Miracle-dz/UniFAR-FTUs huggingface dataset:
Realizing the benefits of the CMMISM with the CeBASE method

Future systems will be increasingly software‐intensive, but the type of software development they will need is not well covered by current development and maturity models such as the waterfall model and Software Capability Maturity Model® (CMM®). Future development of software‐intensive systems will need situation‐specific balancing of discipline and flexibility to address such issues as COTS, open source, distribution, mobility rapid change, agents, collaboration support, and simultaneous achievement of rapid development and high dependability. This article shows how the CMMISM's integration of modern systems engineering, software engineering, and integrated process and product development concepts provides a framework for redressing the shortfalls of the Software CMM®, and for enabling projects and organizations to achieve the right balance of discipline and flexibility for their particular situations. But the CMMISM has shortfalls of its own. It provides little guidance on how to define and execute specific processes for a specific project or organization. The article summarizes various process model generators for software intensive systems such as the spiral, the Rational Unified Process (RUP), MBASE, and the CeBASE Method. It concludes that the CeBASE Method best covers the full range of concerns in the CMMISM, resolves its practice‐focus shortfalls, and covers additional best practices not in the CMMISM, such as business case analysis, requirements prioritization, and evolution requirements. © 2002 John Wiley & Sons, Inc. Syst Eng 5: 73–88, 2002; DOI 10.1002/sys.10016
Mechanisms of Diastolic Dysfunction in Cardiovascular Disease Cardiomyocyte-specific Bmal 1 deletion in mice triggers diastolic dysfunction , extracellular matrix response , and impaired resolution of inflammation

Ingle KA, Kain V, Goel M, Prabhu SD, Young ME, Halade GV. Cardiomyocyte-specific Bmal1 deletion in mice triggers diastolic dysfunction, extracellular matrix response, and impaired resolution of inflammation. Am J Physiol Heart Circ Physiol 309: H1827–H1836, 2015. First published October 2, 2015; doi:10.1152/ajpheart.00608. 2015. —The mammalian circadian clock consists of multiple transcriptional regulators that coordinate biological processes in a time-of-day-dependent manner. Cardiomyocyte-specific deletion of the circadian clock component, Bmal1 (aryl hydrocarbon receptor nuclear translocator-like protein 1), leads to age-dependent dilated cardiomyopathy and decreased lifespan in mice. We investigated whether cardiomyocytespecific Bmal1 knockout (CBK) mice display early alterations in cardiac diastolic function, extracellular matrix (ECM) remodeling, and inflammation modulators by investigating CBK mice and littermate controls at 8 and 28 wk of age (i.e., prior to overt systolic dysfunction). Left ventricles of CBK mice exhibited (P 0.05): 1) progressive abnormal diastolic septal annular wall motion and reduced pulmonary venous flow only at 28 wk of age; 2) progressive worsening of fibrosis in the interstitial and endocardial regions from 8 to 28 wk of age; 3) increased ( 1.5 fold) expression of collagen I and III, as well as the matrix metalloproteinases MMP-9, MMP-13, and MMP-14 at 28 wk of age; 4) increased transcript levels of neutrophil chemotaxis and leukocyte migration genes (Ccl2, Ccl8, Cxcl2, Cxcl1, Cxcr2, Il1 ) with no change in Il-10 and Il-13 genes expression; and 5) decreased levels of 5-LOX, HO-1 and COX-2, enzymes indicating impaired resolution of inflammation. In conclusion, genetic disruption of the cardiomyocyte circadian clock results in diastolic dysfunction, adverse ECM remodeling, and proinflammatory gene expression profiles in the mouse heart, indicating signs of early cardiac aging in CBK mice.
RBF Neural Networks and Genetic Algorithms Based Optimization Control of Aluminum Powder Nitrogen Atomization Process

