Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of noise imbalance on dual-MRC over Rayleigh fading channels

The performance analysis of dual (two-branch) maximal ratio combining (MRC) under imperfect weight, named imperfect MRC, due to the noise imbalance is derived over independent but non-identical Rayleigh fading channels. Considering the system (or channel) conditions that the noise level of each branch is different, we present the accurate performance analysis of imperfect dual MRC in terms of average combined signal-to-noise ratio (SNR), outage probability and average symbol error rate for a large class of modulations in closed-form and compare them with the performance of the perfect MRC and the perfect selection combining (SC) over non-identical but independent Rayleigh fading channels based on the interesting statistical results on the combined SNR. From the performance results we provide the criterion in choosing the imperfect MRC, perfect MRC, or SC depending on the degree of the difference of the noise level between branches.
Strategic Structural Design of a Gel Polymer Electrolyte toward a High Efficiency Lithium-Ion Battery

Electrolytes have played critical roles in electrochemical energy storage. In Li-ion battery, liquid electrolytes have shown their excellent performances over decades, such as high ionic conductivity (∼10–3 S cm–1) and good contacts with electrodes. However, the use of liquid electrolytes often brought risks associated with leakage and combustion of organic electrolytes. Hence, polymer electrolytes become potential candidates to replace liquid electrolyte systems. Although solid polymer electrolytes (SPEs) offer better safety and good mechanical properties to take over liquid electrolytes, most of them only deliver low ionic conductivities (∼10–8 S cm–1) and poor contact with electrodes, resulting in poor cycle performance and low electrical capacity of the batteries. In addition, gel polymer electrolytes (GPEs) have received increasing research attention due to their relevant characteristics, which extend from liquid electrolytes and solid polymer electrolytes. In this review, state-of-the-art samples of.
Methods for Automatic Keyword Extraction on Transcribed Texts : A Comparison

The subject of this thesis is the comparison of different methods for automatic keyword extraction on transcribed texts from the TED database. For this purpose a keyword extraction system based on different features was developed and implemented. The system uses a log linear model to combine the different features and classify potential keyword candidates. It was tested and developed on English texts but is easily adaptable to other languages. In order to discriminate between good and bad keyword candidates for the current document different features are used that rely on both the features of past keywords that are collected during a training procedure and features of N-Grams that are part of the current document. These include the position of first occurrence of a candidate in the document, Part of Speech (POS) tag sequences, the Inverse Document Frequency (IDF), the χ measure as well as the well-known metric TF-IDF. For the parameter tuning of the keyword extraction system the simplex algorithm by John Nelder and Roger Mead as well as Powell search are used. The results of the system with both parameter tuning methods are compared to results achieved using an SVM on the same task. The thesis starts with the description of preprocessing steps that are necessary to give the text the right format. The training process where most of the feature information is gathered as well as the keyword extraction process itself using the three different approaches are described in detail afterwards. The three different keyword extraction approaches are evaluated with different POS thresholds and cleaning methods to prune bad candidates. Further experiments analyse the impact of differing feature combinations on the resulting F-scores. The thesis concludes with a discussion of the experimental results as well as an outlook on prospective future improvements and changes to the developed system. http://www.ted. com/talks
Arthroscopic Retrograde Drilling in Juvenile Osteochondritis Dissecans of the Talus

Background: Juvenile osteochondritis dissecans of the talus is rare, and the literature provides little data to guide treatment. The purpose of the present study was to evaluate our clinical and radiographic results with arthroscopic retrograde drilling in patients who were refractory to conservative care. Methods: We retrospectively evaluated all patients with juvenile osteochondritis dissecans of the talus who underwent surgery for the treatment of stable lesions that failed conservative treatment. Medical records were reviewed for symptoms and demographic information. Preoperative and latest postoperative radiographs were used to determine degree of healing. AOFAS Ankle/Hindfoot scale and visual analog scale for pain were used to evaluate clinical outcomes. Results: We identified 6 patients (6 ankles). The mean age was 13 years, and the mean duration of follow-up was 37 months (range, 16 to 69 mo). All of them had progressed toward healing and were asymptomatic, but only 3 out of 6 had a complete radiographic healing at last follow-up. The average AOFAS Ankle/Hindfoot score improved from 69 points (55 to 75, IQR=10) preoperatively to 98 points (90 to 100, IQR=7) (P<0.0027). Visual analog scale improved from 6.2 (4 to 8, IQR=3) to 0.3 (0 to 2, IQR=1) (P<0.002). All patients expressed satisfaction with operative results. Conclusions: Arthroscopic retrograde drilling seems to be effective for symptoms relief, although 50% of the cases have had persistent lesions on radiographs. A longer follow-up is necessary to assess joint function in those cases with partial radiographic healing. Level of Evidence: Level IV—therapeutic.
Hypnosis for PTSD: Evidence Based Placebo-Controlled Studies

