Miracle-dz/UniFAR-FTUs huggingface dataset:
The importance of early life in childhood obesity and related diseases: a report from the 2014 Gravida Strategic Summit

Obesity and its related non-communicable diseases (NCDs), such as type 2 diabetes, heart disease and cancer, impose huge burdens on society, particularly the healthcare system. Until recently, public health and policy were primarily focused on secondary prevention and treatment of NCDs. However, epidemiological and experimental evidence indicates that early-life exposures influence the risk of childhood obesity and related diseases later in life, and has now focused attention on the health of both mother and child. During pregnancy and the early neonatal period, individuals respond to their environment by establishing anatomical, physiological and biochemical trajectories that shape their future health. This period of developmental plasticity provides an early window of opportunity to mitigate the environmental insults that may increase an individual’s sensitivity to, or risk of, developing obesity or related diseases later in life. Although much investigation has already occurred in the area of Developmental Origins of Health and Disease research, the science itself is still in its infancy. It remains for researchers to tackle the important outstanding questions and translate their knowledge into workable solutions for the public good. The challenge, however, is to decide which areas to focus on. With these opportunities and challenges in mind, the 2014 Gravida Summit convened to examine how its early-life research program can determine which areas of research into mechanisms, biomarkers and interventions could contribute to the international research strategy to fight childhood obesity and its related diseases.
Modelling coral calcification accounting for the impacts of coral bleaching and ocean acidification

Abstract. Coral reefs are diverse ecosystems that are threatened by rising CO2 levels through increases in sea surface temperature and ocean acidification. Here we present a new unified model that links changes in temperature and carbonate chemistry to coral health. Changes in coral health and population are explicitly modelled by linking rates of growth, recovery and calcification to rates of bleaching and temperature-stress-induced mortality. The model is underpinned by four key principles: the Arrhenius equation, thermal specialisation, correlated up- and down-regulation of traits that are consistent with resource allocation trade-offs, and adaption to local environments. These general relationships allow this model to be constructed from a range of experimental and observational data. The performance of the model is assessed against independent data to demonstrate how it can capture the observed response of corals to stress. We also provide new insights into the factors that determine calcification rates and provide a framework based on well-known biological principles to help understand the observed global distribution of calcification rates. Our results suggest that, despite the implicit complexity of the coral reef environment, a simple model based on temperature, carbonate chemistry and different species can give insights into how corals respond to changes in temperature and ocean acidification.
Chapter 2.02.06: The Effects of Dyslexia on the Writing Processes of Students in Higher Education

Undergraduates with dyslexia typically report that writing is the activity that they find most problematic in their studies. Twenty-eight undergraduates registered as dyslexic at a university in the United Kingdom were compared with 32 undergraduates matched for age, gender and academic discipline. Tests of intelligence and of reading and spelling skills showed that the two groups were of equal intelligence but that the dyslexic students performed significantly worse on the reading and spelling tests and on a test of working memory span. Dyslexics made more spelling mistakes than non-dyslexics. Even after texts were corrected for spelling and capitalization, their texts were rated as of much lower quality by two independent judges. There were no differences in proportions of time which dyslexics and non-dyslexics spent on the different writing processes or in the effort they put into the processes. Keywords: dyslexia; dyslexic students; United Kingdom; writing processes
Tracing long‐term vadose zone processes at the Nevada Test Site, USA

The nuclear weapons testing programme of the USA has released radionuclides to the subsurface at the Nevada Test Site. One of these tests has been used to study the hydrological transport of radionuclides for over 25 years in groundwater and the deep unsaturated zone. Ten years after the weapon's test, a 16 year groundwater pumping experiment was initiated to study the mobility of radionuclides from that test in an alluvial aquifer. The continuously pumped groundwater was released into an unlined ditch where some of the water infiltrated into the 200 m deep vadose zone. The pumped groundwater had well‐characterized tritium activities that were utilized to trace water migration in the shallow and deep vadose zones. Within the near‐surface vadose zone, tritium levels in the soil water are modelled by a simple one‐dimensional, analytical wetting front model. In the case of the near‐surface soils at the Cambric Ditch experimental site, water flow and salt accumulation appear to be dominated by rooted vegetation, a mechanism not included within the wetting front model. Simulation results from a two‐dimensional vadose groundwater flow model illustrate the dominance of vertical flow in the vadose zone and the recharge of the aquifer with the pumped groundwater. The long‐time series of hydrological data provides opportunities to understand contaminant transport processes better in the vadose zone with an appropriate level of modelling. Copyright © 2005 John Wiley & Sons, Ltd.
Empirical essays on education policy evaluation

