Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Naturalistic Racialization in the “Post-Racial” United States: On the Shifting rather than Declining Significance of Race

Abstract: This essay contends that a 1950s-1970s shift from coercion-based to “consent”-based modes of US racial ordering corresponded to a contemporaneous shift from naturalistic to “post-naturalistic” modes of US racial identification. My sketch of these transformations points to the limitations of critical theories that work with naturalistic conceptions of race for understanding post-civil rights era US racial politics. While anti-black racism continues to tend toward naturalistic articulations, other racial exclusions and dominations tend to be enacted on post-naturalistic grounds. I conclude that the United States is a post-naturalistic racial rather than a post-racial society.
Rehabilitation of Motor Function after Stroke: A Multiple Systematic Review Focused on Techniques to Stimulate Upper Extremity Recovery

Stroke is one of the leading causes for disability worldwide. Motor function deficits due to stroke affect the patients' mobility, their limitation in daily life activities, their participation in society and their odds of returning to professional activities. All of these factors contribute to a low overall quality of life. Rehabilitation training is the most effective way to reduce motor impairments in stroke patients. This multiple systematic review focuses both on standard treatment methods and on innovating rehabilitation techniques used to promote upper extremity motor function in stroke patients. A total number of 5712 publications on stroke rehabilitation was systematically reviewed for relevance and quality with regards to upper extremity motor outcome. This procedure yielded 270 publications corresponding to the inclusion criteria of the systematic review. Recent technology-based interventions in stroke rehabilitation including non-invasive brain stimulation, robot-assisted training, and virtual reality immersion are addressed. Finally, a decisional tree based on evidence from the literature and characteristics of stroke patients is proposed. At present, the stroke rehabilitation field faces the challenge to tailor evidence-based treatment strategies to the needs of the individual stroke patient. Interventions can be combined in order to achieve the maximal motor function recovery for each patient. Though the efficacy of some interventions may be under debate, motor skill learning, and some new technological approaches give promising outcome prognosis in stroke motor rehabilitation.
Conflict in Teams: Modeling Early and Late Conflict States and the Interactive Effects of Conflict Processes

We introduce a model of teams’ early and late conflict states, conflict processes, and performance. In a study of 529 individuals in 145 teams, we provide a theoretical framework and empirically test a series of hypotheses pertaining to the influence of conflict states, including task and relationship conflict, on performance, as well as the moderating effect of two conflict processes (cooperative and competitive management approaches). We address inconsistencies in the literature related to the effect of team conflict, specifically task conflict, within teams. Our results suggest that task conflict in the end of a team’s life cycle, like relationship conflict, can have a significant negative effect on performance, but only when teams’ conflict management approaches are competitive (rather than cooperative). We also provide evidence that conflict management approaches are affected by the type of conflict teams exhibit in their early life cycle stages. Thus, we present a study of how early levels of conflict types affect conflict management approaches, and how these approaches affect later levels of the conflict type/performance relationship. Our model suggests that conflict types and conflict management approaches should be modeled together to better understand team conflict.
Management Zone Analyst (MZA)

Producers using site-specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones. Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.
Extensibility in ecosystem architectures: an initial study

Organizations that build software-intensive products participate in socio-technical ecosystems that encompass their collaborators, competitors, customers, and suppliers. STREAM, an ecosystem modeling method, provides three views of that ecosystem: business, software, and innovation. In the software view the architecture is the primary structuring element. It is our hypothesis that certain quality attributes of the architecture, such as expressiveness and extensibility, are important factors in the success of the ecosystem. Extensibility is normally presented through the API. We examine the APIs of three platforms, one that is increasing in use, one that is in steady use, and one that is declining in use. We use these initial results to generate hypotheses for further exploration.
"short Term Load Forecasting Using a Bayesian Combination Method" a Bayesian Combination Method for Short Term Load Forecasting

