Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal and spatial variability in snow chemistry at Eclipse Icefield, Yukon Territory, Canada

Samples collected from four snow pits at Eclipse Icefield, Yukon Territory, Canada, were analyzed for stable isotopes and major ions to assess seasonal and spatial variability in snow chemistry. Accumulation since the end of the 2001 summer season over the 0.1 km 2 area sampled ranges from 0.77 to 1.16 m w.e. Snow-pit stratigraphy and chemical records demonstrate that the low accumulation at pit 3 is due to an under-representation of winter snow accumulation at that site. For all major-ion species, chemical concentrations are independent of snow accumulation rate. Seasonal variations are evident in the major-ion records and can be divided between sea-salt species (Na+, Cl - ) that peak in late fall to winter, and dust (Ca 2+ ,M g 2+ ,K + ) and other species (NH4 + ,N O 3 - ,S O 4 2- ,C 2O2 2- ) that peak in late spring to summer. The signal common to all four snow pits identified by empirical orthogonal function analysis ranges from 49% of the total variance for Na + and Cl - to as high as 80% of the total variance for SO4 2- . There is greater spatial variability in species associated with coarse-mode particles (Na + ,C l - , Ca 2+ ,M g 2+ ) than in species present mainly in accumulation-mode aerosols (SO4 2- ,N H 4 + ) or in the gas phase (NO3 - ).
Towards Understanding Cloud Performance Tradeoffs Using Statistical Workload Analysis and Replay

Cloud computing has given rise to a variety of distributed applications that rely on the ability to harness commodity resources for large scale computations. The inherent performance variability in these applications’ workload coupled with the system’s heterogeneity render ineffective heuristics-based design decisions such as system configuration, application partitioning and placement, and job scheduling. Furthermore, the cloud operator’s objective to maximize utilization conflicts with cloud application developers’ goals of minimizing latency, necessitating systematic approaches to tradeoff these optimization angles. One important cloud application that highlights these tradeoffs is MapReduce. In this paper, we demonstrate a systematic approach to reasoning about cloud performance tradeoffs using a tool we developed called Statistical Workload Analysis and Replay for MapReduce (SWARM). We use SWARM to generate realistic workloads to examine latency-utilization tradeoffs in MapReduce. SWARM enables us to infer that batched and multi-tenant execution effectively balance the tradeoff between latency and cluster utilization, a key insight for cloud operators.
Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission-line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission-line objects only. We use two independent methods, Artificial Neural Networks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad-band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify active galactic nuclei and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming Fibre Multi Object Spectrograph (FMOS) survey and the planned Wide Field Multi Object Spectrograph (WFMOS) survey.
BMN Operators and Superconformal Symmetry

Implications of N = 4 superconformal symmetry on Berenstein-Maldacena-Nastase (BMN) operators with two charge defects are studied both at finite charge J and in the BMN limit. We find that all of these belong to a single long supermultiplet explaining a recently discovered degeneracy of anomalous dimensions on the sphere and torus. The lowest dimensional component is an operator of naive dimension J + 2 transforming in the [0, J, 0] representation of SU(4). We thus find that the BMN operators are large J generalisations of the Konishi operator at J = 0. We explicitly construct descendant operators by supersymmetry transformations and investigate their three-point functions using superconformal symmetry.
A Hybrid Heuristic Approach for Scheduling Bug Fix Tasks to Software Developers

Large software projects usually maintain bug repositories where both developers and end users ca n report and track the resolution of software defects . These defects must be fixed and new versions of the software incorporating the patches that solve them must be released. The project manager must schedule a set of error correction tasks with different prio rities in order to minimize the time required to make the next release available and guarantee that the more important issues were fixed. In this paper, we prop ose a hybrid heuristic approach based on an algorithm t o schedule error correction tasks which combines a genetic algorithm and a constructive heuristic. We believe that our method can propose schedules for bug fix tasks with lower cost when compared to an a dhoc schedule for non-trivial projects.
A Real-Time Fraud Detection Algorithm Based on Intelligent Scoring for the Telecom Industry

