Miracle-dz/UniFAR-FTUs huggingface dataset:
Electron microscopy of U4/U6 snRNP reveals a Y-shaped U4 and U6 RNA containing domain protruding from the U4 core RNP

We describe the electron microscopic investigation of purified U4/U6 snRNPs from human and murine cells. The U4/U6 snRNP exhibits two morphological features, a main body approximately 8 nm in diameter and a peripheral filamentous domain, 7-10 nm long. Two lines of evidence suggest that the peripheral domain may consist of RNA and to contain U6 RNA as well as the 5' portion of U4 RNA. (a) Separation of the U4/U6 snRNA interaction regions from the core domains by site-directed cleavage of the U4 snRNA with RNase H gave filament-free, globular core snRNP structures. (b) By immuno and DNA-hybridization EM, both the 5' end of U4 and the 3' end of U6 snRNA were located at the distal region of the filamentous domain, furthest from the core. These results, together with our observation that the filamentous U4/U6 domain is often Y shaped, correlate strikingly with the consensus secondary structure proposed by Brow and Guthrie (1988. Nature (Lond.), 334:213- 218), where U4 and U6 snRNA are base paired in such a way that two U4/U6 helices together with a stem/loop of U4 snRNA make up a Y-shaped U4/U6 interaction domain.
DIRECTIONAL CLASSIFICATION OF CORTICAL SIGNALS USING A LIQUID STATE MACHINE

Abstract Liquid State Machine (LSM) is a newly developed computational model with many interesting properties. It has great advantages of dealing with biologic computing when compared to the traditional computational model. In this paper, the LSM was used to deal with the direction classification problem of the spike series which were distilled from the neurons in motor cortex of a monkey. In the output layer, a linear regression and back-propagation are employed as the training algorithms. Compare to outcomes of the two algorithms, it is showed that ideal classification results were derived when using BP as the training algorithm.
Use of Neural Networks in Testing Analog to Digital Converters

In the past two decades, the techniques of artificial neural networks are growing mature, as a datadriven method, which provides a totally new perspective to fault diagnosis. Testing issues are becoming more and more important with the quick development of both digital and analog circuit industry. Analog-to-digital converters (ADCs) are becoming more and more widespread owing to their fundamental capacity of interfacing analog physical world to digital processing systems. In this paper, we study the use of neural networks in fault diagnosis of ADCs and compare the results with other ADC testing approaches such as histogram, FFT and sine fit test techniques. In this paper, we study the use of neural networks in fault diagnosis of ADCs and compare the results with other ADC testing approaches such as histogram, FFT and sine fit test techniques. In this paper, we introduce two ideas to improve the training phase time. They are separation and indexing of neural network outputs. Finally, we concluded that neural network approach is a robust way for fault diagnosis of ADCs and also other mixed signal circuits. Key-Words: fault, ADC, neural networks, test, histogram, FFT, sine fit, training time, output separation.
Estimation of Covariance Matrix

Estimation of population covariance matrices from samples of multivariate data is important. (1) Estimation of principle components and eigenvalues. (2) Construction of linear discriminant functions. (3) Establishing independence and conditional independence. (4) Setting confidence intervals on linear functions. Suppose we observed p dimensional multivariate samples X1, X2, · · · , Xn i.i. d. with mean 0 and covariance matrix Σp, and write Xi = (Xi1, Xi2, · · · , Xip)′. Our goal is to estimate Σp. For simplicity, we first consider the Gaussian case, where Xi ∼ N(0,Σp).
Sum Secrecy Rate Maximization for Full-Duplex Two-Way Relay Networks Using Alamouti-Based Rank-Two Beamforming