A single manualized abreactive hypnosis session (5-6 hours) based on Ego State Theory (EST) was recently subjected to two placebo-controlled investigations meeting evidence-based criteria. Thirty-six patients in study #1 and 30 patients in study #2 who met PTSD criteria were exposed to either 5-6 hours of a manualized treatment or a placebo in a single session. Abreactive hypnosis emphasized hypnotically activated “reliving” of the trauma experience to physical and psychological exhaustion. In study #1 hypnosis and control group’s reduced PTSD checklist (PCL) scores immediately post treatment (placebo PCL score mean reduction 17. 34 and EST treatment PCL mean reduction 53.11). However, only the hypnosis patients maintained significant treatment effects at followups. Study #2 used the Davidson Trauma Scale (DTS), Beck Depression II (BDI – II), and Beck Anxiety Scales (BAI). Only the hypnosis group showed significant positive effects from pretreatment to all post treatment measurement periods. Abreactive EST was shown to be a highly effective and durable treatment for PTSD. Apparently, EST works because it is emotion focused, activates sub-cortical structures, and because the supportive, interpretive therapist reconstructs the patient’s personality to be resilient and adaptive.
Vestibular Injury After Low-Intensity Blast Exposure

The increased use of close range explosives has led to a higher incidence of exposure to blast-related head trauma. Exposure to primary blast waves is a significant cause of morbidity and mortality. Active service members and civilians who have experienced blast waves report high rates of vestibular dysfunction, such as vertigo, oscillopsia, imbalance, and dizziness. Accumulating evidence suggests that exposure to blast-wave trauma produces damage to both the peripheral and central vestibular system; similar to previous findings that blast exposure results in damage to auditory receptors. In this study, mice were exposed to a 63 kPa peak blast-wave over pressure and were examined for vestibular receptor damage as well as behavioral assays to identify vestibular dysfunction. We observed perforations to the tympanic membrane in all blast animals. We also observed significant loss of stereocilia on hair cells in the cristae and macule up to 1 month after blast-wave exposure; damage that is likely permanent. Significant reductions in the ability to perform the righting reflex and balance on a rotating rod that lasted several weeks after blast exposure were prominent behavioral effects. We also observed a significant reduction in horizontal vestibuloocular reflex gain and phase lags in the eye movement responses that lasted many weeks following a single blast exposure event. OKN responses were absent immediately following blast exposure, but began to return after several weeks’ recovery. These results show that blast-wave exposure can lead to peripheral vestibular damage (possibly central deficits as well) and provides some insight into causes of vestibular dysfunction in blast-trauma victims.
Geringverdiener: Wem und wie gelingt der Aufstieg? / Low-Wage Earners: Who Manages to Reach Higher Wage Levels?

Summary Using representative linked employer-employee data of the German Federal Employment Agency, this paper analyzes to which extent full-time employees who earned low wages (less than two-thirds of the median wage) in 1998/99 were able to earn higher wages in the following years, and which factors play a role in this context. It is shown that just one out of seven of these low-wage earners was able to earn wages above the low wage threshold in 2005. Bivariate probit estimations with endogenous selection indicate that younger and better qualified low-wage earners record a higher probability of getting higher wages, whereas women are less successful. In addition, this probability shows a significant relationship with the size and the employment structure of the establishment. Moving from one establishment to another is an important instrument for leaving low-wage employment.
Automatic cell detection and segmentation from H and E stained pathology slides using colorspace decorrelation stretching

Purpose: Automatic cell segmentation plays an important role in reliable diagnosis and prognosis of patients. Most of the state-of-the-art cell detection and segmentation techniques focus on complicated methods to subtract foreground cells from the background. In this study, we introduce a preprocessing method which leads to a better detection and segmentation results compared to a well-known state-of-the-art work. Method: We transform the original red-green-blue (RGB) space into a new space defined by the top eigenvectors of the RGB space. Stretching is done by manipulating the contrast of each pixel value to equalize the color variances. New pixel values are then inverse transformed to the original RGB space. This altered RGB image is then used to segment cells. Result: The validation of our method with a well-known state-of-the-art technique revealed a statistically significant improvement on an identical validation set. We achieved a mean F1-score of 0.901. Conclusion: Preprocessing steps to decorrelate colorspaces may improve cell segmentation performances.
IMPACT OF TRADING VOLUME ON PREDICTION OF STOCK MARKET DEVELOPMENT

