Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating discrete dynamic strategies in affine models

We consider the problem of measuring the performance of a dynamic strategy, re-balanced at a discrete set of dates, with the objective of hedging a claim in an incomplete market driven by a general multi-dimensional affine process. The main purpose of the paper is to propose a method to efficiently compute the expected value and variance of the hedging error of the strategy. Representing the payoff of the claim as an inverse Laplace transform, we are able to obtain semi-explicit formulas for strategies satisfying a certain property. The result is quite general and can be applied to a very rich class of models and strategies, including Delta hedging. We provide illustrations for the case of the Heston stochastic volatility model.
Adder based residue to binary converters for a new balanced 4-moduli set

In this paper, an efficient residue to binary conversion algorithm is derived for a new four-moduli set {2/sup n/-1, 2/sup n/, 2/sup n/+1, 2/sup n-1/-1} with large dynamic range and well-balanced moduli. Our algorithm exploits the special periodicity properties of the proposed moduli set to obtain hardware efficacy. By using the most efficient residue to binary conversion of the three-moduli set {2/sup n/-1, 2/sup n/, 2/sup n/+1}, the proposed algorithm can be reduced to a simpler two moduli set RNS where fast mixed-radix conversion can be used to obtain the weighted binary number. Four different adder-based architectures are proposed, all are pipelinable to achieve very high throughput rate, and all are more efficient than the residue to binary converters for other four-moduli sets in terms of area and delay.
transformer under short-circuit fault conditions: A multiphysics approach How to evaluate the robustness of a transformer considering currents and Laplace forces under the short-circuit fault conditions?

Transformers’ windings experience mechanical loads from electromagnetic forces due to the currents they carry. During normal operation, the resulting stresses and strains have minor influence, therefore they do not represent the significant risk to the devices’ integrity. However, transformers can suffer from high sudden short-circuit currents that are several times higher than those during the normal operation. These short-circuit currents are a significant threat, not only from an electrical but also from the structural integrity point of view. In this paper, coupled electromagnetic and structural mechanics simulations are carried out to evaluate short-circuit fault risks in a comprehen-sive and accurate way.
Chandra Survey of Nearby Galaxies: The Catalog

We searched the public archive of the Chandra X-ray Observatory as of 2016 March and assembled a sample of 719 galaxies within 50 Mpc with available Advanced CCD Imaging Spectrometer observations. By cross-correlation with the optical or near-infrared nuclei of these galaxies, 314 of them are identified to have an X-ray active galactic nucleus (AGN). The majority of them are low-luminosity AGNs and are unlikely X-ray binaries based upon their spatial distribution and luminosity functions. The AGN fraction is around 60% for elliptical galaxies and early-type spirals, but drops to roughly 20% for Sc and later types, consistent with previous findings in the optical. However, the X-ray survey is more powerful in finding weak AGNs, especially from regions with active star formation that may mask the optical AGN signature. For example, 31% of the H ii nuclei are found to harbor an X-ray AGN. For most objects, a single power-law model subject to interstellar absorption is adequate to fit the spectrum, and the typical photon index is found to be around 1.8. For galaxies with a non-detection, their stacked Chandra image shows an X-ray excess with a luminosity of a few times 1037 erg s−1 on average around the nuclear region, possibly composed of faint X-ray binaries. This paper reports on the technique and results of the survey; in-depth analysis and discussion of the results will be reported in forthcoming papers.
Dipole Anisotropy in the COBE DMR First-Year Sky Maps

