Miracle-dz/UniFAR-FTUs huggingface dataset:
Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris

The effect of a post-industrial megacity on local and regional air quality was assessed via a month-long field measurement campaign in the Paris metropolitan area during winter 2010. Here we present source apportionment results from three aerosol mass spectrometers and two aethalometers deployed at three measurement stations within the Paris region. Submicron aerosol composition is dominated by the organic fraction (30-36%) and nitrate (28-29%), with lower contributions from sulfate (14-16%), ammonium (12-14%) and black carbon (7-13%). Organic source apportionment was performed using positive matrix factorization, resulting in a set of organic factors corresponding both to primary emission sources and secondary production. The dominant primary sources are traffic (11-15% of organic mass), biomass burning (13-15%) and cooking (up to 35% during meal hours). Secondary organic aerosol contributes more than 50% to the total organic mass and includes a highly oxidized factor from indeterminate and/or diverse sources and a less oxidized factor related to wood burning emissions. Black carbon was apportioned to traffic and wood burning sources using a model based on wavelength-dependent light absorption of these two combustion sources. The time series of organic and black carbon factors from related sources were strongly correlated. The similarities in aerosol composition, total mass and temporal variation between the three sites suggest that particulate pollution in Paris is dominated by regional factors, and that the emissions from Paris itself have a relatively low impact on its surroundings.
Exogeneity and Robustness

A common practice for detecting misspecication is to perform a \robustness test", where the researcher examines how a regression coecient of interest behaves when variables are added to the regression. Robustness of the regression coecient is taken as evidence of structural validity. However, there are numerous pitfalls that can befall a researcher when performing such tests. For example, we demonstrate that certain regressors, when added to the regression, will induce a shift in the coecient of interest even when structurally valid. Such robustness tests would produce false alarm, suggesting that the model is misspecied when it is not. For a robustness test to be informative, the variables added to the regression must be carefully chosen based on the model structure. We provide a simple criterion that allows researchers to quickly determine which variables, when added to the regression, constitute informative robustness tests. We also explore the extent to which robustness tests are able to detect bias, demonstrating that robustness tests enable detection of bias due not only to omitted observable variables but omitted unobservable variables as well. Finally, we give two extended examples using simulated data to demonstrate how the material in this paper can be used to conduct informative robustness tests.
High-level robotic control : beyond planning Position paper

We agree with the premise of this workshop that it is time to take seriously the need for high-level cognition in designing robotic systems. In this position paper, we motivate and describe in an informal way the direction we are taking in this regard. Technical details can be found in the papers at our website above. For the past five years or so, a group of us1 at the University of Toronto have been engaged in what we call Cognitive Robotics, which we take to be the study of the knowledge representation and reasoning problems faced by an autonomous robot (or agent) in dynamic and incompletely known world. Central to this effort is to develop an understanding of the relationship between the knowledge, the perception, and the action of such an robot. The sorts of questions we want to be able to answer are
Applying the Framingham risk score for prediction of metabolic syndrome: The Kerman Coronary Artery Disease Risk Study, Iran

BACKGROUND There has been a few studies about the predictability of metabolic syndrome (MetS) based on the Framingham risk score (FRS) as a tool for predicting the risk of 10-years cardiovascular diseases (CVD) in Iranian population. The aim of this study was to compare the risk stratification obtained with the FRS and MetS in a cohort of the Iranian population. METHODS In this population-based study Kerman Coronary Artery Disease Risk study, Iran, MetS was diagnosed as defined by the revised National Cholesterol Education Program definition criteria (ATPIII) and the FRS was calculated using a computer program, previously reported algorithm. RESULTS Overall, the prevalence 10-years risk of CVD for patients with MetS was significantly different with those without MetS (74.3 vs. 86.4% for low-risk patients, 18.1 vs. 12.3% for intermediate-risk people, and 7.6 vs. 1.3% for high-risk individuals) (P < 0.001). The frequency of intermediate-risk and high-risk for 10-year CVD in men with MetS (39.5 and 18.3%, respectively) was considerably higher than women with MetS (3.2 and 0.1%, respectively). Using multiple logistic regression, the odds ratio of MetS in intermediate-risk and high-risk FRS group was 1.7 and 6.7, respectively (P < 0.001). CONCLUSION Significant association between the presence of MetS and high risk for CVD based on FRS was revealed in both men and women indicating a good concordance between MetS and FRS in predicting the risk of CVDs. However, the odds ratio of the development of risk of cardiovascular events among women was higher than men with MetS.
Least mean p-power extreme learning machine for obstacle avoidance of a mobile robot

