Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural interactions between motor cortical hemispheres during bimanual and unimanual arm movements

Cortico‐cortical connections through the corpus callosum are a major candidate for mediating bimanual coordination. However, aside from the deficits observed after lesioning this connection, little positive evidence indicates its function in bimanual tasks. In order to address this issue, we simultaneously recorded neuronal activity at multiple sites within the arm area of motor cortex in both hemispheres of awake primates performing different bimanual and unimanual movements. By employing an adapted form of the joint peri‐stimulus time histogram technique, we discovered rapid movement‐related correlation changes between the local field potentials (LFPs) of the two hemispheres that escaped detection by time‐averaged cross‐correlation methods. The frequency and amplitude of dynamic modifications in correlations between the hemispheres were similar to those within the same hemisphere. As in previous EEG studies, we found that, on average, correlation decreased during movements. However, a subset of recording site pairs did show transiently increased correlations around movement onset (57% of all pairs and conditions in monkey G, 39% in monkey P). In interhemispheric pairs, these increases were consistently related to the mode of coupling between the two arms. Both the correlations between the movements themselves and the interhemispheric LFP correlation increases were strongest during bimanual symmetric movements, and weaker during bimanual asymmetric and unimanual movements. Increased correlations occurred mainly around movement onset, whilst decreases in correlation dominated during movement execution. The task‐specific way in which interhemispheric correlations are modulated is compatible with the notion that interactions between the hemispheres contribute to behavioural coupling between the arms.
A retargetable register allocation framework for embedded processors

This paper describes the FlexCC2 register allocation framework. FlexCC2 is an optimizing retargetable C compiler for embedded processors, and in particular for DSP processors. Embedded processors often contain features such as irregular and constrained register sets that complicate register allocation, making traditional methods inefficient. In this paper, we present a register allocation framework specifically tailored for embedded processor specificities. This framework has been integrated in the FlexCC2 production compiler and is used by FlexCC2 customers.
Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses.
EEG Signal Classification for Motor Imagery

EEG signals comprise accurate patterns which can be valuably exploited during motor imagery of left and right hand movements. Analysis of EEG signals by application of band power, wavelet transform, and autoregressive model AR is performed. These methods allow the creation of discriminating features to be used as input to a linear discriminant analysis LDA classifier and a neural network. The classification results obtained by the application of these features extraction methods allow the identification of EEG temporal segments offering the highest classification rate of left and right hand movements.
ADEA — Active Variable Stiffness Differential Elastic Actuator : Design and application for safe robotics

This paper presents an Active Variable Stiffness Differential Elastic Actuator (ADEA) which is designed and application for robotic systems and more generally for machines that are designed to interact with people and environments under safety constraints. The ADEA consists of two DC-motors to drive two antagonistic warm gears independently. By changing the synchronisation and differentiation of angle displacement of these two antagonistic warm gears, ADEA has the capacity to minimize large impact forces due to shocks, to safely interact with the user or become as stiff as possible to make precise position movements. Besides, the ADEA can achieve fast motion control while guaranteeing safety of human operators in worst-case impact situation because of the stiffness of ADEA is variable dynamically. Experimental results are presented to show performance and safety of a one-link arm actuated by the ADEA.
Design, analysis and control of a fast nanopositioning stage

We present a fast flexure-based, piezoelectric stack-actuated XY nanopositioning stage which is suitable for high-speed, accurate nanoscale positioning applications. The performance of the design are analyzed using finite-element-analysis software. Experiments demonstrate that the design has a high first resonant mode at 2.7 kHz, a low cross-coupling of -35 dB and a relatively large traveling range of 25times25 mum. These results are in close agreement with the predicted FEA results. Non-linearities due to hysteresis of the piezoelectric stack actuators are present in the stage. The hysteresis effect is minimized using charge actuation. The Integral Resonant Control (IRC) method is applied to damp the first resonant mode. By implementing feedforward inversion technique, high-speed and accurate scanning performances, up to 400 Hz, are achieved.
An On-Line Algorithm for Measuring the Translational and Rotational Velocities of a Table Tennis Ball

