Miracle-dz/UniFAR-FTUs huggingface dataset:
Redefinition and global estimation of basal ecosystem respiration rate

Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site‐years of eddy covariance data, from 79 research sites located at latitudes ranging from ∼3°S to ∼70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site‐specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC‐LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective‐Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr −1, with the highest respiration rate over tropical forests and the lowest value in dry and high‐latitude areas.
Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences

Apparent personality analysis (APA) is an important problem of personality computing, and furthermore, automatic APA becomes a hot and challenging topic in computer vision and multimedia. In this paper, we propose a deep learning solution to APA from short video sequences. In order to capture rich information from both the visual and audio modality of videos, we tackle these tasks with our Deep Bimodal Regression (DBR) framework. In DBR, for the visual modality, we modify the traditional convolutional neural networks for exploiting important visual cues. In addition, taking into account the model efficiency, we extract audio representations and build a linear regressor for the audio modality. For combining the complementary information from the two modalities, we ensemble these predicted regression scores by both early fusion and late fusion. Finally, based on the proposed framework, we come up with a solution for the Apparent Personality Analysis competition track in the ChaLearn Looking at People challenge in association with ECCV 2016. Our DBR is the winner (first place) of this challenge with 86 registered participants. Beyond the competition, we further investigate the performance of different loss functions in our visual models, and prove non-convex loss functions for regression are optimal on the human-labeled video data.
Visualization and Enumeration of Marine Planktonic Archaea and Bacteria by Using Polyribonucleotide Probes and Fluorescent In Situ Hybridization

ABSTRACT Fluorescent in situ hybridization (FISH) using rRNA-specific oligonucleotide probes has emerged as a popular technique for identifying individual microbial cells. In natural samples, however, the signal derived from fluor-labeled oligonucleotide probes often is undetectable above background fluorescence in many cells. To circumvent this difficulty, we applied fluorochrome-labeled polyribonucleotide probes to identify and enumerate marine planktonic archaea and bacteria. The approach greatly enhanced the sensitivity and applicability of FISH with seawater samples, allowing confident identification and enumeration of planktonic cells to ocean depths of 3,400 m. Quantitative whole-cell hybridization experiments using these probes accounted for 90 to 100% of the total 4′,6-diamidino-2-phenylindole (DAPI)-stained cells in most samples. As predicted in a previous study (R. Massana, A. E. Murray, C. M. Preston, and E. F. DeLong, Appl. Environ. Microbiol. 63:50–56, 1997), group I and II marine archaea predominate in different zones in the water column, with maximal cell densities of 105/ml. The high cell densities of archaea, extending from surface waters to abyssal depths, suggest that they represent a large and significant fraction of the total picoplankton biomass in coastal ocean waters. The data also show that the vast majority of planktonic prokaryotes contain significant numbers of ribosomes, rendering them easily detectable with polyribonucleotide probes. These results imply that the majority of planktonic cells visualized by DAPI do not represent lysed cells or “ghosts,” as was suggested in a previous report.
Parallel Computing Applied to the Dynamics of One-Dimensional Cellular Automata

Parallel computing is becoming increasingly important for solving technological and scientific problems. In fact, the most powerful computers in the world are now parallel machines. In this paper we utilize a low-cost parallel computing environment, based on the UNIX operating system and the PVM (Parallel Virtual Machine) and MPI (Message Passing Interface) communication protocols, to compute the dynamical evolution of one-dimensional cellular automata with large numbers of sites. Specifically, we develop and validate parallel binary PVM and MPI implementations, analyse their parallel performance measures, and use the relatively faster MPI implementation to obtain some results for cellular automata with large numbers of sites. Key-Words: Parallel computing, PVM, MPI, cellular automata, complex systems.
Learning Human-Like Color Categorization through Interaction

— Human perceives color in categories, which may be identified using color name such as red, blue, etc. The categorization is unique for each human being. However despite the individual differences, the categorization is shared among members in society. This allows communication among them, especially when using color name. Sociable robot, to live coexist with human and become part of human society, must also have the shared color categorization, which can be achieved through learning. Many works have been done to enable computer, as brain of robot, to learn color categorization. Most of them rely on modeling of human color perception and mathematical complexities. Differently, in this work, the computer learns color categorization through interaction with humans. This work aims at developing the innate ability of the computer to learn the human-like color categorization. It focuses on the representation of color categorization and how it is built and developed without much mathematical complexity.
Exploring the link between initial and final diagnosis in a medical intelligent tutoring system