We present a determination of the cosmic microwave background dipole amplitude and direction from the COBE Di(cid:11)erential Microwave Radiometers (DMR) (cid:12)rst year of data. Data from the six DMR channels are consistent with a Doppler-shifted Planck function of dipole amplitude (cid:1)T = 3:365 (cid:6) 0:027 direction (l II ;b II ) The implied velocity of the Local Group with respect to the CMB rest frame is ~vLG = 627 (cid:6) 22 km s (cid:0)1 toward (l II ;b II ) = (276 (cid:14) (cid:6) 3 (cid:14) ;30 (cid:14) (cid:6) 3 (cid:14) ). DMR has also mapped the dipole anisotropy resulting from the Earth's orbital motion about the Solar system barycenter, yielding a measurement of the monopole CMB temperature T0 at 31.5, 53, and 90 GHz, T0 = 2:75 (cid:6) 0:05 K.
Fault Diagnosis of Transformer Winding Based on VMD-SVM

Considering the nonlinear and non-stationary characteristics of the transformer vibration acceleration signal obtained from the surface of the transformer tank, the variational mode decomposition (VMD) theory is introduced. Simulation analysis shows that the VMD decomposition has obvious advantages over EMD when the needle frequency is similar to the signal. It effectively avoids two types of modal aliasing and over-decomposition, and accurately reflects the characteristics of the source signal. Aiming at the problem that the two core parameters of the support vector machine are difficult to determine, the Pareto particle swarm method is used to perform multi-objective parallel optimization of the two core parameters of the support vector machine to obtain the optimal parameters. The VMD-SVM fault diagnosis model is tested using the transformer instance fault data, and compared with the other two methods. The instance test results show that the VMD-SVM proposed in this paper has the highest diagnostic accuracy and realizes the latent fault of the power transformer winding. accurate diagnosis.
Nonperturbative Effective Actions of N=2 Supersymmetric Gauge Theories

We elaborate on our previous work on N=2 supersymmetric Yang-Mills theory. In particular, we show how to explicitly determine the low energy quantum effective action for G = SU(3) from the underlying hyperelliptic Riemann surface, and calculate the leading instanton corrections. This is done by solving Picard-Fuchs equations and asymptotically evaluating period integrals. We find that the dynamics of the SU(3) theory is governed by an Appell system of type F4, and compute the exact quantum gauge coupling explicitly in terms of Appell functions. CERN-TH/95-104 May 1995 a Work supported in part by GIF the German-Israeli Foundation for Scientific Research
Intelligent Electrical Engineering Automation on Account of Particle Swarm Optimization (PSO) Algorithm

Electrical engineering is closely related to people’s life. Many projects have proved that electrical engineering using PSO algorithm provides more convenient applications for people’s production and life, greatly improves people’s living standards, and makes people’s life and work more relaxed and valid. In many production fields, such as subway engineering, if advanced electrical engineering automation is adopted, the subway engineering can be carried out validly from start to finish, and the workload of workers can be greatly reduced. PSO algorithm further improves the automation performance of electrical engineering, and effectively breaks through the traditional electrical engineering operation mode, improve the operation efficiency. This text studies the concept and work flow of PSO algorithm, introduces the intelligent working principle and internal mechanism of electrical engineering. Data analysis, intelligent electrical engineering automation on account of PSO algorithm effectively improve the intelligent effect of electrical engineering automation.
Oracle-guided Contrastive Clustering

Deep clustering aims to learn a clustering representation through deep architectures. Most of the existing methods usually conduct clustering with the unique goal of maximizing clustering performance, that ignores the personalized demand of clustering tasks. % and results in unguided clustering solutions. However, in real scenarios, oracles may tend to cluster unlabeled data by exploiting distinct criteria, such as distinct semantics (background, color, object, etc.), and then put forward personalized clustering tasks. To achieve task-aware clustering results, in this study, Oracle-guided Contrastive Clustering(OCC) is then proposed to cluster by interactively making pairwise ``same-cluster"queries to oracles with distinctive demands. Specifically, inspired by active learning, some informative instance pairs are queried, and evaluated by oracles whether the pairs are in the same cluster according to their desired orientation. And then these queried same-cluster pairs extend the set of positive instance pairs for contrastive learning, guiding OCC to extract orientation-aware feature representation. Accordingly, the query results, guided by oracles with distinctive demands, may drive the OCC's clustering results in a desired orientation. Theoretically, the clustering risk in an active learning manner is given with a tighter upper bound, that guarantees active queries to oracles do mitigate the clustering risk. Experimentally, extensive results verify that OCC can cluster accurately along the specific orientation and it substantially outperforms the SOTA clustering methods as well. To the best of our knowledge, it is the first deep framework to perform personalized clustering.
On Lagrangian tori in K3 surfaces

