Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulsed-power-driven cylindrical liner implosions of laser preheated fuel magnetized with an axial field

The radial convergence required to reach fusion conditions is considerably higher for cylindrical than for spherical implosions since the volume is proportional to r2 versus r3, respectively. Fuel magnetization and preheat significantly lowers the required radial convergence enabling cylindrical implosions to become an attractive path toward generating fusion conditions. Numerical simulations are presented indicating that significant fusion yields may be obtained by pulsed-power-driven implosions of cylindrical metal liners onto magnetized (>10 T) and preheated (100–500 eV) deuterium-tritium (DT) fuel. Yields exceeding 100 kJ could be possible on Z at 25 MA, while yields exceeding 50 MJ could be possible with a more advanced pulsed power machine delivering 60 MA. These implosions occur on a much shorter time scale than previously proposed implosions, about 100 ns as compared to about 10 μs for magnetic target fusion (MTF) [I. R. Lindemuth and R. C. Kirkpatrick, Nucl. Fusion 23, 263 (1983)]. Consequently t.
Today/future importance analysis

SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.
MMSE-Based MDL Method for Accurate Source Number Estimation

In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.
Sliding Mode Control for Flexible Joint using Uncertainty and Disturbance Estimation

Index Terms—sliding mode control, uncertainty and disturbance estimation, flexible joint. Abstract—This paper proposes sliding mode control based on uncertainty and disturbance estimator (UDE), for trajectory tracking control of flexible joint robotic system. UDE is used to estimate plant uncertainty and disturbances. The controller does not requires knowledge of plant uncertainty and external disturbance. Reaching phase is eliminated for robustification. The perturbation is efficiently compensated by feedback of the estimated value. The proposed reference model is to track the plant states according to this model. The closed loop stability for this model with uncertainty and disturbance is also proposed.
Different Classifiers Find Different Defects Although With Different Level of Consistency

BACKGROUND -- During the last 10 years hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. OBJECTIVE -- We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. METHOD -- We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in 12 NASA data sets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty is compared against different classifiers. RESULTS -- Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. CONCLUSIONS -- Our results confirm that a unique sub-set of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Classifier ensembles with decision making strategies not based on majority voting are likely to perform best.
Adaptive Filter for Delay Line Combination (DLC) Receivers

Delay Line Combination (DLC) receivers have been proposed for time hopping impulse radio ultra wideband (TH-IR UWB) systems by Wen and Hsu. In that study, we also presented a non-data aided synchronization algorithm designated as "timing with delay line combination" (TDLC) algorithm. Although channel estimation is unnecessary for the DLC receivers, the system performance is affected by the setting of the integration window length (Tw). By tracking the variance of estimated timing offsets of the TDLC algorithm for each Tw setting, it's found that the minimum variance is obtained at a best Tw setting. However, the best Tw setting is various in different channel environments. To optimize the system performance, for example mean square error (MSE) and bit error rate (BER) of the DLC receivers, we propose a non-data aided adaptive algorithm named "least variance" (LV) adaptive algorithm to estimate the best Tw setting for all channel environments.
A Low Cost Fault Detection Mechanism Based on Cycle Cover Algorithm for Optical Burst Switching Networks

The optical burst switching networks are one of the most potential implementation technology of next generation optical switching networks in nowadays. In this paper, after analysis on the characteristics of HST, SPEM, HDFS, we propose a new fault detection mechanism based on minimum-length probe cycle cover algorithm, then this cycle finding algorithm is used to find cycle cover for OBS networks. After that, a probe module is assigned for each cycle and a fast fault localization algorithm is developed, finally a fault detection mechanism based on probe cycle cover is formed. The computation and statistic results show that in this fault detection mechanism comparing minimum-length probe cycle cover algorithm to the existing algorithms, it has the least amount of network resources (measured by the number of cycles, cycles length, etc.) and higher fault localization degree.
Imaging Glycosylation In Vivo by Metabolic Labeling and Magnetic Resonance Imaging

Abstract Glycosylation is a ubiquitous post‐translational modification, present in over 50 % of the proteins in the human genome,1 with important roles in cell–cell communication and migration. Interest in glycome profiling has increased with the realization that glycans can be used as biomarkers of many diseases,2 including cancer.3 We report here the first tomographic imaging of glycosylated tissues in live mice by using metabolic labeling and a gadolinium‐based bioorthogonal MRI probe. Significant N‐azidoacetylgalactosamine dependent T 1 contrast was observed in vivo two hours after probe administration. Tumor, kidney, and liver showed significant contrast, and several other tissues, including the pancreas, spleen, heart, and intestines, showed a very high contrast (>10‐fold). This approach has the potential to enable the rapid and non‐invasive magnetic resonance imaging of glycosylated tissues in vivo in preclinical models of disease.
Swing-Leg Retraction for Limit Cycle Walkers Improves Disturbance Rejection

