Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical and neural classifiers in estimating rain rate from weather radar measurements

Abstract. Weather radars are used to measure the electromagnetic radiation backscattered by cloud raindrops. Clouds that backscatter more electromagnetic radiation consist of larger droplets of rain and therefore they produce more rain. The idea is to estimate rain rate by using weather radar as an alternative to rain-gauges measuring rainfall on the ground. In an experiment during two days in June and August 1997 over the Italian-Swiss Alps, data from weather radar and surrounding rain-gauges were collected at the same time. The statistical KNN and the neural SOM classifiers were implemented for the classification task using the radar data as input and the rain-gauge measurements as output. The proposed system managed to identify matching pattern waveforms and the rainfall rate on the ground was estimated based on the radar reflectivities with a satisfactory error rate, outperforming the traditional Z/R relationship. It is anticipated that more data, representing a variety of possible meteorological conditions, will lead to improved results. The results in this work show that an estimation of rain rate based on weather radar measurements treated with statistical and neural classifiers is possible.
The Wage Curve and the Phillips Curve

Blanchflower and Oswald (1994) have argued that, in regional data, the level of unemployment is related to the level of wages. This result is at variance with the implications of the original Phillips curve for regional data, which would predict that the change in wages ought to be related to the unemployment rate. On the other hand, there is considerable empirical support for the expectations-augmented Phillips curve using macroeconomic data. I resolve this tension by showing that a standard macroeconomic expectations-augmented Phillips curve can be derived from microfoundations that begin with the wage curve.
Diagnosis and Management of Clostridium difficile Infection by Pediatric Infectious Diseases Physicians.

BACKGROUND The incidence of C difficile infection (CDI) has risen among children; however, optimal management of CDI within a diverse pediatric population remains unclear. Although adult guidelines recommend oral vancomycin for treatment of second recurrence or severe CDI, dedicated pediatric data to support pediatric specific management guidelines are lacking. Our objective was to describe current CDI management practices by pediatric infectious diseases (ID) physicians. METHODS We surveyed pediatric members of the Emerging Infections Network, a network of infectious diseases (ID) physicians across North America, in October 2012. Clinical vignettes were used to determine how physicians modify CDI management based on clinical presentation or presence of comorbidities, including solid organ transplantation, inflammatory bowel disease, and neutropenia. RESULTS Of the 285 physicians surveyed, 167 (59%) responded. There were no significant differences in geography, level of experience, or hospital type between respondents and non-respondents. All respondents (100%) used oral metronidazole for the initial occurrence of mild CDI in a normal host. Management varied substantially for mild CDI in patients with a variety of comorbidities, in whom metronidazole therapy was less frequently preferred (41-79%). For management of severe CDI, 65% preferred oral vancomycin alone or in combination with at least one other agent. For a second recurrence, oral vancomycin alone or in combination was preferred by 92%. Among 125 respondents who reported using alternative therapies for recurrent or severe CDI, 23 (18%) recommend fecal microbiota transplantation, while 20 (16%) reported using fidaxomicin. CONCLUSIONS Pediatric ID physicians prefer metronidazole for treatment of mild CDI in healthy children, but management strategies vary for patients with comorbidities or recurrent or severe disease. These findings highlight the need for pediatric comparative effectiveness studies aimed at determining the optimal treatment for pediatric CDI.
Trpv 1 reporter mice reveal highly restricted brain distribution and functional expression in arteriolar smooth muscle cells

