Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond REDD+ readiness: land-use governance to reduce deforestation in Peru

Peru contains the fourth largest area of tropical forest in the world, yet faces a worsening net deforestation rate. In 2008, to address this threat, the national government announced its ambition to reduce deforestation to zero by 2021. Via literature review and key informant interviews, this study assess two years of REDD+ readiness preparations according to six readiness functions. A mixed pattern of outcomes emerge. Although significant advances were made by various local-level initiatives, national-level efforts continue to struggle. Three crucial challenges persist: (1) greater involvement and coordination of ministries and government agencies associated with REDD+ planning, (2) better understanding of deforestation agents and drivers, and (3) integration of REDD+ policies into national and regional plans, which includes clarification of safeguard procedures and design of incentive mechanisms. Integrated land use planning is presented as a platform to foster dialogue that helps to reconcile divergent stakeholder perspectives, coordinate changes to land use, and resolve overlapping land rights. Policy relevance This article presents the outcomes of a multi-dimensional assessment of the REDD+ readiness process in Peru. The six key functions in the analytical framework provide the opportunity to evaluate the process in an integrated and systematic manner and highlights the persistence of complex, transversal governance challenges across diverse economic sectors and government agencies. Research findings also reveal a need for policy change and continued investment to ensure success of the national process in Peru. Strong leadership is needed to generate consensus in cross-sectoral negotiations and to establish coordinated land governance and monitoring mechanisms.
Microarray data quality assessment for developmental time series

While microarrays have become a routine-tools in modern mol ecular labs, the scientific community came to recognise that findings based solely on microarr ay measurements were not always reproducible, and that studies with inconclusive results w ere quite common. Inter-lab and interplatform comparisons have demonstrated this convincingly , e . Wanget al.(2005). Statisticians have recently started to systematically address the questi on how to quantify the overall quality of large number of massive parallel measurements like the on es produced by microarray experiments. Brettschneider et al. (2006) laid out concepts for microarray data quality assess m nt data in the spirit of Shewhart’s statistical quality assess ment philosophy. In particular, they introduce visual tools for assessing the spatial quality of sh rt oligonucleotide microarrays, and numerical measures (NUSE, RLE) for the quality the pre-proc essed microarray data. The approach is derived based on the RMA model for microarray gene e xpression value estimation by Irizarry et al. (2003). A straight forward application of these quality assessment methods on developmental time course data indicates particularly poor quality for the dat a from all the arrays at the beginning and at the end of each of the developmental series. We hypothe sise that this is potentially due to a misinterpretation of biological variation as technologi cally caused noise. In fact, using NUSE and RLE for quality assessment is based on the assumption tha t the majority of genes is not differentially expressed. Developmental microarray data , however, differs in just this aspect from most other kinds of microarray data. The fraction of non -differentially expressed genes is much higher and it is inhomogeneous over time. Another conce rn is whether the default RMA model we have generally been using in quality assessment is a good fit for the data. It does not have a factor for developmental stage, which may be too si mplistic. On the other hand, microarray time course experiments usually provide no or on ly a small number of technical replicates, which makes fitting more complicated models a qu estionable endeavor. This paper aims at improving our interpretation of the micro ar ay quality measures in the case of developmental experiments. We suggest and test alte rnativ quality assessment strategies. geared toward developmental data. The first approach u ses housekeeping genes selected usinga priori biological knowledge. Restricting the quality assessment to this subset would allow to unambiguously attribute deviations in NUSE and RLE scores to technological causes. Our second approach uses a similar rationale, but uses stati tic l analysis to select the genes with low temporal variation. We further investigate whethe r model-misfit contribute to the poor quality we observed in the data from in the first and last few ch ips of each series. The small number of technical replicates is overcome by grouping by po oling data from several subsequent time points.
Actuator fault estimation based adaptive polytopic observer for a class of LPV descriptor systems

This paper presents an Adaptive Polytopic Observer (APO) design in order to develop an actuator fault estimation method dedicated to polytopic Linear Parameter Varying (LPV) descriptor systems. This paper extends a fault diagnosis method developed for regular LTI systems to polytopic LPV descriptor systems. Here, time‐varying actuator faults are also considered, whereas in many papers, actuator faults are generally assumed to be constant. The design and convergence conditions of this APO are provided. The design is formulated through LMI techniques under equality constraints. The performances of the proposed actuator fault estimation scheme are illustrated using an electrical circuit. Copyright © 2014 John Wiley & Sons, Ltd.
Effect of High Phytase Inclusion Rates on Performance of Broilers Fed Diets Not Severely Limited in Available Phosphorus

