Miracle-dz/UniFAR-FTUs huggingface dataset:
Input-to-State Stability of Infinite-Dimensional Systems: Recent Results and Open Questions

In a pedagogical but exhaustive manner, this survey reviews the main results on input-to-state stability (ISS) for infinite-dimensional systems. This property allows estimating the impact of inputs and initial conditions on both the intermediate values and the asymptotic bound on the solutions. ISS has unified the input-output and Lyapunov stability theories and is a crucial property in the stability theory of control systems as well as for many applications whose dynamics depend on parameters, unknown perturbations, or other inputs. In this paper, starting from classic results for nonlinear ordinary differential equations, we motivate the study of ISS property for distributed parameter systems. Then fundamental properties are given, as an ISS superposition theorem and characterizations of (global and local) ISS in terms of Lyapunov functions. We explain in detail the functional-analytic approach to ISS theory of linear systems with unbounded input operators, with special attention devoted to ISS theory of boundary control systems. The Lyapunov method is shown to be very useful for both linear and nonlinear models, including parabolic and hyperbolic partial differential equations. Next, we show the efficiency of the ISS framework to study the stability of large-scale networks, coupled either via the boundary or via the interior of the spatial domain. ISS methodology allows reducing the stability analysis of complex networks, by considering the stability properties of its components and the interconnection structure between the subsystems. An extra section is devoted to ISS theory of time-delay systems with the emphasis on techniques, which are particularly suited for this class of systems. Finally, numerous applications are considered in this survey, where ISS properties play a crucial role in their study. This survey suggests many open problems throughout the paper.
Muon spin rotation and relaxation in Pr$_{1-x}$Nd$_x$Os$_4$Sb$_{12}$: superconductivity and magnetism in Pr-rich alloys

The Pr-rich end of the alloy series Pr$_{1-x}$Nd$_x$Os$_4$Sb$_{12}$ has been studied using muon spin rotation and relaxation. The end compound PrOs$_4$Sb$_{12}$ is an unconventional heavy-fermion superconductor, which exhibits a spontaneous magnetic field in the superconducting phase associated with broken time-reversal symmetry. No spontaneous field is observed in the Nd-doped alloys for x $>$ 0.05. The superfluid density is insensitive to Nd concentration, and no Nd$^{3+}$ static magnetism is found down to the lowest temperatures of measurement. Together with the slow suppression of the superconducting transition temperature with Nd doping, these results suggest anomalously weak coupling between Nd spins and conduction-band states.
Active Metasurfaces as a Platform for CapacitiveWireless Power Transfer Supporting Multiple Receivers

As wireless power transfer (WPT) repeaters, metasurfaces can enhance field coupling, improving the WPT operation. In this paper, we show that metasurfaces can also be used as transmitters of capacitive WPT systems. Such a metasurface-based WPT system can feed multiple receivers and provide robust operation against load or position variations. We formulate an analytical model of such WPT systems. We also discuss the exact solution of a particular example with N identical receivers.
Sulfur-bearing Molecules in Orion KL

We present an observational study of the sulfur (S)-bearing species toward Orion KL at 1.3 mm by combining ALMA and IRAM-30 m single-dish data. At a linear resolution of ∼800 au and a velocity resolution of 1 km s−1, we have identified 79 molecular lines from six S-bearing species. In these S-bearing species, we found a clear dichotomy between carbon–sulfur compounds and carbon-free S-bearing species for various characteristics, e.g., of line profiles, spatial morphology, and molecular abundances with respect to H2. Lines from the carbon–sulfur compounds (i.e., OCS, 13CS, and H2CS) exhibit spatial distributions concentrated around the continuum peaks and extended to the south ridge. The full width at half maximum (FWHM) linewidth of these molecular lines is in the range of 2 ∼11 km s−1. The molecular abundances of OCS and H2CS decrease slightly from the cold (∼68 K) to the hot (∼176 K) regions. In contrast, lines from the carbon-free S-bearing species (i.e., SO2, 34SO, and H2S) are spatially more extended to the northeast of mm4, exhibiting broader FWHM line widths (15 ∼ 26 km s−1). The molecular abundances of carbon-free S-bearing species increase by over an order of magnitude as the temperature increase from 50 to 100 K. In particular, 34SO/34SO2 and OCS/SO2 are enhanced from the warmer regions (>100 K) to the colder regions (∼50 K). Such enhancements are consistent with the transformation of SO2 at warmer regions and the influence of shocks.
Management of bone disease in patients on long term glucocorticoid therapy

