Miracle-dz/UniFAR-FTUs huggingface dataset:
HyDE – A General Framework for Stochastic and Hybrid Model-based Diagnosis

In recent years several approaches have been proposed for model-based diagnosis of hybrid systems. These approaches deal with discrete or parametric faults, and perform consistency-based, stochastic or mixed reasoning. The major restriction that a diagnosis application designer faces is that each technique uses its own modeling paradigm and the reasoning algorithms implement a single strategy. Diagnosis application designers would like to have the flexibility of building models that are suitable for the task ( e.g. , appropriate abstraction level, appropriate details, type of modeling paradigm used). They would also like to have the flexibility in choosing the strategies used in the diagnostic reasoning process. We propose a general framework for stochastic and hybrid model-based diagnosis called Hybrid Diagnosis Engine (HyDE) that offers this flexibility to the diagnosis application designer. We list the key steps in stochastic and hybrid diagnosis and identify the kinds of models that are needed in those steps. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. We discuss the current status of HyDE and its application in diagnosis of real and conceptual systems.
Global Volatility and Forex Returns in East Asia

During 2001-07, increases in mature market volatility were associated with declines in forex returns for East Asian countries, consistent with an overall "flight to safety" effect. Estimates from GARCH models suggest that a 5 percentage point increase in mature market equity volatility generated an exchange rate depreciation of up to ½ percent. This sensitivity rose during the latter period in the sample, suggesting greater integration of Asian financial markets with global markets. Unconditional standard deviations estimated from these models also provide operational measures of "long-term" and "excess" volatility in forex markets. Long-run forex volatility declined as Asian economies settled down with generally stronger fundamentals in the post-crisis period to more flexible regimes along with a generally lower level of mature market volatility.
6LoWPAN-GHC: Generic Header Compression for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs)

RFC 6282 defines header compression in 6LoWPAN packets (where "6LoWPAN" refers to "IPv6 over Low-Power Wireless Personal Area Network"). The present document specifies a simple addition that enables the compression of generic headers and header-like payloads, without a need to define a new header compression scheme for each such new header or header-like payload.
MOSSE : A Multi Ontological Semantic Search Engine

Search is one of the main motivations behind semantic web. A lot of proposals on semantic web search engines have been appeared in recent years but most of them are restricted to a limited context of web. Here we propose a scalable Multi Ontological Semantic Search Engine to conquer this problem. In addition some components of this architecture have been implemented and their results are reported.
Comparison of Four Bleeding Risk Scores to Identify Rivaroxaban‐treated Patients With Venous Thromboembolism at Low Risk for Major Bleeding

Abstract Objectives Outpatient treatment of acute venous thromboembolism (VTE) requires the selection of patients with a low risk of bleeding during the first few weeks of anticoagulation. The accuracy of four systems, originally derived for predicting bleeding in VTE treated with vitamin K antagonists (VKAs), was assessed in VTE patients treated with rivaroxaban. Methods All patients treated with rivaroxaban in the multinational EINSTEIN deep vein thrombosis (DVT) and pulmonary embolism (PE) trials were included. Major bleeding was defined as ≥2 g/dL drop in hemoglobin or ≥2‐unit blood transfusion, bleeding in critical area, or bleeding contributing to death. The authors examined the incidence of major bleeding in patients with low‐risk assignment by the systems of Ruiz‐Gimenez et al. (score = 0 to 1), Beyth et al. (score = 0), Kuijer et al. (score = 0), and Landefeld and Goldman. (score = 0). For clinical relevance, the definition of low risk for all scores except Kuijer includes all patients < 65 years with no prior bleeding history and no comorbid conditions (current cancer, renal insufficiency, diabetes mellitus, anemia, prior stroke, or myocardial infarction). Results A total of 4,130 patients (1,731 with DVT only, 2,399 with PE with or without DVT) were treated with rivaroxaban for a mean (±SD) duration of 207.6 (±95.9) days. Major bleeding occurred in 1.0% (40 of 4,130; 95% confidence interval [CI] = 0.7% to 1.3%) overall. Rates of major bleeding for low‐risk patients during the entire treatment period were similar: Ruiz‐Gimenez et al., 12 of 2,622 (0.5%; 95% CI = 0.2% to 0.8%); Beyth et al., nine of 2,249 (0.4%; 95% CI = 0.2% to 0.8%); Kuijer et al., four of 1,186 (0.3%; 95% CI = 0.1% to 0.9%); and Landefeld and Goldman, 11 of 2,407 (0.5%; 95% CI = 0.2% to 0.8%). At 30 days, major bleed rates for low‐risk patients were as follows: Ruiz‐Gimenez et al., five of 2,622 (0.2%; 95% CI = 0.1% to 0.4%); Beyth et al., five of 2,249 (0.2%; 95% CI = 0.1% to 0.5%); Kuijer et al., three of 1,186 (0.3%; 95% CI = 0.1% to 0.7%); and Landefeld and Goldman, seven of 2,407 (0.3%; 95% CI = 0.1% to 0.6%). No low‐risk patient had a fatal bleed. Conclusions Four scoring systems that use criteria obtained in routine clinical practice, derived to predict low bleeding risk with VKA treatment for VTE, identified patients with less than a 1% risk of major bleeding during full‐course treatment with rivaroxaban.
The ZmCLA 4 gene in the qLA 4-1 QTL controls leaf angle in maize ( Zea mays

