Miracle-dz/UniFAR-FTUs huggingface dataset:
Random graphs for structure discovery in high-dimensional data.

Originally motivated by computational considerations, we demonstrate how computational efficient and scalable graph constructions can be used to encode both statistical and spatial information and address the problems of dimension reduction and structure discovery in high-dimensional data, with provable results. We discuss the asymptotic behavior of power weighted functionals of minimal Euclidean graphs, proving upper and lower bounds for the respective convergence rates and connecting them to the problem of nonparametric estimation of entropy. We then extend the convergence results from Euclidean graphs to the setting of data that spans a high-dimensional space but which contain fundamental features that are concentrated on lower-dimensional subsets of this space – curves, surfaces or, more generally, lower-dimensional manifolds. In particular, we have developed a novel geometric probability approach to the problem of estimating intrinsic dimension and entropy of manifold data, based on asymptotic properties of graphs such as Minimal Spanning Trees or k-Nearest Neighbor graphs. Unlike previous solutions to this problem, we are able to prove statistical consistency of the obtained estimators for the wide class of Riemann submanifolds of an Euclidean space. We also propose a graph based dimensionality reduction method aimed at extracting lower dimensional features designed expressly to improve classification tasks, with applications to both supervised and semi-supervised learning problems. Finally, using neighborhood graphs and the multidimensional scaling principle,
Research Trends and Features of Critical Thinking Studies in E-Learning Environments: A Review

This study investigates critical thinking (CT) studies in e-learning settings to provide a comprehensive analysis of CT studies. Research indexed in the Web of Science database which includes major peer-reviewed social sciences citation index journals in the e-learning field from January 2006 to November 2017 was searched for a systematic review and analysis. A total of 42 studies were selected and synthesized. The distribution of publishing years, geographical areas, education sectors, publications, disciplines, research methods, activities, CT measurement instruments, teaching instruction, the CT roles applied in the studies, and the experiment durations were reviewed. The results showed that most CT studies were conducted in nontraditional teaching environments using strategies such as online discussion or concept mapping; the most used quantitative method was the California critical thinking skills test (CCTST), whereas the most frequently adopted qualitative method was the practical inquiry model of cognitive presence (PIMCP). The common method to enhance students’ CT was online synchronous or asynchronous discussion, and the role that CT played in these studies was as a dependent variable for evaluating whether the designed e-learning instruction(s) can cultivate CT. This study concludes that with active learning instruction and experienced tutor facilitation, students’ CT could be enhanced in a variety of e-learning environments.
Deriving semantic objects from the structured web

This thesis focuses on the extraction and analysis of Web data objects, investigated from different points of view: temporal, structural, semantic. We first survey different strategies and best practices for deriving temporal aspects of Web pages, together with a more in-depth study on Web feeds for this particular purpose, and other statistics. Next, in the context of dynamically-generated Web pages by content management systems, we present two keyword-based techniques that perform article extraction from such pages. Keywords, automatically acquired, guide the process of object identification, either at the level of a single Web page (SIGFEED), or across different pages sharing the same template (FOREST). We finally present, in the context of the deep Web, a generic framework that aims at discovering the semantic model of a Web object (here, data record) by, first, using FOREST for the extraction of objects, and second, representing the implicit rdf:type similarities between the object attributes and the entity of the form as relationships that, together with the instances extracted from the objects, form a labeled graph. This graph is further aligned to an ontology like YAGO for the discovery of the unknown types and relations.
Nowcasting algorithm for wind fields using ensemble forecasting and aircraft flight data

This study proposes an algorithm that combines ensemble numerical weather‐prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble‐weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium‐Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper‐parameters of SIS on forecast performance are also presented in this study.
Impact of Redundancy on Resilience in Distributed Optimization and Learning

