Miracle-dz/UniFAR-FTUs huggingface dataset:
Obesity and its Relation to Mortality and Morbidity Costs

We reviewed almost 500 research articles on obesity and its relation to mortality and morbidity, focusing primarily on papers published from January 1980 to June 2009. There is substantial evidence that obesity is a worldwide epidemic and that it has a significant negative impact on health, mortality and related costs. Overweight and obesity are associated with increased prevalence of diabetes, cardiovascular disease, hypertension and some cancers. There also is evidence that increased weight is associated with kidney disease, stroke, osteoarthritis and sleep apnea. Moreover, empirical studies report that obesity significantly increases the risk of death. We used the results to estimate costs due to overweight and obesity in the United States and Canada. We estimate that total annual economic cost of overweight and obesity in the United States and Canada caused by medical costs, excess mortality and disability is approximately $300 billion in 2009. JEL Classification: H10, H11, J11, J32 November 30, 2010 The Society of Actuaries supported this project with a research grant. Obesity and its Relation to Mortality and Morbidity Costs 1
Thermal and exhumation history of the Eocene Chumstick Basin, Washington State, USA

This study investigates the Cenozoic evolution of the Cascades Mountains, USA, by means of reconstructing the thermal history of the Chumstick Basin deposits that are located on the eastern flank of the orogen. We present new detrital apatite (U‐Th‐Sm)/He and detrital apatite fission‐track ages from the middle Eocene (~48–37 Ma) Chumstick Formation and modern river detritus. These data are integrated with the published thermochronometric ages of the adjoining Cascade Mountains basement. The new thermochronometer results are combined with existing vitrinite reflectance data for modeling of the basin's thermal history. Results suggest constant temperatures throughout the late Oligocene to early Miocene followed by rapid cooling since 17–10 Ma. Results indicate that the middle to late Miocene uplift of the Cascades resulted in reactivation of the fault zones bounding the Chumstick Basin. The structural reactivation and the weaker lithology of the basin strata in comparison to the harder and more resistant basement rocks of the Cascades resulted in significant basin exhumation. This interpretation is determined from younger apatite (U‐Th‐Sm)/He cooling ages observed in the reset detrital bedrock of the Chumstick Formation than in the adjoining basement rocks. These observations suggest a structural and lithological control on exhumation magnitudes in this region.
LDA + TCP-Friendly Adaptation : A Measurement and Comparison Study

In this paper, we present an end-to-end adaptation scheme, called the enhanced loss-delay based adaptation algorithm (LDA+) for regulating the transmission behavior of multimedia senders in accordance with the network congestion state. LDA+ uses the real-time transport protocol (RTP) for collecting loss and delay statistics which are then used for adjusting the transmission behavior of the senders in a manner similar to TCP connections suffering from equal losses and delays. The performance of LDA+ is then investigated by running several simulations as well as measurements over the Internet. Additionally, by conducting simulations, the performance of LDA+ is compared to that of other TCP-friendly congestion control schemes presented in the literature.
Statistical Parsing of Messages

The recent trend in natural language processing research has been to develop systems that deal with text concerning small, well defined domains. One practical application for such systems is to process messages pertaining to some very specific task or activity [5]. The advantage of dealing with such domains is twofold - firstly, due to the narrowness of the domain, it is possible to encode most of the knowledge related to the domain and to make this knowledge accessible to the natural language processing system, which in turn can use this knowledge to disambiguate the meanings of the messages. Secondly, in such a domain, there is not a great diversity of language constructs and therefore it becomes easier to construct a grammar which will capture all the constructs which exist in this sub-language.
Selectivity to Translational Egomotion in Human Brain Motion Areas

The optic flow generated when a person moves through the environment can be locally decomposed into several basic components, including radial, circular, translational and spiral motion. Since their analysis plays an important part in the visual perception and control of locomotion and posture it is likely that some brain regions in the primate dorsal visual pathway are specialized to distinguish among them. The aim of this study is to explore the sensitivity to different types of egomotion-compatible visual stimulations in the human motion-sensitive regions of the brain. Event-related fMRI experiments, 3D motion and wide-field stimulation, functional localizers and brain mapping methods were used to study the sensitivity of six distinct motion areas (V6, MT, MST+, V3A, CSv and an Intra-Parietal Sulcus motion [IPSmot] region) to different types of optic flow stimuli. Results show that only areas V6, MST+ and IPSmot are specialized in distinguishing among the various types of flow patterns, with a high response for the translational flow which was maximum in V6 and IPSmot and less marked in MST+. Given that during egomotion the translational optic flow conveys differential information about the near and far external objects, areas V6 and IPSmot likely process visual egomotion signals to extract information about the relative distance of objects with respect to the observer. Since area V6 is also involved in distinguishing object-motion from self-motion, it could provide information about location in space of moving and static objects during self-motion, particularly in a dynamically unstable environment.
Local Discriminant Analysis

