Miracle-dz/UniFAR-FTUs huggingface dataset:
Patterns of Knowledge in API Reference Documentation

Reading reference documentation is an important part of programming with application programming interfaces (APIs). Reference documentation complements the API by providing information not obvious from the API syntax. To improve the quality of reference documentation and the efficiency with which the relevant information it contains can be accessed, we must first understand its content. We report on a study of the nature and organization of knowledge contained in the reference documentation of the hundreds of APIs provided as a part of two major technology platforms: Java SDK 6 and . NET 4.0. Our study involved the development of a taxonomy of knowledge types based on grounded methods and independent empirical validation. Seventeen trained coders used the taxonomy to rate a total of 5,574 randomly sampled documentation units to assess the knowledge they contain. Our results provide a comprehensive perspective on the patterns of knowledge in API documentation: observations about the types of knowledge it contains and how this knowledge is distributed throughout the documentation. The taxonomy and patterns of knowledge we present in this paper can be used to help practitioners evaluate the content of their API documentation, better organize their documentation, and limit the amount of low-value content. They also provide a vocabulary that can help structure and facilitate discussions about the content of APIs.
Acyl Peroxy Nitrates Link Oil and Natural Gas Emissions to High Ozone Abundances in the Colorado Front Range During Summer 2015

We present measurements of ozone (O3), acyl peroxy nitrates (APNs), and a suite of O3 precursors made at the Boulder Atmospheric Observatory in Erie, Colorado, during summer 2015. We employ an empirical analysis of the APNs and a previously described positive matrix factorization of the volatile organic compounds (VOCs) to investigate the contribution of different VOC sources to high O3 abundances at Boulder Atmospheric Observatory. Based on the ratio of peroxypropionyl nitrate (PPN) to peroxyacetyl nitrate (PAN), we find that anthropogenic VOC precursors dominate APN production when O3 is most elevated. Propane and larger alkanes, primarily from oil and natural gas emissions in the Colorado Front Range, drive these elevated PPN to PAN ratios during high O3 events. The percentage of OH reactivity associated with oil and gas emissions is also positively correlated with O3 and PPN/PAN. Idealized box model simulations are used to probe the chemical mechanisms potentially responsible for these observations. We find that observed abundances of long‐lived oil and natural gas‐related VOCs are likely high enough such that the oxidation of these VOCs in a single photochemical day produces sufficient peroxy radicals to contribute to O3 formation in the northern Colorado Front Range. Based on our empirical observations and box model simulations, we conclude that oil and natural gas emissions contribute to O3 production on high O3 days in this region during summer 2015.
The Tetraspanins CD9 and CD81 Regulate CD9P1-Induced Effects on Cell Migration

CD9P-1 is a cell surface protein with immunoglobulin domains and an unknown function that specifically associates with tetraspanins CD9 and CD81. Overexpression of CD9P-1 in HEK-293 cells induces dramatic changes in cell spreading and migration on various matrices. Experiments using time-lapse videomicroscopy revealed that CD9P-1 expression has led to higher cell motility on collagen I but lower motility on fibronectin through a β1-integrins dependent mechanism. On collagen I, the increase in cell motility induced by CD9P-1 expression was found to involve integrin α2β1 and CD9P-1 was observed to associate with this collagen receptor. The generation of CD9P-1 mutants demonstrated that the transmembrane and the cytoplasmic domains are necessary for inducing effects on cell motility. On the other hand, expression of tetraspanins CD9 or CD81 was shown to reverse the effects of CD9P-1 on cell motility on collagen I or fibronectin with a concomitant association with CD9P-1. Thus, the ratio of expression levels between CD9P-1 and its tetraspanin partners can regulate cell motility.
Enumerating Maximal Clique Sets with K-plex Constraint by using Meta-cliques

Many variants of pseudo-cliques have been introduced as relaxation models of cliques to detect communities in real world networks. For most types of pseudo-cliques, enumeration algorithms can be designed just similar to maximal clique enumerator. However, the problem of enumerating pseudo-cliques are computationally hard, because the number of maximal pseudo-cliques is huge in general. Furthermore, because of the weak requirement of k-plex, sparse communities are also allowed depending on the parameter k. To obtain a class of more dense pseudo cliques and to improve the computational performance, we introdue a derived graph whose vertices are cliques in the original input graph. Then our target must be cliques or pseudo cliques of the derived graph under an additional constraint requiring high density in the original graph. An enumerator for this new class is designed and its computational efficientcy is experimentally verfied.
Temperature effect on quantum capacitance zig-zag graphene nanoscrolls (ZGNS) (16,0)

