Miracle-dz/UniFAR-FTUs huggingface dataset:
econstor Make Your Publications Visible . A Service of zbw

We construct a theoretical model to capture the compensation and efficiency effects of globalization in a set up where the redistributive tax rate is chosen by the median voter. The model predicts that the two alternative modes of globalizationtrade liberalization and financial opennesscould potentially have different effects on taxation. We then provide some empirical evidence on the relationship between taxation and the alternative modes of globalization using a large cross-country panel data set. On average, globalization is associated with lower taxation but there is some evidence that in countries with high capital-labor ratio, globalization is associated with increased taxation. We make a distinction between de jure and de facto measures of globalization and find a strong negative relationship between taxation and de jure measures of globalization. The results for de facto measures of globalization are mixed. JEL-Codes: F110, F210, F680, H110.
UvA-DARE ( Digital Academic Repository ) Using a digital game for training desirable behavior in cogrnitive-behavioral therapy for burnout syndrome : a controlled study

Burnout is a globally increasing illness, and as a result, many forms of burnout therapy have arisen. The use of digital games can be psychotherapeutically effective because they can transform exercises that are by themselves unattractive into intrinsically motivated action. This pilot study aims to test whether a specially designed game contributes to patients learning desired behavior and achieving other specific therapeutic goals in an online cognitive–behavioral therapy (CBT)-based burnout treatment context. In total, 101 participants took part in the experiment, under four conditions: (a) Game + Therapy, (b) Therapy Only, (c) Game Only, and (d) No Game + No Therapy. Preand postmeasures were taken online. Results showed that the two therapy conditions (Game + Therapy and Therapy Only) showed a greater decrease in complaints and disengagement, and a stronger increase in coping skills than the nontherapy conditions (Game Only and No Game + No Therapy). As expected, the Game + Therapy condition outperformed the Therapy Only condition on combined improvement measures of burnout symptoms. However, analyses of individual measures showed no effects. It can be cautiously concluded that the therapeutic digital game may be a useful tool when embedded in a therapeutic burnout treatment program and is probably more efficient than CBT, as it is used in current practice.
Spatial data clustering in independent mobile environment

Most geolocation applications for mobile devices assume a constant connection with the network and high computational power nodes. However, with ever-developing devices it now becomes possible to establish peer-to-peer networks in case when the network can be unreachable due to special circumstances (like conflicts or natural disasters). In this paper, a method for clustering spatial data in mobile environment is discussed. A simple solution based on OPTICS algorithm with lexical distance is proposed for grouping the observations.
Comparing a Smoothed Particle Hydrodynamics, a Position Based Dynamics, and a Finite Volume Method on a River Section Captured by Bathymetric LiDAR.

Particle-based methods are a widely used technique for ﬂuid-ﬂow simulations nowadays, especially, because GPU hardware became very powerful and methods are very well parallelizable. In this study we apply the smooth particle hydrodynamics (SPH) method, originating from Astrophysics, and the position-based dynamics method (PBD), stemming from Computer Graphics. We compare them to the well-established ﬁnite volume method (FVM) used in engineering in the context of a new data source for river-bed geometry. The river geometry was acquired by airborne bathymetric light detection and ranging (LiDAR). As the data from LiDAR scans is point-based, it is naturally compatible with particle-based methods. We analyze the potential of doing hydraulic computations based on particle methods on bathy-metric LiDAR data. A 160[ m ] long section of a river is selected for the case-study and hydraulic comparison. Particle simulation codes are extended and computations are tuned to meet a water table, known from the LiDAR scan at a reference cross section. The results show 16% higher velocities using PBD compared to the FVM. With further parameter ﬁne-tuning and extensions this can be improved and the method could be an alternative for mesh based river-ﬂow simulations.
Layer Trajectory BLSTM

Recently, we proposed layer trajectory (LT) LSTM (ltLSTM) which significantly outperforms LSTM by decoupling the functions of senone classification and temporal modeling with separate depth and time LSTMs. We further improved ltLSTM with contextual layer trajectory LSTM (cltLSTM) which uses the future context frames to predict target labels. Given bidirectional LSTM (BLSTM) also uses future context frames to improve its modeling power, in this study we first compare the performance between these two models. Then we apply the layer trajectory idea to further improve BLSTM models, in which BLSTM is in charge of modeling the temporal information while depth-LSTM takes care of senone classification. In addition, we also investigate the model performance among different LT component designs on BLSTM models. Trained with 30 thousand hours of EN-US Microsoft internal data, the proposed layer trajectory BLSTM (ltBLSTM) model improved the baseline BLSTM with up to 14.5% relative word error rate (WER) reduction across different tasks.
Deep neural networks learning to solve nonlinear inverse problems for the wave equation