This paper proposes an obstacle avoidance method for navigation of a mobile robot in uncertain environments based on a novel neural learning algorithm, namely least mean p-norm extreme learning machine (LMP-ELM) and Q-learning. The proposed obstacle avoidance method comprises of two behavior modules, Viz., an avoidance behavior and goal-seeking behavior. At the learning phase, the two modules are independently designed using the proposed LMP-ELM and Q-Learning. And then they are combined to navigate the mobile to the goal position without colliding with obstacles based on a switching function at the running phase. The LMP-ELM is used to realize the state-action mapping of the Q-learning. In the novel LMP-ELM, the computationally simple extreme learning machine architecture is maintained but a novel error criterion, namely the least mean p-power (LMP) error criterion provides a mechanism to update the output weights sequentially. The LMP error criterion aims to minimize the mean p-power of the error that is the generalization of the mean square error criterion used in the ELM. The effectiveness of the proposed method is verified by a series of simulations.
UvA-DARE (Digital Academic Repository) A closer look at declarative interpretations

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Support vector machines for automatic data cleanup

Accurate training data plays a very important role in training effective acoustic models for speech recognition. In conversational speech, in several cases, the transcribed data has a signiﬁcant word error rate which leads to bad acoustic models. In this paper we explore a method to automatically identify such mislabelled data in the context of a hybrid Support Vector Machine/hidden Markov model (HMM) system, thereby building accurate acoustic models. The effectiveness of this method is proven on both synthetic and real speech data. A hybrid system for OGI alphadigits using this methodology gives a significant improvement in performance over a comparable baseline HMM system.
Sequence progressive alignment, a framework for practical large-scale probabilistic consistency alignment

MOTIVATION Multiple sequence alignment is a cornerstone of comparative genomics. Much work has been done to improve methods for this task, particularly for the alignment of small sequences, and especially for amino acid sequences. However, less work has been done in making promising methods that work on the small-scale practically for the alignment of much larger genomic sequences. RESULTS We take the method of probabilistic consistency alignment and make it practical for the alignment of large genomic sequences. In so doing we develop a set of new technical methods, combined in a framework we term 'sequence progressive alignment', because it allows us to iteratively compute an alignment by passing over the input sequences from left to right. The result is that we massively decrease the memory consumption of the program relative to a naive implementation. The general engineering of the challenges faced in scaling such a computationally intensive process offer valuable lessons for planning related large-scale sequence analysis algorithms. We also further show the strong performance of Pecan using an extended analysis of ancient repeat alignments. Pecan is now one of the default alignment programs that has and is being used by a number of whole-genome comparative genomic projects. AVAILABILITY The Pecan program is freely available at http://www.ebi. ac.uk/ approximately bjp/pecan/ Pecan whole genome alignments can be found in the Ensembl genome browser.
Economics of new oncology drug development.

PURPOSE Review existing studies and provide new results on the development, regulatory, and market aspects of new oncology drug development. METHODS We utilized data from the US Food and Drug Administration (FDA), company surveys, and publicly available commercial business intelligence databases on new oncology drugs approved in the United States and on investigational oncology drugs to estimate average development and regulatory approval times, clinical approval success rates, first-in-class status, and global market diffusion. RESULTS We found that approved new oncology drugs to have a disproportionately high share of FDA priority review ratings, of orphan drug designations at approval, and of drugs that were granted inclusion in at least one of the FDA's expedited access programs. US regulatory approval times were shorter, on average, for oncology drugs (0.5 years), but US clinical development times were longer on average (1.5 years). Clinical approval success rates were similar for oncology and other drugs, but proportionately more of the oncology failures reached expensive late-stage clinical testing before being abandoned. In relation to other drugs, new oncology drug approvals were more often first-in-class and diffused more widely across important international markets. CONCLUSION The market success of oncology drugs has induced a substantial amount of investment in oncology drug development in the last decade or so. However, given the great need for further progress, the extent to which efforts to develop new oncology drugs will grow depends on future public-sector investment in basic research, developments in translational medicine, and regulatory reforms that advance drug-development science.
Web search personalization using machine learning techniques