This thesis is composed of three empirical essays that share the theme of education policy evaluation. Chapter 1 looks at cognitive spillovers between siblings who live in the same household. The research question asked is whether such spillovers exist, and what their magnitude may be. Using longitudinal data on children aged 5 to 14 and living in the United States, I identify positive, significant and sizable cognitive spillovers between siblings. In the light of these findings, education policies that increase the cognitive achievement of any target population of children may also indirectly benefit their cohabiting siblings. Chapter 2 evaluates the outcome of a decentralization reform that has affected public education in Colombia. Municipalities with more than 100 thousand inhabitants were made autonomous in the management of public education on their territories, while cities below that threshold were not given autonomy. I exploit this quasi-experimental setup to evaluate the impact of local autonomy on educational quality. I find that autonomy was beneficial for municipalities that were highly developed at the time of the reform, but the impact was negative for low-developed ones, increasing inequality across regions. These results sound a note of caution about the potential consequences of decentralization reforms, in educational contexts similar to the Colombian one. Chapter 3 is coauthored with Fabio Sánchez Torres at the University of Los Andes, and looks at how quality assurance mechanisms in the careers of public school teachers have affected student performance in Colombia. The main novelties consist in a selective entry examination, a probation period and permanent evaluation processes. We find that teachers who operate under the new regulation noticeably improve pupils’ achievement with respect to colleagues who follow the old rules, which feature very few incentives for teaching quality. Our findings may provide education policy guidance to several Latin American countries, in which current teacher regulations resemble the former Colombian one.
Adolescent African American Girls as Engaged Readers: Challenging Stereotypical Images of Black Womanhood through Urban Fiction

Abstract:This article explores the ways some adolescent African American girls engage with urban fiction, a genre often maligned as “trash” literature. This project sought to understand the appeal of the genre as well as adolescent African American girls’ perceptions about the texts. Based on findings, which suggest that participants are engaging with the genre in critical ways, conclusions may support non-traditional approaches to helping adolescent Africa American girls deconstruct and challenge stereotypic and dominant messages about the underrepresented lives of African Americans.
Real-time Microwave Photonic technique for Low-Coherence Interferometry applications

Low-Coherence Interferometry (LCI) is a highly relevant optical measurement technique used in a wide variety of applications from sensing to medical imaging among others due to its non-invasive performance with high axial precision. In this work, a novel structure is proposed with the application of Microwave Photonics (MWP) as key enabling technology for real-time LCI applications. The proposed MWP-LCI technique permits the measurement of the optical path difference in the time domain by processing the incoherent optical signal modulated by an electrical RF pulse. The possibility of retrieving the interferogram information by an oscilloscope is experimentally demonstrated achieving a scanning speed of tens of MHz required by real-time LCI applications with moderate signal-to-noise ratio (SNR). We have achieved an acquisition rate of 20 MHz increasing in more than 5 magnitude orders compared to previous techniques operating in frequency domain with similar SNR performance.
Predicting Temperament from Twitter Data

Temperament is an innate psychological characteristic associated with how we relate with the world. This feature is often used to direct careers, manage conflicts, develop leadership, improve teaching, etc. The data generated by social media users represent user behavior facing the various situations of everyday life. With this, machine learning techniques can be used to infer the temperament, as is already done in the vast research on sentiment analysis and the growing research on personality prediction. This paper proposes a framework for temperament classification according to the theory of psychologist David Keirsey. Our results present an accuracy higher than 70% for the Artisan and Guardian types.
Antipsychotic treatment resistance in first-episode psychosis: prevalence, subtypes and predictors

Background We examined longitudinally the course and predictors of treatment resistance in a large cohort of first-episode psychosis (FEP) patients from initiation of antipsychotic treatment. We hypothesized that antipsychotic treatment resistance is: (a) present at illness onset; and (b) differentially associated with clinical and demographic factors. Method The study sample comprised 323 FEP patients who were studied at first contact and at 10-year follow-up. We collated clinical information on severity of symptoms, antipsychotic medication and treatment adherence during the follow-up period to determine the presence, course and predictors of treatment resistance. Results From the 23% of the patients, who were treatment resistant, 84% were treatment resistant from illness onset. Multivariable regression analysis revealed that diagnosis of schizophrenia, negative symptoms, younger age at onset, and longer duration of untreated psychosis predicted treatment resistance from illness onset. Conclusions The striking majority of treatment-resistant patients do not respond to first-line antipsychotic treatment even at time of FEP. Clinicians must be alert to this subgroup of patients and consider clozapine treatment as early as possible during the first presentation of psychosis.
Book Review: The Logic of Charity: Great Expectations in Hard Times by J. Mohan and B. Breeze

