Miracle-dz/UniFAR-FTUs huggingface dataset:
Worst-Case Scenarios and Epidemics

Contingency plans and policies associated with the deliberate release of biological agents must be developed in the absence of data. Hence, the identification of landscapes that facilitate "worst" epidemics (worst-case scenarios) is essential. Common sense suggest that "worst" epidemics are most likely to occur in populations where individuals mix randomly (proportionate mixing). Here, SIR (susceptible-infective-recovered) epidemics that result from the introduction of single or multiple sources are studied on various topologies including small-world and scale-free networks. A simple algorithm is used to compute the average growth rate during the initial exponential growth phase of the epidemic and such a rate is used to estimate the severity of the outbreak. In small-world networks, this average rate of epidemic growth is measured on the full spectrum of the disorder parameter p E [0, 1]. Extensive simulations show that the average rate of growth increases in a nonlinear fashion as the disorder in the network grows. Not surprisingly, simulations show that such average rate of growth is higher when the initial infective source is placed in the most connected node (pressure point) than when it is randomly placed. The average rate of initial growth is a non-decreasing function of the (small) number of initial infectious sources. Simulation results support the view that, worst-case
Assessing the Effects of the Urban Forest Restoration Effort of MillionTreesNYC on the Structure and Functioning of New York City Ecosystems

Current forest restoration practices for New York City’s (NYC) MillionTreesNYC Initiative on public parkland include site preparation with extensive invasive species removal and tree and shrub planting with the goal of creating new multi-layered forests. We have launched a longterm investigation of these sites in order to understand the primary physical, chemical, and biological responses of urban ecosystems to MillionTreesNYC forest restoration practices. This research will examine high and low diversity tree and understory planting combinations in permanent experimental forest restoration plots across NYC. The study assesses how the interactions between soil heterogeneity, plant population dynamics, and forest restoration management strategies drive urban forest ecosystem structure and functioning. Working in collaboration with the NYC Department of Parks & Recreation (NYC Parks) and the MillionTreesNYC tree planting campaign, we are examining different restoration strategies to assess how restoration practices affect the ecological development trajectories of newly established forests in NYC.
Evaluating the drivers of Indo‐Pacific biodiversity: speciation and dispersal of sea snakes (Elapidae: Hydrophiinae)

There are several competing hypotheses to explain the high species richness of the Indo‐Australian Archipelago (IAA) marine biodiversity hotspot centred within Southeast (SE) Asia. We use phylogenetic methods to provide a novel perspective on this problem using viviparous sea snakes, a group with high species richness in the IAA that is highly distinct from other taxa previously studied, both phylogenetically (Reptilia, Amniota) and biologically (e.g. viviparity and direct development).
Multiband RF pulse design for realistic gradient performance

Simultaneous multi‐slice techniques are reliant on multiband RF pulses, for which conventional design strategies result in long pulse durations, lengthening echo‐times so lowering SNR for spin‐echo imaging, and lengthening repetition times for gradient echo sequences. Pulse durations can be reduced with advanced RF pulse design methods that use time‐variable selection gradients. However, the ability of gradient systems to reproduce fast switching pulses is often limited and can lead to image artifacts when ignored. We propose a time‐efficient pulse design method that inherently produces gradient waveforms with lower temporal bandwidth.
No significant differences in rhizosphere bacterial communities between Bt maize cultivar IE09S034 and the near-isogenic non-Bt cultivar Zong31

The release of genetically modified (GM) crops has potential to alter the bacterial population within rhizosphere. Here, the potential effect of GM maize cv. IE09S034 containing the Cry1Ie toxin gene from Bacillus thuringiensis (Bt) was investigated under the field conditions. The community composition and the relative abundance of the bacteria in rhizosphere soil were estimated by analysing 16S rRNA PCR amplicons. Our results indicated that Bt maize IE09S034 has no significant effects on the rhizosphere bacterial community. Instead, it was found that factors such as plant growth stage and year have a stronger effect on the bacterial population dynamics. Our findings therefore provide reliable evidence supporting the potential commercial cultivation of the cv. IE09S034.
Discovery Learning: Development of a Unique Active Learning Environment for Introductory Chemistry

