Miracle-dz/UniFAR-FTUs huggingface dataset:
Orbital error modeling and analysis of spaceborne InSAR

Spaceborne synthetic aperture radar interferometry is a key earth observation technique for large-area deformation monitoring. Measurements can be biased by error originating from different sources. Satellite orbital error affects interferograms in the form of an almost linear signal. The commonly used methods to mitigate the orbital error are to calculate the residual baseline error by the Fast Fourier Transform on the residual phase or to fit it with the quadratic surface. These two methods are applicable in most cases, but do not apply in the presence of a large-scale deformation signal that has similar spatial characteristics, such as tectonic movement or tides. Based on the current research on modeling and analysis of baseline error, this paper realized the orbital error network adjustment of Envisat ASAR data in Los Angeles area according to the principle of network adjustment. For quality control on the network level, iterative data snooping has been used, and the result proved its capability to detect and reject outliers.
Linear response of self assembling systems: mean field solution

We study the linear response of living systems, which break and recombine randomly The mean field approximation gives an analytical solution for the time evolution of the response As an example, the viscosity of living polymers is computed New scaling laws for viscosity are found in the case of very short lifetimes The laws do not depend in an obvious way on the exponent of the chain relaxation time with respect to its length The critical behaviour is confirmed by an exact calculation in the limit of very short lifetimes , 1. Inboduction. New « living » systems, such as living polymers (sulfur [1], selenium [2], grant micelles [3]j or physical gels (See [4], for instance) are now available for physicists. All these Systems contain elementary bodies (e g monomers) These bodies self-assemble in various geometnes (we say in various states) The word «living» means that a given body, at thermodynamical equilibrium, changes randomly and discontinuously of state during time For instance, a monomer in a living polymer system belongs to a chain whose length varies in time by breaking and recombining with an other chain. Hence, at thermodynamical equilibnum, every state has a finite lifetime, and we can define a jump rate from one state of another We are interested in the linear response of iuch systems to any extensive perturbation, such as strain stress relaxation, electric birefnngence, time correlation functions There is a fundamental difference between the effects of finite lifetime and of the polydispersity A polydisperse system response is the averaged response over all the states.
Development of High-Density Radio Frequency Plasma Sources With Very Small Diameter for Propulsion

Radio frequency (RF) plasma sources, especially helicon ones, are very useful in many fields, because of the high-density (up to <inline-formula> <tex-math notation="LaTeX">$\sim 10^{13}$ </tex-math></inline-formula> cm<sup>−3</sup>) and low electron temperature (from a few to several electronvolts) available, using an RF frequency range. Here, we develop and characterize very small-area (from 2-cm down to 0.1–0.3 cm in diameter) RF sources that are useful for a space propulsion system with an advanced concept of an electrodeless condition (no direct contact between the plasma and electrodes), leading to a longer operation lifetime. Measurements by using electrostatic probes as well as by a spectrometer based on the collisional radiative model in a small plasma region were employed to estimate the electron density <inline-formula> <tex-math notation="LaTeX">$n_{\mathrm{ e}}$ </tex-math></inline-formula> and its temperature. Here, <inline-formula> <tex-math notation="LaTeX">$n_{\mathrm{ e}}$ </tex-math></inline-formula> was measured as a function of the input RF power and RF excitation frequency, especially in a higher frequency range than the usual one of ~14 MHz, under the mirror magnetic-field configuration. The electron cyclotron resonance effect in the divergent field was examined under a weaker magnetic field with the RF frequency lower than those generally employed. The particle production efficiency, even in this small source, showed an excellent performance, based on classical diffusion discussions. In addition, applying these sources were applied to a space propulsion system with the concept of the <italic>m</italic> (azimuthal mode number) = 0 half-cycle acceleration, which was operated under all electrodeless conditions in both the plasma generation and electromagnetic acceleration phases.
Spherically symmetric relativistic MHD simulations of pulsar wind nebulae in supernova remnants

Pulsars, formed during supernova explosions, are known to be sources of relativistic magnetized winds whose in- teraction with the expanding supernova remnants (SNRs) gives rise to a pulsar wind nebula (PWN). We present spherically symmetric relativistic magnetohydrodynamics (RMHD) simulations of the interaction of a pulsar wind with the surround- ing SNR, both in particle and magnetically dominated regimes. As shown by previous simulations, the evolution can be divided into three phases: free expansion, a transient phase characterized by the compression and reverberation of the reverse shock, and a final Sedov expansion. The evolution of the contact discontinuity between the PWN and the SNR (and consequently of the SNR itself) is almost independent of the magnetization of the nebula as long as the total (magnetic plus particle) energy is the same. However, a dierent behaviour of the PWN internal structure is observable during the compression-reverberation phase, depending on the degree of magnetization. The simulations were performed using the third order conservative scheme by Del Zanna et al. (2003).
Electrooptic Bragg Deflection Modulator for UV Laser Light Using Periodically Poled MgO:s-LiTaO$_{3}$

