automatic diagnosis of depression, and the existing works suffer two main drawbacks: 1) the high-order interactions between different modalities can not be well exploited; and 2) interpretability of the models are wea

Ido Kaplan, Muhammad Erew, Yonatan Piasetzky, Moshe Goldstein

Error mitigation schemes and error-correcting codes have been the center of much effort in quantum information processing research over the last few decades. While most of the successful proposed schemes for error mitigation are perturbative in the noise and assume deterministic systematic errors, studies of the problem considering the full noise and errors

Sagar Sharma, Yuechun Gu, Keke Chen

Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing solutions are either too expensive to be practical or do not sufficiently protect the confidentiality of data and models. In t

Victor Y. Pan, Soo Go, Qi Luan, Liang Zhao

We approximate the d complex zeros of a univariate polynomial p(x) of a degree d or those zeros that lie in a fixed region of interest on the complex plane such as a disc or a square. Our divide and conquer algorithm of STOC 1995 supports solution of this problem in optimal Boolean time (up to a poly-logarithmic factor), that is, runs nearly as fast as one c

Maria Nareklishvili, Nicholas Polson, Vadim Sokolov

In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available with conventional methods. In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characte

Jiaqi Geng, Dong Huang, Fernando De la Torre

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, nee

Vladimir Markovic, Nathaniel Sagman

We establish the new main inequality as a minimizing criterion for minimal maps to products of $\mathbb{R}$-trees, and the infinitesimal new main inequality as a stability criterion for minimal maps to $\mathbb{R}^n$. Along the way, we develop a new perspective on destabilizing minimal surfaces in $\mathbb{R}^n$, and as a consequence we reprove the instabili

Thomas Dierckx, Jesse Davis, Wim Schoutens

In this study, we predict next-day movements of stock end-of-day implied volatility using random forests. Through an ablation study, we examine the usefulness of different sources of predictors and expose the value of attention and sentiment features extracted from Twitter. We study the approach on a stock universe comprised of the 165 most liquid US stocks

Sayantan Dutta, Adrian Basarab, Bertrand Georgeot, Denis Kouamé

This paper presents a deep neural network called DIVA unfolding a baseline adaptive denoising algorithm (De-QuIP), relying on the theory of quantum many-body physics. Furthermore, it is shown that with very slight modifications, this network can be enhanced to solve more challenging image restoration tasks such as image deblurring, super-resolution and inpai

Henry Adams, Johnathan Bush, Nate Clause, Florian Frick

We explore emerging relationships between the Gromov--Hausdorff distance, Borsuk--Ulam theorems, and Vietoris--Rips simplicial complexes. The Gromov--Hausdorff distance between two metric spaces $X$ and~$Y$ can be lower bounded by the distortion of (possibly discontinuous) functions between them. The more these functions must distort the metrics, the larger

Francesco Reda, Marcella Salvatore, Marco Astarita, Fabio Borbone

Holographic technologies have the potentiality to impact our everyday life in many sectors including science, education, entertainment, art, and healthcare. Although holographic screens and projectors are part of common imagination since long time, they are still at initial stages of development and integration. Recent achievements of metasurface and flat op

Grigorii Konovalov

This paper originated as an attempt to answer a question: what are the natural derived structures on Poisson degeneracy loci? We argue that the question could be possibly answered via a construction of differential graded operads that ``naturally'' act on the degeneracy loci. For each $m \ge 0$, we suggest what looks like a reasonable condition for a Poisson

Florian Kofler, Johannes Wahle, Ivan Ezhov, Sophia Wagner

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-opt

C. Vignat

This is a journey through integrals of involutions and surprising consequences of the Lagrange inversion theorem. On the way, we meet unexpected logarithmic identities, hypergeometric functions with a linear regime and other mysterious objects. This study was inspired by some results from the fascinating article A.E. Holroyd, T.M. Liggett and D. Romik, Integ

Moise Blanchard, Steve Hanneke, Patrick Jaillet

We consider the contextual bandit problem on general action and context spaces, where the learner's rewards depend on their selected actions and an observable context. This generalizes the standard multi-armed bandit to the case where side information is available, e.g., patients' records or customers' history, which allows for personalized treatment. We foc

Abhishek Anand, Songyang Pu, G J Sreejith

A short-ranged, rotationally symmetric multi-Landau-level model Hamiltonian for strongly interacting electrons in a magnetic field was proposed [A. Anand et al, Phys. Rev. Lett. 126, 136601 (2021)] with the key feature that it allows exact many-body eigenfunctions on the disk not just for quasiholes but for all charged and neutral excitations of the entire J

Giacomo Zuccarini, Marisa Michelini

We describe the development of a course of quantum mechanics for secondary school designed to address the challenges related to the revision of classical knowledge, to the building of a well-organized knowledge structure on the discipline, and to the development of a plausible picture of the quantum world. The course is based on a systemic approach to concep

Khalid Zguaid, Fatima-Zahrae El Alaoui, Delfim F. M. Torres

We investigate the regional gradient observability of fractional sub-diffusion equations involving the Caputo derivative. The problem consists of describing a method to find and recover the initial gradient vector in the desired region, which is contained in the spacial domain. After giving necessary notions and definitions, we prove some useful characteriza

Isa E. Hafalir, Fuhito Kojima, M. Bumin Yenmez, Koji Yokote

We provide optimal solutions to an institution that has distributional objectives when choosing from a set of applications based on merit (or priority). For example, in college admissions, administrators may want to admit a diverse class in addition to choosing students with the highest qualifications. We provide a family of choice rules that maximize merit