Kevin Kearns, PhD, is a professor of public and nonprofit management in the Graduate School of Public and International Affairs, University of Pittsburgh. He is also the founding director of the Johnson Institute for Responsible Leadership. He is the 2005 winner of the Chancellor’s Distinguished Teaching Award at the University of Pittsburgh. On six occasions, most recently in 2016, he has won the Goldstein Teacher of the Year Award at the Graduate School of Public and International Affairs.
The Role of Healthy Narcissism in Relating Emotionally to Familiarity andthe Unknown

Narcissism is a greatly maligned human characteristic. It’s understandable. Narcissists tend not to consider others’ feelings. They think only of themselves, and use those who care for them without giving anything in return. In psychological literature, narcissism is predominantly discussed within a pathological context. The focus is on helping the narcissist see past his own self and learn to relate to others more openly. Within this, the evolutionary need for narcissism tends to be ignored. In this paper I intend to elaborate on the other side of narcissism that elicits attraction to the familiar and triggers resistance/rejection of the alien or unknown. What is this function of narcissism? We know that all personality traits, whether they're perceived as healthy or pathological, have an evolutionary basis. They exist in order to ensure our survival as individuals and as a species. So, what about narcissism? What is it that narcissism ensures or preserves? Why do we have this trait, and do we need it?
A new polynomial invariant of knots and links

The purpose of this note is to announce a new isotopy invariant of oriented links of tamely embedded circles in 3-space. We represent links by plane projections, using the customary conventions that the image of the link is a union of transversely intersecting immersed curves, each provided with an orientation, and undercrossings are indicated by broken lines. Following Conway [6], we use the symbols L+, Lo, L_ to denote links having plane projections which agree except in a small disk, and inside that disk are represented by the pictures of Figure 1. Conway showed that the one-variable Alexander polynomials of L+, Lo, L_ (when suitably normalized) satisfy the relation
A fully-differential noise-canceling CMOS front-end amplifier

This paper presents a fully-differential front-end amplifier. The differential cross coupling with tail current is exploited as noise canceling circuitry to improve the Noise Figure (NF). The proposed front-end amplifier is designed with 0.13 um CMOS technology under the supply voltage of 1.2 V. Simulation results show NF of 2.1 dB. The voltage gain and bandwidth of the amplifier are 19.7 dB and 1.75 GHz respectively. The power dissipation is 8.88 mW.
Asynchronous rebalancing of a replicated tree

We study the problem of rebalancing a replicated tree, while the tree is concurrently being updated in a P2P manner. Tree updates are asynchronous and commutative, as we aim at eventual consistency. However, rebalancing requires strong synchronisation, because only replicas that have performed the same rebalances can communicate with one another. In order to scale to large networks, we propose to break this synchronisation into two parts: commitment within a small core of replicas, followed by asynchronous, pairwise catch-up protocol between replicas at different rebalance numbers. We state the requirements and correctness conditions for this distributed algorithm, and propose a correct solution. Keywords: replicated tree, collaborative editing, garbage collection, distributed systems
Multi-View Reconstruction using Narrow-Band Graph-Cuts and Surface Normal Optimization

This paper presents a new algorithm for reducing the minimal surface bias associated with volumetric graph cuts for 3D reconstruction from multiple calibrated images. The algorithm is based on an iterative graph-cut over narrow bands combined with an accurate surface normal estimation. At each iteration, we first optimize the normal to each surface patch in order to obtain a precise value for the photometric consistency measure. This helps in preserving narrow protrusions with high curvature which are very sensitive to the choice of normal. We then apply a volumetric graph-cut on a narrow band around the current surface estimate to determine the optimal surface inside this band. Using graph cuts on a narrow band allows us to avoid local minima inside the band while at the same time reducing the danger of taking ”shortcuts” and converging to a wrong ”global” minimum when using a wide band. Reconstruction results obtained on standard data sets clearly show the merits of the proposed algorithm.
Point-based Dynamic Deformation and Crack Propagation

