Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of discourse data in language use research

Lachlan Mackenzie's current research " [. ] is being conducted with the framework of Functional Discourse Grammar (FDG). This is the most recent version of Functional Grammar (FG), the tradition of linguistic research initiated by Simon Dik (1940-1995). [. ] In the nineties, researchers felt increasing unease with the relation between the organization of the theory and its ambitions. This led to the emergence of Functional Discourse Grammar (FDG) [. ] " (Mackenzie, 2004). The move to discourse data can be considered as a distinctive trend of linguistic research in the past decade. Linguists of every persuasion, be they formal have publicly proclaimed their interest in discourse data. Such a move across the board shows that over the years, the notion of 'discourse' has become more and more important in linguistics, a discipline that used to deal exclusively with sentences in isolation and nowadays includes the study of form and meaning of utterances in context. Presently, formal and functional as well as cognitive approaches exist that consider the discourse level as the core object of study. There seems to be a consensus that what makes a set of utterances a discourse is primarily due to their meaning rather than to their form. More specifically, there is a shared belief that 'discoursehood' is based on the way in which the meanings of the discourse segments can be related to form a coherent message. The 'discoursehood' or coherence is a cognitive phenomenon rather than a textual phenomenon. A discourse is coherent among other things because referential and coherential links exist between the units of which the discourse is made up. And this creates an interesting issue for linguists, who traditionally are eager to rely on explicit linguistic information to base their analyses on, as these links need not be marked explicitly in the text. In his paper on the methodology of cognitive linguistics, Langacker (1999) takes as a starting point for linguistic research the analysis of explicit linguistic elements. This seems to put the analysis of typical discourse phenomena such as coherence relations outside of the realm of linguistics, by definition. Such a stance is at odds with the previously noted move to the discourse level and I believe that it is precipitate: Linguistic regularities behind coherence relations can and should be studied within linguistics. But at the same time I am convinced that the analysis of discourse coherence is in urgent need of reliable …
Large scale natural image classification by sparsity exploration

We consider in this paper the problem of large scale natural image classification. As the explosion and popularity of images in the Internet, there are increasing attentions to utilize millions of or even billions of these images for helping image related research. Beyond the opportunities brought by unlimited data, a great challenge is how to design more effective classification methods under these large scale scenarios. Most of existing attempts are based on k-nearest-neighbor method. However, in spite of the optimistic performance in some tasks, this strategy still suffers from that, one single fixed global parameter k is not robust for different object classes from different semantic levels. In this paper, we propose an alternative method, called ℓ1-nearest-neighbor, based on a sparse representation computed by ℓ1-minimization. We first treat a testing sample as a sparse linear combination of all training samples, and then consider the related samples as the nearest neighbors of the testing sample. Finally, we classify the testing sample based on the majority of these neighbors' classes. We conduct extensive experiments on a 1.6 million natural image database on different semantic levels defined based on WordNet, which demonstrate that the proposed ℓ1-nearest-neighbor algorithm outperforms k-nearest-neighbor in two aspects: 1) the robustness of parameter selection for different semantic levels, and 2) the discriminative capability for large scale image classification task.
A summary review of wireless sensors and sensor networks for structural health monitoring

In recent years, there has been an increasing interest in the adoption of emerging sensing technologies for instrumentation within a variety of structural systems. Wireless sensors and sensor networks are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered monitoring systems. A benefit of wireless structural monitoring systems is that they are inexpensive to install because extensive wiring is no longer required between sensors and the data acquisition system. Researchers are discovering that wireless sensors are an exciting technology that should not be viewed as simply a substitute for traditional tethered monitoring systems. Rather, wireless sensors can play greater roles in the processing of structural response data; this feature can be utilized to screen data for signs of structural damage. Also, wireless sensors have limitations that require novel system architectures and modes of operation. This paper is intended to serve as a summary review of the collective experience the structural engineering community has gained from the use of wireless sensors and sensor networks for monitoring structural performance and health.
Hermansky–Pudlak Syndrome and Related Disorders of Organelle Formation

