Miracle-dz/UniFAR-FTUs huggingface dataset:
Confounding from Cryptic Relatedness in Case-Control Association Studies

Case-control association studies are widely used in the search for genetic variants that contribute to human diseases. It has long been known that such studies may suffer from high rates of false positives if there is unrecognized population structure. It is perhaps less widely appreciated that so-called “cryptic relatedness” (i.e., kinship among the cases or controls that is not known to the investigator) might also potentially inflate the false positive rate. Until now there has been little work to assess how serious this problem is likely to be in practice. In this paper, we develop a formal model of cryptic relatedness, and study its impact on association studies. We provide simple expressions that predict the extent of confounding due to cryptic relatedness. Surprisingly, these expressions are functions of directly observable parameters. Our analytical results show that, for well-designed studies in outbred populations, the degree of confounding due to cryptic relatedness will usually be negligible. However, in contrast, studies where there is a sampling bias toward collecting relatives may indeed suffer from excessive rates of false positives. Furthermore, cryptic relatedness may be a serious concern in founder populations that have grown rapidly and recently from a small size. As an example, we analyze the impact of excess relatedness among cases for six phenotypes measured in the Hutterite population.
Correlation between heart rate, electrodermal activity and player experience in first-person shooter games

Psychophysiological methods are becoming more popular in game research as covert and reliable measures of affective player experience, emotions, and cognition. Since player experience is not well understood, correlations between self-reports from players and psychophysiological data may provide a quantitative understanding of this experience. Measurements of electrodermal activity (EDA) and heart rate (HR) allow making inferences about player arousal (i.e., excitement) and are easy to deploy. This paper reports a case study on HR and EDA correlations with subjective gameplay experience, testing the feasibility of these measures in commercial game development contexts. Results indicate a significant correlation (p < 0.01) between psychophysiological arousal (i.e., HR, EDA) and self-reported gameplay experience. However, the covariance between psychophysiological measures and self-reports varies between the two measures. The results are consistent across three different contemporary major commercial first-person shooter (FPS) games (Prey, Doom 3, and Bioshock).
Generation system adequacy studies in the presence of wind energy resources

Variable renewable energy sources like wind power are in exponential growth worldwide. One of the major challenges regarding high penetration of wind energy in the power system is the degradation of generation adequacy due to the intermittent and random nature of wind. The deterministic approaches applied today to ensure sufficient power system adequacy are illfit to handle stochastic generation behaviour. In response, several generation adequacy metrics with probabilistic basis have been developed in literature. Probabilistic methods for generation adequacy assessment are crucial for securing future power system operation and renewable generation integration. As part of a project to construct a computational framework for power system reliability this thesis focuses on generation adequacy assessment of power systems with the presence of wind power. The applied concepts include generation adequacy metrics (LOLE and EENS)1 and capacity credits (ELCC, EFC and ECC)2. To evaluate the generation adequacy of relevant test systems (Roy Billinton Test System and IEEE-Reliability Test System), MATLAB scripts that compute adequacy metrics and capacity credits have been developed and deployed. To evaluate the reliability effect from wind power generation, a wind farm has been modelled and integrated in the test systems. Evaluations applying several adequacy metrics and capacity credits have been conducted on the test systems including integrated wind power along with relevant sensitivity analyses. From the thesis work it has become evident that the myriad of generation adequacy metrics, capacity credits and their combinations provide a powerful means of evaluating different aspects of power system reliability, thereby contributing to optimising renewable energy integration. However, the multitude of possibilities and lack of an established methodological approach makes the process of identifying the optimal metric combination a knowledge-intensive task. 1Loss-of-Load Expectation (LOLE), Expected Energy Not Served (EENS) 2Effective Load Carrying Capabilities (ELCC), Equivalent Firm Capacity (EFC), Equivalent Conventional Capacity (ECC)
A strategy for finding gravitationally-lensed distant supernovae