Consider a two-way communication scenario where two single-antenna nodes, operating under full-duplex mode, exchange information to one another through the aid of a (full-duplex) multiantenna relay, and there is another single-antenna node who intends to eavesdrop. The relay employs artificial noise (AN) to interfere the eavesdropper's channel, and amplify-forward Alamouti-based rank-two beamforming to establish the two-way communication links of the legitimate nodes. Our problem is to optimize the rank-two beamformer and AN covariance for sum secrecy rate maximization (SSRM). This SSRM problem is nonconvex, and we develop an efficient solution approach using semidefinite relaxation (SDR) and minorization-maximization (MM). We prove that SDR is tight for the SSRM problem and, thus, introduces no loss. Also, we consider an inexact MM method where an approximate but computationally cheap MM solution update is used in place of the exact update in conventional MM. We show that this inexact MM method guarantees convergence to a stationary solution to the SSRM problem. The effectiveness of our proposed approach is further demonstrated by an energy-harvesting scenario extension, and by extensive simulation results.
CORTICAL VISUAL NEUROPROSTHESES FOR THE BLIND

A survey of the present state of research on a visual neuroprostheses, interfaced with the occipital cortex, as a means through which a limited but useful visual sense could be restored to profoundly blind people is given. We review the most important physiological principles regarding this neuroprosthetic approach and emphasize the role of neural plasticity to achieve the desired behaviour of the system. While the full restoration of vision is unlikely in the near future, the discrimination of shape and location of objects could allow blind subjects to 'navigate' in a familiar environment and to read enlarged text, resulting in a substantial improvement in the quality of life of blind and visually impaired persons. Moreover, if we can understand more about the fundamental mechanisms of neuronal coding, and to safely and selectively stimulate nervous system, there will real potential to apply this knowledge clinically. E. Fernandez et al. / Restorative Neurology and Neuroscience. In press (2004).
H.26L Pre-Standard Evaluation

In this report we give an overview of our first evaluations of the upcoming H.26L video encoding standard of the ITU–T. Since this standard is still in the development phase, all of the results presented here are to be seen as preliminary, as is the introduction to the standard itself. The video encodings analyzed in this report have been generated with a preliminary version of the H.26L encoder. The key characteristics of the final H.26L encoder are expected to be very close to the preliminary coder used in our experiments. The traffic characterisations given in this report give therefore a very close approximation of the video traffic and quality produced by the final encoder. In this report we first outline the current state of the standard, our measurement setup, and give an introduction to the analyzed statistical measures. We then present and interpret the statistical characteristics of the H.26L encoded video. We conclude by stating the current problems and outline future work.
Cyber Bullying and University Students: Behaviours, Opinions and Reactions

ABSTRACT In this research, a general survey model was used with the purpose of determining the opinions of the students attending the Faculty of Education at a private university in Northern Cyprus on the subject of cyber bullying. The participants of this research consisted of 200 students studying in in this faculty for the 2016-17 academic year. Also, a questionnaire developed by the researcher was used to obtain opinions. As a result of the analyses conducted, it has been determined that approximately half of the students were bullied online and, when cyber bullying occurred, nearly half of the students did not tell their parents. The students claimed that they found it easier to speak to their teachers than their parents. It was understood that they found it easier to talk with their friends if they experienced cyber bullying behaviours, rather than their teachers.
Recognizing Humans in Motion: Trajectory-based Aerial Video Analysis

We propose a novel method for recognizing people in aerial surveillance videos. Aerial surveillance images cover a wide area at low resolution. In order to detect objects (e.g., pedestrians) from such videos, conventional methods either utilize appearance information from raw videos or extract blob information from background subtraction results. However, people seen in low resolution images have less appearance information, and hence are very difficulty to classify based on their appearance or blob size. In addition, due to heavy camera movements caused by aerial vehicle ego-motion and wind, the system is expected to generate many noisy false detections including parallax. The idea presented in this paper is to detect and classify objects from aerial videos based on their motion: we analyze a trajectory of each object candidate, deciding whether it is a person-of-interest or simple noise based on how it moved. After objects are tracked by a Kalman filter-based tracking, we represent their motion as multi-scale histograms of ‘orientation changes’, which efficiently captures movements displayed by objects. Random forest classifiers are applied to our new representation to make the decision. The experimental results illustrate that our approach recognizes objects-of-interest (i.e., humans) even when there exist a large number of false detection/tracking, and it does it more reliably compared to the approaches with previous paradigm.
La eficacia de la reexperimentación emocional: el papel del optimismo y la alexitimia

