Miracle-dz/UniFAR-FTUs huggingface dataset:
Factor modelling for high-dimensional functional time series

Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables ($p$) diverges as the number of serially dependent observations ($n$) increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. To estimate the number of functional factors and the factor loadings, we propose a fully functional estimation procedure based on an eigenanalysis for a nonnegative definite matrix. Our proposal involves a weight matrix to improve the estimation efficiency and tackle the issue of heterogeneity, the rationality of which is illustrated by formulating the estimation from a novel regression perspective. Asymptotic properties of the proposed method are studied when $p$ diverges at some polynomial rate as $n$ increases. To provide a parsimonious model and enhance interpretability for near-zero factor loadings, we impose sparsity assumptions on the factor loading space and then develop a regularized estimation procedure with theoretical guarantees when $p$ grows exponentially fast relative to $n. $ Finally, we demonstrate that our proposed estimators significantly outperform the competing methods through both simulations and applications to a U.K. temperature dataset and a Japanese mortality dataset.
A P ] 2 4 D ec 2 02 1 NON-SYMMETRIC LÉVY-TYPE OPERATORS

We prove the uniqueness and existence of a weak fundamental solution for the equation ∂t = L for non-symmetric non-local operators Lf(x) := b(x) · ∇f(x) + ∫ R (f(x+ z)− f(x)− 1|z|<1 〈z,∇f(x)〉)κ(x, z)J(z) dz , under certain assumptions on b, κ and J . The result allows us to cover new coefficients even for the 1-stable Lévy measure J(z) = |z|−d−1. We formalize and then use a general abstract approach to the parametrix construction. AMS 2010 Mathematics Subject Classification: Primary 60J35, 47G20; Secondary 47D06, 47A55.
The quantum version of prediction for binary classification problem by ensemble methods

In this work, we consider the performance of using a quantum algorithm to predict the result of a binary classification problem when a machine learning model is an ensemble of any simple classifiers. This approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let N be the number of classifiers from an ensemble model and O(T) be the running time of prediction of one classifier. In classical case, the final result is obtained by ”averaging” outcomes of all ensemble model’s classifiers. The running time in classical case is O (N · T). We propose an algorithm that works in O (√N · T ).
Transient Growth of Accelerated Optimization Algorithms

Optimization algorithms are increasingly being used in applications with limited time budgets. In many real-time and embedded scenarios, only a few iterations can be performed and traditional convergence metrics cannot be used to evaluate performance in these nonasymptotic regimes. In this article, we examine the transient behavior of accelerated first-order optimization algorithms. For convex quadratic problems, we employ tools from linear systems theory to show that transient growth arises from the presence of nonnormal dynamics. We identify the existence of modes that yield an algebraic growth in early iterations and quantify the transient excursion from the optimal solution caused by these modes. For strongly convex smooth optimization problems, we utilize the theory of integral quadratic constraints to establish an upper bound on the magnitude of the transient response of Nesterov’s accelerated algorithm. We show that both the Euclidean distance between the optimization variable and the global minimizer and the rise time to the transient peak are proportional to the square root of the condition number of the problem. Finally, for problems with large condition numbers, we demonstrate tightness of the bounds that we derive up to constant factors.
Husimi-driven many-body systems realised with Bose-Einstein condensates

B.J. Mommers, 2 A. Pritchard, 2 T.A. Bell, 2 R.N. Kohn, Jr., S.E. Olson, M. Baker, 2, 1 and M.W. J. Bromley 2 Centre for Engineered Quantum Systems, The University of Queensland, St Lucia, Australia School of Mathematics and Physics, The University of Queensland, St Lucia, Australia Space Dynamics Laboratory, Albuquerque, New Mexico 87106, USA Air Force Research Laboratory, Kirtland AFB, New Mexico 87117, USA Quantum Technologies, Cyber and Electronic Warfare Division, Defence Science and Technology Group, Brisbane, Australia School of Sciences, University of Southern Queensland, Toowoomba, Australia
Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection

Existing deep learning-based Unsupervised Salient Object Detection (USOD) methods rely on supervised pre-trained deep models. Moreover, they generate pseudo labels based on hand-crafted features, which lack high-level semantic information. In order to overcome these shortcomings, we propose a new two-stage Activation-to-Saliency (A2S) framework that effectively excavates high-quality saliency cues to train a robust saliency detector. It is worth noting that our method does not require any manual annotation, even in the pre-training phase. In the first stage, we transform an unsupervisedly pre-trained network to aggregate multi-level features to a single activation map, where an Adaptive Decision Boundary (ADB) is proposed to assist the training of the transformed network. Moreover, a new loss function is proposed to facilitate the generation of high-quality pseudo labels. In the second stage, a self-rectification learning paradigm strategy is developed to train a saliency detector and refine the pseudo labels online. In addition, we construct a lightweight saliency detector using two Residual Attention Modules (RAMs) to largely reduce the risk of overfitting. Extensive experiments on several SOD benchmarks prove that our framework reports significant performance compared with existing USOD methods. Moreover, training our framework on 3,000 images consumes about 1 hour, which is over 30$\times$ faster than previous state-of-the-art methods.
Analysis of coherence in turbulent stratified wakes using spectral proper orthogonal decomposition

Abstract We use spectral proper orthogonal decomposition (SPOD) to extract and analyse coherent structures in the turbulent wake of a disk at Reynolds number $ {\textit {Re}} = 5 \times 10^{4}$ and Froude numbers $ {\textit {Fr}} = 2$, 10. We find that the SPOD eigenspectra of both wakes exhibit a low-rank behaviour and the relative contribution of low-rank modes to total fluctuation energy increases with $x/D$. The vortex shedding (VS) mechanism, which corresponds to $ {\textit {St}} \approx 0.11 - 0.13$ in both wakes, is active and dominant throughout the domain in both wakes. The continual downstream decay of the SPOD eigenspectrum peak at the VS mode, which is a prominent feature of the unstratified wake, is inhibited by buoyancy, particularly for $ {\textit {Fr}} = 2$. The energy at and near the VS frequency is found to appear in the outer region of the wake when the downstream distance exceeds $Nt = Nx/U = 6 - 8$. Visualizations show that unsteady internal gravity waves (IGWs) emerge at the same $Nt = 6 - 8$. A causal link between the VS mechanism and the unsteady IGW generation is also established using the SPOD-based reconstruction and analysis of the pressure transport term. These IGWs are also picked up in SPOD analysis as a structural change in the shape of the leading SPOD eigenmode. The $ {\textit {Fr}} = 2$ wake shows layering in the wake core at $Nt > 15$ which is captured by the leading SPOD eigenmodes of the VS frequency at downstream locations $x/D > 30$. The VS mode of the $ {\textit {Fr}} = 2$ wake is streamwise coherent, consisting of $V$-shaped structures at $x/D \gtrsim 30$. Overall, we find that the coherence of wakes, initiated by the VS mode at the body, is prolonged by buoyancy to far downstream. Also, this coherence is spatially modified by buoyancy into horizontal layers and IGWs. Low-order truncations of SPOD modes are shown to efficiently reconstruct important second-order statistics.
Good operations practice supervisor profiles in cell-centric manufacturing

The selection and implementation of good operations practice cannot be undertaken in isolation; it must consider the enterprise context. The aim of this paper is to describe a robust process for the development of specific environment good operations practice role profiles for supervisors, and illustrate this through a case study within a complex cell-centric manufacturing environment. The approach identifies the activities undertaken by a cell leader and team leader in a given manufacturing organization, and also by a good operations practice cell leader and team leader in a relevant external organization. Then via a survey of operations managers and functional managers, those activities that a cell leader and team leader should do within the given manufacturing organization are identified. The approach enables the contextual adoption of good operations practice, develops supervisor role profiles which allows them to be proficient within their task domains, contributes to attaining agreement amongst operational and functional management, and has applications across cell-centric manufacturing. The potentially significant impacts available across cell-centric manufacturing are illustrated by the finding that following this examination of operations practice in the case study company two-thirds were classified as requiring change.
Optimal multi-resolvent local laws for Wigner matrices

