Miracle-dz/UniFAR-FTUs huggingface dataset:
Postadaptation orientation discrimination.

An orientational difference of only 0.3-0.5 deg can be discriminated between two gratings or two lines, although psychophysical channels and cortical cells both have comparatively broad orientation bandwidths of 10-25 deg. One proposed explanation for the fineness of orientation discrimination is that, while detection is determined by the most excited orientation-tuned neural elements, superthreshold orientation discrimination is determined by difference signals between these elements [Westheimer et al., J. Opt. Soc. Am. 66, 332 (1976)]. This implies that, if stimulus orientation is changed slightly, the most important elements for discriminating this change will be those whose relative activity changes most, even though the excitation of these elements may be comparatively weak. In accord with this prediction, we found that adapting to a high-contrast grating degraded discrimination for test gratings inclined at about 10-20 deg to the adapting grating while having little effect on the detection of these inclined gratings. For test gratings parallel to the adapting grating, discrimination was improved, but detection was degraded. Either an opponent-process or a line-element model can account for these effects of adaptation. An opponent model can also explain our findings that subjects do not confound orientation change with contrast change and that suprathreshold orientation discrimination is almost independent of contrast, varying by only +/- 10% from about 3 to about 25 times contrast threshold. A discrimination model must incorporate reliable storage of spatial frequency, because discrimination was not affected by increasing the interval between grating presentations from 1 to 10 sec. (ABSTRACT TRUNCATED AT 250 WORDS)
Bmc Evolutionary Biology the Evolution of Imprinting: Chromosomal Mapping of Orthologues of Mammalian Imprinted Domains in Monotreme and Marsupial Mammals

Background: The evolution of genomic imprinting, the parental-origin specific expression of genes, is the subject of much debate. There are several theories to account for how the mechanism evolved including the hypothesis that it was driven by the evolution of X-inactivation, or that it arose from an ancestrally imprinted chromosome.
A data approach alternative at system identification and modeling using the self-organizing associative memory (SAM) system

We introduce a data-based approach alternative to the rule-based parameter approach toward system identification. Motivated by the design-intensive problem of the parameter approach, the self-organizing associative memory (SAM) system seeks to represent the system using a subset of stored training data. We surmise that knowledge is association between memorized objects, not memorized rules. We postulate that only novel and distinct data should be organized into memory, while familiar data may be reproduced to an acceptable degree of accuracy by association between memorized data. The concept is materialized in several computational formats and tested on four different test cases. Results indicate that this data approach has high accuracy, relatively design-free, and requires only one pass of the training data to train.
Performance Analyses of High-Efficiency EDCA for Reducing Contention Collision and Increasing Throughput in QoS-based IEEE 802.11e Wireless Networks

For achieving Quality of Service (QoS) for real-time applications in high-mobility wireless LANs (WLANs), IEEE 802.1 1e proposes a novel Media Access Control (MAC) protocol, namely Hybrid Coordination Function (HCF). HCF provides two channel access modes: (1) a contention-based Enhanced Distributed Channel Access (EDCA) and (2) a contention-free-based mode HCF Controlled Channel Access (HCCA). EDCA achieves random access among mobile nodes but suffers from low throughput under a high traffic load with two main reasons. First, EDCA uses the same minimum contention window (CW) for different classes of traffic, and thus increases the collision probability. Second, after successful transmissions the backoff stage is transited back to the minimum backoff stage, and may lead to collisions from the same class traffic. This paper thus proposes a distributed High-performance EDCA approach (H-EDCA) to partition collision domains of different classes of traffic. The goal is to differentiate CWs for avoiding access collisions from both the same class traffic and different classes of traffic. Moreover, the performance of throughput, average transmission delay and frame dropping probability, are mathematically analyzed in detail based on the discrete-time Markov chain model. Numerical results demonstrate that H-EDCA yields very competitive analytical results to simulation results, which justifies the accuracy of the H-EDCA analytical model. Furthermore, H-EDCA significantly outperforms EDCA of IEEE 802.11e in the compared performance metrics.
Toward an Intelligent Multimodal Interface for Natural Interaction

