Miracle-dz/UniFAR-FTUs huggingface dataset:
The Taxonomic and Functional Diversity of Microbes at a Temperate Coastal Site: A ‘Multi-Omic’ Study of Seasonal and Diel Temporal Variation

How microbial communities change over time in response to the environment is poorly understood. Previously a six-year time series of 16S rRNA V6 data from the Western English Channel demonstrated robust seasonal structure within the bacterial community, with diversity negatively correlated with day-length. Here we determine whether metagenomes and metatranscriptomes follow similar patterns. We generated 16S rRNA datasets, metagenomes (1.2 GB) and metatranscriptomes (157 MB) for eight additional time points sampled in 2008, representing three seasons (Winter, Spring, Summer) and including day and night samples. This is the first microbial ‘multi-omic’ study to combine 16S rRNA amplicon sequencing with metagenomic and metatranscriptomic profiling. Five main conclusions can be drawn from analysis of these data: 1) Archaea follow the same seasonal patterns as Bacteria, but show lower relative diversity; 2) Higher 16S rRNA diversity also reflects a higher diversity of transcripts; 3) Diversity is highest in winter and at night; 4) Community-level changes in 16S-based diversity and metagenomic profiles are better explained by seasonal patterns (with samples closest in time being most similar), while metatranscriptomic profiles are better explained by diel patterns and shifts in particular categories (i.e., functional groups) of genes; 5) Changes in key genes occur among seasons and between day and night (i.e., photosynthesis); but these samples contain large numbers of orphan genes without known homologues and it is these unknown gene sets that appear to contribute most towards defining the differences observed between times. Despite the huge diversity of these microbial communities, there are clear signs of predictable patterns and detectable stability over time. Renewed and intensified efforts are required to reveal fundamental deterministic patterns in the most complex microbial communities. Further, the presence of a substantial proportion of orphan sequences underscores the need to determine the gene products of sequences with currently unknown function.
Network Coding-Aware Fair Opportunistic Scheduling in Lossy Wireless Networks

Users increasingly depend on WLAN for business and entertainment. It is well-recognized that wireless links are prone to errors. Previous work, ER, proposed to use network coding (NC) for providing more ef cient MAC-layer retransmission scheme in WLAN. However, it uses inef cient and costly reception report scheme and does not consider the effect of heterogenous and time-varying wireless conditions and fairness. These issues are critical for getting full bene ts of network coding. We show that, without addressing them, NC may even cause negative effect on the system. In this paper, we present a novel MAC-layer retransmission scheme, namely XORR, which uses reception estimation without extra overhead and adopts NC-aware opportunistic scheduling with maintaining temporal fairness in WLAN. We prove our NC-aware scheduling algorithm is fair and it will always improve the expected goodput for each wireless clients. We further verify XORR with extensive simulation as well as experiment studies and nd that our scheme outperforms traditional opportunistic scheduling (without NC) and 802.11 about 25% and 40%, respectively.
Routing and link scheduling with QoS in IEEE 802.16 mesh networks

Quality of service (QoS) in wireless mesh networks is an active area of research which is driven by the increasing demand for multimedia content delivered wirelessly. The IEEE 802.16 (WiMAX) standard identifies four classes of service targeting throughput, delay, and jitter. In this paper, we achieve two main objectives. (1) We present a routing metric sensitive to both interference and throughput which proves effective in QoS provisioning, a link scheduling algorithm that can easily accommodate all four classes of service by using the concept of feasible intervals, and an effective channel allocation algorithm inspired by a constraint programming heuristic. (2) We prove that the usual approach of constructing routing trees centred at the base station could lock significant network resources. By simply switching to a session based routing strategy, we illustrate that the acceptance ratio under a congested network can improve by more than 10% when compared to the tree based routing driven by our interference and throughput aware metric and by more than 25% when the routing tree is constructed with the metric sensitive to interference only.
PC-Based Prototyping of Audio Applications Using Model-Based Design

Personal computers (PCs) are increasingly being used as the primary development environment for creating, designing, and simulating audio algorithms and complete audio systems with live inputs from multichannel audio devices. In this paper, we show how audio algorithms can be designed and simulated in a textual and graphical modeling environment for a PC. These models interface with typical multichannel audio devices through PortAudio which enables communication with standard audio interfaces, such as Direct Sound, WDM-KS, and ASIO. The paper further delves into some typical challenges that engineers face when working with live multichannel audio algorithm simulation, namely, channel synchronization, dropped frames, and latency issues. We also demonstrate a technique for minimizing and measuring roundtrip latency. We used three sets of audio device hardware for our experiments—Behringer UCA202, M-Audio Delta 66, and M-Audio Firewire 410—to stream live audio through the simulation model. The example used throughout this paper is an automatic gain control algorithm which is modeled using a combination of Simulink, Signal Processing Blockset, Stateflow, and Embedded MATLAB code. Prototyping in this environment can enable designers to explore ideas and verify the correctness of their design early in the design cycle, thus reducing design iterations during the final stages where problems are typically more expensive to resolve.
Profiles Of and Correlations Among Mindset, Grit, and Optimism in Adolescents with Learning Disabilities: A Pilot Study

