Miracle-dz/UniFAR-FTUs huggingface dataset:
Litter quality is in the eye of the beholder: initial decomposition rates as a function of inoculum characteristics

Summary 1. The chemical composition of plant litter is commonly considered to indicate its quality as a resource for decomposer organisms. Litter quality, defined in this way, has been shown to be a major determinant of litter decomposition rates both within and across terrestrial ecosystems. Notably, the structure of the microbial community that is directly responsible for primary decomposition is rarely considered as an empirical predictor of litter decay rates. 2. Microbial communities are generally assumed to perceive litters of the same chemical composition to be of equivalent resource quality but evidence from field studies suggests that these same communities may adapt to the prevalent litter types at a given site. Here, we tested this assumption by assessing how microbial communities sourced from different forest- and herbaceous-dominated ecosystems perceive the quality of novel, foliar litters derived from a tree ( Rhododendron maximum ) and from a grass ( Panicum virgatum ) species. Based on chemical composition, we would expect R. maximum litter to be of lower quality than P. virgatum litter. 3. We used an experimental litter‐soil system which employs a ‘common garden’ approach and measured rates of CO 2 production across 50 days; higher rates of production were assumed to indicate higher quality (i.e. more easily degradable) litter. 4. We found that communities sourced from habitats under differing plant cover perceived litter quality differently. Those communities sourced from herbaceous habitats perceived the grass litter to be of higher quality than the tree litter, whereas communities from forest habitats decomposed both litter types similarly. Within a litter type, differences in both community composition and nutrient availability of the source habitat were related to decomposition rates. 5. Our results suggest that litter quality cannot necessarily be predicted solely from chemical characteristics; instead the perceived quality is dependent on the quality of past resource inputs a community has experienced and the structure of those microbial communities responsible for the initial stages of litter decomposition.
Global Land Surface Emissivity Retrieved From Satellite Ultraspectral IR Measurements

Ultraspectral resolution infrared (IR) radiances obtained from nadir observations provide information about the atmosphere, surface, aerosols, and clouds. Surface spectral emissivity (SSE) and surface skin temperature from current and future operational satellites can and will reveal critical information about the Earth's ecosystem and land-surface-type properties, which might be utilized as a means of long-term monitoring of the Earth's environment and global climate change. In this study, fast radiative transfer models applied to the atmosphere under all weather conditions are used for atmospheric profile and surface or cloud parameter retrieval from ultraspectral and/or hyperspectral spaceborne IR soundings. An inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral IR sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface or cloud microphysical parameters. This inversion scheme has been applied to the Infrared Atmospheric Sounding Interferometer (IASI). Rapidly produced SSE is initially evaluated through quality control checks on the retrievals of other impacted surface and atmospheric parameters. Initial validation of retrieved emissivity spectra is conducted with Namib and Kalahari desert laboratory measurements. Seasonal products of global land SSE and surface skin temperature retrieved with IASI are presented to demonstrate seasonal variation of SSE.
Notes on biextensions over Chow groups

The paper discusses four approaches to biextensions over Chow groups and their equivalence is proved. These are the following: an explicit construction given by S.Bloch, a construction in terms of K-cohomology, a construction in terms of determinant of cohomology of coherent sheaves, and a construction in terms of the Poincaré biextension over dual intermediate Jacobians. As an application, a formula for the Weil pairing of algebraic cycles is obtained.
Software Failures and Risk Management Practices in Nigeria, Kenya and Zimbabwe: A Survey

— Software Project failure particularly in Nigeria, Kenya and Zimbabwe is partly due to lack of adequate knowledge on the part of software developers on software risk management methodologies and the roles they play in software engineering. Over the years and with increase in software project failures, no attention has been paid to the reasons behind project failures. This paper seeks to investigate the degree of awareness of risk principles by software developers and their application of these principles in software development. The instrument used was research questionnaires. Four hundred (400) questionnaires were analyzed using percentages and chi-square and the results shows that software failure is not as a result of ignorance of risk management principles but the non adherence and usage of the principles involved in developing risk mitigated software.
CrowdMR: Integrating Crowdsourcing with MapReduce for AI-Hard Problems

