Miracle-dz/UniFAR-FTUs huggingface dataset:
Mitochondrial DNA analysis of the bark beetle predator Thanasimus dubius F. (Coleoptera: Cleridae) reveals regional genetic differentiation

The checkered beetle, Thanasimus dubius F. , is an important predator of scolytid bark beetles that attack conifers. Relatively few studies exist that have addressed the population genetics of predatory beetles, especially those with potential as biological control agents. This study was conducted to investigate the population genetics of T. dubius across a large part of its range in the eastern United States. A 464‐base pair portion of the mitochondrial cytochrome c oxidase subunit I was sequenced for 85 individuals resulting in 60 haplotypes. Analysis of molecular variance was conducted on the resulting haplotypes for all populations and as a hierarchical analysis between populations defined as broad‐scale northern and southern groups. Results indicate a significant overall ΦST = 0.220 (P < 0.001) for all populations with the hierarchical analysis revealing that this significant ΦST is due to structuring of the populations between northern and southern regions (ΦCT = 0.388, P < 0.009). The observed genetic structure is possibly due to the discontinuous distribution of pine trees, which act as hosts for the prey of T. dubius, which has occurred historically in the central region of the United States that has been covered by prairie.
Agent based modeling and simulation for events hybrid recommendation: application to the handicraft domain

Recommending personalized events from the huge amount of information on the social web is a challenging problem. Besides, dealing with such recommendation improves significantly the professional communities activities. Likewise, it helps them to make appropriate decisions while saving time and efforts. In this paper, we propose a hybrid recommender system which suggests suitable events to the HanDicraft Women (HDWs) from Tunisia and Algeria. The established system considers the final user needs and demographic attributes. Indeed, it combines the knowledge-based and demographic approaches together. Useful information related to the HDWs and available events are represented through a semantic formalization: Friend Of A Friend (FOAF) Ontoloy and Online presence Ontology (OPO). Moreover, the recommender is able to manage the dynamicity and heterogeneity of the HDWs environments since it is based on a multi-agent architecture.
Subspace versus eigenmode quantization for limited feedback block-diagonalization

In this paper, we compare the performance of block-diagonalization and regularized block-diagonalization precoding, assuming quantized channel state information (CSI) at the transmitter as obtained from limited receiver feedback. We identify the necessary CSI for the calculation of the precoders and propose quantization strategies to efficiently represent the CSI in the corresponding Grassmannian and Stiefel manifolds. We evaluate the trade-off between quantizing only subspace information to facilitate block-diagonalization versus quantizing the magnitude and direction of the individual channel eigenmodes to enable precoder regularization, improving the performance at low SNR.
Cybernetics and Systems: An International Journal

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The work-shop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013.
Channel Estimation with Low-Precision Analog-to-Digital Conversion

We consider the problem of estimating the impulse response of a dispersive channel when the channel output is sampled using a low-precision analog-to-digital converter (ADC). While traditional channel estimation techniques require about 6 bits of ADC precision to approach full-precision performance, we are motivated by applications to multiGigabit communication, where we may be forced to use much lower precision (e.g., 1-3 bits) due to considerations of cost, power, and technological feasibility. We show that, even with such low ADC precision, it is possible to attain near full-precision performance using closed-loop estimation, where the ADC input is dithered and scaled. The dither signal is obtained using linear feedback based on the Minimum Mean Squared Error (MMSE) criterion. The dither feedback coefficients and the scaling gains are computed offline using Monte Carlo simulations based on a statistical model for the channel taps, and are found to work well over wide range of channel variations.
Intubation in routine dacryocystorhinostomy: why we do what we do

The role of intubation in routine dacryocystorhinostomy (DCR) surgery is unclear, with there being little evidence in favour of this practice, which appears to have largely come about as a result of history, anecdote and the evolution of DCR surgery. In this paper, a brief history of intubation in DCR surgery is presented and the evidence base is analysed.
Multilevel Full-Chip Gridless Routing With Applications to Optical-Proximity Correction

To handle modern routing with nanometer effects, we need to consider designs with variable wire/via widths and spacings, for which gridless-routing approaches are desirable due to its great flexibility. In this paper, we introduce a gridless-routing model that can obtain design-rule-correct paths and avoid redundant wires. Besides, we propose an enhanced model for the gridless-routing model to reduce the solution space and the runtime. Based on the enhanced gridless-routing model, we present the first multilevel full-chip gridless detailed router (called MGR). The router integrates global routing, detailed routing, and congestion estimation together at each level of multilevel routing. It can handle designs with nonuniform wire/via widths and spacings and consider routability and optical-proximity correction. Experimental results show that MGR achieves the best routing solutions in smaller running times than previous works, based on a set of commonly used benchmarks (with uniform and nonuniform wire widths) and a set of real industrial benchmarks (with a versatile set of design rules)
Lateral entorhinal neurons are not spatially selective in cue‐rich environments