Phytate is not only an unavailable source of phosphorus (P) for broilers but it also acts as an anti-nutrient, reducing protein and mineral absorption, increasing endogenous losses and reducing broiler performance. The objective of this study was to investigate the anti-nutritional effects of phytate by including high levels of phytase in diets not severely limited in available P. A total of 768 male Arbor Acres broilers were distributed in six treatments of eight replicate pens of 16 birds each consisting of a positive control diet (PC), positive control with 500 FTU/kg phytase, negative control (NC) diet with lower available P and calcium (Ca) levels and the same NC diet with 500, 1,000 or 1,500 FTU/kg phytase. Body weight gain (BWG), feed intake (FI), feed conversion ratio (FCR) and mortality were determined at 21 and 35 d of age while foot ash was determined in four birds per pen at 21 d of age. FI, FCR and foot ash where not affected by the lower mineral diets at 21 d of age nor by the enzyme inclusion but broilers fed lower Ca and available P diets had lower BWG. At 35 d of age no difference was observed between broilers fed the positive or NC diets but broilers fed 500, 1,000 and 1,500 FTU/kg on top of the NC diet had better FCR than broilers fed the positive control diet. When compared to birds fed a diet adequate in P, birds fed the same diet included with 500, 1,000 and 1,500 FTU/kg of phytase in marginally deficient available P and Ca diets had an improvement of performance. These results support the concept that hydrolysing phytate and reducing the anti-nutritional effects of phytate improves bird performance on marginally deficient diets that were not covering the P requirement of birds.
Analysis of Kidney stone Detection by Reaction diffusion Level Set Segmentation and Xilinx System Generator

The abnormalities of the kidney can be identified by ultrasound imaging has structural abnormalities like kidney swelling, change in its position appearance, formation of stones, cysts, cancerous cells, congenital anomalies and blockage of urine etc. For surgical operations it is very important to identify the exact and accurate location of stone in the kidney. The ultrasound images are of low contrast and contain speckle noise. This makes the detection of kidney abnormalities rather challenging task. Thus preprocessing of ultrasound images is carried out on Xilinx System generator (XSG) and implemented on Vertex-5 (XC5VLX50T) FPGA to remove speckle noise. In preprocessing, first image restoration is done to reduce speckle noise then it is applied to Gabor filter for smoothening. Next the resultant image is enhanced using convolution. The preprocessed ultrasound image is segmented using reaction diffusion (RD) level set segmentation (LSS), since it yields better results. it uses a two-step splitting methods to iteratively solve the RD-LSS equation, first step is iterating LSS equation, and then solving the diffusion equation. The second step is to regularize the level set function which is the obtained from first step for better stability. The RD is included for LSS for eliminating of anti-leakages on image boundary. The RD-LSS does not require any expensive re-initialization and it is very high speed of operation. The RD-LSS results are compared with distance regularized level set evolution (DRLSE1), DRLSE1 and DRLSE2. Extracted region of the kidney after segmentation is applied to Symlets, Biorthogonal (bio3.7, bio3.9 & bio4.4) and Daubechies wavelet subbands to extract energy levels. These energy level gives an indication about presence of stone in that particular location which significantly vary from that of normal energy level. These energy levels are trained by Multilayer Perceptron (MLP) and Back Propagation (BP) ANN to identify the type of stone with an accuracy of 99.1%.
Long-Term Prognosis of Diabetic Foot Patients and Their Limbs

OBJECTIVE There is a dearth of long-term data regarding patient and limb survival in patients with diabetic foot ulcers (DFUs). The purpose of our study was therefore to prospectively investigate the limb and person survival of DFU patients during a follow-up period of more than 10 years. RESEARCH DESIGN AND METHODS Two hundred forty-seven patients with DFUs and without previous major amputation consecutively presenting to a single diabetes center between June 1998 and December 1999 were included in this study and followed up until May 2011. Mean patient age was 68.8 ± 10.9 years, 58.7% were male, and 55.5% had peripheral arterial disease (PAD). Times to first major amputation and to death were analyzed with Kaplan-Meier curves and Cox multiple regression. RESULTS A first major amputation occurred in 38 patients (15.4%) during follow-up. All but one of these patients had evidence of PAD at inclusion in the study, and 51.4% had severe PAD [ankle-brachial pressure index ≤0.4]). Age (hazard ratio [HR] per year, 1.05 [95% CI, 1.01–1.10]), being on dialysis (3.51 [1.02–12.07]), and PAD (35.34 [4.81–259.79]) were significant predictors for first major amputation. Cumulative mortalities at years 1, 3, 5, and 10 were 15.4, 33.1, 45.8, and 70.4%, respectively. Significant predictors for death were age (HR per year, 1.08 [95% CI, 1.06–1.10]), male sex ([1.18–2.32]), chronic renal insufficiency (1.83 [1.25–2.66]), dialysis (6.43 [3.14–13.16]), and PAD (1.44 [1.05–1.98]). CONCLUSIONS Although long-term limb salvage in this modern series of diabetic foot patients is favorable, long-term survival remains poor, especially among patients with PAD or renal insufficiency.
Workshop on aggregated search

