Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Health Importance of Foodborne Pathogens

2 Abstract: Foodborne pathogens cause a considerable public health burden and challenge. They cause illnesses and deaths in all populations, particularly in groups at risk such as infants, children, elderly and immunocompromised persons. Diarrheal diseases, almost all of which are caused by foodborne or waterborne microbial pathogens, are leading causes of illness and death in less developed countries, killing an estimated 1.9 million people annually at the global level. Even in developed countries, it is estimated that up to one third of the population is affected by microbiological foodborne diseases each year. The majority of the pathogens causing this significant disease burden are now considered to be zoonotic. The occurrence of some of these zoonotic pathogens seems to have increased significantly over recent years. The most important source of foodborne disease is raw or improperly cooked food (meat and poultry, raw eggs, unpasteurized milk, shellfish and rice). Food handlers play a major role in ensuring food safety throughout the chain of food production. The most commonly recognized foodborne infections are those caused by bacteria (Campylobacter spp., Salmonella spp., E. coli O157:H7, L. monocytogenes); viruses (Hepatitis A virus, Hepatitis E virus, Rotavirus); mycotoxins; marine bio toxins and parasites (T. solium, T. saginata, Echinococcus spp., Trich. spiralis, Fasciola, Cryptosporidium parvum, Entamoeba histolytica, T gondii). Symptoms of foodborne diseases greatly depend on the type of pathogen. Numerous organisms cause similar symptoms, especially diarrhea, abdominal cramps and nausea. Pathogens that cause foodborne sporadic diseases and outbreaks are usually detected by specific laboratory tests. The primary goal of foodborne outbreak investigation is to control ongoing and future outbreaks. The general strategy of prevention of foodborne diseases require the application of surveillance and information systems for these diseases and Hazard Analysis and Critical Control Points (HACCP) system in the food production chain from farm to consumers and import/export regulations. Reducing microbiological contamination in meat can be achieved through environmental hygiene, best quality of livestock feed, animal vaccination, mandatory inspection of livestock as well as sanitary standards for slaughter-houses and meat processing plant. Food safety can also be achieved by applying personal hygiene, hygienic handling of food and sanitation of the premises and kitchen utensils. Routine medical and laboratory examination of food handlers should be followed. Planning health education programs for food handlers and consumers are important rules to prevent foodborne diseases.
Social Behavioural Profiles In Characteristic Different OSN Users

We propose an arrangement of social behavioral elements that can successfully portray the client social activities on OSNs. We approve the viability of these behavioral components by gathering and dissecting genuine client click streams to an OSN site. In light of our estimation consider, we devise individual client's social behavioral profile by joining its particular behavioral component measurements. A social behavioral profile precisely mirrors a client's OSN action designs. While a credible proprietor fits in with its record's social behavioral profile automatically, it is hard and exorbitant for impostors to pretend. We assess the capacity of the social behavioral profiles in recognizing distinctive OSN clients, and our work demonstrate the social behavioral profiles can precisely separate individual OSN clients and identify traded off records.
DECOMPOSTION AND DISTRIBUTED COMPUTING

The aim of this paper is the support of the extreme requirements in circuit design in the future. To design millions of gates we use the basic principle of divide and conquer on several levels. First, the bi-decomposition splits a complex Boolean function into smaller parts. Second, the three function decomposition of Boolean functions reduces the necessary computation expenditure. Third, taking advantages of both types of decomposition, the distribute computing on several computers scales down the necessary computation time. Experimental results speak for the new approaches.
Social Media, Science, and Attack Discourse: How Twitter Discussions of Climate Change Use Sarcasm and Incivility

Conflict in online discussions of science has the potential to polarize individuals’ perceptions of science, yet science communication scholarship has paid little attention to systematic study of how verbal attacks play out in online discussions of science. This study analyzes sarcasm and incivility in Twitter discussions of climate change during an extreme weather event (n = 4,094). We found instances of incivility and sarcasm were low overall. Incivility was used in association with political topics, and both incivility and sarcasm were used alongside skeptical perspectives of climate change and by those who mention right-leaning politics in their profiles.
Dynamic adaptive HTTP streaming of live content

MPEG has recently released a first public draft of the new Dynamic Adaptive Streaming over HTTP (DASH) specification. The new streaming technique is based on the 3GPP and partly also on the Open IPTV Forum (OIPF) specifications. In this paper, we give an overview about the new DASH specification with a special focus on Live streaming services. In order to re-use existing web content distribution schemes, the new streaming technique provides the live stream as a sequence of files, which are continuously downloaded by the streaming client. This way of streaming introduces new delay components into the system streaming. Live Streaming technology is often used for events like sport events to allow other users to virtually participate. It is generally preferred to minimize the end-to-end delay for live services. In the paper, we identify and analyze the different delay components of the new adaptive HTTP streaming technique and how they contribute to the end-to-end delay of live services. We also discuss the dependencies and system implications when minimizing the end to end delay. The evaluation principles are also applicable for other adaptive HTTP streaming formats.
Simulation and evaluation of sensor characteristics in vision based advanced driver assistance systems

