Miracle-dz/UniFAR-FTUs huggingface dataset:
Generation Of Monthly Irrigation Maps ForIndia Using Spatial Interpolation Techniques

Considering the current water resources problems and rapid increase in its demand, the importance of effective utilization of water resources cannot be underestimated if India has to remain self reliant in food production. Point ETc (crop evapotranspiration) can be calculated by multiplying point ETo (reference evapotranspiration with appropriate kc (crop coefficient) values on a monthly temporal scale. Subtracting the monthly point effective rainfall values from corresponding monthly cumulative point ETc, the point net monthly irrigation requirements can be estimated. Spatially interpolating the point net monthly irrigation requirements by kriging, monthly net irrigation requirement (NIR) maps can be generated. Such maps, although readily available for most of the developed countries, have not yet been developed for developing countries like India. This kind of readily available standard net irrigation requirement maps can be very useful in obtaining reliable irrigation demands in new areas. Therefore, in this paper, an attempt has been made to develop monthly net irrigation requirement maps for India. To grasp the spatial distribution characteristics of the net irrigation requirement for the dominant crop of the season and agroecological region (AER), geostatistical analysis was performed on the normal monthly NIR derived from 32 years (1971-2002) data of 133 IMD (India Meteorological Department) stations uniformly distributed over the country. It was observed that ordinary kriging produced best results and hence was used to produce the final net monthly irrigation requirement surfaces for all the months.
Reply to Park et al

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Alpine Stream Habitat Classification: An Alternative Approach Incorporating the Role of Dynamic Water Source Contributions

Abstract We review current understanding of dynamic interactions between environmental variables and stream benthic communities within glacierized alpine catchments to provide a context for the central theme of habitat complexity within alpine streams. We present a conceptual model summarizing the important links between environmental variables, from large-scale (regional/catchment) to stream-reach/patch-scale processes, to illustrate this physical habitat complexity. Existing alpine stream classification and zonation criteria are examined, and the environmental characteristics representative of the different stream classes are identified. The theme of habitat complexity is developed to assess the applicability of traditional (principally temperature-based) alpine stream classifications. These traditional classifications do not take into account spatial and temporal variations in water source contributions to alpine streams unless associated temperature changes occur. However, different hydrological stores and pathways impart other physical and chemical influences upon stream benthic communities that are overlooked by traditional classifications. We propose a new classification system to better describe spatial and temporal variability in glacial, snowmelt, and groundwater inputs to alpine streams, based upon the mix of proportions of water contributed from each of these sources. Field data collected in the French Pyrénées are used to support this new alpine stream classification, which we propose as a tool for further research in alpine river catchments.
Improved Electrofacies Characterization and Permeability Predictions in Sandstone Reservoirs Using a Data Mining and Expert System Approach

Permeability is one of the most important parameters in reservoir characterization. It plays a major role in reservoir simulation, enhanced oil recovery, well completion design, and overall field exploitation and development strategies. Yet, despite its vital importance, it is one of the most difficult and controversial petrophysical properties to calculate accurately. Permeability is commonly determined from transient well test analyses; however, well tests yield an average permeability value for the entire drainage area of the well. Core analysis provides the best fine-scale permeability measurements; nevertheless, coring operations are very costly and impractical to perform in all wells. Conversely, well-log responses are widely available; when properly analyzed, wireline logs have the advantage of providing continuous permeability traces as opposed to scanty, discrete core data. The proposed approach combines data mining and expert systems to accurately predict the formation permeability from wireline well logs. It involves a two-step procedure. First, principal components analysis, cluster analysis, and classification-tree analysis are coherently used to identify and characterize electrofacies types; then, the fuzzy-logic model is applied to each electrofacies to determine the formation permeability within the specified interval. Actual field data obtained from a reservoir in the Middle East are used to illustrate the utility and efficiency of the proposed approach. The results are cross-examined with geologic data to ensure that the predictions are consistent with the well-log responses and rock properties. Keywords: Permeability, wireline well logs, electrofacies, fuzzy logic, heterogeneous reservoirs, principal components analysis, cluster analysis, classification-tree analysis.
Image-Text Matching with Multi-View Attention