We present the construction of a deep neural network for solving the nonlinear inverse problem of determining an unknown piecewise smooth wave speed from boundary measurements, where wave propagation is governed by the acoustic wave equation. To achieve this, we utilize an unrolled neural network for overcoming the ill-posedness of the problem, with the novel feature that the data itself form layers in the network. We argue that this construction may apply to more general nonlinear inverse problems, as it seeks to learn optimal and minimal data acquisition when restricted to distributions of models of interest. We also reinterpret the network’s architecture from the point of view of TV 2 regularization as well as provide a conservative estimate of the depth of the network for a given speciﬁed accuracy. Lastly, we train networks with the proposed architectures in the case of piecewise-constant wave speeds in one spatial dimension and provide evidence the resulting networks can indeed recover the wave speed.
Determinants of Trading Volume in Karachi Stock Market

This study aimed to investigate the determinants of trading volume. For this purpose a sample of fifty firms listed at KSE had been considered. 50 firms based on capitalization were selected from non-financial sector covering a time period from 2005 to 2014. Descriptive statistics, Variance inflation factor, and panel data estimation model have been employed for the purpose of analysis. The findings revealed that determinants have significant effect on trading volume. It has been observed that abnormal return, volatility (systematic & residual risk), size, institutional holding, dividend yield, positive returns, and negative returns have positive effect on trading volume, while institutional holding has no effect on trading volume.
COMPARATIVE IN VITRO ANTIOXIDANT ACTIVITY AND TOTAL PHENOLIC CONTENTS OF DIFFERENT PARTS OF Vernonia amygdalina

A comparative in vitro assessment of the leaves, stem and roots of Vernonia amygdalina was done in this study using acetone and methanol extracts by 1, 1-diphenyl-2-picrylhydrazyl (DPPH), β-carotene bleaching, reducing power and ferric reducing antioxidant power (FRAP) assays. Additionally, the total phenolic content (TPC) and total flavonoids contents (TFC) of the extracts were determined. Results showed that V. amygdalina leaves acetone extract had the highest phenol (116.36 ± 5.03 mg GAE/g of extract) and flavonoids (78.38 ± 2.18 mg QE/g of extract) contents with correlated antioxidant potential. The extracts generally displayed potent DPPH, β-carotene bleaching, FRAP and reducing power in concentration dependent manner. Comparison of the two solvent used revealed that the extract of both solvents produced similar concentration dependent antioxidant activities. However, the acetone leave extract showed significantly (p< 0.05) greater antioxidant activity compared to all other extracts studied. Thus, this study has shown clearly that the extracts of V. amygdalina possessed antioxidant activity and could be a valuable therapeutic agent against oxidative stress.
A Comparative Study of Autoencoders against Adversarial Attacks

Many machine learning models can be fooled by adversarial examples. The adversarial examples are the subtly perturbed inputs crafted to fool the machine learning models. While the models correctly classify the original inputs, the perturbed adversarial inputs are misclassified to other classes. The perturbations added to the original inputs are usually not easily perceived by human eyes. Therefore the malicious attacks with the adversarial examples are serious security risks because the attacks can remain stealth. This paper compares various denoising autoencoders applied to inputs before the classifiers to filter the fast gradient sign method (FGSM) adversarial perturbations in the inputs. Both the capacity of removing the attacks and performance change on the original un-attacked inputs are analyzed.
PRODUCTION LINE SCHEDULE BASED ON IMPROVED FISH SWARM ALGORITHM

An Improved Fish Swarm Algorithm (IFSA) is proposed upon the assembly line optimization problem to implement optimal resource scheduling for production line. Taking a conveyor assembly line as a study case, this paper establishes the Parallel Conveyor Flow Shop using the JIT with Non-ideal condition (PCFJ-NC) based on the principle of lean production, after analyzing the influence of machined workpiece itself and other workpieces of conveyor; workpieces are identified by the RFID technology, which is commonly used in the industry informatization. In addition, given that there are some gaps of traditional algorithms if used for solving the conveyor assembly line scheduling problem, the IFSA based on polar coordinates is proposed to solve the conveyor assembly line production model. In the simulation test, for example, simulate a fault occurred on the parallel conveyor for bicycles produced by a plant, the assembly line schedule can be simulated against it. The results reveal that the rescheduling process as simulated by the proposed algorithm can reduce the production cost by 23.71% compared with the original scheduling schemes. The study provides the clues to guiding the assembly line workshop to improve the production efficiency and raising the standard for scientific schedule, as well as saving the workshop production cost.
Image Encryption Scheme Based on 4D4W Hyperchaotic System

