Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy density in density functional theory: Application to crystalline defects and surfaces

We propose a method for decomposing the total energy of a supercell containing defects into contributions of individual atoms, using the energy density formalism within the density functional theory. The spatial energy density is unique up to a gauge transformation, and we show that unique atomic energies can be calculated by integrating over Bader and charge-neutral volumes for each atom. Numerically, we implement the energy density method in the framework of the Vienna ab initio simulation package (VASP) for both norm-conserving and ultrasoft pseudopotentials and the projector augmented-wave method, and we use a weighted integration algorithm to integrate the volumes. Surface energies and point defect energies can be calculated by integrating the energy density over the surface region and the defect region, respectively. We compute energies for several surfaces and defects: the (110) surface energy of GaAs, the monovacancy formation energies of Si, the (100) surface energy of Au, and the interstitial formation energy of O in a hexagonal close-packed Ti crystal. The surface and defect energies calculated using our method agree with size-converged calculations of the difference in the total energies of a system with versus a system without defects. Moreover, the convergence of the defect energies with size can be found from a single calculation.
Observations of the pulsation of the Cepheid l Car with the Sydney University Stellar Interferometer

Observations of the southern Cepheid t Car to yield the mean angular diameter and angular pulsation amplitude have been made with the Sydney University Stellar Interferometer at a wavelength of 696 nm. The resulting mean limb-darkened angular diameter is 2.990 ± 0.017 mas (i.e. ± 0.6 per cent) with a maximum-to-minimum amplitude of 0.560 ± 0.018 mas corresponding to 18.7 ± 0.6 per cent in the mean stellar diameter. Careful attention has been paid to uncertainties, including those in measurements, in the adopted calibrator angular diameters, in the projected values of visibility squared at zero baseline, and to systematic effects. No evidence was found for a circumstellar envelope at 696 nm. The interferometric results have been combined with radial displacements of the stellar atmosphere derived from selected radial velocity data taken from the literature to determine the distance and mean diameter of l Car. The distance is determined to be 525 ± 26 pc and the mean radius 169 ± 8 R ⊙ . Comparison with published values for the distance and mean radius shows excellent agreement, particularly when a common scaling factor from observed radial velocity to pulsation velocity of the stellar atmosphere (the p-factor) is used.
Targeted inhibition of STATs and IRFs as a potential treatment strategy in cardiovascular disease

Key factors contributing to early stages of atherosclerosis and plaque development include the pro-inflammatory cytokines Interferon (IFN)α, IFNγ and Interleukin (IL)-6 and Toll-like receptor 4 (TLR4) stimuli. Together, they trigger activation of Signal Transducer and Activator of Transcription (STAT) and Interferon Regulatory Factor (IRF) families. In particular, STAT1, 2 and 3; IRF1 and 8 have recently been recognized as prominent modulators of inflammation, especially in immune and vascular cells during atherosclerosis. Moreover, inflammation-mediated activation of these STATs and IRFs coordinates a platform for synergistic amplification leading to pro-atherogenic responses. Searches for STAT3-targeting compounds, exploring the pTyr-SH2 interaction area of STAT3, yielded many small molecules including natural products. Only a few inhibitors for other STATs, but none for IRFs, are described. Promising results for several STAT3 inhibitors in recent clinical trials predicts STAT3-inhibiting strategies may find their way to the clinic. However, many of these inhibitors do not seem STAT-specific, display toxicity and are not very potent. This illustrates the need for better models, and screening and validation tools for novel STAT and IRF inhibitors. This review presents a summary of these findings. It postulates STAT1, STAT2 and STAT3 and IRF1 and IRF8 as interesting therapeutic targets and targeted inhibition could be a potential treatment strategy in CVDs. In addition, it proposes a pipeline approach that combines comparative in silico docking of STAT-SH2 and IRF-DBD models with in vitro STAT and IRF activation inhibition validation, as a novel tool to screen multi-million compound libraries and identify specific inhibitors for STATs and IRFs.
Framing Contingencies ∗

