Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the use of Geographic Information Systems as an Environmental and Social Justice Advocacy Tool for Community-Based Organizations: A Case Study of Galena Park, Texas

Environmental factors within communities play a significant role on the health and well-being of residents. These social and physical determinants have a substantial effect on health. This interaction can result in environmental injustices, inequality, and ultimately poor health for residents. The community of Galena Park, Texas, is a predominantly minority community of Hispanic and African-American residents with previously undocumented concerns related to air quality, the built environment, access to healthcare and the food environment. Through participatory engagement with Galena Park residents using Geographic Information Systems (GIS), this research examines the degree to which GIS is an effective tool in illustrating and visualizing environmental and social injustices. Findings from this research suggest that GIS only relays part of the story and is most powerful when the lived experiences of residents are integrated into the analytical process. INDEX WORDS: Environmental justice, Social justice, Health disparities, Residential segregation, Geographic Information Systems, Participatory GIS, Cancer, Built environment, Environmental health EXPLORING THE USE OF GEOGRAPHIC INFORMATION SYSTEMS AS AN ENVIRONMENTAL AND SOCIAL JUSTICE ADVOCACY TOOL FOR COMMUNITY-BASED ORGANIZATIONS: A CASE STUDY OF GALENA PARK, TEXAS
Net Energy Analysis in a Ramsey-Hotelling Growth Model

This article presents a dynamic growth model with energy as an input in the production function. The available stock of energy resources is ordered by a quality parameter based on energy accounting: the “Energy Return on Energy Invested” (EROI). In our knowledge this is the first paper where EROI fits in a neoclassical growth model (with individual utility maximization and market equilibrium), establishing the economic use of “net energy analysis” on a firmer theoretical ground. All necessary concepts to link neoclassical economics and EROI are discussed before their use in the model, and a comparative static analysis of the steady states of a simplified version of the model is presented.
Title: Emotion unfolded by motion: a role for parietal lobe in decoding dynamic facial

Facial expressions convey important emotional and social information and are frequently applied in investigations of human affective processing. Dynamic faces may provide higher ecological validity to examine perceptual and cognitive processing of facial expressions. Higher-order processing of emotional faces was addressed by varying the task and virtual face models systematically. Blood oxygenation level-dependent (BOLD) activation was assessed using functional magnetic resonance imaging (fMRI) in 20 healthy volunteers while viewing and evaluating either emotion or gender intensity of dynamic face stimuli. A general linear model analysis revealed that high valence activated a network of motion responsive areas, indicating that visual motion areas support perceptual coding for the motion-based intensity of facial expressions. The comparison of emotion with gender discrimination task revealed increased activation of inferior parietal lobule (IPL), which highlights the involvement of parietal areas in processing of high level features of faces. Dynamic emotional stimuli may help to emphasize functions of the hypothesized 'extended' over the 'core' system for face processing.
Discovery, segmentation and reactive grasping of unknown objects

Learning the visual appearance and physical properties of unknown objects is an important capability for humanoid robots that are supposed to be working in an open environment. We present an approach that enables a robot to discover new, unknown objects, segment them from the background and grasp them. This gives the robot full control over the object and allows its further multimodal exploration.
Design optimization of a cable-driven two-DOF joint module with a flexible backbone

A cable-driven snake-like robot arm (CDSLRA) with a flexible backbone possesses a number of promising advantages over the conventional rigid-link manipulators, such as lightweight mechanical structure, large reachable workspace, and high maneuverability. In general, a CDSLRA is a hyper-redundant manipulator that consists of a large number of active segments. As the basic building block of a CDSLRA, a 2-DOF cable-driven joint module with a flexible backbone is proposed. This paper focuses on the kinematics, kinetostatics and design optimization of the 2-DOF flexible joint module. Based on the 2-DOF joint module design as well as its resultant motion characteristics, the concept of instantaneous screw axis is proposed to formulate the kinematic model of the 2-DOF joint module, in which the Product-Of-Exponentials (POE) formula is employed. In order to generate the feasible workspace subject to the positive tension constraint, the kinetostatics of the 2-DOF cable-driven joint module is addressed, where the stiffness resulting from both the driving cables and the flexible backbone are considered. A numerical orientation workspace evaluation method is proposed based on an equi-volumetric partition in its parametric space and the volume-element associated integral factor. A multi-performance index, which takes both the stiffness condition index and the workspace volume into account, is employed to optimize the geometric size of the joint module. The simulation results demonstrate the effectiveness of the proposed multi-performance optimization algorithm.
The Effects of Uniform Climate Change on International Cotton Prices and Production