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with the server to find a minimum of the aggregate of the local cost functions. In the context of stochastic learning, the local cost of an agent is the loss function computed over the data at that agent. In this paper, we consider this problem in a system wherein some of the agents may be Byzantine faulty and some of the agents may be slow (also called stragglers). In this setting, we investigate the conditions under which it is possible to obtain an “approximate” solution to the above problem. In particular, we introduce the notion of (f, r;ϵ)-resilience to characterize how well the true solution is approximated in the presence of up to f Byzantine faulty agents, and up to r slow agents (or stragglers) – smaller ϵ represents a better approximation. We also introduce a measure named (f, r;ϵ)-redundancy to characterize the redundancy in the cost functions of the agents. Greater redundancy allows for a better approximation when solving the problem of aggregate cost minimization. In this paper, we constructively show (both theoretically and empirically) that -resilience can indeed be achieved in practice, given that the local cost functions are sufficiently redundant. Our empirical evaluation considers a distributed gradient descent (DGD)-based solution; for distributed learning in the presence of Byzantine and asynchronous agents, we also evaluate a distributed stochastic gradient descent (D-SGD)-based algorithm.
Platelets in sepsis: beyond hemostasis.

In this issue of Blood , [Claushuis et al][1] report on a rigorous evaluation of the associations among baseline platelet counts, biomarkers of host response, and clinical outcomes in a large cohort of critically ill patients with sepsis. [1][2] ! [Figure][3] Three typical examples of how
A review of perception and myth on causes of cholera infection in endemic areas of Nigeria

Cholera epidemic is a recurrent disease in developing countries with poor environmental sanitation and inadequate supply of potable water. In endemic areas of West Africa, opinions and myths surrounding the real causes of cholera diseases have given rise to varying methods of control. This review gives an insight into these various opinions and myths. Some of these are that magico-religious factors, witchcraft, eating soil, god’s will and evil air in the community are responsible for cholera outbreaks. Wrong perception and myth of cholera hinders acceptance and accessibility to launch effective operational response to affected communities during an outbreak. This also lead to delay in providing intervention and treatment during an outbreak. Key words: Myths, aetiology, Vibrio cholerae, epidemic, perception
Boosting with the L2-loss: regression and classification

Boosting With the L2 Loss Peter Bühlmann & Bin Yu a Peter Bühlmann is Associate Professor, Seminar für Statistik, ETH Zürich, CH-8032 Zürich, Switzerland . Bin Yu is Professor, Department of Statistics, University of California, Berkeley, CA 94720 . The authors thank Trevor Hastie and Leo Breiman for very helpful discussions and two referees for constructive comments. Yu was partially supported by National Science Foundation grants DMS-9803063 and FD01-12731 and Army Research Of. ce grants DAAG55-98-1-0341 and DAAD19-01-1-0643. Published online: 31 Dec 2011.
Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

Nowadays, the data-driven diagnosis method, exploiting pattern recognition method to diagnose the fault patterns automatically, achieves much success for rotating machinery. Some popular classification algorithms such as artificial neural networks and support vector machine have been extensively studied and tested with many application cases, while the random forest, one of the present state-of-the-art classifiers based on ensemble learning strategy, is relatively unknown in this field. In this paper, the behavior of random forest for the intelligent diagnosis of rotating machinery is investigated with various features on two datasets. A framework for the comparison of different methods, that is, random forest, extreme learning machine, probabilistic neural network and support vector machine, is presented to find the most efficient one. Random forest has been proven to outperform the comparative classifiers in terms of recognition accuracy, stability and robustness to features, especially with a small training set. Additionally, compared with traditional methods, random forest is not easily influenced by environmental noise. Furthermore, the user-friendly parameters in random forest offer great convenience for practical engineering. These results suggest that random forest is a promising pattern recognition method for the intelligent diagnosis of rotating machinery.
ANALISIS FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN KEJADIAN ANEMIA PADA IBU HAMIL TM III DI BPM KUSMAWATI SURABAYA