Data conﬁdentiality is the basically important concern for everyone in the digital era. In this paper, an image encryption scheme is proposed based on the recently discovered 4D4W hyperchaotic system. Initially, the hyperchaotic system is used to generate the S-Boxes and permutation index matrices. In the ﬁrst stage of image encryption, the column permutation and self-column-forward XOR are executed successively for the setting times. During the second stage, the row permutation and self-row-forward XOR are executed successively for the setting times. Pixel-by-pixel substitutions are implemented during the third stage. The proposed method is simple and easy to be implemented. Several security analyses are performed to quantify the security level and eﬃciency of the proposed method including key space, key sensitivity, plaintext sensitivity, histogram, information entropy, adjacent pixels correlation. The results show that the proposed scheme has a remarkable strength to resist brute-force attacks, statistical attacks, and diﬀerential attacks.
On the Role of Language Learners’ Psychological Reactance, Teacher Stroke, and Teacher Success in the Iranian Context

Given the importance of psychological reactance in social and educational interactions and its influence on language learning/teaching, the present study intended to investigate the relationship among stroke, psychological reactance, and teacher success. To this end, a total number of 300 Iranian English learners from different English language institutes filled out a newly developed scale on psychological reactance along with stroke and teacher success scales. Confirmatory factor analysis (CFA) was exerted to both develop and validate the new scale. Structural equation modeling (SEM) was, then, employed to examine the possible relationships. Results revealed that stroke was a positive predictor of teacher success. Moreover, a negative relationship was found between psychological reactance and teacher success. Findings are discussed and suggestions are provided to pay more attention to the role of psychological reactance in English language teaching/learning.
Comparison of phenotype profiling of 3D cultured micro-tissues for ultra-high content screening by a 2D projection based method and a 3D method

Due to the increased physiological relevance and often more in vivo like functioning of 3D cell cultures compared to 2D cell cultures, 3D cell cultures have been intensively applied in high-content screening to investigate cellular responses to different genetic or chemical perturbations. In previous work, we investigated mouse breast cancer cells which formed characteristic phenotypes of micro-tissues in 3D cultures after being exposed to a diverse set of anti-cancer drugs, using a novel screening and ultra-high content analysis platform. This platform firstly generated a 2D projection for each image stack that was obtained by imaging micro-tissues in z-direction. Subsequent image analysis was performed on the projections, including segmentation of nuclei and micro-tissue structures, and phenotype quantification. We showed that this platform can successfully retrieve the concentration dependent phenotypic trajectory of each compound that could be used to classify compounds based on their biological target. However, this platform did not take into account tissue development in z-direction and this information might play an important role in phenotype characterization. To fill this gap, we developed a 3D image analysis platform for ultra-high content analysis of 3D cultured micro-tissues. Instead of generating 2D projections of image stacks, this platform reconstructed 3D objects from image stacks and measured phenotypes from the reconstructed objects or directly from the image stacks. Here, we compared the performance of both platforms for the analysis of mouse breast cancer cells cultured in 3D in a 384-well micro plate format, following exposure to 12 compounds at different concentrations. A thorough comparison of those two platforms was performed using criterions of sensitivity, classification accuracy and reproducibility. The results showed that while 2D analysis is more sensitive in detecting a biological effect of the test compounds, 3D analysis has an increased classification accuracy and a significant improvement in reproducibility.
CTC Attention Concatenated Multi-Step End-to-End Trainable Two Step Synthetic Data Bootsrapping

With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inevitably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, methodology and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.
Label Driven Time-Frequency Masking for Robust Continuous Speech Recognition

The application of Time-Frequency (T-F) masking based approaches for Automatic Speech Recognition has been shown to provide significant gains in system performance in the presence of additive noise. Such approaches give performance improvement when the T-F masking front-end is trained jointly with the acoustic model. However, such systems still rely on a pre-trained T-F masking enhancement block, trained using pairs of clean and noisy speech signals. Pre-training is necessary due to large number of parameters associated with the enhancement network. In this paper, we propose a flat-start joint training of a network that has both a T-F masking based enhancement block and a phoneme classification block. In particular, we use fully convolutional network as an enhancement front-end to reduce the number of parameters. We train the network by jointly updating the parameters of both these blocks using tied ContextDependent phoneme states as targets. We observe that pretraining of the proposed enhancement block is not necessary for the convergence. In fact, the proposed flat-start joint training converges faster than the baseline multi-condition trained model. The experiments performed on Aurora-4 database show 7.06% relative improvement over multi-conditioned baseline. We get similar improvements for unseen test conditions as well.
On the Scope of Active Probing Methods