The main objective of the work presented here is to introduce a supervised, nonlinear dimensionality reduction technique which performs well-known linear discriminant analysis in a local way and which is able to provide a powerful mapping with less computational effort than other nonlinear reduction methods. Additionally, because of the close connection of the new approach to Fisher's LDA, it is more clear that it acts discriminatively, which is not immediately apparent from previous formulations. The method makes use of the optimal scoring framework advocated by Hastie et al. and it is coined local discriminant analysis (lDA)
The compressed differential heuristic

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) are flexible and can be tuned to fit any size of memory, even smaller than the size of the state space. Furthermore, CDHs can be built without the need to create and store the entire uncompressed DH. Experimental results across different domains show that, for a given amount of memory, a CDH significantly outperforms an uncompressed DH.
Application of Carbon Sorbents for the Concentration and Separation of Metal Ions

Solid-phase extraction is an attractive approach in the preparation of many kinds of samples prior to analysis; highly selective sorbents are desirable for this purpose. The objective of this review is to provide updated information about carbon-based sorbents, their interaction modes and potential application for the concentration and separation of metal ions from environmental samples prior to their determination. New selective phases such as fullerenes and carbon nanotubes are described. Selected examples illustrate the potential of these sorbents.
Applying causal-state splitting reconstruction algorithm to natural language processing tasks

This thesis is focused on the study and use of Causal State Splitting Reconstruction (CSSR) algorithm for Natural Language Processing (NLP) tasks, CSSR is an algorithm that captures patterns from data building automata in the form of visible Markov Models. It is based on the principles of Computational Mechanics and takes advantage of many properties of causal state theory. One of the main advantages of CSSR with respect to Markov Models is that it builds states containing more than one $n$­gram (called history in computational mechanics), so the obtained automata are much smaller than the equivalent Markov Model. In this work, we first study the behavior of the algorithm when learning patterns related to NLP tasks but without performing any annotation task. This first experiments are useful to understand the parameters that affect the algorithm and to check that it is able to capture the patterns present in natural language sentences. Secondly, we propose a way to apply CSSR to NLP annotation tasks. The algorithm is not originally conceived to use the hidden information necessary for annotation tasks, so we devised a way to introduce it into the system in order to obtain automata including this information that can be afterwards used to annotate new text. Also, some methods to deal with unseen events and a modification of the algorithm to make it more suitable for NLP tasks have been presented and tested. These three aspects conform the first line of contributions of this research, altogether with a deep experimental study of the proposed methods. The experimental study of the proposed approach is performed in three different tasks: Named Entity Recognition in general and Biomedical domain and Chunking. The obtained results are promising in the two first tasks though not so good for Chunking. Nevertheless, it is not easy to improve the obtained performance following the same approach, since CSSR needs quite reduced feature sets to build correct automaton and that limits the performance of the developed system. For that reason, we propose to combine CSSR with graphical models, in order to enrich the features that the system can take into account. This combination conforms the second line of contributions of this thesis. There is a variety of possible graphical models that can be used, but for the moment we propose to combine CSSR algorithm with Maximum Entropy (ME) models. ME models can be used as a way of introducing more information into the system, encoding it as features. In this line, we propose and test two methods for combining CSSR and ME models in order to improve the results obtained with original CSSR. The first method is simple and does not modify the automata­building algorithm while the second one is more sophisticated and builds automata taking into account the ME features. We will see that though much more simpler, the first method leads to an important improvement with respect to original CSSR but the second method does not.
Site-directed point mutations in embryonic stem cells: a gene-targeting tag-and-exchange strategy

Sequential gene targeting was used to introduce point mutations into one alpha 2 isoform Na,K-ATPase homolog in mouse embryonic stem (ES) cells. In the first round of targeted replacement, the gene was tagged with selectable markers by insertion of a Neor/HSV-tk gene cassette, and this event was selected for by gain of neomycin (G418) resistance. In the second targeted replacement event, the tagged genomic sequence was exchanged with a vector consisting of homologous genomic sequences carrying five site-directed nucleotide substitutions. Embryonic stem cell clones modified by exchange with the mutation vector were selected for loss of the HSV-tk gene by resistance to ganciclovir. Candidate clones were further screened and identified by polymerase chain reaction and Southern blot analysis. By this strategy, the endogenous alpha 2 isoform Na,K-ATPase gene was altered to encode two other amino acids so that the enzyme is resistant to inhibition by cardiac glycosides while maintaining its transmembrane ion-pumping function. Since the initial tagging event and the subsequent mutation-exchange event are independent of one another, a tagged cell line can be used to generate a variety of mutant lines by exchange with various mutation vectors at the tagged locus. This method should be useful for testing specific mutations introduced into the genomes of tissue culture cells and animals and for developing animal models encompassing the mutational variability of known genetic disorders.
Content Based Microblogger Recommendation

