Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-phase importance sampling for inference about transmission trees

There has been growing interest in the statistics community to develop methods for inferring transmission pathways of infectious pathogens from molecular sequence data. For many datasets, the computational challenge lies in the huge dimension of the missing data. Here, we introduce an importance sampling scheme in which the transmission trees and phylogenies of pathogens are both sampled from reasonable importance distributions, alleviating the inference. Using this approach, arbitrary models of transmission could be considered, contrary to many earlier proposed methods. We illustrate the scheme by analysing transmissions of Streptococcus pneumoniae from household to household within a refugee camp, using data in which only a fraction of hosts is observed, but which is still rich enough to unravel the within-household transmission dynamics and pairs of households between whom transmission is plausible. We observe that while probability of direct transmission is low even for the most prominent cases of transmission, still those pairs of households are geographically much closer to each other than expected under random proximity.
Community structure and interaction dynamics through the lens of quotes

This is the first work investigating community structure and interaction dynamics through the lens of quotes in online discussion forums. We examine four forums of different size, language, and topic. Quote usage, which is surprisingly consistent over time and users, appears to have an important role in aiding intra-thread navigation, and uncovers a hidden "social" structure in communities otherwise lacking all trappings (from friends and followers to reputations) of today's social networks.
A test synthesis technique using redundant register transfers

This paper presents a test synthesis technique for behavioral descriptions. The technique is guided by two testability metrics which quantify the controllability and observability of behavioral variables and structural signals. The method is based on utilizing redundant register transfers in the data path to produce a test behavior with better controllability and observability properties. This approach can avoid unnecessary insertions of test structures in the data path. A test scheme for conditional statements has been developed involving minimal changes in the controller. Our experimental results show improvements in fault coverage at modest hardware overhead.
Training opposing directed models using geometric mean matching

Unsupervised training of deep generative models containing latent variables and performing inference remains a challenging problem for complex, high dimensional distributions. One basic approach to this problem is the so called Helmholtz machine and it involves training an auxiliary model that helps to perform approximate inference jointly with the generative model which is to be fitted to the training data. The top-down generative model is typically realized as a directed model that starts from some prior at the top, down to the empirical distribution at the bottom. The approximate inference model runs in the opposite direction and is typically trained to efficiently infer high probability latent states given some observed data. Here we propose a new method, referred to as geometric mean matching (GMM), that is based on the idea that the generative model should be close to the class of distributions that can be modeled by our approximate inference distribution. We achieve this by interpreting both the top-down and the bottom-up directed models as approximate inference distributions and by defining the target distribution we fit to the training data to be the geometric mean of these two. We present an upper-bound for the log-likelihood of this model and we show that optimizing this bound will pressure the model to stay close to the approximate inference distributions. In the experimental section we demonstrate that we can use this approach to fit deep generative models with many layers of hidden binary stochastic variables to complex and high dimensional training distributions.
Which web browser work best for detecting phishing

Phishing is the technique of social engineering in which important information are hijacked. To prevent phishing the easiest way is to blacklist such web sites which are suspicious;, but there are many new phished websites that are not blacklisted. To protect users from those phishing sites many ways are available; one of those is to implement security toolbar in web browser. Nowaday every modern web browser has its own implicit security toolbar. Our research is based on the evaluation of toolbars attached with Internet Explorer, Mozilla Firefox and Google Chrome. In our experiment we appraised toolbars attached with these most popular web browsers. Results evaluation showed that Google Chrome provides best security against phishing websites.
Diagnosing design problems in object oriented systems

Software decay is a phenomenon that plagues aging software systems. While in recent years, there has been significant progress in the area of automatic detection of "code smells" on one hand, and code refactorings on the other hand, we claim that existing restructuring practices are seriously hampered by their symptomatic and informal (non-repeatable) nature. This paper makes a clear distinction between structural problems and structural symptoms (also known as code smells), and presents a novel, causal approach to restructuring object oriented systems. Our approach is based on two innovations: the encapsulation of correlations of symptoms and additional contextual information into higher-level design problems, and the univocal, explicit mapping of problems to unique refactoring solutions. Due to its explicit, repeatable nature, the approach shows high potential for increased levels of automation in the restructuring process, and consequently a decrease in maintenance costs.
Ethnographic Peace Research: The Underappreciated Benefits of Long-term Fieldwork