A constant topic in medical education is clinical reasoning: how do learners solve cases? Learner interactions with Intelligent Tutoring Systems yield fine-grained data that are useful in generating meaningful information and illuminating understanding about learner behaviors and outcomes. We examine and analyze the log files generated by BioWorld, an Intelligent Tutoring System for the medical domain. More specifically, to further our understanding of the nature of reasoning employed by learners while solving virtual patient cases in BioWorld, one important step is to examine the initial list of selected diagnostic hypotheses before any other learner action is taken in diagnosing a case. By exploring the link between initial selected hypotheses and final submitted hypothesis, a better understanding of the learners' reasoning might be achieved.
Evaluating the Uncanny Valley

Over the last decade, research has been conducted on the potential of androids to evoke uncanny sensations in humans (the so-called uncanny valley phenomenon): Experiments focussing on robot appearance have shown that static images of androids can avoid uncanniness if they are designed to be aesthetically pleasing. However, the appeal of such androids may only be achieved at the expense of physical realism. Concerning robot movement and behaviour, experiments suggest that androids may not be accepted if their behaviour fails to live up to the level of human likeness purported by their appearance. Movements which are reminiscent of those of diseased human beings may elicit strong feelings of eeriness. Exposure to eerie images of androids elicits unconscious defence mechanism usually connected to thoughts of death. However, androids are not genuine reminders of death or mortality. The experiments we discussed seem to suggest that a closer examination of roots of the eerie sensation is imperative.
Predicting Missing Music Components with Bidirectional Long Short-Term Memory Neural Networks

Successfully predicting missing components (entire parts or voices) from complex multipart musical textures has attracted researchers of music information retrieval and music theory. However, these applications were limited to either two-part melody and accompaniment (MA) textures or four-part Soprano-Alto-Tenor-Bass (SATB) textures. This paper proposes a robust framework applicable to both textures using a Bidirectional Long-Short Term Memory (BLSTM) recurrent neural network. The BLSTM system was evaluated using frame-wise accuracies on the Nottingham Folk Song dataset and J. S. Bach Chorales. Experimental results demonstrated that adding bidirectional links to the neural network improves prediction accuracy by 3% on average. Speciﬁcally, BLSTM out-performs other neural-network based methods by 4.6% on average for four-part SATB and two-part MA textures (employing a transition matrix). The high accuracies obtained with BLSTM on both two-part and four-part textures indicated that BLSTM is the most robust and applicable structure for predicting missing components from multi-part musical textures.
A Hyper-heuristic approach towards mitigating Premature Convergence caused by the objective fitness function in GP

This manuscript proposes a hyper-heuristic approach towards mitigating Premature Convergence caused by objective fitness in Genetic Programming (GP). The objective fitness function used in standard GP has the potential to profoundly exacerbate Premature Convergence in the algorithm. Accordingly several alternative fitness measures have been proposed in GP literature. These alternative fitness measures replace the objective function, with the specific aim of mitigating this type of Premature Convergence. However each alternative fitness measure is found to have its own intrinsic limitations. To this end the proposed approach automates the selection of distinct fitness measures during the progression of GP. The power of this methodology lies in the ability to compensate for the weaknesses of each fitness measure by automating the selection of the best alternative fitness measure. Our hyper-heuristic approach is found to achieve generality in the alleviation of Premature Convergence caused by objective fitness. Vitally the approach is unprecedented and highlights a new paradigm in the design of GP systems.
Channel Measurement Based Antenna Synthesis for Mobile Automotive MIMO Communication Systems

In this paper a multiple input multiple output (MIMO) radio channel measurement system is presented that utilizes several software defined radio (SDR) platforms at the transmitter and at the receiver. The system hardware buildup and its calibration technique are presented. The channel measurement results are afterwards exploited for a special antenna synthesis method that was already proofed by raytracing channel simulations. The antenna synthesis method is applied to a mobile single and to a mobile multiple channel receiver. The resulting synthesized antenna systems are evaluated in terms of antenna radiation patterns and the corresponding channel capacities. The results reveal the superiority of synthesized antenna systems compared to conventional omnidirectional antenna systems in the considered urban street scenario. Moreover, the findings from the antenna synthesis based on dynamically measured MIMO radio channels confirm the results from the raytracing channel simulation based antenna synthesis.
Small Differences in Experience Bring Large Differences in Performance

