Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Distributed Concolic Testing Approach: An Empirical Evaluation

Although testing is a standard method for improving the quality of software, conventional testing methods often fail to detect faults. Concolic testing attempts to remedy this by automatically generating test cases to explore execution paths in a program under test, helping testers achieve greater coverage of program behavior in a more automated fashion. Concolic testing, however, consumes a significant amount of computing time to explore execution paths, which is an obstacle toward its practical application. To address this limitation, we have developed a scalable distributed concolic testing framework that utilizes large numbers of computing nodes to generate test cases in a scalable manner. In this paper, we present the results of an empirical study that shows that the proposed framework can achieve a several orders-of-magnitude increase in test case generation speed compared to the original concolic approach, and also demonstrates clear potential for scalability.
Cognitive Mapping and Planning for Visual Navigation Supplementary Material

To demonstrate that our proposed mapper architecture works we test it in isolation on the task of free space prediction. We consider the scenario of an agent rotating about its current position, and the task is to predict free space in a 3.20 meter neighborhood of the agent. We only provide supervision for this experiment at end of the agents rotation. Figure A1 illustrates what the mapper learns. Observe that our mapper is able to make predictions where no observations are made. We also report the mean average precision for various versions of the mapper Table A1 on the test set (consisting of 2000 locations from the testing environment). We compare against an analytic mapping baseline which projects points observed in the depth image into the top view (by back projecting them into space and rotating them into the top-down view).
Developing ML-based Systems to Extract Medical Information from Japanese Medical History Summaries

With the increase of the number of medical records written in an electronic format, natural language processing techniques in the medical domain have become more andmore important. Forthe purposeof the development and evaluation of machine learning-based systems to extract medical information, we recently participated in the NTCIR-10 MedNLP task. The task focused on Japanese medical records and aimed at evaluating different information extraction techniques on the common data set provided by the organizers. We implemented our baseline system based on structured perceptron and have developed its extensions. In this paper, we describe our systems and report on the evaluation of and the analysis on their performance.
The Muscular Dystrophies

Abstract The muscular dystrophies are disorders of progressive muscular degeneration and weakness. As a group they display clinical heterogeneity that reflects the heterogeneity of molecular mechanisms responsible for them, and range from congenital to adulthood onset. Recent advances in the field include improved methods of diagnosis, continued identification of disease genes, and the development of a unified model of pathogenesis in facioscapulohumeral dystrophy. These advances are reflected in the development of new therapeutic approaches, some of which have already led to clinical trials in the dystrophinopathies and limb-girdle dystrophies.
MicroRNAs as Biomarkers for the Diagnostics of Bladder Cancer: a Meta-Analysis.

BACKGROUND Bladder cancer (BCa) is the fifth most common cancer with significant morbidity and mortality. Recently, numerous studies demonstrated that microRNAs (miRNAs) are emerging as diagnostic biomarkers for BCa. However, the findings in these studies are inconsistent. To systematically assess the potential diagnostic value of miRNAs for BCa, a meta-analysis was performed in this study. METHODS Relevant literature was researched in PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), and WanFang databases up to September 1, 2014. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), and area under the SROC curve (AUC) value were analyzed by the random-effects model, whose parameters reflected the overall diagnostic performance of miRNAs. RESULTS Thirty studies from 10 individual publications, including 1019 BCa patients and 690 controls, were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.80 (95% CI: 0.78 - 0.81), 0.74 (95% CI: 0.72 - 0.76), 3.22 (95% CI: 2.68 - 3.87), 0.26 (95% CI: 0.21 - 0.32), 15.20 (95% CI: 10.25 - 22.53) and 0.85, respectively, indicating a moderate diagnostic accuracy for BCa. Moreover, our subgroup analyses showed that analysis of multiple miRNAs (AUC, sensitivity, and specificity of 0.913, 0.86, and 0.80, respectively) yielded a higher diagnostic accuracy than of single miRNAs (AUC, sensitivity, and specificity of 0.84, 0.78, and 0.73, respectively) in BCa diagnosis. In addition, as biomarkers, miRNAs are more suitable for the diagnosis of non-muscle-invasive BCa (NMIBCa) with AUC of 0.84, sensitivity of 0.74, and specificity of 0.77 than muscle-invasive BCa (MIBCa) with AUC of 0.79, sensitivity of 0.73, and specificity of 0.73. CONCLUSIONS The present meta-analysis suggests that miRNAs are potential novel biomarkers for detection of BCa. However, further validation studies are still needed to confirm our findings.
A Hybrid Restaurant Recommender