Micro logs are social networking systems, where users frequently contribute very short (micro) posts. Micro loggers follow others' posts via subscription. However, it is a challenge to determine micro loggers worthy of following, since their contributions are fragmented amidst numerous and various tiny posts. Furthermore, micro posts typically include abbreviations, mispeled words, special tokens, and are not grammatically correct. Consequently, typical NLP techniques are often not very useful in determining what a user posts about. This work proposes a content-based, as opposed to friendship-based, micro blogger recommendation model, where given an user query a ranked list of microbloggers is produced. This paper presents the recommendation model, a prototype implementation, an user evaluation.
Similarity Measures for Computing Relaxed Instances w.r.t. General EL-TBoxes

The notion of concept similarity is central to several ontology tasks and can be employed to realize relaxed versions of classical reasoning services. In this paper we investigate the reasoning service of answering instance queries in a relaxed fashion, where the query concept is relaxed by means of a concept similarity measure (CSM). To this end we investigate CSMs that assess the similarity of EL-concepts defined w.r. t. a general EL-TBox. We derive such a family of CSMs from a family of similarity measures for finite interpretations and show in both cases that the resulting measures enjoy a collection of formal properties. These properties allow us to devise an algorithm for computing relaxed instances w.r. t. general EL-TBoxes, where users can specify the „appropriate“ notion of similarity by instanciating our CSM appropriately.
Possible living fossil in Bolivia: A new genus of flea beetles with modified hind legs (Coleoptera, Chrysomelidae, Galerucinae, Alticini)

Abstract A new genus (Chanealtica) with three new species (Chanealtica cuevas, Chanealtica ellimon, and Chanealtica maxi) from Bolivia is described and illustrated. It is compared with Aphthonoides Jacoby, 1885, Argopistes Motschulsky, 1860, Metroserrapha Bechyne, 1958, Psylliodes Berthold, 1827 and Psyllototus Nadein, 2010. Remarkably, based on the available characters, among all the flea beetles, Chanealtica is mostly similar to an extinct genus Psyllototus. A discussion of diversity and function of the hind leg in flea beetles is provided.
Research of the Communication Model of Botnet Based on P2P

The communication mechanism for P2P botnet existing is classified into two models: the Send communication model and the Request communication model. We propose an evaluation index including concealment, effectiveness, efficiency and robustness and its calculation method. At the same time we using these evaluation index to simulate, evaluate and analysis the two kinds of models, and study the relationship between them and the botnet basic characteristics.
A fast and efficient algorithm to compute BPX- and overlapping preconditioner for adaptive 3D-FEM

In this paper we consider the well-known BPX-preconditioner in conjunction with adaptive FEM. We present an algorithm which enables us to compute the preconditioner with optimal complexity by a total of only O(DoF) additional memory. Furthermore, we show how to combine the BPX-preconditioner with an overlapping Additive-Schwarz-preconditioner to obtain a preconditioner for finite element spaces with arbitrary polynomial degree distributions. Numerical examples illustrate the efficiency of the algorithms.
Evaluation of the Cepheid Xpert MTB/RIF Assay for Direct Detection of Mycobacterium tuberculosis Complex in Respiratory Specimens

ABSTRACT A total of 217 specimens submitted for routine smear and culture from three different sites within the western United States were used to evaluate the GeneXpert MTB/RIF assay (for research use only) (Cepheid, Sunnyvale, CA). Overall agreement compared to culture was 89% (98% for smear positives and 72% for smear negatives) for detection of Mycobacterium tuberculosis.
Supporting Differentiated Services in Computers via Programmable Architecture for Resourcing-on-Demand (PARD)

This paper presents PARD, a programmable architecture for resourcing-on-demand that provides a new programming interface to convey an application's high-level information like quality-of-service requirements to the hardware. PARD enables new functionalities like fully hardware-supported virtualization and differentiated services in computers. PARD is inspired by the observation that a computer is inherently a network in which hardware components communicate via packets (e.g., over the NoC or PCIe). We apply principles of software-defined networking to this intra-computer network and address three major challenges. First, to deal with the semantic gap between high-level applications and underlying hardware packets, PARD attaches a high-level semantic tag (e.g., a virtual machine or thread ID) to each memory-access, I/O, or interrupt packet. Second, to make hardware components more manageable, PARD implements programmable control planes that can be integrated into various shared resources (e.g., cache, DRAM, and I/O devices) and can differentially process packets according to tag-based rules. Third, to facilitate programming, PARD abstracts all control planes as a device file tree to provide a uniform programming interface via which users create and apply tag-based rules. Full-system simulation results show that by co-locating latencycritical memcached applications with other workloads PARD can improve a four-core computer's CPU utilization by up to a factor of four without significantly increasing tail latency. FPGA emulation based on a preliminary RTL implementation demonstrates that the cache control plane introduces no extra latency and that the memory control plane can reduce queueing delay for high-priority memory-access requests by up to a factor of 5.6.
Apoptosis induction mediated by wild-type p53 adenoviral gene transfer in squamous cell carcinoma of the head and neck.