Despite increased interest in the role of “noncognitive skills” in educational contexts, there has been little exploration of their implications for youth with disabilities. We explored several constructs under the “noncognitive skills” umbrella using a survey comprised of items from the Implicit Theories of Intelligence Scale: General (Dweck, 2000) and Self-Theory (De Castella & Byrne, 2015); the Short Grit Scale (Duckworth & Quinn, 2009); and the Life Orientation Test – Revised (Scheier, Carver, & Bridges, 1994). We found notable correlations among scale scores, and preliminarily explored scale reliabilities with this population. Although sample size was small, these data are of value because they represent the first explicit exploration of these variables and their interrelationships in adolescents with learning disabilities.
An efficient method for real-time activity recognition

Real-time feature extraction is a key component for any action recognition system that claims to be truly real-time. In this paper we present a conceptually simple and computationally efficient method for real-time human activity recognition based on simple statistical features. Such features are very cheap to compute and form a relatively low dimensional feature space in which classification can be carried out robustly. On the Weizmann dataset, the proposed method achieves encouraging recognition results with an average rate up to 97.8%. These results are in a good agreement with the literature. Further, the method achieves real-time performance, and thus can offer timing guarantees to real-time applications.
Dynamic Pricing for Nonperishable Products with Demand Learning

A retailer is endowed with a finite inventory of a nonperishable product. Demand for this product is driven by a price-sensitive Poisson process that depends on an unknown parameter that is a proxy for the market size. The retailer has a prior belief on the value of this parameter that he updates as time and available information (prices and sales) evolve. The retailer's objective is to maximize the discounted long-term average profits of his operation using dynamic pricing policies. We consider two cases. In the first case, the retailer is constrained to sell the entire initial stock of the nonperishable product before a different assortment is considered. In the second case, the retailer is able to stop selling the nonperishable product at any time and switch to a different menu of products. For both cases, we formulate the retailer's problem as a (Poisson) intensity control problem and derive structural properties of an optimal solution, and suggest a simple and efficient approximated solution. We use numerical computations, together with asymptotic analysis, to evaluate the performance of our proposed policy.
Response of Woodpecker's Head during Pecking Process Simulated by Material Point Method

Prevention of brain injury in woodpeckers under high deceleration during the pecking process has been an intriguing biomechanical problem for a long time. Several studies have provided different explanations, but the function of the hyoid bone, one of the more interesting skeletal features of a woodpecker, still has not been fully explored. This paper studies the relationship between a woodpecker head’s response to impact and the hyoid bone. Based on micro-CT scanning images, the material point method (MPM) is employed to simulate woodpecker’s pecking process. The maximum shear stress in the brainstem (SSS) is adopted as an indicator of brain injury. The motion and deformation of the first cervical vertebra is found to be the main reason of the shear stress of the brain. Our study found that the existence of the hyoid bone reduces the SSS level, enhances the rigidity of the head, and suppresses the oscillation of the endoskeleton after impact. The mechanism is explained by a brief mechanical analysis while the influence of the material properties of the muscle is also discussed.
Application layer relays for wireless 802 . 11 mesh networks

Multi-hop wireless mesh networks have received considerable attention recently. The predominant network protocol used in such networks is likely to be TCP. However, the performance of TCP degrades quickly as the number of hops increases. How to improve the performance of TCP over multihop wireless mesh network, especially given the existing 802.11 MAC, has been an interesting research problem. Application layer relay method, which splits a traditional end-to-end TCP connection into several short hop connections, has been proposed as a solution in the wired world to improve the TCP performance. In this paper we investigate application layer relays in 802.11 wireless mesh networks and develop a model to analyze TCP performance in such networks. With the present of relays, the nodes in wireless networks act more independently and the round trip time is reduced also. However, relays also increase competition within the network. To reduce the competition, we propose a simple scheduling process to coordinate the relay nodes. The experiments show that relays with scheduling can achieve up to 50% performance gain in a 4-hop network.
Fault Detection and Identi cation in a Mobile Robot Using MultipleModel Estimation and Neural