The paper focuses on the influence of trading volume on quality of prediction of stock market development. The main objective of this article is to assess the influence of stock trading volume level on quality of prediction with use of technical analysis. The research was applied on stocks included in the S & P 500 index. Based on average daily trading volume, three aggregate indexes were constructed. The dynamics of index return volatility was modeled by GARCH-class models. GARCH(1,1), GJR and EGARCH models were estimated for each time series. The in-sample evidence indicated that the return volatility of the indexes can be characterized by significant persistence and asymmetric effects. The best estimate of each model was produced for the index of stocks with the highest average trading volume.However the result could differ based on the observed period, the volatility structure of the examined data supports the idea that influential investors respond to various shocks in the market primarily by closing or opening their largest position.The importance of the level of trading volume for the prediction of financial time series development was shown in the paper. This finding could help generate such volatility structure of time series which would allow to explain development of the time series by various models with better results.
Molecular Behavior of CENP-A and PCNA throughout the Cell Cycle in Living Human HT-1080 Cells

Centromere protein A (CENP-A) is a centromere-specific histone H3 variant present in prekinetochores in nuclei and kinetochores of mitotic chromosomes. The proliferating cell nuclear antigen (PCNA) forms replication foci in S phase but diffuses in nuclei except for nucleoli in other phases. Here to elucidate the molecular behavior of CENP-A and PCNA throughout the cell cycle, we performed a prolong time-lapse imaging of HT-1080 cell line stably expressing mKO-CENP-A and EGFP-PCNA with a highly sensitive EM-CCD camera. We followed the mKO-CENP-A dots in interphase nuclei from G1 through to G2 phase, monitoring the cell cycle progression by the appearance of replication foci of EGFP-PCNA in S phase.
Transamidation of dimethylformamide during alkylammonium lead triiodide film formation for perovskite solar cells

Perovskite-based solar cells, typically CH_3NH_3PbI_3, have reached power conversion efficiencies on par with single crystal silicon solar cells. Perovskite cells prepared with the most common perovskite solvent N,N -dimethylformamide (DMF) by different research groups exhibit disparate efficiencies and stability for nominally identical perovskite films. Although the differences can be related to processing conditions, a consistent physical cause for the differences has been lacking. Highly-sensitive time-of-flight secondary ion mass spectrometry (TOF-SIMS) reveals significant dimethylamine (DMA) included in perovskite films. TOF-SIMS and x-ray photoelectron spectroscopy results suggest DMA levels ranging from roughly 10–50%. Only the highest levels register as perovskite peak shifts in x-ray diffraction; lower levels are invisible. We propose that methylamine (MA) can react with DMF solvent by transamidation to produce dimethylamine (DMA), which then displaces some MA in perovskite crystals, see Fig. 1. Transamidation of DMF can be catalyzed by TiO_2, Al_2O_3, water, or acid, but in perovskite films transamidation is inhibited by water.
Jakub Czajkowski OPTICAL COHERENCE TOMOGRAPHY AS A CHARACTERIZATION METHOD IN PRINTED

This Thesis proposes and describes the use of optical coherence tomography (OCT) as a noncontact and non-destructive characterization technique for printed electronics. It is based on and includes the first published results of such an application of the OCT technique. Several different types of structures were studied to evaluate the feasibility of the application. The measurement data was used to define the surface topography, physical dimensions of the specimen features, and to evaluate the ability to characterize multi-layered and multi-material structures. Presented OCT measurements were done for: screen-printed conductive and insulating structures, microfluidic channels, microscopy glass and organic field effect transistors (OFET), both coated with polymer, and inkjet-printed colour filters. A novel approach to encapsulation inspection was presented. The results show that OCT could be used for full volumetric and non-destructive characterization of the 1-to-2-μm-thin protective layers used in organic and printed electronics. The measurements presented in the Thesis were done using OCT devices in time and in spectral domains. Despite the focus on studying the application of the technique, as a result of observations and limitations of the existing equipment, a new type of OCT device has been developed. A high data acquisition rate of the spectrometer-based systems (SD-OCT) was combined with a broadband supercontinuum light source, used so far mainly in the time-domain (TD-OCT), to enable the sub-micron-resolution spectral domain optical coherence tomography (SMR SD-OCT). The supercontinuum generation effects with virtually white probing light and enables not only superior resolution, but also, e.g., true-colour OCT imaging. The measurements performed on the inkjet-printed colour filters confirm that despite the absorptive properties of the materials, characterization of the few-microns-thin ink layers is possible using visible range of the electromagnetic spectrum and spectral domain OCT. The study shows the potential and versatility of OCT in the printed electronics characterization. In addition, the Thesis discusses further development of the technique, needed to fully match the challenging requirements of the on-line quality inspection.
System-level power management using online machine learning for prediction and adaptation