In many life situations, people choose sequentially between repeating a past action in expectation of a familiar outcome (exploitation), or choosing a novel action whose outcome is largely uncertain (exploration). For instance, in each quarter, a manager can budget advertising for an existing product, earning a predictable boost in sales. Or she can spend to develop a completely new product, whose prospects are more ambiguous. Such decisions are central to economics, psychology, business, and innovation; and they have been studied mostly by modelling in agent-based simulations or examining statistical relationships in archival or survey data. Using experiments across cultures, we add unique evidence about causality and variations. We find that exploration is boosted by three past experiences: When decision-makers fall below top performance; undergo performance stability; or suffer low overall performance. In contrast, individual-level variables, including risk and ambiguity preferences, are poor predictors of exploration. The results provide insights into how decisions are made, substantiating the micro-foundations of strategy and assisting in balancing exploration with exploitation.
The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function

Platelets in the circulation are triggered by vascular damage to activate, aggregate and form a thrombus that prevents excessive blood loss. Platelet activation is stringently regulated by intracellular signalling cascades, which when activated inappropriately lead to myocardial infarction and stroke. Strategies to address platelet dysfunction have included proteomics approaches which have lead to the discovery of a number of novel regulatory proteins of potential therapeutic value. Global analysis of platelet proteomes may enhance the outcome of these studies by arranging this information in a contextual manner that recapitulates established signalling complexes and predicts novel regulatory processes. Platelet signalling networks have already begun to be exploited with interrogation of protein datasets using in silico methodologies that locate functionally feasible protein clusters for subsequent biochemical validation. Characterization of these biological systems through analysis of spatial and temporal organization of component proteins is developing alongside advances in the proteomics field. This focused review highlights advances in platelet proteomics data mining approaches that complement the emerging systems biology field. We have also highlighted nucleated cell types as key examples that can inform platelet research. Therapeutic translation of these modern approaches to understanding platelet regulatory mechanisms will enable the development of novel anti‐thrombotic strategies.
Beneficial Effect of β-Mannanase Feed Enzyme on Performance of Chicks Challenged with Eimeria sp. and Clostridium perfringens

Abstract Two experiments were conducted to determine the effect of a β-mannanase feed enzyme on the performance of broiler chicks subject to a necrotic enteritis disease challenge model involving oral inoculation of Eimeria sp. and Clostridium perfringens. β-Mannanase is known to improve productive performance when added to poultry and swine diets. In both experiments, disease challenge in the absence of feed additives demonstrated significant reductions in performance as measured by weight gain, feed conversion, and the incidence of coccidial lesion scores. Significant mortality was also observed in challenged groups in Experiment 1. The disease challenge model was therefore judged as highly effective. Additions of a commonly used antibiotic, bacitracin methylene disalicilate (BMD), and coccidiostat, salinomycin, were highly effective in partially counteracting negative effects of the disease challenge. In both experiments, addition of β-mannanase significantly improved performance and reduced lesion scores in disease-challenged groups. The degree of improvement was somewhat less than that afforded by a combination of BMD and salinomycin in Experiment 1 but was not different from that afforded by BMD alone in Experiment 2. We conclude that the β-mannanase enzyme can play a role in circumstances where the use of antibiotics is not desired.
The Role of Biogenic Amines in the Regulation of Interaction between the Immune and Nervous Systems in Multiple Sclerosis

Objective: Multiple sclerosis (MS) is a demyelinating, presumably autoimmune disease of the central nervous system. Biogenic amines may participate in MS pathogenesis modulating immune cell activity and cytokine production. Methods: Forty-three patients with relapsing-remitting MS were examined. Serotonin (SE), norepinephrine (NE) and epinephrine (EPI) concentrations in sera were measured by ELISA. The functional activity of Th17 and Th1 cells was assessed by the ability of peripheral blood mononuclear cells (PBMCs) to produce interferon-gamma (IFN-γ) and interleukin-17 (IL-17) and cell proliferation upon stimulation with microbeads coated with anti-CD3 and anti-CD28 antibodies. To evaluate the effect of biogenic amines on Th17 and Th1 cells, PBMCs were cultured in the presence of SE and NE. Statistical analysis was performed using Prism 6 software. Results: Concentrations of SE and EPI in sera were not different between the groups. Concentrations of NE in sera from MS patients were lower than those in the healthy control group. The production of IL-17 and IFN-γ in MS patients in relapse was higher than that in patients in remission or in the control group. SE at a concentration of 10-4M suppressed IL-17 production. NE at a concentration of 10-4M suppressed both IL-17 and IFN-γ production. Conclusions: These data suggest an anti-inflammatory role for biogenic amines in MS.
QoS Scheduling Algorithm Based on Hybrid Particle Swarm Optimization Strategy for Grid Workflow