Every Maslov-zero Lagrangian torus in a K3 surface has non-trivial homology class. The purpose of this note is to extend this result to Lagrangian tori with even Maslov indices. On the other hand, it is shown that every homologically non-trivial Lagrangian torus is Maslov-zero.
Remaining parts of the long-standing 
J/ψ
 polarization puzzle

Based on the nonrelativistic quantum chromodynamics factorization formalism, the polarization parameters λθϕ and λϕ of prompt J/ψ hadroproduction for CMS and LHCb are analyzed in the helicity frame and calculated at QCD next-to-leading order for the first time. For prompt J/ψ production, we take into account the feed-down contributions from χcJ and ψ(2S) decays. The theoretical predictions for the polarization parameters λθϕ and λϕ of J/ψ are presented. Using the theoretical results, we perform a simultaneous fit to the experimental measurements of the yield and polarization of J/ψ hadroproduction, and find that the results describe the experimental measurements at the LHC quite well.
The Numerical Stability of Hyperbolic Representation Learning

Given the exponential growth of the volume of the ball w.r. t. its radius, the hyperbolic space is capable of embedding trees with arbitrarily small distortion and hence has received wide attention for representing hierarchical datasets. However, this exponential growth property comes at a price of numerical instability such that training hyperbolic learning models will sometimes lead to catastrophic NaN problems, encountering unrepresentable values in floating point arithmetic. In this work, we carefully analyze the limitation of two popular models for the hyperbolic space, namely, the Poincar\'e ball and the Lorentz model. We first show that, under the 64 bit arithmetic system, the Poincar\'e ball has a relatively larger capacity than the Lorentz model for correctly representing points. Then, we theoretically validate the superiority of the Lorentz model over the Poincar\'e ball from the perspective of optimization. Given the numerical limitations of both models, we identify one Euclidean parametrization of the hyperbolic space which can alleviate these limitations. We further extend this Euclidean parametrization to hyperbolic hyperplanes and exhibits its ability in improving the performance of hyperbolic SVM.
RF-Chord: Towards Deployable RFID Localization System for Logistic Networks

RFID localization is considered the key enabler of automating the process of inventory tracking and management for high-performance logistic network. A practical and deployable RFID localization system needs to meet reliability, throughput, and range requirements. This paper presents RF-Chord, the first RFID localization system that simultaneously meets all three requirements. RF-Chord features a one-shot multisine-constructed wideband design that can process RF signal with a 200 MHz bandwidth in real-time to facilitate one-shot localization at scale. In addition, multiple SINR enhancement techniques are designed for range extension. On top of that, a kernel-layer-based near-field localization framework and a multipath-suppression algorithm are proposed to reduce the 99% long-tail errors. Our empirical results show that RF-Chord can localize more than 180 tags 6 m away from a reader within 1 second and with 99% long-tail error of 0.786 m, achieving a 0% miss reading rate and ~0.01% cross-reading rate in the warehouse and fresh food delivery store deployment.
Convergence of policy gradient methods for finite-horizon stochastic linear-quadratic control problems

