Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple random generation of smooth connected irregular shapes for cognitive studies

A simple method for generating random smooth connected mildly irregular binary shapes is introduced. It consists of 1) taking the Minkowski sum of a closed linear spline with random vertices and of a disk (in other words, joining consecutive randomly generated points with straight lines drawn with a "large ballpoint pen"); 2) applying Gaussian blur with a large blur radius; and 3) thresholding permissively. With very permissive thresholds and moderately large numbers of seed points, this produces fairly natural-looking "random blobs. " One can also generate "cartoonish shadows" and "boldface alphabets" with less permissive thresholds and smaller numbers of seed points. Rotation invariant families of shapes can be generated by drawing the spline vertices from rotation invariant distributions. Results obtained with the uniform distribution on the disk and the binormal distribution are presented. They are contrasted to those obtained with the uniform distribution on the square. Drawing random points from a binormal distribution gives a collection of shapes that look natural over a wide range of numbers of seed points. The shapes derived with the uniform distributions, however, are more "interesting. " Thresholds close to the most restrictive value yielding an empty shape when there is only one seed point work well. This critical threshold is easy to compute using the drawing software; thresholding more permissively guarantees a nonempty shape. The most restrictive threshold guaranteeing a connected final shape is analytically estimated using the diameter of the "pen nib" and the Gaussian blur sigma. The various bounds are in agreement.
Integrating Conceptual Knowledge Into Relevance Models: A Model and Estimation Method

We address the issue of combining explicit background knowledge with pseudo-relevance feedback from within a document collection. To this end, we use document-level annotations in tandem with generative language models to generate terms from pseudo-relevant documents and bias the probability estimates of expansion terms in a principled manner. By applying the knowledge inherent in document annotations, we aim to control query drift and reap the benefits of automatic query expansion in terms of recall without losing precision. We consider the parameters which are associated with our modeling and describe ways of estimating these automatically. We then evaluate our modeling and estimation methods on two test collections, both provided by the TREC Genomics track.
Semi-Implicit Approximation of Wave Maps into Smooth or Convex Surfaces

A semi-implicit scheme using linear finite elements for the approximation of wave maps into smooth or convex surfaces is devised, and its stability is analyzed. Convergence is established for the case of the unit sphere as a target manifold, which is unconditional in the case of $(2+1)$ Minkowski space. Numerical experiments illustrate the theoretical results.
Magnetically Driven Outflows in a Starburst Environment

We here investigate the possibility that the observed collimated outflows in luminous infrared galaxies (LIGs) and some Seyfert galaxies can be produced in a starburst (SB) environment. In the former source class, in particular, there seems to be some observational evidence for the presence of nuclear SBs in some objects. A nuclear disk can be quickly produced by gas infall during star formation in a rotating, stellar cluster. We find that massive nuclear SBs with core disk masses Md~108-109 M☉ and supernova rates νSN ≃ 5 × 10-3-2 yr-1 (which are consistent with the νSN-values inferred from the observed nonthermal radio power in source candidates) may inject kinetic energies that are high enough to blow out directed flows from the accreting disk surface, within the SB lifetimes. In our models, the acceleration and collimation of the nuclear outflow are provided by magnetic fields anchored into the rotating SB disk. The emerging outflow carries a kinetic power that is only a small fraction (a few percent) of the supernovae energy rate produced in the SB. Based on conditions determined from observed outflows and disks, we find that moderate disk magnetic fields (≳ 8 × 10-4 G) are able to accelerate the outflows up to the observed terminal velocities (≲few 100 km s-1 in the case of the Seyfert galaxies and ~400-950 km s-1 in the case of the LIGs). The outflow is produced within a wind zone in the disk of radius ≲ 100 pc in the LIGs and ≲ 10 pc in the Seyferts, with wind mass loss to disk accretion rate ratios Ṁw/Ṁd ≳ 0.1 (where Ṁd~100 M☉ yr-1). The observation of rotating nuclear disks of gas within ~few 100 pc scales, like that in the prototype LIG, Arp 220 (for which Ṁd~100 M☉ yr-1) and magnetized outflows in Seyfert galaxies and LIGs (with terminal B~10-5 G at the kiloparsec scales), provides some observational support for the magnetocentrifugal disk picture drawn here.
Provisioning Virtual Resources Adaptively in Elastic Compute Cloud Platforms

Provisioning Virtual machines on demand is significant in elastic compute cloud for reliable service delivery. The importance and major difficulty lies in satisfying the conflicting objectives of satisfying contracted service level agreement while lowering used resource costs. In this paper, the authors propose a mathematical multi-tier framework for adaptive virtual resource allocation problem. The framework captures the performance of the virtualized cloud platform gracefully. The authors first use simulations to derive virtual resource allocation policies, and later use real benchmarking applications, to verify the effectiveness of this framework. Experimental results show that the model can be simply and effectively used to satisfy the response time requirement as well as lowering the cost of using the virtual machine resources.
Learning to Communicate and Act in Cooperative Multiagent Systems using Hierarchical Reinforcement Learning