This paper presents the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for Short Term Load Forecasting (STLF) based on the combination of an artificial neural network (ANN) predictor and two linear regression (LR) predictors. The method is applied to STLF for the Greek Public Power Corporation dispatching center of the island of Crete, using 1994 data, and daily load profiles are obtained. Statistical analysis of prediction errors reveals that during given time periods the ANN predictor consistently forecasts better for certain hours of the day, while the LR predictors forecast better during for the rest. This relative prediction advantage may change over different time intervals. The combined prediction is a weighted sum of the ANN and LR predictions, where the weights are computed using an adaptive update of the Bayesian posterior probability of each predictor, based on their past predictive performance. The proposed method outperforms both ANN and LR predictions. This paper appeared in Electrical Power and Energy Systems, Vol.19, pp.171-177, 1997
Semantic Analysis Method for Unstructured Data in Telecom Services

A variety of services have recently been provided depending on highly developed networks and personal equipment. With these advances, connecting this equipment has become increasingly more complicated. Problems such as an increase in no-connection and determining the cause have become difficult in some cases because software is often updated to keep up with advancements in services or security. Telecom operators must understand the situation and act as quickly as possible when they receive customer enquiries. In this paper, we propose a method for analyzing and classifying customer enquiries that enables quick and efficient responses. This method is based upon a dependency parsing and co-occurrence technique to enable classification of a large amount of unstructured data into patterns because customer enquiries are generally stored as unstructured textual data.
Managerial Policy and Knowledge Sharing Performance: A Dialectic Analysis

Knowledge is the fundamental source to the competitive advantage of a firm, and knowledge sharing is a primary approach to make configurations of the knowledge resource. Much of the current researches on knowledge sharing had a common assumption or argument that the managerial policy has positive roles to knowledge sharing performance, while many other researches did not support. This confused circumstance made trouble to the present research on knowledge sharing. Based on the previous literatures, we made a deep analysis on the positive roles and negative roles respectively, and argued that managerial policy has positive roles to knowledge sharing performance when its intension is weak, and vice the versa.
Scaling of Topologically Similar Functional Modules Defines Mouse Primary Auditory and Somatosensory Microcircuitry

Mapping the flow of activity through neocortical microcircuits provides key insights into the underlying circuit architecture. Using a comparative analysis we determined the extent to which the dynamics of microcircuits in mouse primary somatosensory barrel field (S1BF) and auditory (A1) neocortex generalize. We imaged the simultaneous dynamics of up to 1126 neurons spanning multiple columns and layers using high-speed multiphoton imaging. The temporal progression and reliability of reactivation of circuit events in both regions suggested common underlying cortical design features. We used circuit activity flow to generate functional connectivity maps, or graphs, to test the microcircuit hypothesis within a functional framework. S1BF and A1 present a useful test of the postulate as both regions map sensory input anatomically, but each area appears organized according to different design principles. We projected the functional topologies into anatomical space and found benchmarks of organization that had been previously described using physiology and anatomical methods, consistent with a close mapping between anatomy and functional dynamics. By comparing graphs representing activity flow we found that each region is similarly organized as highlighted by hallmarks of small world, scale free, and hierarchical modular topologies. Models of prototypical functional circuits from each area of cortex were sufficient to recapitulate experimentally observed circuit activity. Convergence to common behavior by these models was accomplished using preferential attachment to scale from an auditory up to a somatosensory circuit. These functional data imply that the microcircuit hypothesis be framed as scalable principles of neocortical circuit design.
A kinetic chain approach for shoulder rehabilitation.

OBJECTIVE To introduce an approach to shoulder rehabilitation that integrates the kinetic chain throughout the rehabilitation program while providing the theoretical rationale for this program. BACKGROUND The focus of a typical rehabilitation program is to identify and treat the involved structures. However, in activities of sport and daily life, the body does not operate in isolated segments but rather works as a dynamic unit. Recently, rehabilitation programs have emphasized closed kinetic chain exercises, core-stabilization exercises, and functional programs. These components are implemented as distinct entities and are used toward the end of the rehabilitation program. DESCRIPTION Kinetic chain shoulder rehabilitation incorporates the kinetic link biomechanical model and proximal-to-distal motor-activation patterns with proprioceptive neuromuscular facilitation and closed kinetic chain exercise techniques. This approach focuses on movement patterns rather than isolated muscle exercises. Patterns sequentially use the leg, trunk, and scapular musculature to activate weakened shoulder musculature, gain active range of motion, and increase strength. The paradigm of kinetic chain shoulder rehabilitation suggests that functional movement patterns and closed kinetic chain exercises should be incorporated throughout the rehabilitation process. CLINICAL ADVANTAGES The exercises in this approach are consistent with biomechanical models, apply biomechanical and motor control theory, and work toward sport specificity. The exercises are designed to stimulate weakened tissue by motion and force production in the adjacent kinetic link segments.
A User Model-Based Resource Scheduling Framework