It is well established that active learning results in greater gains in student conceptual knowledge and retention compared to traditional modes of learning. However, active learning can be very difficult to implement in a large-enrollment course due to various course and institutional barriers. Herein, we describe the development and implementation of Discovery Learning, a novel active learning discussion/recitation for a large enrollment general chemistry course. Drawing on the very successful cooperative learning pedagogies Process-Oriented Guided Inquiry Learning (POGIL) and Student-Centered Active Learning Environment with Upside-down Pedagogies (SCALE_UP), Discovery Learning involves students working in self-managed teams on inquiry problems in a unique learning environment, the Chemistry Discovery Center. In this case study, we will describe the design and implementation of Discovery Learning and report data on its successes, which include increased student performance and retention.
Threat Prediction Based on Opinion Extracted from Twitter

The availability of technology and infrastructure create opportunities for citizens to publicly voice their opinions over social media, but this has created serious problems when it comes to making sense of these opinions. Government and companies don’t yet have an effective way to make sense of this users’ conversation and interact importantly with thousands of others. Huge amount of opinions are posted and tweeted on the Web. Such opinions are a very important source of information for governments and companies. A lot of researchers describe that users are relying on online opinions to make effective use of user opinions. Unfortunately, due to the increasing number of social media users, many government-opposing groups use access of the web to initiate people in anti-government protests and for violent actions. Many researches are being conducted in the domain and many attempts to approach the topic have been presented. However, researches conducted so far, using opinion mining for threat prediction are rare. In this research, we present how to model and automatically monitor trends and detect opinions which are considered as threats on Twitter. Specifically, we propose a supervised machine learning algorithm which classifies opinions using n-bag of word features.
zk-SNARK explained: Basic Principles

This document is an informal guide to zk-SNARK – a zero-knowledge Argument-of-Knowledge. We do not discuss security or implementation. Our aim is to go through the mathematics of the zk-SNARK protocol used in Zcash and the smart contract system Hawk . This protocol can be used in many other applications, like Veriﬁable Computation (VC).
Maryland Striped Bass: Recruitment Declining below Replacement

Abstract A mathematical technique was developed to examine interrelationships among first-year survival rates, adult fecundity, and adult survival of striped bass Morone saxatilis based on indices of year-class strength. Application of this technique to striped bass in Maryland waters of the Chesapeake Bay provided evidence for reduced survival in the life cycle. If adult fecundity and survival have remained constant, first-year survival declined significantly from 1969 to 1983, and averaged less than that needed for replacement for the last 10 years. Treatment of the individual spawning grounds separately indicated that the downward trend in survival for the pooled data was the result of declines in the upper bay and, to a lesser extent, in the Choptank River. Alternatively, if first-year survival and adult fecundity were assumed to have remained constant, an annual decline of about 1.9% in adult survival would have been required to produce the observed trend in the pooled year-class data. This would be .
PHENOTYPIC AND GENETIC ASSOCIATIONS BETWEEN READING AND ADHD 1 Phenotypic and Genetic Associations Between Reading Comprehension , Decoding Skills , and ADHD Dimensions : Evidence from two population-based studies

Background: The phenotypic and genetic associations between decoding skills and ADHD dimensions have been documented but less is known about the association with reading comprehension. The aim of the study is to document the phenotypic and genetic associations between reading comprehension and ADHD dimensions of inattention and hyperactivity/impulsivity in early schooling and compare them to those with decoding skills. Methods: Data were collected in two population-based samples of twins (Quebec Newborn Twin Study – QNTS) and singletons (Quebec Longitudinal Study of Child Development – QLSCD) totaling ≈ 2300 children. Reading was assessed with normed measures in second or third grade. Teachers assessed ADHD dimensions in kindergarten and first grade. Results: Both decoding and reading comprehension were correlated with ADHD dimensions in a similar way: associations with inattention remained after controlling for the other ADHD dimension, behavior disorder symptoms and nonverbal abilities, whereas associations with hyperactivity/impulsivity did not. Genetic modeling showed that decoding and comprehension largely shared the same genetic etiology at this age and that their associations with inattention were mostly explained by shared genetic influences. Conclusion: Both reading comprehension and decoding are uniquely associated with inattention through a shared genetic etiology.
On one-dimensional Riccati diffusions