Distant Type Ia and II supernovae (SNe) can serve as valuable probes of the history of the cosmic expansion and star formation, and provide important information on their progenitor models. At present, however, there are few observational constraints on the abundance of SNe at high redshifts. A major science driver for the Next Generation Space Telescope is the study of such very distant SNe. In this paper we discuss strategies for finding and counting distant SNe by using repeat imaging of supercritical intermediate redshift clusters whose mass distributions are well constrained via modelling of strongly lensed features. For a variety of different models for the star formation history and supernova progenitors, we estimate the likelihood of detecting lensed SNe as a function of their redshift. In the case of a survey conducted with Hubble Space Telescope (HST), we predict a high probability of seeing a supernova in a single return visit with either Wide Field Planetary Camera 2 or Advanced Camera for Surveys, and a much higher probability of detecting examples with z > 1 in the lensed case. Most events would represent magnified SNe II at z ≃ 1, and a fraction will be more distant examples. We discuss various ways to classify such events using ground-based infrared photometry. We demonstrate an application of the method using the HST archival data and discuss the case of a possible event found in the rich cluster AC 114 (z = 0.31).
Fabbriche religiose e itinerari di culto. Temi di storia e recupero

La presenza sul territorio italiano di itinerari di culto storico, lungo i quali sono sorte reti di edifici religiosi, offre un ricchissimo patrimonio, architettonico e paesaggistico, in buona parte oggi dismesso, se non ridotto in condizioni di abbandono. Tra gli altri esempi si segnala il ricco sistema di abbazie, conventi e monasteri, funzionale al culto di San Michele Arcangelo, diffusa nel Gargano dal VI secolo che sosteneva la "via sacra" percorsa dai pellegrini, spesso sulla pista del percorsi piu antichi. Questo articolo ripercorre la storia articolata di questi edifici, sulla base di documenti di prima mano risultanti dalle misurazioni accurate della loro geometria e la loro residua struttura formale e materiale. Lo scopo non e solo l’inserimento di un edificio in una rete, in realta inscindibile dal territorio di appartenenza, ma anche la verifica dell’ampio orizzonte dei progetti di recupero.
A Review of Cascade Correlation Neural Network for Software Cost Estimation

Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks and classification techniques. Rather than adjusting the weights in a network of predefined topology, Cascade-Correlation begins with a minimal network and it automatically trains, adds new hidden units one after the other by creating a multi-layer structure. As soon as a new hidden unit has been added to the network, its input-side weights are getting fixed. After that these unit then becomes a permanent feature-identifier in the network, present for producing outputs, then cascadecorrelation is behaves as more complex feature detectors. The Cascade-Correlation networks have several benefits over existing algorithms as it learns very fast. It determines its own size and topology fast. It maintains the structures which it has built even after the training set changes, and it doesn’t need back-propagation of error signals through the connections of the network and its component. Cascade Correlation Neural Network (CCNN) types such as recurrent CCNN, evolving CCNN, genetic CCNN are used to predict software effort from Use Case diagrams in advance manner which helps further for software cost estimation. The use case diagrams are developed in the early stages of the software development and they are used for input. This paper is an overview of cascade-correlation neural networks in which we study different types of cascade-correlation neural network. They are based on a special architecture which autonomously adapts to the application and makes the training much more efficient than the widely used backpropagation algorithm. This review focuses on different types of CCNN and also describes the cascade-correlation architecture variants.
Looks Are (Almost) Everything: Where Drivers Look to Get Information