Maize architecture is a major contributing factor to their high level of productivity. Maize varieties with an erect-leaf-angle (LA) phenotype, which increases light harvesting for photosynthesis and grain-filling, have elevated grain yields. Although a large body of information is available on the map positions of quantitative trait loci (QTL) for LA, little is known about the molecular mechanism of these QTL. In this study, the ZmCLA4 gene, which is responsible for the qLA4-1 QTL associated with LA, was identified and isolated by fine mapping and positional cloning. The ZmCLA4 gene is an orthologue of LAZY1 in rice and Arabidopsis. Sequence analysis revealed two SNPs and two indel sites in ZmCLA4 between the D132 and D132-NIL inbred maize lines. Association analysis showed that C/T/mutation667 and CA/indel965 were strongly associated with LA. Subcellular localization verified the functions of a predicted transmembrane domain and a nuclear localization signal in ZmCLA4. Transgenic maize plants with a down-regulated ZmCLA4 RNAi construct and transgenic rice plants over-expressing ZmCLA4 confirmed that the ZmCLA4 gene located in the qLA4 QTL regulated LA. The allelic variants of ZmCLA4 in the D132 and D132-NIL lines exhibited significant differences in leaf angle. ZmCLA4 transcript accumulation was higher in D132-NIL than in D132 during all the developmental stages and was negatively correlated with LA. The gravitropic response was increased and cell shape and number at the leaf and stem junctions were altered in D132-NIL relative to D132. These findings suggest that ZmCLA4 plays a negative role in the control of maize LA through the alteration of mRNA accumulation, leading to altered shoot gravitropism and cell development. The cloning of the gene responsible for the qLA4-1 QTL provides information on the molecular mechanisms of LA in maize and an opportunity for the improvement of plant architecture with regard to LA through maize breeding.
Improving the heart disease diagnosis by evolutionary algorithm of PSO and Feed Forward Neural Network

The considerable growing of cardiovascular disease and its effects and complications as well as the high costs on society makes medical community seek for solutions to prevention, early identification and effective treatment with lower costs. Thus, valuable knowledge can be established by using artificial intelligence and data mining; the discovered knowledge makes improve the quality of service. Until now, different researches have been carried out in order to predict heart disease based on data mining methods such as classification and clustering methods; however, what has been less noticed is the exact diagnosis of disease with the lowest cost and time. In this paper, by using feature ranking on effective factors of disease related to Cleveland clinic database and by using Particle Swarm Optimization as well as Neural Network Feed Forward Back-Propagation, 13 effective factors reduced to 8 optimized features in terms of cost and accuracy. The assessment of selected features of classified methods also showed that PSO method along with Neural Networks of Feed Forward Back-Propagation has the best accurate criteria of the rate of 91.94% on these features.
Enhancing Sentiment Classification Performance Using Bi-Tagged Phrases

Sentiment analysis research mainly aims to determine the orientation of an opinionated stretch of text into positive or negative polarity. The key motivation of sentiment analysis is getting to know what consumers think about products and services by analyzing their opinions on online portals, blogs, discussion boards, reviews etc. The main objective of this paper is to incorporate the information of POS-based sentiment-rich phrases in a machine-learning algorithm that determines the semantic orientation of a given text. In this paper, bi-tagged phrases are used as features in combination with unigram features for sentiment classification. Joint feature vectors of unigrams and bi-tagged phrases have high dimensions consisting of noisy and irrelevant features. Therefore, a feature selection method is used to select only relevant features from the feature vector. Experimental results show that the combination of prominent unigrams and bi-tagged phrases outperforms other features for sentiment classification in a movie review dataset.
Simulating the red:far-red ratio of individual plant organs, a key issue for hytochrome-driven processes

Introduction The red:far-red ratio (R:FR) is a key variable in many biological processes from basic ones such as the response of phytochrome to more integrated ones, such as tillering or weed competition. Accurate estimation of the red:far-red ratio of plant organs in field condition is an important issue to be able to integrate the increasing knowledge on phytochrome-driven processes from cell and organ scale to canopy scale. In this paper, we interfaced an architectural plant model (ADEL-wheat) with a 3D light model (nested radiosity) to simulate the red:far-red signal actually perceived by plant organs in crop canopies.
How do Organizations Align their Marketing and Technological Capabilities in a Turbulent Market Environment ?