The hippocampus is a brain region that is critical for spatial learning, context‐dependent memory, and episodic memory. It receives major inputs from the medial entorhinal cortex (MEC) and the lateral EC (LEC). MEC neurons show much greater spatial firing than LEC neurons in a recording chamber with a single, salient landmark. The MEC cells are thought to derive their spatial tuning through path integration, which permits spatially selective firing in such a cue‐deprived environment. In accordance with theories that postulate two spatial mapping systems that provide input to the hippocampus—an internal, path‐integration system and an external, landmark‐based system—it was possible that LEC neurons can also convey a spatial signal, but that the signal requires multiple landmarks to define locations, rather than movement integration. To test this hypothesis, neurons from the MEC and LEC were recorded as rats foraged for food in cue‐rich environments. In both environments, LEC neurons showed little spatial specificity, whereas many MEC neurons showed a robust spatial signal. These data strongly support the notion that the MEC and LEC convey fundamentally different types of information to the hippocampus, in terms of their spatial firing characteristics, under various environmental and behavioral conditions. © 2010 Wiley Periodicals, Inc.
Reconfigurable architectures for bio-sequence database scanning on FPGAs

Protein sequences with unknown functionality are often compared to a set of known sequences to detect functional similarities. Efficient dynamic-programming algorithms exist for solving this problem, however current solutions still require significant scan times. These scan time requirements are likely to become even more severe due to the rapid growth in the size of these databases. In this paper, we present a new approach to bio-sequence database scanning using re-configurable field-programmable gate array (FPGA)-based hardware platforms to gain high performance at low cost. Efficient mappings of the Smith-Waterman algorithm using fine-grained parallel processing elements (PEs) that are tailored toward the parameters of a query have been designed. We use customization opportunities available at run time to dynamically reconfigure the PEs to make better use of available resources. Our FPGA implementation achieves a speedup of approximately 170 for linear gap penalties and 125 for affine gap penalties compared to a standard desktop computing platform. We show how run-time reconfiguration can be used to further improve performance.
Interaction of Mitogen-activated Protein Kinases with the Kinase Interaction Motif of the Tyrosine Phosphatase PTP-SL Provides Substrate Specificity and Retains ERK2 in the Cytoplasm*

ERK1 and ERK2 associate with the tyrosine phosphatase PTP-SL through a kinase interaction motif (KIM) located in the juxtamembrane region of PTP-SL. A glutathioneS-transferase (GST)-PTP-SL fusion protein containing the KIM associated with ERK1 and ERK2 as well as with p38/HOG, but not with the related JNK1 kinase or with protein kinase A or C. Accordingly, ERK2 showed in vitro substrate specificity to phosphorylate GST-PTP-SL in comparison with GST-c-Jun. Furthermore, tyrosine dephosphorylation of ERK2 by the PTP-SLΔKIM mutant was impaired. Thein vitro association of ERK1/2 with GST-PTP-SL was highly stable; however, low concentrations of nucleotides partially dissociated the ERK1/2·PTP-SL complex. Partial deletions of the KIM abrogated the association of PTP-SL with ERK1/2, indicating that KIM integrity is required for interaction. Amino acid substitution analysis revealed that Arg and Leu residues within the KIM are essential for the interaction and suggested a regulatory role for Ser231. Finally, coexpression of PTP-SL and ERK2 in COS-7 cells resulted in the retention of ERK2 in the cytoplasm in a KIM-dependent manner. Our results demonstrate that the noncatalytic region of PTP-SL associates with mitogen-activated protein kinases with high affinity and specificity, providing a mechanism for substrate specificity, and suggest a role for PTP-SL in the regulation of mitogen-activated protein kinase translocation to the nucleus upon activation.
Content-Based Image Retrieval : A Comprehensive Study