Device scaling of the electronic devices has brings the dominancy of quantum effect in nano-size device characterization. This paper presented the first band analytical model of the quantum capacitance for (16,0) zigzag graphene nanoscroll (ZGNS). The derivation of quantum capacitance is based on the differentiation of carrier density towards the Fermi energy. The Taylor's series expansion is employed on parabolic energy band structure so that it can be modified in the form of Fermi Intergal. Owing to its unique geometry structure that provides high intercalation area, it is expected that ZGNS exhibit high quantum capacitance.
Exploring relevance for clicks

Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. This paper proposes an approach to recognize reliable and meaningful user clicks (referred to as Relevant Clicks, RCs) in click-through data. By modeling user click-through behavior on search result lists, we propose several features to separate RCs from click noises. A learning algorithm is presented to estimate the quality of user clicks. Experimental results on large scale dataset show that: 1) our model effectively identifies RCs in noisy click-through data; 2) Different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries.
Hayek and Lachmann and the Complexity of Capital

Friederich Hayek was a scholar of uncommon breadth and depth whose work will be the subject of study and discovery for a long time to come. The span of his career makes it difficult, probably impossible, to account for all of the influences that the many scholars who crossed his path might have had on his work, or the influences that he might have had on theirs. No doubt this will motivate diverse contributions. Some of the usual suspects appear in other chapters of this volume. In this chapter I investigate the intellectual relationship between Hayek and his student and younger colleague Ludwig M. Lachmann. I begin at the beginning, the LSE years, during which Hayek and Lachmann both worked on the related topics of the trade-cycle and capital-theory. I follow with an examination of their diverging but related subsequent work. The capital-theory experience is pivotal.
Combination chemotherapy with carboplatin and bleomycin for advanced and recurrent head and neck cancer: A phase II study

Carboplatin is a platinum analogue with activity reported in head and neck cancer. We conducted a phase II trial with 14 patients who had recurrent head and neck cancer. They were treated with carboplatin 300 mg/m2 intravenously (I.V.) and bleomycin 30 units I.V. every 4 weeks. No responses were observed in this group of patients. Dose intensity of carboplatin administration may be an important determinant of response.
A reputation-based trust mechanism for ad hoc networks

The main characteristics of ad hoc networks are the lack of predefined infrastructure and the dynamic topology. These characteristics present some new security vulnerabilities for this emerging networking paradigm. Usually, security in ad hoc networks is handled through authentication and encryption. This can be considered as a first line of defense, however, this remain inefficient against some other kind of attacks such as malicious packet dropping. The purpose of this work is to provide a mechanism for detecting malicious incorrect packet forwarding attacks. To this end, a trust model extending routing protocols and based on the reputation concept is developed. Our model provides two main functionalities: monitoring the behavior of the neighboring nodes in the network and computing their reputations based on the information provided by the monitoring. This paper also discusses how the reputation information is gathered, stored and exchanged between the nodes, and computed according to the different scenarios. Our mechanism is also validated with some simulation work showing its feasibility, performance and benefits.
MEASUREMENT OF SEAWARD GROUND DISPLACEMENTS ON COASTAL LANDFILL AREA USING RADAR INTERFEROMETRY

Abstract. In order to understand the mechanism of subsidence and help reducing damage, researchers has been observed the line-of-sight subsidence on the Noksan industrial complex using SAR Interferometry(InSAR) and suggested subsidence prediction models. Although these researches explained a spatially uneven ground subsidence near the seaside, they could not have been explained the occurrence of the newly proposed seaward horizontal, especially nearly north-ward, displacement because of the geometric limitation of InSAR measurements. In this study, we measured the seaward ground displacements trend on the coastal landfill area, Noksan Industrial Complex. We set the interferometric pairs from an ascending and a descending orbits strip map data of ALOS PALSAR2. We employed InSAR and MAI stacking approaches for the both orbits respectively in order to improve the measurement. Finally, seaward deformation was estimated by retrieving three-dimensional displacements from multi-geometric displacements. As a results, maximally 3.3 and 0.7 cm/year of ground displacements for the vertical and seaward directions. In further study, we plan to generate InSAR and MAI stacking measurements with additional SAR data to mitigate tropospheric effect and noise well. Such a seaward observation approach using spaceborne radar is expected to be effective in observing the long-term movements on coastal landfill area.
Exploiting Structured Reference Data for Unsupervised Text Segmentation with Conditional Random Fields