Osteoporosis is a well documented complication of gastrointestinal disease. Its pathogenesis is multifactorial but glucocorticoid therapy is likely be an important contributory factor, particularly in patients with inflammatory bowel disease.1A widely recognised condition, glucocorticoid induced osteoporosis is under diagnosed and often inadequately treated. Two recent surveys, one from a large teaching hospital2 and one community based,3 reported that only a small minority of patients receiving long term glucocorticoid therapy were offered prophylaxis against bone loss. However, recent evidence shows that intervention can prevent bone loss and reduce the significant morbidity associated with glucocorticoid induced osteoporosis.4 5 This emphasises the need to adopt a more aggressive approach towards prevention of osteoporosis, and to define management strategies for patients who are treated with glucocorticoids. The time course of glucocorticoid induced bone loss has not been well documented but there is evidence that the rate of loss is most rapid in the first six to 12 months of treatment and decreases thereafter.6 7 If these findings are confirmed, there are clear implications for the timing of intervention. Although not all studies are in agreement, most data indicate that the spine and proximal femur are affected similarly8 and observational studies suggest that the risk both of hip and vertebral fractures is higher in patients treated with glucocorticoids.9 10 Data from placebo controlled primary prevention trials indicate rates of bone loss in untreated patients in the region of 2–4% each year, both in the spine and proximal femur.4 11 This is considerably in excess of rates of bone loss expected in a healthy population of similar age and sex distribution. Attempts to show a correlation between the dose and duration of glucocorticoid therapy, and bone loss, have produced conflicting results. This may partly reflect the confounding …
Daniel@FinTOC’2 Shared Task: Title Detection and Structure Extraction

We present our contributions for the 2020 FinTOC Shared Tasks: Title Detection and Table of Contents Extraction. For the Structure Extraction task, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge. For the title detection task, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.
Alcohol Consumption Among Racial/Ethnic Minorities

Ethnic minorities (e.g., Hispanics, blacks, Asian-Americans, and Native Americans) are still underrepresented in alcohol research in the United States. Furthermore, existing studies often do not take into consideration the variability that exists within each ethnic group, resulting in inaccurate generalizations. Studies among Hispanics have found substantial differences among Hispanic subgroups in drinking patterns and rates of alcohol-related problems. Moreover, no single variable can explain the observed patterns. Similarly, numerous factors have been shown to shape drinking patterns among blacks, including individual and environmental characteristics as well as historical and cultural factors. Different subgroups of Asian-Americans also vary substantially in their rates of drinking and heavy drinking, although their lifetime alcohol use is lower than the national average. Genetic and cultural factors, as well as stress and historic experiences, may influence drinking patterns of Asian-Americans. The widely differing drinking patterns among Native Americans also are likely shaped by a variety of influences.
A middleware solution for integrating and exploring IoT and HPC capabilities

Even with the considerable advances in the development of middleware solutions, there is still a substantial gap in Internet of Things (IoT) and high‐performance computing (HPC) integration. It is not possible to expose services such as processing, storage, sensing, security, context awareness, and actuating in a unified manner with the existing middleware solutions. The consequence is the utilization of several solutions with their particularities, thus requiring different skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce the gap between IoT and HPC technologies, we present the JavaCá&Lá (JCL), a middleware used to help the implementation of distributed user‐applications classified as IoT‐HPC. This ubiquity is possible because JCL incorporates (1) a single application programming interface to program different device categories; (2) the support for different programming models; (3) the interoperability of sensing, processing, storage, and actuating services; (4) the integration with MQTT technology; and (5) security, context awareness, and actions services introduced through JCL application programming interface. Experimental evaluations demonstrated that JCL scales when doing the IoT‐HPC services. Additionally, we identify that customized JCL deployments become an alternative when Java‐Android and vice‐versa code conversion is necessary. The MQTT brokers usually are faster than JCL HashMap sensing storage, but they do not perform distributed, so they cannot handle a huge amount of sensing data. Finally, a short example for monitoring moving objects exemplifies JCL facilities for IoT‐HPC development.
Does oestrogen allow women to store fat more efficiently? A biological advantage for fertility and gestation

In normal healthy‐weight humans, women have a higher percentage body fat than men, a difference that commences at puberty and continues throughout adult life, suggesting that the mechanism is related to sex steroids. The first half of pregnancy is also a stage of body fat gain in women. From an energy balance point, there is no explanation why women should be fatter than men, as the latter consume more calories proportionately. Moreover, women store fat in early pregnancy when caloric intake does not significantly change. The aim of this review is to focus on evidence supporting one mechanism that may account for these findings. That is, oestrogen reduces postprandial fatty acid oxidation leading to an increase in body fat which may account for the greater fat mass observed in women compared with men and the fat gain in early pregnancy. Therefore, female puberty and early pregnancy could be seen as states of efficient fat storage of energy in preparation for fertility, foetal development and lactation providing an obvious biological advantage. Further research into this mechanism of fat storage may provide further insights into the regulation of body fat.
Diagnosis of Cardiovascular Diseases using Hybrid Feature Selection and Classification Algorithms