Advances in technology are enabling novel approaches to human-computer interaction (HCI) in a wide variety of devices and settings (e.g., the Microsoft@ Surface, the Nintendo@ Wii, iPhone@, etc.). While many of these devices have been commercially successful, the use of multimodal interaction technology is still not well understood from a more principled system design or cognitive science perspective. The long-term goal of our research is to build an intelligent multimodal interface for natural interaction that can serve as a testbed for enabling the formulation of a more principled system design framework for multimodal HCI. This thesis focuses on the gesture input modality. Using a new hand tracking technology capable of tracking 3D hand postures in real-time, we developed a recognition system for continuous natural gestures. By nature gestures, we mean the ones encountered in spontaneous interaction, rather than a set of artificial gestures designed for the convenience of recognition. To date we have achieved 96% accuracy on isolated gesture recognition, and 74% correct rate on continuous gesture recognition with data from different users and twelve gesture classes. We are able to connect the gesture recognition system with Google Earth, enabling gestural control of a 3D map. In particular, users can do 3D tilting of the map using nontouch-based gesture which is more intuitive than touch-based ones. We also did an exploratory user study to observe natural behavior under a urban search and rescue scenario with a large tabletop display. The qualitative results from the study provides us with good starting points for understanding how users naturally gesture, and how to integrate different modalities. This thesis has set the stage for further development towards our long-term goal. Thesis Supervisor: Randall Davis Title: Professor of Electrical Engineering and Computer Science
The association between perceived social support and posttraumatic stress symptoms among Eritrean and Sudanese male asylum seekers in Israel

ABSTRACT We investigated the combined effect of the severity of exposure to traumatic events and perceived social support on posttraumatic stress disorder (PTSD) symptoms among male asylum seekers in Israel. A convenience sample of 90 men who sought asylum from Eritrea and Sudan and presented at the Open-Clinic in Tel-Aviv, Israel, participated in the study. Participants completed measures assessing exposure to traumatic events, perceived social support and PTSD symptoms in their native language. The majority of participants had been exposed to traumatic events. Lack of shelter, ill health without access to medical care, imprisonment and torture were most prevalent. Perceived social support was associated with lower PTSD symptoms only among those who reported low exposure to traumatic events. Among asylees who reported high exposure to traumatic events, social support did not affect the association between exposure to traumatic events and PTSD symptoms. Our findings show that perceived social support serves as a significant moderator in the relationship between exposure to traumatic events and PTSD symptoms among asylum seekers, depending on the severity of exposure to traumatic events. The complex relationship between protective factors such as perceived social support, exposure to trauma and mental health should inform mental health services for forced migrants.
An On-chip Data Storage with ElGamal Elliptic Curve Cryptosystems

An On-chip Data Storage with cryptosystem proposed in this paper was designed for modern smart card system and “on the fly data encryption/decryption”. To ensure the security, the ElGamal Elliptic curve cryptosystems with the key size of 176 bits was embeded. The design can be configured to be either the encrypter or the decryptor with simple control signals. To minimize the hardware cost, size and complexity, the slight modified fix-coefficient bit-serial finite field multiplier has been evoked. The FPGA-based prototype implementation had proved the concept idea and mathematics behind.
Mechanical sensorless drives of IPMSM with online parameter identification

The mechanical sensorless drive system for an interior permanent magnet synchronous motor (IPMSM), for which parameters including the inverter are identified, is proposed in this paper. The rotor position is estimated by a signal injection sensorless scheme at standstill. The resistance, including the on-resistance of the insulated gate bipolar transistor (IGBT), the voltage error caused by the dead time of the inverter, and the d-axis and q-axis inductances are identified. After the motor starts by the signal injection sensorless control, the sensorless scheme changes to a scheme based on the extended electromotive force (EMF) estimation, which uses the identified parameters. The magnet flux linkage is also identified for the sensorless operation. The effectiveness of the proposed method is verified by several experimental results.
Contribution of the Runx1 transcription factor to axonal pathfinding and muscle innervation by hypoglossal motoneurons

The runt‐related transcription factor Runx1 contributes to cell type specification and axonal targeting projections of the nociceptive dorsal root ganglion neurons. Runx1 is also expressed in the central nervous system, but little is known of its functions in brain development. At mouse embryonic day (E) 17.5, Runx1‐positive neurons were detected in the ventrocaudal subdivision of the hypoglossal nucleus. Runx1‐positive neurons lacked calcitonin gene‐related peptide (CGRP) expression, whereas Runx1‐negative neurons expressed CGRP. Expression of CGRP was not changed in Runx1‐deficient mice at E17.5, suggesting that Runx1 alone does not suppress CGRP expression. Hypoglossal axon projections to the intrinsic vertical (V) and transverse (T) tongue muscles were sparser in Runx1‐deficient mice at E17.5 compared to age‐matched wild‐type littermates. Concomitantly, vesicular acetylcholine transporter‐positive axon terminals and acetylcholine receptor clusters were less dense in the V and T tongue muscles of Runx1‐deficient mice. These abnormalities in axonal projection were not caused by a reduction in the total number hypoglossal neurons, failed synaptogenesis, or tongue muscles deficits. Our results implicate Runx1 in the targeting of ventrocaudal hypoglossal axons to specific tongue muscles. However, Runx1 deficiency did not alter neuronal survival or the expression of multiple motoneuron markers as in other neuronal populations. Thus, Runx1 appears to have distinct developmental functions in different brain regions. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 75: 1295–1314, 2015
Folk classification as evidence of transformed landscapes and adaptative strategies: a case study in the semiarid region of northeastern Brazil

