Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Care-Seeking Behavior During Childhood Diarrheal Illness: Results of Health Care Utilization and Attitudes Surveys of Caretakers in Western Kenya, 2007–2010

We interviewed caretakers of 1,043 children < 5 years old in a baseline cross-sectional survey (April to May 2007) and > 20,000 children on five separate subsequent occasions (May of 2009 to December 31, 2010) to assess healthcare seeking patterns for diarrhea. Diarrhea prevalence during the preceding 2 weeks ranged from 26% at baseline to 4–11% during 2009–2010. Caretakers were less likely to seek healthcare outside the home for infants (versus older children) with diarrhea (adjusted odds ratio [aOR] = 0.33, confidence interval [CI] = 0.12–0.87). Caretakers of children with reduced food intake (aOR = 3.42, CI = 1.37–8.53) and sunken eyes during their diarrheal episode were more likely to seek care outside home (aOR = 4.76, CI = 1.13–8.89). Caretakers with formal education were more likely to provide oral rehydration solution (aOR = 3.01, CI = 1.41–6.42) and visit a healthcare facility (aOR = 3.32, CI = 1.56–7.07). Studies calculating diarrheal incidence and healthcare seeking should account for seasonal trends. Improving caretakers' knowledge of home management could prevent severe diarrhea.
PALM: Preprocessed Apriori For Logical Matching Using Map Reduce Algorithm

With the recent explosive growth of the amount of data content and information on the Internet, it has become increasingly difficult for users to find and maximize the utilization of the information found in the internet. Traditional web search engines often return hundreds or thousands of results for a particular search, which is time consuming . In order to overcome these problems, we have described the implementation and design of the PALM Algorithm (PREPROCESSED APRIORI FOR LOGICAL MATCHING) in mining information data from the World Wide Web. The PALM- ALGORITHM provides us with a very efficient and simple way for finding related patterns while maneuvering through the internet. The PALM-ALGORITHM is implemented in two steps. The first step includes a Map-Reducing Algorithm which is used to traverse and analyze all the items of a large database and preprocess them using a variable called MINIMUM THRESHOLD SUPPORT to find the INITIAL CANDIDATE SET(C) AND LARGEITEM SET (L). The second step includes a pre-processing algorithm to find both the CANDIDATE(C) and LARGEITEM SET (L) for the further scans.
Multimodel H∞ Loop shaping control of a DC motor under variable loads

The multimodel and multicontrol approachs are two powerful techniques which are often useful in literature, to the analyse, the modeling and the control of complex systems. Indeed, the operating range of the process is partitioned into a set of operating regions. For each operation region a local model or/and controller is applied. The effective control applied to the process is a fusion of all these local control laws. In this paper, a multimodel H∞ Loop shaping controller (MM-HILSC) is designed and applied to a variably loaded DC motor. In order to use the system in variable load applications such as robot actuators, a multi-model controller is designed based on the H∞ Loop shaping controllers (HILSC). Simulation results show a notable improvement relatively to the classical HILSC. The design controller is simplified by considering a reduced model obtained by decomposition of the global system into two time-scales singularly perturbed model of the DC motor.
On the Synthesis of Haptic Textures

Advanced, synthetic haptic virtual environments require textured virtual surfaces. We found that texturing smooth surfaces often reduces the system passivity margin of a haptic simulation. As a result, a smooth virtual surface that can be rendered in a passive manner may loose this property once textured. We propose that any texture algorithm is associated with a characteristic number that expresses the relative change in loop gain. We further found that a passive virtual interaction can have severe unwanted artifacts if the synthesized force field is not conservative. The energy characteristics of seven algorithms are analyzed. Finally, a new texture synthesis algorithm, which operates by modulating a friction force during scanning, is shown to have several advantages over previous ones.
A New Statistical Approach to Personal Name Extraction

We propose a new statistical approach to extracting personal names from a corpus. One of the key points of our approach is that it can both automatically learn the characteristics of personal names from a large training corpus and make good use of human empirical knowledge (e.g., Context Free Grammar). Furthermore, our approach also assigns confidence measures to the extracted personal names, compared with traditional simple true/false determination. Another main contribution of this work is that we have applied the personal name extraction technology into a real application, which is a Chinese inputting system and have achieved an approximately 7% error rate reduction for all characters and 30% error rate reduction for personal names.
Belief Revision with General Epistemic States

