Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

This paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble.
A compactness result in $GSBV^p$ and applications to $\Gamma$-convergence for free discontinuity problems

We present a compactness result in the space $GSBV^p$ which extends the classical statement due to Ambrosio to problems without a priori bounds on the deformations. As an application, we revisit the $\Gamma$-convergence results for free discontinuity functionals established recently by Cagnetti, Dal Maso, Scardia, and Zeppieri. We investigate sequences of boundary value problems and show convergence of minimum values and minimizers.
Granger causality in mixed frequency time series

Kaiji Motegi: Granger Causality in Mixed Frequency Time Series (Under the direction of Eric Ghysels) It is a classic topic in time series econometrics to test Granger causality among multiple variables. While many Granger causality tests have been invented in the literature, they are often vulnerable to temporal aggregation which potentially generates or hides causality. Based on the growing literature of Mixed Data Sampling (MIDAS) analysis, this dissertation proposes a set of mixed frequency Granger causality tests which are robust against temporal aggregation. The mixed frequency causality tests take an explicit treatment of data sampled at different frequencies, and hence enable more accurate statistical inference than the conventional approach that aggregates all time series into the common lowest frequency. Depending on the magnitude of the ratio of sampling frequencies, this dissertation proposes two types of mixed frequency causality tests. The first one handles a small ratio of sampling frequencies like month vs. quarter. Exploiting Ghysels’ mixed frequency vector autoregressive (MF-VAR) models, we extend Dufour, Pelletier, and Renault’s VAR-based causality test to the mixed frequency context. We prove that the mixed frequency approach better recovers the underlying causal patterns than the existing low frequency approach. Moreover, we demonstrate via local asymptotic power analysis and simulations that the mixed frequency test has higher power than the low frequency test in both large sample and small sample. In an empirical application on U.S. macroeconomy, we show that the mixed frequency approach and the low frequency approach produce very different causal implications, with the former yielding more intuitive results. The second part of this dissertation deals with a relatively large ratio of sampling frequencies like month vs. year. Inspired by Sims’ regression-based causality tests, we develop a new test that achieves higher power than the conventional test in both large sample and small sample. In this framework, a larger ratio of sampling frequencies is likely to improve power since our
MFCC feature extraction of speech based on pitch period

This paper proposed a MFCC feature extraction method based on pitch period to improve the performance of speaker recognition.The original speech signals were decomposed into two parts: the speech within the length of multiple pitch periods and that out of the pitch period.The former part was reserved to minimize the spectral distortion.The improved MFCC feature extraction method was also used in the verification experiment of the text-independent speaker recognition.The result shows the recognition rate is improved.
Survey of Vision-based Automatic Incident Detection Technology

In this paper,video-based automatic incident detection technology is introduced,which is through the vehicle tracking and the motion features analysis of vehicles to determine the traffic incidents.According to three common traffic incidents,such as vehicle retrograde,vehicle stop and traffic jam,the modeling is made.The test results indicate that the detection method has good practical effect.
Watermarking scar as an ultimate copy protection

Strong and powerful attacks are able to make the watermark extraction impossible even if there is some watermarking. In this work, we call this residual information the scar and we use it to prove the existence of an attacked watermark. We evaluate the Scar by the mutual information between the embedded watermark and the attacked copies. It is well known that the mutual information between two random variables gives an evaluation of the shared information between the two variables. Thus, the attack can compromise the correct decoding by removing partialy the watermarking information. However, it is possible to make the watermarking existence proof possible when the mutual information between the embedded message and the attacked copies is above a certain threshold. We propose a practical way to use the scar by measuring the correlation between the attacked watermark and the original one.
Volterra-wiener Characterization Of A Recurrent Neural Network

This paper studies the relationship of the Volterra-Wiener theory of nonlinear systems to neural network modeling. A recursive formula to compute the Volterra kernels of a well known recurrent neural network is presented here. Expressing the neural network input-output relationship in this framework allows addressing issues like generalization, functional representation power, and optimal choice of the neurons activation function. We also address the issue of neural network realization of a subclass of systems admitting Volterra expansions.
Face shape processing via visual-to-auditory sensory substitution activates regions within the face processing networks in the absence of visual experience