Previous research has found that organizations that align their operational marketing and technological capabilities with changing market conditions eventually achieve evolutionary fitness in terms of organizational survival and firm growth. Dynamic capabilities comprise the processes of opportunity identification and reconfiguring of operational capabilities which are required to align operational capabilities with changing market environments. In this paper, we develop and test a model of dynamic capabilities. Drawing on data from a survey of 228 organizations, we investigate how dynamic capabilities influence marketing and technological capabilities and how market turbulence moderates these relationships. Our results find positive relationships between dynamic and operational capabilities. FIMIX-PLS analysis – a response-based segmentation approach which accounts for unobserved heterogeneity – further uncovers latent segments within our data and shows that differences in the relationships between dynamic and operational capabilities between firms can be explained by the degree of market turbulence that firms face.
iSarProjection: A KinectFusion Based Handheld Dynamic Spatial Augmented Reality System

We introduce a general technique of dynamically augmenting physical models. Using a handheld projector attached with an RGBD camera, we map the physical model with corresponding textures, and develop a real-time dynamic spatial augmented reality (SAR) system named iSarProjection. Compared to traditional huge and complex tracking system, this technique, based on KinectFusion, supports precise registration between the physical model point cloud and the reconstructed 3D scene, and provides real-time pose estimation to track the physical display surface effectively. Through projecting medical anatomy visualizations on a white body model, it presents that we ease the development of dynamic SAR system and make it possible to be applied in various research fields.
Prediction of Forage Energy Content by Near Infrared Reflectance Spectroscopy and Summative Equations

In livestock nutrition, summative models (SM) are displacing empirical models as a preferred method to predict energy content of forages. The objective of this study was to determine the effect of using near infrared reflectance spectroscopy (NIRS) to determine nutrient subcomponents required of SM on overall ability to predict energy content of corn and legume-grass silages. Corn (n = 90) and legume-grass (n = 70) silages were collected and analyzed for CP, ADF CP, NDF, NDF CP, in vitro (IV) digestible (d) NDF, ash, and fat by standard laboratory techniques. Samples were scanned on a Model 6500 NIRS, and calibration equations were developed for each nutrient. The TDN contents of corn and legume-grass silages were then estimated using a SM, where the model nutrients were determined by laboratory or NIRS methods. The predicted TDN content of corn and legume-grass silages was compared to IV d OM to assess overall utility. The NIRS calibrations were adequate (R 2 > 0.90) for CP and NDF for both corn and legume-grass silages with standard errors of calibration (SEC) <0.55
Automated Vehicle Velocity Estimation Using a Dual-Line Asynchronous Sensor

This paper proposes a vision system for vehicle velocity estimation based on asynchronous output data. For real-time velocity estimation, a processing method has been developed and implemented on a digital signal processor. It exploits the sparse asynchronous data representation from the dual-line sensor with high temporal resolution (better than 100 mus) and low latency. The processing concept includes vehicle contours extraction, velocity estimation and scaling. The estimation approach uses the vehicle's leading edge on both lines to extract the detection time difference, which is used for the vehicle velocity estimation. The dual-line sensor system including this processing concept has been evaluated on several vehicles for velocities ranging from 10 to 120 km/h. The experiments have been performed at a test site with a two-lane road and the results obtained show an absolute velocity estimation error < 4 km/h.
Navigation Planning for Legged Robots Thesis Proposal

As legged robots gain the abilities to walk and balance on more than just flat, obstacle-free floors, they grow closer to fulfilling the potential of legged locomotion shown by biological systems. To truly fulfill this potential these robots must successfully traverse complicated, rough terrain, requiring the robots to step onto or over various features of the environment. Furthermore, when a robot must spend a significant amount of time supported by a single foot, the contact that foot makes with the ground is very important for stability, requiring that the robot properly assess foot placement to maintain balance during locomotion. This thesis will address the problem of navigation for legged robots by using a global planning approach built on top of existing walking and running controllers. The planning process will reason about foot placement and contact configurations within the terrain, and the connectivity of those contact configurations. This thesis will provide a general global navigation strategy for a wide range of legged robots through complicated environments, while utilizing the advantages of their legs, as well as ensuring their safety and stability during locomotion.
Cognitive RF Systems and EM Fratricide

In many parts of our world the radio frequency (RF) spectrum is overcrowded. The Department of Defense and researchers throughout the US have been addressing this problem by developing cognitive radios, networks, and radar systems to intelligentl y choose frequencies, waveform parameters, antenna beam patterns, etc. to operate with conventional receivers without causing electromagnetic (EM) fratricide. In most of the documented work that addresses EM fratricide to date, there is an inhere nt assumption that the cognitive system knows when and where the fratricide occurs. However, the authors usually do not make known how this information is obtained. In this paper we propose two approaches o f how the victim receivers can work together with cognitive systems and share the knowledge of their EM status.
Trajectory tracking and point stabilization of noholonomic mobile robot