Objective: To describe the impact of Rockwell's early eye movements research. Background: The advent of a new technology enabling measurements of eye movements in natural environments launched the seminal research of a Human Factors pioneer, Tom Rockwell, into how drivers process visual information. Method: In two seminal Human Factors articles —“Mapping Eye-Movement Pattern to the Visual Scene in Driving: An Exploratory Study” (Mourant & Rockwell, 1970) and “Strategies of Visual Search by Novice and Experienced Drivers” (Mourant & Rockwell, 1972) — Rockwell and his student, Ron Mourant, examined drivers' eye movements in naturalistic driving environments. Results: The analyses of the visual fixations revealed systematic relationships between the sources of information the drivers needed to drive safely and the spatial distributions of their visual fixations. In addition, they showed that as drivers gain skill and experience, their pattern of fixations changes in a systematic manner. Conclusions: The research demonstrated that fixations and saccadic eye movements provide important insights into drivers' visual search behavior, information needs, and information acquisition processes. Application: This research has been a cornerstone for a myriad of driving-related studies, by Rockwell and other researchers. Building on Rockwell's pioneering work, these studies used eye-tracking systems to describe cognitive aspects of skill acquisition, and the effects of fatigue and other impairments on the process of attention and information gathering. A novel and potentially revolutionary application of this research is to use eye movement recordings for vehicle control and activation of in-vehicle safety systems.
Edge-selenated graphene nanoplatelets as durable metal-free catalysts for iodine reduction reaction in dye-sensitized solar cells

The I3− reduction mechanism on carbon-based materials is clarified by electrochemical kinetics and DFT-NEGF calculations. Metal-free carbon-based electrocatalysts for dye-sensitized solar cells (DSSCs) are sufficiently active in Co(II)/Co(III) electrolytes but are not satisfactory in the most commonly used iodide/triiodide (I−/I3−) electrolytes. Thus, developing active and stable metal-free electrocatalysts in both electrolytes is one of the most important issues in DSSC research. We report the synthesis of edge-selenated graphene nanoplatelets (SeGnPs) prepared by a simple mechanochemical reaction between graphite and selenium (Se) powders, and their application to the counter electrode (CE) for DSSCs in both I−/I3− and Co(II)/Co(III) electrolytes. The edge-selective doping and the preservation of the pristine graphene basal plane in the SeGnPs were confirmed by various analytical techniques, including atomic-resolution transmission electron microscopy. Tested as the DSSC CE in both Co(bpy)32+/3+ (bpy = 2,2′-bipyridine) and I−/I3− electrolytes, the SeGnP-CEs exhibited outstanding electrocatalytic performance with ultimately high stability. The SeGnP-CE–based DSSCs displayed a higher photovoltaic performance than did the Pt-CE–based DSSCs in both SM315 sensitizer with Co(bpy)32+/3+ and N719 sensitizer with I−/I3− electrolytes. Furthermore, the I3− reduction mechanism, which has not been fully understood in carbon-based CE materials to date, was clarified by an electrochemical kinetics study combined with density functional theory and nonequilibrium Green’s function calculations.
4FP-Structure: A Robust Local Region Feature Descriptor

Establishing reliable correspondence for images of the same scene is still challenging work due to repetitive texture and unknown distortion. In this paper, we propose a region-matching method to simultaneously filter false matches and maximize good correspondence between images, even those with irregular distortion. First, a novel region descriptor, represented by a structure formed by four feature points ( 4FP -Structure), is presented to simplify matching with severe deformation. Furthermore, an expansion stage based on the special 4FP -Structure is adapted to detect and select as many high location accuracy correspondences as possible under a local affine-transformation constraint. Extensive experiments on both rigid and non-rigid image datasets demonstrate that the proposed algorithm has a very high degree of correctness and significantly outperforms other state-of-the-art methods.
Substance P Causes Seizures in Neurocysticercosis