any e-commerce application, the recommender systems play a vital role as they assist the prospective buyers in making proper decisions on the basis of the recommendations that the system provides. Recommender systems aim at providing the users with effective recommendations based on their intuitions and preferences. The two very old techniques commonly used for providing automated recommendations are collaborative filtering and knowledge based filtering techniques. However, both these techniques have certain drawbacks when used separately. In this paper, we propose architecture for designing hybrid recommender system that combines the advantages of both the techniques; thereby improving accuracy. The proposed approach uses a combination of personalised recommendations (based on individuals past behaviour), social recommendations (based on past behaviour of similar users) and item-based recommendations (based on restaurant database). This combination overcomes all the drawbacks that are faced when these techniques are used separately. In this paper, we have described the application of such a system within the domain of restaurants.
Survey of Wild Mammal Hosts of Cutaneous Leishmaniasis Parasites in Panamá and Costa Rica

The eco-epidemiology of American cutaneous leishmaniasis (ACL) is driven by animal reservoir species that are a source of infection for sand flies that serve as vectors infecting humans with Leishmania spp parasites. The emergence and re-emergence of this disease across Latin America calls for further studies to identify reservoir species associated with enzootic transmission. Here, we present results from a survey of 52 individuals from 13 wild mammal species at endemic sites in Costa Rica and Panama where ACL mammal hosts have not been previously studied. For Leishmania spp. diagnostics we employed a novel PCR technique using blood samples collected on filter paper. We only found Leishmania spp parasites in one host, the two-toed sloth, Choloepus hoffmanni. Our findings add further support to the role of two-toed sloths as an important ACL reservoir in Central America.
A solution to estimate robot motion with large rotation by matching laser scans

This paper presents an Iterative Closest Point (ICP) preprocessor named Iterative Closest Normal (ICN). The proposed ICN algorithm is mainly focused on the estimation of large rotation between two successive scans during the robot motion in the indoor environment. After the large rotation has been reduced, the standard ICP and point-to-line metric based ICP algorithm (PLICP) is applied to estimate the residual transformation. The experiments were conducted in a noisy environment and the robot was controlled to move with sharp turning. The results suggest that ICP and PLICP needs less iterations and becomes more robust after introducing the ICN as a preprocessor.
Polynomial expansion detector for uniform linear arrays

In this paper, we design a low complexity linear MMSE decoder to recover the signal transmitted by M mobile users to a base station equipped with N receiving antennas, arranged as a uniform linear array (ULA). The angles of arrival are supposed to be uniformly distributed. As the dimension increases, it is hard to invert the system and therefore recent results on random matrix theory and polynomial expansion detectors are helpful to approximate the optimum linear MMSE receiver. Simulation results confirm the validity of this approximation.
Galactomannan does not precede major signs on a pulmonary computerized tomographic scan suggestive of invasive aspergillosis in patients with hematological malignancies.

BACKGROUND Detection of serum galactomannan (GM) antigen and presence of the halo sign on a pulmonary computerized tomographic (CT) scan have a high specificity but a low sensitivity to diagnose invasive aspergillosis (IA) in patients at risk for this disease. To our knowledge, the relationship between the time at which pulmonary infiltrates are detected by CT and the time at which GM antigens are detected by enzyme immunoassay (EIA) has not been studied. METHODS In a prospective study, tests for detection of GM were performed twice weekly for patients with hematological malignancies who had undergone hematopoetic stem cell transplantation (HSCT) or had received induction and/or consolidation chemotherapy. A pulmonary CT scan was performed once weekly. Infiltrates were defined as either major or minor signs. IA was classified as proven, probable, or possible, in accordance with the definition stated by the European Organization for Research and Treatment of Cancer-Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group. RESULTS We analyzed 161 episodes of infection in 107 patients (65 allogeneic HSCT recipients, 30 autologous HSCT recipients, and 66 induction and/or consolidation chemotherapy recipients). A total of 109 episodes with no IA, 32 episodes with possible IA, and 20 episodes with probable or proven IA were identified. Minor pulmonary signs were detected by CT in 70 episodes (43%), and major pulmonary signs were detected by CT in 11 episodes (7%). Univariate and multivariate analyses revealed no significant association between detection of GM by EIA and detection of abnormal pulmonary signs by CT. A significant association was found between GM levels and receipt of piperacillin-tazobactam. GM test results were not positive before major signs were seen on CT images. Only 7 (10%) of 70 patients with minor pulmonary signs had positive GM test results before detection of the greatest pathologic change by CT. CONCLUSIONS We show that detection of GM by EIA does not precede detection of major lesions by pulmonary CT. In the clinical setting, the decision to administer mold-active treatment should based on detection of new pulmonary infiltrates on CT performed early during infection, rather than on results of EIA for detection of GM.
Country-by-genotype-by-environment interaction in childhood academic achievement