ABSTRACT While Peace Studies has always incorporated different research methodologies, large-N quantitative methods and state-level findings have dominated the literature and had most influence on policy and practice. Today, however, the limitations of peace interventions are commonly identified with the institutional, state-centric, and technocratic approaches associated with such limited understandings and their resultant policies. This paper argues, therefore, that the inability of these methods to examine local experiences of conflict, transition, and peace in diverse sociocultural settings contributes to inadequate policy formation and, thus, to problematic interventions. Indeed, the recent ‘local turn’ and its focus on the everyday, resistance, hybridity, and friction demands research that can better interpret local experiences of conflict, transition, and peace and, thereby, discover more locally salient practice. While this paper argues that an Ethnographic Peace Research (EPR) agenda must be central to such efforts, it also argues against applying the ethnographic label to work that is more suitably described as qualitative (site visits, interviews, focus groups, etc.). The paper argues that long-term fieldwork and close engagement with the subjects of peacebuilding must be required within any EPR agenda. The underappreciated benefits of such fieldwork are illustrated with examples from research in northern Sierra Leone.
A Framework for Performance Evaluation of Large-scale Interactive Distributed Virtual Environments

Interactivity is a key consideration in large-scale distributed virtual environments (DVEs), such as massively multi-player online games. Due to the Internet dynamics as well as the large-scale and distributed nature of many DVEs, it is difficult to quantify the possible improvements claimed by various DVE interactivity enhancement approaches. In this paper, we propose a scalable framework that supports evaluation of interactivity indicators, under dynamic Internet conditions. We have developed and used this framework to conduct extensive experiments on PlanetLab with many distributed client and server locations. We have collected the latency experienced by over 700 client locations distributed around the globe over a few days; and used it for evaluating performance of some recently proposed zone mapping algorithms.
A Compton-thick active galactic nucleus powering the hyperluminous infrared galaxy IRAS 00182−7112

We present X-ray observations of the hyperluminous infrared galaxy (HLIRG) IRAS 00182− 7112 obtained using the XMM–Newton EPIC camera. A luminous hard X-ray source coincident with the nucleus is revealed, along with weaker soft X-ray emission which may be extended or offset from the hard emission. The EPIC spectrum is extremely flat and shows Fe Kα emission with very high equivalent width: both are typical characteristics of a buried, Compton-thick active galactic nucleus (AGN) which is seen only in scattered light. Perhaps the most remarkable characteristic of the spectrum is that the Fe Kα line energy is that of He-like iron, making IRAS 00182−7112 the first hidden AGN known to be dominated by ionized, Compton-thick reflection. Taking an appropriate bolometric correction, we find that this AGN could easily dominate the far-infrared (FIR) energetics. The nuclear reflection spectrum is seen through a relatively cold absorber with column density consistent with recent Spitzer observations. The soft X-ray emission, which may be thermal in nature and associated with star-forming activity, is seen unabsorbed. The soft X-rays and weak polycyclic aromatic hydrocarbon features both give estimates of the star formation rate ∼300 M⊙ yr−1, insufficient to power the FIR emission and supportive of the idea that this HLIRG is AGN-dominated.
Acquiring Growth

I develop a model of mergers in which M&A deals are used to reallocate investment opportunities. In equilibrium, acquirers lack internal growth options and seek out projects from targets in the M&A market. The model is able to reconcile many features of the merger data that I document, including the high productivity, investment, and valuation of target firms. Furthermore, in my model, profitability is highly predictive of acquisition, and merger transactions naturally lead to a substantial drop in profitability despite creating value for the acquirer.
On the mathematical structure of messages and message processing systems

In this paper we want to explore the prospects for a physics of information. In the first chapter we will summarise the present situation. Chapter 2 demonstrates the present limitations of information processing. Chapter 3 suggests, that these practical limitations might be traced back to limitations in the underlying theoretical concepts. Chapter 4 suggests a more physics oriented way of defining concepts like “program” or “complexity”. Chapter 5 explores, whether the concepts discussed in chapter 4 can be applied to the processing of physical messages, for instance video images. In a first step in chapter 5.1 we note a difference between messages in information theory and physical messages – physical messages can always be finite. In chapter 5.2 we note, that messages can be considered vectors and that already this very simple statement is leading to relevant practical consequences, leading to a new type of OTP cipher. Chapter 5.3 discusses some general mathematical properties of vector spaces and creates a connection between the mathematical concepts and the physical observables (measurement). In chapter 5.4 we try to gain first hints, of how the mathematical structure of the visible world might be used in order to calculate the description of an image to mathematical precision.
Flavonoid Bioavailability and Attempts for Bioavailability Enhancement

