Miracle-dz/UniFAR-FTUs huggingface dataset:
A tale of two villages: assessing the dynamics of fuelwood supply in communal landscapes in South Africa

SUMMARY Fuelwood is the dominant source of energy used by most rural households in southern Africa to meet daily domestic energy requirements. Due to limited financial resources, most rural households are unable to make the transition to electricity thus they remain dependant on the woodlands surrounding their settlements as a source of cheap energy. Unsustainable fuelwood harvesting due to increasing demand as a result of growing human populations may result in environmental degradation particularly in the high-density, communal savannah woodlands of South Africa. Evaluating the sustainability of current fuelwood harvesting patterns requires an understanding of the environmental impacts of past logging practices to establish patterns of woodland degradation. This study evaluates impacts of fuelwood harvesting from 1992–2009 on the woodland structure and species composition surrounding two rural villages located within the Kruger to Canyons Biosphere Reserve (Mpumalanga Province, South Africa). Both villages (Welverdiend and Athol) were of similar spatial extent and exhibited similar socioeconomic characteristics. The total wood stock in the communal woodlands of both villages declined overall (with greater losses seen in Welverdiend) and, in Welverdiend, there were also changes in the woodland structure and species diversity of the species commonly harvested for fuelwood over this period. The woodlands in Welverdiend have become degraded and no longer produce fuelwood of preferred species and stem size in sufficient quantity or quality. The absence of similar negative impacts in Athol suggests more sustainable harvesting regimes exist there because of the lower human population and lower fuelwood extraction pressure. The Welverdiend community has annexed neighbouring unoccupied private land in a social response to fuelwood scarcity. Athol residents behaved similarly during drought periods. The potential for future conflict with neighbouring conservation areas within the Kruger to Canyons Biosphere is high if current land uses and fuelwood extraction patterns are maintained.
Investigating Internal Gettering of Iron at Grain Boundaries in Multicrystalline Silicon via Photoluminescence Imaging

In this paper, we present measurements and modeling of the reduction in dissolved iron Fe; concentrations near grain boundaries in multicrystalline silicon (mc-Si) wafers. The measurements of the interstitial Fe concentrations are obtained via photoluminescence images taken before and after iron-boron pair dissociation. A simple diffusion-capture model was developed to characterize the removal of interstitial Fe by the gettering sites. The model is based on a numerical solution to the 1-D diffusion equation with two fitting parameters: the diffusion length of dissolved Fe atoms and the effective gettering velocity at the gettering site. By comparing the simulation with a controlled phosphorous gettering process, the model is shown to give good estimation of the diffusion length of Fe atoms. For as-cut multicrystalline silicon wafers from different parts of the ingot, that is, wafers with different average dissolved Fe concentrations [Fei], the diffusion lengths of Fe atoms are found to decrease with decreasing average [Fei] This suggests the presence of relaxation precipitation during the internal gettering of dissolved Fe by the grain boundaries in mc-Si during ingot cooling.
Frequency-domain-based least-squares estimation of multifrequency signal parameters

This paper proposes a frequency-based least squares algorithm applied to the estimation of multifrequency signal parameters. It is based on the processing of the joint information carried by nearby frequency components of Fourier-transformed data. Theoretical and simulated analysis are carried out on the algorithm accuracy while its performance is compared to the corresponding Cramer-Rao lower bound.
What to Reuse?: A Probabilistic Model to Transfer User Annotations in a Surveillance Video

Techniques to extract or understand interactions between moving objects in video is becoming increasingly important as the amount of video increases. Applications in surveillance range from understanding traffic to studying fish schooling behaviour. Because of the massive amount of data, fast, approximate techniques based on statistical models are common. These models connect user annotations (labels) to scenes in a (short) video segment. The connection forms a domain, which associates information about moving objects in scenes with the labels, such as to indicate whether a user considers a particular traffic scene to be "dangerous. " Unfortunately a statistical model trained in one domain often yields low precision and recall when applied to another domain because the random variables that explain video content exhibit changing marginal and conditional probability distributions over time (e.g., due to different backgrounds, changes in illumination, shading, and numbers of moving objects). This problem is exacerbated when new domains continuously arise (e.g., in the real-time processing of video) and user annotations are only limited to training data, a common scenario for surveillance video. In this paper, we propose a new, cross-domain technique that reuses labelled content from source domains to improve the prediction of user annotations in a target domain. Our model probabilistically learns how users annotate scenes based on the similarity of target to source domains. Two domains that are similar will share a large number of observable features. We encode the similarity in a covariance matrix, which flexibly allows allows users to set an arbitrary covariance structure between pairs of domains before training the model. Experiments show that our method improves state-of-the-art techniques (SVM and CF) in predicting dangerous scenes in real-world traffic surveillance videos.
A Novel MST based Multi-Prototype Clustering Algorithm