Why children differ in their cognitive abilities, including their academic achievement, is a function of their environment and genotype, and this function may be complex, involving gene–environment (GxE) interaction and gene–environment (GE) covariation. The paper by Figlio et al. (1) in PNAS on academic achievement in reading and mathematics considers the importance of GxE interaction in a unique state-wide sample of twin children from Florida. GxE interaction can be thought of as the genetic control over sensitivity to environmental conditions or, alternatively, as the environment determining the expression of the genotype. Several scenarios may be distinguished: Across different environments, the same genes are expressed, but their absolute or relative influences vary (quantitative differences), or different genes are expressed across different environments (qualitative differences). Obviously, a combination of these two scenarios is also possible. A third alternative states that under GxE interaction, environmental influences vary conditional on a child’s genotypic value (e.g., a low or high genotypic value for mathematical ability) (2). As to how GxE interaction may manifest, several theoretical models each offer their own prediction. The diathesis-stress model predicts that genetic vulnerability, or diathesis, increases the likelihood of a trait in the presence of environmental stress. It also predicts that the heritability of the trait will be higher for children in risk environments (3). In contrast, the bioecological model predicts that risk environments will mask genetic differences between children and enriched environments will amplify genetic differences (4). In studies of human behavior, researchers usually rely on cross-sectional designs with groups of related or genotyped individuals exposed to different environments to assess quantitative and qualitative GxE interactions. Such a cross-sectional design was employed by Figlio et al. (1): Twin children’s academic achievement in mathematics and reading in primary school was observed under different socioeconomic status (SES) circumstances. Birth and … [↵][1]1To whom correspondence should be addressed. Email: di.boomsma{at}vu.nl. [1]: #xref-corresp-1-1
An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks

Recent advances in environmental energy harvesting technologies have provided great potentials for traditional battery powered sensor networks to achieve perpetual operations. Due to dynamics from the temporal profiles of ambient energy sources, most of the studies so far have focused on designing and optimizing energy management schemes on single sensor node, but overlooked the impact of spatial variations of energy distribution when sensors work together at different locations. To design a robust sensor network, in this paper, we use mobility to circumvent communication bottlenecks caused by spatial energy variations. We employ a mobile collector, called SenCar, to collect data from designated sensors and balance energy consumptions in the network. To show spatial-temporal energy variations, we first conduct a case study in a solar-powered network and analyze possible impact on network performance. Next, we present a two-step approach for mobile data collection. First, we adaptively select a subset of sensor locations where the SenCar stops to collect data packets in a multi-hop fashion. We develop an adaptive algorithm to search for nodes based on their energy and guarantee data collection tour length is bounded. Second, we focus on designing distributed algorithms to achieve maximum network utility by adjusting data rates, link scheduling, and flow routing that adapts to the spatial-temporal environmental energy fluctuations. Finally, our numerical results indicate the distributed algorithms can converge to optimality very fast and validate its convergence in case of node failure. We also show advantages of our framework such as it can adapt to spatial-temporal energy variations and demonstrate its superiority compared to the network with static data sink.
How to measure local active information storage in neural systems

Any act of information processing can be decomposed into the component operations of transfer, storage and modification, and neural information processing is no exception. Here, we review two recent attempts to quantify active information storage carried by neural signals, and suggest an optimal experimental strategy to measure the active information storage as seen by other neurons or brain areas.
Texture Analysis Using Gaussian Graphical Models

Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects.
Rapid innovation diffusion in social networks

Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks.
Antifungal Resistance and New Strategies to Control Fungal Infections