Existing two-stream models for image-text matching show good performance while ensuring retrieval speed and have received extensive attention from industry and academia. These methods use a single representation to encode image and text separately and get a matching score with cosine similarity or the inner product of vectors. However, the performance of the two-stream model is often sub-optimal. On the one hand, a single representation is challenging to cover complex content comprehensively. On the other hand, in this framework of lack of interaction, it is challenging to match multiple meanings which leads to information being ignored. To address the problems mentioned above and facilitate the performance of the two-stream model, we propose a multi-view attention approach for two-stream image-text matching MVAM (\textbf{M}ulti-\textbf{V}iew \textbf{A}ttention \textbf{M}odel). It first learns multiple image and text representations by diverse attention heads with different view codes. And then concatenate these representations into one for matching. A diversity objective is also used to promote diversity between attention heads. With this method, models are able to encode images and text from different views and attend to more key points. So we can get representations that contain more information. When doing retrieval tasks, the matching scores between images and texts can be calculated from different aspects, leading to better matching performance. Experiment results on MSCOCO and Flickr30K show that our proposed model brings improvements over existing models. Further case studies show that different attention heads can focus on different contents and finally obtain a more comprehensive representation.
Patterns of intelligent and mobile agents

Almost all agent development to date has been “homegrown” [4] and done from scratch, independently, byeach development team. This has led to the followingproblems:• Lack of an agreed definition: Agents built bydifferent teams have different capabilities. • Duplication of effort: There has been little reuse ofagent architectures, designs, or components. • Inability to satisfy industrial strengthrequirements: Agents must integrate with existingsoftware and computer infrastructure. They must alsoaddress security and scaling concerns.Agents are complex and ambitious software systems thatwill be entrusted with critical applications. As such,agent based systems must be engineered with validsoftware engineering principles and not constructed in anad hoc fashion.Agent systems must have a strong foundation based onmasterful software patterns. Software patterns arose outof Alexander’s [2] work in architecture and urbanplanning. Many urban plans and architectures aregrandiose and ill-fated. Overly ambitious agent basedsystems built in an ad hoc fashion risk the same fate.They may never be built, or, due to their fragile nature,they may be built and either never used or used once andthen abandoned. A software pattern is a recurringproblem and solution; it may address conceptual,architectural or design problems.A pattern is described in a set format to ease itsdissemination. The format states the problem addressedby the pattern and the forces acting on it. There is also acontext that must be present for the pattern to be valid, astatement of the solution, and any known uses. Thefollowing sections summarize some key patterns of agentbased systems; for brevity, many of the patterns arepresented in an abbreviated “patlet” form. When kn ownuses are not listed for an individual pattern, it means thatthe pattern has arisen from the JAFIMA activity. Thepatterns presented in this paper represent progress towarda pattern language or living methodology for intelligentand mobile agents.
Black Box Polynomial Identity Testing of Depth-3 Arithmetic Circuits with Bounded Top Fan-in

In this paper we consider the problem of determining whether an unknown arithmetic circuit, for which we have oracle access, computes the identically zero polynomial. Our focus is on depth-3 circuits with a bounded top fan-in. We obtain the following results. 1. A quasi-polynomial time deterministic black-box identity testing algorithm for ���(k) circuits (depth-3 circuits with top fan-in equal k). 2. A randomized black-box algorithm for identity testing of ���(k) circuits, that uses a polylogarithmic number of random bits, and makes a single query to the black-box.
Aberrant Neurogenesis After Stroke: A Retroviral Cell Labeling Study

Background and Purpose— Adult neurogenesis in the dentate gyrus is a unique form of brain plasticity that is strongly stimulated after stroke. We investigate the morphological properties of new granule cells, which are born and develop after the ischemic insult, and query whether these adult-born neurons properly integrate into the pre-existing hippocampal circuitries. Methods— Two well-established models were used to induce either small cortical infarcts (photothrombosis model) or large territorial infarcts (transient middle cerebral artery occlusion model). New granule cells were labeled 4 days after the initial insult by intrahippocampal injection of a retroviral vector encoding green fluorescent protein and newborn neurons were morphologically analyzed using a semiautomatic Neurolucida system and confocal laser scanning microscopy at 6 weeks. Results— Approximately 5% to 10% of newborn granule cells displayed significant morphological abnormalities comprising additional basal dendrites and, after middle cerebral artery occlusion, also ectopic cell position. The extent of morphological abnormalities was higher after large territorial infarcts and seems to depend on the severity of ischemic damage. An increased portion of mushroom spines in aberrant neurons suggests stable synaptic integration. However, poststroke generated granule cells with regular appearance also demonstrate alterations in dendritic complexity and spine morphology. Conclusions— The remarkable stimulation of dentate neurogenesis after stroke coincides with an increased rate of aberrantly integrated neurons, which may contribute to functional impairments and, hypothetically, favor pathogenesis of adjustment disorders, cognitive deficits, or epilepsy often seen in stroke patients.
Non-invasive Software Process Data Collection for Expert Identification

Software companies depend heavily on knowledgeable employees. Competence and skills management are essential instruments to understand how to employ the available skills in an optimal way. Unfortunately, implementing knowledge management strategies like competence and skills management is challenging because resources, time and effort are required before benefits become visible. This paper shows an approach to collect noninvasively (i.e., without requiring any effort by developers) data about “who” is working on “what” during software production. We present two examples to show how to answer three questions: “who is the expert of a specific part of the code? ”, “who should do pair programming with whom? ”, and “what knowledge gap arises if a specific developer leaves? ”.
Identification of a second Asian soybean rust resistance gene in Hyuuga soybean.