Previous evidence suggests that visual experience is crucial for the emergence and tuning of the typical neural system for face recognition. To challenge this conclusion, we trained congenitally blind adults to recognize faces via visual-to-auditory sensory-substitution (SDD). Our results showed a preference for trained faces over other SSD-conveyed visual categories in the fusiform gyrus and in other known face-responsive-regions of the deprived ventral visual stream. We also observed a parametric modulation in the same cortical regions, for face orientation (upright vs. inverted) and face novelty (trained vs. untrained). Our results strengthen the conclusion that there is a predisposition for sensory-independent and computation-specific processing in specific cortical regions that can be retained in life-long sensory deprivation, independently of previous perceptual experience. They also highlight that if the right training is provided, such cortical preference maintains its tuning to what were considered visual-specific face features.
Transport service quality and perceived service value: an international comparative survey in European and Indian context

Road transportation plays a critical role as a means to commute between places. Ensuring quality in this service, therefore, is crucial. In this paper, we explore the service quality characteristics of intercity bus passenger transport and analyse the determinants of commuters' perceived service value in the European and Indian context. A structured questionnaire was administered face-to-face and captured the passengers' perception of service quality. Factor analysis and multiple linear regression were used to test the research hypotheses of the study. Empirical results demonstrate that timely service, women-friendliness, ticket price affordability and service to price satisfaction are common to passengers from Europe and India, and significantly impact commuters' perceived service value devised as overall satisfaction. In particular, external tangibles such as clean drinking water, clean bus stops are however significantly more important for passengers in Europe, while handling of luggage is a more appropriate tangible for passengers in the Indian context. Additional analysis suggests that technology also significantly impacts the overall satisfaction of passengers in both contexts. Several comparative results were discussed for their context similarities and differences.
The structure of children’s writing: moving from spoken to adult written norms

Treebanking work, in which a consistent system of grammatical annotation is applied to samples of the spoken and written output of cross-sections of the British population at different ages, is opening up the possibility of studying the route taken by children from mastery of the spoken language towards mastery of the somewhat different structural norms characteristic of skilled writing. Perhaps not surprisingly, it appears that children adapt to adult written norms sooner with respect to phrase than to clause usage. A less predictable finding is that use of relative clauses, and constructions logically equivalent to relative clauses, matures earlier than usage of other categories of subordinate clause, despite the fact that relatives can be strikingly complex in terms of their formal properties.
Picture/Graphics Classification Using Intensity Features

Classification of images with different rendering intents is required in the adaptive gamut mapping technics in computer color management. From the statistic of image intensity histogram,this paper presents an picture/graphics classification algorithm based on such image intensity features as intensity levels and intensity histogram smoothness. The algorithm can help to determine whether a image is a natural picture or a synthetic graphic.
Probabilistic Model of Technical Queuing Systems with Subsystems for Detection and Recovery of Failures

The given article presents an analytical model developed for a multichannel technical queuing system with time redundancy for operability management, subsystems of reliable periodical control of sudden and gradual failure detection and a subsystem for system recovery after failure. It is assumed in the model that the failures are governed by the Poisson probability distribution law and the times of reliable control and system recovery after failures are governed by an arbitrary law of probability distribution, in the general case depending on the number of servicers. The paper defines probabilities of accomplishment of certain task volumes by the system within a priori defined time intervals, taking into consideration its reliability characteristics.
Measuring Knowledge Translation Uptake Using Citation Metrics: A Case Study of a Pan-Canadian Network of Pharmacoepidemiology Researchers

ABSTRACT Collecting citation metric data is important, as research funders are increasingly demanding impact assessment, but there is limited consensus on the most rigorous and accurate approach. We compared three sources of citation counts (Google Scholar, Web of Science, Scopus) to determine their reliability, comprehensiveness, and currency. We identified each tool’s strengths and limitations, particularly when considering team outputs. Citation counts varied, with poor overall agreement: Fleiss’ kappa, 0.075 (95% CI [0.01, 0.12]). Researchers, funders, and administrators need to understand each tool’s unique strengths and limitations and develop guidelines for use within specific contexts.
Predicting Drivers of Mobile Entertainment Adoption: A Two-Stage SEM-Artificial-Neural-Network Analysis