The subjective likelihood of a contingency often depends on the manner in which it is described to the decision maker. To accommodate this dependence, we introduce a model of decision making under uncertainty which takes as primitive a family of preferences indexed by partitions of the state space. Each partition corresponds to a description of the state space. We characterize the following partition-dependent expected utility representation. The decision maker has a nonadditive set function ν over events. Given a partition of the state space, she computes expected utility with respect to her partition-dependent belief, which weights each cell in the partition by ν. Nonadditivity of ν allows the probability of an event to depend on the way in which the state space is described. We propose behavioral definitions for those events which are transparent to the decision maker and those which are completely overlooked, and connect these definitions to conditions on the representation. ∗This paper supersedes an earlier draft titled “Unawareness and Framing. ” Comments from anonymous referees, Eddie Dekel, Raphaël Giraud, and Todd Sarver were very helpful. Klaus Nehring deserves special thanks for discovering and carefully explaining the relationship between binary bet acyclicity and the product rule to us, leading to the material in Section 4.4. We thank the National Science Foundation for financial support under Grants SES-0550224 and SES-0835944. †Department of Economics, University of California, 508-1 Evans Hall #3880, Berkeley, CA 94720-3880. Email: dahn@econ.berkeley. edu. ‡Department of Economics, Washington University in Saint Louis, Campus Box 1208, Saint Louis, MO 63130. Email: hergin@artsci.wustl. edu.
The adversarial stochastic shortest path problem with unknown transition probabilities

We consider online learning in a special class of episodic Markovian decision processes, namely, loop-free stochastic shortest path problems. In this problem, an agent has to traverse through a finite directed acyclic graph with random transitions while maximizing the obtained rewards along the way. We assume that the reward function can change arbitrarily between consecutive episodes, and is entirely revealed to the agent at the end of each episode. Previous work was concerned with the case when the stochastic dynamics is known ahead of time, whereas the main novelty of this paper is that this assumption is lifted. We propose an algorithm called “follow the perturbed optimistic policy” that combines ideas from the “follow the perturbed leader” method for online learning of arbitrary sequences and “upper confidence reinforcement learning”, an algorithm for regret minimization in Markovian decision processes (with a fixed reward function). We prove that the expected cumulative regret of our algorithm is of order L|X||A| p T up to logarithmic factors, where L is the length of the longest path in the graph,X is the state space, A is the action space and T is the number of episodes. To our knowledge this is the first algorithm that learns and controls stochastic and adversarial components in an online fashion at the same time.
Rationing (higher) education : a mixed methods study of economic, cultural and institutional factors in progression from further to higher education in England

Theories involving the metaphor of 'capital' in both the economics and sociology of education are frequently used to explain progression to Higher Education (HE). However, in employing these theories there is often a lack of consideration given to the role of educational institutions, such as Further Education (FE) colleges. In particular, FE colleges may have their own objectives that impinge upon student progression. Following Gillborn and Youdell's (2000) work on schools, they ration access to HE. Through a mixed methods approach employing multivariate analysis, interview and ethnography it is shown that the ways in which FE colleges ration access to academic courses is vital in understanding educational progression. Considerations of capital are important, but there are limitations to how far they provide a complete explanation of progression. Although human capital considerations partly explain the intention to attend HE they are found not to predict actual attendance nor are they explicit in students' own accounts of their decision making. Similarly, cultural and social capital are important, but limited when faced with the countervailing demands of FE colleges. Indeed, through rationing, what may appear to depend upon one's individual or community characteristics, such as human or social capital are ascribed value within institutions. For example, there have been recent concerns with the academic potential and community cohesion of white, working class communities. Through ethnographic analysis it is shown how this manifests itself in one FE site in tenns of the students in the study being particularly pathologised as 'white trash'. This has particular consequences for their progression to HE. Because institutions, particularly FE colleges, have a key role in rationing opportunity and classifying students they are a key site in the 'widening participation' debate. According to my research, to 'widen participation' in its fullest sense, those who work in FE will need to take actions which would lead to institutional change across the sector.
4 CMOS Photodetectors

The inclusion of cameras in everything from cell phones to pens to children’s’ toys is possible because of the low cost and low power consumption of the imaging arrays that form the core of the cameras. However, these arrays are low cost and low power because they are CMOS-based; this allows for the devices to be made with the same processes and facilities that are used to make memory and computer chips. Yet, the continued surge in CMOS imager popularity goes beyond the lower cost to other factors such as ability to integrate the sensors with electronics, and the ability to achieve fast, customizable frame rates [1]. In this chapter, we will cover the design and layout of the various types of CMOS photodetectors and fundamentals of photo-conversion processes in these devices, including a brief review of CMOS photodetector history. We will then describe the emerging CMOS based technologies in photodetector design optimization to tune device responsivity, integration of micro-optics to achieve enhanced detection in low-light conditions, integration of photonic grating structures on photodetectors for spectral response selectivity, and bio-inspired CMOS imaging devices. We will conclude the chapter with some examples of applications of these technologies.
Transitions to tertiary education: Measuring and minimising inequality between private and public school students in a university outreach program