This paper proposes an on-line method for estimating both translational and rotational velocities of a table tennis ball by using only a few consecutive frames of image data which are sensed by two high speed cameras. In order to estimate the translational velocity, three-dimensional (3D) position of the ball’s center at each instant of camera frame is obtained, where the on-line method of reconstructing the 3D position from the two-dimensional (2D) image data of two cameras is proposed without the pattern matching process. The proposed method of estimating the rotational velocity belongs to the image registration methods, where in order to avoid the pattern matching process too, a rotation model of the ball is used to make an estimated image data from an image data sensed at the previous instant of camera frame and then the estimated image data are compared with the image data sensed at the next instant of camera frame to obtain the most plausible rotational velocity by using the least square and the conjugate gradient method. The effectiveness of the proposed method is shown by some experimental results in the case of a ball rotated by a rotation machine as well as in the case of a flying ball shot from a catapult machine.
Added Sources of Costs in Maintaining COTS-Intensive Systems

T he past 10 to 15 years have seen a strong push within the Department of Defense and other government agencies toward the use of COTS software products in system acquisition. On the surface, this makes a lot of sense – why build something from scratch that already exists, especially if it is a mature product? In fact, with the increasing complexity of today's systems, a total custom system is no longer practical. With the continued use of COTS components in building systems, it is a worthwhile objective to identify sources of cost and approaches to managing them. It is the intent of this article to identify these sources. As part of an effort to collect data to calibrate a cost model for systems consisting of COTS components, a number of interviews were conducted with project managers, team leads, and other project members maintaining COTS-intensive systems. People consistently told us that these systems were more expensive to maintain than originally estimated and, in fact, were more costly than a comparable custom-built system to maintain. At the same time, we heard frustration expressed about the difficulty of communicating to upper management the reasons why COTS-based systems are so expensive to maintain. If COTS-based systems really are more costly to maintain, what are these additional costs? Are there strategies for managing or minimizing them? These questions are addressed in this article 3. Before proceeding to answer these questions, we need to define some terms. A COTS software component is defined as it was used in the COCOTS model [2]. This definition contains the following four parts: 1. A COTS component is sold, leased, or licensed for a fee (which includes vendor support in fixing defects if they are found). 2. The source code is unavailable. 3. The component evolves over time as the vendor provides periodic releases of the product (upgrades) containing fixes and new or enhanced functional-ity. 4. Any given version of a COTS component will reach eventual obsolescence or end of life in which it will no longer be supported by the vendor. All four parts of this definition have major implications for the added costs of maintaining COTS-intensive systems (compared to comparable custom-developed systems). At any given time for any given component , there is a choice between upgrading to the next version or doing nothing. There are risks inherent in either strategy. If the first choice is made …
Estimation of Object Motion Parameters from Noisy Images

An approach is presented for the estimation of object motion parameters based on a sequence of noisy images. The problem considered is that of a rigid body undergoing unknown rotational and translational motion. The measurement data consists of a sequence of noisy image coordinates of two or more object correspondence points. By modeling the object dynamics as a function of time, estimates of the model parameters (including motion parameters) can be extracted from the data using recursive and/or batch techniques. This permits a desired degree of smoothing to be achieved through the use of an arbitrarily large number of images. Some assumptions regarding object structure are presently made. Results are presented for a recursive estimation procedure: the case considered here is that of a sequence of one dimensional images of a two dimensional object. Thus, the object moves in one transverse dimension, and in depth, preserving the fundamental ambiguity of the central projection image model (loss of depth information). An iterated extended Kalman filter is used for the recursive solution. Noise levels of 5-10 percent of the object image size are used. Approximate Cramer-Rao lower bounds are derived for the model parameter estimates as a function of object trajectory and noise level. This approach may be of use in situations where it is difficult to resolve large numbers of object match points, but relatively long sequences of images (10 to 20 or more) are available.
Pattern recognition of multidimensional PBMC flow cytometry histograms for prostate cancer identification

Flow cytometry is a technique that is used to count cells and to char- acterize property of the cells. In spite of enormous information content on the cells provided by flow cytometry, cytometry data is still analyzed based on step- by-step gating, either manually or automatically via bioinformatics. This paper presents a new strategy of interpreting cytometry data in a different manner. The proposed strategy utilizes clustering approach to identify cell population of interest and supervised approach to identify statistical significant cell regions in the population that can differentiate prostate cancer patients from the benign patients.
How Social Identity Influences Social and Emotional Loneliness