Fraud detection is one of the biggest challenges in the telecom industry. Commonly used approaches, such as rule sets, outlier detection, and classification, have high computational cost, so they don't work well on mass data in terms of accuracy and speed. Besides, those algorithms are not good at detecting new fraud patterns. In this paper, we propose an UIS (United Intelligent Scoring) algorithm for fraud detection which has three merits. First, it has lower computational complexity. We use Manhattan distance instead of Euclidean distance to measure similarity between fraud samples and ordinaries. Second, new fraud patterns can be detected effectively by joint fraud probability. Finally, UIS is able to generate and update real-time scores, which detects early-time fraud and minimizes economic losses. Integrated experiments on real datasets of the telecom industry demonstrate that UIS is real-time, effective, and robust in different situations.
Linguistic Markers of Psychological Change Surrounding September 11, 2001

The diaries of 1,084 U.S. users of an on-line journaling service were downloaded for a period of 4 months spanning the 2 months prior to and after the September 11 attacks. Linguistic analyses of the journal entries revealed pronounced psychological changes in response to the attacks. In the short term, participants expressed more negative emotions, were more cognitively and socially engaged, and wrote with greater psychological distance. After 2 weeks, their moods and social referencing returned to baseline, and their use of cognitive-analytic words dropped below baseline. Over the next 6 weeks, social referencing decreased, and psychological distancing remained elevated relative to baseline. Although the effects were generally stronger for individuals highly preoccupied with September 11, even participants who hardly wrote about the events showed comparable language changes. This study bypasses many of the methodological obstacles of trauma research and provides a finegrained analysis of the time line of human coping with upheaval.
Manhattonian proximity in a simple polygon

Let <italic>P</italic> be a simple planar polygon. We present a linear worst-case time algorithm for constructing the bounded Voronoi diagram of <italic>P</italic> in the Manhattan metric, where each point <italic>z</italic> in <italic>P</italic> belongs to the region of the closest vertex of <italic>P</italic> that is visible from <italic>z</italic>. Among other consequences, the minimal spanning tree of the vertices in the Manhattan metric that is contained in <italic>P</italic> can be computed within optimal linear time.
Perinatal psychological distress in the South African context: The road to task shifting evidence based interventions

Inadequate public health resources coupled with a chronically overburdened health system leave a large proportion of South Africans unable to access mental health care. Low-income pregnant women with common mental disorders (CMDs) are arguably more vulnerable to falling through the treatment gap, given the low rates of detection during pregnancy and the numerous additional barriers to care. The direct and indirect financial and personal costs associated with perinatal mental illness are substantial, while the high prevalence rates of perinatal CMDs make this an area in need of urgent attention. Integrating task shifting approaches into perinatal primary health care services is a promising solution. The first chapter introduces the thesis, providing context to the studies that are presented in later chapters and an overview of the research questions that informed them. The second chapter constitutes a systematic review of the literature relevant to the studies. Chapters 3 to 6 report on the findings of the studies, briefly described in the abstract below. The prevalence and risk factors associated with perinatal psychological distress a plausible precursor for common mental disorders (CMDs) are not widely understood in underresourced settings. The first study (Chapter 3) investigates the prevalence and predictors of psychological distress in the antenatal period. Data were collected from 664 pregnant women who reported for antenatal care to any one of 11 Midwife and Obstetric Units (MOU) across the greater Cape Town area. Psychological distress was measured using the Symptom Response Questionnaire (SRQ-20; cut-off value of 7/8), while data pertaining to risk factors were collected via a demographic questionnaire, the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) and the Multidimensional Scale of Perceived Social Support (MSPSS). The prevalence of antenatal psychological distress was 38.6%. Risk factors included low socio-economic status (SES) (OR = 1.45, 95% CI: 1.24-1.68); recent physical abuse and/or rape (OR = 1.94, 95% CI: 1.57-2.40); complications during a previous delivery (OR = 1.18, 95% CI: 1.01-1.38); having given birth before (OR = 1.61, 95% CI: 1.21-2.14). The high prevalence rate of psychological distress is consistent with those found in other South African studies of perinatal CMDs. Appropriate, context-specific, and effective interventions are better served by investigating a broader range of symptoms associated with perinatal CMDs in these settings. The second study (Chapter 4) examines the mental health literacy (MHL) of pregnant women, including their perceptions of the causes of mental illness during pregnancy and best treatment
Dimethylarginine dimethylaminohydrolase (DDAH) regulates trophoblast invasion and motility through effects on nitric oxide.

