Miracle-dz/UniFAR-FTUs huggingface dataset:
Metallicities of the β Cephei stars from low-resolution ultraviolet spectra

We derive basic stellar parameters (angular diameters, effective temperatures, metallicities) and interstellar reddening for all β Cephei stars observed during the IUE satellite mission, including those belonging to three open clusters. The parameters are derived by means of an algorithmic procedure of fitting theoretical flux distributions to the low-resolution IUE spectra and ground-based spectrophotometric observations. Since the metallicity has a special importance for pulsating B-type stars, we focus our attention in particular on this parameter.
Impact of a community-based integrated management of childhood illnesses (IMCI) programme in Gegharkunik, Armenia.

BACKGROUND Maternal and child health status in the Martuni region of Gegharkunik marz, Armenia, precipitously declined following Armenia's independence in 1991. In response, the American Red Cross (ARC) and the Armenian Red Cross Society (ARCS) implemented the WHO community-level Integrated Management of Childhood Illnesses (IMCI) strategy, complementing recent clinical IMCI training in the region in which 387 community health volunteers from 16 villages were trained as peer educators, and approximately 5000 caretakers of children under age 5 were counselled on key nutrition and health practices. METHODS A pre-post independent sample design was used to assess the programme's impact. The evaluation instrument collected respondent demographic characteristics and knowledge, attitudes and practices consistent with 10 health indicators typical of child survival interventions. At baseline and at follow-up, 300 mothers were interviewed using a stratified simple random sampling of households with at least one child less than age 2. RESULTS The assessment confirmed the population's poor health status and limited knowledge and application of recommended child care practices. The campaign reached its target: at follow-up, 67% had seen media messages within the past month, 82% had received the IMCI informational booklet, and 30% had seen other materials. Evidence of the success of the programme included the following: exclusive breastfeeding increased 31.4%, maternal knowledge of child illness signs increased 30%, knowledge of HIV increased 28.5%, and physician attended deliveries increased 15%. CONCLUSIONS This evaluation documented the significant and substantial impact of the community IMCI programme on both knowledge and practice in rural areas of Armenia. Consideration should be given to continuing and expanding this project as a complement to health sector development activities in this region.
Global Optimal Routing, Scheduling and Power Control for Multi-Hop Wireless Networks with Interference

We consider the problem of joint routing, scheduling and power control in multi-hop wireless networks. We use a linear relation between link capacity and signal to interference noise ratio in our formulation. In a previous work, using a duality approach, the optimal link scheduling and power control that minimizes the total average transmission power is found. We formulate this problem as a linear programming problem with exponential number of constraints. To cope with the exponential number of constraints, we propose an iterative algorithm based on the cutting plane method. The separation oracle for the cutting plane algorithm turns out to be an element-wise concave optimization problem that can be effectively solved using branch and bound algorithm. We extend the same method to find the optimal routing scheduling and power control. Simulation results show that this methodology is more efficient and scalable compare to the previously proposed algorithm.
A statistical approach for semantic relation extraction

Semantic relations are an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. Automatic semantic relation extraction system is a crucial tool that can reduce the bottleneck of knowledge acquisition in the ontologies construction. In this paper, we present a statistical approach for learning the semantic relations between concepts of an ontology in the agricultural domain. The semantic relations are acquired by using verbs to indicate the relations between ontology concepts. The co-occurrences of domain-verbs with their components, which are annotated the concepts, are analyzed by using several statistical methodologies. Moreover, we expand the sets of verb expressing the same semantic relation by using the extracted patterns of concept pairs of the seed verb's component. Our experiment has been done on a collection of Thai shallow parsed texts in the domain of agriculture. The precision and recall of the presented system is 65% and 82%, respectively.
Counting Pedestrian in Crowded Subway Scene