Nowadays embedded devices have the need to be portable, battery powered and high performance. This need for high performance makes power management a matter of critical priority. Power management algorithms exist, but most of the approaches focus on an energy-performance trade-off oblivious to the applications running on the system. Others are application-specific and their solution cannot be applied to other applications. This work proposes Shepherd, a cross-layer runtime management system for reduction of energy consumption whilst offering soft real-time performance. It is cross-layer because it takes the performance requirements from the application, and learns to adjust the power management knobs to provide the expected performance at the minimum cost of energy. Shepherd is implemented as a Linux governor running at OS level, this layer offers a low-overhead interface to change the CPU voltage and frequency dynamically. As opposed to the reactive behaviour of Linux Governors, Shepherd adapts to the application-specific performance requirements dynamically, and proactively selects the power state that fulfils these requirements while consuming the least power. Proactiveness is achieved by using AEWMA for adapting to the upcoming workload. These adaptations are facilitated using a model-free reinforcement learning algorithm, that once it learns the optimal decisions it starts exploiting them. This work enables Shep-herd to work with different applications. A programming framework was designed to allow programmers to develop their applications to be power-aware, by enabling them to send their performance requirements and annotations to Shepherd and provide the cross-layer soft real-time performance desired. Shepherd is implemented within the Linux Kernel 3.7. 10, interfacing with the application and hardware to select an appropriate voltage-frequency control for the executing application. The performance of Shepherd is demonstrated on an ARM Cortex-A8 pro-cessor. Experiments conducted with multimedia applications demonstrate that Shep-herd minimises energy consumption by up to 30% against existing Governors. Also, the framework has been used to adapt example applications to work with Shepherd, achieving 60% energy savings compared to the existing approaches.
Smart trailer: Automatic generation of movie trailer using only subtitles

With the enormous growth rate in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. Video summarization is considered a promising approach for efficacious realization of video content through Identifying and picking out descriptive frames of the video. In this paper, we propose an adaptive framework called Smart-Trailer (S-Trailer) to automate the process of creating an online trailer for any movie based only on its subtitle. The language used in the subtitle is English. The framework analyzes the movie subtitle file to extract relevant textual features that are used to classify the movie into its corresponding genre(s). Initial experimentation resulted in generating genre-classification corpus. The generated corpus is tested against real movies dataset and showed high classification accuracy rate (0.89) in classifying movies into their corresponding genre(s). The proposed system returned automated trailers that contain on average 47% accuracy in terms of recalling scenes appeared on the original movie trailer for different movie genres. Currently, we employ deep learning techniques to captures user behaviors and opinions in order to adapt our system to provide users with relevant video scenes recommendations that match their preferences.
WebPropagate : Network Propag

Network propagation is a powerful tool for genetic analysis which is widely used to identify genes and genetic modules that underlie a process of interest. Here we provide a graphical, web-based platform (http://anat.cs. tau.ac.il/WebPropagate/) in which researchers can easily apply variants of this method to data sets of interest using up-to-date networks of protein–protein interactions in several organisms. © 2018 Elsevier Ltd. All rights reserved.
Trajectory Design for Distributed Estimation in UAV-Enabled Wireless Sensor Network

In this paper, we study an unmanned aerial vehicle (UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is nonconvex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves a significant performance gain in terms of the number of SNs whose data are successfully collected, as compared with other benchmark schemes.
Description and Evaluation of the MIT Earth System Model (MESM)

The Massachusetts Institute of Technology Integrated Global System Model (IGSM) is designed for analyzing the global environmental changes that may result from anthropogenic causes, quantifying the uncertainties associated with the projected changes, and assessing the costs and environmental effectiveness of proposed policies to mitigate climate risk. The IGSM consists of the Massachusetts Institute of Technology Earth System Model (MESM) of intermediate complexity and the Economic Projections and Policy Analysis model. This paper documents the current version of the MESM, which includes a two‐dimensional (zonally averaged) atmospheric model with interactive chemistry coupled to the zonally averaged version of Global Land System model and an anomaly‐diffusing ocean model.
Large vocabulary recognition using linked predictive neural networks

A large-vocabulary isolated word recognition system based on linked predictive neural networks (LPNNs) is presented. In this system, neural networks are used as predictors of speech frames, enabling a pool of such networks to serve as phoneme models. Higher-level algorithms are used to organize these networks, linking them into sequences corresponding to the phonetic spellings of words, and to train and evaluate the networks for word recognition. By virtue of linking phonemic networks, the LPNN is vocabulary independent and can be applied to large-vocabulary recognition. Recognition rates of 94% for a 234-word Japanese vocabulary of acoustically similar words and 90% for a larger vocabulary of 924 words are obtained. <<ETX>>