This article is concerned with the fluctuation analysis and the stability properties of a class of one-dimensional Riccati diffusions. This class of Riccati diffusion is quite general, and arises, for example, in data assimilation applications, and more particularly in ensemble (Kalman-type) filtering theory. These one-dimensional stochastic differential equations exhibit a quadratic drift function and a non-Lipschitz continuous diffusion function. We present a novel approach, combining tangent process techniques, Feynman-Kac path integration, and exponential change of measures, to derive sharp exponential decays to equilibrium. We also provide uniform estimates with respect to the time horizon, quantifying with some precision the fluctuations of these diffusions around a limiting deterministic Riccati differential equation. These results provide a stronger and almost sure version of the conventional central limit theorem. We illustrate these results in the context of ensemble Kalman-Bucy filtering. In this context, the time-uniform convergence results developed in this work do not require a stable signal. To the best of our knowledge, the exponential stability and the fluctuation analysis developed in this work are the first results of this kind for this class of nonlinear diffusions.
Development and Validation of a B.E.A.N. Survey for College Students

College students often do not consume the recommended amounts of beans and legumes. To develop interventions aimed at improving the intake of beans and legumes in the college population, more information is needed on the behaviors, environment, attitudes, and nutrition knowledge (B.E.A.N.) of college students regarding beans. Validated tools to assess these bean‐related constructs are currently lacking.
Feedback, Cribbing, and Causal State Information on the Multiple-Access Channel

The benefits afforded by feedback and/or causal state information (SI) on the state-dependent discrete memoryless multiple-access channel (SD-MAC) with cribbing encoder/s are studied. Capacity regions are derived for communication scenarios whose capacities without cribbing are still unknown. It is shown that when the encoders can crib, the SD-MAC behaves less like a MAC and more like a single-user channel: 1) feedback does not help; 2) strictly causal SI does not help; and 3) causal SI to both encoders is best utilized using Shannon strategies. However, in asymmetric settings, the single-user-like behavior may or may not occur. For example, the SD-MAC with only one cribbing encoder is single-user-like when the state is revealed to the cribbing encoder, but not if it is revealed to the noncribbing encoder.
Towards Multi-user Private Keyword Search for Cloud Computing

Working in various service models ranging from SaaS, PaaS, to IaaS, cloud computing is viewed by many as anew revolution in IT, and could reshape the paradigm of how IT industry works today. Storage services are a fundamental function offered by cloud computing. A typical use scenario of the cloud storage services is that an enterprise outsources its database to the cloud and authorizes multiple users to access the database. In such cases of database outsourcing, data encryption is a good approach enabling the data owner to retain its control over the outsourced data. {\em Searchable encryption} is a cryptographic primitive allowing for private keyword based search over the encrypted database. The above setting of enterprise outsourcing database to the cloud requires multi-user searchable encryption, whereas virtually all existing schemes consider the single-user setting. To bridge this gap, we are motivated to propose a practical multi-user searchable encryption scheme, which has a number of advantages over the known approaches. The associated model and security requirements are also formulated.
Querying and ranking XML documents

XML represents both content and structure of documents. Taking advantage of the document structure promises to greatly improve the retrieval precision. In this article, we present a retrieval technique that adopts the similarity measure of the vector space model, incorporates the document structure, and supports structured queries. Our query model is based on tree matching as a simple and elegant means to formulate queries without knowing the exact structure of the data. Using this query model we propose a logical document concept by deciding on the document boundaries at query time. We combine structured queries and term-based ranking by extending the term concept to structural terms that include substructures of queries and documents. The notions of term frequency and inverse document frequency are adapted to logical documents and structural terms. We introduce an efficient technique to calculate all necessary term frequencies and inverse document frequencies at query time. By adjusting parameters of the retrieval process we are able to model two contrary approaches: the classical vector space model, and the original tree matching approach.
Ray tracing on a connection machine

A new ray-tracing algorithm designed specifically for massively parallel hypercube connected processors is introduced. World space is subdivided according to a fixed size 3-D orthogonal grid. Rays are traced using ray-points which move along the rays, referencing the voxels intersected. One processor is assigned per ray, as well as one processor per object fragment. A pointerless data structure called an induced octree is used. This structure exists only as an ordering of the data in processor-address space, yet with it, all processors can determine the jump-distance to the nearest non-empty voxel in parallel to within a power of two. Ray-points move by large jumps whenever possible, and ray-object intersection proceeds in log time in the average ray length. Sorting via the network is the parallel analogue to octree-traversal. A code-scheduling mechanism is introduced which improves processor usage for SIMD architectures. Preliminary results are presented for an implementation using a CM-1 CPU equipped with 16k processors.
Three-Dimensional Sound Field Reproduction Using Multiple Circular Loudspeaker Arrays