How Social Identity Influences Social and Emotional Loneliness by Curtis N. Peterson MS, Walden University, 2005 BS, Idaho State University, 2002 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Psychology Walden University November 2018 Abstract Social identity theory (SIT) is a robust theory that explains in-group versus out-groupSocial identity theory (SIT) is a robust theory that explains in-group versus out-group behaviors. Two qualities of one’s social identity include emotional connection and social connection with others, which someone who is experiencing loneliness tends to lack in their current situation. This dissertation explored whether when one’s social identity becomes salient it results in a lower evaluation of one’s current state of loneliness. An experiment was conducted in which college student participants, who were 18 years of age or older and currently enrolled in college courses, were randomly assigned to a social identity saliency group (college student) or 1 of 3 control conditions (personal identity group, cognitive control condition, and no prime condition). The sample consisted of 207 participants of which 189 were analyzed for social loneliness and 190 were analyzed for emotional loneliness, after excluding participants who did not meet scoring criteria. To analyze the data a planned contrast procedure was conducted in which the social identity group’s mean was compared to the combined means of the 3 control conditions. Results indicated that when social identity is made salient, participants report a lower level of emotional and social loneliness when compared to the other 3 conditions. Loneliness, which is being considered a major public health crisis, is becoming more common in modern society, making finding mechanisms to reduce loneliness important. This research supports the notion that social identification can reduce one’s evaluation of loneliness. As an example, from the findings in this research, to reduce loneliness among college students, college programs should focus on the positive attributions of being a college student. How Social Identity Influences Social and Emotional Loneliness by Curtis N. Peterson MS, Walden University, 2005 BS, Idaho State University, 2002 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Psychology Walden University November 2018 Dedication This dissertation is dedicated to my two adult children Curtis (Taylor) and Latasha, the love of my life Elsa, and my mother Becky. Without their support and love this would have never been possible. Acknowledgments I like to acknowledge my dissertation chair Dr. Brad Bell, for his dedication and persistence in mentoring and helping me through the dissertation process. Thank you to Dr. Anthony Perry for his support as committee member, and his feedback and continued
The Analgesic Effects of Different Extracts of Aerial Parts of Coriandrum Sativum in Mice

Regarding the effects of Coriandrum sativum (C. sativum) on central nervous system, in the present study analgesic properties of different extracts of C. sativum aerial partswere investigated. The mice were treated by saline, morphine, three doses (20, 100 and 500 mg/kg) of aqueous, ethanolic, choloroformic extracts of C. sativum and one dose (100 mg/kg) of aqueous, two doses of ethanolic (100 and 500 mg/kg) and one dose of choloroformic (20 mg/kg) extracts of C. sativum pretreated by naloxone. Recording of the hot plate test was performed 10 min before injection of the drugs as a base and it was consequently repeated every 10 minutes after the extracts injection. The maximal percent effect (MPE) in the groups treated by three doses of aqueous, ethanolic and chloroformic extracts were significantly higher than saline group which were comparable to the effect of morphine. The effects of most effective doses of extracts were reversed by naloxone. The results of present study showed analgesic effect of aqueous, ethanolic and chloroformic extracts of C. sativum extract. These effects of the extracts may be mediated by opioid system. However, more investigations are needed to elucidate the exact responsible mechanism(s) and the effective compound(s).
Robust Text Reading in Natural Scene Images

In this paper, we consider using two-layer, convolutional neural networks (CNNs) to construct an end-to-end text recognition system for natural images. The approach we take is similar to traditional approaches to text recognition in which we first train a text detector that finds regions in the image that contain text, and then train a character classifier and apply it to the regions of interest to perform word-level recognition. While the basic two-stage system we adopt is similar to other text recognition systems, we note that in all existing systems, the detector and classifier are treated as two distinct systems with different sets of hand-crafted features. Here, we take a decisively different approach by leveraging the power of unsupervised feature learning and fine-tuning to construct a common framework for both the detector and recognizer. Such a system demonstrates the possibility of using a single, unified architecture for both detection and recognition in the construction of a full end-to-end system. In constructing such a system, we present state-of-the-art results in cropped word recognition and reputable results in full end-to-end recognition.
An Evaluation of Orbital Angular Momentum Multiplexing Technology

This paper reports our investigation of wireless communication performance obtained using orbital angular momentum (OAM) multiplexing, from theoretical evaluation to experimental study. First, we show how we performed a basic theoretical study on wireless OAM multiplexing performance regarding modulation, demodulation, multiplexing, and demultiplexing. This provided a clear picture of the effects of mode attenuation and gave us insight into the potential and limitations of OAM wireless communications. Then, we expanded our study to experimental evaluation of a dielectric lens and end-to-end wireless transmission on 28 gigahertz frequency bands. To overcome the beam divergence of OAM multiplexing, we propose a combination of multi-input multi-output (MIMO) and OAM technology, named OAM-MIMO multiplexing. We achieved 45 Gbps (gigabits per second) throughput using OAM multiplexing with five OAM modes. We also experimentally demonstrated the effectiveness of the proposed OAM-MIMO multiplexing using a total of 11 OAM modes. Experimental OAM-MIMO multiplexing results reached a new milestone for point-to-point transmission rates when 100 Gbps was achieved at a 10-m transmission distance.
Short Acculturation Scale for Hispanics (SASH-Y and SASH) Reliability and Validity in Low-Income, Mexican-Heritage Youth and Parents

