Miracle-dz/UniFAR-FTUs huggingface dataset:
The Materialized Form of Military Supply Chain Management Platform: Transaction Cost Economics Analysis

The materialized form of supply chain management platform based on transaction cost theory is composed of market, outsourcing (including internal outsourcing and external outsourcing), self-operation in house. It is suggested that application service provision (ASP) belongs to the third wave of outsourcing. Based on Williamson's heuristic model, Firstly, this paper deeply analyzes the efficiency of the ASP and the firm's vertical boundary with asset specificity as variable expressed with k while the sum of the production cost difference expressed with Delta C and transaction cost difference with DeltaT as the dependence variable. It is found that if (DeltaC + DeltaT)>0, then the efficiency of the market is higher than that of the internal integration; If (DeltaC + DeltaT)<0, it concludes to the contrary. K* is the threshold point, that is the vertical boundary. Secondly, this paper expounds the IT external outsourcing area. Research founds that with the development of IT and the good relationship established with ASP, the firm's vertical boundary moves to the right side K **. The range between K* and K** is the general IT external outsourcing Area. At the same time, the efficiency of IT external outsourcing is increased. Thirdly, background of internal outsourcing and its area is explained. It is found that, with the occurrence of oligopoly to IT industry, the boundary of the firm is adjusted to K*** and external market space shrinks. The area between K*** to K* becomes the internal outsourcing space. As the supply chain management of military, it is quite different with the general production enterprises. The platform of military supply chain management requires much more customizable, personality, and it is greatly dependent on the IT providers and IT quality/level. It shows the characteristics of oligopoly. As a conclusion that IT internal outsourcing may be the best selection to the military supply chain management. The military appliance system may co-invest with the research bureau of the military in IT, and delivers the regarding service to internal ASPs.
Low-cost real-time stereo vision hardware with binary confidence metric and disparity refinement

This paper presents a real-time stereo vision System-on-Chip (SoC) architecture for a depth-field generation processor as required in 3D TV applications. This architecture includes post-processing steps like a decision tree based confidence metric and a disparity refinement module while still fitting in a low cost FPGA. A real-time stereo matching calculation at a frame rate of 56 Hz with a resolution of 800x600 and a disparity of 80 has been realized using this architecture without the need for external memories.
The Human Brain Project: High Performance Computing for Brain Cells Hw/Sw Simulation and Understanding

This paper describes the challenge of understanding brain function through high performance computing dealt with by the Human Brain Project, the European Commission Future and Emerging Technologies Flagship involving a consortium of 112 partners spread in 24 European countries. In particular, we describe the activity, performed by one of the Italian units involved into the project, aiming at identifying very accurate models of cerebellum neurons. These models are processed through high end Graphic Processing Units (GPUs) during the tuning phase and later implemented on FPGA-based application specific processors for respecting real time requirements together with embedded implantability. Models and performance of granular neurons implementations are given in the results section.
Towards Readable Layouts for Modeling Data Warehouses (PREPRINT)

Data warehouses are large-scale databases that are usually managed by means of diagram-based conceptual models. However, the complexity of those models often imposes significant design challenges. In particular, this article studies their different underlying graph layouts. The working hypothesis is that graph layouts influence diagram readability, with the latter being significant for facilitating the design process. We define the main viewpoints involved in conceptual modeling. For each one, surveyed as well as alternative layouts were evaluated against a set of aesthetics and efficiency measures. As a result, more readable graph layouts than those found in the literature were identified.
The Principalship: New Roles in a Professional Learning Community

In its revised sixth edition,The Principalship continues to emphasize school culture, standards, and building community, while also providing a moral basis for leadership as a means of navigating contextual issues and constraints that principals routinely face. Studying The Principalship, future principals will explore in-depth the array of decisions that they will be confronted with and how those decisions will affect students, teachers, and school policy. Extensively updated, the new edition emphasizes diversity as a part of community building; the importance of instructional and shared leadership; and the building of communities of practice, while the book's trademark concentration on reflective practice is maintained throughout the text. The sixth edition also contains a new "Some Reflections" feature that appears in most chapters and that offers students a chance for further critical thinking and enriched class discussions.
High Fault Tolerant Image Processing System Based on Stochastic Computing

In order to decrease impact of noise caused by nanometer devices and cosmic radiation in image processing area, stochastic computing system is proposed as a high fault tolerant computing structure. Implementation of image averaging is used to illustrate the advantages of stochastic computing, and both traditional computing architecture and stochastic computing architecture are implemented with TSMC 0.18um technology, the result shows that stochastic computing offers a much better capacity of fault tolerance compared with traditional computing method, and it is very flexible in design tradeoff between computing time, power consumption and chip area, which makes stochastic computing a better way to accomplish severe design requirements.
Framework for combination aware AU intensity recognition

