Miracle-dz/UniFAR-FTUs huggingface dataset:
Tropical Cyclone Central Pressure Estimation Using Doppler Radar Observations

The Meteorological Research Institute of the Japan Meteorological Agency has developed a new system for estimating the intensity (central pressure) of tropical cyclones (TCs) at 5-min intervals using single ground-based Doppler radar observations. The method involves the use of the ground-based velocity track display (GBVTD) technique, in which tangential winds are retrieved, and the gradient wind balance. In terms of the accuracy of this method, the root mean square error (RMSE) and bias are 8.37 and 1.51 hPa, respectively, to the best track data of the Regional Specialized Meteorological Center (RSMC) Tokyo. This level of accuracy is comparable to or better than the accuracies of conventional methods such as Dvorak and satellite microwave-derived estimates. In particular, for TCs with a radius of maximum wind of 20 – 70 km, the estimated central pressures have an RMSE of 5.55 hPa. The method enables TC monitoring at 5-min intervals from several hours before landfall in populated areas and helps to clarify intensity changes in real time. This report details the characteristics, utilities and limitations of the Doppler radar intensity estimation method and outlines examples of related estimation.
Selegiline Protects Motoneurons at Acute Phase of Spinal Cord Injury in Hemisectioned rats

Selegiline Protects Motoneurons at Acute Phase of Spinal Cord Injury in Hemisectioned rats Mostafa Ashrafi OSLAU, Taki TIRAIHI, Rahim HOBBENEGHI, Taher TAHERI Department of Anatomical Sciences, School of Medical Sciences, Tarbiat Modares University Tehran, Iran Department of Anatomical Sciences, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran Department of Pathology, Faculty of Veterinary Medicine, Urmia University, Urmia, Iran Shefa Neurosciences Research Center, Khatam AlAnbia Hospital, Tehran, Iran
EXPECTATIONS FOR DEFENDANT EMOTION

Participants (N = 160) read scenarios in which a defendant’s guilt status (guilty, not guilty); type of emotion judgment made (feeling, displaying); and defendant/victim relationship (husband and stranger) were varied to determine their influences on expectations for defendants’ emotional responses. In a second experiment, we presented these same variables between-subjects to 324 online participants. In a third experiment, 329 online participants provided expectations for a defendant’s crying behavior when defendant gender, guilt status, and defendant/victim relationship varied. Generally, more emotion (e.g., sadness) was expected when a spouse versus stranger was killed, but only when the defendant was not guilty. Expectations varied with defendant gender and participants’ self-reported emotionality. The implications of having defendant emotion expectations are discussed within a wrongful conviction context.
Global distribution of mafic minerals abundances and associated chemical composition at Mars: a legacy of OMEGA

One of the major goals of the OMEGA/Mars Express instrument was to constrain the igneous terrains of Mars. The entire NIR OMEGA dataset, acquired over 3.6 Martian years, was used and formatted for such an analysis. To derive the modal composition and grain sizes at a planetary scale, a radiative transfer model was used to reproduce these millions of spectra representative of igneous terrains of Mars. The lithology can be summarized in five mineral maps at km-scale (Figure 1): plagioclase, pyroxenes, olivine and Martian dust analogue. The corresponding oxide composition translates into a Martian crust of basaltic composition. These data products are a part of the OMEGA/MarsExpress legacy and will be distributed throughout the PSUP portal and PSA archive.
COMPETING RISKS MODEL WITH A NON-TRADITIONAL APPLICATION

The competing risks scheme is used to the analysis of time to the first goal in a football (soccer) match. The competing random variables are two latent times (of two teams) to scoring. It is assumed that these times are exponentially distributed, their mutual dependence is described by a copula ensuring the model identifiability. As a real example the data from two seasons 2014–2016 of the Czech First League are analyzed and compared.
ffect of agricultural activities on prevalence rates , and clinical and resumptive malaria episodes in central Côte d ’ Ivoire enjamin