In order to properly regulate iterated belief revision, Darwiche and Pearl (1997) model belief revision as revising epistemic states by propositions. An epistemic state in their sense consists of a belief set and a set of conditional beliefs. Although the denotation of an epistemic state can be indirectly captured by a total preorder on the set of worlds, it is unclear how to directly capture the structure in terms of the beliefs and conditional beliefs it contains. In this paper, we first provide an axiomatic characterisation for epistemic states by using nine rules about beliefs and conditional beliefs, and then argue that the last two rules are too strong and should be eliminated for characterising the belief state of an agent. We call a structure which satisfies the first seven rules a general epistemic state (GEP). To provide a semantical characterisation of GEPs, we introduce a mathematical structure called belief algebra, which is in essence a certain binary relation defined on the power set of worlds.We then establish a 1-1 correspondence between GEPs and belief algebras, and show that total preorders on worlds are special cases of belief algebras. Furthermore, using the notion of belief algebras, we extend the classical iterated belief revision rules of Darwiche and Pearl to our setting of general epistemic states.
Exposure to H1 genotype measles virus at an international airport in Japan on 31 July 2016 results in a measles outbreak

a Field Epidemiology Training Program, National Institute of Infectious Diseases. b Department of Epidemiology for Infectious Diseases, Graduate School of Medicine, Osaka University, Japan. c Infectious Disease Surveillance Center, National Institute of Infectious Diseases. d Department of Virology III, National Institute of Infectious Diseases. e Division of Global Infectious Diseases, Department of Infection and Epidemiology, Graduate School of Medicine, Tohoku University, Miyagi, Japan. Submitted: 22 December 2016; Published: 07 February 2017 doi: 10.5365/wpsar.2016. 7.4. 007 Exposure to H1 genotype measles virus at an international airport in Japan on 31 July 2016 results in a measles outbreak
Lability of secondary organic particulate matter

Significance Mass lability of atmospheric organic particulate matter, meaning its tendency to evaporate, is an important property in the mechanisms governing the climate and health effects. This property can be governed by both the volatility and the molecular diffusivity of the constituent organic species. An experimental approach presented herein highlights a nonlabile-to-labile transition at a threshold humidity for material representative of anthropogenic particulate matter. The behavior differs markedly from materials representing biogenic sources, which lack such a transition. Accounting for this transition in the interpretation of laboratory datasets collected at low relative humidity can potentially reduce discrepancies between modeled and observed concentrations of particulate matter in urban regions. The energy flows in Earth’s natural and modified climate systems are strongly influenced by the concentrations of atmospheric particulate matter (PM). For predictions of concentration, equilibrium partitioning of semivolatile organic compounds (SVOCs) between organic PM and the surrounding vapor has widely been assumed, yet recent observations show that organic PM can be semisolid or solid for some atmospheric conditions, possibly suggesting that SVOC uptake and release can be slow enough that equilibrium does not prevail on timescales relevant to atmospheric processes. Herein, in a series of laboratory experiments, the mass labilities of films of secondary organic material representative of similar atmospheric organic PM were directly determined by quartz crystal microbalance measurements of evaporation rates and vapor mass concentrations. There were strong differences between films representative of anthropogenic compared with biogenic sources. For films representing anthropogenic PM, evaporation rates and vapor mass concentrations increased above a threshold relative humidity (RH) between 20% and 30%, indicating rapid partitioning above a transition RH but not below. Below the threshold, the characteristic time for equilibration is estimated as up to 1 wk for a typically sized particle. In contrast, for films representing biogenic PM, no RH threshold was observed, suggesting equilibrium partitioning is rapidly obtained for all RHs. The effective diffusion rate Dorg for the biogenic case is at least 103 times greater than that of the anthropogenic case. These differences should be accounted for in the interpretation of laboratory data as well as in modeling of organic PM in Earth’s atmosphere.
Reactive power control of DFIG wind farm using online supplementary learning controller based on approximate dynamic programming

Dynamic reactive power control of doubly fed induction generators (DFIGs) plays a crucially important role in maintaining transient stability of power systems with high penetration of DFIG based wind generation. Based on approximate dynamic programming (ADP), this paper proposes an optimal adaptive supplementary reactive power controller for DFIGs. By augmenting a corrective regulation signal to the reactive power command of rotor-side converter (RSC) of a DFIG, the supplementary controller is designed to reduce voltage sag at the point of common connection (PCC) during a fault, and to mitigate output active power oscillation of the wind farm after a fault. As a result, the transient stability of both DFIG and the power grid is enhanced. An action dependent cost function is introduced to provide real-time online ADP learning control. Furthermore, a policy iteration algorithm using high-efficiency least square method is employed to train the supplementary controller in an online model-free manner. By using such techniques, the supplementary reactive power controller is endowed with capability of online optimization and adaptation. Simulations carried out on a benchmark power system integrating a large DFIG wind farm show that the ADP based supplementary reactive power controller can significantly improve the transient system stability in changing operation conditions.
Weighted one-norm minimization with inaccurate support estimates: Sharp analysis via the null-space property

