Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiology of neurocognitive disorders in France.

Neurocognitive disorders are common: every year, any physician or health professional comes into contact with patients or relatives with early symptoms of dementia. Nevertheless, their epidemiology remains difficult to estimate, due to real differences in risk factors within a country or region (average age, genetics, level of education, socio-economic level, etc.), differences in data collection, in the interpretation of these data with regard to scientific findings and changes in diagnostic criteria. In this review, we present a state of knowledge of the epidemiology of dementia in France. Epidemiological data on neurocognitive disorders in France come mainly from 3 sources: two prospective cohorts (PAQUID and 3C) and large databases (SNIIRAM, RSI data, PMSI). Neurocognitive disorders are estimated to directly affect more than 1.2 million people in France and about 2 out of 3 cases are attributed to Alzheimer's disease. The prevalence rate is estimated at about 40/1,000 people after 60 years of age and gradually increases to 180/1,000 after 75 years of age, reaching almost one in two people after 90 years of age. The incidence rate is estimated at between 13 and 19/1,000 person-years, and seems to decrease slightly over the decades. The PAQUID and 3C cohorts are coming to an end; despite the development of large databases (SNIIRAM, PMSI, etc.), it seems necessary to continue to set up new prospective cohorts in the general population to monitor the epidemiology of neurocognitive disorders in France.
Pilot testing a manualized equine-facilitated cognitive processing therapy (EF-CPT) intervention for PTSD in veterans.

OBJECTIVE The objective of this study was to test effectiveness and feasibility of equine-facilitated cognitive processing therapy (EF-CPT), a manualized adaptation of the cognitive processing therapy model for veterans with posttraumatic stress disorder (PTSD) championed by the Department of Veterans Affairs, in which equine-facilitated activities are integrated into face-to-face sessions. METHOD Twenty-seven veterans with a diagnosis of PTSD participated (Mage = 51; 78% male) in a pretest-posttest design. Veterans were seen by a single psychologist for 12 sessions of individual EF-CPT. Instruments included: PTSD Checklist (PCL), Trauma Related Guilt Inventory (TRGI), Working Alliance Inventory (WAI), and the Human Animal Bond Scale (HABS). We hypothesized improvement on all measures, low attrition, and good model fidelity. Paired sample t tests were conducted using SPSS. RESULTS PCL scores improved significantly (M1 = 68.25, M2 = 35.96, p ≤ .001), as did TRGI scores (p ≤ .001 on all scales). HABS and WAI indicated good working relationship. Two individuals attended one session and did not return (both under the age of 50); there was no other attrition (7% rate). Audio of sessions was reviewed for fidelity; there were variations in temporal order of session plans, which is within the acceptable flexibility of the manual. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE This manualized intervention has promise as an effective and well-tolerated treatment for veterans with PTSD. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Scalable Model-Based Management of Correlated Dimensional Time Series in ModelarDB+

To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used. However, as they produce huge amounts of data, only simple aggregates are stored. This removes outliers and fluctuations that could indicate problems. As a remedy, we present a model-based approach for managing time series with dimensions that exploits correlation in and among time series. Specifically, we propose compressing groups of correlated time series using an extensible set of model types within a user-defined error bound (possibly zero). We name this new category of model-based compression methods for time series Multi-Model Group Compression (MMGC). We present the first MMGC method GOLEMM and extend model types to compress time series groups. We propose primitives for users to effectively define groups for differently sized data sets, and based on these, an automated grouping method using only the time series dimensions. We propose algorithms for executing simple and multi-dimensional aggregate queries on models. Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB (ModelarDB+). Our evaluation shows that compared to widely used formats, ModelarDB+ provides up to 13.7x faster ingestion due to high compression, 113x better compression due to the adaptivity of GOLEMM, 573x faster aggregates by using models, and close to linear scalability. It is also extensible and supports online query processing.
Learning Low-Rank Regularized Generic Representation With Block-Sparse Structure for Single Sample Face Recognition