In this paper, we address the issue of rational communication behavior among autonomous agents. We extend our previously reported cooperative hierarchical reinforcement learning (HRL) algorithm to include communication decision and propose a new multiagent HRL algorithm, called COM-Cooperative HRL. In this algorithm, at specific levels of the hierarchy, called cooperation levels, a group of subtasks, in which coordination among agents has significant effect on the performance of the overall task, are defined as cooperative subtasks. Coordination skills among agents are learned faster by sharing information at cooperation levels, rather than the level of primitive actions. We add a communication level to the hierarchical decomposition of the problem, below each cooperation level. A communication action has a certain cost and is used by each agent to obtain the actions selected by the cooperative subtasks of the other agents. Before making a decision at a cooperative subtask, agents decide if it is worthwhile to perform a communication action in order to acquire the actions chosen by the cooperative subtasks of the other agents. Using this algorithm, agents learn a policy to balance the amount of communication needed for proper coordination, and communication cost. We demonstrate the efficacy of the COM-Cooperative HRL algorithm as well as the relation between communication cost and the learned communication policy, using a multiagent taxi domain.1
Advertising Models on Social Networks for SMEs-An Advertising Methodology

The proposed paper highlights the present scenario of advertising on social networks for SMEs (small and medium sized enterprises). It discusses the significance of the growth of social networks and its utilization as a platform for advertising by SMEs to reach millions of internet users. The paper consists of two segments mainly the general discussion of advertisement models on social networks and its implementation on 'The Dina Malkova Fashion Company', an SME, which deals with embroidered and silk designer wear from the silk route. The findings of the surveys and interviews conducted with social network users and the 'Dina Malkova Fashion Company' customers reveal the need for an approach to advertising for small businesses. This lead to the introduction of an advertising methodology discussed. The methodology is a systematic approach to advertising for small businesses. Its effectiveness and usability is affirmed with its implementation on 'The Dina Malkova Fashion Company'. The paper further discusses the implementation, advantages and the post implementation precautions of the introduced methodology and provides general advertising recommendations for small businesses on social networks.
Bayesian neural networks on the inference of distillation product quality

The control of the distillation process in oil refineries requires the evaluation of product quality throughout the operation of the plant. This paper uses Bayesian neural networks, combined to several pre-processing and variable selection techniques, to develop systems for inferencing the quality of distillation products, for REPAR refinery (Refinaria do Parana), operated by PETROBRAS.
EVALUATING THE PROJECT EMPOWERING YOUNG LGBT ADULTS: METHODOLOGY AND KEY FINDINGS OF A EUROPEAN ACTION RESEARCH

© 2016 Romanian Society of Experimental Applied Psychology. Content in the UH Research Archive is made available for personal research, educational, and non-commercial purposes only. Unless otherwise stated, all content is protected by copyright, and in the absence of an open license, permissions for further re-use should be sought from the publisher, the author, or other copyright holder.
The Level of Ets-1 Protein Is Regulated by Poly(ADP-Ribose) Polymerase-1 (PARP-1) in Cancer Cells to Prevent DNA Damage

Ets-1 is a transcription factor that regulates many genes involved in cancer progression and in tumour invasion. It is a poor prognostic marker for breast, lung, colorectal and ovary carcinomas. Here, we identified poly(ADP-ribose) polymerase-1 (PARP-1) as a novel interaction partner of Ets-1. We show that Ets-1 activates, by direct interaction, the catalytic activity of PARP-1 and is then poly(ADP-ribosyl)ated in a DNA-independent manner. The catalytic inhibition of PARP-1 enhanced Ets-1 transcriptional activity and caused its massive accumulation in cell nuclei. Ets-1 expression was correlated with an increase in DNA damage when PARP-1 was inhibited, leading to cancer cell death. Moreover, PARP-1 inhibitors caused only Ets-1-expressing cells to accumulate DNA damage. These results provide new insight into Ets-1 regulation in cancer cells and its link with DNA repair proteins. Furthermore, our findings suggest that PARP-1 inhibitors would be useful in a new therapeutic strategy that specifically targets Ets-1-expressing tumours.
Inference of Identity-by-Descent in Sib Pairs : Analysis with and without Linkage Disequilibrium