Information on the web is increasing at an enormous speed. Every user has a distinct background and aspecific goal when searching for information on the web. Present search engines produce results that are best suited to given query. But these engines are unaware of user's individual preferences which in turn can vary with individual interest and these interests most of the time change with individual working environment time. To provide such personalized results, user's topical preferences could be stored and utilized for the purpose. Different approaches have been implemented for the same such as, Collaborative Filtering, Document-Based or Concept based profiling etc. We are proposing hybrid approach based on Document Based as well as Concept Based Profiling. Proposed framework aims to re-rank results for a given query obtained from existing search engines. Thus this system would provide an adaptive methodology for learning changing user preferences to re-rank results according to one's individual interests.
The relation between sleep and violent aggression

Good sleep is important for our emotional stability and aggression control. Although most people do not become violent after a period of poor sleep, this may be different for certain vulnerable individuals. Forensic psychiatric patients may represent a group of such individuals. We studied patients who committed a crime, often violent in nature, but are not completely held accountable for this due to a mental disorder. They usually get sentenced by the court to follow treatment. A novel finding in this thesis is that poor sleep quality is highly prevalent among these forensic populations. The more sleep difficulties these patients experience, the more impulsive and aggressive they are. Individuals with violent traits appear particularly poor sleepers. The causal relationship between sleep quality and violence was studied in an animal model of violence. We found some indications that also violent rats differ slightly from non-violent rats in their sleep patterns as measured by brain activity after exposure to challenging conditions. Moreover, lack of sleep in rats causes increased impulsive behavior. The effect of sleep loss on the prefrontal cortex, a brain area which serves as the “brake” for our behavior, is likely to be responsible for the negative effects of sleep debt on our behavioral inhibitory capabilities. More work needs to be done to understand the neurobiological mechanisms underlying the relation between sleep and violent aggression. Treatment of sleep disorders may open new avenues for the development of aggression control in forensic patients.
Histopathological changes in broiler chickens fed aflatoxin and cyclopiazonic acid.

Experimental mycotoxicoses was induced into broiler chickens by feeding 1 ppm afl atoxin (AF) and 20 ppm cyclopiazonic acid (CPA) from 0 to 28 days of age to evaluate the gross and histopathological changes. Grossly, AF and AF-CPA fed birds showed enlargement, yellowish discoloration of the liver while the CPA fed birds showed enlargement and congestion. The CPA and AF-CPA fed birds showed thickening of crop and necrosis and thickening of proventricular mucosa. Histopathologically, degenerative and necrotic changes were observed in the liver, kidneys, intestine, pancreas, heart, pectoral muscle, spleen and bursa of Fabricius of all toxin fed birds. Besides, hyperplastic changes were also observed in the crop, proventriculus and gizzard in the CPA fed birds. The lesions were more marked in the AF-CPA group. The study revealed that AF and CPA in combination could act cumulatively and adversely affect the health of broiler chicken.
Bank organization and lending behavior: the independence of the Credit Supervision Office and its relation to the bad loan problem in Japan

In this paper, I focus on the structural management reforms of Japanese banks during the pre and post bubble period, and analyse whether these efforts have had any positive effects on their lending behaviour. First I try to index management organisation reforms based on the degree of independence displayed by the banks’ credit supervision departments. Then, I examine the relationships between 1) the organisational reforms of the 1980s and the subsequent bad loan problem and 2) the organisational reforms of the 1990s and the banks’ lending behaviour (whether or not there was a credit crunch). Conclusions of this paper are as follows: 1) introducing the divisional system (or headquarters system) in the 1980s weakened the influence of credit supervision departments, and increased bad loans during the bubble economy. 2) abolishing the divisional system (returning to the traditional organizational system) during the 1990s made Japanese banks more cautious about lending. In either case, reforms of the organisational system have had non-negligible effects on banking behaviour. This may also mean that we need to consider the effects that introducing financial holding companies had as well. JEL Classification: G21; G32
Presence Mediator: Creating Social Presence through Spatial Positioning of Everyday Objects

In the situation where people live together in one household, the rearrangement of everyday objects (either accidental or deliberate) becomes a trace of social presence that people may use as part of their social interactions. This research aims at simulating that natural phenomenon in the context of a long-distance relationship. That is, mediation of social presence through domestic objects is extended over time and space. Our concept, the Presence Mediator, is an interactive system that is attached to an ordinary domestic object. Participants of a field test were in the belief that two mediating objects are synchronized and spatially representing each other’s action in the social relation. In fact, the prototype moved a few times per day at random distance and direction. The results illustrate the complexity mediating objects entail as they bring further consequences for the social relation into play. We discuss the difference between traces of social presence that form part of the common background of attention, versus social interaction as mediated by objects that form the center of attention. Furthermore, we discuss the object as a mediating trace of another person’s presence, versus the object as an explicit (anthropomorphic) representation of that other person.
The determination of shock ramp width using the noncoplanar magnetic field component