Large-scale distributed computing has made available the resources necessary to solve "AI-hard" problems. As a result, it becomes feasible to automate the processing of such problems, but accuracy is not very high due to the conceptual difficulty of these problems. In this paper, we integrated crowdsourcing with MapReduce to provide a scalable innovative human-machine solution to AI-hard problems, which is called CrowdMR. In CrowdMR, the majority of problem instances are automatically processed by machine while the troublesome instances are redirected to human via crowdsourcing. The results returned from crowdsourcing are validated in the form of CAPTCHA (Completely Automated Public Turing test to Tell Computers and Humans Apart) before adding to the output. An incremental scheduling method was brought forward to combine the results from machine and human in a "pay-as-you-go" way.
Collaborative annotation for pseudo relevance feedback

We present a pseudo relevance feedback technique for infor- mation retrieval, which expands keyword queries with semantic annota- tion found in the freely available Del.icio. us collaborative tagging system. We hypothesise that collaborative tags represent semantic information that may render queries more informative, and hence enhance retrieval performance. Experiments with three different techniques of enriching queries with Del.icio. us tags, and also varying the number of tags used for expansion between 1-10, show small improvement in retrieval preci- sion, over a baseline of short keyword queries.
Geographical Distribution of Mortality from Diabetes Complications in Yazd

Article history: Received: 23 Jan 2013 Accepted: 5 Feb 2013 Available online: 3 June 2013 ZJRMS 2014; 16(6): 89-92 Background: The purpose of this study is to present a geographical map of mortality rate due to diabetes complications in different areas of Yazd during 2001-2010. Materials and Methods: In this ecological study, a sample data of mortality due to diabetic complications in Yazd was gathered. Mortality rate calculation and geographical distribution mapping was performed then using this data. Results: General rate of mortality from diabetic complications is higher in internal and marginal areas than high-income and new-built regions. This pattern is also consistent regardless of patients’ gender. Conclusion: It seems that the observed pattern is due to inactivity in internal areas, as well as unawareness and lack of access to medical facilities in margins. Copyright © 2014 Zahedan University of Medical Sciences. All rights reserved.
Classification multi-classes au prix d'un classifieur binaire

Cet article traite du probleme de classification multi-classe en reconnaissance des formes. La resolution de ce type de problemes necessite des algorithmes au cout calculatoire souvent beaucoup plus eleve que les methodes d'apprentissage dediees a la classification binaire. On propose dans cet article une nouvelle formulation pour la conception de classifieurs multi-classes, necessitant essentiellement la meme complexite calculatoire que l'apprentissage d'un classifieur binaire. On montre que ce socle commun offre un cadre pour elaborer des algorithmes multi-classes en utilisant les memes routines d'optimisation que celles utilisees pour les problemes de classification binaire. On illustre ce resultat avec les algorithmes SVM, LS-SVM et RLSC.
A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individual's daily life that can further help to analyse more complex and abnormal routines of human behaviour.
Recursion and Random Walks

This paper examines conditions for recurrence and transience for random walks on discrete surfaces, such as Z, trees, and random environments. 1 Random Walks on Non-Random Environments 1.1 Definitions Definition 1. A class of subsets F of a set Ω is an algebra if the following hold: 1. ∅ ∈ F and Ω ∈ F . 2. A1, A2, . , An ∈ F =⇒ ⋃n i=1Ai ∈ F . 3. A ∈ F =⇒ A ∈ F . If additionally A1, A2, . ∈ F =⇒ ⋃∞ i=1Ai ∈ F , then F is called a σ-algebra. Definition 2. A Probability Measure is a function P : F → [0, 1], where F is a σ-algebra, satisfying: 1. P{∅} = 0 2. If A1, A2, . is a collection of disjoint members of F , so that Ai ∩ Aj = ∅ ∀i 6= j, then P { ∞ ⋃
Comparative study on weather derivatives and conventional financial derivatives

Weather derivatives are conjunct product of avoidance of weather risks and financial tools innovation. As a kind of financial derivative products, weather derivatives possess comment features of general financial derivatives, such as product forms, trading models, hedging function, etc. While due to the difference of basic assets that construct the derivatives, there are significant deferent points between the two others: both the risks they transferred and the pricing models and methods are different. In addition, market participators and the roles they play are also different.
Bayesian Channel Estimation Techniques for AF MIMO Relaying Systems