In this paper, a mixed controller for solving the trajectory tracking and point stabilization problems of a mobile robot is presented, applying the integration of backstepping technique and neural dynamics. By introducing a virtual target point, the whole motion process is divided into two parts. The first one is employed to realize tracking control and the other one is adopted to implement point stabilization. Each part produces a feedback control law by using backstepping technique. Moreover, to solve the speed and torque jump problems and make the controller generate smooth and continuous signal when controllers switch, the neural dynamics model is integrated into the backstepping. The stability of the proposed control system is analyzed by using Lyapunov theory. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.
Synergetic control for DC-DC buck converters with constant power load

An algorithm for design of synergetic control is derived, using as an example a system containing buck choppers with constant power load J.G. Ciezki et al. (1998). The closed loop system behavior is compared to that of the same system controlled by feedback linearization control. As a result, it is shown that synergetic control has better dynamics and a faster response. This system nullifies steady state error not only of output voltage but also in current sharing among parallel converters.
Locality sensitive discriminative dictionary learning

Discriminative dictionary learning (DDL) has been applied to various pattern classification problems. Despite satisfying experimental results, most existing discriminative dictionary learning methods emphasize too much on the role of l0 or l1-norm sparsity, while the underlying local structure of original data is totally ignored. In this paper, we present a novel dictionary learning method, named Locality Sensitive Discriminative Dictionary Learning (LSDDL), which combines basic dictionary learning scheme and locality relationship of original data which is propagated to the coding vectors. The learned discriminative dictionary can map the original data points into a new space in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments clearly show that our method has very competitive performance in contrast to previous discriminative dictionary learning methods.
A bit allocation scheme by controlling code rate for closed-loop V-BLAST OFDM system with soft channel decoding

In this paper, we present a bit allocation scheme for a closed-loop MIMO-OFDM (multiple input multiple output-orthogonal frequency division multiplexing) system using V-BLAST (vertical-Bell laboratories layered space-time) detector with soft channel decoding. A fully adaptive modulation and coding scheme may provide the optimal performance, but requires excessive feedback information. Instead, the proposed scheme controls the code rate of the channel coding and assigns the same modulation and coding to the set of selected sub-channels, which greatly reduces the feedback overhead while achieving good performance. Simulation results show that the proposed scheme with minimal feedback provides significant performance improvement over the conventional bit allocation schemes.
A Case for Heterogeneous Technology-Mapping: Soft Versus Hard Multiplexers

Lookup table-based FPGAs offer flexibility but compromise on performance, as compared to custom CMOS implementations. This paper explores the idea of minimising this performance gap by using fixed, fine-grained, nonprogrammable logic structures in place of lookup tables (LUTs). Functions previously mapped onto LUTs can now be diverted to these structures, resulting in reduced LUT usage and higher operating speed. This paper presents a generic heterogeneous technology-mapping scheme for segregating LUTs and hard logic blocks. For the proof-of-concept, we choose to isolate multiplexers present in most general-purpose circuits. These multiplexers are mapped onto hard blocks of multiplexers that are present in existing commercial FPGA fabrics, but often unused. Since the hard multiplexers are already present, there is no additional performance or area penalty. Using this approach, an average reduction in LUT usage of 16% and an average speedup of 8% has been observed for the VTR benchmarks as compared to the LUTs-only implementation.
Computing multivariate approximate GCD based on Barnett's theorem

We present algorithms for multivariate GCD and approximate GCD by modifying Barnett's theorem, which is based on the LU-decomposition of Bézout matrix. Our method is suited for multivariate polynomials with large degrees. Also, we analyze ill-conditioned cases of our method. We show our method is stabler and faster than many other methods.
A Multi-staged System for Efficient Visual Person Reidentification

An important field in today’s computer vision is person centric video analysis. The basis of this person centric analysis is the detection and tracking of people in video data. In many cases it is not sufficient to track people when they continuously appear in the camera’s field of view, but to also reacquire a track after a person has left a field of view and reenters it. In this paper, we introduce a technique that conducts this person reidentification based on SIFT features only. This approach fits into an Implicit Shape Model (ISM) based person tracking approach by employing the SIFT features collected during tracking for reidentification. The ISM characteristics of a person are used to perform reidentification in an efficient 3-staged approach which combines computation efficiency with high distinctiveness. The evaluation is performed in an open-set classification approach on a public dataset of 60 persons which was acquired with a thermal camera. Despite the challenges of person reidentification in thermal imagery, the approach shows nearly perfect performance and outperforms other reidentification approaches on this dataset.