Text segmentation is the process of converting information in unstructured text into structured records. This is an important problem since structured data is amenable to efficient query processing. CRFs are a class of discriminative probabilistic models that are gaining acceptance as an effective computing machinery for text segmentation. An important aspect of CRFs is learning model parameters from labeled training data. Labeling can be a labor intensive process. One can avoid the labeling step by using structured reference tables whose data domains and that of the input text data given for segmentation, coincide. In other words the labels in the training data drawn from reference tables “come for free”. Inspired by recent work on their use for training HMMs, we developed an unsupervised technique for text segmentation with CRFs using reference tables. Assuming text sequences to be segmented come in batches and sequences in a batch conform to the same attribute order, we build CRF models for each attribute in the reference table, use them to decide the attribute order of a batch of input sequences, derive labeled training data from the reference table according to that order, and train a global CRF model to segment the input sequences in the batch. Preliminary experimental results indicate that our technique works well in practice.
Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a popular method to deal with nonconvex and/or stochastic optimization problems when the gradient information is not available. Being based on the CMA-ES, the recently proposed Matrix Adaptation Evolution Strategy (MA-ES) provides a rather surprising result that the covariance matrix and all associated operations (e.g., potentially unstable eigendecomposition) can be replaced in the CMA-ES by a updated transformation matrix without any loss of performance. In order to further simplify MA-ES and reduce its $\mathcal{O}\big(n^2\big)$ time and storage complexity to $\mathcal{O}\big(n\log(n)\big)$, we present the Limited-Memory Matrix Adaptation Evolution Strategy (LM-MA-ES) for efficient zeroth order large-scale optimization. The algorithm demonstrates state-of-the-art performance on a set of established large-scale benchmarks. We explore the algorithm on the problem of generating adversarial inputs for a (non-smooth) random forest classifier, demonstrating a surprising vulnerability of the classifier.
Context-Based Technique for Biomedical Term Classification

The existing volumes of biomedical texts available online drive the increasing need for automated techniques to analyze and extract knowledge from these information repositories. Recognizing and classifying biomedical terms in these texts is an important step for developing efficient techniques for knowledge discovery and information extraction from the literature. This paper presents a new technique for biomedical term classification in biomedical texts. The method is based on combing successful feature selection techniques {MI, X2) with machine learning (SVM) for biomedical term classification. We utilize the advances in feature selection techniques in IR and use them to select the key features for term identification and classification. We evaluated the method using Genia 3.0 corpus with about 3,000 to more than 34,000 biomedical term instances. The technique is effective, achieving impressive accuracy, precision, and recall; and with F-score approaching -90%, the method is superior or very competitive with the recently published results.
Prevalence and Genotyping of Torque Teno Virus in HBV/HIV and Chronic HBV Patients in Iran

Background: Torque teno virus (TTV) was the first human Anelloviridae detected in a Japanese patient with an unknown type of hepatitis in 1997. TTV is by far the first known single-stranded circular DNA virus infecting human. In spite of its widespread nature in human population, its pathogenesis is still unclear. In addition, information regarding TTV infection in Iranian population is limited. Therefore, we attempted to determine the prevalence and genotype of TTV in three groups: HIV/HBV patients, chronic hepatitis B patients, and healthy individuals. Methods: The presence of TTV DNA in sera was investigated using PCR. The primer sets encompassing two 5’-UTR and N22 regions were used, and the positive products were collected for sequencing. Phylogenetic tree was generated based on N22 region and using the MEGA 7 software. Results: TTV DNA was detected in 452 patients with HIV/HBV and chronic hepatitis B, as well as in healthy control groups. The results from PCR indicated positive rates for these three groups 48%, 54%, and 49.3% using 5’-UTR primer and 15.1%, 12%, and 8% using N22 primer, respectively. Conclusion: Five genogroups were observed, which the second group was found to be the most frequent. The results of 5’-UTR primer showed more prevalence of TTV DNA comparing to N22 primer in patients and healthy control.
The TIGR rice genome annotation resource: annotating the rice genome and creating resources for plant biologists