In gene mapping, after an initial genome-wide linkage scan, the next step often involves candidate region studies or fine mapping using dense markers. Dense genotyping, however, introduces linkage disequilibrium (LD). Traditional linkage analysis assuming no LD leads to increased false positive rates. Hence, we develop models to incorporate linkage disequilibrium, focusing on the sib pair design. Changes in sib-pair identity-by-descent (IBD) sharing along the chromosome can be modeled as a random walk. A hidden Markov model (HMM) framework is adopted to infer IBD sharing from genotypic data on sib pairs. In our first model, linkage equilibrium (LE) is assumed and only the IBD sharing is the hidden process. The effect of misspecification of allele frequencies is studied. We then use a first-order Markov model to incorporate LD into parental haplotypes and investigate the impact of linkage disequilibrium on IBD sharing estimation and lod score calculation. A cluster-based HMM developed by Scheet and Stephens [1] is then used to model LD in parental haplotypes, in order to capture better the LD patterns. However, parameter estimation for this cluster-based model is complicated. Moreover, at the dense mapping scales for which such models are appropriate, recombinant-based methods for linkage detection and estimation are impractical due to the low numbers of recombination events. Nonetheless, the hidden Markov model framework we have developed here provides a way to simulate genotypic data for related individuals with dense markers. It may also be useful in family-based association studies where this framework can be adopted to impute missing genotypes. The research described in this report was carried out between 2004 and 2007, while Audrey Q. Fu was a Research Assistant on the NIH grant GM-46255.
Docking Studies reveal Phytochemicals as the long searched Anticancer Drugs for Breast Cancer

Natural products including phytochemicals have been recently proposed as tumor suppressors. In this paper, docking study is presented to use these phytochemicals for their prospective role in cancers including breast and prostate cancer. The most common type of cancer in women all over the world is breast cancer. The breast cells including cancerous breast cells have receptors for binding with estrogen and progesterone to stimulate a growth response. This crucial property has been exploited to investigate binding properties of phytochemicals with these receptors to generate an antagonist response in order to resolve uncontrolled cancerous growth. The most commonly used breast cancer drugs mainly work against the effects of estrogen on these cells. In this context groups of different set of phytochemicals (3-IMG-Glucosinolates, Anthocyanins, Apigenin, Carnosol, Daidzein, Genistein, Isoflavones and Quercetin) were taken and docked into the active site of Human estrogen receptor (PDB ID: 2IOK). In this study, based on molecular docking, potential phytochemicals have successfully been identified which may be used as anticancer drugs against breast cancer. These studies based on binding energy, docking energy, drug likeness and other relevant scores show that Daidzein, Genistein and Quercetin could be the potential lead molecule for the inhibition of signals potent for Human breast cancer and Leu346, Leu384, Leu387, Phe404 and Leu525 are the most important residues for potential drug targets. This paper is the initial step towards a rational design of novel selective and potent Human estrogen inhibitors for the treatment of cancer.
Explaining Classifications For Individual Instances

We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities. It is based on the decomposition of a model's predictions on individual contributions of each attribute. Our method works for the so-called black box models such as support vector machines, neural networks, and nearest neighbor algorithms, as well as for ensemble methods such as boosting and random forests. We demonstrate that the generated explanations closely follow the learned models and present a visualization technique that shows the utility of our approach and enables the comparison of different prediction methods.
Epistemic cultures in complementary medicine: knowledge-making in university departments of osteopathy and Chinese medicine

ABSTRACT There is increasing pressure on complementary and alternative medicine (CAM) to follow the evidence-based approach promoted in allied health and medicine, in which the randomised control trial represents the evidence gold standard. However, many CAM advocates see these methods as undermining the holism of CAM practice. This paper explores how such tensions are managed in CAM university departments – settings in which particular forms of knowledge and evidence are given ‘official’ imprimatur by CAM educators and researchers. By comparing two types of CAM, the paper also unpacks differences within this broad category, asking whether CAM academic disciplines comprise different ‘epistemic cultures’ (Knorr-Cetina, K. (1999). Epistemic cultures: How the sciences make knowledge. Cambridge, MA: Harvard University Press). Interviews were conducted with 20 lecturers in Chinese medicine and osteopathy, across five Australian universities, and augmented with observation in two degree programs. Findings reveal contrasting ontological and epistemological perspectives between the two academic fields. Chinese medicine lecturers had largely adopted bioscientific models of research, typically conducting laboratory work and trials, although teaching included traditional theories. Osteopathy academics were more critical of dominant approaches and were focused on reframing notions of evidence to account for experiences, with some advocating qualitative research. The study illustrates CAM’s ‘epistemic disunity’ while also highlighting the particular challenges facing academic CAM.
Software Product and Process Assessment through Profile-Based Evaluation

Software entities (software products or processes) are characterized by many attributes, each one in its turn can be measured by one or more measures. In several cases the software entities have to be evaluated as a whole, thus raising the problem of aggregating measures to give an overall, single view on the software entity. This paper presents a method to aggregate measures, which works by comparing the product/process with predefined, ideal entities, or profiles. Profiles are defined starting from ranges of values on measures of attributes. The method is based on two main phases, namely definition of the evaluation model and application of the evaluation model. It is presented through a simplified case study that deals with evaluating the level of quality of an asset to decide whether to accept it in a reuse repository. A plausible way of how the method could be applied to process maturity assessment is also provided. The advantages of the method are that it allows using ordinal scales, while it deals explicitly with preferences expressed, implicitly or explicitly, by the evaluator.
Evolution of the Digital Society Reveals Balance between Viral and Mass Media Influence