Neurocysticercosis (NCC), a helminth infection of the brain, is a major cause of seizures. The mediators responsible for seizures in NCC are unknown, and their management remains controversial. Substance P (SP) is a neuropeptide produced by neurons, endothelial cells and immunocytes. The current studies examined the hypothesis that SP mediates seizures in NCC. We demonstrated by immunostaining that 5 of 5 brain biopsies from NCC patients contained substance P (SP)-positive (+) cells adjacent to but not distant from degenerating worms; no SP+ cells were detected in uninfected brains. In a rodent model of NCC, seizures were induced after intrahippocampal injection of SP alone or after injection of extracts of cysticercosis granuloma obtained from infected wild type (WT), but not from infected SP precursor-deficient mice. Seizure activity correlated with SP levels within WT granuloma extracts and was prevented by intrahippocampal pre-injection of SP receptor antagonist. Furthermore, extracts of granulomas from WT mice caused seizures when injected into the hippocampus of WT mice, but not when injected into SP receptor (NK1R) deficient mice. These findings indicate that SP causes seizures in NCC, and, suggests that seizures in NCC in humans may be prevented and/or treated with SP-receptor antagonists.
Thermal Actuation Based 3-DoF Non-Resonant Microgyroscope Using MetalMUMPs

High force, large displacement and low voltage consumption are a primary concern for microgyroscopes. The chevron-shaped thermal actuators are unique in terms of high force generation combined with the large displacements at a low operating voltage in comparison with traditional electrostatic actuators. A Nickel based 3-DoF micromachined gyroscope comprising 2-DoF drive mode and 1-DoF sense mode oscillator utilizing the chevron-shaped thermal actuators is presented here. Analytical derivations and finite element simulations are carried out to predict the performance of the proposed device using the thermo-physical properties of electroplated nickel. The device sensitivity is improved by utilizing the dynamical amplification of the oscillation in 2-DoF drive mode using an active-passive mass configuration. A comprehensive theoretical description, dynamics and mechanical design considerations of the proposed gyroscopes model are discussed in detail. Parametric optimization of gyroscope, its prototype modeling and fabrication using MetalMUMPs has also been investigated. Dynamic transient simulation results predicted that the sense mass of the proposed device achieved a drive displacement of 4. 1μm when a sinusoidal voltage of 0.5V is applied at 1.77 kHz exhibiting a mechanical sensitivity of 1. 7μm /°/s in vacuum. The wide bandwidth frequency response of the 2-DoF drive mode oscillator consists of two resonant peaks and a flat region of 2.11 kHz between the peaks defining the operational frequency region. The sense mode resonant frequency can lie anywhere within this region and therefore the amplitude of the response is insensitive to structural parameter variations, enhancing device robustness against such variations. The proposed device has a size of 2.2 × 2.6 mm2, almost one third in comparison with existing M-DoF vibratory gyroscope with an estimated power consumption of 0.26 Watts. These predicted results illustrate that the chevron-shaped thermal actuator has a large voltage-stroke ratio shifting the paradigm in MEMS gyroscope design from the traditional interdigitated comb drive electrostatic actuator. These actuators have low damping compared to electrostatic comb drive actuators which may result in high quality factor microgyroscopes operating at atmospheric pressure.
An New Data Parallelism Approach with High Performace Clouds

This paper presents a new high performance cloud framework that support simplified data parallel applications. and describes the design and implementation of it. This cloud implements stream processing paradigm over clusters of computers, either within single data center or across wide area. Unlike MapReduce, this Cloud’s stream processing paradigm is simpler, more straightforward, and reaches better performance in certain cases. The experimental results show this cloud is more straightforward, simpler to use, and it is about twice as fast as Hadoop.
Cost and Benefit of Using WordNet Senses for Sentiment Analysis

Typically, accuracy is used to represent the performance of an NLP system. However, accuracy attainment is a function of investment in annotation. Typically, the more the amount and sophistication of annotation, higher is the accuracy. However, a moot question is """"is the accuracy improvement commensurate with the cost incurred in annotation""""? We present an economic model to assess the marginal benefit accruing from increase in cost of annotation. In particular, as a case in point we have chosen the sentiment analysis (SA) problem. In SA, documents normally are polarity classified by running them through classifiers trained on document vectors constructed from lexeme features, i.e., words. If, however, instead of words, one uses word senses (synset ids in wordnets) as features, the accuracy improves dramatically. But is this improvement significant enough to justify the cost of annotation? This question, to the best of our knowledge, has not been investigated with the seriousness it deserves. We perform a cost benefit study based on a vendor-machine model. By setting up a cost price, selling price and profit scenario, we show that although extra cost is incurred in sense annotation, the profit margin is high, justifying the cost.
Certified reputation: how an agent can trust a stranger