We study the problem of recovering sparse vectors given possibly erroneous support estimates. First, we provide necessary and sufficient conditions for weighted ℓ1 minimization to successfully recovery all sparse signals whose support estimate is sufficiently accurate. We relate these conditions to the analogous ones for ℓ1 minimization, showing that they are equivalent when the support estimate is 50% accurate but that the weighted ℓ1 conditions are easier to satisfy when the support is more than 50% accurate. Second, to quantify this improvement, we provide bounds on the number of Gaussian measurements that ensure, with high probability, that weighted ℓ1 minimization succeeds. The resulting number of measurements can be significantly less than what is needed to ensure recovery via ℓ1 minimization. Finally, we illustrate our results via numerical experiments.
Incremental learning to rank with partially-labeled data

In this paper we present a semi-supervised learning method for a problem of learning to rank where we exploit Markov random walks and graph regularization in order to incorporate not only "labeled" web pages but also plenty of "un-labeled" web pages (click logs of which are not given) into learning a ranking function. In order to cope with scalability which existing semi-supervised learning methods suffer from, we develop a scalable and incremental method for semi-supervised learning to rank. In the graph regularization framework, we first determine features which well reflects data manifold and then make use of them to train a linear ranking function. We introduce a matrix-fee technique where we compute the eigenvectors of a huge similarity matrix without constructing the matrix itself. Then we present an incremental algorithm to learn a linear ranking function using features determined by projecting data onto the eigenvectors of the similarity matrix, which can be applied to a task of web-scale ranking. We evaluate our method on Live Search query log, showing that search performance is much improved when Live Search yields unsatisfactory search results.
Inference of the Russian Drug Community from One of the Largest Social Networks in the Russian Federation

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Developing a multivariable prognostic model for pancreatic endocrine tumors using the clinical data warehouse resources of a single institution

Summary Objective: Current staging systems are not accurate for classifying pancreatic endocrine tumors (PETs) by risk. Here, we developed a prognostic model for PETs and compared it to the WHO classification system. Methods: We identified 98 patients diagnosed with PET at NewYork-Presbyterian Hospital/Columbia University Medical Center (1999 to 2009). Tumor and clinical characteristics were retrieved and associations with survival were assessed by univariate Cox analysis. A multivariable model was constructed and a risk score was calculated; the prognostic strength of our model was assessed with the concordance index. Results: Our cohort had median age of 60 years and consisted of 61.2% women; median follow-up time was 10.4 months (range: 0.1-99.6) with a 5-year survival of 61.5%. The majority of PETs were non-functional and no difference was observed between functional and non-functional tumors with respect to WHO stage, age, pathologic characteristics or survival. Distant metastases, aspartate aminotransferase-AST and surgical resection (HR=3.39, 95% CI: 1.38-8.35, p=0.008, HR=3.73, 95% CI: 1.20-11.57, p=0.023 and HR=0.20, 95% CI: 0.08-0.51, p<0.001 respectively) were the strongest predictors in the univariate analysis. Age, perineural and/or lymphovascular invasion, distant metastases and AST were the independent prognostic factors in the final multivariable model; a risk score was calculated and classified patients into low (n=40), intermediate (n=48) and high risk (n=10) groups. The concordance index of our model was 0.93 compared to 0.72 for the WHO system. Conclusion: Our prognostic model was highly accurate in stratifying patients by risk; novel approaches as such could thus be incorporated into clinical decisions.
Kinematic manipulability of general constrained rigid multibody systems

Extends the kinematic manipulability concept commonly used for serial manipulators to general constrained rigid multibody systems. Examples of such systems include multiple cooperating manipulators, multiple fingers holding a payload, multi-leg walking robots, and variable geometry trusses. Explicit formulas for velocity and force manipulability ellipsoids are derived and their duality explained. The concept of unstable grasp and manipulable grasp are also extended and illustrated with examples.
A DFT technique for delay fault testability and diagnostics in 32-bit high performance CMOS ALUs

Aggressive technology scaling has been the mainstay of digital CMOS circuit design for the past 30 years. This has resulted in the design of multi-gigahertz microprocessors with unprecedented levels of integration. However, this is posing serious challenges to IC testing and long-term reliability. A major source of failures and test escapes in high performance ICs can be attributed to timing-only parametric failures. We implement a DFT technique to detect delay faults in a full custom 32-bit high performance ALU. We present the energy-delay tradeoffs and scaling trends associated with our DFT technique for the 180 nm-65 nm CMOS technologies. In addition, we demonstrate how this technique can be used to detect delay faults with improved resolution (/spl sim/60 ps for 180 nm technology) at relatively low, test mode clock frequencies.
Make Up Your Mind: The Price of Online Queries in Differential Privacy