The Short Acculturation Scale for Hispanics (SASH) is a popular acculturation measure. Although SASH has been validated, conflicting information has been published about the youth version (SASH-Y). We examined the psychometric properties (i.e., validity and reliability) of SASH-Y in low-income, Mexican-heritage youth (aged 8-18; n = 100) and SASH with their parents (n = 100; total N = 200). Confirmatory factor analyses comparing the three published structural variations of the SASH-Y revealed the three-factor structure originally proposed for SASH-Y fit the data best, and the frequently used unidimensional structure did not fit. The adult SASH fit parent data well. For researchers interested in comparing parent and child scores (e.g., acculturation gap research), we examined the 10 identical parent and child items for invariance. Invariance was established for parent and child data with the traditional SASH structure. However, internal consistency was inadequate for the child data regardless of variable structure. Implications are discussed.
Multi-Level Fuzzy Score Fusion for Client Specific Linear Discriminant Analysis Based Face Authentication System

A client specific linear discriminant analysis (CSLDA) based face authentication system has been developed with multi-level fuzzy score fusion. The CSLDA method provides two measures for authentication: distance to the client (Client Score) and distance to the mean of impostors (Impostor Score). A two-level multi-sample score fusion method has been proposed. A fuzzy inference module has also been developed to combine the scores of the CSLDA in the first level. The performance of fuzzy inference score fusion is then compared with several existing fusion methods and the conventional CSLDA face authentication system (without score fusion). Overall, the proposed fusion methods improve the performance of the algorithm and are more robust to variability of the inputs. Evaluation experiments were carried out with two different databases (AT&T and BANCA) where each contains face images of 40 subjects. Experimental results showed that the proposed multi-level fuzzy score fusion method improves the performance of the CSLDA based face authentication system compared to the other fusion techniques examined in this work.
Saliency-based video segmentation with graph cuts and sequentially updated priors

This paper proposes a new method for achieving precise video segmentation without any supervision or interaction. The main contributions of this report include 1) the introduction of fully automatic segmentation based on the maximum a posteriori (MAP) estimation of the Markov random field (MRF) with graph cuts and saliencydriven priors and 2) the updating of priors and feature likelihoods by integrating the previous segmentation results and the currently estimated saliency-based visual attention. Test results indicate that our new method precisely extracts probable regions from videos without any supervised interactions.
Neutron-loaded outflows in gamma-ray bursts

Relativistic neutron-loaded outflows in gamma-ray bursts are studied at their early stages, before deceleration by a surrounding medium. The outflow has four components: radiation, electrons, protons and neutrons. The components interact with each other and exchange energy as the outflow expands. The presence of neutrons significantly changes the outflow evolution. Before neutrons decouple from protons, friction between the two components increases their temperatures by many orders of magnitude. After the decoupling, the gradual neutron decay inside the outflow has a drag effect on the protons and reduces their final Lorentz factor. In inhomogenous outflows, this drag can reduce the Lorentz-factor contrast and affect the development and strength of internal shocks.
From Fast to Lightweight Atomic Memory in Large-Scale Dynamic Distributed Systems

Failure resilience is a fundamental problem. If failures are frequent, it is reasonable to envisage the replication on multiple servers. Although replicating an object (i.e., data) over multiple network locations copes with failures, this makes object modification costly in term of message exchanges to guarantee consistency. In large-scale dynamic systems, nodes join or leave the system arbitrarily often, while the tremendous number of participant prevents nodes from gathering global information. Therefore there is an inherent tradeoff in dynamic distributed systems between the operation timecomplexity and message complexity required for consistency maintenance in atomic memory. This paper addresses this trade-off by presenting and classifying existing solutions.
Deriving a statistical syntactic parsing from a treebank

The study presented in this article is dedicated to a syntactic parser for Romanian. The central goal of the presented technique is to learn a model which is able to discriminate between probability for a word to be head of another word in a dependency structure corresponding to a sentence in the considered language. The model described in this paper was trained on a dependency treebank linguistic resource and is intended to be used in order to develop a dependency syntactic parser.