ABSTRACT This study aims to understand users’ motivations to adopt mobile entertainment (m-entertainment). Extending the Technology Acceptance Model (TAM), this study examined the effects of trust, perceived financial cost (PFC), and quality of the service on consumers’ decision in adopting the m-entertainment. Survey data were collected from 524 mobile users and analyzed using both structural equation modeling (SEM) and neural network (NN) . The result showed that perceived usefulness (PU), perceived ease of use (PEOU), and quality of service (QS) are important predictors of m-entertainment adoption. The study contributes to the existing literature by extending the TAM model as well as examining m-entertainment, an important and emerging business model in mobile commerce. A new analytical approach using both SEM and NN was also employed in this study.
8. Some old and some new results in inverse obstacle scattering

We will survey on uniqueness, that is, identifiability and on reconstruction issues for inverse obstacle scattering for time-harmonic acoustic and electromagnetic waves. In the first part we begin by presenting two classical uniqueness proofs and after that proceed with two recent uniqueness results for inverse obstacle scattering subject to a generalized impedance boundary condition. Then we proceed with an iterative reconstruction algorithm via non-linear boundary integral equations for the case of the generalized impedance boundary condition. In the final part we present new integral equation formulations for transmission eigenvalues that play an important role through their connections with the linear sampling method and the factorization method for inverse scattering problems for penetrable objects. 1 Uniqueness in inverse obstacle scattering Scattering theory is concerned with the effects that obstacles and inhomogeneities have on the propagation of waves and in particular time-harmonic waves. For simplicity, we focus our attention on acoustic waves and only give passing references to electromagnetic waves. Throughout the paper we will consider scattering objects within a homogeneous background that are described by a bounded domain D ⊂ R for m = 2, 3 with a connected C smooth boundary ∂D and can be either impenetrable or penetrable. We note ∗Institut für Numerische und Angewandte Mathematik, Universität Göttingen, Göttingen, Germany (kress@math.uni-goettingen.de)
Portfolio Selection in Mean-Minimum Return Level-Expected Bounded First Passage Time Framework

This paper explores the selection of optimal portfolio by replacing the standard Mean-Variance model by Mean-Minimum Return Level (MRL) framework and adding one important dimension—expectation of bounded First Passage Time (FPT) towards the MRL. To measure how much a given portfolio is exposed to risk, the new model can capture both, the amount of the largest possible loss at a certain confidence level and time to such an event occurring. The novelty of this paper is the introduction of bounded first passage time towards MRL and taking its expectation into consideration as an additional factor in portfolio selection decision making. Assuming that the asset price dynamics follow multi-dimensional Geometric Brownian Motion with drift, we obtain a portfolio wealth process for multiple assets and we evaluate the lowest possible value to which it can drop by a high confidence level. Then we extend our examination of the optimal portfolio selection by ultimately obtaining the efficient surface of risky portfolios. As a result, the paper shows that the third dimension can make a significant difference while choosing the asset weights compared to classical models ignoring the portfolio return paths as long as they achieve a desired combination of risk and return.
A genetic-fuzzy approach for mobile robot navigation

A genetic-fuzzy approach is developed to solve the navigation problem of a mobile robot in dynamic environment. The improved genetic algorithms are used for tuning the scaling factors of the state variables and rule sets of a fuzzy logic controller which a robot uses to navigate among moving obstacles. The results of simulation experiment indicate the effectiveness of the proposed method.
Banach spaces with shortest network length depending only on pairwise distances between points

For a real Banach space realising shortest networks for all finite subsets, we prove that a necessary and sufficient condition for the shortest network length to be expressed as a function only of pairwise distances between its points is that the space is either predual to or a Hilbert space. We give a characterization of spaces predual to and Hilbert spaces in terms of shortest networks. Bibliography: 23 titles.
Classifying airborne bathymetry data using the Random Forest algorithm