Agricultural activities, among other factors, can influence the transmission of malaria. In two villages of central Côte d’Ivoire (Tiémélékro and Zatta) with distinctively different agro-ecological characteristics, we assessed Plasmodium prevalence rates, fever and clinically confirmed malaria episodes among children aged 15 years and below by means of repeated cross-sectional surveys. Additionally, presumptive malaria cases were monitored in dispensaries for a 4-year period. In Tiémélékro, we observed a decrease in malaria prevalence rates from 2002 to 2005, which might be partially explained by changes in agricultural activities from subsistence farming to cash crop production. In Zatta, where an irrigated rice perimeter is located in close proximity to human habitations, malaria prevalence rates in 2003 were significantly lower ransmission revalence rate resumptive malaria linical malaria gricultural activity ôte d’Ivoire than in 2002 and 2005, which coincided with the interruption of irrigated rice farming in 2003/2004. Although malaria transmission differed by an order of magnitude in the two villages in 2003, there was no statistically significant difference between the proportions of severe malaria episodes (i.e. axillary temperature > 37.5 ◦C plus parasitaemia > 5000 parasites/ l blood). Our study underscores the complex relationship between malaria transmission, prevalence rate and the dynamics of malaria episodes. A better understanding of local contextual determinants, including the effect of agricultural activities, will epide help to improve the local
Hao Zhang: Robot Adaptation to Unstructured Terrains by Joint Representation and Apprenticeship Learning

—When a mobile robot is deployed in a ﬁeld environment, e.g., during a disaster response application, the capability of adapting its navigational behaviors to unstructured terrains is essential for effective and safe robot navigation. In this paper, we introduce a novel joint terrain representation and apprenticeship learning approach to implement robot adaptation to unstructured terrains. Different from conventional learning-based adaptation techniques, our approach provides a uniﬁed problem formulation that integrates representation and apprenticeship learning under a uniﬁed regularized optimization framework, instead of treating them as separate and independent procedures. Our approach also has the capability to automatically identify discriminative feature modalities, which can improve the robustness of robot adaptation. In addition, we implement a new optimization algorithm to solve the formulated problem, which provides a theoretical guarantee to converge to the global optimal solution. In the experiments, we extensively evaluate the proposed approach in real-world scenarios, in which a mobile robot navigates on familiar and unfamiliar unstructured terrains. Experimental results have shown that the proposed approach is able to transfer human expertise to robots with small errors, achieve superior performance compared with previous and baseline methods, and provide intuitive insights on the importance of terrain feature modalities.
Supplement to “ User-Friendly Covariance Estimation for Heavy-Tailed Distributions ”

holds with probability at least δ, as claimed. ∗Department of Statistics, University of Georgia, Athens, GA 30602, USA. E-mail: yuan.ke@uga.edu. †Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA. E-mail: minsker@usc.edu. ‡Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA. E-mail: zren@pitt.edu. §Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, Canada. E-mail: qsun@utstat.toronto. edu. ¶Department of Mathematics, University of California, San Diego, La Jolla, CA 92093, USA. E-mail: wez243@ucsd.edu.
Characteristics of high-resolution sea ice dynamics in the Gulf of Finland , Baltic Sea

High-resolution Eulerian and Lagrangian sea ice kinematics are examined based on an ADCP and four icebound drifting buoys in the Gulf of Finland, Baltic Sea. The measurements were performed in the central Gulf of Finland in winter 2010 from January to March. Ice drift was mostly in near-free drift state driven by winds and ice-independent currents in the basin. Internal friction was significant, apart from off-ice forcing toward the open boundary. The averaged asymptotic ice/wind speed ratio and deviation angle at higher wind speeds were 0.034 and 9° in the oceanic boundary layer, respectively. The ratio depended on the direction of the wind indicating the role of morphometry of the coast to the local ice kinematics. The maximum values of wind ratio were observed in cases of NE winds and minimum values in cases of SW winds. Coherent ice drift was observed up to a distance of 42 km.
THE ACQUISITION OF THE ENGLISH ARTICLE SYSTEM BY ADULT NATIVE SPEAKERS OF ARABIC : L 1 TRANSFER AT THE LEVEL OF ARTICLE SEMANTICS