espanolLos efectos beneficiosos sobre la salud de la reexperimentacion emocional mediante la utilizacion de la escritura expresiva de vivencias traumaticas estan bien establecidos. La efectividad de la utilizacion de la reexperimentacion parece verse afectada por ciertas condiciones. Entre las variables personales que han mostrado tener influencia sobre la recuperacion de tales vivencias se ha destacado el optimismo y la alexitimia. El objetivo del presente trabajo fue evaluar los efectos de una intervencion basada en la reexperimentacion emocional sobre la salud mental. Se aplico un diseno experimental intergrupo pre y post-tratamiento. La muestra estuvo compuesta por un total de 60 participantes, de los que 40 llevaron a cabo un procedimiento de reexperimentacion emocional mediante tres sesiones de escritura expresiva. El resto formaron el grupo control y escribieron sobre situaciones triviales. Todos fueron evaluados antes y despues de la reexperimentacion emocional en afecto positivo y negativo, ansiedad, depresion, pensamientos intrusivos y evitacion cognitiva. Ademas, se controlo el nivel de optimismo y la alexitimia. Los resultados mostraron la eficacia de la escritura expresiva sobre el afecto positivo y negativo y el nivel de depresion. Entre las covariables, unicamente el optimismo produjo un efecto significativo sobre las variables mas emocionales. Se discuten las condiciones contextuales y personales que pueden favorecer el uso clinico de la reexperimentacion emocional. EnglishThe beneficial effects on the health of the re-experimentation of traumatic experiences are well-established. The effectiveness of using reexperimentation seems to be affected by certain conditions. Among personal variables that have been shown to influence the recovery of such experiences it has highlighted the optimism and alexithymia. The aim of this study was to evaluate the effects on mental health of an intervention based on re-experimentation. The sample consisted in a total of 60 participants, of which 40 carried out a process of emotional re-experimentation through three sessions of expressive writing. The rest formed the control group. All participants were evaluated before and after the emotional disclosure in positive and negative affect, anxiety, depression, intrusive thoughts and cognitive avoidance. Furthermore, the level of optimism and alexithymia were controlled. The results showed the effectiveness of expressive writing on the positive and negative affect and on depression. Among the covariables, only optimism affected significantly to effect emotional variables. The contextual and personal conditions that can be ameliorated the clinical use of emotional disclosure were discussed.
Analysis of a hybrid key management solution for ad hoc networks

Designing a key management system is both important and challenging for wireless ad hoc networks. We have developed a secure, scalable, decentralized and robust key management solution using a hybrid (symmetric/asymmetric) key based methodology that is well suited for ad hoc networks. The nodes are grouped into clusters, and keys are distributed such that intra-cluster communication is secured using a symmetric cryptosystem and inter-cluster communication is secured using an asymmetric cryptosystem. We present a detailed analysis of the solution and simulation results. We observe that the hybrid solution provides a significant improvement in the performance of the key management solution in a highly hostile environment, and scales well to large networks.
Seismic hazard assessment and site-dependent response spectra parameters of the current seismic design code in Albania

A probabilistically based hazard map of Albania expressed in terms of PGA for 475 years of return period following the spatially smoothed seismicity approach proposed by Frankel (1995) and developed by Slovenian researchers (Lapajne et al. 1997), is presented. Taking advantage of this development, an effort has been made to analyze the parameters of site-dependent acceleration response spectra parameters of the current seismic design code KTP-N.2-89 in Albania.
Heterogeneous models matching for consistency management