The Workshop on Aggregated seeks to define the research problems for aggregated search results, and to build a community of researchers working in this emerging area. Aggregated search addresses the issues of how to select and rank resources, how to present them to a user in such a way that information can be found efficiently and effectively. The aim of the workshop was to encourage discussion and the exchange of ideas about the directions for aggregated search. The workshop presented prototype systems, user studies of existing systems, as well as papers about component technologies.
A Novel Lightweight Virtual Machine Based Decompiler to Generate C / C + + Code with High Readability

As a key part of reverse engineering, decompilation plays a very important role in software security and maintenance. Many decompilation techniques and tools have been developed while all of them have defects in different aspects. For example, IDA Hex rays generates pseudocodes with poor readability, and Boomerang is unable to identify composite structures such as Classes and multidimensional arrays. In this paper we present C-Decompiler, an integrated C/C++ decompiler based on lightweight virtual machine, which is capable to analyze the data flow especially the data dependency across basic blocks with high accuracy. C-Decompiler is able to recognize composite structures and libraries, such as Standard Template Library. Our experimental results show that on average C-Decompiler has the highest reduction rate of 55.91% and lowest expansion rate of 55.79%, which prove that the code decompiled by C-Decompiler is most similar to the original code in comparison with other existing decompilers such as Boomerang and IDA Hex rays. Furthermore, C-Decompiler is able to recognize all the functions without any false positive nor false negative, and it is convincing to support that the code decompiled by CDecompiler is of high readability by the least redundancy and most accuracy.
Statistical sequential analysis for particle size distribution of magnetic nanoparticles

We present two statistical sequential analytical approaches to estimate particle size distribution of magnetic nanoparticles. They are termed sequential least square estimation and sequential linear minimum mean square error estimation, respectively. These approaches are implemented and quantified within the formalism of sequential estimation theory. The proposed methods are based on the data sampled sequentially in time and no matrix inversions are required in the implementation. To illustrate the efficiency of the proposed approaches, we give two examples of the particle size distributions in ferrofluid with normal sample and lognormal sample, respectively. In both cases we compare the reconstruction distributions using our methods with those calculated via the electron microscopy images of the ferrofluid particles.
On the Nature of GSE Organizational Social Structures: An Empirical Study

In Global Software Engineering (GSE), people are organized in teams, distanced in space, time and culture. Organizational research calls this interplay of people an Organizational Social Structure (OSS). Previous literature in GSE shows that its OSS is highly dynamic and unpredictable. This paper presents a mapping of OSS types onto GSE organizational factors, based on empirical evidence. We made two observations: first, current OSS types don't support factors related to GSE process management and organizational efficiency (e.g. risk management, language, etc.). Second, OSSs in GSE have attributes which don't map onto any GSE factor, rather introduce a new one, awareness management (e.g. awareness of skills to others, awareness of tasks, tasks (re-)localization, etc.). Our conclusions are twofold. First, OSSs for GSE should focus on increasing support to process management and organizational efficiency. Lastly, research in GSE should include factors focusing on awareness management.
Anisotropic Elliptic PDEs for Feature Classification

The extraction and classification of multitype (point, curve, patch) features on manifolds are extremely challenging, due to the lack of rigorous definition for diverse feature forms. This paper seeks a novel solution of multitype features in a mathematically rigorous way and proposes an efficient method for feature classification on manifolds. We tackle this challenge by exploring a quasi-harmonic field (QHF) generated by elliptic PDEs, which is the stable state of heat diffusion governed by anisotropic diffusion tensor. Diffusion tensor locally encodes shape geometry and controls velocity and direction of the diffusion process. The global QHF weaves points into smooth regions separated by ridges and has superior performance in combating noise/holes. Our method's originality is highlighted by the integration of locally defined diffusion tensor and globally defined elliptic PDEs in an anisotropic manner. At the computational front, the heat diffusion PDE becomes a linear system with Dirichlet condition at heat sources (called seeds). Our new algorithms afford automatic seed selection, enhanced by a fast update procedure in a high-dimensional space. By employing diffusion probability, our method can handle both manufactured parts and organic objects. Various experiments demonstrate the flexibility and high performance of our method.
Determination of propagation constant using 1D-FDTD with MATLAB

This report mainly concentrates on determination of propagation constant of a planar transmission lines such as Microstrip line and coplanar wave guide (CPW) by 1D-FDTD using conventional MATLAB code. Apart from that, this article presents study of basic phenomenon like reflection at an interface between two media, and design of material absorbers. The fields Ey and Hz are simulated along the line Y=Z=0 i.e. propagation along X-axis. Implementation of source (Gaussian pulse, sinusoidal) and effects of various boundaries such as Mur ABC, PML on incident/scattered/total fields are investigated. Also presented a comparison plot of phase constant by using CST microwave studio and MATLAB.
Application of ant-colony optimisation to compute diversified entity summarisation on semantic knowledge graphs