This study investigates the role of L1 transfer in the acquisition of L2 English article system by adult native speakers of Arabic. The main research question is: do adult Arabic-speaking learners of English transfer the definiteness setting of the Article Choice Parameter? Based on the underlying assumptions of two generative hypotheses: the Full Transfer/ Full Access hypothesis and the Fluctuation Hypothesis, two particular hypotheses were formulated: 1. H1: Since both Arabic and English have definiteness-based article systems, adult Arabicspeaking learners of English will show accurate use of the in all definite categories and accurate use of a in all indefinite categories. 2. H2: Since neither Arabic nor English encode specificity in their article systems, adult Arabic-speaking learners of English will show no sensitivity to context specificity in their article choices. To test the study hypotheses, a forced-choice elicitation task was used. The findings reveal that adult Arabic-speaking L2 learners were influenced by their L1 (that is, Arabic) in acquiring L2 English article system; intermediate level Arabic-speaking English learners exhibited relatively native-like use of English articles because the semantic definiteness-based distinction between articles is instantiated in Arabic. However, the study results do not fully confirm the predictions of the Fluctuation Hypothesis concerning the role of context specificity because low-intermediate level Arabic-speaking L2 learners of English were unexpectedly affected by a marginally significant impact of specificity which was manifested in overusing the English definite article the in specific indefinite contexts. Hence, the study findings reveal a significant effect of L1transfer in the acquisition of L2 article system and a potential role of context specificity in causing article misuse. The study undertakes some pedagogical implications and implies some recommendations for future research regarding the underlying assumptions of the Fluctuation Hypothesis and the Article Choice parameter.
Neural Coreference Resolution with Limited Lexical Context and Explicit Mention Detection for Oral French

We propose an end-to-end coreference resolution system obtained by adapting neural models that have recently improved the state-of-the-art on the OntoNotes benchmark to make them applicable to other paradigms for this task. We report the performances of our system on ANCOR, a corpus of transcribed oral French, for which it constitutes a new baseline with proper evaluation.
D C ] 1 0 A ug 2 01 7 Simulating a Shared Register in a System that Never Stops Changing ∗

Simulating a shared register can mask the intricacies of designing algorithms for asynchronous message-passing systems subject to crash failures, since it allows them to run algorithms designed for the simpler shared-memory model. Typically such simulations replicate the value of the register in multiple servers and require readers and writers to communicate with a majority of servers. The success of this approach for static systems, where the set of nodes (readers, writers, and servers) is fixed, has motivated several similar simulations for dynamic systems, where nodes may enter and leave. However, existing simulations need to assume that the system eventually stops changing for a long enough period or that the system size is bounded. This paper presents the first simulation of an atomic read/write register in a crash-prone asynchronous system that can change size and withstand nodes continually entering and leaving. The simulation allows the system to keep changing, provided that the number of nodes entering and leaving during a fixed time interval is at most a constant fraction of the current system size. The simulation also tolerates node crashes as long as the number of failed nodes in the system is at most a constant fraction of the current system size.
The BASE Plug-in Architecture-Composable Communication Support for Pervasive Systems

Pervasive computing is based upon spontaneously networked devices that are invisibly integrated into everyday objects. Due to the integration and mobility of devices, pervasive systems can be highly heterogeneous and dynamic. To enable cost-effective application development despite such harsh conditions, we have developed the BASE middleware. In its original implementation, BASE relied on monolithic plugins to provide tailored support for different communication abstractions, protocols and technologies. In this paper, we describe and evaluate a major architectural redesign that introduces modular plug-ins. The key benefits of the new architecture are increased flexibility and code reuse due to the efficient runtime composition of plug-ins.
A Flexible Heuristic to Schedule Distributed Analytic Applications in Compute Clusters