In this paper, we take the yield impacts of Schlenker and Roberts (2009), specifically on cotton, under a range of uniform temperature changes and apply it to a global fiber model to map changes in cotton production and prices. Although we use the 2011-2020 time period, the results should be viewed more as potential long-term market adjustments in the absence of new technology rather than a specific forecast. We find that in terms of extreme higher temperatures in the U.S. alone (+5°C) results in higher cotton prices as much as 17% against the baseline over the 2011-2020 projection period as production is cut back, on average, by 1.8%. Meanwhile, a 5°C increase in temperature in the U.S. and the rest-of-the-world (ROW) induces a price increase of as much as 135%, on average, throughout the projection period given a lower production of 20% from baseline levels that ignore temperature changes. More modest temperature changes (+1°C) result in much more modest (+6% in price and -1.3% in global production) changes in the cotton market.
Coping with motion in MRI: Developments since TRELLIS

At IVCNZ in 2005, we presented “Correcting motion artifacts in magnetic resonance images”. In that paper we described a new algorithm, TRELLIS, which our group had developed to detect and correct bulk motion in certain types of magnetic resonance (MR) images. A similar algorithm, PROPELLER, has become the method of choice in MR imaging, but a lot more work has occurred since 2005 in the area. The paper briefly recaps on PROPELLER and TRELLIS and then reviews the methods that have been proposed in the intervening period. It is clear that much work remains in the area of motion detection and correction as other aspects of MR imaging continue to improve. Researchers with a range of expertise are needed to collaborate in this work.
AlN cantilever for differential pressure sensor

The differential pressure sensors are used for various applications. In this work, a piezoelectric differential pressure sensor using an aluminum nitride (AlN) thin film cantilever is proposed to achieve high sensitivity and low power consumption. The dimensions of the cantilever are 1500 μm × 1000 μm × 2 μm. The proposed sensor needs no power consumption in the AlN sensing element in principle. The sensor design was validated by measuring the performance of the sensor, which showed sensitivity of 8.2 mV/Pa from -20 Pa to 20 Pa.
7 When Words FailSyariah Law in Indonesia: Revival, Reform or Transplantation?

Most comparatist scholarship on legal transplantation has been conducted in a European setting and does not itself always transplant usefully into other geographic contexts. The notion of 'legal transplantation' is particularly complex in south-east Asia, because almost all formal institutions of the legal systems of most south-east Asian countries are, to a large extent, 'transplants'. Indonesia is not an Islamic state and its Constitution (Undang-Undang Dasar 1945) does not refer to religion except in vague terms and in formulae asserting freedom of religious expression. The application of syariah was determined not by reference to the scriptures of Islam, but by an inquiry into the ethnicity and folk beliefs of the person involved. Success at the local level in Indonesia encouraged proponents of legal Islamisation to revive efforts at the national level. Keywords: Indonesia; Islamic revival; legal Islamisation; reform; south-east Asia; Syariah Law
Adversarial Prediction Games for Multivariate Losses

Multivariate loss functions are used to assess performance in many modern prediction tasks, including information retrieval and ranking applications. Convex approximations are typically optimized in their place to avoid NP-hard empirical risk minimization problems. We propose to approximate the training data instead of the loss function by posing multivariate prediction as an adversarial game between a loss-minimizing prediction player and a loss-maximizing evaluation player constrained to match specified properties of training data. This avoids the non-convexity of empirical risk minimization, but game sizes are exponential in the number of predicted variables. We overcome this intractability using the double oracle constraint generation method. We demonstrate the efficiency and predictive performance of our approach on tasks evaluated using the precision at k, the F-score and the discounted cumulative gain.
Core Techniques of Ontology-Based Question Answering Systems : a Survey

The Semantic Web contains an enormous amount of information in the form of ontologies. To make this information available, many ontology-based question answering (QA) systems were created in the last years. Building a QA system is difficult since many different challenges have to be solved. To address these challenges, QA systems generally combine techniques from natural language processing, machine learning and Semantic Web. The aim of this survey is to give an overview over the techniques used in current QA systems. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data (QALD). These techniques are presented in a novel way: techniques that solve the same task are first grouped together and then described. For each technique advantages and disadvantages are discussed. This allows a direct comparison of similar techniques. We conclude that there is a need for a framework to interweave current approaches.
A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, this paper reports on a study on how to make an existing algorithm, named C³E from consensus between classification and clustering ensembles, more convenient by automatically tuning its main parameters. The C³E algorithm is based on a general optimisation framework that takes as input class membership estimates from existing classifiers, and a similarity matrix from a cluster ensemble operating solely on the new target data to be classified, in order to yield a consensus labelling of the new data. To do so, two parameters have to be defined a priori by the user: the relative importance of classifier and cluster ensembles, and the number of iterations of the algorithm. We propose a differential evolution DE algorithm, named dynamic DE D²E, which is a computationally efficient alternative for optimising such parameters. D²E provides better results than DE by dynamically updating its control parameters. Moreover, competitive results were achieved when comparing D²E with three state-of-the-art algorithms.
Measuring atrophy by simultaneous segmentation of serial MR images using 4-D graph-cuts