We propose a method to detect and identify faults in wheeled mobile robots. The idea behind the method is to use adaptive estimation to predict the outcome of several faults, and to learn them collectively as a failure pattern. Models of the system behavior under each type of fault are embedded in multiple parallel Kalman Filter (KF) estimators. Each KF is tuned to a particular fault and predicts, using its embedded model, the expected values for the sensor readings. The residual, the diierence between the predicted readings (based on certain assumptions for the system model and the sensor models) and the actual sensor readings, is used as an indicator of how well each lter is performing. A backpropagation Neural Network processes this set of residuals as a pattern and decides which fault has occurred , that is, which lter is better tuned to the correct state of the mobile robot. The technique has been implemented on a physical robot and results from experiments are discussed.
Convex Approaches to Text Summarization

This dissertation presents techniques for the summarization and exploration of text documents. Many approaches taken towards analysis of news media can be analogized to well-defined, well-studied problems from statistical machine learning. The problem of feature selection, for classification and dimensionality reduction tasks, is formulated to help assist with these media analysis tasks. Taking advantage of e1 regularization, convex programs can be used to efficiently solve these feature selection problems efficiently. There is a demonstrated potential to conduct media analysis at a scale commensurate with the growing volume of data available to news consumers. There is first a presentation of an example text mining over a vector space model. Given two news articles on a related theme, a series of additional articles are pulled from a large pool of candidates to help link these two input items. The novel algorithm used is based on finding the documents whose vector representations are nearest the convex combinations of the inputs. Comparisons to competing algorithms show performance matching a state-of-the-art method, at a lower computational complexity. Design of a relational database for typical text mining tasks is discussed. The architecture trades off the organizational and data quality advantages of normalization versus the performance boosts from replicating entity attributes across tables. The vector space model of text is implemented explicitly as a three-column table. The predictive framework, connecting news analysis tasks to feature selection and classification problems, is then explicitly explored. The validity of this analogy is tested with a particular task: given a query term and a corpus of news articles, provide a short list of word tokens which distinguish how this word appears within the corpus. Example summary lists were produced by five algorithms, and presented to volunteer readers. Evidence suggests that an implementation of e1-regularized logistic regression model, trained over the documents with labels indicating the presence or absence of the query word, selected word-features best summarizing the query. To contend with tasks that do not lend themselves this a predictive framework, a sparse variant of latent semantic indexing is investigated. Sparse principal components were calculated to define corpora built of two distinct styles of news. The results are used to both summarize with word lists, as before, and extract documents representative of their larger collection. The work concludes with discussion of further study of the impact of the underlying vector space models of text on the resulting summaries, of real-world deployments of this kind of news analysis, and for distributed calculation of these results to support larger and larger corpora.
Chapter 6 Discours and Dialogue ( Taken from the Book : “ Survey of the state of the art in human language technology ” ) 6 . 1 Overview

Computational work in discourse has focused on two different types of discourse: extended texts and dialogues, both spoken and written. Although there are clear overlaps between these—dialogues contain text-like sequences spoken by a single individual and texts may contain dialogues—the current state of the art leads research to focus on different questions for each. In addition, application opportunities and needs are different. Work on text is of direct relevance to document analysis and retrieval applications, whereas work on dialogue is of import for human-computer interfaces regardless of the modality of interaction. A good sense of the current state of research in text interpretation can be gained from reading the papers on text interpretation published in a recent special issue of Artificial Intelligence (hereafter, AIJ-SI), (Hobbs, Stickel, et al., 1994; Jacobs & Rau, 1994; Palmer, Passonneau, et al., 1994).
Memorability in Narration: An Overview of Mnemonic Features in Oral and Written Tradition

How does narration effect memory? This paper investigates memorability in narratology, specifically in oral tradition. I will primarily investigate the effects of discourse and theme elements on human memory. Firstly, by understanding how memories are formed; secondly, by exploring how oral stories have been remembered and transmitted through long spans of time. Finally, I will briefly examine the effect of writing on narration. The investigation of narrative elements in the oral tradition and writing will then be used to illuminate what features make narration memorable. Modern narrative theories will also be discussed briefly to compare what features overlap with oral tradition. Primarily, the aim will be to understand the ways in which narratives enact on long term memory based on salient, emotive, and relatable attributes.DOI: http://dx.doi. org/10.5755/j01.sal. 32.0. 19186
A Viral Nanoparticle with Dual Function as an Anthrax Antitoxin and Vaccine

The recent use of Bacillus anthracis as a bioweapon has stimulated the search for novel antitoxins and vaccines that act rapidly and with minimal adverse effects. B. anthracis produces an AB-type toxin composed of the receptor-binding moiety protective antigen (PA) and the enzymatic moieties edema factor and lethal factor. PA is a key target for both antitoxin and vaccine development. We used the icosahedral insect virus Flock House virus as a platform to display 180 copies of the high affinity, PA-binding von Willebrand A domain of the ANTXR2 cellular receptor. The chimeric virus-like particles (VLPs) correctly displayed the receptor von Willebrand A domain on their surface and inhibited lethal toxin action in in vitro and in vivo models of anthrax intoxication. Moreover, VLPs complexed with PA elicited a potent toxin-neutralizing antibody response that protected rats from anthrax lethal toxin challenge after a single immunization without adjuvant. This recombinant VLP platform represents a novel and highly effective, dually-acting reagent for treatment and protection against anthrax.
An Empirical Study of Dimensional Reduction Techniques for Facial Action Units Detection