Cancer gene therapy strategies for inducing apoptosis in solid tumors may allow contemporary medicine to reassess its management of these cancers. We demonstrated previously that overexpression of the wild-type p53 gene in squamous cell carcinoma of the head and neck cell lines via adenovirus-mediated gene transfer suppressed growth both in vitro and in vivo. Here, we characterize the mechanism of the growth suppression by the exogenous p53 gene as a consequence of programmed cell death (apoptosis). One of the cell lines used in this study, Tu-138, harbors a mutated p53 gene, whereas the other cell line, MDA 686LN, possesses a wild-type p53 gene. DNA fragmentation was detected by electrophoresis in both cell lines after infection with the wild-type p53 adenovirus, Ad5CMV-p53. With the use of the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling method, 4.4% of the remaining viable Tu-138 cell population was identified as apoptotic as early as 15 h after inoculation with Ad5CMV-p53. The percentage of apoptotic cells increased to 31% at 22 h. In contrast, only 10% of the viable MDA 686LN cells (wt-p53) had undergone apoptosis 30 h after Ad5CMV-p53 infection, although the percentage of apoptotic cells rapidly increased to 60% at 48 h after infection. For in vivo analysis of apoptosis, nude mice in which squamous cell carcinoma of the head and neck cell lines had been implanted s.c. had exogenous wt-p53 transiently introduced to the tumor cells via Ad5CMV-p53 2 days later. In situ end labeling clearly illustrated apoptosis in the tumor cells. These results suggest that wt-p53 plays an important role in the induction of apoptosis in human head and neck cancer cell lines and that selective induction of apoptosis in cancer cells can be further explored as a strategy for cancer gene therapy.
Geodetic Iterative Methods for Nonlinear Acoustic Sources Localization : Application to Cracks E ects Detected with Acoustic Emission

This paper is related to Acoustic Emission (AE) principle, which is a Nondestructive Testing method (NDT) for structural health monitoring and many other applications. We deal with the geodetic curve based localization of the defects on surfaces of solid bodies mathematically described as a conjunction of several simpler parametrized shapes like cylinders, toroids, spheres, cones,. with intersections. We demonstrate a numerical solution of geodesic equations by the algorithm NewtonRaphson method. For faster computations we propose a few improvements using Sequential algorithm applied to bisected points of the intersections, which is compared with trivial Fundamental method. The whole algorithm for nding geodesics on combined surfaces is applied to virtual testing body which was designed according to the solid watering can.
A Computational Model of Empathy: Empirical Evaluation

Empathy can be defined as the ability to perceive and understand others' emotional states. Neuropsychological evidence has shown that humans empathize with each other to different degrees depending on factors such as their mood, personality, and social relationships. Although artificial agents have been endowed with features such as affect, personality, and the ability to build social relationships, little attention has been devoted to the role of such features as factors that can modulate their empathic behavior. In this paper, we present and discuss the results of an empirical evaluation of a computational model of empathy which allows a virtual human to exhibit different degrees of empathy. Our model is supported by psychological models of empathy and is applied and evaluated in the context of a conversational agent scenario.
Depression Diagnosis Based on Ontologies and Bayesian Networks

Recently, depression become a general disease in the world due to the promotion of life quality and technology development. Most of people are not aware of the possibility of getting depressed himself in daily life. To accurately diagnose getting depressed becomes an important issue. In this paper, we utilize ontologies and Bayesian networks techniques to build the inference model for inferring the possibility of depression. We propose an ontology model to build the terminology of depression and utilize the Bayesian networks to infer the probability of depression. In addition, the paper also proposes an agent-based platform and addresses the implementation issue. The result shows that it can be well-inferring in the depression diagnosis.
3D facial expression recognition: A perspective on promises and challenges

This survey focuses on discrete expression classification and facial action unit recognition performed using 3D face data, possibly including a corresponding 2D texture image. Research trends to date are summarized and the limitations of current methods are discussed. The challenges towards the development of more accurate and automated 3D facial expression recognition methods are identified. We also call for standardized experimental protocols in order to draw fair and meaningful comparisons between different systems.