We study the global linear convergence of policy gradient (PG) methods for finite-horizon continuous-time exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the state variable and whose covariance is state-independent. Contrary to discrete-time problems, the cost is noncoercive in the policy and not all descent directions lead to bounded iterates. We propose geometry-aware gradient descents for the mean and covariance of the policy using the Fisher geometry and the Bures-Wasserstein geometry, respectively. The policy iterates are shown to satisfy an a-priori bound, and converge globally to the optimal policy with a linear rate. We further propose a novel PG method with discrete-time policies. The algorithm leverages the continuous-time analysis, and achieves a robust linear convergence across different action frequencies. A numerical experiment confirms the convergence and robustness of the proposed algorithm.
A Damped Newton Method Achieves Global $O\left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate

In this paper, we present the ﬁrst stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an O (cid:0) 1 k 2 (cid:1) global rate, which matches the state-of-the-art global rate of cubically regularized Newton method of Polyak and Nesterov (2006) and of regularized Newton method of Mishchenko (2021) and Doikov and Nesterov (2021), b) we prove a local quadratic rate, which matches the best-known local rate of second-order methods, and c) our stepsize formula is simple, explicit, and does not require solving any subproblem. Our convergence proofs hold under afﬁne-invariance assumptions closely related to the notion of self-concordance. Finally, our method has competitive performance when compared to existing baselines, which share the same fast global convergence guarantees.
Tensionless limit of super-strings and its Majorana condition

We compute the deformation of the spinor fields of the super-Polyakov action leading to the so-called tensionless super-string theory. This allows us to obtain a satisfying Majorana condition in the tensionless limit, which is the main result of this paper. Our tensionless limit is shown to possess previously computed features of tensionless super-string theory.
Un ambiente integrado de ejecución para determinar la satisfacibilidad de fórmulas en forma normal conjuntiva

The satisfiability problem (SAT) is a classical NP-complete problem of useful application in plenty of computer science areas, electric engineering, and mathematics. In this work we present an integration of all the components of the DPLL system enhanced with Neural Networks (DPLL NN) in order to create a user friendly execution environment. One of the main components of DPLL NN is an algorithm that is capable of solving some instances of the 3SAT problem, a well-knownNP-complete problem derived from SAT. DPLL NN combines the Davis-Putnam rules with neural networks which are capable enough to learn complex problems. The environment proposed allows the user determine the satisfiability of a particular formula either randomly or manually generated. In addition, the environment provides a graphical tree representation of the corresponding assignation values that satisfy that formula (if it can be satisfied). Resumen El problema de satisfacibilidad (SAT), el primero en demostrarse que es NP-completo, es fundamental en muchos campos de las ciencias de la computacion, la ingenieria electrica y matematica. En el presente trabajo se integran todas las componentes del sistema DPLL con Redes Neuronales (DPLL RN), para crear un ambiente de ejecucion amigable al usuario. Una de las componentes fundamentales del sistema DPLL RN consta de un algoritmo que permite resolver algunas instancias del problema 3SAT, un conocido problema NP-completo derivado de SAT. El sistema DPLL RN combina las reglas de Davis-Putnam con redes neuronales (DPRN), las cuales tienen una gran capacidad para aprender problemas complejos. El ambiente propuesto permite al usuario determinar la satisfacibilidad o no de una formula en logica proposicional, permitiendole ingresar una formula cualquiera o generar una aleatoriamente y luego se refleja en un arbol el camino seguido para obtener las asignaciones que hacen verdadera a dicha formula si tales asignaciones existen.
Automated Imbalanced Learning

Automated Machine Learning has grown very successful in automat-ing the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when the data is imbalanced. Since many real-world datasets are naturally imbalanced, and improper handling of this issue can lead to quite useless models, this issue should be handled carefully. This paper ﬁrst intro-duces a new benchmark to study how diﬀerent AutoML methods are aﬀected by label imbalance. Second, we propose strategies to better deal with imbalance and integrate them into an existing AutoML framework. Finally, we present a systematic study which evaluates the impact of these strategies and ﬁnd that their inclusion in AutoML systems signiﬁcantly increases their robustness against label imbalance.
Truly Lorentzian quantum cosmology