This work addresses the problem of scheduling user-defined analytic applications, which we define as high-level compositions of frameworks, their components, and the logic necessary to carry out work. The key idea in our application definition, is to distinguish classes of components, including core and elastic types: the first being required for an application to make progress, the latter contributing to reduced execution times. We show that the problem of scheduling such applications poses new challenges, which existing approaches address inefficiently. Thus, we present the design and evaluation of a novel, flexible heuristic to schedule analytic applications, that aims at high system responsiveness, by allocating resources efficiently. Our algorithm is evaluated using trace-driven simulations and with large-scale real system traces: our flexible scheduler outperforms current alternatives across a variety of metrics, including application turnaround times, and resource allocation efficiency. We also present the design and evaluation of a full-fledged system, which we have called Zoe, that incorporates the ideas presented in this paper, and report concrete improvements in terms of efficiency and performance, with respect to prior generations of our system.
Poststroke Fatigue and its Effect on Functional Status and Quality of Life in Patient with Ischemic Stroke

Poststroke Fatigue and its Effect on Functional Status and Quality of Life in Patient with Ischemic Stroke Vildan YAYLA, Hacı Ali ERDOĞAN, Filiz AYDIN, Meltem VURAL, Murat ÇABALAR, Sedef ERSOY, Bahar ÇAKMAK BOZAN Bakırköy Dr. Sadi Konuk Training ve Research Hospital Department of Neurology, Istanbul, Turkey Bakırköy Dr. Sadi Konuk Training ve Research Hospital Department of Physical Medicine and Rehabilitation, Istanbul, Turkey
3311 / CCC 2018-007 Creative Construction Conference 2018 , CCC 2018 , 30 June-3 July 2018 , Ljubljana , Slovenia Evaluation of the construction project success with use of neural networks

Construction project success is determined in terms of cost, schedule, performance and safety through many events and resultant interactions, plans, facilities and changes in participants and the environment. In the construction industry there are myriad uncertainties that make management exceedingly complex. Factors for success vary from project to project. Human experts can often achieve a satisfactory project outcome, however, shortfalls nearly always occur due to managers failing to take all relevant factors into consideration, in addition to lacking access to all relevant information. Statistical methods represent a basic approach to identifying significant factors from historical data or questionnaire results. However, the dynamic nature of critical factors means that changes in project conditions must be monitored continuously. Artificial intelligence techniques have a wide range of applications, including monitoring and forecasting of long-term projects; their main advantage is the ability to track and predict trends in changing project implementation factors. In this article, the authors describe the structure and algorithm of the neural network for assessing the success of construction projects, taking into account the individual influence of the initial conditions as well as their combined impact. © 2018 The Authors. Published by Diamond Congress Ltd. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2018.
Alteration Of Insulin-Like Growth Factor-1 Expression Following The Middle Cerebral Artery Occlusion in Monkeys And Rats : cDNA Microarray , Immunohistochemistry and in Situ Hybridization Studies

It is well established that insulin-like growth factor-1 (IGF-1) has potent neuroprotective effects on the cerebral ischemia in the rat and sheep model. In order to investigate whether it has neuroprotective effects on brain insult in human stroke, the present study was designed as one part of a serial subhuman primate stroke research to observe whether IGF-1mRNA and protein expressed in the middle cerebral artery occlusion (MCAO) in monkeys. 12800 dots cDNA microarray, in situ hybridization and immunohistochemistry were used. cDNA microarray showed that among the nearly 8000 genes, approximately 8% of the total number of genes examined were affected after ischemia/reperfusion injury especially in the growth factor family including IGF-1 in the ischemic region. The decreased IGF-1mRNA and protein expression was found in the insular striatum, but an increase mRNA expression and unchanged protein expression in the hippocampus 24 hours following ischemia. The results suggested that, IGF-1 might contribute to the neuroprotective pathway in a pattern different from that of rats, and it might play a role in protection of ischemic injured neuronal cells following the monkey focal cerebral ischemia.
Prevalence of Extended Spectrum Beta-lactamase (ESBL) producing Enterobacteriaceae from clinical samples in a tertiary care hospital in Mumbai