We prove local laws, i.e. optimal concentration estimates for arbitrary products of resolvents of a Wigner random matrix with deterministic matrices in between. We find that the size of such products heavily depends on whether some of the deterministic matrices are traceless. Our estimates correctly account for this dependence and they hold optimally down to the smallest possible spectral scale.
Louisiana Highway Evacuation Plan for Hurricane Katrina: Proactive Management of a Regional Evacuation

In the days preceding Hurricane Katrina in 2005, over one million people evacuated from southeast Louisiana. Preliminary evidence suggests that, given the few available routes and short advance warning, this achievement was the most successful highway-based evacuation in U.S. history. This paper presents the contraflow plan that was developed to evacuate the greater New Orleans area, the transportation goals and philosophies that underlie it, and the unique features that permitted it to be used so effectively. The plan was unprecedented in its size and scope. It required the closure of long segments of interstate freeway, forced movement of traffic onto alternative routes, established reversible flow segments of freeway that crossed state boundaries, forced coordination of parallel nonfreeway routes, and forced reconfiguration of busy urban freeway interchanges to more effectively load evacuees from the surface street network onto the interstate system. The lessons learned from this experience can be applied by emergency planning experts in their own jurisdictions.
Multiplication noise in uniform avalanche diodes

A general expression is derived from which the spectral density of the noise generated in a uniformly multiplying p-n junction can be calculated for any distribution of injected carriers. The analysis is limited to the white noise part of the noise spectrum only, and to diodes having large potential drops across the multiplying region of the depletion layer. It is shown for the special case in which \beta = k\alpha , where k is a constant and α and β are the ionization coefficients of electrons and holes, respectively, that the noise spectral density is given by 2eI_{in}M^{3}[1 + (\frac{1 - k}{k})(\frac{M - 1}{M})^{2}] where M is the current multiplication factor and I in the injected current, if the only carriers injected into the depletion layer are holes, and by 2eI_{in}M^{3}[1 - (1 - k)(\frac{M - 1}{M})^{2}] if the only injected carriers are electrons. An expression is also derived for the noise power which will be delivered to an external load for the limit M \rightarrow \infin .
Athlete Graduation Rate Gaps at Division-I State Flagship Universities: An Exploratory Analysis Emphasizing Black Males

Discrepancies in Black male graduation rates at NCAA Division-I state flagship institutions have raised questions about claims of unilateral academic progress among certain revenue sport athletes. Researchers have identified gaps in NCAA and federal graduation rates between athletes and non-athletes based on race and type of sport participation. This exploratory study examines the degree to which graduation rates vary between football and male basketball athletes and male undergraduates at state flagship institutions. We pay particular attention to gaps in graduation rates for Black male athletes. We then seek theoretical explanations for these gaps by drawing on the athletic role-engulfment and key-player hypothesis, the mismatch education hypothesis, and the institutional isomorphism theory.
Fast Mixing of Hard Negative Samples for Contrastive Learning and Use for COVID-19

The new type of coronavirus pneumonia (COVID-19) is spreading around the world, and one of the main reasons is the low detection efficiency and lack of detection materials. Image recognition algorithms based on deep learning are an effective method to assist epidemic detection. However, COVID-19 medical image data has the problems of difficulty in obtaining case samples and high data labeling costs. Contrastive learning algorithm is the best performing method among self-supervised learning algorithms without label training. To solve the problem of insufficient annotation data for COVID-19 medical images, this paper proposes a contrastive learning algorithm that incorporates feature synthesis components of hard negative samples. The feature of hard negative samples is a key factor to promote network training among a large number of negative samples features. Generating more hard negative samples features and adding them to the contrastive loss function for calculation allows the comparison learning algorithm to learn better semantic features. At the same time, the contrastive learning algorithm proposed in this paper is used in the transfer learning of COVID-19 CT images, which can deal with the problem of large differences between the original domain and the target domain. The algorithm proposed in this paper uses common methods of self-supervised learning to evaluate the classification accuracy of 0.798 on the COVID-CT data set, which has a significant improvement. The training method of transfer learning has better classification performance than common methods, and the classification accuracy is 0.852.
Seismic Performance of Geosynthetic-Encased Stone Columns