We propose and demonstrate an electrooptic Bragg deflection optical modulator using a periodically poled MgO:s- LiTaO3 thin crystal for ultraviolet (UV) laser light. This modulator is a building block for implementation of a one-dimensional spatial light modulator, and can be driven by voltage lower than the conventional bulk-type device. The device was constructed by bonding a thin periodically poled MgO:SLT and a robust support plate with gold layers in-between. A thin crystal structure with good surface flatness was obtained. Good performances, which include a diffraction efficiency as high as 97.8% and a high extinction ratio, were accomplished at 355-nm wavelength.
Automated Coding of Open-Ended Survey Responses Dallas Card and

Background: Manual coding of open-ended survey responses is time-consuming, expensive, and error-prone. However, this task can be recast as a multi-label text classification problem, to which natural language processing and machine learning techniques can be applied. Aim: The aim of this work is to assess the suitability of various machine learning techniques to the problem of automated coding of open-ended survey responses, and to explore the use of techniques to automate the search for an appropriate model. Data: The data for this project consist of approximately 28,500 manually-coded responses by 2,000 respondents to open-ended survey questions from the 2008 American National Election Studies survey. Methods: We compare the application of two types of machine learning methods to this task: L1-regularized logistic regression, and recurrent neural networks. In an effort to provide a fair comparison, while minimizing bias in favor of novelty, we make use of Bayesian optimization to perform an automated search over the configuration space of these two types of models, and make use of a reusable holdout in an attempt to prevent overfitting. Results: In the case of a traditional logistic regression model, we find that Bayesian optimization is an effective way to do feature selection and hyperparameter tuning. For recurrent neural networks, which involve a much larger space of possible configurations, we have been unable to produce a consistent improvement over the baseline in performance on this data. Conclusions: Although we do not presently recommend recurrent neural networks as a effective or easy-to-use approach to this kind of limited-data multi-label classification problem, we believe that with further work, recurrent neural networks do have advantages which can be realized through an automated model selection process.
Secure Communication through Prior Key Distribution System for Nodes

In near future, the Wireless Sensor Networks (WSN) is widely used in many applications like military and civil domains. The wireless sensor networks are always deployed in hostile and pervasive environment. Security is major concern in the wireless sensor network. The traditional network security methods are not suitable for wireless sensor networks because of limited resource. Several Key management and establishment schemes have been proposed to provide light weight ciphers. The key management schemes play an important role in wireless sensor networks to achieve security. In this paper a survey of key management schemes in wireless sensor networks has been executed. Each and every key management schemes has some trade off between limited resource and security. Classification of key management schemes based on encryption was done. Further based on key pre-distribution (with or without deployment knowledge) and rekeying mechanism, the key management schemes have been analysed and their limitations and advantages have been summarized.
Design Optimization of Charge Preamplifiers With CMOS Processes in the 100 nm Gate Length Regime

Low noise design of charge sensitive amplifiers in deep submicron CMOS technologies is discussed based on the experimental characterization of transistors belonging to a 130 nm and a 90 nm minimum channel length processes. After briefly examining the main preamplifier noise sources, residing in the input element, achievable resolution limits in charge measuring systems employing such technologies are discussed under different detector capacitance, processing time and power dissipation constraints. The equivalent noise charge (ENC) model adopted in this work takes into account the behavior of series 1/f noise as a function of the overdrive voltage in PMOS devices. Moreover, noise in the gate current, whose effects could be neglected in past CMOS technologies featuring larger gate oxide thickness, is shown to play a role in the optimization process, significantly affecting the preamplifier performance at long shaping times. The extent of this contribution, besides depending on the drain current in the input device, is also determined by its drain voltage, which therefore may become a critical parameter in the design of low noise analog blocks.
Partial price adjustment and autocorrelation in foreign exchange markets

This paper studies autocorrelation in the won/dollar Foreign Exchange (FX) market. In contrast to FX markets in developed countries, we find significant positive autocorrelation until quite recently, with partial price adjustment the only plausible explanation. We find that the autocorrelation was eliminated as the FX market was gradually liberalized. The informational efficiency of the market was significantly improved, with the time required for new information to be fully incorporated into the exchange rate diminishing from eight trading days to less than one day. We study the determinants of the autocorrelation from a market microstructure point of view, finding that the autocorrelation was not related to volatility, and was related to trading volume only after the final stage of the FX liberalization. While the liberalization allowed greater freedom to make speculative trades, our results suggest that the bulk of the trade was for hedging rather than speculative purposes
Optic disc segmentation based on Otsu's thresholding and level set