Hermansky–Pudlak syndrome (HPS) consists of a group of genetically heterogeneous disorders which share the clinical findings of oculocutaneous albinism, a platelet storage pool deficiency, and some degree of ceroid lipofuscinosis. Related diseases share some of these findings and may exhibit other symptoms and signs but the underlying defect in the entire group of disorders involves defective intracellular vesicle formation, transport or fusion. Two HPS‐causing genes, HPS1 and ADTB3A, have been isolated but the function of only the latter has been determined. ADTB3A codes for the β3A subunit of adaptor complex‐3, responsible for vesicle formation from the trans‐Golgi network (TGN). The many HPS patients who do not have HPS1 or ADTB3A mutations have their disease because of mutations in other genes. Candidates for these HPS‐causing genes include those responsible for mouse models of HPS or for the ‘granule’ group of eye color genes in Drosophila. Each gene responsible for a subset of HPS or a related disorder codes for a protein which almost certainly plays a pivotal role in vesicular trafficking, inextricably linking clinical and cell biological interests in this group of diseases.
A clustering and opinion mining approach to socio-political analysis of the blogosphere

Blogosphere is the name associated to universe of all the blog sites. A blog is a website that allows people to write about topics they want to share with others. The ease & simplicity of creating blog posts and their free form and unedited nature have made blogging a happening thing. The blogosphere today contains a large number of posts on virtually every topic of interest. This rich and unique source of data has attracted people and companies across disciplines to exploit it for varied purposes. However, it not only provides a rich data source for commercial exploitation but also for psychological & socio-political analysis purposes. This paper tries to identify the methodology and technical framework required for an analytical experiment of this kind, and demonstrates the plausibility of this idea through our clustering & opinion mining experiment on analysis of blog posts about a recent socio-political issue.
POSBIOTM-NER: a trainable biomedical named-entity recognition system

SUMMARY POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus, GENE-NER based on BioCreative data and GPCR-NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction.
Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play

Interactions between natural selection and environmental change are well recognized and sit at the core of ecology and evolutionary biology. Reciprocal interactions between ecology and evolution, eco-evolutionary feedbacks, are less well studied, even though they may be critical for understanding the evolution of biological diversity, the structure of communities and the function of ecosystems. Eco-evolutionary feedbacks require that populations alter their environment (niche construction) and that those changes in the environment feed back to influence the subsequent evolution of the population. There is strong evidence that organisms influence their environment through predation, nutrient excretion and habitat modification, and that populations evolve in response to changes in their environment at time-scales congruent with ecological change (contemporary evolution). Here, we outline how the niche construction and contemporary evolution interact to alter the direction of evolution and the structure and function of communities and ecosystems. We then present five empirical systems that highlight important characteristics of eco-evolutionary feedbacks: rotifer–algae chemostats; alewife–zooplankton interactions in lakes; guppy life-history evolution and nutrient cycling in streams; avian seed predators and plants; and tree leaf chemistry and soil processes. The alewife–zooplankton system provides the most complete evidence for eco-evolutionary feedbacks, but other systems highlight the potential for eco-evolutionary feedbacks in a wide variety of natural systems.
Characterizing and Assessing a Large-Scale Software Maintenance Organization

One important component of a software process is the organizational context in which the process is enacted. This component is ofien missing or incomplete in current process modeling approaches. One technique for modeling this perspective is the Actor-Dependency (AD) Model. This paper reports on a case study which used this approach to analyze and assess a large software maintenance organization. Our goal was to identify the approach's strengths and weaknesses while providing practical recommendations for improvement and research directions. The AD model was found to be very useful in capturing the important properties of the organizational context of the maintenance process, and aided in the understanding of the flaws found in this process. However, a number of opportunities for extending and improving the AD model were identified. Among others, there is a need to incorporate quantitative information to complement the qualitative model.
Influences on the design of exception handling: ACM SIGSOFT project on the impact of software engineering research on programming language design