Flavonoids are a group of phytochemicals that have shown numerous health effects and have therefore been studied extensively. Of the six common food flavonoid classes, flavonols are distributed ubiquitously among different plant foods whereas appreciable amounts of isoflavones are found in leguminous plant-based foods. Flavonoids have shown promising health promoting effects in human cell culture, experimental animal and human clinical studies. They have shown antioxidant, hypocholesterolemic, anti-inflammatory effects as well as ability to modulate cell signaling and gene expression related disease development. Low bioavailability of flavonoids has been a concern as it can limit or even hinder their health effects. Therefore, attempts to improve their bioavailability in order to improve the efficacy of flavonoids are being studied. Further investigations on bioavailability are warranted as it is a determining factor for flavonoid biological activity.
Antibodies Biotinylated Using a Synthetic Z-domain from Protein A Provide Stringent In Situ Protein Detection

Antibody-based protein profiling on a global scale using immunohistochemistry constitutes an emerging strategy for mapping of the human proteome, which is crucial for an increased understanding of biological processes in the cell. Immunohistochemistry is often performed indirectly using secondary antibodies for detection, with the benefit of signal amplification. Direct immunohistochemistry instead brings the advantage of multiplexing; however, it requires labeling of the primary antibody. Many antibody-labeling kits do not specifically target IgG and may therefore cause labeling of stabilizing proteins present in the antibody solution. A new conjugation method has been developed that utilizes a modified Z-domain of protein A (ZBPA) to specifically target the Fc part of antibodies. The aim of the present study was to compare the ZBPA conjugation method and a commercially available labeling kit, Lightning-Link, for in situ protein detection. Fourteen antibodies were biotinylated with each method and stained using immunohistochemistry. For all antibodies tested, ZBPA biotinylation resulted in distinct immunoreactivity without off-target staining, regardless of the presence of stabilizing proteins in the buffer, whereas the majority of the Lightning-Link biotinylated antibodies displayed a characteristic pattern of nonspecific staining. We conclude that biotinylated ZBPA domain provides a stringent method for antibody biotinylation, advantageous for in situ protein detection in tissues.
The effect of altruism on social discounting of environmental gain and loss

After decades of rapid economic growth, societies around the world are facing severe challenges with environmental pollution. In some developing countries, environmental issues are threatening their sustainable development and people’s health. In economic and psychological literatures, environmental problems are usually recognized as social dilemmas because natural resources are public goods, which imply conflicts between individual and collective benefits. Empirical studies found that social distance had an effect on cooperative behavior in social dilemmas. Meanwhile, the phenomenon of social discounting on monetary outcomes was also revealed by many studies. However, the impact of social distance on environmental decisions remains unknown. The purpose of this study was to explore the phenomenon of social discounting of environmental outcomes, fit the behavioral data of social discounting, and investigate the effect of altruism on social discounting under gain and loss frames. The experiment was designed as a 4 (social distance level: a family member, friend, mere acquaintance, and stranger) × 2 (task frame: environmental gain, environmental loss) study in which social distance was manipulated within the subject, but task frames were assigned between subjects. The choice titration procedure was used to measure the social discounting rate of environmental outcomes. Participants were asked to make a series of choices between “I live in high (or poor in environmental loss frame) quality air for X days” and “Another person (in different social distances) lives in high (or poor) quality air for 110 days. ” The value of “X” in the choice series varied from 10 days to 100 days with an interval of 10 days. In addition, participants’ altruism was measured by the Self-report Altruism Scale. The results showed that: (1) in both environmental gain and loss frames, exponential models could fit social discounting of environmental outcomes better than hyperbolic model; (2) the interaction between social distance and task frame could influence the social discounting rate. As social distance increased, the discounting rate increased more rapidly for environmental gain than for environmental loss; (3) altruism had moderate effect on the relationship between social distance and social discounting. For participants with high level of altruism, social distance had less impact on social discounting. In conclusion, this study suggests that social distance, altruism, and task frame may play important roles in people's environmental decision-making. These findings enlighten us that decision makers may become more pro-environmental if some measures are taken to enhance their altruistic tendencies and reduce their psychological distance with others.
Using rough set theory for mining the level of hearing loss diagnosis knowledge