We determine a simple expression for the ramp width of a collisionless fast shock, based upon the relationship between the noncoplanar and main magnetic field components. By comparing this predicted width with that measured during a shock observation, the shock velocity can be determined from a single spacecraft. For a range of low‐Mach, low‐β bow shock observations made by ISEE‐1 and ‐2, ramp widths determined from two‐spacecraft comparison and from this noncoplanar component relationship agree within 30%. When two‐spacecraft measurements are not available or are inefficient, this technique provides a reasonable scale size estimation for low Mach shocks.
Review on Improving Transmission efficiency in VANET using MAC and Multichannel

Vehicular Adhoc Network (VANET) is the form of Mobile Adhoc Network(MANET) in which we use cars as nodes in the network same as computers are nodes in Mobile Adhoc Network. These nodes are assumed as mobile devices carried by people from one place to another. Every car that is participating in the VANET turns into wireless router. When any car goes out of the particular signal range the connection drop out and in place of that new connection is established. There are number of issues related to VANET that need to be resolved. Media Access Control(MAC) and Multichannel assignment are only the two issues from them. Multi channels easily increase the capacity of network but there is need of new protocols that can expand the capacity of the available network. In this paper we have present the various aspects of data transmissions. We will also discuss for efficient transmission with mac and multichannel.
Integer-weight approximation of continuous-weight multilayer feedforward nets

Multilayer feedforward neural nets with integer weights can be used to approximate the response of their counterparts with continuous-weights. Integer weights, when restricted to a maximum magnitude of 3, require just 3 binary bits for storage, and therefore are very attractive for hardware implementation of neural nets. However, these integer-weight nets have a weaker learning capability and lack the affine group invariance of continuous-weight nets. These weaknesses, although compensatable by the addition of hidden neurons, can be used to one's benefit for closely matching the network complexity with that of the learning task. This paper discusses theses issues with the help of the decision and error surfaces of 2D classification problems of various complexities, whose results suggest that in many cases, limited weight resolution can be offset by an increase in the size of the hidden layer in the network.
iAtheroSim: atherosclerosis process simulator on smart devices

Atherosclerosis is the major cause of heart attacks, some strokes, aneurysms, and peripheral artery disease. Arteries are a well organized system: it provides the organs and tissues of the body with the blood needed to extract sufficient amounts of nutrients and oxygen. However, today, we are exposed to high levels of potentially toxic lipids and other factors. This may lead to the development of inflammation, scarring and disruption of the tunica intima, of which the sub-endothelial layer is initially affected in the process of atherogenesis. For these reasons, any increased awareness of potential atherosclerosis risk is a great improvement over current healthcare. We present iAtheroSim, an iOS application easy to use and freely available on the Apple Store. It allows the general public an easy way to access to the SimAthero simulator, which predicts atherogenesis risks taking into account some personal bio-physiological indicators. This, at least, may convince people to schedule a medical doctor appointment in order to better evaluate the atherogenesis risk and to take counter- measures before that the disease process could cause severe complications. This is one of the first example in which it is showed that modern biological approaches based on computational modeling could reach the general public by means of extremely diffused architectures like smart- phones. iAtheroSim application is freely available on Apple Store.
Modeling and behaviour of partial discharge in nanocomposite materials

Partial Discharge (PD) behaviour in electrical apparatus leads to identify the degradation of insulating materials. This paper provides an analysis of Electric Field (E-Field), Electric potential distributions and Temperature analysis of the nanocomposite materials. The ultimate aim of this simulation studies are carried out to improve the breakdown strength of the nanocomposite materials for long term performances. The major factors that governs the performances of the insulating materials is E-Field and Electric potential distribution which leads to PD and temperature rise which reduces the breakdown strength of the materials. The analysis are performed by modeling the nanocomposite materials in two dimensions (2D) using Finite Element Method (FEM) with nanoparticles of different size for different combinations. According to the results, Polycarbonate (base material) with nanoparticle (SiO2) of 5% size when compared to base materials shows better performances and nominal temperature rise.