We propose a method for simultaneous segmentation of serially acquired magnetic resonance (MR) images. An existing graph-cuts based algorithm is extended and applied to 4-D images. A probabilistic atlas is generated for each baseline scan by intersubject registration of multiple labeled images. The atlases are used for baseline and aligned follow-up images and are combined with an intensity model to define a weighted graph that connects the timepoints. A minimal cut on this graph yields the segmentation for all timepoints. The resulting segmentations are consistent over time in boundary regions with weak gray scale definition, but reflect atrophy well where the structure boundary is well defined by MR intensity. The hippocampus was segmented in 568 baseline and follow-up images provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI). The estimated atrophy rates correctly classified AD patients from controls at a rate of 82% (Atrophy rates: AD 3.85%/y.; MCI 2.31%/y.; controls: 0.85%/y.)
The Future of Assessment : Shaping Teaching and Learning

@text:This volume stems from the 2003 Educational Testing Service Invitational Conference that convened leading scholars and practitioners from education, psychology, economics, statistics and public policy to discuss the important topic of measurement and accountability. The chapters cover all significant aspects of the current accountability scene, with careful but not exclusive attention to the No Child Left Behind act. Written by nationally recognized scholars with a mandate to write in a non-technical style, this volume will appeal to anyone seriously interested in school reform and the educational accountability movement.
Learning invariant features by harnessing the aperture problem

The energy model is a simple, biologically inspired approach to extracting relationships between images in tasks like stereopsis and motion analysis. We discuss how adding an extra pooling layer to the energy model makes it possible to learn encodings of transformations that are mostly invariant with respect to image content, and to learn encodings of images that are mostly invariant with respect to the observed transformations. We show how this makes it possible to learn 3D pose-invariant features of objects by watching videos of the objects. We test our approach on a dataset of videos derived from the NORB dataset.
Firm Strategies in the "Mid Tail" of Platform-Based Retailing

While millions of products are sold on its retail platform, Amazon.com itself stocks and sells only a very small fraction of them. Most of these products are sold by third-party sellers who pay Amazon a fee for each unit sold. Empirical evidence clearly suggests that Amazon tends to sell high-demand products and leave long-tail products for independent sellers to offer. We investigate how a platform owner such as Amazon, facing ex ante demand uncertainty, may strategically learn from these sellers' early sales which of the “mid-tail” products are worthwhile for its direct selling and which are best left for others to sell. The platform owner's “cherry-picking” of the successful products, however, gives an independent seller the incentive to mask any high demand by lowering his sales with a reduced service level unobserved by the platform owner. We analyze this strategic interaction between a platform owner and an independent seller using a game-theoretic model with two types of sellers---one with high demand and one with low demand. We show that it may not always be optimal for the platform owner to identify the seller's demand. Interestingly, the platform owner may be worse off by retaining its option to sell the independent seller's product, whereas both types of sellers may benefit from the platform owner's threat of entry. The platform owner's entry option may reduce consumer surplus in the early period, although it increases consumer surplus in the later period. We also investigate how consumer reviews influence the market outcome.
The eco-social approach in social work

A co-edited refereed output of EU Framework 4 ‘Targeted Socio-Economic Research’ project, 'Making New Policies against Social Exclusion in European Cities: The Eco-social Approach and Social Impact Assessment in Social Work' (646,000 ECU), a comparative action-research project undertaken jointly with Fachhochschule Magedeburg in Germany and University of Jyvaskyla in Finland. In bringing forward the eco-social perspective, the book introduces a new theoretical understanding and new practical approaches to the questions of sustainability in social environments and social inclusion. The two co-authored chapters cover the main concepts developed and used across the project and the research undertaken in England, in Leicester.
The Stimulating Effect of Bright Light on Physical Performance Depends on Internal Time