"Trpv1 reporter mice reveal highly restricted brain distribution and functional expression in arteriolar smooth muscle cells. The heat and capsaicin receptor, TRPV1, is required for the detection of painful heat by primary afferent pain fibers (nociceptors), but the extent to which functional TRPV1 channels are expressed in the CNS is debated. Because previous evidence is based primarily on indirect physiological responses to capsaicin, here we genetically modified the Trpv1 locus to reveal, with excellent sensitivity and specificity, the distribution of TRPV1 in all neuronal and non-neuronal tissues. In contrast to reports of widespread and robust expression in the CNS, we find that neuronal TRPV1 is primarily restricted to nociceptors in primary sensory ganglia, with minimal expression in a few discrete brain regions, most notably in a contiguous band of cells within and adjacent to the caudal hypothalamus. We confirm hypothalamic expression in the mouse using several complementary approaches, including in situ hybridization, calcium imaging, and electrophysi-ological recordings. Additional in situ hybridization experiments in rat, monkey, and human brain demonstrate that the restricted expression of TRPV1 in the CNS is conserved across species. Outside of the CNS, we find TRPV1 expression in a subset of arteriolar smooth muscle cells within thermoregulatory tissues. Here, capsaicin increases calcium uptake and induces vasoconstriction, an effect that likely counteracts the vasodilation produced by activation of neuronal TRPV1. Introduction TRPV1 is a nonselective cation channel that is activated by noxious heat, protons, the vanilloid compounds capsaicin (CAP) and resiniferatoxin (RTX), and membrane-derived lipids, including anandamide (Caterina et al., 1997; Caterina and Julius, 2001). Studies of Trpv1 knockout mice demonstrated a critical contribution of TRPV1 to the cellular and behavioral responses to noxious heat (Caterina et al., 2000; Davis et al., 2000). Although expression of TRPV1 was originally reported to be restricted to primary afferent nociceptors of the dorsal root ganglia (DRGs), trigeminal ganglia, subsequent studies argued for a much wider distribution, both in the CNS and in non-neuronal tissues. Expression of TRPV1 in the CNS has been reported using a variety of methods, including pharmacological characterization and reverse transcription (RT)-PCR (Mezey et al., 2000). Despite this abundance of studies, the existence of TRPV1 in the brain remains a controversial topic, mainly because the extent and lo-calization of TRPV1 varies considerably among studies, even those using very similar assays. For example, RTX binding studies reported results ranging from a lack of binding in the CNS (Szal-lasi et …
Don't Do It! Cortical Inhibition and Self-attribution during Action Observation

Numerous studies suggest that both self-generated and observed actions of others activate overlapping neural networks, implying a shared, agent-neutral representation of self and other. Contrary to the shared representation hypothesis, we recently showed that the human motor system is not neutral with respect to the agent of an observed action [Schtz-Bosbach, S. , Mancini, B. , Aglioti, S. M. , & Haggard, P. Self and other in the human motor system. Current Biology, 16, 18301834, 2006]. Observation of actions attributed to another agent facilitated the motor system, whereas observation of identical actions linked to the self did not. Here we investigate whether the absence of motor facilitation for observing one's own actions reflects a specific process of cortical inhibition associated with self-representation. We analyzed the duration of the silent period induced by transcranial magnetic stimulation of the motor cortex in active muscles as an indicator of motor inhibition. We manipulated whether an observed action was attributed to another agent, or to the subjects themselves, using a manipulation of body ownership on the basis of the rubber hand illusion. Observation of actions linked to the self led to longer silent periods than observation of a static hand, but the opposite effect occurred when observing identical actions attributed to another agent. This finding suggests a specific inhibition of the motor system associated with self-representation. Cortical suppression for actions linked to the self might prevent inappropriate perseveration within the motor system.
Finding frequent items in data streams

The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been much comparison of the different methods under uniform experimental conditions. It is common to find papers touching on this topic in which important related work is mischaracterized, overlooked, or reinvented. In this paper, we aim to present the most important algorithms for this problem in a common framework. We have created baseline implementations of the algorithms, and used these to perform a thorough experimental study of their properties. We give empirical evidence that there is considerable variation in the performance of frequent items algorithms. The best methods can be implemented to find frequent items with high accuracy using only tens of kilobytes of memory, at rates of millions of items per second on cheap modern hardware.
Cultural analysis and what designers need to know---a case of sometimes too much, sometimes too little, and always too late: why ethnography needs to show its relevance

Bader and Nyce's article raises intriguing issues that have concerned researchers in HCI and user-centered systems design for much of the last decade: to what extent can a deep social science methodology influence the process of technology design usefully. Their conclusion, that cultural analysis yields knowledge perceived to be of little value by system designers, is in my view, largely correct. However, while I share their conclusion, I do not accept their rationale. In the present paper I will attempt to demonstrate that the root of the problem lies less with the system designers than the inappropriate application of the specific social science methods Bader and Nyce invoke, which itself can be traced to their overly narrow view of the design process and their assumption that cultural analysis is the most useful social scientific method.
Knowledge Exploitation for Human Micro-Doppler Classification

Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.
Exploring and Exploiting Opportunistic Network Routing in a DTN Environment