There has long been a close association between research in software engineering and the design of programming languages. Part of the IMPACT project involves an exploration of the interrelations of these two fields and documentation in a report of how fundamental research in software engineering has been a valuable resource for programming language features commonly used today. The resulting report investigates the relationship by considering features in currently used languages, including exceptions, control and data abstractions, types, inheritance, concurrency and visualization mechanisms.This paper, exerpted from the report, focuses on the influence of software engineering research on the development of exceptions. The paper demonstrates that there is a symbiotic relationship between software engineering research and the design of exception handing in programming languages. Publication of these partial results is aimed at soliciting feedback and comments from both the programming languages and software engineering communities.
Nonlinearity of hermetically encapsulated high-Q double balanced breathe-mode ring resonator

We present a hermetically encapsulated breathe-mode ring resonator with very high quality factor (∼473,000) and frequency at 10.65 MHz and 13.29 MHz. The resonator's nonlinear behavior is experimentally characterized and theoretically analyzed for the first time. The maximum current handling is measured and verified using two different methods. We find that material nonlinearity (nonlinear Young's modulus of silicon) limits the device's maximum output current. The theoretical analysis presented here is valid for any type of MEMS resonators, and the results provide guidance for future design of high current handling devices.
Particle swarm intelligence for channel assignment problem in mobile cellular communication system

This paper deals with the Channel Assignment Problem (CAP) in a hexagonal cellular network. The CAP in cellular network is known to belong to the class of Non Deterministic Polynomial (NP) complete optimisation problems. This problem is being solved by tools like simulated annealing, neural networks, Genetic Algorithm (GA) and other heuristic methods. In this paper, a Modified Particle Swarm Optimisation (MPSO) algorithm has been developed for CAP. The proposed approach mainly focuses on accelerating the convergence speed. The performance of the proposed approach is analysed by applying it to a well-known benchmark problem. The result of MPSO algorithm shows that the proposed technique is an attractive one than the other existing techniques.
Field strength measurements in vegetated residential environments

This paper investigates the propagation measurement conducted in a vegetated residential environment. In [1], the theoretical modeling of a propagation prediction model in vegetated residential environments was presented. The model extends the Walfisch-Bertoni urban and the COST-231 Walfisch-Ikegami model for application in vegetated residential environments. In this paper, we further verify the proposed propagation prediction model through various measurement campaigns at 860 and 3500 MHz at a vegetated rural residential area in Germany. With the aid of the high precision terrain data, predictions were compared with measurement data. The prediction from the proposed model in vegetated residential areas outperforms the COST-231 Walfisch-Ikegami model at 3500 MHz where no vegetation was considered.
DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number

Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some assumptions about the underlying data set. If the assumptions hold, good clusterings can be expected. It is hard, in some cases impossible, to satisfy all the assumptions. Therefore, it is beneficial to apply different clustering methods on the same data set, or the same method with varying input parameters or both. We propose a novel method, DICLENS, which combines a set of clusterings into a final clustering having better overall quality. Our method produces the final clustering automatically and does not take any input parameters, a feature missing in many existing algorithms. Extensive experimental studies on real, artificial, and gene expression data sets demonstrate that DICLENS produces very good quality clusterings in a short amount of time. DICLENS implementation runs on standard personal computers by being scalable, and by consuming very little memory and CPU.
Web server workload characterization: the search for invariants

The phenomenal growth in popularity of the World Wide Web (WWW, or the Web) has made WWW traffic the largest contributor to packet and byte traffic on the NSFNET backbone. This growth has triggered recent research aimed at reducing the volume of network traffic produced by Web clients and servers, by using caching, and reducing the latency for WWW users, by using improved protocols for Web interaction.Fundamental to the goal of improving WWW performance is an understanding of WWW workloads. This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in this study: three from academic (i.e., university) environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year of activity.Throughout the study, emphasis is placed on finding workload invariants: observations that apply across all the data sets studied. Ten invariants are identified. These invariants are deemed important since they (potentially) represent universal truths for all Internet Web servers. The paper concludes with a discussion of caching and performance issues, using the invariants to suggest performance enhancements that seem most promising for Internet Web servers.
Specific molecular mutation patterns delineate chronic neutrophilic leukemia, atypical chronic myeloid leukemia, and chronic myelomonocytic leukemia

Chronic neutrophilic leukemia (CNL) and atypical chronic myeloid leukemia (aCML) are rare entities grouped into the World Health Organisation (WHO) categories myeloproliferative and myelodysplastic/myeloproliferative neoplasms (MPN and MDS/MPN overlap), respectively. According to the WHO 2008
Dynamic-group scheduling scheme for maneuvering target tracking in wireless sensor networks