In traditional resource scheduling algorithms, hardware condition and network performance, such as CPU, storage space, network bandwidth and delay, play important roles. In previous studies, researchers have ignored imbalance of resource requirement of different user who has special interest affecting the quality of service. In this paper, we present a User Model-based Resource Scheduling (UMRS) framework, which allocates storage resources according to both user information and hardware condition. By predicting the interest of user and pushing possible desired resource to user node or nodes nearby actively, the overall schedule performance can be improved.
Algorithm 879: EIGENTEST—a test matrix generator for large-scale eigenproblems

Eigentest is a package that produces real test matrices with known eigensystems. A test matrix, called an eigenmat, is generated in a factored form, in which the user can specify the eigenvalues and has some control over the condition of the eigenvalues and eigenvectors. An eigenmat <i>A</i> of order <i>n</i> requires only <i>O</i>(<i>n</i>) storage for its representation. Auxiliary programs permit the computation of (<i>A</i> − <i>sI</i>)<i>b</i>, (<i>A</i> − <i>sI</i>)<sup>T</sup><i>b</i>, (<i>A</i> − <i>sI</i>)<sup>−1</sup> <i>b</i>, and (<i>A</i> − <i>sI</i>)<sup>−T</sup> <i>b</i> in <i>O</i>(<i>n</i>) operations. A special routine computes specified eigenvectors of an eigenmat and the condition of its eigenvalue. Thus eigenmats are suitable for testing algorithms based on Krylov sequences, as well as others based on matrix-vector products. This article introduces the eigenmat and describes implementations in Fortran 77, Fortran 95, C, and Matlab.
A hardware/software cosimulator for Network-on-Chip

Network-on-Chip (NoC) is considered as a promising interconnection scheme for many-core System-on-a-Chip (SoC) since it offers better scalability than traditional bus-based interconnection. In this paper, we proposed a fast simulator of NoC architectures using QEMU and Noxim. QEMU is an open-source CPU emulator, and Noxim is an open-source SystemC base NoC simulator for on-chip interconnection network. Experimental results show that proposed simulator successfully simulates a 108 core NoC in a practical time.
A Network Coding Approach to Overlay Network Monitoring

Monitoring and diagnosis of network conditions is a central problem in networking. As such, it has received a lot of attention in the Internet community in general and in the context of overlay networks in particular. Independently, recent advances in network coding have shown that it is possible to increase network capacity and better share the available resources by allowing intermediate nodes to perform processing operations, in addition to just forwarding packets. In this work, we propose the use of network coding techniques to improve several aspects of network monitoring in overlay networks. As a specific application, we use our approach for the well-known problem of network tomography, and in particular for inferring link loss rates from end-to-end measurements. We demonstrate that our approach can decrease the bandwidth used by probes, improve the accuracy of estimation, and decrease the complexity of selecting paths or trees to send probes.
Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements

We present a new method to accurately locate persons indoors by fusing inertial navigation system (INS) techniques with active RFID technology. A foot-mounted inertial measuring units (IMUs)-based position estimation method, is aided by the received signal strengths (RSSs) obtained from several active RFID tags placed at known locations in a building. In contrast to other authors that integrate IMUs and RSS with a loose Kalman filter (KF)-based coupling (by using the residuals of inertial- and RSS-calculated positions), we present a tight KF-based INS/RFID integration, using the residuals between the INS-predicted reader-to-tag ranges and the ranges derived from a generic RSS path-loss model. Our approach also includes other drift reduction methods such as zero velocity updates (ZUPTs) at foot stance detections, zero angular-rate updates (ZARUs) when the user is motionless, and heading corrections using magnetometers. A complementary extended Kalman filter (EKF), throughout its 15-element error state vector, compensates the position, velocity and attitude errors of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (forward, lateral or backward walk, at different speeds), and does not require an offline calibration for the user gait. The integrated INS+RFID methodology eliminates the typical drift of IMU-alone solutions (approximately 1% of the total traveled distance), resulting in typical positioning errors along the walking path (no matter its length) of approximately 1.5 m.
Neuroscience in Information Systems Research: Applying Knowledge of Brain Functionality Without Neuroscience Tools