In this paper, we propose a novel low-rank regularized generic representation method to address the single sample per person problem in face recognition, which simultaneously employs the structure information from the probe dataset and the generic variation information. Each face image is divided into overlapped patches, whose classification results are aggregated to produce the final result. We generate a subspace for each patch by extracting its eight nearest neighbor patches and explore the relationships between subspaces by imposing a low-rank constraint on the reconstruction coefficients. Moreover, a block-sparsity constraint on the coefficient matrix is imposed to further promote the discrimination of representations. We also propose a dictionary learning method to learn the intra-class facial variations from the generic face datasets, which separates the face contour noise from the variation dictionary by an incoherence regularization item. The experimental results on four public face databases not only show the robustness of our approach to expression, illumination, occlusion, and time variation but also demonstrate the effectiveness of face sketch recognition.
A 220 GHz Broadband Sub-Harmonic Mixer Based on Global Design Method

A terahertz mixer is one of terahertz key components and widely used on terahertz wireless communication, remote sensing, aerospace exploration, and biomedical system. The performance of the terahertz mixer has a great effect on the entire terahertz transceiver system performance. This paper proposes a 220 GHz broadband sub-harmonic mixer based on global design method (GDM), which is different from the traditional subdivision design method of which the mixer circuit is composed of functional unit circuits. Instead, the GDM adopts the overall performance of the mixer (conversion loss) as an optimization goal. The continuous structures of the circuit are described by ideal transmission line models and waveguide models, meanwhile, the S-parameters of the de-embedded model are used to describe the discontinuous structures. The measured single-sideband (SSB) conversion loss is basically consistent with the simulation results, which verifies the accuracy and reliability of the GDM. When local oscillator (LO) frequency is fixed at 110 GHz, the measured SSB conversion loss is less than 10 dB with the average value of 8.1 dB from 198 to 238 GHz. The minimum conversion loss of the mixer is 6.35 dB at 207 GHz. The measured double-sideband noise temperature of the mixer is less than 924 K at an intermediate frequency of 1.6 GHz. The GDM has the characteristics with a wide frequency band, small circuit size, simple structure, low LO power, and stable performance, and can also be applied to other terahertz components.
Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced hyperspectral remote sensing image classification techniques based on manifold learning, sparse representation and deep learning have been proposed and reported a good performance in accuracy and efficiency on state-of-the-art public datasets. However, most existing methods still face challenges in dealing with large-scale hyperspectral image datasets due to their high computational complexity. In this work, we propose an improved spectral clustering method for large-scale hyperspectral image classification without any prior information. The proposed algorithm introduces two efficient approximation techniques based on Nyström extension and anchor-based graph to construct the affinity matrix. We also propose an effective solution to solve the eigenvalue decomposition problem by multiplicative update optimization. Experiments on both the synthetic datasets and the hyperspectral image datasets were conducted to demonstrate the efficiency and effectiveness of the proposed algorithm.
Guessing models imply the singular cardinal hypothesis

<p>In this article we prove three main theorems: (1) guessing models are internally unbounded, (2) for any regular cardinal <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="kappa greater-than-or-equal-to omega 2"> <mml:semantics> <mml:mrow> <mml:mi>κ<! -- κ --></mml:mi> <mml:mo>≥<! -- ≥ --></mml:mo> <mml:msub> <mml:mi>ω<! -- ω --></mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:annotation encoding="application/x-tex">\kappa \ge \omega _2</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="sans-serif upper I sans-serif upper S sans-serif upper P left-parenthesis kappa right-parenthesis"> <mml:semantics> <mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="sans-serif">I</mml:mi> <mml:mi mathvariant="sans-serif">S</mml:mi> <mml:mi mathvariant="sans-serif">P</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>κ<! -- κ --></mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathsf {ISP}(\kappa )</mml:annotation> </mml:semantics> </mml:math> </inline-formula> implies that <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="sans-serif upper S sans-serif upper C sans-serif upper H"> <mml:semantics> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="sans-serif">S</mml:mi> <mml:mi mathvariant="sans-serif">C</mml:mi> <mml:mi mathvariant="sans-serif">H</mml:mi> </mml:mrow> <mml:annotation encoding="application/x-tex">\mathsf {SCH}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> holds above <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="kappa"> <mml:semantics> <mml:mi>κ<! -- κ --></mml:mi> <mml:annotation encoding="application/x-tex">\kappa</mml:annotation> </mml:semantics> </mml:math> </inline-formula>, and (3) forcing posets which have the <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3. org/1998/Math/MathML" alttext="omega 1"> <mml:semantics> <mml:msub> <mml:mi>ω<! -- ω --></mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:annotation encoding="application/x-tex">\omega _1</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-approximation property also have the countable covering property. These results solve open problems of Viale [Ann. Pure Appl. Logic 163 (2012), no. 11, 1660–1678] and Hachtman and Sinapova [J. Symb. Log. 84 (2019), no. 2, 713–725]. </p>
Instantaneous Symmetrical Component Theory Based Parallel Grid Side Converter Control Strategy for Microgrid Power Management