We present a framework for combination aware AU intensity recognition. It includes a feature extraction approach that can handle small head movements which does not require face alignment. A three layered structure is used for the AU classification. The first layer is dedicated to independent AU recognition, and the second layer incorporates AU combination knowledge. At a third layer, AU dynamics are handled based on variable duration semi-Markov model. The first two layers are modeled using extreme learning machines (ELMs). ELMs have equal performance to support vector machines but are computationally more efficient, and can handle multi-class classification directly. Moreover, they include feature selection via manifold regularization. We show that the proposed layered classification scheme can improve results by considering AU combinations as well as intensity recognition.
Mining User Preferences of Multimedia Interfaces with K-modes

Interactive multimedia learning systems use sophisticated techniques to present advanced interface features. However, not all users appreciate the strengths of such interface features because of the variations of user backgrounds and skills. In this context, human factors are important issues in deciding user preferences. This study applies a data mining approach to examine user preferences of interface features and to identify the influence of human factors on this issue. K-modes, a data mining technique extensively applied to user modeling, was used to group users' preferences. The results indicated that users' preferences could be divided into eight groups where gender and computer experience significantly influenced the choices made by users.
System-Level Design Methodologies for Networked Multiprocessor Systems-on-Chip

models of various processing elements and simulating the whole system to check if the tasks meet their local and/or end-to-end deadlines under precedence and resource constraints. This work has also formed the basis for other system-level modeling-related research activities such as networks-on-chip and reconfigurable computing platforms. Chapter 6 describes a further extension of our earlier work on SystemC-based multiprocessor system-on-chip modeling framework which can provide the wireless sensor network designers a system-level abstraction of the sensor network for system-level design-space exploration to meet the requirements mentioned above. Though our aim has been to develop a general sensor network modeling environment, we have actually been driven by a real-life sensor network application − the Hogthrob project which, as described above, is concerned with the development of a wireless sensor network infrastructure for sow monitoring. In our SystemC-based modeling framework, a sensor network model is designed following the principle of composition. We model a sensor network at two levels: the sensor network level and the sensor node level. At the sensor node level, a sensor node platform model is split into two sections: the software section for functional simultion of the sensor node platform and the hardware section to enable estimation of the energy consumption of the sensor node platform. The software section of the sensor node platform model consists of the application model, comprising a set of task models and the RTOS model, composed of a set of RTOS services. At the sensor network level, a sensor node platform model is embedded in an environment model that models the environmental phenomena to be sensed by the sensor network application. To bridge the abstraction gap between the system-level abstract sensor network model (mentioned above) and the implementation-level, cycle-accurate sensor node model (mentioned later), we have introduced an intermediate-level bridging model based on transaction-level modeling concepts that attempts to connect the top-level and the bottom-level models in a consistent manner. This model is described in Chapter 6. 1.2 An Outline of the Thesis 23 1.2. 3 Wireless Sensor Node Design & Test and Cycle-
Interference Prediction Using IMM Estimator in Broadband Wireless Packet Networks

–An interference prediction scheme is proposed for power control in packet-switched TDMA wireless networks. The prediction scheme is based on the interacting multiple model (IMM) estimator, and it is adaptive to a wide range of nonstationary dynamic characteristics of the interference power. Compared to Leung's scheme, it can predict the interference power more accurately and allow us to adjust the transmit power more efficiently in achieving a desired signal-to-interference-plus-noise ratio (SINR).
Object Recognition, A Particular Reference to Face Recognition

Pattern recognition problem rely upon the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face regions are identified from this skin pixel region. Facial features such as eyes, nose and mouth are then located. Faces are recognized from color images using an RBF based neural network. K means clustering algorithm is used to locate different facial elements. Orientation is corrected by using eyes. Parameters like inter eye distance, nose length, mouth position, Discrete Cosine Transform (DCT) coefficients etc. are computed and used for a Radial Basis Function (RBF) based neural network. This approach reliably works for face sequence with orientation in head, expressions etc.
Incorporating Context Correlation into Context-aware Matrix Factorization

Context-aware recommender systems (CARS) go beyond traditional recommender systems, that only consider users' profiles, by adapting their recommendations also to users' contextual situations. Several contextual recommendation algorithms have been developed by incorporating context into recommendation algorithms in different ways. The most effective approaches try to model deviations in ratings among contexts, but ignore the correlations that may exist among these contexts. In this paper, we highlight the importance of contextual correlations and propose a correlation-based context-aware matrix factorization algorithm. Through detailed experimental evaluation we demonstrate that adopting contextual correlations leads to improved performance.
Transcranial Direct Current Stimulation of the Dorsolateral Prefrontal Cortex Modulates Repetition Suppression to Unfamiliar Faces: An ERP Study