— We provide a ﬁrm foundation for the estimation of route and trafﬁc metrics using active probing. The central concepts of Initial and Busy probes are identiﬁed then used to deﬁne four signature types. Precise conditions for signatures to survive end-to-end are given, and illustrated with simulation. Techniques are provided for the ef-ﬁcient ampliﬁcation, extraction, and interpretation of signatures, notably peaks, leading to canonical estimation meth-ods, some of them new, for link rate, cross trafﬁc, and available bandwidth. The concept of secondary bottlenecks is clearly deﬁned, and a symmetry law for delay variation given. We show how all link rates can be measured, not only bottlenecks. The effects of independently varying probe size and inter-probe separation are explained. Finally packet size dependencies of link rate estimates observed in high precision measurements are explained.
Use of electronic communication aids by temporarily nonvocal patients in the ICU

Purpose: To examine the enactment and progression of assistive and augmentative communication (AAC) device use by nonvocal intensive care unit (ICU) patients during nurse-patient communication over two days. Background: Patient-nurse communication in the ICU is a complex process. Endotracheal intubation or tracheostomy renders patients temporarily unable to speak. ICU nurses occupy a crucial role in facilitating patient communication. The Study of Patient-Nurse Effectiveness with Assisted Communication Strategies (SPEACS) presented basic communication skills training and training in electronic communication devices to nurses in two ICUs to improve communication with nonvocal patients. Methods: We used a descriptive multiple case study design and applied microanalytic communication coding with descriptive analysis using primarily qualitative techniques. A subset of patients (n = 9) who were physically (i.e., upper motor) and cognitively intact (RASS = 0, CAM-ICU negative for delirium), throughout all study observations was extracted from the SPEACS sample. The patients were > 21 years old, intubated, scored 13 on the Glasgow Coma Scale, and received a communication plan and electronic AAC device matched to patient ability and preference from a speech language pathologist. For two days directly after the plan was developed, the nurse-patient dyad was videotaped four times (twice daily) during routine care. The full-length videos (3:25-19:38 minutes) were coded for patient use of natural, low tech AAC, and high tech AAC communication modality; nurse facilitative behaviors/strategies; and patient communication topic. Results/Conclusion: Five patients used the high tech AAC device during observation; 4 patients did not. The Lightwriter device was most common (n=4) and most popular among the high tech AAC users. Nurse facilitative behaviors were observed with 7 dyads; positioning the high tech device appropriately was the most common facilitative behavior for patients who used high tech AAC. The most common topics were comfort care/needs, patient’s condition, and greetings/small talk. These were also common topics during high tech AAC communication. All patients used multiple communication modalities and used natural communication modalities most frequently. Although the pattern of AAC use over time differed among the patients, communication rates dropped in most (8/9) cases during session 4. This information regarding how patients use AAC devices may help nurses to better predict their patients’ communication needs and to facilitate effective communication.
Anxiety Level Recognition for Virtual Reality Therapy System Using Physiological Signals

Virtual reality exposure therapy (VRET) can have a significant impact towards assessing and potentially treating various anxiety disorders. One of the main strengths of VRET systems is that they provide an opportunity for a psychologist to interact with virtual 3D environments and change therapy scenarios according to the individual patient’s needs. However, to do this efficiently the patient’s anxiety level should be tracked throughout the VRET session. Therefore, in order to fully use all advantages provided by the VRET system, a mental stress detection system is needed. The patient’s physiological signals can be collected with wearable biofeedback sensors. Signals like blood volume pressure (BVP), galvanic skin response (GSR), and skin temperature can be processed and used to train the anxiety level classification models. In this paper, we combine VRET with mental stress detection and highlight potential uses of this kind of VRET system. We discuss and present a framework for anxiety level recognition, which is a part of our developed cloud-based VRET system. Physiological signals of 30 participants were collected during VRET-based public speaking anxiety treatment sessions. The acquired data were used to train a four-level anxiety recognition model (where each level of ‘low’, ‘mild’, ‘moderate’, and ‘high’ refer to the levels of anxiety rather than to separate classes of the anxiety disorder). We achieved an 80.1% cross-subject accuracy (using leave-one-subject-out cross-validation) and 86.3% accuracy (using 10 × 10 fold cross-validation) with the signal fusion-based support vector machine (SVM) classifier.
Structure and function of the Rad 9 binding region of the DNA damage checkpoint adaptor

TopBP1 is a scaffold protein that coordinates activation of the DNA-damage-checkpoint response by coupling binding of the 9-1-1 checkpoint clamp at sites of ssDNA, to activation of the ATR–ATRIP checkpoint kinase complex. We have now determined the crystal structure of the N-terminal region of human TopBP1, revealing an unexpected triple-BRCT domain structure. The arrangement of the BRCT domains differs significantly from previously described tandem BRCT domain structures, and presents two distinct sites for binding phosphopeptides in the second and third BRCT domains. We show that the site in the second but not third BRCT domain in the N-terminus of TopBP1, provides specific interaction with a phosphorylated motif at pSer387 in Rad9, which can be generated