Retinal image inspection is crucial to identify and supervise a class of retinal diseases. Image processing procedures are widely considered to extract the infected region of retinal image in order to have a clear idea about the disease. In this paper, heuristic algorithm assisted multi-level thresholding and level set approaches are considered to extract the optic disc from the retinal image dataset. The proposed approach has three processing sections, such as (i) Heuristic algorithm guided multi-level thresholding, (ii) K-means clustering based separation and (iii) Level set based segmentation. The proposed approach is implemented on the RIM-ONE dataset and validated using the image similarity index values, such as Jaccard, Dice, False Positive (FP) rate and False Negative (FN) rate. The investigational outcome illustrate that, the proposed approach offers better values of image similarity index. Hence, the proposed technique is very significant for the segmentation and analysis of optic disc images.
Cognitive and Brain Development in Adolescence

Resume La theorie piagetienne des operations formelles a inaugure le travail empirique sur le developpement cognitif de l’adolescent. Ce lancer a abouti a deux resultats principaux. D’une part, il n’y a pas eu confirmation de stades, d’âge d’acquisition ni de conditions logiques specifiques ; d’autre part, des recherches se sont developpees sur les mecanismes sous-tendant les changements lies a l’adolescence. Des changements importants concernent la vitesse de traitement, la memoire de travail, le controle inhibiteur, les capacites de planification strategique, toutes capacites en rapport avec les fonctions executives. Les recherches montrent en particulier que les progres executifs concernent davantage les domaines de plus grande expertise.Plus recemment, on note un focus sur la cognition de l’adolescent dans des modeles duels faisant une distinction entre la pensee heuristique et la pensee analytique plus exigeante, et entre une fonction executive raisonnee (cold) ou spontanee (hot) selon le degre d’investissement emotionnel et social.Les modeles de traitement duels et les neurosciences developpementales s’accordent sur le role de la myelinisation dans la vitesse de traitement, sur le developpement accelere du systeme limbique, sur la predominance du cortex prefrontal (PFC) dans le jugement et pour l’orchestration des autres systemes neuraux. Les donnees experimentales cognitives confirment. Dans le futur, la relation directe entre le developpement cognitif et le developpement cerebral sera sans nul doute privilegiee, avec les rapides progres des technologies d’imagerie.
Sir J.C. Bose and radio science

Sir J.C. Bose's pioneering works in quasi-optic millimeter wave research in Calcutta, India about 100 years back during 1890s are highlighted. He developed an elegant millimeter wave spark transmitter, self recovering coherer detector, wire grid polariser, cylindrical diffraction grating, dielectric lens and prism, rectangular waveguide, horn antenna and microwave absorber, for the studies of reflection, refraction, absorption and polarisation of millimeter waves and its application to wireless remote control for firing a gun. All these pioneering activities indicate that he was well ahead of his time and prompted us to call him the father of Radio Science.
Exploiting Approximate MLC-PCM in Low-Power Embedded Systems

Multi-level cell phase change memory (MLC-PCM), because of its very low leakage power and high density, is promising for embedded systems. Furthermore, for applications with inherent low sensitivity to errors, approximate write operations can be exploited in MLC-PCM to improve endurance and performance. However, data that reside in the approximate MLC-PCM for a rather long time without refreshing are prone to soft errors due to resistance drift phenomenon, while even for an application with inherent low sensitivity to errors, a high soft error rate can degrade its Quality of Result (QoR). The architecture-level approaches to decrease the drift effect incur considerable power overhead (about 100%), which is a prominent issue in embedded systems, and are dependent on the number of logic levels stored in the PCM cell (e.g., most of them are designed for 4LC-PCM). This article, taking a different approach, proposes a drift-aware frequency and voltage management to alleviate the drift-based soft-error rate. To this end, first we characterize the application data based on the degree of being exposed to the drift to identify the drift-prone application data. Then we assign the execution frequency and voltage to different regions of the application considering the drift. This frequency assignment speeds up the application regions wherein the drift-prone data are accessed to shorten the lifetime of the drift-prone data, thereby decreasing the soft error rate. An integer linear programming model implements our proposed Dynamic Voltage Frequency Scaling (DVFS). Also, the proposed approach is independent of the number of levels of PCM cells and can be applied to any MLC-PCM system. To evaluate the approach, the approximate MLC-PCM is simulated using empirical models and is integrated into a full-system simulator as data memory. The experimental results show that, by exploiting the approach, QoR is in the acceptable range, while its power overhead is about 84% (on average) less than that of the architecture-level approach.
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row