Current computational trust models are usually built either on an agent's direct experience of an interaction partner (interaction trust) or reports provided by third parties about their experiences with a partner (witness reputation). However, both of these approaches have their limitations. Models using direct experience often result in poor performance until an agent has had a sufficient number of interactions to build up a reliable picture of a particular partner and witness reports rely on self-interested agents being willing to freely share their experience. To this end, this paper presents Certified Reputation (CR), a novel model of trust that can overcome these limitations. Specifically, CR works by allowing agents to actively provide third-party references about their previous performance as a means of building up the trust in them of their potential interaction partners. By so doing, trust relationships can quickly be established with very little cost to the involved parties. Here we empirically evaluate CR and show that it helps agents pick better interaction partners more quickly than models that do not incorporate this form of trust.
Utilizing SMAP Soil Moisture Data to Constrain Irrigation in the Community Land Model

Irrigation representation in land surface models has been advanced over the past decade, but the soil moisture (SM) data from SMAP satellite have not yet been utilized in large‐scale irrigation modeling. Here we investigate the potential of improving irrigation representation in the Community Land Model version‐4.5 (CLM4.5) by assimilating SMAP data. Simulations are conducted over the heavily irrigated central U.S. region. We find that constraining the target SM in CLM4.5 using SMAP data assimilation with 1‐D Kalman filter reduces the root‐mean‐square error of simulated irrigation water requirement by 50% on average (for Nebraska, Kansas, and Texas) and significantly improves irrigation simulations by reducing the bias in irrigation water requirement by up to 60%. An a priori bias correction of SMAP data further improves these results in some regions but incrementally. Data assimilation also enhances SM simulations in CLM4.5. These results could provide a basis for improved modeling of irrigation and land‐atmosphere interactions.
New biotechnology enhances the application of cisgenesis in plant breeding

Cisgenesis is genetic modification to transfer beneficial alleles from crossable species into a recipient plant. The donor genes transferred by cisgenesis are the same as those used in traditional breeding. It can avoid linkage drag, enhance the use of existing gene alleles. This approach combines traditional breeding techniques with modern biotechnology and dramatically speeds up the breeding process. This allows plant genomes to be modified while remaining plants within the gene pool. Therefore, cisgenic plants should not be assessed as transgenics for environmental impacts.
A multi-modal feature fusion framework for kinect-based facial expression recognition using Dual Kernel Discriminant Analysis (DKDA)

We present a multi-modal feature fusion framework for Kinect-based Facial Expression Recognition (FER). The framework extracts and pre-processes 2D and 3D features separately. The types of 2D and 3D features are selected to maximize the accuracy of the system, with the Histogram of Oriented Gradient (HOG) features for 2D data and statistically selected angles for 3D data giving the best performance. The sets of 2D features and 3D features are reduced and later combined using a novel Dual Kernel Discriminant Analysis (DKDA) approach. Final classification is done using SVMs. The framework is benchmarked on a public Kinect-based FER dataset which includes data for 32 subjects (in both frontal and non-frontal poses and two expression intensities) and 6 basic expressions (plus neutral), namely: happiness, sadness, anger, disgust, fear, and surprise. The framework shows that the proposed combination of 2D and 3D features outperforms simpler existing combinations of 2D and 3D features, as well as systems that use either 2D or 3D features only. The proposed system also outperforms Linear Discriminant Analysis (LDA)-transformed and traditional Kernel Discriminant Analysis (KDA)-transformed systems, with an average accuracy improving of 10%. It also outperforms the state of the art by more than 13% in frontal poses.