We consider the problem of answering queries about a sensitive dataset subject to differential privacy. The queries may be chosen adversarially from a larger set $Q$ of allowable queries in one of three ways, which we list in order from easiest to hardest to answer:Offline: The queries are chosen all at once and the differentially private mechanism answers the queries in a single batch. Online: The queries are chosen all at once, but the mechanism only receives the queries in a streaming fashion and must answer each query before seeing the next query. Adaptive: The queries are chosen one at a time and the mechanism must answer each query before the next query is chosen. In particular, each query may depend on the answers given to previous queries.Many differentially private mechanisms are just as efficient in the adaptive model as they are in the offline model. Meanwhile, most lower bounds for differential privacy hold in the offline setting. This suggests that the three models may be equivalent. We prove that these models are all, in fact, distinct. Specifically, we show that there is a family of statistical queries such that exponentially more queries from this family can be answered in the offline model than in the online model. We also exhibit a family of search queries such that exponentially more queries from this family can be answered in the online model than in the adaptive model. We also investigate whether such separations might hold for simple queries like threshold queries over the real line.
Associative Memory for Online Learning in Noisy Environments Using Self-Organizing Incremental Neural Network

Associative memory operating in a real environment must perform well in online incremental learning and be robust to noisy data because noisy associative patterns are presented sequentially in a real environment. We propose a novel associative memory that satisfies these requirements. Using the proposed method, new associative pairs that are presented sequentially can be learned accurately without forgetting previously learned patterns. The memory size of the proposed method increases adaptively with learning patterns. Therefore, it suffers neither redundancy nor insufficiency of memory size, even in an environment in which the maximum number of associative pairs to be presented is unknown before learning. Noisy inputs in real environments are classifiable into two types: noise-added original patterns and faultily presented random patterns. The proposed method deals with two types of noise. To our knowledge, no conventional associative memory addresses noise of both types. The proposed associative memory performs as a bidirectional one-to-many or many-to-one associative memory and deals not only with bipolar data, but also with real-valued data. Results demonstrate that the proposed method's features are important for application to an intelligent robot operating in a real environment. The originality of our work consists of two points: employing a growing self-organizing network for an associative memory, and discussing what features are necessary for an associative memory for an intelligent robot and proposing an associative memory that satisfies those requirements.
Understanding variation in measles-mumps-rubella immunization coverage--a population-based study.

BACKGROUND Coverage of the Measles-Mumps-Rubella combined vaccine (MMR) has declined in recent years in the UK, following adverse publicity about possible links between the vaccine, autism, and Crohn's disease. The objectives of this study were to assess geographical variation in trends in MMR coverage and to identify the factors affecting MMR uptake at population level. METHODS We conducted an ecological study of immunization coverage by second birthday, based on routinely collected data from 1993-2004 for England. Trends in MMR uptake were assessed in 95 District Health Authorities in England over the study period. We investigated the relationship between MMR immunization uptake and deprivation, ethnicity, education, population density, rurality, and socioeconomic class. RESULTS Since 2000, MMR coverage has declined significantly in virtually all areas of England. Population density and deprivation were both strongly correlated with low MMR uptake. The decline in coverage since 1993-94 was significantly related to the proportion of educated population and was greater in densely populated areas. CONCLUSION Decline in MMR coverage now affects most areas of England. The lowest rates of MMR coverage remain in urban areas, particularly in inner cities, which also tend to show high levels of deprivation. Public health resources should continue to target inner city areas, as well as focus on the concerns of the better educated about vaccine safety.
Experiences in assessing product family software architecture for evolution

Software architecture assessments are a means to detect architectural problems before the bulk of development work is done. They facilitate planning of improvement activities early in the lifecycle and allow limiting the changes on any existing software. This is particularly beneficial when the architecture has been planned to (or already does) support a whole product family, or a set of products that share common requirements, architecture, components or code. As the family requirements evolve and new products are added, the need to assess the evolvability of the existing architecture is vital. The author illustrates two assessment case studies in the mobile telephone software domain: the Symbian operating system platform and the network resource access control software system. By means of simple experimental data, evidence is shown of the usefulness of architectural assessment as rated by the participating stakeholders. Both assessments have led to the identification of previously unknown architectural defects, and to the consequent planning of improvement initiatives. In both cases, stakeholders noted that a number of side benefits, including improvement of communication and architectural documentation, were also of considerable importance. The lessons learned and suggestions for future research and experimentation are outlined.