Much of the literature concerned with evaluating public and private schooling focuses upon year 12 examination results. Investigating the transition to university, some studies have compared these results with first-year university marks. Very few researchers, however, have looked beyond students’ marks. This paper examines how “school type” affects student performance, participation, and experience in a university outreach program – SmARTS. SmARTS is run through The University of Western Australia’s (UWA) Faculty of Arts, Humanities, and Social Sciences (FAHSS). It engages year 11 students in a six-month research project, facilitated by the use of both e-learning and more traditional face-to-face methods. This paper is based on research that evaluated the 2009, 2010, and 2011 programs. The methods employed include analyses of student completion rates and results, as well as 198 student surveys, ten school coordinator surveys, and three group interviews with tutors. Based on schools’ socio-economic backgrounds, fees, and examination results, we have divided schools into four types: top-tier private, second-tier private, top-tier public, and second-tier public. Our findings suggest that top-tier private and top-tier public school students have the highest levels of participation, the lowest drop-out rates, and gain the highest results in SmARTS, while the opposite is evident for second-tier public school students. We also found, however, that second-tier public school students reported to have gained more generic skills from the program than did other groups. Our findings suggest that analysing examination results provides only a limited picture of how students experience the transition to university. We argue that through research and practice such as ours, inequalities can be more accurately measured, and thus minimised, before students enter university.
New dynamic heuristics in the client-agent-server model

MCT is a widely used heuristic for scheduling tasks onto Grid platforms. However, when dealing with many tasks, MCT tends to dramatically delay already mapped task completion time, while scheduling a new task. In this paper we propose heuristics based on two features: the historical trace manager that simulates the environment and the perturbation that defines the impact a new allocated task has on already mapped tasks. Our simulations and experiments on a real environment show that the proposed heuristics outperform MCT.
Video Shot Detection Using Hidden Markov Models with Complementary Features

Shot detection is the first stage of video analysis. In this paper, we present a machine learning based shot detection approach using hidden Markov models (HMMs), in which both the color and shape clues are utilized. Its advantages are twofold. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for each type of shot transitions, including cut and gradual transitions. As trained HMMs are used to recognize the shot transition patterns automatically, it does not suffer from any trouble of threshold selection problem. Second, two complementary features, statistical corner change ratio (SCCR) and HSV color histogram difference, are used. The former summarizes the shape well whereas the latter summarizes the appearance well. Experimental results on a set of test videos demonstrate the efficacy of this shot detection approach
Maternal depression during early childhood, persistent aggression into emerging adulthood: neurodevelopmental pathways of risk?

Despite an accumulation of evidence documenting prospective links between maternal depression and aggression in offspring, the mechanisms underlying this association remain somewhat mysterious. Mothers' depressive symptoms could undermine offspring's learning of stage-adaptive emotion regulation (ER) skills during early childhood (e.g., Seifer, Schiller, Sameroff, Resnick, & Riordan, 1996; Silk, Shaw, Skuban, Oland, & Kovacs, 2006). Some longitudinal studies link maternal depression to disruptions in young children's ER, which has been found to predict elevated aggressive behavior in later childhood and emerging adolescence (e.g., Gilliom et al., 2002; Trentacosta & Shaw, 2009). Neurodevelopmental mechanisms such as altered organization or refinement in cortico-limbic pathways could also play a role in prospective associations between mothers' depression during early childhood and dysregulated aggression in offspring (Callaghan & Tottenham, 2016; Sheikh et al., 2014). To further inform future inquiries into these mechanisms of risk, the present study tested whether maternal depression in early childhood was prospectively linked to persistent patterns of aggression at school entry and in emerging adulthood via disruptions in early ER processes and related patterns of neuroanatomical connectivity. Participants were drawn from a sample of 310 males at elevated risk for disruptive behavior problems based on their gender and low socioeconomic status. Direct paths from maternal depression and preschool-age ER in early childhood to offspring aggression at school-age were supported. Unexpectedly, aggressive behavior was not found to be stable from the early school-age period into young adulthood across informant and context. Children's aggressive behavior was inversely associated with uncinate fasciculus structural integrity in emerging adulthood, such that higher aggression at school-age predicted lower fractional anisotropy at age 20. Another index of uncinate structural integrity (i.e., mean diffusivity) was positively associated with general antisocial behavior and depressive symptoms in young adulthood. The present findings add new, longitudinal evidence to inform nascent theories for neurodevelopmental mechanisms underlying antisocial behavior and clarify directions for future research endeavors to illuminate other potential neurodevelopmental mechanisms of risk related to mothers' depression.
Local Histogram Equalization to Improve Segmentation of Low Contrast Structures