The geotextile-encased columns (GECs) foundation system for embankments on soft or problematic soils was introduced in 1994. The GECs consist of compacted granular fill similar to common stone columns, but with a main decisive difference: GECs are confined in a high-strength woven geotextile cylinder (encasement). Consequently, they work properly even in extremely soft soils. Granular columns under compressive loads experience different failure modes, such as bulging, general shear failure, and sliding. However, the most common failure mode for stone columns in soft clays is bulging. With the help of GECs, the bulging failure can be prevented. The risk of bulging is even higher for earthquake loading conditions. A vast amount of numerical, analytical, and experimental research has been done to study different aspects of GEC's behavior. Despite this available literature from several researchers, there is not much research, numerically or experimentally, about the seismic behavior of GECs. To analyze the effect of earthquakes on stone columns and to determine how the presence of GECs improves the soil behavior, three-dimensional finite element analysis was used. The results show that the encapsulation of the stone column by a geosynthetic reinforcement (i.e., instead of installing ordinary stone columns, using geosynthetic-encapsulated columns) helps with the integrity of the column and also improves the system behavior under earthquake loading conditions.
Re-Emphasizing the Roles of General Medical and Dental Practitioners Regarding Oral Cancer Eradication in Nigeria

Abstract The burden of oral cancer in Nigeria is largely under-reported. The available data on oral cancer etiology/risk factors, clinical features, disease burden, and literacy rate in Nigeria points towards a possible explosion in prevalence in the near future, which poses a serious public health concern. The general medical and dental practitioners (GMDPs) in Nigeria can salvage this looming problem through appropriate public health and clinical interventions. This narrative review article re-emphasizes the key roles of GMDPs towards oral cancer eradication in Nigeria. It also discusses oral cancer case definition, etiology, risk factors, and the epidemiological burden in the Nigerian context.
The Careful Assessment Tool for Managing Patients with Medically Unexplained Symptoms – the Experience of Slovenian Family Medicine Trainees: a Qualitative Study

Abstract Introduction Primary care physicians use various tools and methods to identify medically unexplained symptoms (MUS). The main purpose of our study is to determine the views of Slovenian family medicine trainees (FMT) about using the “Careful Assessment” tool for managing patients with MUS. Methods A qualitative study using open survey questions focused on the experience of family medicine trainees in managing patients with MUS. The sample consisted of surveys from 184 family medicine trainees. These trainees analysed a total of 702 patients with MUS. Manual coding was used for quantitative content analysis. Results In the coding process, 49 codes were developed that included broader research fields about using the “Careful Assessment” tool for managing patients with MUS. The codes were grouped into four theoretically grounded, logical categories in accordance with the elaborated theoretical concept: multi-purpose utility; improved patient management; in-depth knowledge and new skills; and patient response. Conclusion The study demonstrated that, in the view of Slovenian FMT, the “Careful Assessment” tool has multi-purpose utility. The study showed that FMT felt that this tool helps them in systematic patient management. Their opinion is that it helps them establish a trusting relationship with patients, which is a precondition for providing further treatment.
Effects of farmer’s computer abilities and self-efficacy on their learning performance and adoption intention of the farming management information system