Learning efficient options illustrations and equivalency metric measures are imperative to the searching performance of a content-based image retrieval (CBIR) machine. Despite in depth analysis efforts for many years, it remains one amongst the foremost difficult open issues that significantly hinders the success of real- world CBIR systems. The key issue has been associated to the commonly known “linguistic gap” problem that exists between low-level image pixels captured by machines and high-level linguistics ideas perceived by humans. Among varied techniques, machine learning has been actively investigated as a potential direction to bridge the linguistics gap in the long run. Motivated by recent success of deep learning techniques for computer vision and other applications, In this paper, we'll conceive to address an open problem: if deep learning could be a hope for bridging the linguistics gap in CBIR and the way a lot of enhancements in CBIR tasks may be achieved by exploring the progressive deep learning methodologies for learning options illustrations and equivalency measures. Speci? cally, we'll investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a progressive deep learning technique (Convolutional Neural Networks) for CBIR tasks in varied settings. From our empirical studies, we found some encouraging results and summarized some vital insights for future analysis. CBIR tasks may be achieved by exploring the progressive deep learning techniques for learning options illustrations and equivalency measures.
Wikilinks: A Large-scale Cross-Document Coreference Corpus Labeled via Links to Wikipedia

Cross-document coreference resolution is the task of grouping the entity mentions in a collection of documents into sets that each represent a distinct entity. It is central to knowledge base construction and also useful for joint inference with other NLP components. Obtaining large, organic labeled datasets for training and testing cross-document coreference has previously been difficult. This paper presents a method for automatically gathering massive amounts of naturally-occurring cross-document reference data. We also present the Wikilinks dataset comprising of 40 million mentions over 3 million entities, gathered using this method. Our method is based on finding hyperlinks to Wikipedia from a web crawl and using anchor text as mentions. In addition to providing large-scale labeled data without human effort, we are able to include many styles of text beyond newswire and many entity types beyond people.
Confounding and misrounding in age-period-cohort models

A natural starting point for an age-period-cohort analysis is to assess the suitability of an independence model. If necessary, modifications are then made to the independence model to account for the effects of period and cohort. Data are usually made available for analysis in a two-way contingency table categorised in terms of rounded age and rounded period. The linear relationship age=period-cohort does not hold exactly under rounding and the observation in a cell rounded age-by-period is not necessarily the same as an observation in a cell rounded age-by-cohort. In practice, independence models are discretised incorrectly such that the age-by-period data are used for model fitting as if the data are rounded age-by-cohort. The independence model is often deemed to be unsuitable as a description of the data and modifications are made to the independence model according to a proportional hazards assumption. We investigate whether the need for modifications is only apparent due to the misrounded treatment of the data. The case of Bovine Spongiform Encephalopathy is used as an illustrative example.
Insulin Resistance Syndrome

Insulin resistance syndrome, also referred to as the metabolic syndrome, affects 1 in 3 to 4 adults older than 20 years. This syndrome consists of a clustering of metabolic abnormalities that put people at risk for type 2 diabetes and cardiovascular disease. These clinical abnormalities include dyslipidemia, specifically elevated triglycerides and low high-density lipoprotein cholesterol, elevated glucose, and hypertension. The incidence of this morbid syndrome is expected to continue to grow both in the United States and worldwide, and thus is of tremendous interest to nurses seeking to measure their impact on patient outcomes. The key lifestyle interventions essential to treating this syndrome are weight loss and physical activity. The purpose of this article is to (1) describe the insulin resistance syndrome and discuss the current focuses for inquiry in major outcome areas (eg, mortality, morbidity, costs); (2) describe the status of specific lifestyle interventions (weight loss, diet, and exercise); (3) identify outcomes that nurses could measure to assess their impact on patient care; and (4) identify areas for future nursing research.
Implications of Spatial Habitat Diversity on Diet Selection of European Bison and Przewalski´s Horses in a Rewilding Area

In Europe, the interest in introducing megaherbivores to achieve ambitious habitat restoration goals is increasing. In this study, we present the results of a one-year monitoring program in a rewilding project in Germany (Doeberitzer Heide), where European bison (Bison bonasus) and Przewalski´s horses (Equus ferus przewalskii) were introduced for ecological restoration purposes. Our objectives were to investigate diet and habitat preferences of Przewalski´s horses and European bison under free-choice conditions without fodder supplementation. In a random forest classification approach, we used multitemporal RapidEye time series imagery to map the diversity of available habitats within the study area. This spatially explicit habitat distribution from satellite imagery was combined with direct field observations of seasonal diet preferences of both species. In line with the availability of preferred forage plants, European bison and Przewalski´s horses both showed seasonal habitat preferences. Because of their different preferences for forage plants, they did not overlap in habitat use except for a short time in the colder season. European bison used open habitats and especially wet open habitats more than expected based on available habitats in the study area. Comparative foraging and feeding niches should be considered in the establishment of multispecies projects to maximize the outcome of restoration processes.
An Automatic Infants Cry Detection Using Linear Frequency Cepstrum Coefficients(LFCC)