Aluminum powder nitrogen atomizing process is with nonlinearities, large time delay, strong coupling and severe uncertainty, thus it is difficult to obtain the deterministic model and implement process optimization control by conventional methods. In this paper, the optimization control of aluminum powder nitrogen atomization process is presented to improve the fine powder rate. The process model of nitrogen atomization is established using RBF neural networks and the set values of control variables are optimized dynamically by means of implement of the optimization strategy based on enhanced genetic algorithms. Comparisons of the aluminum powder particle size distribution before and after optimization illustrate that the implement of process optimization control can improve the effect of nitrogen atomization and promote the percentages of ultra-fine aluminum powder greatly.
CALL FOR ARTICLES IEEE Design & Test of Computers Special Issue on Microprocessors

new types of sophisticated fieldprogrammable devices (FPDs) has dramatically changed the process of designing digital hardware. Unlike previous generations of hardware technology in which board level designs included large numbers of SSI (small-scale integration) chips containing basic gates, virtually every digital design produced today consists mostly of high-density devices. This is true not only of custom devices such as processors and memory but also of logic circuits such as state machine controllers, counters, registers, and decoders. When such circuits are destined for high-volume systems, designers integrate them into high-density gate arrays. However, the high nonrecurring engineering costs and long manufacturing time of gate arrays make them unsuitable for prototyping or other lowvolume scenarios. Therefore, most prototypes and many production designs now use FPDs. The most compelling advantages of FPDs are low startup cost, low financial risk, and, because the end user programs the device, quick manufacturing turnaround and easy design changes. The FPD market has grown over the past decade to the point where there is now a wide assortment of devices to choose from. To choose a product, designers face the daunting task of researching the best uses of the various chips and learning the intricacies of vendor-specific software. Adding to the difficulty is the complexity of the more sophisticated devices. To help sort out the confusion, we provide an overview of the various FPD architectures and discuss the most important commercial products, emphasizing devices with relatively high logic capacity.
A data adaptive compressed sensing approach to polarimetric SAR tomography

Super-resolution imaging via compressed sensing (CS) based spectral estimators has been recently introduced to synthetic aperture radar (SAR) tomography. In the case of partial scatterers, the mainstream has so far been twofold, in that the tomographic reconstruction is conducted by either working directly with multiple looks and/or polarimetric channels or by exploiting the corresponding single-channel second order statistics. In this paper, we unify these two methodologies in the context of covariance fitting. In essence, we exploit the fact that both vertical structures as well as the unknown polarimetric signatures can be approximated in a low dimensional subspace. For this purpose, we make use of a wavelet basis in order to sparsely represent vertical structures. Additionally, we synthesize a data adaptive orthonormal basis that spans the space of polarimetric signatures. Finally, we validate this approach by using fully polarimetric L-band data acquired by the E-SAR sensor of the German Aerospace Center (DLR).
Semi-supervised Learning for Large Scale Image Cosegmentation

This paper introduces to use semi-supervised learning for large scale image co segmentation. Different from traditional unsupervised cosegmentation that does not use any segmentation ground truth, semi-supervised cosegmentation exploits the similarity from both the very limited training image foregrounds, as well as the common object shared between the large number of unsegmented images. This would be a much practical way to effectively co segment a large number of related images simultaneously, where previous unsupervised co segmentation work poorly due to the large variances in appearance between different images and the lack of segmentation ground truth for guidance in co segmentation. For semi-supervised co segmentation in large scale, we propose an effective method by minimizing an energy function, which consists of the inter-image distance, the intra-image distance and the balance term. We also propose an iterative updating algorithm to efficiently solve this energy function, which decomposes the original energy minimization problem into sub-problems, and updates each image alternatively to reduce the number of variables in each sub-problem for computation efficiency. Experiment results on iCoseg and Pascal VOC datasets show that the proposed co segmentation method can effectively co segment hundreds of images in less than one minute. And our semi-supervised co segmentation is able to outperform both unsupervised co segmentation as well as fully supervised single image segmentation, especially when the training data is limited.
Bmc Medical Informatics and Decision Making Description and Validation of a Markov Model of Survival for Individuals Free of Cardiovascular Disease That Uses Framingham Risk Factors

