Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data Analytic Based on Scalable PANFIS for RFID Localization

RFID technology has gained popularity to address localization problem in the manufacturing shopfloor by tracking the manufacturing object location to increase the production's efficiency. However, the signals (data) used for localization task is not easy to analyze because it is generated from the nonstationary environment. It also continuously arrive over time and yields the large-volume of data. Therefore, an advanced big data analytic is required to overcome this problem. We propose a distributed big data analytic framework based on PANFIS (Scalable PANFIS), where PANFIS is an evolving algorithm which has capability to learn data stream in the single pass mode. Scalable PANFIS can learn big data stream by processing many chunks/partitions of data stream. Scalable PANFIS is also equipped with rule' structure merging to eliminate the redundancy among rules. Scalable PANFIS is validated by measuring its performance against single PANFIS and other Spark's scalable machine learning algorithms. The result shows that Scalable PANFIS performs running time more than 20 times faster than single PANFIS. The rule merging process in Scalable PANFIS shows that there is no significant reduction of accuracy in classification task with 96.67 percent of accuracy in comparison with single PANFIS of 98.71 percent. Scalable PANFIS also generally outperforms some Spark MLib machine learnings to classify RFID data with the comparable speed in running time.
GANViz: A Visual Analytics Approach to Understand the Adversarial Game

Generative models bear promising implications to learn data representations in an unsupervised fashion with deep learning. Generative Adversarial Nets (GAN) is one of the most popular frameworks in this arena. Despite the promising results from different types of GANs, in-depth understanding on the adversarial training process of the models remains a challenge to domain experts. The complexity and the potential long-time training process of the models make it hard to evaluate, interpret, and optimize them. In this work, guided by practical needs from domain experts, we design and develop a visual analytics system, GANViz, aiming to help experts understand the adversarial process of GANs in-depth. Specifically, GANViz evaluates the model performance of two subnetworks of GANs, provides evidence and interpretations of the models’ performance, and empowers comparative analysis with the evidence. Through our case studies with two real-world datasets, we demonstrate that GANViz can provide useful insight into helping domain experts understand, interpret, evaluate, and potentially improve GAN models.
Adipocytokines, Hepatic and Inflammatory Biomarkers and Incidence of Type 2 Diabetes. The CoLaus Study

Context There is contradictory information regarding the prognostic importance of adipocytokines, hepatic and inflammatory biomarkers on the incidence of type 2 diabetes. The objective was to assess the prognostic relevance of adipocytokine and inflammatory markers (C-reactive protein – CRP; interleukin-1beta – IL-1β; interleukin-6– IL-6; tumour necrosis factor-α – TNF-α; leptin and adiponectin) and gamma-glutamyl transpeptidase (γGT) on the incidence of type 2 diabetes. Methods Prospective, population-based study including 3,842 non-diabetic participants (43.3% men, age range 35 to 75 years), followed for an average of 5.5 years (2003–2008). The endpoint was the occurrence of type 2 diabetes. Results 208 participants (5.4%, 66 women) developed type 2 diabetes during follow-up. On univariate analysis, participants who developed type 2 diabetes had significantly higher baseline levels of IL-6, CRP, leptin and γGT, and lower levels of adiponectin than participants who remained free of type 2 diabetes. After adjusting for a validated type 2 diabetes risk score, only the associations with adiponectin: Odds Ratio and (95% confidence interval): 0.97 (0.64–1.47), 0.84 (0.55–1.30) and 0.64 (0.40–1.03) for the second, third and forth gender-specific quartiles respectively, remained significant (P-value for trend = 0.05). Adding each marker to a validated type 2 diabetes risk score (including age, family history of type 2 diabetes, height, waist circumference, resting heart rate, presence of hypertension, HDL cholesterol, triglycerides, fasting glucose and serum uric acid) did not improve the area under the ROC or the net reclassification index; similar findings were obtained when the markers were combined, when the markers were used as continuous (log-transformed) variables or when gender-specific quartiles were used. Conclusion Decreased adiponectin levels are associated with an increased risk for incident type 2 diabetes, but they seem to add little information regarding the risk of developing type 2 diabetes to a validated risk score.
Class Structure, Demography, and Selection: Reproductive-Value Weighting in Nonequilibrium, Polymorphic Populations