ABSTRACT Asian soybean rust (ASR) is an economically significant disease caused by the fungus Phakopsora pachyrhizi. The soybean genes Rpp3 and Rpp? (Hyuuga) confer resistance to specific isolates of the pathogen. Both genes map to chromosome 6 (Gm06) (linkage group [LG] C2). We recently identified 12 additional soybean accessions that harbor ASR resistance mapping to Gm06, within 5 centimorgans of Rpp3 and Rpp? (Hyuuga). To further characterize genotypes with resistance on Gm06, we used a set of eight P. pachyrhizi isolates collected from geographically diverse areas to inoculate plants and evaluate them for differential phenotypic responses. Three isolates elicited different responses from soybean accessions PI 462312 (Ankur) (Rpp3) and PI 506764 (Hyuuga) (Rpp? [Hyuuga]). In all, 11 of the new accessions yielded responses identical to either PI 462312 or Hyuuga and 1 of the new accessions, PI 417089B (Kuro daizu), differed from all others. Additional screening of Hyuuga-derived recombinant inbred lines indicated that Hyuuga carries two resistance genes, one at the Rpp3 locus on Gm06 and a second, unlinked ASR resistance gene mapping to Gm03 (LG-N) near Rpp5. These findings reveal a natural case of gene pyramiding for ASR resistance in Hyuuga and underscore the importance of utilizing multiple isolates of P. pachyrhizi when screening for ASR resistance.
Massively parallel stream processing with latency guarantees

A new class of stream processing engines has recently established itself as a platform for applications in numerous domains, such as personalized contentand ad-serving, online recommender systems or social media analytics. These new engines draw inspiration from Big Data batch processing frameworks (Google’s MapReduce and its descendants) as well existing stream processing engines (e.g. Borealis and STREAM). They process data on-the-ŕy without őrst storing it in a őle system and their core programming abstractions hide the complexity of distributed-parallel programming. Although stream processing applications commonly need to process ingested data within time bounds, this new class of engines so far computes results las fast as possiblež. As variations in workload characteristics are often hard to predict and outside the control of the application, this may quickly lead to a situation where las fast as possiblež becomes lnot fast enoughž for the application. This thesis revisits the design of this new class of stream processing engines. The core question addressed by this thesis is how latency requirements can be speciőed and continuously enforced within these engines in a resourceeicient manner. To this end, this thesis contributes (1) a formalism and associated semantics for expressing latency requirements for stream processing applications, (2) a set of techniques for engines to enforce them and (3) an evaluation of the efectiveness of the presented techniques. The proposed techniques optimize resource eiciency by automatically adjusting the data shipping strategy between data ŕow tasks and adapting the mapping between tasks and execution threads at runtime. Furthermore, based on workload statistics measured at runtime, they adapt the application’s parallelism by exploiting the elasticity of shared, resource-managed compute clusters. To evaluate their efectiveness, they have been implemented in a research prototype and an experimental evaluation with several application workloads has been conducted on a large commodity cluster.
PABench: Designing a Taxonomy and Implementing a Benchmark for Spatial Entity Matching

The tremendous increase of data sources containing spatial information is bound up with the diversity of geospatial applications such as location-based services (LBS) and global positioning systems. LBS providers use maps to locate spatial entities referring to points of interest (POI), for instance restaurants or locations of events. In our study, we specifically focus on places that tourists can get through LBS. The multiplication of these providers has an impact on the quality of data: spatial entities referring to the same POI may include spatial and terminological properties with incomplete, inconsistent, inaccurate or even wrong data. Thus, entity matching approaches have been proposed to discover correspondences between spatial entities, and experimental validations are traditionally performed to demonstrate the performance of these approaches in terms of effectiveness and efficiency. However, the datasets used in these experiments are rarely available, thus limiting their reuse and a fair comparison between the proposed approaches. This paper introduces PABench, a benchmark for spatial entity matching, which is available for researchers to assess their algorithms. Our benchmark includes a taxonomy of observed differences, inconsistencies and errors, which enables the characterization of different LBS providers. PABench can provide a complete and accurate evaluation of the different aspects of spatial entity matching approaches, and also facilitate an understanding of their weaknesses and abilities with respect to spatial integration. This paper provides a conceptual platform to enable LBS interoperability.
Making Evidence-Based Psychotherapy More Accessible in Canada