Clustering is the process of partitioning the data objects such that the degree of similarity among the objects in the same partition is more than the degree of dissimilarity between the objects of different classes. There are many applications of clustering like medicine, marketing, insurance, image analysis etc. The squared-error clustering algorithm uses single prototype to represent a cluster. However, the algorithm is incapable of detecting clusters with complex shapes. To resolve this issue, multi-prototype clustering algorithm has been proposed which can discover clusters of complex shapes, but it uses squared error clustering to produce prototypes. This paper proposes multi-prototype clustering algorithm based on Minimum Spanning Tree. Minimum Spanning Tree is known to be capable of detecting clusters with irregular boundaries. Two closest prototypes are merged based on Euclidean distance. At each step of merging, weighted standard deviation is calculated. The result is output for the set of clusters for which the weighted standard deviation is least. Experimental results for real data demonstrate the effectiveness of the proposed algorithm.
Determination of Sesquiterpenes in Wines by HS-SPME Coupled with GC-MS

The sesquiterpene compounds present in red wines were characterized and quantified by Headspace Solid-Phase Microextraction in combination with Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS). Sixteen sesquiterpenes were identified, mainly hydrocarbons but also derived oxygenated compounds. Sesquiterpenes were acyclic, monocyclic, byciclic and tryciclic. Sesquiterpenes were detected in SIM (selected ion monitoring) mode using their characteristics ions. All the sesquiterpenes were identified by mass spectral data, linear retention indices (LRI), literature data and injection of standards where available. Quantitative results were obtained using the method of standard additions. The method showed an average LOD = 0.05 µg L−1 and LOQ = 0.15 µg L−1. The monocyclic sesquiterpene with the germacrene skeleton, Germacrene D and the bicyclic sesquiterpene with the muurolane skeleton, α-muurolene were present in all the wine samples analysed. Syrah wines were the samples richest in sesquiterpenes in agreement with their typical spicy and woody notes. The results evidenced the possibility to use sesquiterpenes for wine authenticity and traceability.
Escape: a target selection technique using visually-cued gestures

Many mobile devices have touch-sensitive screens that people interact with using fingers or thumbs. However, such interaction is difficult because targets become occluded, and because fingers and thumbs have low input resolution. Recent research has addressed occlusion through visual techniques. However, the poor resolution of finger and thumb selection still limits selection speed. In this paper, we address the selection speed problem through a new target selection technique called Escape. In Escape, targets are selected by gestures cued by icon position and appearance. A user study shows that for targets six to twelve pixels wide, Escape performs at a similar error rate and at least 30% faster than Shift, an alternative technique, on a similar task. We evaluate Escape's performance in different circumstances, including different icon sizes, icon overlap, use of color, and gesture direction. We also describe an algorithm that assigns icons to targets, thereby improving Escape's performance.
Mining the Change of Events in Environmental Scanning for Decision Support

An organization’ s environment is increasingly complex. Business demand on environmental scanning has significantly increased in recent years due to an attempt to assist management in planning an organization’ s strategies and responses. The conventional technique for environmental scanning is event detection from text documents such as new s stories. Event detection methods recognize events while they neglect to discover the changes of events. This work develops an event change detection (ECD) approach that combines association rule mining and change mining techniques. Detecting changes of events aids managers in making fast responses to the change of external environments. Association rule mining is employed to discover the subject behaviors of events from news stories. Changes of events are identified by comparing the subject behaviors of events from different time periods. The discovered event changes can provide effective decision support for decision makers to capture environmental information in a timely manner and make adequate decisions.
A hybrid meta-cognitive restricted boltzmann machine classifier for credit scoring

Meta-cognition with self-regulated learning equips a machine learning algorithm to make judicious decisions about every sample in the training data set. Due to this capability, meta-cognitive machine learning algorithms exhibit better generalization behavior. In the past, numerous works have focused on studying the effect on meta-cognition for learning patterns from data in a supervised fashion. In this paper, we attempt to study the effect of meta-cognition towards efficient feature representation in a Restricted Boltzmann Machine (RBM). To this end, we develop a Meta-cognitive Restricted Boltzmann Machine (McRBM) that decides what-to-represent, how-to-represent and when-to-represent for each sample in the training data set through its Sample deletion, Feature Representation and Sample Reserve strategies, respectively. The meta-cognitive component of McRBM helps to improve the generative training of RBM. The features thus generated by RBM are then used in classification through back propagation learning, which is derived based on the hinge-loss error function. The McRBM is used to solve credit scoring problem using the German credit data set, Australian credit data set and the KAGGLE credit data set. Performance of McRBM is compared against the Support Vector Machines (SVM), Extreme Learning Machines (ELM), Multi-layer Perceptron with Back Propagation (MLP-BP), and Classification Restricted Boltzmann Machine (ClassRBM). Performance results show superior classification ability of McRBM.
Relationship between RPE and Blood Lactate after Fatiguing Handgrip Exercise in Taekwondo and Sedentary Subjects