Limit cycle walkers are bipeds that exhibit a stable cyclic gait without requiring local controllability at all times during gait. A well-known example of limit cycle walking is McGeer's ldquopassive dynamic walking,rdquo but the concept expands to actuated bipeds as involved in this study. One of the stabilizing effects in limit cycle walkers is the dissipation of energy that occurs when the swing foot hits the ground. We hypothesize that this effect can be enhanced with a negative relation between the step length and step time. This relation is implemented through an open-loop strategy called swing-leg retraction; a predefined time trajectory for the swing leg makes the swing leg move backwards just prior to foot impact. In this paper, we study the effect of swing-leg retraction through three bipeds; a simple point mass simulation model, a realistic simulation model, and a physical prototype. Their stability is analyzed using Floquet multipliers, followed by an evaluation of how well disturbances are handled using the Gait Sensitivity Norm. We find that mild swing-leg retraction is optimal for the disturbance rejection of a limit cycle walker, as it results in a system response that is close to critically damped, rejecting the disturbance in the fewest steps. Slower retraction results in an overdamped response, characterized by a positive dominant Floquet multiplier. Likewise, faster retraction results in an underdamped response, characterized by a negative Floquet multiplier.
Mirror, Mirror on the Wall, How Does My Brain Recognize My Image at All?

For decades researchers have used mirrors to study self-recognition. However, attempts to identify neural processes underlying this ability have used photographs instead. Here we used event related potentials (ERPs) to compare self-face recognition in photographs versus mirrors and found distinct neural signatures. Measures of visual self-recognition are therefore not independent of the medium employed.
Logistics Integration with Third Party Logistics providers

With increasing competition and globalization, it is supply chain to compete rather than single firm. Integration in supply chain has been conceptualized and documented in literature from multiple perspectives. In this study, we conceptualize logistics integration with third party logistics providers (3PLs) from supply chin integration perspective, and then empirically explore its relational antecedents and its impact on performance. Structural equation model analyses of data collected from 134 logistics users in mainland China support that trust, commitment, and dependence are positively related to logistics integration with 3PLs. The study also provides evidence supporting the contributive effect of logistics integration on performance, especially, the contributive effect is moderated by legal contract.
The development of APE-PCR for the cloning of genomic insertion sites of DNA elements

Insertional mutagenesis is a productive strategy for the exploration of genetic regulation of important biological and pathological processes, such as tumorigenesis. Successful implementation of this strategy depends heavily on an efficient approach to the identification of insertion sites present in the host genome. Here, we have introduced an easy and efficient protocol, called Adenosine-ended Primer Extension Polymerase Chain Reaction (APE-PCR), which represents several advantages, including the Addition technique we previously developed, primer extension approach coupled with biotin-streptavidin based purification, introduction of nano-scale magnetic particles, and digestion of DNA with a combination of enzymes. We have demonstrated that APE-PCR is able to amplify more and larger specific proviral insertion site (PIS)-derived fragments, with a lower non-specific background produced, fewer steps and less DNA samples required, flexibility in choice of restriction enzymes applied, at a lower cost. Replacement of regular magnetic beads with nano-scale ones in the protocol can further increase its power. Moreover, even with small amount of sample DNA, PISs can be recovered and analyzed. Thus, based on the results provided from this study, we believe that APE-PCR represents an efficient method in mapping of PISs and likely, the insertion sites of other types of DNA elements as well.
Adaptive Weighted Nearest Feature Space Analysis and Its Application to Feature Extraction

In this paper, a new feature extraction algorithm named Adaptive Weighted Nearest Feature Space Analysis (AWNFSA) is proposed. AWNFSA is a Nearest Feature Space (NFS) based subspace learning approach. In Discriminant Nearest Feature Space Analysis (DNFSA) algorithm based on NFS, it may lead the result into misclassification when the between class scatter is very big or within class scatter is very small. Different from DNFSA, AWNFSA evaluates the effect of two scatter for classification through choosing their weights adaptively. The proposed AWNFSA is applied to image classification on ORL face Database. The experimental results demonstrate the efficiency of the proposed AWNFSA.
Phytoplankton Biomass Dynamics in the Strait of Malacca within the Period of the SeaWiFS Full Mission: Seasonal Cycles, Interannual Variations and Decadal-Scale Trends