A wide number of magnetic resonance imaging (MRI) analysis techniques rely on brain tissue segmentation. Automated and reliable tissue classii cation is a challenging task since the intensity representation of the data typically does not allow a clear delimitation of the dii erent tissue types present in a MRI. Clear delineation is hampered by partial volume ee ects, image noise and intensity non-uniformities caused by magnetic eld in homogeneities. Moreover, some brain regions are characterized by a higher iron content causing a signal increase in the T1-image (Drayer et al. 1986, Helms et al. 2009, Fukunaga et al. 2010). These structures (e.g. visual cortex) exhibit reduced contrast from white matter (WM) and are thus often misclassii ed by tissue classii cation. Recently, a locally adaptive cortical segmentation approach was introduced to minimize segmentation bias by using modii ed prior information (Cardoso et al. 2011). We propose an approach that does not rely on prior information for segmentation but uses modi ed prior information to locally equalize the histograms before segmentation in regions that are in uenced by high iron content. METHODS In order to create a mask of regions that should be corrected we used a modi cation of the prior information of deep structures (Cardoso et al. 2011) and additionally regions dee ned by the AAL atlas (Tzourio-Mazoyer et al. 2002). In the AAL atlas we used precentral and postcentral regions to dee ne the motor cortex and superior and middle occipital areas to dee ne the visual cortex. All regions were merged to a mask that is dee ned in the MNI space (Fig. 1). This mask is transformed to the native space of each image by using the inverse nonlinear spatial transformation from the image to MNI space. The local image intensities within this mask are now equalized according to the histogram that is obtained for the remaining brain regions. The deviating peak for gray matter (GM) is displayed in Fig. 1. We used a 3T dataset (MPRAGE, voxel size 0.75 mm isotropic; Halle et al. 2011) to test our approach. Histogram equalized images are nally segmented using the VBM8 toolbox RESULTS After histogram equalization the image intensity in the aa ected GM regions is decreased leading to increased contrast from white matter and better delineation of these structures. The visual improvement in image contrast in the T1-image is quite obvious in the basal ganglia (Fig. 2, …
Disambiguating entity references within an ontological model

In our everyday conversations, entities (persons, companies, etc.) are referred to by natural language identifiers (NLIs). Humans employ personal experience and situational context to interpret such identifiers. However, due to ambiguity, even humans run the risk of misinterpretations. In our prior work, we presented a novel method to resolve entity references in texts under the aspect of ambiguity. We explore ontological background knowledge represented in an RDF(S) graph. The different interpretation possibilities lead to different subgraphs of the underlying ontology, each subgraph describing one consistent, non-ambiguous interpretation of the ambiguous NLIs within the ontological knowledge base. Our domain-independent approach is based on spreading activation and uses a semantic relational ranking. In this paper, we suggest three extensions to our original algorithm. First, we process in a two-step interpretation---instead of the whole original input text---at first hand smaller text windows in order to get more precise reference interpretations through a smaller local text context. Second, we extend the spreading-activation algorithm within the RDF(S) graph towards a bidirectional exploration of edges which shall speed-up the algorithm. Third we use reinforcement learning in order to take advantage of re-occurring information. We present first experimental results with these algorithmic extensions and derive directions for future work.
Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data

suitable membership function (MF) is the core step of fuzzy classification system. This paper presents a novel learning algorithm that generates automatically reasonable MFs for quantitative attributes. In addition, a set of an appropriate fuzzy classification rules (FCRs) are discovered from a given numerical data. Each fuzzy rule (FR) is of the form IF-THEN rule. The antecedent IF-part and consequent THEN-part contain fuzzy sets. Since MFs are generated automatically, the proposed fuzzy learning algorithm can be viewed as a knowledge acquisition tool for classification problems. Experimental results on Iris dataset are presented to demonstrate the contribution of the proposed approach for generating MFs.