This study explores how farmer’s self-efficacy influences their performance to use the farming management information system, FMIS. Six instruments were developed to evaluate computer ability, self-efficacy, performance, acceptance and attitude of 23 farmers who were enrolled in a two-day training workshop on FMIS. The results of this study have shown the training program improved farmer’s self-efficacy in the FMIS, and perceived usefulness could increase participants’ usage motivation of the FMIS in the future. The results suggested farmer’s efficacy has no significant impact on their choices over tasks with different levels of difficulties, and performance of using FMIS. This could be attributed to the planning and limited duration of the training. This study contributes to the field of research by its genuine and systematic implementation of farming information system training. Several suggestions including the involvement of subject-matter experts, experience sharing activities as well as peer modeling strategies were proved to be effective to facilitate farmer’s self-efficacy and can be served as references for future practices and studies.
Effect of Al2O3 nano-particles on Ba(Zr0.95Ti0.05)O3 ceramics prepared by mixed oxide method

Abstract In this study, the effect of Al2O3 nano-particles on Ba(Zr0.95Ti0.05)O3 or BZT ceramics prepared by mixed oxide method with various amount of Al2O3 were investigated. The BZT powders were calcined at 1100 °C for 3 h with heating rate of 5 °C/min. The sintering procedure of BZT + x wt. % Al2O3 nano-particles (x = 0.3, 0.7 and 1.0) was carried out at 1300 °C for 2 h with heating rate of 5 °C/min. The results showed that, all XRD patterns of BZT + x wt. % Al2O3 nano-particles ceramics could be matched with a JCPDS data file number 06-0399. The BZT + 0.7 wt. % Al2O3 nano-particles ceramic was showed the highest density about 5.9 g/cm3, Vickers micro-hardness was 8.9 GPa, dielectric constant was 2005 and dielectric loss was 0.01 at 1 kHz frequency.
Lyapunov-Derived Control and Adaptive Update Laws for Inner and Outer Layer Weights of a Deep Neural Network

Lyapunov-based real-time update laws are well-known for neural network (NN)-based adaptive controllers that control nonlinear dynamical systems using single-hidden-layer NNs. However, developing real-time weight update laws for deep NNs (DNNs) remains an open question. This letter presents the first result with Lyapunov-based real-time weight adaptation laws for each layer of a feedforward DNN-based control architecture, with stability guarantees. Additionally, the developed method allows nonsmooth activation functions to be used in the DNN to facilitate improved transient performance. A nonsmooth Lyapunov-based stability analysis proves global asymptotic tracking error convergence. Simulation results are provided for a nonlinear system using DNNs with leaky rectified linear unit (LReLU) and hyperbolic tangent activation functions to demonstrate the efficacy of the developed method.
Bandwidth Enhancement of UWB Microstrip Antenna with a Modified Ground Plane

This article presents a bandwidth enhancing technique using a modified ground plane with diagonal edges, rectangular slot, and T-shape cut for the design of compact antennas. The proposed low-cost, compact-size circular patch antenna on 3 cm × 5.1 cm printed circuit board (FR-4) is designed and validated through simulations and experiments. Results show that the T-shaped ground plane with the presence of the diagonal cuts at the top corners and the rectangular slots can increase the bandwidth. Return losses of −19 and −26 dB for the first and second resonant frequencies, respectively, can be achieved when the depth of the diagonal cut is 5 mm, the dimension of each rectangular slot is 5×3 mm, and the T-shaped size is 8×4 mm, providing a 28.67% wider bandwidth than FCC standard.
Effect of Clonally Expanded PD‐1highCXCR5–CD4+ Peripheral T Helper Cells on B Cell Differentiation in the Joints of Patients With Antinuclear Antibody–Positive Juvenile Idiopathic Arthritis

Antinuclear antibody (ANA)–positive juvenile idiopathic arthritis (JIA) is characterized by synovial B cell hyperactivity, but the precise role of CD4+ T cells in promoting local B cell activation is unknown. This study was undertaken to determine the phenotype and function of synovial CD4+ T cells that promote aberrant B cell activation in JIA.