Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaining insight with recursive partitioning of generalized linear models

Recursive partitioning algorithms separate a feature space into a set of disjoint rectangles. Then, usually, a constant in every partition is fitted. While this is a simple and intuitive approach, it may still lack interpretability as to how a specific relationship between dependent and independent variables may look. Or it may be that a certain model is assumed or of interest and there is a number of candidate variables that may non-linearly give rise to different model parameter values. We present an approach that combines generalized linear models (GLM) with recursive partitioning that offers enhanced interpretability of classical trees as well as providing an explorative way to assess a candidate variable's influence on a parametric model. This method conducts recursive partitioning of a GLM by (1) fitting the model to the data set, (2) testing for parameter instability over a set of partitioning variables, (3) splitting the data set with respect to the variable associated with the highest instability. The outcome is a tree where each terminal node is associated with a GLM. We will show the method's versatility and suitability to gain additional insight into the relationship of dependent and independent variables by two examples, modelling voting behaviour and a failure model for debt amortization, and compare it to alternative approaches.
IPO pricing: growth rates implied in offer prices

This paper studies the valuation of companies going public and defines a methodology to infer the growth expectations implicit in the prices of their Initial Public Offering (IPO). The proposed reverse-engineered DCF model is operable by individual investors, as it does not require access to private information or sell-side analysts’ forecasts. Applying the procedure to a sample of IPOs in three European countries (France, Italy, and Germany), we estimate the cash flow growth implied by offer prices and examine the bias of implied growth in comparison to the realized. We find that the estimated growth in cash flow is much higher than its actual realization, with the median IPO firm overvalued at the offering by 74%. Estimation errors increase with IPO firms’ leverage and underpricing, and decrease with age, size, and book-to-market ratios. Further tests find that post-IPO stock returns are lower for issues whose implied growth is more upward biased.
Effect of a Computerized Provider Order Entry (CPOE) System on Medication Orders at a Community Hospital and University Hospital

Computerized Provider Order Entry (CPOE) has been demonstrated to improve the medication ordering process, but most published studies have been performed at academic hospitals. Little is known about the effects of CPOE at community hospitals. With a pre-post study design, we assessed the effects of a CPOE system on the medication ordering process at both a community and university hospital. The time from provider ordering to pharmacist verification decreased by two hours with CPOE at the community hospital (p<0.0001) and by one hour at the university hospital (p<0.0001). The rate of medication clarifications requiring signature was 2.80 percent pre-CPOE and 0.40 percent with CPOE (p<0.0001) at the community hospital. The university hospital was 2.76 percent pre-CPOE and 0.46 percent with CPOE (p<0.0001). CPOE improved medication order processing at both community and university hospitals. These findings add to the limited literature on CPOE in community hospitals.
Automatically assessing code understandability: How far are we?

Program understanding plays a pivotal role in software maintenance and evolution: a deep understanding of code is the stepping stone for most software-related activities, such as bug fixing or testing. Being able to measure the understandability of a piece of code might help in estimating the effort required for a maintenance activity, in comparing the quality of alternative implementations, or even in predicting bugs. Unfortunately, there are no existing metrics specifically designed to assess the understandability of a given code snippet. In this paper, we perform a first step in this direction, by studying the extent to which several types of metrics computed on code, documentation, and developers correlate with code understandability. To perform such an investigation we ran a study with 46 participants who were asked to understand eight code snippets each. We collected a total of 324 evaluations aiming at assessing the perceived understandability, the actual level of understanding, and the time needed to understand a code snippet. Our results demonstrate that none of the (existing and new) metrics we considered is able to capture code understandability, not even the ones assumed to assess quality attributes strongly related with it, such as code readability and complexity.
Dublin City University Video Track Experiments for TREC 2001

Dublin City University participated in the interactive search task and Shot Boundary Detection task of the TREC Video Track. In the interactive search task experiment thirty people used three different digital video browsers to find video segments matching the given topics. Each user was under a time constraint of six minutes for each topic assigned to them. The purpose of this experiment was to compare video browsers and so a method was developed for combining independent users' results for a topic into one set of results. Collated results based on thirty users are available herein though individual users' and browsers' results are currently unavailable for comparison. Our purpose in participating in this TREC track was to create the ground truth within the TREC framework, which will allow us to do direct browser performance comparisons.
The ATILF-LLF System for Parseme Shared Task: a Transition-based Verbal Multiword Expression Tagger

We describe the ATILF-LLF system built for the MWE 2017 Shared Task on automatic identification of verbal multiword expressions. We participated in the closed track only, for all the 18 available languages. Our system is a robust greedy transition-based system, in which MWE are identified through a MERGE transition. The system was meant to accommodate the variety of linguistic resources provided for each language, in terms of accompanying morphological and syntactic information. Using per-MWE Fscore, the system was ranked first for all but two languages (Hungarian and Romanian).
Metrology for the Electrical Characterization of Semiconductor Nanowires