This book shows how information systems (IS) scholars can effectively apply neuroscience expertise in ways that do not require neuroscience tools. However, the approach described here is intended to complement neuroscience tools, not to supplant them. Written by leading scholars in the field, it presents a review of the empirical literature on NeuroIS and provides a conceptual description of basic brain function from a cognitive neuroscience perspective. Drawing upon the cognitive neuroscience knowledge developed in non-IS contexts, the book enables IS scholars to reinterpret existing behavioral findings, develop new hypotheses and eventually test the hypotheses with non-neuroscience tools. At its core, the book conveys how neuroscience knowledge makes a deeper understanding of IS phenomena possible by connecting the behavioral and neural levels of analysis.
Exploration and Development of High Entropy Alloys for Structural Applications

We develop a strategy to design and evaluate high-entropy alloys (HEAs) for structural use in the transportation and energy industries. We give HEA goal properties for low (≤150 °C), medium (≤450 °C) and high (≥1,100 °C) use temperatures. A systematic design approach uses palettes of elements chosen to meet target properties of each HEA family and gives methods to build HEAs from these palettes. We show that intermetallic phases are consistent with HEA definitions, and the strategy developed here includes both single-phase, solid solution HEAs and HEAs with intentional addition of a 2nd phase for particulate hardening. A thermodynamic estimate of the effectiveness of configurational entropy to suppress or delay compound formation is given. A 3-stage approach is given to systematically screen and evaluate a vast number of HEAs by integrating high-throughput computations and experiments. CALPHAD methods are used to predict phase equilibria, and high-throughput experiments on materials libraries with controlled composition and microstructure gradients are suggested. Much of this evaluation can be done now, but key components (materials libraries with microstructure gradients and high-throughput tensile testing) are currently missing. Suggestions for future HEA efforts are given.
Preferences for Model Selection in Explanation

The range of possible domain models on which an explanation can be based is often large, yet human explainers are able to choose models that address a questioner's informative needs without undue obscurity. However, few existing explanation systems use knowledge bases providing multiple models of their topics of explanation let alone account for the selection of a model for a given explanation. This paper demonstrates the utility of a preference-based mechanism for model selection. Selection heuristics are made explicit as preferences and can be added, modified, or removed without modifying the interpreter or other planning knowledge. The mechanism is more general than previous mechanisms for model selection or “perspective,” and can apply previously identified model selection criteria.
Digit recognition system for camera mobile phones

We present the evaluation of different methods for digit recognition for mobile camera phones. The recognition system follows the typical paradigm of object recognition: a) image segmentation, b) feature extraction, and c) object recognition. The image segmentation is based on a local adaptive thresholding method for separating the digits from the background. Then, 22 features derived from the statistical distribution of points were calculated from the binarized digits. For digit recognition, two minimum distance classifiers were compared: Euclidean and Mahalanobis. The results pointed out that Mahalanobis classifier reached the best performance with 98.9% of accuracy when recognizing single digits and 93.1% when recognizing complete lectures (array of 4 or 5 digits).
Success & Failure of Worker Co-Ops: The Role of Internal and External Environmental Factors*

In an enlightened society, one critical goal is the creation of social and economic equity among its membership to free it from the bonds of poverty and racism. The belief that all people deserve the right to economic security and to fulfillment and dignity in their work is fundamental. A major path to this goal can be the democratization of the economy through the spread of worker ownership and control. This paper was written to share the experiences from some of the pathbreaking efforts in the field to implement the cooperative ideal and to contribute to the transformation of the capitalist system into a cooperative commonwealth. This study examines some conditions conducive to the success or failure of worker cooperatives, derived from an examination of several case studies including the U.S. plywood co-ops, the Jamaican sugar worker co-ops, the Mondragon network, and the Philadelphia area O&O markets.