This work is situated in the context of the application of Model Driven Engineering to complex systems view-based modelling. In fact, view-based models - called also partial models - are manipulated by different actors (designers), and are thus generally heterogeneous, that is, described with different DSLs (Domain Specific Languages). Instead of building a single global model, which is not realistic, we propose to organize the different partial models as a network of related models, which provides a global view of the system through a correspondence model. As models are modelled separately by different designers, they also evolve separately that induces a problem of consistency. To solve it, we propose a semi-automatic process based on the correspondence model allowing detecting changes, calculating their impacts, and proposing modifications to maintain the consistency among them. The approach is supported by a tool chain and illustrated by the example of a Bug Tracking System.
Location-Dependent Network Performance and Design Strategies for Wireless Mesh Networks

In this paper, we model the location-dependent throughput and delay in wireless mesh networks. We analyze packet arrival rates and packet departure rates for the forwarding queues at relaying nodes and then derive the throughput and packet delay experienced by nodes at different hop count distances to the gateway. Based on this model, we further analyze how network design strategies affect the throughput and delay of each node. We then conduct simulations to validate our analytical model and evaluate the performance of different network design strategies. This paper not only provides a framework for studying the location-dependent throughput and delay in wireless mesh networks but also gives insights into the network design strategy for wireless mesh networks.
Understanding Audio Pattern Using Convolutional Neural Network From Raw Waveforms

One key step in audio signal processing is to transform the raw signal into representations that are efficient for encoding the original information. Traditionally, people transform the audio into spectral representations, as a function of frequency, amplitude and phase transformation. In this work, we take a purely data-driven approach to understand the temporal dynamics of audio at the raw signal level. We maximize the information extracted from the raw signal through a deep convolutional neural network (CNN) model. Our CNN model is trained on the urbansound8k dataset. We discover that salient audio patterns embedded in the raw waveforms can be efficiently extracted through a combination of nonlinear filters learned by the CNN model.
Object-oriented techniques in robot manipulator control software development

Software development for the control of robotic manipulators is a complex task because it requires expertise in many areas (e.g., robotics, real-time programming, hardware integration, concurrency, etc.). Because of this fact, it is difficult to develop a common software platform supporting the diversity of robotic research areas and robotic hardware. Even though a large number of robotic languages, libraries, and tools have been created, they are seldom reused. That is, many research teams develop their own software platform from scratch because existing platforms are too inflexible with regard to modifications and too complex to understand. The authors of this paper believe that code reuse is fostered by providing a lightweight platform rather than requiring the user to modify a complex system. Along this line of reasoning, this paper describes the QMotor Robotic Toolkit (QMotor RTK). The RTK is a set of C++ libraries and programs that follow object-oriented concepts to ensure code reuse, modularity, scalability, and an intuitive code structure. The RTK is a homogeneous system that consists only of PC software. The RTK includes joint level control programs for the Puma 560 manipulator, the Barren Whole Arm Manipulator (WAM), and the Integrated Motion Inc. (IMI) two-link manipulator as well as a joint level trajectory generator and a graphical user interface (GUI).
Multi-Phase Ant Colony System for Multi-Party Data-Intensive Service Provision

The rapid proliferation of enormous sources of digital data has led to greater dependence on data-intensive services. Each service may actually request or create a large amount of data sets. To compose these services will be more challenging. Issues such as autonomy, scalability, adaptability, and robustness, become difficult to resolve. In order to automate the process of reaching an agreement among service composers, service providers, and data providers, an ant-inspired negotiation mechanism is considered in this paper. We exploit a group of agents automatically negotiating to establish agreeable service contracts. Two-stage negotiation procedures are used in our data-intensive service provision model, which will provide effective and efficient service selection for service composers. We also present a multi-phase, multi-party negotiation protocol, where the ant colony system is applied to select services with the best or near-optimal utility outputs. In order to adapt the ant colony system to handle the dynamic scenarios during negotiations, we also discuss several strategies for modifying the pheromone information in the first place. The experimental results show that our negotiation-based approach can facilitate the data-intensive service provision with better outcome.
A Reversible Watermarking Algorithm Based on Four-Neighbors Context Prediction for Tongue Images