Background: Estimation of cardiovascular disease risk is increasingly used to inform decisions on interventions, such as the use of antihypertensives and statins, or to communicate the risks of smoking. Crude 10-year cardiovascular disease risk risks may not give a realistic view of the likely impact of an intervention over a lifetime and will underestimate of the risks of smoking. A validated model of survival to act as a decision aid in the consultation may help to address these problems. This study aims to describe the development of such a model for use with people free of cardiovascular disease and evaluates its accuracy against data from a United Kingdom cohort.
Wireless Energy Harvesting Sensor Networks: Boolean–Poisson Modeling and Analysis

Wireless radio frequency energy harvesting has been adopted in wireless networks as a method to supply energy to wireless nodes. In this paper, we analyze a wireless energy harvesting network based on a Boolean-Poisson model. This model assumes that energy sources are distributed according to a Poisson point process and have disc-shaped coverage regions with random radii. We introduce a distribution for the coverage radii, which takes aggregated harvested power into account. The union of the coverage regions of the energy sources forms the energy harvesting zone. We derive the transmission success probability of single-hop networks characterized by the probability that two sensor nodes are located in the energy harvesting zone. Then, we analyze the performance of multi-hop networks in the cases, where the locations of the sensor nodes are either fixed or randomly distributed. Moreover, we consider a star-shaped topology, which reflects the scenario wherein some sensor nodes simultaneously transmit data to a data collector. In this setting, we derive an approximation of the average throughput at the data collector. Numerical results validate the accuracy of our analysis in the single-hop and multi-hop networks and confirm the tightness of our approximation in the case of the star-shaped topology.
Physical structure and dust reprocessing in a sample of HH jets

Context. Stellar jets are an essential ingredient of the star formation process and a wealth of information can be derived from their characteristic emission-line spectra. Aims. We investigate the physical structure and dust reprocessing in the shocks along the beam of a number of classical Herbig-Haro (HH) jets in the Orion and Lupus molecular clouds (HH 111, HH 1/2, HH 83, HH 24 M/A/E/C, and Sz68). Parameters describing plasma conditions, as well as dust content, are derived as a function of distance from the source and, for HH 111, of gas velocity. Methods. Spectral diagnostic techniques are applied to obtain the jet physical conditions (the electron and total density, n e and n H , the ionisation fraction, x e , and the temperature, T e ) from the ratios of selected forbidden lines. The presence of dust grains is investigated by estimating the gas-phase abundance of calcium with respect to its solar value. Results. We find the electron density varies between 0.05–4$\times$10 3 cm -3 , the ionisation fraction x e from 0.01–0.7, the temperature ranges between 0.6–3$\times$10 4 K, and the hydrogen density between 0.01–6$\times$10 4 cm -3 . Interestingly, in the HH 111 jet, n e , x e , and T e peak in the high velocity interval (HVI) of the strongest working surfaces, confirming a prediction from shocks models. Calcium turns out to be depleted with respect to its solar value, but its gas-phase abundance is higher than estimates for the interstellar medium in Orion. The depletion is high (up to 80%) along the low-excited jets, while low or no depletion is measured in those jets which show higher excitation conditions. Moreover, for HH 111 the depletion is lower in the HVI of the fastest shock. Conclusions. Our results confirm the shock structure predicted by models and indicate that shocks occurring along jets, and presumably those present in the launch zone, only partially destroy dust grains and that the efficiency of dust reprocessing strongly depends on shock velocity. However, the high Ca gas-phase abundance estimated in some of the knots, is not well understood in terms of existing models of dust reprocessing in shocks, and indicates that the dust must have been partially reprocessed in the region where the flow originates.
Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.
Solitude as an Approach to Affective Self-Regulation