<para> Nanoelectronic devices based upon self-assembled semiconductor nanowires are excellent research tools for investigating the behavior of structures with sublithographic features as well as a promising basis for future information processing technologies. New test structures and associated electrical measurement methods are the primary metrology needs necessary to enable the development, assessment, and adoption of emerging nanowire electronics. We describe two unique approaches to successfully fabricate nanowire devices: one based upon harvesting and positioning nanowires and one based upon the direct growth of nanowires in predefined locations. Test structures are fabricated and electronically characterized to probe the fundamental properties of chemical-vapor-deposition-grown silicon nanowires. Important information about current transport and fluctuations in materials and devices can be derived from noise measurements, and low-frequency <formula formulatype="inline"><tex Notation="TeX">$ \hbox{1}/f$</tex></formula> noise has traditionally been utilized as a quality and reliability indicator for semiconductor devices. Both low-frequency <formula formulatype="inline"><tex Notation="TeX">$\hbox{1}/f$</tex></formula> noise and random telegraph signals are shown here to be powerful methods for probing trapping defects in nanoelectronic devices. </para>
XFlow: 1D-2D Cross-modal Deep Neural Networks for Audiovisual Classification

We propose two multimodal deep learning architectures that allow for cross-modal dataflow (XFlow) between the feature extractors, thereby extracting more interpretable features and obtaining a better representation than through unimodal learning, for the same amount of training data. These models can usefully exploit correlations between audio and visual data, which have a different dimensionality and are therefore nontrivially exchangeable. Our work improves on existing multimodal deep learning metholodogies in two essential ways: (1) it presents a novel method for performing cross-modality (before features are learned from individual modalities) and (2) extends the previously proposed cross-connections, which only transfer information between streams that process compatible data. Both cross-modal architectures outperformed their baselines (by up to 7.5%) when evaluated on the AVletters dataset.
Underwater visual SLAM with loop-closure using image-to-image link recovery

This study develops an underwater visual simultaneous localization and mapping (SLAM) algorithm using a monocular vision as a major measurement sensor, focusing in particular on the loop-closure problem. Although most vision-based loop-closure approaches have been implemented by feature-based pairwise image matching, matching underwater images is generally a challenging task due to the limited number of feature correspondences or relatively small overlapping regions between the compared images. The lack of loop-closing events caused by these challenges can degrade the navigation and mapping performance of visual SLAM. For robust visual SLAM, a loop-closure algorithm that can recover image-to-image matching links is presented. The proposed loop-closure algorithm is more resilient to failures of pairwise matching and thus can maximize the use of image-to-image links, thereby improving the estimation performance in the context of visual SLAM. To validate the effectiveness of the proposed algorithm, a hover-capable unmanned underwater vehicle was used for in-water experiments, and the proposed algorithm was also evaluated through a series of comparative results drawn from an experimental dataset.
Offloading in HCNs: Congestion-Aware Network Selection and User Incentive Design

To accommodate exponentially increasing traffic demands, operators are seeking to offload cellular traffic to small base stations (BSs) in heterogeneous cellular networks (HCNs), which is promising in alleviating traffic congestion. In HCNs, operators are eager to balance the traffic globally, where users may be pushed to less preferred small BSs, resulting in possible conflict with user local preference. Thus, it is a big challenge to achieve dynamic load balancing for operators and provide participation incentive for users simultaneously. Due to the dynamics of network state and user traffic demand, we are inspired to utilize Lyapunov optimization to develop a congestion-aware cellular offloading scheme. Specifically, an operator profit maximization problem involving network selection and rate control is formulated. To achieve long-term network stability, we propose a congestion-aware network selection algorithm, obtaining the BS alternative set that maintains traffic congestion constraint. By exploring the heterogeneity of user quality sensitivity, we devise the optimal quality-price contract, which maximizes operator profit. With effective pricing and resource allocation, users are motivated to make proper association strategy chosen from the BS alternative set. Simulation results demonstrate the effectiveness of our scheme in improving operator profit. User incentive and network stability are also validated.
Mechanical characterization of single-walled carbon nanotubes: Numerical simulation study

The mechanical behaviour of non-chiral and chiral single-walled carbon nanotubes under tensile and bending loading conditions is investigated. For this purpose, three-dimensional finite element modelling is used in order to evaluate the tensile and bending rigidities and, subsequently, the Young's moduli. It is shown that the evolution of rigidity, tensile and bending, as a function of diameter can be described by a unique function for non-chiral and chiral single-walled nanotubes, i.e. regardless of the index or angles of chirality. A comprehensive study of the influence of the nanotube wall thickness and diameter on the Young's modulus values is also carried out. It is established that the evolution of the Young's modulus as a function of the inverse of the wall thickness follows a quasi-linear trend for nanotubes with diameters larger than 1.085 nm. The current numerical simulation results are compared with data reported in the literature. This work provides a benchmark in relation to ascertaining the mechanical properties of chiral and non-chiral single-walled carbon nanotubes by nanoscale continuum models.
Improving Database Performance on Simultaneous Multithreading Processors