Biologically inspired features, such as Gabor filters, result in very high dimensional measurement. Does reducing the dimensionality of the feature space afford advantages beyond computational efficiency? Do some approaches to dimensionality reduction (DR) yield improved action unit detection? To answer these questions, we compared DR approaches in two relatively large databases of spontaneous facial behavior (45 participants in total with over 2 minutes of FACS-coded video per participant). Facial features were tracked and aligned using active appearance models (AAM). SIFT and Gabor features were extracted from local facial regions. We compared linear (PCA and KPCA), manifold (LPP and LLE), supervised (LDA and KDA) and hybrid approaches (LSDA) to DR with respect to AU detection. For further comparison, a no-DR control condition was included as well. Linear support vector machine classifiers with independent train and test sets were used for AU detection. AU detection was quantified using area under the ROC curve and F1. Baseline results for PCA with Gabor features were comparable with previous research. With some notable exceptions, DR improved AU detection relative to no-DR. Locality embedding approaches proved vulnerable to \emph{out-of-sample} problems. Gradient-based SIFT lead to better AU detection than the filter-based Gabor features. For area under the curve, few differences were found between linear and other DR approaches. For F1, results were mixed. For both metrics, the pattern of results varied among action units. These findings suggest that action unit detection may be optimized by using specific DR for specific action units. PCA and LDA were the most efficient approaches; KDA was the least efficient.
Empirical study of the influencing factors on work engagement of female entrepreneurs

With the rise and development of positive psychology, people concentrate more on their forte and positive psychological capacities. On this background, as a positive personal state, work engagement has become a new research hot spot in the fields of organizational behavior and human resource management. Based on previous study and interviews, this article takes female entrepreneurs as the research object and explores the influencing factors on work engagement of female entrepreneurs through quantitative study.
Foreign Language Enrollments in United States Institutions of Higher Education, Fall 2002

In September 2003 the MLA finished compiling the figures from its fall 2002 survey of foreign language enrollments in United States institutions of higher education. This latest survey is the twentieth in a series con ducted since 1958 with the support of grants from the United States De partment of Education (or from its predecessor, the United States Office of Education). The following report presents fall 2002 enrollments for indi vidual languages and examines trends through time. Using procedures developed for previous surveys, the MLA sent a questionnaire to the registrars of 2,781 twoand four-year institutions, soliciting information on credit-bearing enrollments for fall 2002 in all language courses other than English. Although the instructions on the questionnaire made it clear that the survey was seeking information on all language courses offered on the campuses of these institutions, the MLA has no way of knowing whether the registrars in all cases provided com plete information. The questionnaire was not mailed until mid-October 2002, to ensure that the figures provided would be final (or nearly so) rather than preliminary. A second mailing was sent in early December, a third in mid-February 2003, and a series of follow-up telephone calls was begun in April.
Towards Basque Oral Poetry Analysis: A Machine Learning Approach

This work aims to study the narrative structure of Basque greeting verses from a text classification approach. We propose a set of thematic categories for the correct classification of verses, and then, use those categories to analyse the verses based on Machine Learning techniques. Classification methods such as Naive Bayes, kNN, Support Vector Machines and Decision Tree Learner have been selected. Dimensionality reduction techniques have been applied in order to reduce the term space. The results shown by the experiments give an indication of the suitability of the proposed approach for the task at hands.
Diversity and Abundance of Woody Plant Species of Assosa Forest Field Gene Bank, Benishangul Gumuz Regional State, Western Ethiopia

The study was carried out to investigate the diversity and abundance of woody plant species of Assosa forest gene bank. An intensive inventory was made to collect the required data. Sample plot of size 20 × 20 m quadrant was used for this particular study. Sixteen quadrats of sample plot were systematically laid down with an interval of 100 m along four transect lines. Shannon-Wiener index was applied to quantify species diversity, richness, and evenness. Total of 44 woody plant species representing 26 families of were identified. Besides, from 44 identified woody plant species 938 number of individual plants were counted and recorded. Most of the woody plant species identified from the gene bank were belongs to Combretaceae family. The current study reveal that Assosa forest field gene bank has more diverse (H=3.49), unevenly distributed (E=0.0037) and unrelated abundance of all individuals of plant species. Keywords: Woody species, diversity, field gene bank, richness, evenness