Abstract How do people classify the living world? Solid theoretical and methodological foundations advocate for utilitarian or intellectual strategies. Some authors argue that people tend to classify natural resources that are necessary for their survival. However, in the case of landscapes, we do not know the true criteria that people use to classify their space. We present strong evidence that people name landscapes according to utilitarian (economic) criteria. We also seek to identify which factors and variables explain people’s knowledge about landscapes and how this knowledge can generate information about landscape modifications. This study was conducted in the Brazilian National Forest of Araripe (FLONA Araripe), NE Brazil, with local people who live near this forest. A total of 304 nomenclatural types of landscapes were recounted, and these landscapes were mainly associated with indicators of current or past use. The variables that best explained the knowledge about landscapes were useful species known and the number of categories of use, showing that utilitarian features are strongly associated with local forest classification. Our findings suggest an adaptive strategy in which people name the landscape based on past and current uses, either as a whole or with respect to particular features of certain areas. In addition, we interpret this finding as a mnemonic strategy for optimising the use of the landscape.
Prediction of Gyro Motor's State Based on Grey Model and BP Neural Network

The prediction accuracy of grey theory was limited by it’s high requirement of data’s smoothness. BP neural is adept in solving nonlinear problem and performs well in self-adaption and self organization, but it’s training effect and efficiency was limited by the number of data. A hybrid model combined advantages of grey theory and BP neural network is put forward based on analysis of gyro motor’s state parameters. And then, grey theory, BP neural network and the hybrid model were constructed respectively to model and predict the parameters. The results prove the validity and accuracy of hybrid model.
Extending a Model-Driven Engineering Environment to Support Product Line Engineering

In order to make model-driven product line engineering a reality, variability and feature modeling techniques are necessary, but not sufficient. Adequate tool support is of equal importance, and presents its own set of challenges. In this paper, we present an approach to the activities of modeling product lines, defining and verifying products, and exporting product models. Our approach pioneers innovations such as the active product view and an efficient decision propagation logic, and supports advanced concepts such as incomplete product definitions. We have implemented our approach on top of a UML tool.
Test-retest study of the six-minute walk test in people with bipolar disorder.

BACKGROUND The multidisciplinary care for bipolar disorder is highly fragmented with limited opportunities for prevention and treatment of medical co-morbidities. We examined the reliability of the 6-minute walk test (6MWT). Secondary aims were to assess minimal detectable changes (MDC(95)), practice effects and the impact of clinical conditions. SUBJECTS AND METHODS Two 6MWTs were administered within 3 days to 46 (23♂) inpatients with a DSM-V diagnosis of bipolar disorder. Physical complaints before and after the 6MWT were recorded. Patients completed the Quick Inventory of Depressive Symptomatology Self Report (QIDS-SR) and Hypomania Check List-32. RESULTS Patients walked 594.7±121.3 meters and 600.0±122.9 meters at the first and second test. The intraclass correlation coefficient was 0.98 (95% confidence interval 0.97-0.99). The MDC(95) was 37.8 meters for men and 52.9 meters for women. No practice effect was detected. Longer illness duration, higher QIDS-SR scores and the presence of feet or ankle static problems or pain were independently related to shorter 6MWT distance accounting for 59.8% of the variance. CONCLUSION The 6MWT is a clinically feasible tool for evaluating the functional exercise capacity in patients with bipolar disorder. Health care professionals should consider depression and physical pain when developing rehabilitation programmes.
CHARACTER RECOGNITION OF KANNADA TEXT IN SCENE IMAGES USING NEURAL NETWORK

Character recognition in scene images is one of the most fascinating and challenging areas of pattern recognition with various practical application potentials. It can contribute immensely to the advancement of an automation process and can improve the interface between man and machine in many applications. Some practical application potentials of character recognition system are: reading aid for the blind, traffic guidance systems, tour guide systems, location aware systems and many more. In this work, a novel method for recognizing basic Kannada characters in natural scene images is proposed. The proposed method uses zone wise horizontal and vertical profile based features of character images. The method works in two phases. During training, zone wise vertical and horizontal profile based features are extracted from training samples and neural network is trained. During testing, the test image is processed to obtain features and recognized using neural network classifier. The method has been evaluated on 490 Kannada character images captured from 2 Mega Pixels cameras on mobile phones at various sizes 240x320, 600x800 and 900x1200, which contains samples of different sizes, styles and with different degradations, and achieves an average recognition accuracy of 92%. The system is efficient and insensitive to the variations in size and font, noise, blur and other degradations.
A new tunable fuzzy filter for image smoothing