ABSTRAK : Anemia dalam kehamilan masih merupakan masalah kronik di Indonesia terbukti dalam prevalensi pada wanita hamil persentase mencapai 63,5%. Dalam empat tahun terakhir prevalensi anemia tidak menunjukkan penurunan yang cukup berarti. Anemia pada kehamilan dapat berpengaruh buruk terutama saat kehamilan, persalinan dan nifas.Penelitian ini bertujuan untuk menganalisis faktor-faktor yang berhubungan dengan kejadian anemia pada ibu hamil TM III. Penelitian ini menggunakan rancangan kuantitatif dengan desain penelitian cross sectional. Populasi dan Sampel adalah ibu hamil TM III yang periksa di BPM Kusmawati Surabaya selama 3 bulan. Sampel di ambil menggunakan total sampling sebesar 45 responden. Pengumpulan data menggunakan data primer berupa kuesioner dan data sekunder berupa buku register PWS KIA, kemudian data dianalisis menggunakan uji statistik Chi-Square. Hasil uji statistic, didapatkan tidak ada hubungan signifikan antara umur ibu dengan kejadian anemia (p = 0,358), ada hubungan signifikan status gizi dengan kejadian anemia (p = 0,000), tidak ada hubungan signifikan antara paritas dengan kejadian anemia (p = 0,222), ada hubungan signifikan antara frekuensi antenatal care dengan kejadian anemia (p = 0,000), ada hubungan signifikan antara tingkat pengetahuan dengan kejadian anemia (p=0,034) dan ada hubungan signifikan antara kepatuhan konsumsi tablet Fe dengan kejadian anemia (p=0,024). Faktor-faktor yang berhubungan dengan kejadian anemia pada ibu hamil TM III di BPM Kusmawati adalah faktor status gizi, frekuensi antenatal care, tingkat pengetahuan dan kepatuhan konsumsi tablet Fe. Diharapkan bagi petugas kesehatan agar terus meningkatkan penyuluhan dan konseling bagi ibu hamil untuk mencegah terjadinya anemia
Inferring processes of community assembly from macroscopic patterns: the case for inclusive and mechanistic approaches

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as the causal link between process and pattern is often lacking and processes known to be important are ignored. Here, we revisit current knowledge on community assembly across scales and, in line with several reviews that have outlined the features and challenges associated with current inference techniques, we identify a discrepancy between features of real communities and current inference techniques. We argue, that mechanistic eco-evolutionary models in combination with novel model fitting and model evaluation techniques can provide avenues for more accurate, reliable and inclusive inference. To exemplify, we implement a trait-based and spatially explicit dynamic eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources. This suggested approach can be computationally intensive, and model fitting and parameter estimation can be challenging. We discuss optimization of model implementation, data requirements and availability, and Approximate Bayesian Computation (ABC) as potential solutions to challenges that may arise in our quest for better inference techniques.
Dynamic biological adhesion: mechanisms for controlling attachment during locomotion

The rapid control of surface attachment is a key feature of natural adhesive systems used for locomotion, and a property highly desirable for man-made adhesives. Here, we describe the challenges of adhesion control and the timescales involved across diverse biological attachment systems and different adhesive mechanisms. The most widespread control principle for dynamic surface attachment in climbing animals is that adhesion is ‘shear-sensitive’ (directional): pulling adhesive pads towards the body results in strong attachment, whereas pushing them away from it leads to easy detachment, providing a rapid mechanical ‘switch’. Shear-sensitivity is based on changes of contact area and adhesive strength, which in turn arise from non-adhesive default positions, the mechanics of peeling, pad sliding, and the targeted storage and controlled release of elastic strain energy. The control of adhesion via shear forces is deeply integrated with the climbing animals’ anatomy and locomotion, and involves both active neuromuscular control, and rapid passive responses of sophisticated mechanical systems. The resulting dynamic adhesive systems are robust, reliable, versatile and nevertheless remarkably simple. This article is part of the theme issue ‘Transdisciplinary approaches to the study of adhesion and adhesives in biological systems’.
The effects of tail damage on tadpole development and leaping ability after metamorphosis in Cuban tree frogs (Osteopilus septentrionalis)

Abstract. Tadpoles have many predators in the wild and are able to escape from them because the structure of their tail allows it to tear off if attacked. We examined the effects of tail damage in the early tadpole stages on the leaping and swimming abilities of Cuban tree frogs after metamorphosis. We obtained 120 Cuban tree frogs, and to simulate predation, we initially removed a piece of the top of the tadpole tails in one experimental group, cut off a piece of the bottom of the tadpole tails in the second experimental group, and did not make any cuts to tails of the control group. Following tail regeneration by all tadpoles in both experimental groups, we treated all experimental tadpoles as one group, and simulated a second predation event by removing 10% of the distal tip of the tail. After tadpoles completed metamorphosis, they were subjected to a swimming endurance test and subsequent measurement of their leaping abilities. We found no significant effect of tail damage on the leaping abilities of frogs after metamorphosis, and no significant difference in body size at metamorphosis among the groups. The experiment was then replicated and tested tadpole corticosterone (CORT) levels to determine if tadpoles exhibited stress from the tail damage. We found significantly elevated CORT levels in tadpoles with tail damage compared to the tadpoles without tail damage. We conclude that the regeneration abilities of the Cuban tree frog tadpoles afford them the opportunity to survive minor predation events and recover with no apparent long-lasting ill effects even though they were physiologically stressed from the damage. These abilities likely contribute to the Cuban tree frogs' success as an invasive species throughout the Florida peninsula.
Cost Sharing over Combinatorial Domains: Complement-Free Cost Functions and Beyond