This paper proposes a centralized control strategy for power management of hybrid microgrid connected to the grid using a parallel combination of grid side converters (GSCs). An improved version of instantaneous symmetrical component theory is developed and is used for the control of parallel operated GSCs, which results in reduced sensor requirement, control complexity, and communication bandwidth. In addition, a simple power management algorithm is developed to test the efficacy of the proposed parallel grid side converter control strategy for all the microgrid modes considering state of charge limits of hybrid energy storage system, load changes, and renewable power variations. In the proposed system, a better dc link voltage regulation is achieved and usage of supercapacitor reduces the current stresses on the battery. With the proposed control strategy, the essential features of grid side converters like power quality, power injection, bidirectional power flow, and proportional power sharing are achieved. The effectiveness of the developed control strategy for the proposed system is tested using MATLAB based simulink environment and validated experimentally using a laboratory prototype.
Numerical De-embedding of Periodic Guided-wave Structures via SOC/SOL in FEM Algorithm

This paper presents a 3D full-wave finite element method (FEM) combined with short-open calibration (SOC) and short-open-load (SOL) technique. In order to accurately acquire the intrinsic propagation characteristics of various guided-wave structures, our proposed SOC/SOL technique is successfully incorporated into 3D full-wave FEM algorithm/solver. Hence, the port discontinuities between the core guided-wave structure and the feeding network can be effectively removed out. Thus, the intrinsic unite-length parameters including their propagation constants and characteristic/wave impedances can be finally achieved. A few numerical examples are given out to demonstrate the efficiency and accuracy of our proposed full-wave approach.
Relationships and the development of transition readiness skills into early emerging adulthood for individuals with type 1 diabetes

ABSTRACT The study examined how “transition readiness” skills develop from relationship processes with parents, friends, and healthcare providers. During their senior year of high school and one year later, participants (n = 217) with type 1 diabetes completed measures of transition readiness skills (Self-Management; Self-Advocacy), adherence, HbA1c, and relationships with providers (patient-centered communication), parents (monitoring/knowledge), and friends (knowledge/helpfulness) surrounding diabetes. Self-Management skills increased across time. Higher friend knowledge/helpfulness during emerging adulthood was associated with increased Self-Management skills. Adherence improved when relationships with providers and friends matched transition readiness skills, indicating that these relationships may facilitate transition skills in early emerging adulthood.
Privacy-Aware Crowdsourced Spectrum Sensing and Multi-User Sharing Mechanism in Dynamic Spectrum Access Networks

Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multi-user sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based $k$ -anonymity grouping algorithm are proposed to prevent each helper’s privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers’ detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multi-user transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms.
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks

High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning, a prominent method in artificial intelligence, to design an energy-preserving MAC protocol, with the aim to extend the network lifetime. Our QL-MAC protocol is derived from Q-learning, which iteratively tweaks the MAC parameters through a trial-and-error process to converge to a low energy state. This has a dual benefit of 1) solving this minimization problem without the need of predetermining the system model and 2) providing a self-adaptive protocol to topological and other external changes. QL-MAC self-adjusts the WSN node duty-cycle, reducing energy consumption without detrimental effects on the other network parameters. This is achieved by adjusting the radio sleeping and active periods based on traffic predictions and transmission state of neighboring nodes. Our findings are corroborated by an extensive set of experiments carried out on off-the-shelf devices, alongside large-scale simulations.
A Denoising Autoencoder based wireless channel transfer function estimator for OFDM communication system