Rice is not only a major food staple for the world's population but it also is a model species for a major group of flowering plants, the monocotyledonous plants. Draft genomic sequence of two subspecies of rice, Oryza sativa spp. japonica and indica ssp. are publicly available. To provide the community with a resource to data-mine the rice genome, we have constructed an annotation resource for rice (http://www.tigr.org/tdb/e2k1/osa1/). In this resource, we have annotated the rice genome for gene content, identified motifs/domains within the predicted genes, constructed a rice repeat database, identified related sequences in other plant species, and identified syntenic sequences between rice and maize. All of the data is available through web-based interfaces, FTP downloads, and a Distributed Annotation System.
Evolvement of Information Security Research on Employees' Behavior: A Systematic Review and Future Direction

Information Security (IS) is one of the biggest concerns for many organizations. This concern has led many to focus a huge effort into studying different IS areas. One of these critical areas is the human aspect, where investigation of employees' behaviors has emerged as an important topic. In this paper, we conduct a systematic review of all empirical studies published on this topic. The review will highlight the theoretical and methodological development and the dissemination of related empirical studies in academic journals throughout the years. At the end of the review, future research considerations are discussed and shared.
Prevalence of Extracranial Carotid Artery Disease Detectable by Echo‐Doppler in an Elderly Population

Background and Purpose Little information is available on extracranial carotid artery disease in free-living elderly individuals. We sought to evaluate the prevalence of carotid lesions in the elderly. Methods Using echo-Doppler, we assessed the prevalence of possible atherosclerotic lesions in the internal carotid arteries (n=478) and the external and common carotid arteries (n=956) of 239 subjects 65–94 years of age living in retirement homes in Seattle, Wash. Results We found that 152 (31.8%) internal carotid arteries were affected by nonstenosing plaque and 37 arteries (7.7%) had stenosis or occlusion. In addition, 193 (20.2%) external or common carotid arteries showed nonstenosing plaques. There were 128 subjects (53.6%) with internal carotid disease, 106 (44.3%) with evidence of external or common carotid disease, and 75 (31.4%) affected by disease in all three sites. There were 80 subjects (33.5%) with no ultrasound evidence of carotid disease. We found that the presence and severity of carotid disease increased between the decades 65–74 and 75–84. We also demonstrated a positive association between systolic blood pressure and ultrasound evidence of carotid disease that was independent of age. Conclusions The prevalence of extracranial artery disease in an apparently healthy population was high, although stenoses in most instances were not severe. We conclude that noninvasive ultrasound methods identify a relatively small fraction of individuals (5% of the total) at high risk for stroke or transient ischemic attack. Echo-Doppler might be used to monitor further disease progression and to evaluate the efficacy of different therapeutic or preventive interventions.
RECENT ADVANCES IN AUTOMATIC SPEECH SUMMARIZATION

Speech summarization technology, which extracts important information and removes irrelevant information from speech, is expected to play an important role in building speech archives and improving the efficiency of spoken document retrieval. However, speech summarization has a number of significant challenges that distinguish it from general text summarization. Fundamental problems with speech summarization include speech recognition errors, disfluencies, and difficulties of sentence segmentation. Typical speech summarization systems consist of speech recognition, sentence segmentation, sentence extraction, and sentence compaction components. Most of the research has focuses on sentence extraction, using LSA (latent semantic analysis), MMR (maximal marginal relevance), or feature-based approaches, among which no decisive method has yet been found. Proper sentence segmentation is also essential to achieve good summarization performance. How to objectively evaluate speech summarization results is an important issue. Several measures, including families of SumACCY and ROUGE measures, have been proposed, and correlation analyses between subjective and objective evaluation scores have been performed. Although these measures are useful for ranking various summarization methods, they do not correlate well with human evaluations, especially when spontaneous speech is targeted.
Learning to rank images using semantic and aesthetic labels

Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recentlypublished large-scale dataset and we show that the second family of techniques significantly outperforms the first one.