In this research, we showed that solitude generally has a deactivation effect on people’s affective experiences, decreasing both positive and negative high-arousal affects. In Study 1, we found that the deactivation effect occurred when people were alone, but not when they were with another person. Study 2 showed that this deactivation effect did not depend on whether or not the person was engaged in an activity such as reading when alone. In Study 3, high-arousal positive affect did not drop in a solitude condition in which participants specifically engaged in positive thinking or when they actively chose what to think about. Finally, in Study 4, we found that solitude could lead to relaxation and reduced stress when individuals actively chose to be alone. This research thus shed light on solitude effects in the past literature, and on people’s experiences when alone and the different factors that moderate these effects.
On the Hardness of Recognizing Bundles in Time Table Graphs

In a cooperation with the national German railway company, we construct a directed graph from a set of train time tables where train stations correspond to vertices, and where pairs of consecutive stops of trains correspond to edges. We consider the problem of locating vertices of this time table graph that intuitively correspond to train stations where the underlying railroad network branches into several directions, and that induce a partition of the edge set into bundles. We formulate this problem as a graph theoretic optimization problem, and show for two versions of the problem that they are NP-hard. For the first version we show that it is even NP-complete to decide whether any other solution besides the trivial one exists.
Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data

In data mining, decision tree algorithms are very popular methodologies since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model in the form of a decision tree. Over the past years, fuzzy decision tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This paper investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, although both do not reach the accuracy values of the best performing classifier.
Biodegradation of carbofuran in soils within Nzoia River Basin, Kenya

Carbofuran (2,3-dihydro-2,2-dimethylbenzofuran-7-yl methylcarbamate) has been used within the Nzoia River Basin (NRB), especially in Bunyala Rice Irrigation Schemes, in Kenya for the control of pests. In this study, the capacity of native bacteria to degrade carbofuran in soils from NRB was investigated. A gram positive, rod-shaped bacteria capable of degrading carbofuran was isolated through liquid cultures with carbofuran as the only carbon and nitrogen source. The isolate degraded 98% of 100-μg mL−1 carbofuran within 10 days with the formation of carbofuran phenol as the only detectable metabolite. The degradation of carbofuran was followed by measuring its residues in liquid cultures using high performance liquid chromatography (HPLC). Physical and morphological characteristics as well as molecular characterization confirmed the bacterial isolate to be a member of Bacillus species. The results indicate that this strain of Bacillus sp. could be considered as Bacillus cereus or Bacillus thuringiensis with a bootstrap value of 100% similar to the 16S rRNA gene sequences. The biodegradation capability of the native strains in this study indicates that they have great potential for application in bioremediation of carbofuran-contaminated soil sites.
Automatic Discovery of Part-Whole Relations

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.
Fast and general distributed transactions using RDMA and HTM

Recent transaction processing systems attempt to leverage advanced hardware features like RDMA and HTM to significantly boost performance, which, however, pose several limitations like requiring priori knowledge of read/write sets of transactions and providing no availability support. In this paper, we present DrTM+R, a fast in-memory transaction processing system that retains the performance benefit from advanced hardware features, while supporting general transactional workloads and high availability through replication. DrTM+R addresses the generality issue by designing a hybrid OCC and locking scheme, which leverages the strong atomicity of HTM and the strong consistency of RDMA to preserve strict serializability with high performance. To resolve the race condition between the immediate visibility of records updated by HTM transactions and the unready replication of such records, DrTM+R leverages an optimistic replication scheme that uses seqlock-like versioning to distinguish the visibility of tuples and the readiness of record replication. Evaluation using typical OLTP workloads like TPC-C and SmallBank shows that DrTM+R scales well on a 6-node cluster and achieves over 5.69 and 94 million transactions per second without replication for TPC-C and SmallBank respectively. Enabling 3-way replication on DrTM+R only incurs at most 41% overhead before reaching network bottleneck, and is still an order-of-magnitude faster than a state-of-the-art distributed transaction system (Calvin).