Seasonal cycles, interannual variations and decadal trends of Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-retrieved chlorophyll-a concentration (Chl-a) in the Strait of Malacca (SM) were investigated with reconstructed, cloud-free SeaWiFS Chl-a during the period of the SeaWiFS full mission (September 1997 to December 2010). Pixel-based non-parametric correlations of SeaWiFS Chl-a on environmental variables were used to identify the probable causes of the observed spatio-temporal variations of SeaWiFS Chl-a in northern, middle and southern regions of the SM. Chl-a was high (low) during the northeast (southwest) monsoon. The principal causes of the seasonality were wind-driven vertical mixing in the northern region and wind-driven coastal upwelling and possibly river discharges in the middle region. Among the three regions, the southern region showed the largest interannual variations of Chl-a. These variations were associated with the El Nino/Southern Oscillation (ENSO) and river runoff. Interannual variations of Chl-a in the middle and northern regions were more responsive to the Indian Ocean Dipole and ENSO, respectively, with atmospheric deposition being the most important driver. The most significant decadal-scale trend of increasing Chl-a was in the southern region; the trend was moderate in the middle region. This increasing trend was probably caused by environmental changes unrelated to the variables investigated in this study.
Genomic expression responses to DNA-damaging agents and the regulatory role of the yeast ATR homolog Mec1p.

Eukaryotic cells respond to DNA damage by arresting the cell cycle and modulating gene expression to ensure efficient DNA repair. The human ATR kinase and its homolog in yeast, MEC1, play central roles in transducing the damage signal. To characterize the role of the Mec1 pathway in modulating the cellular response to DNA damage, we used DNA microarrays to observe genomic expression in Saccharomyces cerevisiae responding to two different DNA-damaging agents. We compared the genome-wide expression patterns of wild-type cells and mutants defective in Mec1 signaling, including mec1, dun1, and crt1 mutants, under normal growth conditions and in response to the methylating-agent methylmethane sulfonate (MMS) and ionizing radiation. Here, we present a comparative analysis of wild-type and mutant cells responding to these DNA-damaging agents, and identify specific features of the gene expression responses that are dependent on the Mec1 pathway. Among the hundreds of genes whose expression was affected by Mec1p, one set of genes appears to represent an MEC1-dependent expression signature of DNA damage. Other aspects of the genomic responses were independent of Mec1p, and likely independent of DNA damage, suggesting the pleiotropic effects of MMS and ionizing radiation. The complete data set as well as supplemental materials is available at http://www-genome.stanford. edu/mec1.
$k$-Connectivity Analysis of One-Dimensional Linear VANETs

In a 1-D linear vehicular ad hoc network (1-DL-VANET), some vehicles may leave the network (e.g., at highway exits), which may make the 1-DL-VANET disconnected. Thus, it is important to analyze the connectivity of the 1-DL-VANET. When removal of any (k - 1) arbitrary nodes from a network does not disconnect the network, the network is said to be k-connected. In this paper, we investigate the k-connectivity of the 1-DL-VANET. Sufficient and necessary conditions are derived for the 1-DL-VANET to be k-connected, and based on this, a method is provided, with the help of matrix decomposition, to obtain expression of the probability of the 1-DL-VANET being k-connected. The expectation of the maximum number of tolerable vehicle departures is also derived. Simulation results confirm the accuracy of our analysis and indicate that the expectation of the maximum number of tolerable vehicle departures almost linearly increases with the total number of vehicles.
2.1 Notation

In the past few years gene expression microarrays have become important tools in biology and genetics. Microarrays have had a comparably large impact on quantitative fields. A multitude of computational and statistical issues accompany microarrays, and computational scientists have been invigorated by the problems. In recent years statisticians and other mathematical scientists have produced a variety of techniques specifically addressed to microarrays. A biologist with microarray data who seeks guidance in the literature may be overwhelmed by the large number of different methods. As different statisticians and others promote methodologies that they helped develop, there is little guidance for a conscientious scientist who needs to decide what analyses to perform. In fact, many method-ologies are substantially similar, but this is often not apparent in the literature. By understanding the similarities among methods, an investigator might then understand the differences and has a better chance of making a truly informed decision. The goal of this paper is to conceptually organize some of the key methods for microarray data analysis. It is intended to summarize some of the methodologies in a way that illuminates the hidden similarities and true differences among them. " True differences " refers to the fact that methods may be presented differently but yield the same effective analysis and have identical implications for experimental design. For example, it is not a coincidence that the comparisons of microarray designs using a linear model as in Yang and Speed (2002) reproduce the results in Kerr and Churchill (2002), although the data 3 models discussed appear to be quite different. As will be shown here, such models actually differ only marginally. By understanding such connections, the differences between various methodologies should also become clearer. A microarray experiment involves many decisions that can effect the conclusions, including (1.) What RNA samples will be collected and which pairs will be hybridized together (experimental design)? (2.) What method of image analysis will be used and will the data be adjusted for background (data extraction) ? (3.) How will the raw data be used to estimate relative gene expression , and how will differential expression be decided (normaliza-tion, estimation, statistical inference)? (4.) How can the data be explored to suggest high-order structure, or how can gene expression be used as predictors, classifiers, etc. This paper concentrates on methodologies contained in (3.), but is not intended as a comprehensive review of all techniques described by (3.). …
Growing vascular endothelial cells express somatostatin subtype 2 receptors