When the high occlusion occurs in crowded scene, face detection is a better substitute for detecting pedestrian. In this paper, we present a novel crowd analysis method based on discriminative descriptor of faces and support vector machine (SVM) ensemble. Through manipulating the input features in the same sample set, the different input features of faces are extracted to train two SVM classifiers. The classification scores of two generated classifiers are combined adaptively to make a collective decision. The first SVM, as the principal classifier gives out most of face hypotheses, while the second SVM serves as secondary one to rejecting the false positive. We present experiment to test the proposed method in crowded subway video, and the result shows that the SVM ensemble outperforms the single SVM in counting the pedestrian.
Accelerating Neuronal Genetic Research in C . elegans with Computer Vision & Machine Learning

This final project for CS229 implements a software system to classify C. elegans as wild-type or mutant based on biological fluorescence images of synapses, the connections between neurons and muscles, in the worms. The project uses features of two neurons in the images, the DA8 and DA9 motorneurons, which were calculated as part of the CS231A final project. Using these features, the software trains an SVM using a Radial Basis Function kernel. The software was cross-validated on over 150 images with 75% accuracy. The input images are very noisy and blurry, so human accuracy can be as low as 60%. In comparison, the software is precise. Future Distribution Permission The author(s) of this report give permission for this document to be distributed to Stanfordaffiliated students taking future courses.
A Parallel Workflow for Real-time Correlation and Clustering of High-Frequency Stock Market Data

We investigate the design and implementation of a parallel workflow environment targeted towards the financial industry. The system performs real-time correlation analysis and clustering to identify trends within streaming high-frequency intra-day trading data. Our system utilizes state-of-the-art methods to optimize the delivery of computationally-expensive real-time stock market data analysis, with direct applications in automated/algorithmic trading as well as knowledge discovery in high-throughput electronic exchanges. This paper describes the design of the system including the key online parallel algorithms for robust correlation calculation and clique-based clustering using stochastic local search. We evaluate the performance and scalability of the system, followed by a preliminary analysis of the results using data from the Toronto Stock Exchange.
A Model of Eye Movements and Visual Attention

This paper introduces the EMMA model of eye movements and visual attention. EMMA provides a formal model of the temporal and spatial aspects of eye movements as they arise from shifts in visual attention. To implement the model, EMMA is integrated into the ACT-R/PM cognitive architecture (Anderson & Lebiere, 1998) and requires minimal modification of existing ACT-R/PM models. This paper details the EMMA model, theory, and implementation and also demonstrates how the extended framework helps to capture important aspects of behavior in an equation-solving task.
Data compression with restricted parsings

We consider a class of algorithms related to Lempel-Ziv that incorporate restrictions on the manner in which the data can be parsed with the goal of introducing new tradeoffs between implementation complexity and data compression ratios. Our main motivation lies within the field of compressed memory computer systems. Here requirements include extremely fast decompression and compression speeds, adequate compression performance on small data block lengths, and minimal hardware area and energy requirements. We describe the approach and provide experimental data concerning its compression performance with respect to known alternatives. We show that for a variety of data sets stored in a typical main memory, this direction yields results close to those of earlier techniques, but with significantly lower energy consumption at comparable or better area requirements. The technique thus may be of eventual interest for a number of applications requiring high compression bandwidths and efficient hardware implementation
Matrix Metalloproteinases-2, -3, -7, -9 and -10, But Not MMP-11, Are Differentially Expressed in Normal, Benign Tumorigenic and Malignant Human Keratinocyte Cell Lines