Current diagnostic systems in order to identify cardiovascular diseases (CVDs) such as Echocardiography (ECG) require highly skilled physicians to evaluate complex combinations of clinical and pathological data. Inaccurate decision decision making is the challenge in the process and thus can’t be permitted in healthcare industry. Data mining methodologies can be applied to large medical datasets to extract insights that aid healthcare professionals in the diagnosis of cardiovascular diseases. In CVDs data mining, classification categorize a patient as having CVDs or free from it based on their similarities to previous examples of other patients. The classification accuracy rate is highly influenced by feature selection technique which eliminates features or attributes with practically no or little information from the dataset. Thus, feature selection and classification algorithms are considered as a concern of global "combinatorial optimization". The aim of this research is to investigate the optimal hybrid model of feature selection and classification algorithms in the diagnosis of cardiovascular diseases based on three performance metrics namely accuracy, sensitivity and specificity. It followed the Cross Industry Standard Process for Data Mining (CRISP-DM). The effect of hybrid feature selection and classification algorithms is examined on heart disease dataset acquired from University of California, Irvine - Machine Learning Repository (UCI-ML). The feature selection algorithm used is Particle Swarm Optimization (PSO). The classification algorithms used are Support Vector Machines (SVM), Artificial Neural Network (ANN), Naive Bayes, K-Nearest Neighbour (KNN), Random Forest and C5.0 Decision Tree. The hybrid feature selection and classification algorithms are evaluated based on accuracy, sensitivity and specificity with the objective of achieving superior predictive performance. Results demonstrated that hybrid combination of PSO with SVM (PSO_SVM) achieves superior predictive performance over other models. The research will thus empower physicians to diagnose cardiovascular diseases and initiate timely treatment without the intervention of a trained cardiologist.
Enhancing Baccalaureate Nursing Information Technology Outcomes: Faculty Perspectives

Baccalaureate nurses must be prepared to meet information technology expectations for practice and future professional development. Therefore, educational programs must evaluate curriculum and student outcomes and address areas for improvement. Faculty members were surveyed regarding barriers and strategies for improving information technology outcomes. Project findings have educational, clinical agency, legal, and policy implications.
Astroinformatics-based search for globular clusters in the Fornax Deep Survey

In the last years, Astroinformatics has become a well-defined paradigm for many fields of Astronomy. In this work, we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multiband photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analysed in this work consist of deep, multiband, partially overlapping images centred on the core of the Fornax cluster. In this work, we use a Neural Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method (ΦLAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics-based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single-band HST data and two approaches based, respectively, on a morpho-photometric and a Principal Component Analysis using the same data discussed in this work.
Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Federated learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints. Albeit its popularity, it has been observed that FL yields suboptimal results if the local clients’ data distributions diverge. To address this issue, we present clustered FL (CFL), a novel federated multitask learning (FMTL) framework, which exploits geometric properties of the FL loss surface to group the client population into clusters with jointly trainable data distributions. In contrast to existing FMTL approaches, CFL does not require any modifications to the FL communication protocol to be made, is applicable to general nonconvex objectives (in particular, deep neural networks), does not require the number of clusters to be known a priori, and comes with strong mathematical guarantees on the clustering quality. CFL is flexible enough to handle client populations that vary over time and can be implemented in a privacy-preserving way. As clustering is only performed after FL has converged to a stationary point, CFL can be viewed as a postprocessing method that will always achieve greater or equal performance than conventional FL by allowing clients to arrive at more specialized models. We verify our theoretical analysis in experiments with deep convolutional and recurrent neural networks on commonly used FL data sets.
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with respect to so-called Neural Tangent Kernels (NTKs) (Jacot et al., 2018). An efficient algorithm to compute the NTK, as well as its convolutional counterparts, appears in Arora et al. (2019a), which allowed studying performance of infinitely wide nets on datasets like CIFAR-10. However, super-quadratic running time of kernel methods makes them best suited for small-data tasks. We report results suggesting neural tangent kernels perform strongly on low-data tasks. 1. On a standard testbed of classification/regression tasks from the UCI database, NTK SVM beats the previous gold standard, Random Forests (RF), and also the corresponding finite nets. 2. On CIFAR-10 with 10 - 640 training samples, Convolutional NTK consistently beats ResNet-34 by 1% - 3%. 3. On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance. 4. Comparing the performance of NTK with the finite-width net it was derived from, NTK behavior starts at lower net widths than suggested by theoretical analysis(Arora et al., 2019a). NTK's efficacy may trace to lower variance of output.
Approximate policy iteration using neural networks for storage problems