Reversible watermarking has been used in many sensitive digital images, such as medical and military images, because of the ability to embed data with zero loss of host information. This paper describes a reversible watermark embedding algorithm, based on context prediction error for color tongue images, that can achieve high data embedding rates and effective copyright protection for such images. The algorithm exploits the high correlation that is inherent among the neighboring pixels of tongue images, forming a more effective method for embedding data by using the proposed prediction and extended difference-expansion techniques in different components. When extracting, the algorithm can restore the original tongue image accurately, meeting the requirements required for medical images. The experimental results show that the algorithm is easy to implement and that it achieves very high data embedding rates while keeping the resulting distortion low.
Effect of motor imagery therapy on cognitive function of patients with stroke

Objective To explore the rehabilitation effect of motor imagery therapy on cognitive function of stroke patients. Methods A total of 99 stroke patients with mild to moderate cognitive dysfunction were randomly divided into 3 groups: control group (N = 33), cognitive training group (N = 33) and motor imagery training group (N = 33). All patients received conventional rehabilitation training. Before and after 8-week training, all subjects were assessed with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). At the same time, event-related potential (ERP) was examined to detect P300 latency and amplitude. Results ompared with before training, MMSE ( P = 0.000) and MoCA ( P = 0.000) scores were significantly increased, P300 latency was shortened ( P = 0.000) and P300 amplitude was increased ( P = 0.000) in 3 groups after 8 - week training. There were significant differences among 3 groups on MMSE ( P = 0.030) and MoCA ( P = 0.013) scores, P300 latency ( P = 0.004) and P300 amplitude ( P = 0.009) before and after training. Among them, cognitive training group and motor imagery training group had significantly higher MMSE ( P = 0.019, 0.021) and MoCA ( P = 0.003, 0.031) scores, shorter P300 latency ( P = 0.020, 0.003) and higher P300 amplitude ( P = 0.003, 0.002) than control group. Conclusions Motor imagery training can not only improve motor function of stroke patients, but also improve their cognitive function. DOI: 10.3969/j.issn. 1672-6731.2017. 06.005
Leveraging RGB-D Data: Adaptive fusion and domain adaptation for object detection

Vision and range sensing belong to the richest sensory modalities for perception in robotics and related fields. This paper addresses the problem of how to best combine image and range data for the task of object detection. In particular, we propose a novel adaptive fusion approach, hierarchical Gaussian Process mixtures of experts, able to account for missing information and cross-cue data consistency. The hierarchy is a two-tier architecture that for each modality, each frame and each detection computes a weight function using Gaussian Processes that reflects the confidence of the respective information. We further propose a method called cross-cue domain adaptation that makes use of large image data sets to improve the depth-based object detector for which only few training samples exist. In the experiments that include a comparison with alternative sensor fusion schemes, we demonstrate the viability of the proposed methods and achieve significant improvements in classification accuracy.
The 2dF Galaxy Redshift Survey: Voids and hierarchical scaling models

We measure the redshift space reduced void probability function (VPF) for 2dFGRS volume limited galaxy samples covering the absolute magnitude range MbJ 5log10 h = 18 to 22. Theoretically, the VPF connects the distribution of voids to the moments of galaxy clustering of all orders, and can be used to discriminate clustering models in the weakly non-linear regime. The reduced VPF measured from the 2dFGRS is in excellent agreement with the paradigm of hierarchical scaling of the galaxy clustering moments. The accuracy of our measurement is such that we can rule out, at a very high significance, popular models for galaxy clustering, including the lognormal distribution. We demonstrate that the negative binomial model gives a very good approximation to the 2dFGRS data over a wide range of scales,