Purpose: To get information on the effort during vigorous exercise there are different ways and, the most popular, is the scale to measure subjective exertion or the rating of perceived exertion (RPE), such as the Borg scale. This scale is an effective methods to quantify and monitor the intensity of the exercise The aims of this study were to investigate the association between RPE, blood lactate concentration and maximal voluntary handgrip (MVG) force, after fatiguing exercise in athletes and non-athletes. Method: Twelve right-handed male taekwondo athletes (age: 27 ± 5 years) and 12 matched controls (age: 26 ± 5 years) were recruited. Peak force was measured and, after a subsequent rest, subjects were asked to reproduce 30% of their MVG for 4 s, then relax for 2 s, and then repeat this sequence for 10 minutes. Blood lactate was measured at rest (pre), at the end (0 min) as well as 3 and 10 min of the recovery. The RPE for the work performed were measured at the end of fatiguing handgrip exercise with Borg scale (range: 6-20). Conclusion: The results of this study show significant correlation between RPE and blood lactate peak (p<0.01), RPE and MVG post exercise (p<0.01) and between RPE and Δ blood lactate (blood lactate post-blood lactate pre; p<0.01).
2D NMR metabonomic analysis: a novel method for automated peak alignment

MOTIVATION Comparative metabolic profiling by nuclear magnetic resonance (NMR) is showing increasing promise for identifying inter-individual differences to drug response. Two dimensional (2D) (1)H (13)C NMR can reduce spectral overlap, a common problem of 1D (1)H NMR. However, the peak alignment tools for 1D NMR spectra are not well suited for 2D NMR. An automated and statistically robust method for aligning 2D NMR peaks is required to enable comparative metabonomic analysis using 2D NMR. RESULTS A novel statistical method was developed to align NMR peaks that represent the same chemical groups across multiple 2D NMR spectra. The degree of local pattern match among peaks in different spectra is assessed using a similarity measure, and a heuristic algorithm maximizes the similarity measure for peaks across the whole spectrum. This peak alignment method was used to align peaks in 2D NMR spectra of endogenous metabolites in liver extracts obtained from four inbred mouse strains in the study of acetaminophen-induced liver toxicity. This automated alignment method was validated by manual examination of the top 50 peaks as ranked by signal intensity. Manual inspection of 1872 peaks in 39 different spectra demonstrated that the automated algorithm correctly aligned 1810 (96.7%) peaks. AVAILABILITY Algorithm is available upon request.
Initial intracellular proteome profile of Aspergillus niger biofilms.

An initial profiling of the intracellular proteome of Aspergillus niger ATCC 10864 biofilm cultures developed on polyester cloth was carried out by using 2D-PAGE and MS-TOF analysis and it was compared to the proteome of conventionally grown free-living submerged cultures. A number of 2D-PAGE protein spots from both types of cultures were subjected to MS-TOF analysis and data interrogation of the NCBI nr database available for this species. Proteomic maps showed different expression patterns in both culture systems with differentially expressed proteins in each case. In biofilm cultures, 19% and 32% of the selected protein spots were overexpressed and differentially expressed, respectively. On the contrary, in free-living cultures, 44% and 7% of the selected protein spots were over-expressed and differentially expressed, respectively. Although preliminary, results presented in this paper show that there are significant differences between the proteomes of A. niger biofilm and free-living mycelia. It seems that cell adhesion is the most important stimulus responsible for biofilm development which is the basis of Surface Adhesion Fermentation.
Elite Opposition-Based Differential Evolution for Solving Large-Scale Optimization Problems and Its Implementation on GPU

Recently, the interests of solving large-scale optimization problems have increased in the field of evolutionary algorithms. This paper presents a novel differential evolution, namely EOBDE, to solve these kinds of problems by using elite opposition-based learning strategy. In the proposed algorithm, the opposite solutions of some selected elite individuals from the current population are generated at a certain probability for generation jumping. Then a corresponding opposite population is constructed to compete with the current population for providing more chances of finding out the global optimum. This approach is helpful to obtain a tradeoff between exploration and exploitation ability of DE. As another contribution, a parallel version of the proposed algorithm is implemented on Graphics Processing Units (GPU) based on CUDA platform for accelerating computing speed. The experiments are carried out on a set of representative problems with D=500 and 1000. The results of EOBDE are compared with other four state-of-the-art evolutionary algorithms in order to investigate the performance, which show that our proposed algorithm outperform the compared algorithms in terms of solution accuracy. Also the parallel version based on GPU shows promising performance in terms of the computational time.
A Portal Based Approach to Viewing Aggregated Network Performance Data in Distributed Brokering Systems