This paper presents a method for the simulation of images in the scope of virtual camera prototypes under the constraint of color correctness. This is a first step to gain a complete simulatable camera model that can be used to generate synthetic images using real data. Each real camera system has its own color processing characteristics. Real images recorded with a reference camera model can be computationally simulated as if they have been recorded with another real or virtual camera. The resulting images are transformed to underly the color characteristics of the targeted virtual camera system. Our approach can be used at the design phase of vision-based advance driver assistance systems to verify the exact behaviour under varying optical properties of the optical system and to test and evaluate the overall robustness of the system when color processing changes. It can as well lead to a decision basis for the selection of the hardware to be used. In this paper, we show how cameras can be calibrated and in a second step we evaluate the simulation errors. Finally, we apply our simulation to a traffic sign recognition algorithm and evaluate its behaviour in relation to ground truth data.
MOL # 105064 Title: Dynamin Functions and Ligands: Classical Mechanisms Behind Running title: Dynamin Function and Ligands

Dynamin is a GTPase that plays a vital role in clathrin-dependent endocytosis and other vesicular trafficking processes by acting as a pair of molecular scissors for newly formed vesicles originating from the plasma membrane. Dynamins and related proteins are important components for cleavage of clathrin-coated vesicles, phagosomes, mitochondria, etc. These proteins help in organelle division, viral resistance, and mitochondrial fusion/fission. Dysfunction and mutations in dynamin have been implicated in the pathophysiology of various disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Charcot-Marie-Tooth disease (CMT), Heart failure (HF), schizophrenia, epilepsy, cancer, Dominant optic atrophy (DOA), osteoporosis, Down’s syndrome (DS), etc. This review is an attempt to illustrate the dynamin related mechanisms involved in above mentioned disorders and to help medicinal chemists to design novel dynamin ligands, which could be useful in the treatment of dynamin-related disorders. This article has not been copyedited and formatted. The final version may differ from this version. Molecular Pharmacology Fast Forward. Published on November 22, 2016 as DOI: 10.1124/mol.116. 105064 at A PE T Jornals on Jauary 7, 2017 m oharm . aspeurnals.org D ow nladed from MOL # 105064
Artifacts mitigation in ambulatory ECG telemetry

In remote monitoring applications of vital signs including ECG, it is extremely important to ensure that the diagnostic integrity of the signals is not compromised due to the presence of sensing artifacts and channel errors. It is also important for the platform to be extremely power efficient in order to facilitate wearable sensors with user friendly form factors. We present a novel, low power application layer solution that is agnostic to wireless protocols and mitigates artifacts due to packet losses in Body Area Networks (BANs). In our previous work, we presented initial results based on this approach and demonstrated that greater than 99% beat detection accuracy can be achieved even at a packet loss rate as high as 20%. Our contributions in this work include validation of the above on a platform with an ultra low power wearable single lead ECG pendant. We present details of implementation and then extend the platform to mitigate ECG sensing artifacts including power line interference and baseline wandering. The proposed approach enables us to offload most of the complex processing from sensor nodes to the receiver node with better a battery budget, for improved sensor life. Finally, present a qualitative and quantitative assessment of the system.
Fluorine production in intermediate-mass stars.

The 19F production during the first dozen thermal pulses of AGB stars with (M=3,Z=0.02), (M=6,Z=0.02) and (M=3,Z=0.001) is investigated on grounds of detailed stellar models and of revised rates for 15N(a,g)19F and 18O(a,g)22Ne. These calculations confirm an early expectation that 19F {\it is} produced in AGB thermal pulses. They also enlarge substantially these previous results by showing that the variations of the level of 19F production during the evolution is very sensitive to the maximum temperature reached at the base of the pulse. These variations are analyzed in detail, and are shown to result from a subtle balance between different nuclear effects (mainly 19F production or destruction in a pulse, and 15N synthesis during the interpulse), possibly super-imposed on dilution effects in more or less extended pulse convective tongues. Our calculations, as most others, do not predict the third dredge-up self- consistently. When parametrized, it appears that our models of intermediate-mass AGB stars are able to account only for the lowest 19F overabundances observed in solar-metallicity MS, S and C stars. That conclusion is expected to hold true for low-mass stars when F production results from secondary 13C only. Massive AGB stars, on the other hand, are not expected to build up large surface F abundanc- es. Therefore, the large F overabundance reported for the super Li-rich star WZ Cas (where HBB is supposed to be operating) remains unexplained so far. Our results for the (M=3,Z=0.001) star indicate that F surface overabundances can also be expected in low-metallicity stars provided that third dredge-ups occur after the early cool pulses. The relative increase in the surface 19F/12C ratio is, however, lower in the low-metallicity than in the solar-metallicity star. No observations are
Convalescing Cluster Configuration Using a Superlative Framework