We present a machine learning approach that ranked on the first place in the Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial Evaluation Campaign. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech or phonetic transcripts, we also use a kernel based on dialectal embeddings generated from audio recordings by the organizers. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Preliminary experiments indicate that KRR provides better classification results. Our approach is shallow and simple, but the empirical results obtained in the 2018 ADI Closed Shared Task prove that it achieves the best performance. Furthermore, our top macro-F1 score (58.92%) is significantly better than the second best score (57.59%) in the 2018 ADI Shared Task, according to the statistical significance test performed by the organizers. Nevertheless, we obtain even better post-competition results (a macro-F1 score of 62.28%) using the audio embeddings released by the organizers after the competition. With a very similar approach (that did not include phonetic features), we also ranked first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign, surpassing the second best method by 4.62%. We therefore conclude that our multiple kernel learning method is the best approach to date for Arabic dialect identification.
Quantification and examination of depression-related mental health literacy.

OBJECTIVE The aim of the study was to create an algorithm to measure depression-related mental health literacy. METHOD Participants were those recruited for the 1998 and 2004 South Australian Health Omnibus Surveys. Two unprompted questions were selected from a mental health literacy questionnaire and responses were quantified through allocation of scores agreed by a focus group of mental health professionals. RESULTS An application of the algorithm resulted in findings consistent with those of other research, providing face validity for the method developed. The application allowed for examination of overall levels of depression-related mental health literacy, with improvement found between 1998 and 2004. Similarly, differences in overall literacy according to demographics were also uncovered. CONCLUSIONS Tracking mental health literacy within populations is important as it has been shown to be related to individual help-seeking as well as provision of support for those with a mental disorder. The method devised in this study allows for comparisons of literacy levels to be made across populations and time. It also offers increased capacity for statistical application.
Gene Expression Analysis Using Clustering

s--Data Mining has become an important topic in effective analysis of gene expression data due to its wide application in the biomedical industry. In this paper, k-means clustering algorithm has been extensively studied for gene expression analysis. Since our purpose is to demonstrate the effectiveness of the k-means algorithm for a wide variety of data sets, we have chosen two pattern recognition data and thirteen microarray data sets with both overlapping and non-overlapping cluster boundaries, where the number of features/genes ranges from 4 to 7129 and number of sample ranges from 32 to 683. The number of clusters ranges from two to eleven. We use the clustering error rate (or, clustering accuracy) as evaluation metrics to measure the performance of k-means algorithm.
Evaluating the benefits of a youth mental health curriculum for students in Nicaragua: a parallel-group, controlled pilot investigation

Background. High rates of mental illness and addictions are well documented among youth in Nicaragua. Limited mental health services, poor mental health knowledge and stigma reduce help-seeking. The Mental Health Curriculum (MHC) is a Canadian school-based program that has shown a positive impact on such contributing factors. This pilot project evaluated the impact of the MHC on mental wellness and functioning among youth in Leon, Nicaragua. Methods. High school and university students (aged 14–25 years) were assigned to intervention (12-week MHC; n = 567) and control (wait-list; n = 346) groups in a non-randomized design. Both groups completed measures of mental health knowledge, stigma and function at baseline and 12 weeks. Multivariate analyses and repeated measures analyses were used to compare group outcomes. Results. At baseline, intervention students showed higher substance use (mean difference [MD] = 0.24) and lower perceived stress (MD = −1.36) than controls (p < 0.05); there were no other group differences in function. At 12 weeks, controlling for baseline differences, intervention students reported significantly higher mental health knowledge (MD = 1.75), lower stigma (MD = 1.78), more adaptive coping (MD = 0.82), better lifestyle choices (MD = 0.06) and lower perceived stress (MD = −1.63) (p < 0.05) than controls. The clinical significance as measured by effect sizes was moderate for mental health knowledge, small to moderate for stigma and modest for the other variables. Substance use also decreased among intervention students to similar levels as controls (MD = 0.03) (p > 0.05). Conclusions. This pilot investigation demonstrates the benefits of the MHC in a low-and-middle-income youth population. The findings replicate results found in Canadian student populations and support its cross-cultural applicability.
An agent based model of a frequency activated electricity reserve market

This paper introduces an agent based model for Frequency Activated Reserve Markets. Generation Units (GenUns) bid both prices and quantity in interconnected and dynamically congested Market Areas in order to reach their optimal production point. The units are limited by their spare capacity after their actions on the spot market. Generation Companies (GenCos) manage the strategy portfolios of their subordinate agents with the goal of coordinating the bidding behavior and subsequently increasing profits. A case study of Monte Carlo simulated units will show the dominance of Marginal Cost bidding over different periods and pricing modes (System Price and Pay-as-Bid) as well as the quality of the chosen modeling approach.