This paper focused on the development of diagnosis knowledge model of the level of hearing loss in the audiology clinic patients using rough set theory. A knowledge model contains a set of knowledge via rules that are obtained from mining certain amount of data. These data consist of valuable knowledge that impossible for the audiologist or audio therapist to extract without powerful mining techniques or tools. These rules help doctors in decision making such as setting up new strategy to improve the efficiency of the operation. In this work, a data mining technique, rough set theory was used for the knowledge modelling. It was used based on its capability of handling uncertain data that often occurs in real world problems. The results from the modelling produced a classifier called rough classifier. The classifier was used to classify the level of hearing loss. A total of 500 data obtained from the audiology clinic. The data consisted of 24 attributes from four categories namely demography, antenatal, neonatal and medical categories. These attributes were used as an input and one attribute called diagnostic category as an output. In order to facilitate the modelling process requirement, these attributes have been gone a pre-process stage. The best model has been obtained from 10 experiments using 10 sets of different training and test data. The experiment showed promising results with 76% accuracy. The developed knowledge model has a great potential to be embedded in the development of the medical decision support system.
Registration and matching of temporal mammograms for detecting abnormalities

Our aim is to establish accurate correspondences between temporal mammograms in order to improve the detection of tumours. We propose a method for detecting abnormalities after aligning a pair of mammograms using thin-plate spline interpolation based on corresponding points on the breast boundary. We also suggest a method for the automatic detection of landmarks within the breast tissue. We show how using suitable boundary points yields reasonable results whilst finding internal landmarks is difficult and using points on the pectoral muscle unreliable.
Dual Power: Integrating Renewable Energy into Green Datacenters without Grid Tie Inverter

There has been growing concern about energy consumption and environmental impact of data centers. Some pioneers begin to power data centers with renewable energy to offset carbon footprint. However, it is challenging to integrate intermittent renewable energy into data center power system. Grid-tied system is widely deployed in renewable energy powered data centers. But the drawbacks (e.g. Harmonic disturbance and costliness) of grid tie inverter harass this design. Besides, the mixture of green load and brown load makes power management heavily depend on software measurement and monitoring, which often suffers inaccuracy. We propose Dual Power, a novel power provisioning architecture that enables green data centers to integrate renewable power supply without grid tie inverters. To optimize Dual Power operation, we propose two optimization schemes (LM and PS) under different data center operation scenarios. The experimental results show that Dual Power can be as efficient as grid-tied system. In contrast to previous works, Daul Power integrates renewable power at lower cost and maintains full availability of data center servers.
TRADITIONAL AND INDEX TRACKING METHODS FOR PORTFOLIO CONSTRUCTION BY MEANS OF NEURAL NETWORKS

Neural networks have seen an explosion of interest over the last few years, and are being successfully applied across an extraordinary range of problem domains, in areas as diverse as finance, medicine, engineering, geology and physics. Indeed, wherever that there are problems of prediction, classification or control, neural networks are being introduced. Neural networks are very sophisticated modelling techniques, capable of modelling extremely complex functions. In particular, neural networks are non-linear. For many years linear modelling has been the commonly used technique in most modelling domains, since linear models had well-known optimisation strategies. Where the linear approximation was not valid (which was frequently the case) the models suffered accordingly. Neural networks also keep in check the curse of dimensionality problem, which bedevils attempts to model non-linear functions with large numbers of variables. Forecasting the behaviour of a given system follows two approaches which are trying to predict future values of time series by extracting knowledge from the past. One common approach is to relate the changes in one time series to other phenomena in the economy. The other approach also adopted in this paper states that stock exchange indices embody all the knowledge that is required to predict future behaviour. The experiments presented here are based on the assumption, that the past values of the indices contain all the knowledge that is needed to predict future behaviour. Portfolio management involves the construction of a portfolio of securities (stocks, bonds etc.) that maximises the investor’s utility. The construction of such a portfolio consists of two main stages. In the first stage, the investor evaluates the securities available in the market. There are two main factors, which must be taken into account in the portfolio construction process: the expected return and risk. The biggest problem is to forecast the behaviour of securities (expected returns). Using superior neural network abilities can solve this problem. When all the information is available, one can use simple techniques for portfolio construction. Another approach for portfolio construction is the so called Index Tracking method. It is a kind of passive portfolio management which attempts to match the performance of a theoretical portfolio, such as Dow Jones Riga Stock Exchange index (DJ RSE), as closely as possible. In this particular case we can also use a neural network to predict the index future value. Both of these methods are discussed in this paper.