Competent data mining methods are vital to discover knowledge from databases which are built as a result of enormous growth of data. Various techniques of data mining are applied to obtain knowledge from these databases. Data clustering is one such descriptive data mining technique which guides in partitioning data objects into disjoint segments. K-means algorithm is a versatile algorithm among the various approaches used in data clustering. The algorithm and its diverse adaptation methods suffer certain problems in their performance. To overcome these issues a superlative algorithm has been proposed in this paper to perform data clustering. The specific feature of the proposed algorithm is discretizing the dataset, thereby improving the accuracy of clustering, and also adopting the binary search initialization method to generate cluster centroids. The generated centroids are fed as input to K-means approach which iteratively segments the data objects into respective clusters. The clustered results are measured for accuracy and validity. Experiments conducted by testing the approach on datasets from the UC Irvine Machine Learning Repository evidently show that the accuracy and validity measure is higher than the other two approaches, namely, simple K-means and Binary Search method. Thus, the proposed approach proves that discretization process will improve the efficacy of descriptive data mining tasks.
On stability analysis of discrete-time homogeneous dynamics

This paper considers the problem of stability analysis of discrete-time dynamics that are positively homogeneous of degree one. An example of a homogeneous and even continuous dynamics that is globally exponentially stable and that does not admit any λ-contractive proper C-set is presented. This motivates us to propose a natural generalization of this concept, namely, (k, λ)-contractive proper C-sets. It is proven that this simple generalization yields a non-conservative Lyapunov-type tool for stability analysis of homogeneous dynamics, namely, sublinear finite-time Lyapunov functions. Moreover, scalable and non-conservative stability tests are established for relevant classes of homogeneous dynamics.
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis

In this paper, the UNITOR system participating in the SemEval-2013 Sentiment Analysis in Twitter task is presented. The polarity detection of a tweet is modeled as a classification task, tackled through a Multiple Kernel approach. It allows to combine the contribution of complex kernel functions, such as the Latent Semantic Kernel and Smoothed Partial Tree Kernel, to implicitly integrate syntactic and lexical information of annotated examples. In the challenge, UNITOR system achieves good results, even considering that no manual feature engineering is performed and no manually coded resources are employed. These kernels in-fact embed distributional models of lexical semantics to determine expressive generalization of tweets.
Solutions For Optimizing The Stream Compaction Algorithmic Function Using The Compute Unified Device Architecture

In this paper, I have researched and developed solutions for optimizing the stream compaction algorithmic function using the Compute Unified Device Architecture (CUDA). The stream compaction is a common parallel primitive, an essential building block for many data processing algorithms, whose optimization improves the performance of a wide class of parallel algorithms useful in data processing. A particular interest in this research was to develop solutions for optimizing the stream compaction algorithmic function that offers optimal solutions over an entire range of CUDA enabled GPUs: Tesla GT200, Fermi GF100 and the latest Kepler GK104 architecture, released on 22 March 2012. In order to confirm the utility of the developed optimization solutions, I have extensively benchmarked and evaluated the performance of the stream compaction algorithmic function in CUDA.
A Robbins-Monro type learning algorithm for an entropy maximizing version of stochastic Optimality Theory

The object of the present work is the analysis of the convergence behaviour of a learning algorithm for grammars belonging to a special version  the maximum entropy version  of stochastic Optimality Theory. Stochastic Optimality Theory is like its deterministic predecessor, namely Optimality Theory as introduced by Prince and Smolensky, in that both are theories of universal grammar in the sense of generative linguistics. We give formal de nitions of basic notions of stochastic Optimality Theory and introduce the learning problem as it appears in this context. A by now popular version of stochastic Optimality Theory is the one introduced by Boersma, which we brie y discuss. The maximum entropy version of stochastic Optimality Theory is derived in great generality from fundamental principles of information theory. The main part of this work is dedicated to the analysis of a learning algorithm proposed by Jager (2003) for maximum entropy grammars. We show in which sense and under what conditions the algorithm converges. Connections with well known procedures and classical results are made explicit. The present work is a slightly modi ed version of my Master's thesis, which was submitted to the Department of German Language and Linguistics at Humboldt University Berlin in June 2005. The thesis was supervised by Prof. Manfred Krifka and Prof. Gerhard Jager. Author's address: Markus Fischer Reichenberger Str. 166 10999 Berlin Germany E-Mail: markus. scher@alumni.hu-berlin.de
Monadic Second Order Logic and Automata on Infinite Words : Büchi ’ s Theorem