Repeated visual processing of an unfamiliar face suppresses neural activity in face-specific areas of the occipito-temporal cortex. This "repetition suppression" (RS) is a primitive mechanism involved in learning of unfamiliar faces, which can be detected through amplitude reduction of the N170 event-related potential (ERP). The dorsolateral prefrontal cortex (DLPFC) exerts top-down influence on early visual processing. However, its contribution to N170 RS and learning of unfamiliar faces remains unclear. Transcranial direct current stimulation (tDCS) transiently increases or decreases cortical excitability, as a function of polarity. We hypothesized that DLPFC excitability modulation by tDCS would cause polarity-dependent modulations of N170 RS during encoding of unfamiliar faces. tDCS-induced N170 RS enhancement would improve long-term recognition reaction time (RT) and/or accuracy rates, whereas N170 RS impairment would compromise recognition ability. Participants underwent three tDCS conditions in random order at ∼72 hour intervals: right anodal/left cathodal, right cathodal/left anodal and sham. Immediately following tDCS conditions, an EEG was recorded during encoding of unfamiliar faces for assessment of P100 and N170 visual ERPs. The P3a component was analyzed to detect prefrontal function modulation. Recognition tasks were administered ∼72 hours following encoding. Results indicate the right anodal/left cathodal condition facilitated N170 RS and induced larger P3a amplitudes, leading to faster recognition RT. Conversely, the right cathodal/left anodal condition caused N170 amplitude and RTs to increase, and a delay in P3a latency. These data demonstrate that DLPFC excitability modulation can influence early visual encoding of unfamiliar faces, highlighting the importance of DLPFC in basic learning mechanisms.
Master Erasmus Mundus in Color in Informatics and Media Technology ( CIMET ) Digital Inpainting for Artwork Restoration : Algorithms and Evaluation Master Thesis Report

Digital image inpainting refers to a technique used for filling in the missing or corrupted regions of an image using information from the surrounding area. Inpainting techniques have found widespread use in applications that include, but are not limited to error recovery, red-eye removal, multimedia editing. This thesis will discuss inpainting techniques in the context of digital artwork restoration. Due to an extensive research in the field of digital inpainting and to rapid advances in technology, new and improved inpainting techniques are continuously proposed. Improved digital image inpainting algorithms could provide substantial support for future artwork restoration. However, as the latter is characterised by a high demand in quality, methods for an accurate evaluation of the performance of inpainting algorithms are needed. A literature review showed however, that currently, there is an acknowledged lack of quantitative metrics for image inpainting evaluation. In this thesis the performance of eight representative inpainting algorithms will first be evaluated by means of a psychophysical experiment. Based on the obtained perceptual data, a ranking of the algorithms will be established, that confirms that exemplar based methods generally outperform Partial Differential Equation based methods. Two novel inpainting quality metrics, proposed in this thesis, eight general image quality metrics and four quality metrics specially developed for inpainting assessment will then be evaluated against human observers. Two types of evaluation will be carried out; one investigates the performance of the metrics over the entire image database considered, and the other assesses the correlation of the metrics for individual images. Results show that none of the considered metrics can adequately predict inpainting quality over the entire image database, and that the performance of the metrics is imagedependent. The two newly proposed metrics outperform some of the existent inpainting quality metrics for the first type of evaluation. However, additional work is needed to find metrics that correlate well with the percept.
Selectively Disrupted Functional Connectivity Networks in Type 2 Diabetes Mellitus

Background The high prevalence of type 2 diabetes mellitus (T2DM) in individuals over 65 years old and cognitive deficits caused by T2DM have attracted broad attention. The pathophysiological mechanism of T2DM-induced cognitive impairments, however, remains poorly understood. Previous studies have suggested that the cognitive impairments can be attributed not only to local functional and structural abnormalities but also to specific brain networks. Thus, our aim is to investigate the changes of global networks selectively affected by T2DM. Methods A resting state functional network analysis was conducted to investigate the intrinsic functional connectivity in 37 patients with diabetes and 40 healthy controls who were recruited from local communities in Beijing, China. Results We found that patients with T2DM exhibited cognitive function declines and functional connectivity disruptions within the default mode network, left frontal parietal network, and sensorimotor network. More importantly, the fasting glucose level was correlated with abnormal functional connectivity. Conclusion These findings could help to understand the neural mechanisms of cognitive impairments in T2DM and provide potential neuroimaging biomarkers that may be used for early diagnosis and intervention in cognitive decline.
A BICS for CMOS OpAmps by Monitoring the Supply Current Peak