In this paper, we articulate a novel meshless computational paradigm for the effective modeling, accurate physical simulation, and rapid visualization of solid objects. The uniqueness of our approach is that both the interior and the boundary of our new volumetric representation are point-based, generalizing the powerful and popular method of point-sampled surfaces. We also build the point-based physical model founded upon continuum mechanics, which allow us to effectively model the dynamic behavior of point-based volumetric objects ranging, from elastic deformation to crack propagation. Our prototype system takes any point-sampled surfaces as input and generates both interior volumetric points and a volumetric distance field with an octree structure, which can be utilized to facilitate the crack surface evolution. The physics of these volumetric points in a solid interior are simulated using the Element-Free Galerkin (EFG) method. In sharp contrast to the traditional finite element method, the meshless property of our new technique expedites the accurate representation and precise simulation of the underlying discrete model, without the need of domain remeshing. Furthermore, we develop the new modeling and animation techniques for point-sampled surfaces to dynamically generate cracked surfaces based on the underlying distance field. The accuracy and continuity advantages of the meshless method also enable the direct visualization of the physical quantities of volumetric objects for mechanical and material analysis. CR Categories: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling—Curve, surface, solid, and object representations; Physically based modeling; I.6. 8 [Simulation and Modeling]: Types of Simulation—Animation
Optimal transportation to the entropy-power inequality

We present a simple proof of the entropy-power inequality using an optimal transportation argument which takes the form of a simple change of variables. The same argument yields a reverse inequality involving a conditional differential entropy which has its own interest. It can also be generalized in various ways. The equality case is easily captured by this method and the proof is formally identical in one and several dimensions.
Content Based Image Retrieval in Similarity Integrated Network Using Combined Ranking Algorithm

In image rich websites like flickr, Photobucket retrieving images in such a large network is very useful but also very tedious process. It also consumes more time and the retrieved contents are not exactly relevant always. In this paper, three algorithms have been proposed to improve the performance of such sites. To compare the similarity of the images efficiently, HMok-SimRank algorithm is used. It’s derived from similarity algorithm. Integrated Weighted Similarity Learning(IWSL) is used to integrate meta information descriptions with image content. Finally, Ranking algorithm is used to rank the images for the order of retrieval. Benefits of our proposed system applied in flickr are experimentally shown in terms of both relevance and speed.
Switching Between OSTBC and Spatial Multiplexing with Linear Receivers in Spatially Correlated MIMO Channels

We present a low complexity adaptive transmission approach for spatially correlated MIMO channels. The proposed scheme adaptively switches between orthogonal space-time block codes (OSTBC) and spatial multiplexing (SM), depending of the channel correlation and SNR. We derive an exact closed-form expression and tight upper bound on the OSTBC capacity in double-correlated channels, and examine the relative capacity of OSTBC and SM in terms of the spatial correlation. We then derive efficient closed-form BER expressions for practical OSTBC transmission employing bit-interleaved coded modulation (BICM). Based on these results, we propose a practical adaptive algorithm which selects the combination of MIMO transmission scheme (OSTBC or SM), and BICM mode, which achieves the highest spectral efficiency whilst satisfying a pre-defined BER
Thalamic and Subthalamic Deep Brain Stimulation for Essential Tremor: Where Is the Optimal Target?

BACKGROUND: The ventrolateral thalamus (ventral intermediate nucleus [Vim]) is the traditional target for neurosurgical treatment of essential tremor. The target, however, has varied substantially among different neurosurgeons. OBJECTIVE: To evaluate the effect of deep brain stimulation in the thalamus and posterior subthalamic area (PSA) in relation to electrode location. METHODS: Thirty-six (17 Vim/19 PSA) patients with 44 deep brain stimulation electrodes were included in this retrospective study. The effect of stimulation was evaluated with standardized settings for each contact using items from the Essential Tremor Rating Scale. RESULTS: When each contact was evaluated in terms of the treated hand with standardized stimulation, the electrode contact providing the best effect in the individual patient was located in the zona incerta or radiation prelemniscalis in 54% and the Vim in 12%. Forty contacts provided a tremor reduction of > 90%. Of these, 43% were located in the PSA and 18% in the Vim according to the Schaltenbrand atlas. Of these 40 contacts, 37 were found in the PSA group. CONCLUSION: More contacts yielding an optimal effect were found in the PSA group than in the Vim. Many patients operated on in the Vim got the best effect from a contact located in the PSA. This might suggest that the PSA is a more efficient target than the Vim.
Block learning Bayesian network structure from data

Existing methods for learning Bayesian network structures run into the computational and statistical problems because of the following two reasons: a large number of variables and a small sample size for enormous variables. Adopting the divide and conquer strategies, we propose a novel algorithm, called block learning algorithm, to learn Bayesian network structures. The method partitions the variables into several blocks that are overlapped with each other. The blocks are learned individually with some constraints obtained from the learned overlap structures. After that, the whole network is recovered by combining the learned blocks. Comparing with some typical learning algorithms on golden Bayesian networks, our proposed methods are efficient and effective. It shows a large potential capability to be scaled up.