Simultaneous multithreading (SMT) allows multiple threads to supply instructions to the instruction pipeline of a superscalar processor. Because threads share processor resources, an SMT system is inherently different from a multiprocessor system and, therefore, utilizing multiple threads on an SMT processor creates new challenges for database implementers.We investigate three thread-based techniques to exploit SMT architectures on memory-resident data. First, we consider running independent operations in separate threads, a technique applied to conventional multi-processor systems. Second, we describe a novel implementation strategy in which individual operators are implemented in a multi-threaded fashion. Finally, we introduce a new data-structure called a work-ahead set that allows us to use one of the threads to aggressively preload data into the cache.We evaluate each method with respect to its performance, implementation complexity, and other measures. We also provide guidance regarding when and how to best utilize the various threading techniques. Our experimental results show that by taking advantage of SMT technology we achieve a 30% to 70% improvement in throughput over single threaded implementations on in-memory database operations.
The Methods of Maximum Flow and Minimum Cost Flow Finding in Fuzzy Network

This article considers the problems of maximum flow and minimum cost flow determining in fuzzy network. Parameters of fuzzy network are fuzzy arc capacities and transmission costs of one flow unit represented as fuzzy triangular numbers. Conventional rules of operating with fuzzy triangular numbers lead to a strong “blurring” of their borders, resulting in loss of selfdescriptiveness of calculations with them. The following technique of addition and subtraction of fuzzy triangular numbers is proposed in the presented paper: the centers are added (subtracted) by the conventional methods, and the borders of the deviations are calculated using linear combinations of the borders of adjacent values. The fact that the limits of uncertainty of fuzzy triangular numbers should increase with the increasing of central values is taken into account. To illustrate the proposed method numerical examples are presented.
SemEval-2014 Task 7: Analysis of Clinical Text

This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It focused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Resources (ShARe) 1 corpus. This task was a follow-up to the ShARe/CLEF eHealth 2013 shared task, subtasks 1a and 1b, 2 but using a larger test set. A total of 21 teams competed in Task A, and 18 of those also participated in Task B. For Task A, the best system had a strict F1-score of 81.3, with a precision of 84.3 and recall of 78.6. For Task B, the same group had the best strict accuracy of 74.1. The organizers have made the text corpora, annotations, and evaluation tools available for future research and development at the shared task website. 3
Antifungal Activity of Endophyte Cultures of Morus alba L. against Phytopathogenic Fungi

A total of 106 strains of endophytic fungi were isolated from mulberry tree. 86 strains of the total isolates belonged to 18 genera. The dominant species were Fusarium spp. (22.6%) and Alternaria spp. (15.1%). The isolation frequency of endophytes was around 47, 38 and 15%, respectively to roots, branches and leaves. About 54% of the isolates could produce antifungal metabolites against at least one plant pathogenic fungi. Endophytic Aspergillus sp. A204, Colletotrichum sp. C103 and Penicillium sp. P306 showed broader antifungal spectrum. The strain C103 showed stronger potential with growth inhibition rates higher than 50% against all of the selected phytopathogens. The minimum inhibition concentrations (MICs) of the EtOAc extract of C103 culture against Botrytis cinerea, Fusarium oxysporum, Myrothecium rorideum was 1.5, 0.5 and 0.5 mg/mL, respectively, indicating that endophytic fungi may play an important role in protection of mulberry tree from disease as well as an excellent resource for natural antifungal compounds.
Pace independent mobile gait biometrics

Accelerometers embedded in mobile devices have shown great potential for non-obtrusive gait biometrics by directly capturing a user's characteristic locomotion. Although gait analysis using these sensors has achieved highly accurate authentication and identification performance under controlled experimental settings, the robustness of such algorithms in the presence of assorted variations typical in real world scenarios remains a major challenge. In this paper, we propose a novel pace independent mobile gait biometrics algorithm that is insensitive to variability in walking speed. Our approach also exploits recent advances in invariant mobile gait representation to be independent of sensor rotation. Performance evaluations on a realistic mobile gait dataset containing 51 subjects confirm the merits of the proposed algorithm toward practical mobile gait authentication.
Briefing Paper “Characterizing the Range of Children’s Pollutant Exposure during School Bus