In this paper, we mainly focused on automation of Infant's Cry. For this implementation we use LFCC for feature extraction and VQ codebook for matching samples using LBG algorithm. The baby crying samples collected from various crying baby having 0-6months age. There are 150 baby's sound as training data, each of which represents the 30 hungry infant cries, 30 sleepy infant cries, 30 wanted to burp infant cries, 30 in pain infant cries, and 30 uncomfortable infant cries (could be because his/her diaper is wet/too hot/cold air or anything else). The testing data is 40, respectively 8 infant cries for each type of infant cry. The identification of infant cries based the minimum distance of Euclidean distance. The, classification of the cry in five classes neh - hunger owh - sleepy, heh - discomfort ,eair - lower gas, eh - burp.Here for classification of the cry our system is divided into two phases. First, in training phase, in which LFCC is applied for feature extraction, and then VQ codebooks are generated to compress the feature vectors. Second, is the testing phase in which features extraction and codebook generation of samples are repeated. Here, comparison of the codebook template of samples to the all the available templates in the database are carried based on Euclidian distance between them. LFCC effectively capture the lower as well as higher frequency characteristics than MFCC, hence we will get good results over MFCC.
Improvement of Renamed Trace Cache through the Reduction of Dependent Path Length for High Energy Efficiency

SUMMARY Out-of-order superscalar processors rename register numbers to remove false dependencies between instructions. A renaming logic for register renaming is a high-cost module in a superscalar processor, and it consumes considerable energy. A renamed trace cache (RTC) was proposed for reducing the energy consumption of a renaming logic. An RTC caches and reuses renamed operands, and thus, register renaming can be omitted on RTC hits. However, conventional RTCs su ﬀ er from several performance, energy consumption, and hardware overhead problems. We propose a semi-global renamed trace cache (SGRTC) that caches only re-named operands that are short distance from producers outside traces, and solves the problems of conventional RTCs. Evaluation results show that SGRTC achieves 64% lower energy consumption for renaming with a 0.2% performance overhead as compared to a conventional processor.
Evaluating feature types for encoding clinical notes

This paper proposes a machine learning approach to the task of assigning the international standard on classification of diseases ICD-9-CM codes to clinical records. By treating the task as a text categorisation problem, a classification system was built which explores a variety of features including negation, different strategies of measuring gloss overlaps between the content of clinical records and ICD-9-CM code descriptions together with expansion of the glosses from the ICD-9-CM hierarchy. The best classifier achieved an overall F1 value of 88.2 on a data set of 978 free text clinical records, and was better than the performance of two out of three human annotators.
Obstructive Sleep Apnea Syndrome analysis in children by Decreases in the Amplitude Fluctuations of Pulse Photoplethysmography: role of recording duration and Heart Rate Variability

Heart rate variability (HRV) during decreases in the amplitude fluctuations of pulse photopletysmography signal events (DAP) is studied. The DAP duration and the time span of the polysomnography (PSG) recording under study are also considered for identification of obstructive sleep apnea events in children. Oxygen desaturation is used as reference. The study involves recordings of 21 children. Periods of time associated with a drop in SaO2 of 3% from baseline during at least 5% of the time were labeled as pathologic. DAP events were detected using an automatic detector, HRV signal processing using the Smooth pseudo Wigner-Ville distribution was carried out in order to obtain several time and frequency HRV indexes. T-test was used to analyze differences between groups. The results show discrimination power between groups that increases as the duration of the time period under analysis, becomes shorter, and so more tune to the apneic episodes. Also HRV increase the discrimination power helping in discriminating DAP event non related to apnea.
Designing digital games for rural children: a study of traditional village games in India

Low educational levels hinder economic empowerment in developing countries. We make the case that educational games can impact children in the developing world. We report on exploratory studies with three communities in North and South India to show some problems with digital games that fail to match rural children's understanding of games, to highlight that there is much for us to learn about designing games that are culturally meaningful to them. We describe 28 traditional village games that they play, based on our contextual interviews. We analyze the mechanics in these games and compare these mechanics against existing videogames to show what makes traditional games unique. Our analysis has helped us to interpret the playability issues that we observed in our exploratory studies, and informed the design of a new videogame that rural children found to be more intuitive and engaging.