Despite improvement of antifungal therapies over the last 30 years, the phenomenon of antifungal resistance is still of major concern in clinical practice. In the last 10 years the molecular mechanisms underlying this phenomenon were extensively unraveled. In this paper, after a brief overview of currently available antifungals, molecular mechanisms of antifungal resistance will be detailed. It appears that major mechanisms of resistance are essential due to the deregulation of antifungal resistance effector genes. This deregulation is a consequence of point mutations occurring in transcriptional regulators of these effector genes. Resistance can also follow the emergence of point mutations directly in the genes coding antifungal targets. In addition we further describe new strategies currently undertaken to discover alternative therapy targets and antifungals. Identification of new antifungals is essentially achieved by the screening of natural or synthetic chemical compound collections. Discovery of new putative antifungal targets is performed through genome-wide approaches for a better understanding of the human pathogenic fungi biology.
Why Do SMEs File Trademarks? Insights from Firms in Innovative Industries

Trademark filings have increased markedly over time. Although prior research has investigated the outcomes of trademark registration, including its effects on firm market valuation and productivity, little is known about why firms file trademarks. However, to interpret the increase in trademark filings and its economic effects, it is important to know and understand why firms file trademarks. Because trademarks are particularly important to small and medium-sized enterprises (SMEs), this study analyzes trademarking motives using a survey of 600 SMEs in innovative industries. An exploratory factor analysis yields three distinct motives: protection, marketing, and exchange. A cluster analysis reveals four distinct clusters of firms with respect to the three trademarking motives. A comparison of these clusters reveals significant differences in several industry- and firm-level characteristics, including participation in service industries and relationships with external parties. Implications for research on SMEs, trademarks, and intellectual property management are discussed.
Sikker : A High-Performance Distributed System Architecture for Secure Service-Oriented Computing

In this paper, we present Sikker1, a high-performance distributed system architecture for secure service-oriented computing. Sikker includes a novel service-oriented application model upon which security and isolation policies are derived and enforced. The workhorse of Sikker is a custom network interface controller, called the Network Management Unit (NMU), that enforces Sikker’s security and isolation policies while providing high-performance network access. Sikker’s application model satisfies the complex interactions of modern large-scale distributed applications. Our experimentation results show that even when implemented on very large clusters, the NMU adds a negligible message latency of 41 ns under realistic workloads and 66 ns at the 99th percentile of worst case access patterns. Our analysis shows that the NMU can easily support over 100 Gbps with a single logic engine and that over 500 Gbps is achievable with more aggressive designs. Sikker’s service-oriented security and isolation mechanism removes high overheads imposed by current systems. Sikker allows distributed applications to operate in an environment with fine-grained security and isolation while experiencing supercomputer-like network performance.
3D path planning with continuous bounded curvature and pitch angle profiles using 7th order curves

Path planning is a fundamental task for any kind of autonomous mobile robot. In this paper, we present a motion planning technique on the three-dimensional space considering vehicles with spatial curvature and pitch (climb or dive) angle constraints. Concerning real fixed-wing Unmanned Aerial Vehicles or underwater Remotely Operated Vehicles, we face the problem of calculating paths with continuous acceleration profiles, preventing the robot from abrupt turning or climbing/diving rates during its navigation. We also focus on the generation of paths with reduced length, allowing energy savings and minimizing the time for the task fulfillments. The proposed methodology provides, in a fast and iterative way, near-optimal paths in some scenarios, but with the advantage of continuous curvature and pitch rate profiles, a fundamental issue regarding the dynamics of real-world robots. The methodology was validated through numerous trials, under different scenarios in a simulated environment, and the results compared to the ones obtained by state-of-the-art techniques, providing a thorough evaluation.
Assessment of Spontaneous Fluctuations of Viral Load in Untreated Patients with Chronic Hepatitis C by Two Standardized Quantitation Methods: Branched DNA and Amplicor Monitor

ABSTRACT Quantitation of hepatitis C virus (HCV) RNA in serum has been used to predict and monitor the efficacy of interferon therapy in chronic HCV infection. We prospectively studied the fluctuation of viremia by a longitudinal follow-up of HCV RNA levels for 2 months in six untreated patients. Spontaneous fluctuations of HCV RNA ranged from 2.8- to 5.7-fold with branched DNA assay and from 2.9- to 5.6-fold with Monitor. These large spontaneous fluctuations (up to 0.75 log), observed daily, weekly, and monthly, raise doubt about the clinical value of a single assessment of pretherapeutic viremia.