Psychological interventions are effective for many psychiatric disorders. In particular, cognitive behavioural therapy (CBT) is rigorously evidence based. The Canadian Network for Mood and Anxiety Treatments (CANMAT) depression guidelines state that CBT is as effective for mild and moderate depression as antidepressant medications, and combined psychopharmacology and CBT is superior to either modality alone. New research shows CBT can help cancer patients endure chemotherapy, assist the obese in weight management, and even help return patients with depression to full-time employment. For major mental illnesses, CBT is very effective, with low numbers needed to treat (for generalized anxiety disorder, posttraumatic stress disorder [PTSD], and depression: 2.3, 1.7, and 4.4, respectively). At the same time, it must be acknowledged that it is not the only effective from of psychotherapy and that there is emerging evidence of decreasing effectiveness in more recent compared to earlier studies. CBT and other evidence-based psychotherapies require a major investment of resources and time. Thus, in public health care systems like Canada’s, these psychotherapies have limited availability. In this journal, Dezetter et al. surveyed nearly 1300 attendees at Quebec primary care clinics, finding unmet mental health needs in 40% of participants, including psychotherapy (in part because of a perceived inability to pay); Hadjipavlou et al. surveyed British Columbia psychiatrists, finding confidence in their psychotherapy efficacy but few patients are served because of time constraints. Not surprisingly, then, the lack of access has promoted calls for change. In its ‘‘Changing Directions, Changing Lives: The Mental Health Strategy for Canada,’’ the Mental Health Commission of Canada focused on access, stating that ‘‘services for which we know people are waiting too long, if they can get it at all, are psychotherapies and clinical counseling,’’ recommending an ‘‘increase to the availability and coordination of mental health services in the community. ’’ More recently, in a major report, Quebec’s L’Institut National d’Excellence en Santé et en Services Sociaux (INESSS), which advises evidence-based health policy in that province, called for greater access to psychotherapies for people with depression and anxiety. But how? In this article, which offers a perspective rather than a systematic review, we consider several options, including increasing the supply of psychiatrists and expanding the range of publicly funded services and providers. We also look abroad for answers, in particular to the United Kingdom and Australia, where recent efforts have increased access to evidence-based therapies. This article will show that—from Liverpool, England, to Liverpool, New South Wales—these countries experiment with ideas that are relevant here in their applicability.
On the Approximation of Functional Classes Equipped with a Uniform Measure Using Ridge Functions

We introduce a construction of a uniform measure over a functional class Br which is similar to a Besov class with smoothness index r. We then consider the problem of approximating Br using a manifold Mn which consists of all linear manifolds spanned by n ridge functions, i.e., Mn={? ni=1gi(ai·x):ai? Sd? 1, gi? L2(? 1, 1])}, x? Bd. It is proved that for some subset A? Br of probabilistic measure 1? , for all f? A the degree of approximation of Mn behaves asymptotically as 1/nr/(d? 1). As a direct consequence the probabilistic (n, ? )-width for nonlinear approximation denoted as dn, ? (Br, ? , Mn), where ? is a uniform measure over Br, is similarly bounded. The lower bound holds also for the specific case of approximation using a manifold of one hidden layer neural networks with n hidden units.
GCMA: Guaranteed Contiguous Memory Allocator

The importance of physically contiguous memory has increased in modern computing environments, including both low- and high-end systems. Existing physically contiguous memory allocators generally have critical limitations. For example, the most commonly adopted solution, the memory reservation technique, wastes a significant amount of memory space. Scatter/Gather direct memory access (DMA) and input-output memory management units (IOMMUs) avoid this problem by utilizing additional hardware for address space virtualization. However, additional hardware means an increase in costs and power consumption, which is especially disadvantageous for small systems and they do not provide real contiguous memory. Linux Contiguous Memory Allocator (CMA) aims to provide both contiguous memory allocation and to maximize memory utilization based on page migration, but they suffer from unpredictably long latency and a high probability of allocation failure. Therefore, we introduce a new solution to this problem, the guaranteed contiguous memory allocator (GCMA). This guarantees efficient memory space utilization, short latency, and successful allocation. The GCMA uses a reservation scheme and increases memory utilization by sharing the memory with immediately discardable data. Our evaluation of a GCMA on a Raspberry Pi 2 finds a latency that is 15-130 times lower compared to a CMA, and a latency that is up to 10 times lower when taking a photo. Using a large working set in a memory-fragmented high-end system, the GCMA is able to produce a 2.27x speedup.