Abstract In order to investigate the correlations between constitutive proteinase expression and the degree of tumorigenicity of cancer cells we have studied a model system of three keratinocyte cell lines. RT-PCR studies showed that the cell lines express the genes of matrix metalloproteinase-2, -3, -7, -9, -10 and -11, indicating that they are able to synthesize the corresponding enzymes. Actual MMP synthesis was proven by zymography and Western blotting. In conditioned media gelatinolytic activities or immunoreactive forms of MMP-2, -3, -7, -9, -10 and -11 were detected. The signal intensities showed that MMP secretion increases in the order HaCaT < A5 ≤ II-4RT, whereas only MMP-11 is secreted by all cell lines in equal amounts. Intracellularly, enhanced levels of one or both of the tumorigenic variants were only found for MMP-3, -9 and -10, suggesting special functions of these intracellular MMP pools for the tumorigenic cell lines. For MMP-11 exclusive expression in stromal fibroblasts of tumor tissues is widely accepted; however, our results and three other recent reports demonstrate that this concept is not generally valid. In conclusion, the three keratinocyte cell lines investigated here represent an excellent model for studying constitutive expression and secretion of MMPs in correlation to the degree of in vivo tumorigenicity.
A Climate Policy for the Short and Medium Term: Stabilization or Adaptation?

An evaluation of analyses sponsored by the predecessor to the U.K. Department for Environment, Food and Rural Affairs (DEFRA) of the global impacts of climate change under various mitigation scenarios (including CO2. stabilization at 550 and 750 ppm) coupled with an examination of the relative costs associated with different schemes to either mitigate climate change or reduce vulnerability to various climate-sensitive hazards (namely, malaria, hunger, water shortage, coastal flooding, and losses of global forests and coastal wetlands) indicates that, at least for the next few decades, risks and/or threats associated with these hazards would be lowered much more effectively and economically by reducing current and future vulnerability to those hazards rather than through stabilization. Accordingly, over the next few decades the focus of climate policy should be to: (a) broadly advance sustainable development (particularly in developing countries since that would generally enhance their adaptive capacity to cope with numerous problems that currently beset them, including climate-sensitive problems), (b) reduce vulnerabilities to climate-sensitive problems that are urgent today and might be exacerbated by future climate change, and (c) implement “no-regret” emission reduction measures while at the same time striving to expand the universe of such measures through research and development of cleaner and more affordable technologies. Such a policy would help solve current urgent problems facing humanity while preparing it to face future problems that might be caused by climate change.
Consistent and efficient sampler for geometric computation

This paper shows how the random sampling, M-estimators, random walk can be combined to create a consistent sampler for generic models in problems that are difficult due to outliers and multimodality of the solution. Our method contains three major steps: (1) finding the local peaks of the selected robust cost function (M-estimator) using seeds from random sampling of minimal configurations, (2) constructing an approximation of the posterior density by using the local Hessian approximations of the cost function, and (3) sampling by the Metropolis-Hastings selection rule with a mixture proposal distribution containing both draws from the approximated posterior density and random walk to achieve consistence of the samples. The experiments verify that the sampler has much better mixing properties than a conventional random walk sampler. Thus, the approach is promising MCMC method for Bayesian inference that need to numerically evaluate integrals over the posterior density.
Inhibition of platelet-derived growth factor receptor phosphorylation by STI571 (Gleevec) reduces growth and metastasis of human pancreatic carcinoma in an orthotopic nude mouse model.