A communications network that is capable of storing packets temporarily in intermediate nodes, until the time an end-to-end route is re-established or regenerated is known as a delay tolerant network/s, in short, DTN. This paper aims to detail basic & general aspects specific to information needs in DTN routing and present classification chart. We start with evolution of some relevant routing objectives and issues since their inception, discuss in some detail routing types, groups and classifications. Our study culminates in identification of various schemes and techniques that will provide an insight and help us to exploit the delay and effectively utilize the time in a judicious manner to help improve the efficiency of information exchange in a DTN network. The basic objective is to utilize efficiently this temporal-period or adhoc-period by opportunistically establishing connectivity - in a strategic manner, to exchange information across locally distributed resources, thus making optimal use of available network resources and enhancing the efficiency of information exchange.
Opinion Mining and Social Networks: A Promising Match

In this paper we discuss the role and importance of social networks as preferred environments for opinion mining and sentiment analysis especially. We begin by briefly describing selected properties of social networks that are relevant with respect to opinion mining and we outline the general relationships between the two disciplines. We present the related work and provide basic definitions used in opinion mining. Then, we introduce our original method of opinion classification and we test the presented algorithm on real world datasets acquired from popular Polish social networks, reporting on the results. The results are promising and soundly support the main thesis of the paper, namely, that social networks exhibit properties that make them very suitable for opinion mining activities.
Algorithms for Nash Equilibria in General-Sum Stochastic Games

The field of stochastic games has been actively pursued over the last seven decades because of several of its important applications in oligopolistic economics. In the past, zerosum stochastic games have been modelled and solved for Nash equilibria using the standard techniques of Markov decision processes. General-sum stochastic games on the contrary have posed difficulty as they cannot be reduced to Markov decision processes. Over the past few decades the quest for algorithms to compute Nash equilibria in general-sum stochastic games has intensified and several important algorithms such as stochastic tracing procedure [Herings and Peeters, 2004], NashQ [Hu and Wellman, 2003], FFQ [Littman, 2001], etc., and their generalised representations such as the optimization problem formulations for various reward structures [Filar and Vrieze, 2004] have been proposed. However, they suffer from either lack of generality or are intractable for even medium sized problems or both. In this paper, we propose three algorithms, OFF-SGSP, ON-SGSP and DON-SGSP, respectively, which we show provide Nash equilibrium strategies for general-sum discounted stochastic games. Here OFF-SGSP is an off-line algorithm while ON-SGSP and DON-SGSP are online algorithms. In particular, we believe that DON-SGSP is the first decentralized on-line algorithm. We show that both our on-line algorithms are computationally efficient.
Tropomyosin-like properties of clathrin light chains allow a rapid, high-yield purification

The light chains (LCa and LCb) of bovine brain clathrin are resistant to heat denaturation by boiling, a property shared by tropomyosin (Bailey, K., 1948, Biochem. J., 43:271-281). Light chains were partially purified by boiling and centrifugation of a Tris-extract of crude membranes prepared from bovine brains (Keen, J. H., M. C. Willingham, and I. H. Pastan, 1979, Cell., 16:303-312). Contaminant polypeptides were then removed by size-exclusion high-pressure liquid chromatography. The purified light chains were separated from each other by using an immunoaffinity column prepared from a monoclonal antibody CVC.7 specific for LCa and not LCb.
A Review of Machine Vision based on Moving Objects : Object Detection from UAV Aerial Images

Moving object detection from UAV aerial images has stated to get attention in recent years where fast abrupt motion, low resolution, noisy imagery, cluttered background, low contrast and small target size need to be compensated with an updated moving object detection framework to increase detection performance. This paper presents 6 UCF (Uncertainty Constraint Factors) issues which are the recent categorized challenges to handle performance metric for moving object detection from UAV aerial images. A critical review for adaptive motion analysis to handle these 6 UCF factors is presented in this paper. Besides these 6 UCF factors, feature extraction for moving object detection is another unsolved issue. Large feature vector is suitable for optimum detection performance but increase computation time for overall detection process. This paper presents critical review of existing feature extraction techniques which can be compensated with adaptive motion analysis framework to influence optimum detection performance. After detection of moving object, classification becomes one of the critical tasks when classification needs to perform based on pixel basis. This paper presents a critical review of different kinds of classification techniques which are suitable for UAV aerial images so that it becomes easy to choose appropriate classification technique in future research. Finally, this paper presents an updated framework to handle 6 UCF issues by analysis of existing frameworks for moving object detection from UAV aerial images. The overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.
A Perspective on Multiple Waves of Influenza Pandemics