In this paper, we consider the fundamental problem of channel estimation in multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems operating over random channels. Using the Bayesian framework, linear minimum mean square error (LMMSE) and expectation-maximization (EM) based maximum a posteriori (MAP) channel estimation algorithms are developed, that provide the destination with full knowledge of all channel parameters involved in the transmission. The performance of the proposed algorithms is evaluated in terms of the mean square error (MSE) as a function of the signal-tonoise ratio (SNR) during the training interval. Our simulation results show that the incorporation of prior knowledge into the channel estimation algorithm offers improved performance, especially in the low SNR regime.
Sequential processing JIPDA for multitarget tracking in clutter using multistatic passive radar

The target tracking using multistatic passive radar in a digital audio/video broadcast (DAB/DVB) network with uncertain illuminators of opportunity faces two main challenges: one is the three-dimensional (3-D) data association problem among the target-measurement-illuminator due to the uncertainty of illuminators of opportunity; the other is that only the bistatic range and range-rate measurements are available since the angle information is unavailable or of very poor quality. In this paper, the authors propose two tracking algorithms directly in a two dimensional (2-D) Cartesian coordinates with the capability of false track discrimination (FTD) using the probability of target existence: the modified joint integrated data association (MJIPDA) and the sequential processing joint integrated data association (SP-JIPDA). The MJIPDA algorithm is enhanced based on the existing MJPDA algorithm by using the probability of target existence for FTD. The SP-JIPDA algorithm sequentially operates the JIPDA tracker to update each track for each illuminator with all the measurements in the common measurement set at each time. A simulation study is performed to verify the validity of both algorithms in this multistatic passive radar system.
Fixpoint Logics for Reasoning about Probabilistic Systems

Abstract. We consider exogenous logics for reasoning about probabilistic systems: a variant of probabilistic state logic EPPL[24], and its fixpoint extension MEPL, which is enriched with operators from the modal μ-calculus. System states correspond to probability distributions over classical states and the system evolution is modeled by parametrized Kripke structures that capture both stochastic and non–deterministic transitions. We introduce two approaches to the verification of properties expressed in these logics, one syntactic (a weakly complete Hilbert calculus) and the other semantic (a model– checking algorithm). The completeness proof of MEPL builds on the decidability of the existential theory of the real numbers and on a polynomial-space sat algorithm for EPPL. The model checking problem for MEPL is also analysed and the logic is related to previous work. The semantics of EPPL and MEPL are defined in terms of probability distributions over sets of propositional symbols, whereas the usual approaches are designed for reasoning about distributions over paths of possible behaviour. The intended application of our logics is as a specification formalism for properties of probabilistic systems. We illustrate the use of the logics for specifying system properties with some simple examples. 1 Background and Motivation There are numerous applications in science where reasoning about probabilistic behaviour is necessary. In computing, applications include proba-
Basics for Biosystems of the Cell DNA , RNA and Protein Structure Prediction

Introduction DNA RNA Proteins Methods RNA and DNA secondary structure prediction mFold Vienna RNA Package Circles Protein structure prediction kPROT Jnet SSA References 3 Abstract In this study we explore some DNA, RNA and protein structure prediction software, which is found in the Internet [1]. The single-stranded DNA and RNA fold into specific three-dimensional conformations, which are determined by the sequence of nucleotides. The prediction of RNA folding is important in understanding many biological processes, including translation regulation in messenger RNA, replication of single-stranded RNA viruses, and the function of structural RNAs and RNA/protein complexes. The prediction of DNA folding is important for example in PCR, where DNA is in a single-stranded form. Proteins are generally self-folding. The three-dimensional structures of proteins are therefore also extremely diverse, ranging from completely fibrous to globular. The prediction of protein folding is important because the structure of a protein is related to its function. Experimental structure determination, or structure prediction, aids the elucidation of protein function; conversely, synthetic protein sequences might be designed so that the protein performs a desired function. The study of protein structure therefore produces very valuable practical benefits for medicine, agriculture and industry. The understanding of enzyme function allows the design of drugs which inhibit specific enzyme targets for therapeutic purposes. The field of research dealing with the prediction of structure from sequence is generally known as bioinformatics [2].
Learning Functionality Graph of Products : Substitute and Complementary Relations on Amazon