Online social networks (OSNs) enable researchers to study the social universe at a previously unattainable scale. The worldwide impact and the necessity to sustain the rapid growth of OSNs emphasize the importance of unraveling the laws governing their evolution. Empirical results show that, unlike many real-world growing networked systems, OSNs follow an intricate path that includes a dynamical percolation transition. In light of these results, we present a quantitative two-parameter model that reproduces the entire topological evolution of a quasi-isolated OSN with unprecedented precision from the birth of the network. This allows us to precisely gauge the fundamental macroscopic and microscopic mechanisms involved. Our findings suggest that the coupling between the real preexisting underlying social structure, a viral spreading mechanism, and mass media influence govern the evolution of OSNs. The empirical validation of our model, on a macroscopic scale, reveals that virality is 4–5 times stronger than mass media influence and, on a microscopic scale, individuals have a higher subscription probability if invited by weaker social contacts, in agreement with the “strength of weak ties” paradigm.
Novel laser tissue-soldering technique for dural reconstruction.

OBJECT The goal of this study was to use a modified version of the CO2 laser-soldering system to develop a simple and reliable technique for the repair of dural defects after excision of brain tumors. METHODS The authors used a CO2 fiber optic laser system that they had developed for heating, monitoring, and controlling tissue temperature in situ and in real time, thereby reducing damage to the brain parenchyma. They adapted the system for dural closure by using free fascial grafts in a porcine model. Measures for estimation of reconstruction quality included visual assessment under magnification and direct measurements of adhesive strength and cerebrospinal fluid leak (CSF) pressure. Reliable soldering was achieved in 54 of 57 experiments, providing a 95% success rate. The average peak adhesive strength was 82 +/- 3 mN/cm2. The measured leak pressure of the fascia-dura mater bond was 66 +/- 5 mm Hg. Conventional suturing performed using Prolene stitches resulted in immediate CSF leakage from areas between the stitches and from the area of the needle hole itself. CONCLUSIONS Fascia-dura mater soldering using the CO2 laser is feasible and may support CSF pressure up to six times higher than normal intracranial pressure. Findings of this study may provide a basis for the development of new tools for dural reconstruction.
HIDDEN COSTS OF QUALITY: MEASUREMENT & ANALYSIS

Quality is the assurance of adherence to the customer specifications and it is a measure of excellence or a state of being free from defects, deficiencies and significant variation from standards. Customer specification of the product can be met by strictly adhering to the quality control measures in the production process and can be ensured in a cost effective manner only if the quality of each and every process in the organization is well defined and ensured without any lapses. Every activity in the supply chain line to be critically verified to identify the quality deviations incurring additional expense or loss to the organization. This is in line with the continual improvement principle of TQM philosophy . The cost of quality management system acts as the most significant tool in measuring, monitoring, controlling and decision making activities in a firm which aims on business excellence.
Size Constancy in Bat Biosonar? Perceptual Interaction of Object Aperture and Distance

Perception and encoding of object size is an important feature of sensory systems. In the visual system object size is encoded by the visual angle (visual aperture) on the retina, but the aperture depends on the distance of the object. As object distance is not unambiguously encoded in the visual system, higher computational mechanisms are needed. This phenomenon is termed “size constancy”. It is assumed to reflect an automatic re-scaling of visual aperture with perceived object distance. Recently, it was found that in echolocating bats, the ‘sonar aperture’, i.e., the range of angles from which sound is reflected from an object back to the bat, is unambiguously perceived and neurally encoded. Moreover, it is well known that object distance is accurately perceived and explicitly encoded in bat sonar. Here, we addressed size constancy in bat biosonar, recruiting virtual-object techniques. Bats of the species Phyllostomus discolor learned to discriminate two simple virtual objects that only differed in sonar aperture. Upon successful discrimination, test trials were randomly interspersed using virtual objects that differed in both aperture and distance. It was tested whether the bats spontaneously assigned absolute width information to these objects by combining distance and aperture. The results showed that while the isolated perceptual cues encoding object width, aperture, and distance were all perceptually well resolved by the bats, the animals did not assign absolute width information to the test objects. This lack of sonar size constancy may result from the bats relying on different modalities to extract size information at different distances. Alternatively, it is conceivable that familiarity with a behaviorally relevant, conspicuous object is required for sonar size constancy, as it has been argued for visual size constancy. Based on the current data, it appears that size constancy is not necessarily an essential feature of sonar perception in bats.