This paper proposes a channel estimation method for Orthogonal Frequency Division Multiple Access (OFDM) communication system by utilizing a Neural Network (NN) based a Machine Learning (ML). Especially, Autoencoder is utilized to estimate Channel Transfer Function (CTF) and to reduce a noise on the estimate. Japanese Digital TV broadcast system is assumed as target system. Then 8k FFT/IFFT is used and number of sub-carriers are 5617 such as mode3 in Integrated Services Digital Broadcasting-Terrestrial (ISDB-T) spec. 5617 complex CTF points must be estimated by limited number of scattered pilot sub-carriers. Assumed channel condition is 2 wave multipath channel with Additive White Gaussian Noise (AWGN). The multipath parameters are randomly generated. To train the autoencoder, 5000 CTFs are generated and pre-training was performed. System performance was evaluated by measuring Bit Error Rate (BER). The system with conventional frequency-domain interpolator and the system with autoencoder based were compared. According to BER simulation results, the autoencoder based system has shown lower BER than the conventional. At BER=10$^{-5}$, autoencoder system shows roughly 2dB gain than conventional system.
The Effects of Natural Language Processing on Big Data Analysis: Sentiment Analysis Case Study

The social networks are one of the main sources of big data. Continuously, it produce huge volume of variety types of data at high velocity rates. This huge volume of data contains valuable information that requires efficient and scalable analysis techniques to be extracted. Hadoop/MapReduce is considered the most suitable framework for handling big data because of its scalability, reliability and simplicity. One of the basic applications to extract valuable information from data is the sentiment analysis. The sentiment analysis studies peoples' opinion by classifying their written text into positive or negative polarity. In this work, a sentiment analysis method for analyzing a Twitter data set is analyzed. The method uses the Naive Bayes algorithm for classifying the text into positive and negative polarity. Several linguistic and NLP preprocessing techniques were applied on the data set. The aim of these preprocessing techniques is to study their effects on the quality of big data classification. The applied preprocessing techniques have achieved an enhancement in the classification accuracy of the Naive Bayes algorithm. The experiments prove that the performance of the sentiment analysis is enhanced by 5% using NLP and linguistic processing, yielding an accuracy of 73 % on the used data set.
Temporal Logic Guided Safe Reinforcement Learning Using Control Barrier Functions

Using reinforcement learning to learn control policies is a challenge when the task is complex with potentially long horizons. Ensuring adequate but safe exploration is also crucial for controlling physical systems. In this paper, we use temporal logic to facilitate specification and learning of complex tasks. We combine temporal logic with control Lyapunov functions to improve exploration. We incorporate control barrier functions to safeguard the exploration and deployment process. We develop a flexible and learnable system that allows users to specify task objectives and constraints in different forms and at various levels. The framework is also able to take advantage of known system dynamics and handle unknown environmental dynamics by integrating model-free learning with model-based planning.
Estimation of Fiber-Waveguide Coupling Loss and Waveguide Propagation Loss by Spectral Analysis

This letter presents a non-destructive and simple method for simultaneous measurement of fiber-waveguide coupling loss and propagation loss of single-mode optical waveguides. The experiment includes the coupling of a medium bandwidth laser source to optical waveguide under test and the analysis of its transmission spectra by using a grating monochromator. The technique has been demonstrated for a 46.5-mm-long fabricated titanium-indiffused lithium niobate (Ti:LiNbO3) waveguide for transverse magnetic polarization, and our first experiment yields a 2.18-dB fiber-waveguide coupling loss and a 0.58-dB/cm propagation loss at around 1557-nm transmitting wavelength. The measurement process needs no external perturbation, such as electric field or controlled heating, and hence is applicable for optical waveguides as-fabricated.
Reflected Self-Image and Commitment to a Career Role

Twenty-three university students enrolled in an introductory Army Reserve Officers Training Corps (ROTC) course described thirteen aspects of themselves by choosing terms from an idiographically generated list of traits. Among the self-images described were the reflected self-image in the ROTC (the way others see me now in the ROTC), actual career self, and the desired career identity. Each participant's data was individually analyzed using HICLAS. Discrepancies between the reflected self-image and (a) the actual career self and (b) desired career identity were scored from each participant's HICLAS output. Both discrepancy scores predicted intention to continue in the ROTC, assessed six months after the self-descriptive data were collected. The reflected self-image/actual career self discrepancy was the better predictor of intention to continue, and was the only discrepancy score to predict whether or not students actually remained in ROTC in the following academic year. The actual self may be the more reliable reference point to which the reflected self image is compared, and the discrepancy between these images of self may inform people's judgment of whether they fit in with a group.