Three-dimensional spatial sound field reproduction enables enhanced immersive acoustic experience for a listener. Recreating an arbitrary 3-D spatial sound field using a practically realizable array of loudspeakers is a challenging problem in acoustic signal processing. This paper exploits the underlying characteristics of wavefield propagation to devise a strategy for accurate 3-D sound field reproduction inside a 3-D region of interest with practical array geometries. Specifically, we use the properties of the associated Legendre functions and the spherical Hankel functions, which are part of the solution to the wave equation in spherical coordinates, for loudspeaker placement on a set of multiple circular arrays and provide a technique for spherical harmonic mode-selection to control the reproduced sound field. We also analyze the artifacts of spatial aliasing due to the use of discrete loudspeaker arrays in the region of interest. As an illustration, we design a third-order reproduction system to operate at a frequency of 500 Hz with 18 loudspeakers arranged in a practically realizable configuration.
THE UP SYSTEM FOR THE 2016 DCASE CHALLENGE USING DEEP RECURRENT NEURAL NETWORK AND MULTISCALE KERNEL SUBSPACE LEARNING

We propose a system for acoustic scene classification using pairwise decomposition with deep neural networks and dimensionality reduction by multiscale kernel subspace learning. It is our contribution to the Acoustic Scene Classification task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2016). The system classifies 15 different acoustic scenes. First, auditory spectral features are extracted and fed into 15 binary deep multilayer perceptron neural networks (MLP). MLP are trained with the ‘one-against-all’ paradigm to perform a pairwise decomposition. In a second stage, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Multiscale Gaussian kernels are then used in constructing optimal linear combination of Gram matrices for multiple kernel subspace learning. The reduced feature set is fed into a nearest-neighbour classifier. Predictions from the two systems are then combined by a threshold-based decision function. On the official development set of the challenge, an accuracy of 81.5% is achieved. In this technical report, we provide a description of the actual system submitted to the challenge.
A Semiparametric Factor Model for Electricity Forward Curve Dynamics

In this paper we introduce the dynamic semiparametric factor model (DSFM) for electricity forward curves. The biggest advantage of our approach is that it not only leads to smooth, seasonal forward curves extracted from exchange traded futures and forward electricity contracts, but also to a parsimonious factor representation of the curve. Using closing prices from the Nordic power market Nord Pool we provide empirical evidence that the DSFM is an efficient tool for approximating forward curve dynamics.
Galaxy Zoo: Major Galaxy Mergers Are Not a Significant Quenching Pathway

We use stellar mass functions to study the properties and the significance of quenching through major galaxy mergers. In addition to SDSS DR7 and Galaxy Zoo 1 data, we use samples of visually selected major galaxy mergers and post-merger galaxies. We determine the stellar mass functions of the stages that we would expect major-merger-quenched galaxies to pass through on their way from the blue cloud to the red sequence: (1) major merger, (2) post-merger, (3) blue early type, (4) green early type, and (5) red early type. Based on their similar mass function shapes, we conclude that major mergers are likely to form an evolutionary sequence from star formation to quiescence via quenching. Relative to all blue galaxies, the major-merger fraction increases as a function of stellar mass. Major-merger quenching is inconsistent with the mass and environment quenching model. At , major-merger-quenched galaxies are unlikely to constitute the majority of galaxies that transition through the green valley. Furthermore, between , major-merger-quenched galaxies account for 1%–5% of all quenched galaxies at a given stellar mass. Major galaxy mergers are therefore not a significant quenching pathway, neither at nor within the last 5 Gyr. The majority of red galaxies must have been quenched through an alternative quenching mechanism that causes a slow blue to red evolution.
The Relationship among Perfectionism, Motivation and Self- Efficacy of EFL Learners

The purpose of the present study was to investigate the relationship among perfectionism, motivation and self-efficacy of EFL learners. Among the 132 participants, 87 were female and 45 were male with different majors in high school. There were three questionnaires in this study; The Almost Perfect Scale - Revised, The Academic Motivation Scale and The General Self-Efficacy Scale. Regarding the first research hypothesis, the results showed that there was a significance correlation between perfectionism and efficacy. As perfectionism increases, efficacy of the participants increases, too. Concerning the second hypothesis, it is revealed that there is a significance correlation between motivation and efficacy. As for hypotheses three and four, it was shown that there was a strong and positive relationship among the variables. The largest number of significant correlations in this study went to the correlations between perfectionism and other variables whereas the correlation between perfectionism and self-efficacy was low. In addition, in most cases, the correlations among perfectionism and motivation and other variables were positive and high.