ABSTRACT The information on the topography of the seabed included in data from airborne lidar bathymetry can be used for the detection of changes occurring at the bottom of the basin and for the detection of objects deposited on it. Processing of full waveform data enables obtaining data on the water surface and the identification of the underwater situation. The classification process based on the Random Forest (RF) algorithm is presented using data from lidar bathymetry. The classification was performed in two independent approaches using input vector consisting of 16 features. In the first approach, the entire point cloud was classified, in the second the point cloud did not contain points of the water surface. In the classification, traits based on the full waveform and resulting from point cloud geometry were used. The quantitative efficiency of classification was verified through error matrices. The obtained efficiency (100% water surface, 99.9% seabed and 60% objects) of the point classification of the objects enables the possibility for using the RF algorithm for detection of objects on the seabed. In comparison to Support Vector Machines, the RF algorithm has better results in the detection of points on the objects in point cloud with water surface.
Sensitivity analysis of Mixed Tempered Stable parameters with implications in portfolio optimization

This paper investigates the use, in practical financial problems, of the Mixed Tempered Stable distribution both in its univariate and multivariate formulation. In the univariate context, we study the dependence of a given coherent risk measure on the distribution parameters. The latter allows to identify the parameters that seem to have a greater influence on the given measure of risk. The multivariate Mixed Tempered Stable distribution enters in a portfolio optimization problem built considering a real market dataset of seventeen hedge fund indexes. We combine the flexibility of the multivariate Mixed Tempered Stable distribution, in capturing different tail behaviors, with the ability of the ARMA-GARCH model in capturing the time dependence observed in the data.
Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods

This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and blocked designs, and models with weights and covariates. Because of the linearity of the regression model underlying RCTs, the asymptotic properties of design-based estimators using group-level averages—formed randomly or by covariates for nonclustered designs and as cluster-level averages for clustered designs—match those using individual data. Furthermore, design effects from aggregation are tolerable with moderate numbers of groups and few covariates, suggesting little information is lost in these cases. Ecological inference methods for subgroup analyses, however, yield large design effects. Several empirical examples using real-world education RCT data demonstrate the theory.
Delta-doped SrTiO3 top-gated field effect transistor

Oxide heterostructures are an attractive platform for incorporation in field-effect transistors (FETs) due to their diverse physical properties which can be tuned by electrostatic gating. We report a top-gated FET based on a SrTiO3 delta-doped structure, which operates down to cryogenic temperatures. The device shows excellent DC characteristics with an on/off ratio greater than 104 and field effect mobility estimated to be 2125 cm2/V s at 2 K. The high field effect mobility was consistent with the Hall mobility and is attributed to the formation of a two-dimensional electron system in the delta-doped layer: two-dimensional gate-tunable Shubnikov-de Haas oscillations confirm this. The achievement of an electron density of 3 × 1012 cm−2 in a gate-tunable geometry allows for the exploration of the interplay between magnetic, ferroelectric, and superconducting properties of SrTiO3 in the quantum limit.
A new surrogate modeling method combining polynomial chaos expansion and Gaussian kernel in a sparse Bayesian learning framework

Surrogate modeling techniques have been increasingly developed for optimization and uncertainty quantification problems in many engineering fields. The development of surrogates requires modeling high‐dimensional and nonsmooth functions with limited information. To this end, the hybrid surrogate modeling method, where different surrogate models are combined, offers an effective solution. In this paper, a new hybrid modeling technique is proposed by combining polynomial chaos expansion and kernel function in a sparse Bayesian learning framework. The proposed hybrid model possesses both the global characteristic advantage of polynomial chaos expansion and the local characteristic advantage of the Gaussian kernel. The parameterized priors are utilized to encourage the sparsity of the model. Moreover, an optimization algorithm aiming at maximizing Bayesian evidence is proposed for parameter optimization. To assess the performance of the proposed method, a detailed comparison is made with the well‐established PC‐Kriging technique. The results show that the proposed method is superior in terms of accuracy and robustness.
Characteristic Analysis and Modeling of Network Traffic for the Electromagnetic Launch System

The electromagnetic launch system (EMLS) is a kind of large-scale complex system with strong mechanical, electrical, and hydraulic coupling. The effective operation of the system requires the support of advanced and reliable health management system, and the function of the health management system relies on the network traffic to complete data transmission and interaction. Through protocol filtering and time-frequency characteristic analysis of network traffic, it is found that the system mainly includes CIP and TCP; traffic of CIP shows continuity and stability, while traffic of TCP shows sparsity and impact. Based on the analysis of traffic characteristics, an SVR-ARMA network traffic model is established, which improves the prediction accuracy and performance of network traffic and provides a reliable technical guarantee for the fault diagnosis and health management of the EMLS.