We present a Built-In-Current-Sensor (BICS) based on monitoring a signature of the supply current peak of CMOS OpAmps using the oscillation-test-strategy. The BICS takes a weighted sample of the current through two OpAmp current branches and monitors a signature of the peak value under oscillation. Two current-based comparators and some digital circuitry are used to provide a pass/fail flag. Simulation results demonstrate a high defect coverage with a very small impact both on the OpAmp nominal operation and the area overhead.
Hybridization of Genetic Algorithm and IBMUSIC applied to DOA estimation under coherent signals

This paper introduces a new hybrid of genetic algorithm (GA) and iteration beamspace multiple signal classification (IBMUSIC) suitable for making direction-of-arrival (DOA) in coherent signals. In general, the problem of estimation the DOA in coherent signals is more difficult to solve and conventional DOA estimation methods such as minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC) algorithm produce mistake results owing to the existence of coherent terms. In this situation, we adopt the method of conjugate with a GA for selecting the initial search angle of IBMUSIC estimation. Moreover, the received data is projected onto the steering vectors of signal complement space by GA estimation, and then the coherent terms can be decrease. We use this tool to estimate the DOA in coherent signals to achieve low computational complexity and an excellent performance. Finally, numerical example was analyzed to illustrate the design procedure and confirm the performance of the proposed method.
Attenuation of Amydgala and Frontal Cortical Responses to Low Blood Glucose Concentration in Asymptomatic Hypoglycemia in Type 1 Diabetes

OBJECTIVE—Loss of ability to recognize hypoglycemia (hypoglycemia unawareness) increases risk of severe hypoglycemia threefold in insulin-treated diabetes. We set out to investigate the cerebral correlates of unawareness in type 1 patients. RESEARCH DESIGN AND METHODS—Regional changes in brain glucose kinetics were measured using [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET), in 13 men with type 1 diabetes—6 with hypoglycemia awareness and 7 with hypoglycemia unawareness—at euglycemia (5 mmol/l) and hypoglycemia (2.6 mmol/l), in random order. RESULTS—Epinephrine responses to hypoglycemia were reduced in hypoglycemia unawareness (P < 0.0003), as were symptoms. Statistical parametric mapping (SPM) of FDG uptake using SPM2 at a statistical threshold of P < 0.005 showed increased FDG uptake in left amygdala in hypoglycemia awareness, but not in hypoglycemia unawareness (region of interest analysis −0.40 ± 1.03 vs. 3.66 ± 0.42, respectively; P = 0.007), and robust increase in bilateral ventral striatum during hypoglycemia (region of interest analysis hypoglycemia unawareness 3.52 ± 1.02 vs. awareness 6.1 ± 0.53; P = 0.054). Further analysis at the statistical threshold of P < 0.01 showed bilateral attenuated activation of brain stem regions and less deactivation in lateral orbitofrontal cortex in hypoglycemia unawareness. CONCLUSIONS—Ventral striatal, amygdala, brain stem, and orbitofrontal responses to hypoglycemia indicate engagement of appetitive motivational networks, associated with integrated behavioral responses to hypoglycemia. Reduced responses in these networks in hypoglycemia unawareness, particularly failure of amygdala and orbifrontal cortex responses, suggest habituation of higher behavioral responses to hypoglycemia as a basis for unawareness. New approaches may be needed to restore awareness effectively in practice.
Salt Stress Causes Peroxisome Proliferation, but Inducing Peroxisome Proliferation Does Not Improve NaCl Tolerance in Arabidopsis thaliana

The PEX11 family of peroxisome membrane proteins have been shown to be involved in regulation of peroxisome size and number in plant, animals, and yeast cells. We and others have previously suggested that peroxisome proliferation as a result of abiotic stress may be important in plant stress responses, and recently it was reported that several rice PEX11 genes were up regulated in response to abiotic stress. We sought to test the hypothesis that promoting peroxisome proliferation in Arabidopsis thaliana by over expression of one PEX11 family member, PEX11e, would give increased resistance to salt stress. We could demonstrate up regulation of PEX11e by salt stress and increased peroxisome number by both PEX11e over expression and salt stress, however our experiments failed to find a correlation between PEX11e over expression and increased peroxisome metabolic activity or resistance to salt stress. This suggests that although peroxisome proliferation may be a consequence of salt stress, it does not affect the ability of Arabidopsis plants to tolerate saline conditions.
Hedging effectiveness of European wheat futures markets

The instability of commodity prices and the hypothesis that speculative behaviour was one of its causes has brought renewed interest in futures markets. In this paper, the hedging effectiveness of European and US wheat futures markets were studied to test whether they were affected by the high price instability after 2007. Implicitly, this is a test of whether the increasing presence of speculation in futures markets have made them divorced from the physical markets. A multivariate GARCH model was applied to compute optimal hedging ratios. No important evidence was found of a change in the effectiveness of hedging after 2007.