This study of children's exposures while riding school buses was the most comprehensive study of its type ever conducted. Pollutant concentrations were measured on board buses of various ages and types, driven over actual bus routes in Los Angeles. The three factors with the greatest overall effect on bus cabin concentrations were (a) " self-pollution " from the bus's own emissions (b) emissions from other vehicles on the road as reflected by traffic density, and (c) ambient " background " concentrations in the vicinity of the roadways traveled. The self-pollution effect was highest for older buses under closed window conditions, with diesel vehicle-related pollutant (DRP) concentrations up to 2.5 times higher than during open window conditions. During runs of different traffic density, DRP concentrations were two to three times higher on urban routes with dense traffic as compared to the same bus driving on rural/suburban routes with light traffic. Accurate interpretation of the study results requires consideration of several caveats. The most important caveats are that both the number of buses studied and the number of runs conducted for each bus were small, and the variability in measured concentrations from bus to bus and from run to run was large. To put the results into an exposure perspective, Air Resources Board (ARB) staff compared riding uncontrolled diesel school buses, at concentrations measured in this study, for school years K-12, two hours per school day, to concentrations measured in passenger cars in Los Angeles in a different ARB study. Lifetime diesel exhaust particulate matter (DPM) exposure and cancer risk due to DPM 2 increased by about 5%. This risk was estimated to be significantly less than the risk of death in an auto accident if the same travel was made in a passenger car. Although these two aspects of risk are not directly comparable, they indicate that there are considerable personal safety benefits associated with using buses rather than passenger cars to transport children to school. Nevertheless, this and previous studies provide compelling data that exposures occurring during commutes, whether on buses or in cars, are significantly elevated and need to be reduced. Exposure reduction recommendations from this report include replacing older diesel school buses with natural gas-powered or particulate trap-equipped buses, assigning the newest and cleanest buses to the longest routes, and avoiding caravanning of buses through staggered departure times.
Improved ant colony algorithm for solving assignment problem

Assignment problem is one of the fundamental combinatorial optimization problems, ant colony algorithm is a kind of bionic optimization algorithm. The method of applying improved ant colony algorithm to assignment problem is analyzed. Based on the definitions of migration matrix, cost matrix, pheromone matrix, the node selection strategy, local pheromone updating rules and global pheromone updating rules are introduced in detail, steps of the improved ant colony algorithm to solving assignment problem is described. Using two different sizes of data sets, the range of optimal values of number of ants, information heuristic factor, expectation heuristic factor, global pheromone evaporation factor, local pheromone evaporation factor, interference factor are determined, and the performance of the algorithm presented is analyzed and validated.
A Modified Hepatitis B Virus Core Particle Containing Multiple Epitopes of the Plasmodium falciparum Circumsporozoite Protein Provides a Highly Immunogenic Malaria Vaccine in Preclinical Analyses in Rodent and Primate Hosts

ABSTRACT Despite extensive public health efforts, there are presently 200 to 400 million malaria infections and 1 to 2 million deaths each year due to the Plasmodium parasite. A prime target for malaria vaccine development is the circumsporozoite (CS) protein, which is expressed on the extracellular sporozoite and the intracellular hepatic stages of the parasite. Previous studies in rodent malaria models have shown that CS repeat B-cell epitopes expressed in a recombinant hepatitis B virus core (HBc) protein can elicit protective immunity. To design a vaccine for human use, a series of recombinant HBc proteins containing epitopes of Plasmodium falciparum CS protein were assayed for immunogenicity in mice [A. Birkett, B. Thornton, D. Milich, G. A. Oliveira, A. Siddique, R. Nussenzweig, J. M. Calvo-Calle, and E. H. Nardin, abstract from the 50th Annual Meeting of the American Society of Tropical Medicine and Hygiene 2001, Am. J. Trop. Med. Hyg. 65(Suppl. 3):258, 2001; D. R. Milich, J. Hughes, J. Jones, M. Sallberg, and T. R. Phillips, Vaccine 20:771-788, 2001]. The present paper summarizes preclinical analyses of the optimal P. falciparum HBc vaccine candidate, termed ICC-1132, which contains T- and B-cell epitopes from the repeat region and a universal T-cell epitope from the C terminus of the CS protein. The vaccine was highly immunogenic in mice and in Macaca fascicularis (cynomolgus) monkeys. When formulated in adjuvants suitable for human use, the vaccine elicited antisporozoite antibody titers that were logs higher than those obtained in previous studies. Human malaria-specific CD4+-T-cell clones and T cells of ICC-1132-immunized mice specifically recognized malaria T-cell epitopes contained in the vaccine. In addition to inducing strong malaria-specific immune responses in naïve hosts, ICC-1132 elicited potent anamnestic antibody responses in mice primed with P. falciparum sporozoites, suggesting potential efficacy in enhancing the sporozoite-primed immune responses of individuals living in areas where malaria is endemic.