BACKGROUND Invasion of trophoblast into the uterine environment is crucial for establishing a successful pregnancy. Physiological production of nitric oxide (NO) by extravillous trophoblasts results in significant pro-invasive effects. NO synthesis is competitively inhibited by methylated arginine analogues such as asymmetric dimethylarginine (ADMA) but not the enantiomer symmetric dimethylarginine (SDMA). Within cells, the concentration of ADMA is regulated by the activity of the enzyme dimethylarginine dimethylaminohydrolase (DDAH). The aim of this study was to examine DDAH expression and function in trophoblasts. METHODS AND RESULTS DDAH-1 and DDAH-2 messenger RNA and protein were demonstrated in first trimester placental tissue, primary extravillous trophoblasts and extravillous trophoblast-derived cell lines. DDAH activity was demonstrated in both cells and tissue. Overexpression of DDAH-1 in trophoblasts led to a number of significant changes, including an 8-fold increase in enzymatic activity, a 59% decrease in production of ADMA (but not SDMA), a 1.9-fold increase in NO and a 1.6-fold increase in cyclic guanosine monophosphate (cGMP) production. Functional assays showed that increased DDAH activity led to significantly increased cell motility and invasion in response to hepatocyte growth factor (HGF). CONCLUSIONS DDAH may play an important role in the regulation of extravillous trophoblast function via its effects on ADMA and NO production.
Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.
Massively parallel data analysis with PACTs on Nephele

Large-scale data analysis applications require processing and analyzing of Terabytes or even Petabytes of data, particularly in the areas of web analysis or scientific data management. This trend has been discussed as "web-scale data management" in a panel at VLDB 2009. Formerly, parallel data processing was the domain of parallel database systems. Today, novel requirements like scaling out to thousands of machines, improved fault-tolerance, and schema free processing have made a case for new approaches.
De Novo Unbalanced Translocations in Prader-Willi and Angelman Syndrome Might Be the Reciprocal Product of inv dup(15)s

The 15q11-q13 region is characterized by high instability, caused by the presence of several paralogous segmental duplications. Although most mechanisms dealing with cryptic deletions and amplifications have been at least partly characterized, little is known about the rare translocations involving this region. We characterized at the molecular level five unbalanced translocations, including a jumping one, having most of 15q transposed to the end of another chromosome, whereas the der(15)(pter->q11-q13) was missing. Imbalances were associated either with Prader-Willi or Angelman syndrome. Array-CGH demonstrated the absence of any copy number changes in the recipient chromosome in three cases, while one carried a cryptic terminal deletion and another a large terminal deletion, already diagnosed by classical cytogenetics. We cloned the breakpoint junctions in two cases, whereas cloning was impaired by complex regional genomic architecture and mosaicism in the others. Our results strongly indicate that some of our translocations originated through a prezygotic/postzygotic two-hit mechanism starting with the formation of an acentric 15qter->q1::q1->qter representing the reciprocal product of the inv dup(15) supernumerary marker chromosome. An embryo with such an acentric chromosome plus a normal chromosome 15 inherited from the other parent could survive only if partial trisomy 15 rescue would occur through elimination of part of the acentric chromosome, stabilization of the remaining portion with telomere capture, and formation of a derivative chromosome. All these events likely do not happen concurrently in a single cell but are rather the result of successive stabilization attempts occurring in different cells of which only the fittest will finally survive. Accordingly, jumping translocations might represent successful rescue attempts in different cells rather than transfer of the same 15q portion to different chromosomes. We also hypothesize that neocentromerization of the original acentric chromosome during early embryogenesis may be required to avoid its loss before cell survival is finally assured.
Characterizing Fluid Saturation Distribution Using Cross-Well Seismic and Well Data: A Geostatistical Study