Quantum cosmology based on Lorentzian path integrals is a promising avenue. However, many previous works allow non-Lorentzian configurations by integrating the squared scale factor over the whole real line. Here we show that restricting the minisuperspace path integral to Lorentzian configurations with positive squared scale factor can significantly change the expectation values. In addition, this enables the study of causal horizons and their quantum fluctuations, and achieves singularity avoidance trivially by excluding singular minisuperspace geometries as non-Lorentzian. The results indicate that semiclassical saddle point approximation is not always valid in truly Lorentzian quantum cosmology. As a consequence, related works on the tunnelling and no-boundary proposals, bouncing cosmology, and the quantum origin of inflation etc. need to be reexamined.
Titan: Bringing the Deep Image Prior to Implicit Representations

We study the interpolation capabilities of implicit neural representations (INRs) of images. In principle, INRs promise a number of advantages, such as continuous derivatives and arbitrary sampling, being freed from the restrictions of a raster grid. However, empirically, INRs have been observed to poorly interpolate between the pixels of the fit image; in other words, they do not inherently possess a suitable prior for natural images. In this paper, we propose to address and improve INRs' interpolation capabilities by explicitly integrating image prior information into the INR architecture via deep decoder, a specific implementation of the deep image prior (DIP). Our method, which we call TITAN, leverages a residual connection from the input which enables integrating the principles of the grid-based DIP into the grid-free INR. Through super-resolution and computed tomography experiments, we demonstrate that our method significantly improves upon classic INRs, thanks to the induced natural image bias. We also find that by constraining the weights to be sparse, image quality and sharpness are enhanced, increasing the Lipschitz constant.
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms

This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations of FL such as communication bottlenecks and statistical heterogeneity. Recently, Federated Averaging Langevin Dynamics (FALD) was introduced, which extends the Federated Averaging algorithm to Bayesian inference. We obtain a novel tight non-asymptotic upper bound on the Wasserstein distance to the global posterior for FALD. This bound highlights the effects of statistical heterogeneity, which causes a drift in the local updates that negatively impacts convergence. We propose a new algorithm VR-FALD* that uses control variates to correct the client drift. We establish non-asymptotic bounds showing that VR-FALD* is not affected by statistical heterogeneity. Finally, we illustrate our results on several FL benchmarks for Bayesian inference.
True Concurrency Can Be Easy

Step net bisimulation is a coinductive behavioral relation for finite Petri nets, which is a smooth generalization of the definition of standard step bisimulation \cite{NT84} on finite Petri nets. Its induced equivalence offers an alternative, much simpler characterization of causal-net bisimilarity \cite{G15,Gor22}, as it does not resort to any causality structure, and of structure-preserving bisimilarity \cite{G15}, as it does not require bijective mappings between related markings. We show that step net bisimilarity can be characterized logically by means of a suitable modal logic, called NML (acronym of net modal logic): two markings are step net bisimilar if and only if they satisfy the same NML formulae.
Fundamental Limits of Low-Rank Matrix Estimation with Diverging Aspect Ratios

We consider the problem of estimating the factors of a low-rank $n \times d$ matrix, when this is corrupted by additive Gaussian noise. A special example of our setting corresponds to clustering mixtures of Gaussians with equal (known) covariances. Simple spectral methods do not take into account the distribution of the entries of these factors and are therefore often suboptimal. Here, we characterize the asymptotics of the minimum estimation error under the assumption that the distribution of the entries is known to the statistician. Our results apply to the high-dimensional regime $n, d \to \infty$ and $d / n \to \infty$ (or $d / n \to 0$) and generalize earlier work that focused on the proportional asymptotics $n, d \to \infty$, $d / n \to \delta \in (0, \infty)$. We outline an interesting signal strength regime in which $d / n \to \infty$ and partial recovery is possible for the left singular vectors while impossible for the right singular vectors. We illustrate the general theory by deriving consequences for Gaussian mixture clustering and carrying out a numerical study on genomics data.