Büchi’s theorem establishes the equivalence of the satisfiability relation for monadic second-order logic, and the acceptance relation for Büchi automata. The development of the theories of monadic second-order logic and Büchi automata follows Thomas’s survey[6] closely, in that all of the concepts and results found in this report are also in [6]. However, because the scope of Thomas’s survey is much greater, he develops the theories in a more general (and more complicated) way than is necessary to understand Büchi’s theorem, and he only sketches the proof of Büchi’s theorem, which is given in detail here.
Short-Term Projects, Long-Term Benefits: Four Student NLP Projects for Low-Resource Languages

This paper describes a local effort to bridge the gap between computational and documentary linguistics by teaching students and young researchers in computational linguistics about doing research and developing systems for low-resource languages. We describe four student software projects developed within one semester. The projects range from a front-end for building small-vocabulary speech recognition systems, to a broad-coverage (more than 1000 languages) language identification system, to language-specific systems: a lemmatizer for the Mayan language Uspanteko and named entity recognition systems for both Slovak and Persian. Teaching efforts such as these are an excellent way to develop not only tools for low-resource languages, but also computational linguists well-equipped to work on endangered and low-resource languages.
ELECTRON-CLOUD MODULE FOR THE ORBIT CODE

A new electron cloud module has been developed and inserted into the beam dynamics code, ORBIT, which is used for simulations in high intensity proton rings. In addition to incorporating the dynamics of electron cloud build up, the model simulates the self consistent dynamics of the proton beam and the electrons. This new model includes full 3D descriptions of the proton bunch and the electron cloud, including both their space charge interactions and their motion in external electric and magnetic fields. The secondary emission of electrons is calculated using a set of models based on those of M. Furman and M. Pivi. The structure, algorithms, parallel implementation, and benchmarks of the new ORBIT module with analytic results are presented.
Evolutionary reduction of developmental plasticity in desert spadefoot toads

Organisms vary their rates of growth and development in response to environmental inputs. Such developmental plasticity may be adaptive and positively correlate with environmental heterogeneity. However, the evolution of developmental plasticity among closely related taxa is not well understood. To determine the evolutionary pattern of plasticity, we compared plasticity in time to and size at metamorphosis in response to water desiccation in tadpoles among spadefoot species that differ in breeding pond and larval period durations. Like most tadpoles, spadefoot tadpoles possess the remarkable ability to accelerate development in response to pond drying to avoid desiccation. Here, we hypothesize that desert spadefoot tadpoles have evolved reduced plasticity to avoid desiccation in ephemeral desert pools compared to their nondesert relatives that breed in long‐duration ponds. We recorded time to and size at metamorphosis following experimental manipulation of water levels and found that desert‐adapted species had much less plasticity in larval period and size at metamorphosis than nondesert species, which retain the hypothetical ancestral state of plasticity. Furthermore, we observed a correlation between degree of plasticity and fat body content that may provide mechanistic insights into the evolution of developmental plasticity in amphibians.
INTERPRETING TIME IN TEXT SUMMARIZING TEXT WITH TIME

In this thesis, I study two key steps in building a logical representation of temporal information — a timeline — found within text from newswire articles: 1) intra-sentence event-timex (E-T ) temporal relationship classification, and 2) article-wide event-event (E-E ) temporal relationship classification. Events and time expressions (timexes) are basic units of temporal information in text. These two steps allow us to build an understanding of the relative ordering between these basic temporal units. For both of these classification tasks, I propose more semantically motivated features, namely the use of typed dependency parses and discourse analyses, to achieve better classification performance. This is in contrast to much work in the existing literature, which have focused on lexicosyntactic features. Working on E-T temporal relationship classification, I also show that crowdsourcing is a very cost-effective and viable avenue through which a high-quality temporal corpus can be built. Making use of the structure of a sentence, I propose a unique way to identify instances which are computationally and cognitively easier. Excluding these instances from a corpus does not degrade subsequent classifier performance significantly. This allows cost savings of up to 37% when building a E-T temporal corpus. Besides putting together a state-of-the-art temporal processing system, this thesis also validates the efficacy and utility of the timelines that are automatically derived. Temporal information from these timelines is incorporated into a competitive baseline multi-document summarization system. I propose several features derived from timelines and show that they lead to a 4.1% improvement in summarization performance. I also introduce a modification to the traditional Maximal Marginal Relevance (MMR) algorithm, TimeMMR. TimeMMR is shown to be useful in the summarization of some document sets. To further improve the performance gains derived from the use of temporal information, I propose a reliability filtering metric which gauges how accurate and useful a timeline is. By selectively making use of timelines guided by this reliability filtering metric, overall summarization performance is increased by a statistically significant 5.9%.