We consider the stochastic single node energy storage problem (SNES) and revisit Approximate Policy Iteration (API) to solve SNES. We show that the performance of API can be boosted by using neural networks as an approximation architecture at the policy evaluation stage. To achieve this, we use a model different to that in literature with aggregate variables reducing the dimensionality of the decision vector, which in turn makes it viable to use neural network predictions in the policy improvement stage. We show that performance improvement by neural networks is even more significant in the case when charging efficiency of storage systems is low.
Nonvolatile Multilevel States in Multiferroic Tunnel Junctions

Manipulation of tunneling spin-polarized electrons via a ferroelectric interlayer sandwiched between two ferromagnetic electrodes, dubbed Multiferroic Tunnel Junctions (MFTJs), can be achieved not only by the magnetic alignments of two ferromagnets but also by the electric polarization of the ferroelectric interlayer, providing great opportunities for next-generation multi-state memory devices. Here we show that a La0.67Sr0. 33MnO3 (LSMO)/PbZr0.2Ti0. 8O3(PZT)/Co structured MFTJ device can exhibit multilevel resistance states in the presence of gradually reversed ferroelectric domains via tunneling electro-resistance and tunneling magnetoresistance, respectively. The nonvolatile ferroelectric control in the MFTJ can be attributed to separate contributions arising from two independent ferroelectric channels in the PZT interlayer with opposite polarization. Our study shows the dominant role of "mixed" ferroelectric states on achieving accumulative electrical modulation of multilevel resistance states in MFTJs, paving the way for multifunctional device applications.
Co-optimization of Speed and Gearshift Control for Battery Electric Vehicles Using Preview Information

This paper addresses the co-optimization of speed and gearshift control for battery electric vehicles using short-range traffic information. To achieve greater electric motor efficiency, a multi-speed transmission is employed, whose control involves discrete-valued gearshift signals. To overcome the computational difficulties in solving the integrated speed-and-gearshift optimal control problem that involves both continuous and discrete-valued optimization variables, we propose a hierarchical procedure to decompose the integrated hybrid problem into purely continuous and discrete sub-problems, each of which can be efficiently solved. We show, by simulations in various driving scenarios, that the co-optimization of speed and gearshift control using our proposed hierarchical procedure can achieve greater energy efficiency than other typical approaches.
Near-horizon Bondi-Metzner-Sachs symmetry, dimensional reduction, and black hole entropy

Author(s): Carlip, S | Abstract: © 2020 authors. Published by the American Physical Society. In an earlier short paper [Phys. Rev. Lett. 120, 101301 (2018)PRLTAO0031-900710.1103/PhysRevLett.120. 101301], I argued that the horizon-preserving diffeomorphisms of a generic black hole are enhanced to a larger three-dimensional Bondi-Metzner-Sachs symmetry, which is powerful enough to determine the Bekenstein-Hawking entropy. Here, I provide details and extensions of that argument, including a loosening of horizon boundary conditions and a more thorough treatment of dimensional reduction and meaning of a "near-horizon symmetry. "
Zero-temperature phases of the two-dimensional Hubbard-Holstein model: A non-Gaussian exact diagonalization study

We propose a novel numerical method which embeds the variational non-Gaussian wavefunction approach with exact diagonalization, allowing for efficient treatment of correlated systems with both electron-electron and electron-phonon interactions. Using a generalized polaron transformation, we construct a variational wavefunction that minimizes the entanglement between electrons and phonons; exact diagonalization is then used to treat the electronic part of the wavefunction exactly, thus taking into account high-order correlation effects beyond the Gaussian level. Keeping the full electronic Hilbert space, the complexity is increased only by a polynomial factor relative to the exact diagonalization calculation for pure electrons. As an example, we use this method to study ground-state properties of the two-dimensional Hubbard-Holstein model, providing evidence for the existence of intervening phases between the spin and charge-ordered states. In particular, we find one of the intervening phases has strong charge susceptibility and binding energy, but is distinct from a CDW-ordered state; while the other intervening phase displays superconductivity at weak couplings. This method, as a general framework, can be extended to treat excited states and dynamics, as well as a wide range of systems with both electron-electron and electron-boson interactions.