One of the goals of reservoir characterization, particularly in mature reservoirs, is to identify unswept regions containing high oil saturation for targeted infill drilling or enhanced recovery. A common approach to the problem is to generate high resolution distribution of reservoir properties such as permeability and porosity and then conduct flow simulations to identify regions of high oil saturation. One possible alternative to flow simulations would be to generate spatial distribution of properties that are related to fluid saturations and then infer fluid saturation distribution through the use of appropriate correlations. In this paper we present a field application to infer interwell water saturation distribution by combining crosswell seismic and well data. First, we use a non-parametric transformational approach to correlate sonic velocity with resistivity and porosity at the wells. An iterative procedure using alternating conditional expectations (ACE) forms the basis for this calibration. Next, stochastic cosimulation is carried out to generate conditional realizations of resistivity and porosity in the interwell region. In this cosimulation, cross-well seismic velocity is considered as the secondary data while resistivity and porosity are treated as primary data. Finally, water saturation distribution is deduced from the resistivity and porosity distributions through the use of Archie’s law.
Inhibitor peptide design for NF-кB: Markov model & genetic algorithm

Two peptide design approaches are proposed to block activities of disease related proteins. First approach employs a probabilistic method; the problem is set as Markov chain. The possible binding site of target protein and a path on this binding site are determined. 20 natural amino acids and 400 dipeptides are docked to the selected path using the AutoDock software. The statistical weight matrices for the binding energies are derived from AutoDock results; matrices are used to determine top 100 peptide sequences with affinity to target protein. Second approach utilizes a heuristic method for peptide sequence determination; genetic algorithm (GA) with tournament selection. The amino acids are the genes; the peptide sequences are the chromosomes of GA. Initial random population of 100 chromosomes leads to determination of 100 possible binding peptides, after 8–10 generations of GA. Thermodynamic properties of the peptides are analyzed by a method that we proposed previously. NF-кB protein is selected as case-study.
Developing Exergame 1 Running Head: Developing a Video Game Developing a Video Game to Increase Intrinsic Motivation to Exercise

This study sought to understand how to design an exergame to increase motivation for physical activity with regard to college students. The current research reports the findings of the initial phase of developing the exergame grounded in Self-Determination Theory. Two rounds of formative evaluation studies were conducted. In the first round, four focus groups were conducted to gain insights from the target population about their reception of the proposed exergame concept. In the second round, participants as a group played the game prototype that was developed based on the first round focus groups and provide feedback and suggestions in a focus group format. In addition, four participants played the game with a device that recorded their energy expenditure during game play on an individual basis and were interviewed after game play using the same questions used in the focus groups. The two rounds of formative evaluation confirmed many of the proposed design features. The design team also obtained valuable suggestions from the focus group participants on how to improve usability and playability of the game.
Open-M3: smart space with COTS devices

Support of legacy devices and services is crucial for the adoption of new smart space technologies. We present two technologies which enable the formation of local ad-hoc smart spaces with commercial off-the-shelf (COTS) devices. First of these technologies is NoTA, which is a service oriented architecture enabling networks of devices with different physical transports. Second one is Smart-M3, which is a semantic information sharing architecture for smart spaces. It aims at opening physical world information for the use of services and applications in the information world, thus enabling new types of mash-up applications. In our demonstration - Open-M3 - we show how these technologies are used to build a small, yet extendable smart space for sensor monitoring using COTS devices.
Religiousness on mental health in older adults: the mediating role of social support and healthy behaviours

ABSTRACT The positive association between religiousness and mental health among the faithful is well-established; here, social support (SS) and healthy behaviours (HB) are investigated as mechanisms underlying the benefits of faith on depression and anxiety in a survey sample of 97 religious older adults aged 62–96 (Mage = 79). Initial regression models revealed a significant direct effect (higher religiousness = less depression and less anxiety). For depressive symptoms, both individual mediators rendered the effect of religiousness non-significant, with HB explaining more variance (36% vs. 27%); in the combined model, both demonstrated independent, additive effects (SS = −.18, p = .006; HB = −.34, p < .0001). For anxiety, only SS emerged as a significant mediator and predicted anxiety in the combined model (SS = −.25, p = .005). The results help inform those working with religious older adults facing depression and anxiety by highlighting key aspect(s) of the person’s faith experience that will be most effective in helping to improve his or her mental health.