PURPOSE We evaluated the expression of platelet-derived growth factor (PDGF) ligands and receptors in clinical specimens of human pancreatic adenocarcinomas and determined the therapeutic effect of STI571 (Gleevec), a protein tyrosine kinase inhibitor of PDGF receptor (PDGFR), on human pancreatic carcinoma cells growing in the pancreas and liver of nude mice. EXPERIMENTAL DESIGN Immunohistochemical staining for PDGF-AA and -BB ligands, PDGFR-alpha and -beta, and phosphorylated PDGFR-alpha and -beta was performed on 31 specimens of human pancreatic cancer and L3.6pl human pancreatic adenocarcinoma cell line. To determine the in vivo effects of STI571, nude mice with L3.6pl cells injected into the pancreas were randomized 7 days later to receive one of the following treatments: sterile water p.o. (control), STI571, gemcitabine, or a combination of STI571 and gemcitabine. RESULTS In 29 of 31 clinical specimens of human pancreatic adenocarcinoma, both tumor cells and tumor-associated endothelial cells expressed phosphorylated PDGFR-alpha and -beta. L3.6pl cells growing in culture expressed moderate amounts of PDGF-AA and little to no PDGFR-alpha or -beta, whereas L3.6pl cells growing in the pancreas of nude mice expressed a high level of PDGF and receptors. Colocalization immunohistochemical analysis demonstrated expression of activated PDGFR-beta by tumor-associated endothelial cells in both the pancreas and in liver metastases. Tumors of mice treated for 4 weeks with STI571 (50 mg/kg or 100 mg/kg p.o. daily) were slightly smaller than controls. Tumors treated with gemcitabine and STI571 (50 mg/kg) were >70% smaller than tumors in control mice and 36% smaller than those in mice treated with gemcitabine only (P < 0.0002 and P < 0.04, respectively). Combination therapy also inhibited spontaneous metastasis to the liver. Tumors from mice treated with both STI571 and gemcitabine had decreased expression of activated (phosphorylated) PDGFR-alpha and -beta, decreased mean vessel density, decreased cell proliferation, and increased apoptosis of tumor cells. CONCLUSIONS Collectively, these data show that activated PDGFR on tumor cells and tumor-endothelial cells can be a novel target for therapy of pancreatic carcinoma.
A Novel Maximum Likelihood Detection Algorithm for Orthogonal Space-Time Block Codes

In this letter, we propose a low complexity Maximum Likelihood (ML) decoding algorithm for orthogonal space-time block codes (OSTBCs) based on the real-valued lattice representation and QR decomposition. We show that for a system with rate r = K/T , where K is the number of transmitted symbols per T time slots, the proposed algorithm decomposes the original complex-valued system into a parallel system represented by 2K real-valued components, thus allowing for a simple and independent detection of the real and imaginary parts of each complex transmitted symbol. We further show that for square L-QAM constellations, the proposed algorithm reduces the decoding computational complexity from O(L) for conventional ML to O( √ L) without sacrificing the performance.
Sparse Approximations for Drum Sound Classification

Up to now, there has only been little work on using features from temporal approximations of signals for audio recognition. Time-frequency tradeoffs are an important issue in signal processing; sparse representations using overcomplete dictionaries may (or may not, depending on the dictionary) have more time-frequency flexibility than standard short-time Fourier transform. Also, the precise temporal structure of signals cannot be captured by spectral-based feature methods. Here, we present a biologically inspired three-step process for audio classification: 1) Efficient atomic functions are learned in an unsupervised manner on mixtures of percussion sounds (drum phrases), optimizing the length as well as the shape of the atoms. 2) An analog spike model is used to sparsely approximate percussion sound signals (bass drum, snare drum, hi-hat). The spike model consists of temporally shifted versions of the learned atomic functions, each having a precise temporal position and amplitude. To obtain the decomposition given a set of atomic functions, matching pursuit is used. 3) Features are extracted from the resulting spike representation of the signal. The classification accuracy of our method using a support vector machine (SVM) in a 3-class database transfer task is 87.8%. Using gammatone functions instead of the learned sparse functions yields an even better classification rate of 97.6%. Testing the features on sounds containing additive white Gaussian noise reveals that sparse approximation features are far more robust to such distortions than our benchmark feature set of timbre descriptor (TD) features.
Does Money Growth Granger Cause Inflation in the Euro Area? Evidence from Out-of-Sample Forecasts Using Bayesian VARS

We use a mean-adjusted Bayesian VAR model as an out-of-sample forecasting tool to test whether money growth Granger-causes inflation in the euro area. Based on data from 1970 to 2006 and forecasting horizons of up to 12 quarters, there is surprisingly strong evidence that including money improves forecasting accuracy. The results are very robust with regard to alternative treatments of priors and sample periods. That said, there is also reason not to overemphasize the role of money. The predictive power of money growth for inflation is substantially lower in more recent sample periods compared to the 1970s and 1980s. This cautions against using money-based inflation models anchored in very long samples for policy advice.
Fundamentals of Information Studies: Understanding Information and Its Environment, Second Edition