Effective utilization of networks and responsiveness to changing network conditions are essential to mitigate network utilization problems associated with distributed messaging systems. Monitoring and aggregation of network performance data is a pre-cursor to making intelligent decisions to optimize network utilizations based on detected network conditions. In this paper, we present a scheme to view aggregated performance information inside a portal and specify constraints on the accumulated performance information to detect network conditions, based on specified thresholds on measured performance factors. This work builds upon our previous work involving the design of a framework for aggregation network performance. The investigations that we report in this paper are in the context of NaradaBrokering a distributed brokering system.
Decomposed Learning for Joint Object Segmentation and Categorization

We present a learning algorithm for joint object segmentation and categorization that decomposes the original problem into two sub-tasks and admits their bidirectional interaction. In the first stage, in order to decompose output space, we train category-specific segmentation models to generate figure-ground hypotheses. In the second stage, by taking advantage of object figure-ground information, we train a multi-class segment-based categorization model to determine the object class. A re-ranking strategy is then applied to classified segments to obtain the final category-level segmentation results. Experiments on the Graz-02 and Caltech-101 datasets show that the proposed algorithm performs favorably against the state-of-the-art methods.
Stratum approaches to temporal DBMS implementation

Previous approaches to implementing temporal DBMSs have assumed that a temporal DBMS must be built from scratch, employing an integrated architecture and using new temporal implementation techniques such as temporal indexes and join algorithms. However, this is a very large and time-consuming task. The paper explores approaches to implementing a temporal DBMS as a stratum on top of an existing non-temporal DBMS, rendering implementation more feasible by reusing much of the functionality of the underlying conventional DBMS. More specifically, the paper introduces three stratum meta-architectures, each with several specific architectures. Based on a new set of evaluation criteria, advantages and disadvantages of the specific architectures are identified. The paper also classifies all existing temporal DBMS implementations according to the specific architectures they, employ. It is concluded that a stratum architecture is the best short, medium, and perhaps even long-term, approach to implementing a temporal DBMS.
cis-Tetraaquabis{5-[4-(1H-imidazol-1-yl-κN 3)phenyl]tetrazolido}manganese(II) dihydrate

In the title compound, [Mn(C10H7N6)2(H2O)4]·2H2O, the complex unit comprises an Mn2+ ion, coordinated by two imidazole N atoms from cis-related monodentate 5-[4-(imidazol-1-yl)phenyl]tetrazolide ligands and four water molecules, together with two water molecules of solvation. The Mn2+ ion lies on a twofold rotation axis and has a slightly distorted octahedral geometry. The molecules are connected by O—H⋯N and O—H⋯O hydrogen bonds involving both coordinated and solvent water molecules, generating a three-dimensional structure. Two C atoms of the imidazole ring of the ligand are each disordered over two sites with occupancy factors of 0.75 and 0.25.
An Overview of the Singularity Project 1

Singularity is a research project in Microsoft Research that started with the question: what would a software platform look like if it was designed from scratch with the primary goal of dependability? Singularity is working to answer this question by building on advances in programming languages and tools to develop a new system architecture and operating system (named Singularity), with the aim of producing a more robust and dependable software platform. Singularity demonstrates the practicality of new technologies and architectural decisions, which should lead to the construction of more robust and dependable systems. 1 This report is a snapshot of a project in motion. In particular, performance measurements are preliminary and subject to improvement. Please contact Galen Hunt (galenh@microsoft.com) or Jim Larus (larus@microsoft.com) or check at http://research.microsoft.com/os/singularity for the latest results and citation information. 2 Martín Abadi is affiliated with the Computer Science Department, University of California at Santa Cruz. Paul Barham is at Microsoft Research Cambridge. Martín Abadi, Nick Murphy, and Ted Wobber are at Microsoft Research Silicon Valley. We’ve also been fortunate to have a great group of interns who made many contributions to this project over the past couple years: Michael Carbin, Fernando Castor, Adam Chlipala, Jeremy Condit, Daniel Frampton, Chip Killian, Prince Mahajan, Bill McCloskey, Martin Murray, Martin Pohlack, Tom Roeder, Avi Shinnar, Mike Spear, Yaron Weinsberg, and Aydan Yumerefendi.
Detection of mobile botnets using neural networks

This poster deals with botnets, the most dangerous kind of mobile malware, and their detection using neural networks. Unlike common mobile malware, botnets often have a complicated pattern of behavior because they are not managed by predictable algorithms but they are controlled by humans via command and control servers (C&C servers) or via peer-to-peer networks. However, they have certain common features which have been revealed by analysis of contemporary mobile botnets. These features have been used for creation of a neural network training set. Finally, the design of parallel architecture using neural network for useful detection of mobile botnets has been described.