Different applications sometimes require varying degrees of smoothing enhancement so as not to tradeoff important image detail with increased smoothing. A sigmoid membership function is proposed in this paper to model the image information in the spatial domain. This function acts as a smoothing intensification operator or a tunable fuzzy filter. With a proper choice of two parameters 't' and 'a', the filter strength can be tuned for removal of noise in gray images. A visible improvement in the smoothness of images is observed, and the output of filter is also compared with those of other standard smoothing methods.
Multi-objective genetic algorithms for courses of action planning

Planning military courses of action is a very complex and difficult activity. Planners should take into consideration environmental information, predictions, the end state targeted and resource constraints. Development of courses of action involves solving simultaneously planning and scheduling problems. In this work, a new approach based on genetic algorithms (GA) and multi-objective optimisation is proposed to support resource-constrained courses of action development where both cardinal and ordinal objectives are considered. A vector of fitness evaluations is proposed to control the proportion of the infeasible solutions. Crossover and mutation operators are designed to diversity the search space and improve solutions on all objectives from one generation to another. In the replacement strategy, a selection procedure, based on the dominance concept and multi-criteria filtering method, is proposed. Such a strategy is applied when the population reaches a critical size. Different GA schemes are compared and their strengths and weaknesses are discussed. The multi-criteria filtering procedure used in the replacement strategy proved very efficient in the diversification of the Pareto front.
EMG Sensors as Virtual Input Devices

Electromyography (EMG), i.e. the detection and analysis of electric signals related to activity of muscles in humans, is often used to control prosthetic limbs. Recently, its importance started to grow also in the field of the human--computer interaction. We have established a simple electromyographic signal pipeline using two off-the-shelf hardware devices NIA in order to implement a virtual input device capable of emulation of mouse. A single NIA device is capable of signal composed of various components, including the EMG signal. We tested the reliability of recognition of elementary activities, such as fist clench, eye blink, etc. and the use of NIA as device which controls an emulation of the mouse. The placement on the forehead yielded least amount of errors.
Antithrombin-Independent Effects of Heparins on Fibrin Clot Nanostructure

Objective—Because of the widespread clinical use of heparins, their effects on the enzymatic cascade are very well known. In contrast, little is known about the direct effect of heparins on the nanostructure of fibrin fibers, even though this nanostructure plays a major role in the mechanical strength and lysis of clots. This lack of reliable data can be correlated with the lack of a nonintrusive, quantitative method to determine this structure. We recently developed such a method that allows the simultaneous determination of the average fiber radius and the protein content using spectrometric data. In this study, we assessed the nanostructure of fibrin in a system composed of human thrombin and fibrinogen. Methods and Results—We provide quantitative evidence showing that both unfractionated heparin and low molecular weight heparin directly alter the nanostructure of fibrin fibers independent of their other actions on the coagulation cascade; as expected, the pentasaccharide fondaparinux has no effect. Conclusion—Our results show that in addition to the effect of heparin on the coagulation cascade, modifications of the fibrin nanostructure may also contribute to improved fibrinolysis.
Evaluating the Causal Explanatory Value of Bayesian Network Structure Learning Algorithms

This paper presents a new technique for evaluating the causal correctness of structural models produced by Bayesian network learning algorithms. In this method, Bayesian networks are generated randomly using a chosen model distribution, data sets of various sizes are produced using the generated networks, the data sets are passed to learning algorithms, and the network structures output by the learning algorithms are compared to the original generated networks. Similar methods have in the past used individually hand-selected networks rather than generating large families of networks. Meaningful results from applying the technique to several search-and-score algorithms are shown. One crucial observation obtained from the results is that when using a search-and-score algorithm over the space of possible graphs to produce a causal network, the choice of scoring function (i.e. MDL or BDeu) has a much greater effect on the resulting model than the choice of search method (i.e. K2 or hill-climbing). A second, corollary, observation is that different scoring functions present different tradeoffs in explanatory performance, rather than there being a single scoring function which is automatically best for all purposes.
Using GPU Simulation to Accurately Fit to the Power-Law Distribution

This article describes a methodology for fitting experimental data to the discrete power-law distribution and provides the results of a detailed simulation exercise used to calculate accurate cutoff values used to assess the fit to a power-law distribution when using the maximum likelihood estimation for the exponent of the distribution. Using massively parallel programming computing, we were able to accelerate by a factor of 60 the computational time required for these calculations across a range of parameters and construct a series of detailed tables containing the test values to be used in a Kolmogorov-Smirnov goodness-of-fit test, allowing for an accurate assessment of the power-law fit from empirical data.