We present ant colony optimisation approach, enriched with a novel self-adaptation mechanism, applied to solve DIVERSUM Problem that consist of generating a small diversified entity summarisation in a knowledge graph. The recently proposed DIVERSUM problem is viewed in this paper in a novel way as a NP-hard combinatorial optimisation problem. The presented preliminary experimental results indicate superiority of this approach to the previously proposed solutions to the DIVERSUM problem.
TR 00-034 A Comparison of Document Clustering Techniques

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K-means and its variants have a time complexity which is linear in the number of documents, but are thought to produce inferior clusters. Sometimes K-means and agglomerative hierarchical approaches are combined so as to “get the best of both worlds. ” However, our results indicate that the bisecting K-means technique is better than the standard K-means approach and as good or better than the hierarchical approaches that we tested for a variety of cluster evaluation metrics. We propose an explanation for these results that is based on an analysis of the specifics of the clustering algorithms and the nature of document data. 1 Background and Motivation Document clustering has been investigated for use in a number of different areas of text mining and information retrieval. Initially, document clustering was investigated for improving the precision or recall in information retrieval systems [Rij79, Kow97] and as an efficient way of finding the nearest neighbors of a document [BL85]. More recently, clustering has been proposed for use in browsing a collection of documents [CKPT92] or in organizing the results returned by a search engine in response to a user’s query [ZEMK97]. Document clustering has also been used to automatically generate hierarchical clusters of documents [KS97]. (The automatic generation of a taxonomy of Web documents like that provided by Yahoo! (www.yahoo.com) is often cited as a goal. ) A somewhat different approach [AGY99] finds the
Design and Synthesis of Pyrazole-3-one Derivatives as Hypoglycaemic Agents

Pyrazole-3-one compounds were designed on the basis of docking studies of previously reported antidiabetic pyrazole compounds. The amino acid residues found during docking studies were used as guidelines for the modification of aromatic substitutions on pyrazole-3-one structure. Depending on the docking score, the designed compounds were selectively prioritized for synthesis. The synthesized compounds were subjected to in vivo hypoglycemic activity using alloxan induced diabetic rats and metformin as a standard. Compound 4 having sulphonamide derivative was found to be the most potent compound among the series.
The relationship between attachment dimensions and emotion regulation in individuals who have committed sexual crimes

ABSTRACT Using attachment theory as an explanatory theory for sexual offending, this cross-sectional study examined the relationship between attachment and emotion regulation capacities utilising data that were collected from 59 incarcerated adult male sex offenders. Attachment dimensions were measured using the Relationship Scales Questionnaire and the Adult Attachment Survey and emotion regulation capacities were measured using the Difficulties with Emotion Regulation Scale. Findings indicated subjects who were more anxiously attached demonstrated more deficits in emotion regulation. Implications for practice and research are discussed.
Human action recognition via multi-view learning

In this paper, we propose a novel approach to automatically learn a compact and yet discriminative representation for humane action recognition. Considering the static visual information and motion information, each frame is represented in two feature subsets (views) and Gaussian Mixture Model (GMM) is adopted to model the distributions of those features. In order to complement the strengths of the different features (views), a Co-EM based multiview learning framework is introduced to estimate the parameters of GMM instead of conventional single view based EM. Then Gaussian components are considered as video words to describe videos with different time resolutions. Compared with the traditional method to recognize action, there are several advantages with the proposed method using Co-EM strategy. First, complex actions are efficiently modeled by GMM, and the number of its component is automatically determined with the Minimum Description Length (MDL). Second, because the imperfectness of single view can be compensated by the other view in the Co-EM, the resulting bag of video words are superior to that formed by any single view. To the best of our knowledge, we are the first to try the Co-EM based multi-view learning method for action recognition and obtain significantly better results. We extensively verify our proposed approach on two publicly available challenging datasets: the KTH dataset and Weizmann dataset. The experimental results show the validity of our proposed method.
Supporting News Article Understanding by Detecting Subject-Background Event Relations

Typically, news articles mention not just one but multiple events. These events can be classified into subject or background events. The former are events that the article is written about, while the latter are additional events referred to in order to explain the background of the subject events (e.g., causal relations, circumstances or the consequences of the main event). Background events are considered to play an important role in helping to understand articles. In this paper, we first propose to classify content of news articles into subject or background event descriptions. In the second part of the paper, we demonstrate a novel solution for improving the news article search. Based on the subject and background relationship structure between events and articles, our method outputs news articles that help with understanding of a given target article.