We study mechanism design for combinatorial cost sharing models. Imagine that multiple items or services are available to be shared among a set of interested agents. The outcome of a mechanism in this setting consists of an assignment, determining for each item the set of players who are granted service, together with respective payments. Although there are several works studying specialized versions of such problems, there has been almost no progress for general combinatorial cost sharing domains until recently [7]. Still, many questions about the interplay between strategyproofness, cost recovery and economic efficiency remain unanswered. The main goal of our work is to further understand this interplay in terms of budget balance and social cost approximation. Towards this, we provide a refinement of cross-monotonicity (which we term trace-monotonicity) that is applicable to iterative mechanisms. The trace here refers to the order in which players become finalized. On top of this, we also provide two parameterizations (complementary to a certain extent) of cost functions which capture the behavior of their average cost-shares. Based on our trace-monotonicity property, we design a scheme of ascending cost sharing mechanisms which is applicable to the combinatorial cost sharing setting with symmetric submodular valuations. Using our first cost function parameterization, we identify conditions under which our mechanism is weakly group-strategyproof, O(1)-budget-balanced and O(Hn)-approximate with respect to the social cost. Further, we show that our mechanism is budget-balanced and Hn-approximate if both the valuations and the cost functions are symmetric submodular; given existing impossibility results, this is best possible. Finally, we consider general valuation functions and exploit our second parameterization to derive a more fine-grained analysis of the Sequential Mechanism introduced by Moulin. This mechanism is budget balanced by construction, but in general only guarantees a poor social cost approximation of n. We identify conditions under which the mechanism achieves improved social cost approximation guarantees. In particular, we derive improved mechanisms for fundamental cost sharing problems, including Vertex Cover and Set Cover.
Obviously Strategyproof Mechanisms for Machine Scheduling

Catering to the incentives of people with limited rationality is a challenging research direction that requires novel paradigms to design mechanisms and approximation algorithms. Obviously strategyproof (OSP) mechanisms have recently emerged as the concept of interest to this research agenda. However, the majority of the literature in the area has either highlighted the shortcomings of OSP or focused on the “right” deﬁnition rather than on the construction of these mechanisms. We here give the ﬁrst set of tight results on the approximation guarantee of OSP mechanisms for scheduling related machines. By extending the well-known cycle monotonicity technique, we are able to concentrate on the algorithmic component of OSP mechanisms and provide some novel paradigms for their design.
Multiarray Passive Acoustic Localization and Tracking

Wireless sensor networks and data fusion has received increasing attention in recent years, due to the ever increasing computational power, battery and wireless technology, and proliferation of sensor modalities. Notably, the application of acoustic sensors and arrays of sensors has expanded to encompass surveillance, teleconferencing, and sound source localization in adverse environments. The ability to passively locate and track acoustic sources, be they gunfire, animals, or geological events, is crucial to a wide range of applications. The challenge addressed herein is how to best utilize the massive amount of data collected from spatially distributed sensors. Localization in two acoustic propagation scenarios is addressed: the free-field assumption and the general case. In both cases, it is found that performance is highly dependent on the array-source geometry which in turn drives the design of localization strategies. First, the general surveillance problem including signal detection, classification, data association, localization and tracking is studied. Signal detectors are designed with a focus on robustness and capacity for real time implementation. Specifics of the data association problem relevant to acoustic measurements are addressed. Assuming freefield propagation, a localization algorithm is developed to harness some of the vast potential and robust nature of a sensor networks. In addition, a prototypical sensor network has been constructed to accompany the theoretical development, address real world situations, and demonstrate applicability. Experimental results obtained confirm the practicality of theoretical models, support numerical results, and illustrate the effectiveness of the proposed strategies and the system as a whole.