In natural populations, individuals of a given genotype may belong to different classes. Such classes can represent different age groups, developmental stages, or habitats. Class structure has important evolutionary consequences because the fitness of individuals with the same genetic background may vary depending on their class. As a result, demographic transitions between classes can cause fluctuations in the trait mean that need to be removed when estimating selection on a trait. Intrinsic differences between classes are classically taken into account by weighting individuals by class-specific reproductive values, defined as the relative contribution of individuals in a given class to the future of the population. These reproductive values are generally constant weights calculated from a constant projection matrix. Here, I show for large populations and clonal reproduction that reproductive values can be defined as time-dependent weights satisfying dynamical demographic equations that depend only on the average between-class transition rates over all genotypes. Using these time-dependent demographic reproductive values yields a simple Price equation where the nonselective effects of between-class transitions are removed from the dynamics of the trait. This generalizes previous theory to a large class of ecological scenarios, taking into account density dependence, ecological feedbacks, arbitrary strength of selection, and arbitrary trait distributions. I discuss the role of reproductive values for prospective and retrospective analyses of the dynamics of phenotypic traits.
Efficient Subpixel Refinement with Symbolic Linear Predictors

We present an efficient subpixel refinement method using a learning-based approach called Linear Predictors. Two key ideas are shown in this paper. Firstly, we present a novel technique, called Symbolic Linear Predictors, which makes the learning step efficient for subpixel refinement. This makes our approach feasible for online applications without compromising accuracy, while taking advantage of the run-time efficiency of learning based approaches. Secondly, we show how Linear Predictors can be used to predict the expected alignment error, allowing us to use only the best keypoints in resource constrained applications. We show the efficiency and accuracy of our method through extensive experiments.
Cell size and morphological properties of yeast Saccharomyces cerevisiae in relation to growth temperature

ABSTRACT Cell volume is an important parameter for modelling cellular processes. Temperature‐induced variability of cellular size, volume, intracellular granularity, a fraction of budding cells of yeast Saccharomyces cerevisiae CEN.PK 113‐7D (in anaerobic glucose unlimited batch cultures) were measured by flow cytometry and matched with the performance of the biomass growth (maximal specific growth rate (&mgr;max), specific rate of glucose consumption, the rate of maintenance, biomass yield on glucose). The critical diameter of single cells was 7.94 &mgr;m and it is invariant at growth temperatures above 18.5°C. Below 18.5°C, it exponentially increases up to 10.2 &mgr;m. The size of the bud linearly depends on &mgr;max, and it is between 50% at 5°C and 90% at 31°C of the averaged single cell. The intracellular granularity (side scatter channel (SSC)‐index) negatively depends on &mgr;max. There are two temperature regions (5‐31°C vs. 33‐40°C) where the relationship between SSC‐index and various cellular parameters differ significantly. In supraoptimal temperature range (33‐40°C), cells are less granulated perhaps due to a higher rate of the maintenance. There is temperature dependent passage through the checkpoints in the cell cycle which influences the &mgr;max. The results point to the existence of two different morphological states of yeasts in these different temperature regions.
Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents

Movies provide us with a mass of visual content as well as attracting stories. Existing methods have illustrated that understanding movie stories through only visual content is still a hard problem. In this paper, for answering questions about movies, we put forward a Layered Memory Network (LMN) that represents frame-level and clip-level movie content by the Static Word Memory module and the Dynamic Subtitle Memory module, respectively. Particularly, we firstly extract words and sentences from the training movie subtitles. Then the hierarchically formed movie representations, which are learned from LMN, not only encode the correspondence between words and visual content inside frames, but also encode the temporal alignment between sentences and frames inside movie clips. We also extend our LMN model into three variant frameworks to illustrate the good extendable capabilities. We conduct extensive experiments on the MovieQA dataset. With only visual content as inputs, LMN with frame-level representation obtains a large performance improvement. When incorporating subtitles into LMN to form the clip-level representation, we achieve the state-of-the-art performance on the online evaluation task of 'Video+Subtitles'. The good performance successfully demonstrates that the proposed framework of LMN is effective and the hierarchically formed movie representations have good potential for the applications of movie question answering.
Identifying and using energy-critical paths

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energy-reduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimality-scalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.
Association of FHIT expression and FHIT gene hypermethylation with liver cancer risk: a PRISMA-compliant meta-analysis

Background There have been suggestions that fragile histidine triad protein (FHIT) expression and FHIT gene hypermethylation were crucial to the pathogenesis of liver cancer. However, the conclusions remained unclear because of small sample size, disease subtype, and different detection techniques. Therefore, we performed a meta-analysis to estimate the associations of FHIT expression and FHIT gene hypermethylation with liver cancer pathogenesis. Methods Studies that were published in electronic databases, such as PubMed, Web of Knowledge, China National Knowledge Infrastructure (CNKI), VIP, and WanFang, were retrieved and selected for the meta-analysis. Relative risk (RR) and 95% confidence interval (CI) were calculated to determine the correlations of FHIT expression and FHIT gene hyper-methylation with liver cancer pathogenesis with Stata 12.0 software. Results A total of 17 papers that evaluated the associations of FHIT expression (14 articles) and FHIT gene methylation (3 articles) with liver cancer pathogenesis were included in this meta-analysis. In the overall analysis, the pooled relative risk was 1.93 (95% CI =1.72–2.17), which indicated a significant association between FHIT low expression and liver cancer risk. According to the results of clinical information, there were significant associations of FHIT expression with TNM-stage (RR =2.13, 95% CI =1.72–2.64), tumor size (RR =1.67, 95% CI =1.36–2.05), and merger of cirrhosis (RR =1.34, 95% CI =1.06–1.69) of liver cancer in the Chinese population. In addition, the FHIT gene hypermethylation was significantly associated with the risk of liver cancer (RR =1.45, 95% CI =1.08–1.93). Conclusion The FHIT expression and hypermethylation of FHIT gene were significantly associated with the risk of liver cancer, especially in the Chinese population. Furthermore, the results indicated significant associations between FHIT low expression and TNM-stage, tumor size, and merging of cirrhosis of liver cancer in the Chinese population.
Association between Body Mass Index and Sputum Culture Conversion among South Korean Patients with Multidrug Resistant Tuberculosis in a Tuberculosis Referral Hospital