In general, machine learning algorithms focus on predicting outputs from ’flat’ data. However, increasingly a lot of the data available inherently has a relational structure, either explicit or implicit. This implies that the data values are not independent from each other. Further, individual data values may be related to each other through different classes of relationships. These relationships,in turn, may not be independent of each other and may influence the existence of other relationships between the same or other pairs of nodes. We consider the inherent challenges that such a problem formulation presents and try to use a relational Markov network model that learns over the individual attributes as well as the relations among the entities. We apply the model to a dataset containing Amazon product items, which has complementary and substitute item relations. A major focus on efficacy of the approach is on the consistency of the resultant functionality relationship graph, in addition to accuracy in relation predictions. Through carefully designed evaluation scenarios, we demonstrate the superior performance of the RMN model as opposed to ’flat’ classifiers.
Repeated withdrawal from ethanol spares contextual fear conditioning and spatial learning but impairs negative patterning and induces over‐responding: evidence for effect on frontal cortical but not hippocampal function?

Repeated exposure of rats to withdrawal from chronic ethanol reduces hippocampal long‐term potentiation and gives rise to epileptiform‐like activity in hippocampus. We investigated whether such withdrawal experience also affects learning in tasks thought to be sensitive to hippocampal damage. Rats fed an ethanol‐containing diet for 24 days with two intermediate 3‐day withdrawal episodes, resulting in intakes of 13–14 g/kg ethanol per day, showed impaired negative patterning discrimination compared with controls and animals that had continuous 24‐day ethanol treatment, but did not differ from these animals in the degree of contextual freezing 24 h after training or in spatial learning in the Barnes maze. Repeatedly withdrawn animals also showed increased numbers of responses in the period immediately before reinforcement became available in an operant task employing a fixed‐interval schedule although overall temporal organization of responding was unimpaired. Thus, in our model of repeated withdrawal from ethanol, previously observed changes in hippocampal function did not manifest at the behavioural level in the tests employed. The deficit seen after repeated withdrawal in the negative patterning discrimination and over‐responding in the fixed‐interval paradigm might be related to the changes in the functioning of the cortex after withdrawal.
Spatiotemporal Saliency Detection and Its Applications in Static and Dynamic Scenes

This paper presents a novel method for detecting salient regions in both images and videos based on a discriminant center-surround hypothesis that the salient region stands out from its surroundings. To this end, our spatiotemporal approach combines the spatial saliency by computing distances between ordinal signatures of edge and color orientations obtained from the center and the surrounding regions and the temporal saliency by simply computing the sum of absolute difference between temporal gradients of the center and the surrounding regions. Our proposed method is computationally efficient, reliable, and simple to implement and thus it can be easily extended to various applications such as image retargeting and moving object extraction. The proposed method has been extensively tested and the results show that the proposed scheme is effective in detecting saliency compared to various state-of-the-art methods.
Expressional Analysis and Role of Calcium Regulated Kinases in Abiotic Stress Signaling

Perception of stimuli and activation of a signaling cascade is an intrinsic characteristic feature of all living organisms. Till date, several signaling pathways have been elucidated that are involved in multiple facets of growth and development of an organism. Exposure to unfavorable stimuli or stress condition activates different signaling cascades in both plants and animal. Being sessile, plants cannot move away from an unfavorable condition, and hence activate the molecular machinery to cope up or adjust against that particular stress condition. In plants, role of calcium as second messenger has been studied in detail in both abiotic and biotic stress signaling. Several calcium sensor proteins such as calmodulin (CaM), calcium dependent protein kinases (CDPK) and calcinuerin B-like (CBL) were discovered to play a crucial role in abiotic stress signaling in plants. Unlike CDPK, CBL and CaM are calcium-binding proteins, which do not have any protein kinase enzyme activity and interact with a target protein kinase termed as CBL-interacting protein kinase (CIPK) and CaM kinases respectively. Genome sequence analysis of Arabidopsis and rice has led to the identification of multigene familes of these calcium signaling protein kinases. Individual and global gene expression analysis of these protein kinase family members has been analyzed under several developmental and different abiotic stress conditions. In this review, we are trying to overview and emphasize the expressional analysis of calcium signaling protein kinases under different abiotic stress and developmental stages, and linking the expression to possible function for these kinases.