Aim: The aim of this study was to detect prevalence of Extended Spectrum Beta Lactamase (ESBL) among Enterobacteriaceae isolated from various clinical samples received in a tertiary care hospital of Mumbai. Materials and Methods: This study was a retrospective study of 13 months (April 2017 to April 2018) done in the section of Microbiology of our institute. All gram negative isolates were identified and their antibiotic sensitivity pattern was studied according to standard microbiological procedures. ESBL activity among Enterbacteriaceae class of bacteria were confirmed by phenotypic confirmatory disc diffusion test according to Clinical Laboratory Standards Institute guidelines (CLSI) 2017. Clinical and demographic data was collected from Hospital Information and Management system (HIMS). Result: Total Enterobacteriaceae isolates studied were 1194 out of which 32.7% isolates were ESBL producers. ESBL producing Escherichia coli (E.coli) accounted to 34.8% of the total E.coli isolates and 30.7% of total Klebsiella pneumoniae (K.pneumoniae) isolates were ESBL producers. ESBL producing Klebsiella oxytoca (K.oxytoca) and Proteus mirabilis (P.mirabilis) were 29.7% and 18.6% respectively. Conclusion: Knowledge of prevalence of ESBL isolates in our institute will play a pivotal role in curtailing the use of unnecessary antibiotics and assist in taking measures to prevent their spread. Keywords: Extended spectrum beta lactamase, Enterobacteriaceae, Multidrug resistance.
The Effect of Income on Housing Instability and Living Arrangements: Evidence from the Earned Income Tax Credit

As rents have risen and wages have not kept pace, housing affordability has declined over the last 15 years, impacting the housing and living arrangements of low-income families (moves, eviction/homelessness, affordability, shared living arrangements/doubling up, crowding). Housing subsidies are effective in improving the housing situations among low-income families, but less than one in four eligible families receive a voucher. In this paper, we ask whether one of the largest anti-poverty programs in the United States, the Earned Income Tax Credit (EITC) affects the housing and living arrangements of low-income families. Using the Current Population Survey, the American Community Survey/decennial Census, and the Fragile Families and Child Wellbeing Study, we employ a parameterized difference-in-differences strategy to examine whether policy-induced expansions to the EITC affect the housing and living arrangements of single mothers. Results suggest that a $1,000 increase in the EITC improves housing outcomes by reducing frequent moves, housing cost burden, the number of people in the household, and crowding. Increases in the EITC also reduce doubling up (living with additional, non-nuclear family adults) and three-generation/multigenerational coresidence, indicating that mothers prefer to live independently. Although the EITC is not an explicit housing policy, expansions to the EITC are generally linked with improved housing outcomes for single mothers
Prior Consistent CNN with Multi-Task Learning for Colon Image Classification

As adenocarcinoma is the most common cancer, the pathology diagnoses for it is of great significance. In the field of digital pathology, although deep learning method has achieved good results, it is theorem agnostic and the accumulated pathology-level knowledge is ignored. Specifically, the degree of gland differentiation is vital for defining the grade of adenocarcinoma. Following this domain knowledge, we encoded gland tissue regions as prior information in a multi-task convolutional neural network (CNN), guiding the network’s preference for gland information when inferring. Firstly, we validated the effectiveness of the gland prior information by single task with gland ground truth annotations. Then we constructed a multi-task framework with segmentation and classification branches simultaneously. In this architecture, the segmentation probability map acted as the spatial attention for classification, emphasizing the region of gland and masking the noise of irrelevant parts. Experiments showed that the proposed prior consistent CNN with a multi-task learning method achieved 97.04% accuracy, compared with 93.82% of the single task classification model. Meanwhile, proposed multi-task model outputted gland tissue segmentation results. Most importantly, our model is based on the clinical-pathological diagnostic criteria of adenocarcinoma, which provides more ideas on how to make deep learning methods in the field of digital pathology more interpretable.