Background Multidrug-resistant tuberculosis (MDR-TB) is an important global health problem. Furthermore, the time to identify a positive sputum culture is an important risk factor for the spread of tuberculosis, and several factors can predict a prolonged time to culture conversion. Moreover, the relationship between poor nutritional status and infectious disease is clearly established. Therefore, the present study aimed to investigate the association between body mass index (BMI) and sputum culture conversion within 3 months among patients with MDR-TB. Materials and Methods We retrospectively evaluated 218 patients with MDR-TB who were treated at a large tuberculosis referral hospital in South Korea between January 2005 and December 2010. The outcome of interest was defined as sputum culture conversion within 3 months, and we analyzed the association between BMI and this outcome. Results Among the 218 patients, 53 patients (24.3%) had a low BMI (<18.5 kg/m2). In the multivariate Cox proportional-hazards regression analysis, failure to achieve sputum culture conversion within 3 months was independently associated with having a low BMI (hazard ratio [HR]: 1.741, 95% confidence interval [CI]: 1.006–3.013; P = 0.047) and a positive sputum smear at the initiation of therapy (HR: 8.440, 95% CI: 1.146–62.138, P = 0.036). Conclusion Low BMI (<18.5 kg/m2) was an independent risk factor for failure to achieve sputum culture conversion within 3 months among patients with MDR-TB.
Clinical developments in the treatment of relapsed or relapsed and refractory multiple myeloma: impact of panobinostat, the first-in-class histone deacetylase inhibitor

Background Panobinostat is a new agent for the treatment of relapsed and refractory multiple myeloma (rrMM) as part of a combination regimen. This article presents an overview of the mechanism of action, pharmacokinetics, safety, efficacy, patient care strategies, and role of the agent in treating rrMM patients. Results Panobinostat belongs to the class of drugs known as histone deacetylase inhibitors, and has high activity against Class I, II, and IV nonhistone deacetylases and histone deacetylases. It represents the first of its class to receive approval for use in MM, and received priority review and orphan drug status in both US and Europe, when used in combination with bortezomib and dexamethasone in the treatment of rrMM. Approval of panobinostat was based on subgroup analysis of Phase III data obtained in the PANORAMA trial program for evaluation of the combination of panobinostat, bortezomib, and dexamethasone. Additional clinical trials have continued to explore optimal dosing regimens and novel combination regimens to further clarify the optimal role of panobinostat in the arsenal of drugs for rrMM. Panobinostat has shown a manageable safety profile characterized primarily by hematologic toxicities (thrombocytopenia, neutropenia, lymphopenia, and anemia), gastrointestinal toxicities, notably diarrhea and nausea, as well as fatigue/asthenia, electrolyte abnormalities, and less commonly cardiac toxicities. Conclusion Panobinostat represents an important addition to the treatment armamentarium for patients with rrMM, and studies are underway evaluating its optimal dosing strategy and role in combination with other drugs used to treat this patient population.
eHealth WBAN : Energy-Efficient and Priority-Based Enhanced IEEE 802 . 15 . 6 CSMA / CA MAC Protocol Wireless Body Area Network

This paper provided a general study of Wireless Body Area Network (WBAN) in health monitoring system as well as the study of the application of wearable and implanted BioMedical-Sensors (BMS) which are used to monitor the vital signs of a patient in early detection. Energy efficiency is a significant issue in WBAN which can be achieved by reducing the overhead of control packets, prioritizing sensor-nodes and sink-node selection. Moreover, uncertainty in network topologies, such as distance and link affect between sensor-nodes occurs due to the mobility of human. In this research, we propose a scheme to reduce the overhead of control packets and prioritizing the threshold values of vital signs by assigning low and high transmission power with enhanced IEEE802.15. 6 CSMA/CA as well as introduce a Mobility Link Table (MLT) for selecting a sink-node to communicate with the coordinator. Compare it with existing IEEE802.15. 6 CSMA/CA technique and results shows the proposed techniques regarding mean power consumption, network delay, network throughput. Keywords—Wireless Body Area Network (WBAN); node; IEEE802.15. 6; MAC; CSMA/CA; Mobility Link Table (MLT)
Two-Dimensional Modeling of Ablation and Pyrolysis with Application to Rocket Nozzles