Limited resource allocation and scheduling are important problems in distributed wireless sensor networks (WSNs). Saving energy and real-time performance deserve research for low-cost sensor nodes in the target tracking application. In this paper, we propose a new dynamic-group idea and apply it to our scheduling scheme. An Extended Kalman Filter (EKF) is employed to predict tracking accuracy, and an energy model is adopted to predict the energy consumption. Simulation results show that, compared with the information-driven sensor quering (IDSQ) and the adaptive sensor scheduling strategy, the proposed scheme can achieve significant improvement on real-time property and tracking accuracy for target tracking in WSN. At the same time, it approaches to the adaptive sensor scheduling strategy in energy consumption.
Negative Mood and Obsessive-Compulsive Related Clinical Constructs: An Examination of Underlying Factors

Emerging evidence suggests that many of the clinical constructs used to help understand and explain obsessive-compulsive (OC) symptoms, and negative mood, may be causally interrelated. One approach to understanding this interrelatedness is a motivational systems approach. This approach suggests that rather than considering clinical constructs and negative affect as separable entities, they are all features of an integrated threat management system, and as such are highly coordinated and interdependent. The aim of the present study was to examine if clinical constructs related to OC symptoms and negative mood are best treated as separable or, alternatively, if these clinical constructs and negative mood are best seen as indicators of an underlying superordinate variable, as would be predicted by a motivational systems approach. A sample of 370 student participants completed measures of mood and the clinical constructs of inflated responsibility, intolerance of uncertainty, not just right experiences, and checking stop rules. An exploratory factor analysis suggested two plausible factor structures, one where all construct items and negative mood items loaded onto one underlying superordinate variable, and a second structure comprising of five factors, where each item loaded onto a factor representative of what the item was originally intended to measure. A confirmatory factor analysis showed that the five factor model was preferential to the one factor model, suggesting the four constructs and negative mood are best conceptualized as separate variables. Given the predictions of a motivational systems approach were not supported in the current study, other possible explanations for the causal interrelatedness between clinical constructs and negative mood are discussed.
Prediction of transcription regulatory sites in Archaea by a comparative genomic approach.

Intragenomic and intergenomic comparisons of upstream nucleotide sequences of archaeal genes were performed with the goal of predicting transcription regulatory sites (operators) and identifying likely regulons. Learning sets for the detection of regulatory sites were constructed using the available experimental data on archaeal transcription regulation or by analogy with known bacterial regulons, and further analysis was performed using iterative profile searches. The information content of the candidate signals detected by this method is insufficient for reliable predictions to be made. Therefore, this approach has to be complemented by examination of evolutionary conservation in different archaeal genomes. This combined strategy resulted in the prediction of a conserved heat shock regulon in all euryarchaea, a nitrogen fixation regulon in the methanogens Methanococcus jannaschii and Methanobacterium thermoautotrophicum and an aromatic amino acid regulon in M.thermoautotrophicum. Unexpectedly, the heat shock regulatory site was detected not only for genes that encode known chaperone proteins but also for archaeal histone genes. This suggests a possible function for archaeal histones in stress-related changes in DNA condensation. In addition, comparative analysis of the genomes of three Pyrococcus species resulted in the prediction of their purine metabolism and transport regulon. The results demonstrate the feasibility of prediction of at least some transcription regulatory sites by comparing poorly characterized prokaryotic genomes, particularly when several closely related genome sequences are available.
Sound, Music and Textual Associations on the World Wide Web

Sound files on the World Wide Web are accessed from web pages. To date, this relationship has not been explored extensively in the MIR literature. This paper details a series of experiments designed to measure the similarity between the public text visible on a web page and the linked sound files, the name of which is normally unseen by the user. A collection of web pages was retrieved from the web using a specially-constructed crawler. Sound file information and associated text were parsed from the pages and analyzed for similarity using common IR techniques such as TFIDF cosine measures. The results are intended to be used in the improvement of a web crawler for audio and music, as well as for MIR purposes in general.