We hypothesized that non-proliferating (quiescent) human vascular endothelial cells would not express somatostatin receptor subtype 2 (sst 2) and that this receptor would be expressed when the endothelial cells begin to grow. To test this hypothesis, placental veins were harvested from 6 human placentas and 2 mm vein disks were cultured in 0.3% fibrin gels. Morphometric analysis confirmed that 50–75% of cultured vein disks developed radial capillary growth within 15 days. Sst 2 gene expression was determined by reverse transcription-polymerase chain reaction (RT-PCR) analysis of the RNA from veins before culture and from tissue-matched vein disks that exhibited an angiogenic response. The sst 2 gene was expressed in the proliferating angiogenic sprouts of human vascular endothelium. The presence of sst 2 receptors on proliferating angiogenic vessels was confirmed by immunohistochemical staining and in vivo scintigraphy. These results suggest that sst 2 may be a unique target for antiangiogenic therapy with sst 2 preferring somatostatin analogues conjugated to radioisotopes or cytotoxic agents. © 2001 Cancer Research Campaign www.bjcancer. com
A new TORA-based energy aware routing protocol in mobile ad hoc networks

Energy aware routing protocol plays an important role in the performance of mobile ad hoc networks because mobile hosts in such networks are usually battery-operated. In this paper, based on TORA, we present an energy-aware routing protocol E-TORA. E-TORA selects routes according to hop count and residual energy of nodes. The nodes that have more energy have more probability chosen by E-TORA. Simulation results comparing with TORA in NS-2 show that E-TORA can balance effectively energy consumption of each node and increase evidently the lifetime of the network.
NFAT5 Regulates T Lymphocyte Homeostasis and CD24-Dependent T Cell Expansion under Pathologic Hypernatremia

Immune cells rely on the transcription factor NFAT5 to adapt to hypertonic stress. The hypertonicity-dependent role of NFAT5 in T cells in vivo remains unclear because mouse models of NFAT5 deficiency have produced substantially different T cell phenotypes. In this study, we analyzed the T cell compartment in NFAT5-null and T cell-specific NFAT5 knockout mice. We found that NFAT5-null mice had constitutive, pronounced hypernatremia and suffered a severe immunodeficiency, with T cell lymphopenia, altered CD8 naive/memory homeostasis, and inability to reject allogeneic tumors. By contrast, T cell-specific NFAT5 knockout mice had normal plasma tonicity, rejected allogeneic tumors, and exhibited only a mild, low-penetrance memory bias in CD8 cells. Notably, when T cells from these mice were cultured ex vivo in hypernatremic media, they exhibited features found in NFAT5-null mice, with pronounced naive/memory imbalance and impaired homeostatic survival in response to IL-7, as well as a severe inhibition of their mitogen-induced proliferation. By analyzing surface receptors whose expression might be affected in NFAT5-deficient cells, we identified CD24 as a novel NFAT5 target induced by hypertonicity both in vitro and in vivo, and required to sustain T cell expansion under osmostress. NFAT5 bound to the Cd24 promoter in response to hypertonicity facilitated the local derepression of chromatin and enhanced the expression of CD24 mRNA and protein. Altogether, our results indicate that the systemic hypernatremia of NFAT5-null mice is a major contributor to their immunodeficiency, and highlight the role of NFAT5 and CD24 in the homeostasis of T cells under osmostress in vivo.
A sparsity-based algorithm for power-efficient node localization

We devise a centralized node localization algorithm, which relies on linearization and employs the orthogonal matching pursuit algorithm at its core, albeit with modifications and additional steps in order to address the localisation problem. The main feature of the algorithm is that it can determine the position of more than one simultaneously transmitting unknown nodes. This renders it generic and efficient in terms of power consumption and use of time and frequency resources. Moreover, efficiency and computational complexity can be traded off for improved localization accuracy.