A new two-dimensional ablation analysis code (MOPAR-MD) capable of modeling pyrolyzing thermal protection system materials is presented. Favorable agreement with analytical solutions and results from other (one-dimensional) ablation solvers for a wide range of test cases indicates a correct implementation consistent with other codes. This new material response code can be coupled to the LeMANS reacting flow solver. New capabilities required for modeling nozzle flowfields are added to LeMANS, including the Menter baseline and shear stress transport turbulence models and a “two-gas” method for capturing the thermodynamics of gas–particle flow found in many rocket nozzles. These updated codes are used to perform uncoupled simulations, predicting the thermal and ablation response of the HIPPO nozzle test case. Radiation is found to have minimal impact on the response of the throat and downstream portions of the rocket nozzle, but remains significant for the motor chamber and upstream portions of the nozzle. E.
Convergence analysis for second‐order accurate schemes for the periodic nonlocal Allen‐Cahn and Cahn‐Hilliard equations

In this paper, we provide a detailed convergence analysis for fully discrete second‐order (in both time and space) numerical schemes for nonlocal Allen‐Cahn and nonlocal Cahn‐Hilliard equations. The unconditional unique solvability and energy stability ensures ℓ4 stability. The convergence analysis for the nonlocal Allen‐Cahn equation follows the standard procedure of consistency and stability estimate for the numerical error function. For the nonlocal Cahn‐Hilliard equation, because of the complicated form of the nonlinear term, a careful expansion of its discrete gradient is undertaken, and an H−1 inner‐product estimate of this nonlinear numerical error is derived to establish convergence. In addition, an a priori W1,∞ bound of the numerical solution at the discrete level is needed in the error estimate. Such a bound can be obtained by performing a higher order consistency analysis by using asymptotic expansions for the numerical solution. Following the technique originally proposed by Strang (eg, 1964), instead of the standard comparison between the exact and numerical solutions, an error estimate between the numerical solution and the constructed approximate solution yields an O(s3+h4) convergence in ℓ∞(0,T;ℓ2) norm, in which s and h denote the time step and spatial mesh sizes, respectively. This in turn leads to the necessary bound under a standard constraint s≤Ch. Here, we also prove convergence of the scheme in the maximum norm under the same constraint.
Leucine-rich glioma inactivated 3: integrative analyses support its prognostic role in glioma

Background Leucine-rich glioma inactivated 3 (LGI3) is a secreted protein member of LGI family. We previously reported that LGI3 was expressed in brain, adipose tissues and skin, where it played roles as a multifunctional cytokine. We postulated that LGI3 may be involved in cytokine network in cancers. Aim This study aimed to analyze differentially expressed genes in glioma tissues and glioma cohort data to investigate the prognostic role of LGI3 and its receptors. Materials and methods Expression microarray data from Gene Expression Omnibus and glioma cohort data were analyzed using bioinformatic tools for statistical analysis, protein–protein interactions, functional enrichment and pathway analyses and prognostic association analysis. Results We found that LGI3 and its receptors, ADAM22 and ADAM23, were significantly downregulated in glioma tissues. Eleven upregulated genes and two downregulated genes in glioma tissues were found to be the previously reported LGI3-regulated genes. Protein–protein interaction network analysis showed that 85% of the LGI3-regulated and glioma-altered genes formed a cluster of interaction network. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed the association of these genes with hypoxia responses, p53 and Akt signaling and various cancer-related pathways including glioma. Analysis of expression microarray data of glioma cohorts demonstrated that low expression levels of LGI3, ADAM22 and ADAM23 were significantly associated with poor prognosis of glioma. Conclusion These results propose that LGI3 and its receptors may play a prognostic role in glioma.
Transformação da periferia e novas formas de desigualdades nas metrópoles brasileiras: um olhar sobre as mudanças na produção habitacional

This article’s objective is to discuss recent transformations in the periphery of Brazil’s largest cities, considering the expansion of real estate in these territories, strongly supported by the Government’s housing program Minha Casa Minha Vida. Using historical and theoretical analyses, we demonstrate the shift in the primacy of the contradiction between capital and labor, dominated by industrial capital, towards the primacy of an urban contradiction, dominated by financial capital. Our hypothesis is that the appropriation of the periphery has consolidated into an essential strategy in the amplification of gains and in the expansion of real estate. This strategy of capital appreciation seems to impose new forms of inequality, which we have attempted to explore here.