The service oriented grid workflow has been a research focus in grid technology. As an NP complete problem, grid service scheduling is difficult to solve by means of classic algorithms This paper presents an algorithm HPSOA (hybrid particle swarm optimization algorithm) to resolve dynamic Web services selection with QoS global optimal in grid workflow.The essence of the algorithm is that the problem of dynamic Web Service selection with QoS global optimal is transformed into a multi-objective services composition optimization with QoS constraints. The operations of the cross and mutation in genetic algorithm are brought into PSOA (particle swarm optimization algorithm) to form a mix algorithm, which called HPSOA solve the QoS global optimal problem. Theoretical analysis and experimental results indicate the feasibility and efficiency of the algorithm.
Making Sense of Loss: A Content Analysis of End-of-Life Practitioners' Therapeutic Approaches

Clinical professionals working in end-of-life (EOL) contexts are frequently relied upon to address questions of meaning with dying and bereaved persons. Similar to the gulf between researchers and practitioners besetting the larger healthcare community, the voices of EOL practitioners are often underrepresented in the empirical literature. This study aimed to further the dialogue in the field of thanatology by surveying and describing the therapeutic approaches that EOL practitioners most commonly report using to facilitate meaning-making. A total of 119 practitioners from a range of EOL disciplines were surveyed to write about their intervention strategies for helping clients/patients make sense of loss. Overall, participants discussed using 23 different therapeutic approaches that comprised three overarching categories: 1) presence of the helping professional; 2) elements of the process; and 3) therapeutic procedures. Importantly, the results also indicated that practitioners from the different EOL occupations are converging on many of the same strategies for promoting meaning-making. Implications for future research on evaluating the effectiveness of meaning-making interventions are also discussed.
Wavelet analysis of surface electromyography

Muscle fatigue is often a result of unhealthy work practice. It has been known for some time that there is a significant change in the spectrum of the electromyography (EMG) of the muscle when it is fatigued. Due to the very complex nature of this signal however, it has been difficult to use this information to reliably automate the process of fatigue onset determination. If such a process implementation were feasible, it could be used as an indicator to reduce the chances of work-place injury. This research report on the effectiveness of the wavelet transform applied to the EMG signal as a means of identifying muscle fatigue. We report that with the appropriate choice of wavelet functions and scaling factors, it is possible to achieve reliable discrimination of the fatigue phenomenon, appropriate to an automated fatigue identification system.
Radio Resource Allocation for D2D-Enabled Massive Machine Communication in the 5G Era

Device-to-Device (D2D) and Massive Machine Communication (MMC) are believed to be the cornerstones of future 5th generation (5G) cellular technologies. As a method to increase spectrum utilization, extend cellular coverage, and offload backhaul traffic, D2D has been recently incorporated into Release 12 of 3rd Generation Partnership Project (3GPP) Long Term Evolution Advanced (LTE-A) specifications. Devices in physical proximity can thus discover each other and communicate via a direct path using licensed LTE spectrums. Leveraging on the ubiquity of cellular coverage and harnessing LTE-A D2D for networks with massive number of machine-type communications, such as large-scale sensor networks and vehicular networks, introduces a paradigm shift and opens up new opportunities for proximity-based services. In this paper, we propose a novel radio resource allocation method for D2D discovery in clustered MMC networks. With a large number of nodes, the proposed method can still maintain the signaling overhead at a reasonable level while achieving high discovery rate. Experimental results show that the proposed approach significantly outperforms the existing 3GPP random resource allocation mechanism.
Challenging tourism contexts for innovation: the revitalisation of mature destinations

SAŽETAK: Inovacije su ključne za održivost i konkurentnost destinacija. Zrele destinacije hladnih mora/voda suočavaju se sa specifi čnim izazovima prilikom uvođenja inovacija u svrhu svoje revitalizacije. Ovaj rad istražuje okolnosti tih destinacija s obzirom na karakteristike ponude i potražnje te analizira u kojoj mjeri one mogu potaknuti ili onemogućiti uvođenje inovacija. U radu se navode čimbenici uspjeha poput potrebe za postojanjem šampiona, cjelovitog pristupa razvoju destinacije i odgovarajućih fi nancijskih sredstava za revitalizaciju. Na kraju, u radu se analiziraju okolnosti za inovacije u odredištu poput krajolika dionika, analize mreža i ‘zajednica prakse’. KLJUČNE RIJEČI: inovacija, pomlađivanje, analiza mreža, zajednice prakse