Background A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms could provide knowledge for health authorities to develop and implement prevention and control strategies. Materials and Methods We exhibit five distinct mechanisms, each of which can generate two waves of infections for an acute infectious disease. The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography), where each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Each mechanism is incorporated into separate mathematical models, and outbreaks are then simulated. We use the models to examine the effects of the initial number of infected individuals (e.g., border control at the beginning of the outbreak) and the timing of and amount of available vaccinations. Results Four models, individually or in any combination, reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively. All models indicate that significantly reducing or delaying the initial numbers of infected individuals would have little impact on the attack rate. Instead, this reduction or delay results in a single wave as opposed to two waves. Furthermore, four of these models also indicate that a vaccination program started earlier than October 2009 (when the H1N1 vaccine was initially distributed) could have eliminated the second wave of infection, while more vaccine available starting in October would not have eliminated the second wave.
Bound - states for truncated Coulomb potentials

The pseudoperturbative shifted-l expansion technique (PSLET) is generalized for states with an arbitrary number of nodal zeros. Bound-states energy eigenvalues for two truncated Coulombic potentials are calculated using PSLET. In contrast with the shifted large-N expansion technique, PSLET results compare excellently with those from a direct numerical integration.
For Interest or for Meaning : A Comparative Analysis of Two Perspectives on Collective Action

This paper argues that we can discern and fruitfully compare two scholarly approaches to the study of collective action: a rational choice perspective and an identity-based perspective. In order to detail and analyze each perspective, I draw on select works of political science that comprise them and conceptually disaggregate each piece into the following five components: (1) theoretical orientation; (2) social context; (3) political context; (4) conception of individuals; (5) methodology. I begin in Part II by very briefly defining the foregoing five dimensions. I then commence the core of my analysis in Part III by discussing the rational choice perspective, focusing on the work of Mancur Olson, Timur Kuran, and Elinor Ostrom. I transition to the identity-based perspective in Part IV, focusing on the contributions of Mark Beissinger, Deborah Yashar, and Elisabeth Jean Wood. Because of the admittedly restricted set of works receiving close attention in both parts, throughout my analysis I shall reference echoes – cognate scholarship that shares the particular attribute being described but which were omitted from the more in-depth analysis – primarily in the footnotes. Further, as I transition from analyzing one perspective to the next, I will highlight the overarching differences between the two, particularly along the five dimensions outlined above. Part V concludes by pondering whether future scholarship should seek to develop a third perspective occupying the theoretical space between the rational choice and the identity-based approaches, and posits that such an effort would be both premature and unwise.
Study of the current stressing in nanomanipulated three-dimensional carbon nanotube structures

We report the fabrication of free-standing carbon nanotube structures. The welding of individual carbon nanotubes to other nanotubes and metal substrates has been performed, on a selective basis, to produce joints of both good electrical conductivity and mechanical integrity, without the need for a joining material. As a result of this unique process, we study the damage to the microstructure of the nanotube as a function of current. When the current densities are in excess of 2×106A∕cm2, particular care must be taken with regard to the quality of the nanotube and the heat dissipation. This is crucial for the use and application of nanotubes in any future device structure for it gives the upper limits to the “average” current density calculations. This process now allows for the fabrication of bespoke carbon nanotube devices for the prototyping of device performance.
NORM SUPPORT VECTOR MACHINE

In this paper we propose a new support vector machine (SVM), the F∞norm SVM, to perform automatic factor selection in classification. The F∞-norm SVM methodology is motivated by the feature selection problem in cases where the input features are generated by factors, and the model is best interpreted in terms of significant factors. This type of problem arises naturally when a set of dummy variables is used to represent a categorical factor and/or a set of basis functions of a continuous variable is included in the predictor set. In problems without such obvious group information, we propose to first create groups among features by clustering, and then apply the F∞-norm SVM. We show that the F∞-norm SVM is equivalent to a linear programming problem and can be efficiently solved using standard techniques. Analysis on simulated and real data shows that the F∞-norm SVM enjoys competitive performance when compared with the 1-norm and 2-norm SVMs.