Sandipan Sarma, Arijit Sur

Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods. In this work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I) which, given an attribute-based dataset, can intelligently yield

Feiyu Chen

In this work we study a more restricted class of quasi-topological gravity theories where the higher curvature terms have no contribution to the equation of motion on general static spherically symmetric metric where $g_{tt} g_{rr} \ne \mathrm{constant}$. We construct such theories up to quintic order in Riemann tensor and observe an important property of th

Wei-Liang Qian, Pan-Pan Wang, Zhang-Qi Wu, Cheng-Gang Shao

Inspired by the combinatorial algebraic approach proposed by Dhurandhar {\it et al.}, we propose two novel classes of second-generation time-delay interferometry (TDI) solutions and their further generalization. The primary strategy of the algorithm is to enumerate specific types of residual laser frequency noise associated with second-order commutators in p

Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng

With the increasing capabilities of large language models (LLMs), in-context learning (ICL) has emerged as a new paradigm for natural language processing (NLP), where LLMs make predictions based on contexts augmented with a few examples. It has been a significant trend to explore ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to s

Maor Ben-Shahar, Lucia Garozzo, Henrik Johansson

Scattering amplitudes in Yang-Mills theory are known to exhibit kinematic structures which hint to an underlying kinematic algebra that is dual to the gauge group color algebra. This color-kinematics duality is still poorly understood in terms of conventional Feynman rules, or from a Lagrangian formalism. In this work, we present explicit Lagrangians whose F

Isa E. Hafalir, Fuhito Kojima, M. Bumin Yenmez

Given an initial matching and a policy objective on the distribution of agent types to institutions, we study the existence of a mechanism that weakly improves the distributional objective and satisfies constrained efficiency, individual rationality, and strategy-proofness. We show that such a mechanism need not exist in general. We introduce a new notion of

Dipankar Das, Miguel Levy, Palash B. Pal, Anugrah M. Prasad

We study two novel aspects of democratic 3HDMs -- the custodial limit and the possibility of wrong-sign Yukawa couplings. In the custodial limit, the democratic 3HDMs can easily negotiate the constraints from the electroweak $T$-parameter. We also uncover the possibility of having wrong-sign Yukawa couplings in democratic 3HDMs, as in the case of 2HDMs. We s

Xin Ma, Chang Liu, Chunyu Xie, Long Ye

Masked image modeling (MIM) has shown great promise for self-supervised learning (SSL) yet been criticized for learning inefficiency. We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, we introduce a conceptually simple yet learning-efficient MIM training scheme, termed Disjoint Masking with Joint Dist

V. Hocdé, R. Smolec, P. Moskalik, O. Ziółkowska

Estimating metallicity of classical Cepheids is of prime importance for studying metallicity effect on stellar evolution, chemical evolution of galaxies, and ultimately its impact on period-luminosity relation used in the extragalactic distance scale. We aim at establishing new empirical relations for estimating the iron content of classical Cepheids for sho

Alexander Schlüter, Henning Müller, Sikang Yan, Erik Faust

The Lattice Boltzmann Method (LBM), e.g. in [ 1] and [2 ], can be interpreted as an alternative method for the numerical solution of partial differential equations. Consequently, although the LBM is usually applied to solve fluid flows, the above interpretation of the LBM as a general numerical tool, allows the LBM to be extended to solid mechanics as well.

Susanna Terracini, Giorgio Tortone, Stefano Vita

As a first result we prove higher order Schauder estimates for solutions to singular/degenerate elliptic equations of type: \[ -\mathrm{div}\left(\rho^aA\nabla w\right)=\rho^af+\mathrm{div}\left(\rho^aF\right) \quad\textrm{in}\; \Omega \] for exponents $a>-1$, where the weight $\rho$ vanishes in a non degenerate manner on a regular hypersurface $\Gamma$ whic

Xin Meng, Youwei Zhang, Xichang Zhang, Shenchao Jin

We propose a novel paradigm to vector magnetometry based on machine learning. Unlike conventional schemes where one measured signal explicitly connects to one parameter, here we encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals, i.e., the oscillating optical rotation signal's harmonics of a frequency m

Fabian Bleitner, Camilla Nobili

We consider two-dimensional Rayleigh-B\'enard convection with Navier-slip and fixed temperature boundary conditions at the two horizontal rough walls described by the height function $h$. We prove rigorous upper bounds on the Nusselt number $\text{Nu}$ which capture the dependence on the curvature of the boundary $\kappa$ and the (non-constant) friction coef

Valentina Kiritchenko

We construct simple geometric operations on faces of the Cayley sum of two polytopes. These operations can be thought of as convex geometric counterparts of divided difference operators in Schubert calculus. We show that these operations give a uniform construction of Knutson-Miller mitosis (in type A) and (simplified) Fujita mitosis (in type C) on Kogan fac

Anup Biswas, Vivek S. Borkar

Risk-sensitive control has received considerable interest since the seminal work of Howard and Matheson [120] because of its ability to account for fluctuations about the mean, its connection with $H_\infty$ control, and its application to financial mathematics. In this article, we attempt to put together a comprehensive survey on the research done on ergodi

Ahlam Alqasim, Mat J. Page

Almost all massive galaxies today are understood to contain supermassive black holes (SMBH) at their centers. SMBHs grew by accreting material from their surroundings, emitting X-rays as they did so. X-ray Luminosity Functions (XLFs) of Active Galactic Nuclei (AGN) have been extensively studied in order to understand the AGN population's cosmological propert