The human circadian clock regulates the daily timing of sleep, alertness and performance and is synchronized to the 24-h day by the environmental light-dark cycle. Bright light exposure has been shown to positively affect sleepiness and alertness, yet little is known about its effects on physical performance, especially in relation to chronotype. We, therefore, exposed 43 male participants (mean age 24.5 yrs ± SD 2.3 yrs) in a randomized crossover study to 160 minutes of bright (BL: ≈ 4.420 lx) and dim light (DL: ≈ 230 lx). During the last 40 minutes of these exposures, participants performed a bicycle ergometer test. Time-of-day of the exercise sessions did not differ between the BL and DL condition. Chronotype (MSFsc, mid-sleep time on free days corrected for oversleep due to sleep debt on workdays) was assessed by the Munich ChronoType Questionnaire (MCTQ). Total work was significantly higher in BL (median 548.4 kJ, min 411.82 kJ, max 875.20 kJ) than in DL (median 521.5 kJ, min 384.33 kJ, max 861.23 kJ) (p = 0.004) going along with increased exhaustion levels in BL (blood lactate (+12.7%, p = 0.009), heart rate (+1.8%, p = 0.031), and Borg scale ratings (+2.6%, p = 0.005)) in all participants. The differences between total work levels in BL and DL were significantly higher (p = 0.004) if participants were tested at a respectively later time point after their individual mid-sleep (chronotype). These novel results demonstrate, that timed BL exposure enhances physical performance with concomitant increase in individual strain, and is related not only to local (external) time, but also to an individual’s internal time.
Development and evaluation of the aerosol dynamics and gas phase chemistry model

The aim of this work was to develop a model suited for detailed studies of aerosol dynamics, gas and particle phase chemistry within urban plumes, from local scale (1× 1 km2) to regional scale. This article describes and evaluates the trajectory model for Aerosol Dynamics, gas and particle phase CHEMistry and radiative transfer (ADCHEM). The model treats both vertical and horizontal dispersion perpendicular to an air mass trajectory (2-space dimensions). The Lagrangian approach enables a more detailed representation of the aerosol dynamics, gas and particle phase chemistry and a finer spatial and temporal resolution compared to that of available regional 3D-CTMs. These features make it among others well suited for urban plume studies. The aerosol dynamics model includes Brownian coagulation, dry deposition, wet deposition, in-cloud processing, condensation, evaporation, primary particle emissions and homogeneous nucleation. The organic mass partitioning was either modeled with a 2-dimensional volatility basis set (2D-VBS) or with the traditional two-product model approach. In ADCHEM these models consider the diffusion limited and particle size dependent condensation and evaporation of 110 and 40 different organic compounds respectively. The gas phase chemistry model calculates the gas phase concentrations of 61 different species, using 130 different chemical reactions. Daily isoprene and monoterpene emissions from European forests were simulated separately with the vegetation model LPJ-GUESS, and included as inCorrespondence to: P. Roldin (pontus.roldin@nuclear.lu.se) put to ADCHEM. ADCHEM was used to simulate the ageing of the urban plumes from the city of Malm̈ o in southern Sweden (280 000 inhabitants). Several sensitivity tests were performed concerning the number of size bins, size structure method, aerosol dynamic processes, vertical and horizontal mixing, coupled or uncoupled condensation and the secondary organic aerosol formation. The simulations show that the full-stationary size structure gives accurate results with little numerical diffusion when more than 50 size bins are used between 1.5 and 2500 nm, while the movingcenter method is preferable when only a few size bins are selected. The particle number size distribution in the center of the urban plume from Malm̈o was mainly affected by dry deposition, coagulation and vertical dilution. The modeled PM2.5 mass was dominated by organic material, nitrate, sulfate and ammonium. If the condensation of HNO 3 and NH3 was treated as a coupled process (pH independent) the model gave lower nitrate PM2.5 mass than if considering uncoupled condensation. Although the time of ageing from that SOA precursors are emitted until condensable products are formed is substantially different with the 2D-VBS and two product model, the models gave similar total organic mass concentrations.
Cerebral impairment in chronic solvent‐induced encephalopathy

Worldwide, many workers experience occupational exposure to organic solvents, which may induce chronic solvent‐induced encephalopathy (CSE). Disturbances within the frontostriatothalamic (FST) circuitry might explain the symptomatology of CSE. We tested the hypothesis of FST circuitry abnormalities in CSE, as well as associations with performance of psychomotor speed, attention, and solvent exposure. To detect preclinical, solvent‐related effects, we also studied the FST circuitry in solvent‐exposed, but asymptomatic workers.
The Handling of Disjunctive Fuzzy Information via Neural Network Approach

This paper proposes a neural network approach for the handling of disjunctive fuzzy information in the feature space. This neural network model consists of two types of nodes in the hidden layer. The prototype nodes and the exemplar nodes represent cluster centroids and exceptions in the feature space, respectively. This classifier can automatically generate and refine prototypes for distinct clusters in the feature space. The prototypes will form nearoptimal decision regions to meet the distribution of input patterns and classify as many input patterns as possible. Next, exemplars will be created and expanded to learn the patterns that cannot be classified by the prototypes. Such a training strategy can reduce the memory requirement and speed up the process of nonlinear classification. In addition, on-line learning is supplied in this classifier and the computational load is lightened.