In this paper, a new method for fuzzy clustering is proposed that combines generative topographic mapping (GTM) and Fuzzy c-means (FCM) clustering. GTM is used to generate latent variables and their posterior probabilities. These two provide the distribution of the input data in the latent space. FCM determines the seeds of clusters, as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from GTM. Experiments are conducted to compare the results obtained using FCM and the Gustafson-Kessel (GK) algorithm with the proposed method in terms of four cluster-validity indexes. Using simulated and benchmark data sets, it is observed that the hybrid method (GTMFCM) performs better than FCM and GK algorithms in terms of these indexes. It is also found that the superiority of GTMFCM over FCM and GK algorithms becomes more pronounced with the increase in the dimensionality of the input data set. © 2009 Wiley Periodicals, Inc.
Dynamic Competing Mechanisms

Competing mechanism games involve contracting environments with multiple principals. This paper extends the static model with three or more agents to the dynamic setting, where agents’ types evolve over time according to Markov processes. Agents observe contracts oered and actions taken, but principals observe only private messages from agents. Perhaps surprisingly, the dynamic game with low discounting is more tractable than the static version. A principal’s minmax value relative to arbitrarily general mechanisms equals that relative to simpler classes of mechanisms, allowing equilibrium payos to be expressed in terms of primitives: In one-shot games, the minmax cannot be explicitly computed. The dynamic game allows weaker notions of incentive compatibility than the static game, lowering players’ mimnax values. Therefore, the dynamic game supports more equilibrium payos with simple mechanisms. From the applied perspective, this
Fuzzy Location and Tracking on Ambient Networks

This paper describes user location and tracking on indoor scenarios through an ambient network. User location and tracking are considered in order to provide complete intelligent location based services. Imprecise location based on radio-frequency estimations can be processed by a fuzzy location algorithm in order to provide high location near to 90%. Fuzzy tracking is also provided by means of a fuzzy automaton. Fuzzy location techniques here presented allow increasing location ratios even when the user triangulation can not be as precise as desired.
An Upper Limit to Arcminute-Scale Anisotropy in the Cosmic Microwave Background Radiation at 142 GHz

We present limits to anisotropies in the cosmic microwave background radiation (CMB) at angular scales of a few arcminutes. The observations were made at a frequency of 142 GHz using a six-element bolometer array (the Sunyaev-Zel'dovich Infrared Experiment) at the Caltech Submillimeter Observatory. Two patches of sky, each approximately 36' × 4' and free of known sources, were observed for a total of 6-8 hours each, resulting in approximately 80 independent 1. ′7 full width half-maximum pixels. Each pixel is observed with both a dual-beam and a triple-beam chop, with a sensitivity per pixel of 90-150 μK in each chop. These data have been analyzed using maximum-likelihood techniques by assuming a Gaussian autocorrelation function for the distribution of CMB fluctuations on the sky. We set an upper limit of ΔT/T ≤ 2.1 × 10-5 (95% confidence) for a coherence angle to the fluctuations of 1. ′1. These limits are comparable to the best limits obtained from centimeter-wavelength observations on similar angular scales but have the advantage that the contribution from known point sources is negligible at these frequencies. They are the most sensitive millimeter-wavelength limits for coherence angles ≤3'. The results are also considered in the context of secondary sources of anisotropy, specifically the Sunyaev-Zel'dovich effect from galaxy clusters.
Listening to MEMS: An acoustic vibrometer

A new way to characterize vibrating MEMS devices is presented. Using an acoustic particle velocity sensor the coupled sound field is measured, which is a measure for the movement of the MEMS device. We present several possible applications of this measurement method. It can be used as a read-out system for a mass flow sensor, and for characterization of in- and out-of-plane movements of MEMS devices. The method is an interesting alternative to laser scanning vibrometry due to its small size and low complexity; furthermore, it allows the user to ‘listen’ directly to MEMS devices.