Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth of Micropipe-Free Single Crystal Silicon Carbide (SiC) Ingots Via Physical Vapor Transport (PVT)

The move towards commercialization of SiC based devices places increasing demands on the quality of the substrate material. While the industry has steadily decreased the micropipe (MP) levels in commercial SiC substrates over the past years, the achievement of wafers that are entirely free of MPs marks an important milestone in commercialization of SiC based devices. We present the results of a study for controlling the nucleation and propagation of MP defects in SiC ingots grown via PVT. Our studies confirm that during bulk growth of SiC, foreign polytype nucleation such as 3C-polytype occurs at the initial stages of growth (nucleation period) and/or during subsequent growth in the presence of facets. Results in this investigation suggest that polytype instability during crystal growth adversely impacts the MP density. Based on this key concept, growth conditions for nucleation and growth stages were optimized. These conditions were subsequently implemented in an innovative PVT growth environment to achieve a growth technique with highly effective polytype control. Under continuously modulated growth conditions, MPs induced by seed material and/or formed during the growth were eliminated. 2-inch and 3-inch diameter MP-free (zero MP density) conducting 4H-SiC ingots were obtained.
Beryllium Concentrations at European Workplaces: Comparison of ‘Total’ and Inhalable Particulate Measurements

A field study was carried out in order to derive a factor for the conversion of historic worker exposure data on airborne beryllium (Be) obtained by sampling according to the 37-mm closed faced filter cassette (CFC) ‘total’ particulate method into exposure concentration values to be expected when sampling using the ‘Gesamtstaubprobenahmesystem’ (GSP) inhalable sampling convention. Workplaces selected to represent the different copper Be work processing operations that typically occur in Germany and the EU were monitored revealing a broad spectrum of prevailing Be size distributions. In total, 39 personal samples were taken using a 37-mm CFC and a GSP worn side by side for simultaneous collection of the ‘total’ dust and the inhalable particulates, respectively. In addition, 20 static general area measurements were carried out using GSP, CFC, and Respicon samplers in parallel, the latter one providing information on the extra-thoracic fraction of the workplace aerosol. The study showed that there is a linear relationship between the concentrations measured with the CFC and those measured with the GSP sampler. The geometric mean value of the ratios of time-weighted average concentrations determined from GSP and CFC samples of all personal samples was 2.88. The individual values covered a range between 1 and 17 related to differences in size distributions of the Be-containing particulates. This was supported by the area measurements showing that the conversion factor increases with increasing values of the extra-thoracic fraction covering a range between 0 and 79%.
Longitudinal effects of prenatal exposure to air pollutants on self-regulatory capacities and social competence.

BACKGROUND We evaluated the influence of prenatal exposure to widespread urban air pollutants on the development of self-regulation and social competence in a longitudinal prospective cohort of children born to nonsmoking minority women in New York City. METHODS Air pollutant exposure was estimated categorically by level of polycyclic aromatic hydrocarbon (PAH)-DNA adducts in maternal blood collected at delivery, providing a biomarker of maternal exposure to PAH over a 2- to 3-month period. Deficient emotional self-regulation (DESR) was defined as moderate elevations on three specific scales of the child behavior checklist (anxious/depressed, aggressive behavior, and attention problems). We used generalized estimating equations to assess the influence of prenatal exposure to PAH on DESR in children at 3-5, 7, 9, and 11 years of age, adjusted for gender and race/ethnicity. Next, we assessed the association of prenatal exposure to PAH with social competence, as measured by the social responsiveness scale (SRS), the association of impaired self-regulation with social competence, and whether impairment in self-regulation mediated the association of prenatal exposure to PAH with social competence. RESULTS We detected a significant interaction (at p = .05) of exposure with time, in which the developmental trajectory of self-regulatory capacity was delayed in the exposed children. Multiple linear regression revealed a positive association between presence of PAH-DNA adducts and problems with social competence (p < .04), level of dysregulation and problems with social competence (p < .0001), and evidence that self-regulation mediates the association of prenatal exposure to PAH with social competence (p < .0007). CONCLUSIONS These data suggest that prenatal exposure to PAH produces long-lasting effects on self-regulatory capacities across early and middle childhood, and that these deficits point to emerging social problems with real-world consequences for high-risk adolescent behaviors in this minority urban cohort.
Beyond the T Dwarfs: Theoretical Spectra, Colors, and Detectability of the Coolest Brown Dwarfs

We explore the spectral and atmospheric properties of brown dwarfs cooler than the latest known T dwarfs. Our focus is on the yet-to-be-discovered free-floating brown dwarfs in the Teff range from ~800 to ~130 K and with masses from 25 to 1 MJ. This study is in anticipation of the new characterization capabilities enabled by the launch of the Space Infrared Telescope Facility (SIRTF) and the eventual launch of the James Webb Space Telescope (JWST). In addition, it is in support of the continuing ground-based searches for the coolest substellar objects. We provide spectra from ~0.4 to 30 μm, highlight the evolution and mass dependence of the dominant H2O, CH4, and NH3 molecular bands, consider the formation and effects of water ice clouds, and compare our theoretical flux densities with the putative sensitivities of the instruments on board SIRTF and JWST. The latter can be used to determine the detection ranges from space of cool brown dwarfs. In the process, we determine the reversal point of the blueward trend in the near-infrared colors with decreasing Teff (a prominent feature of the hotter T dwarf family), the Teff's at which water and ammonia clouds appear, the strengths of gas-phase ammonia and methane bands, the masses and ages of the objects for which the neutral alkali metal lines (signatures of L and T dwarfs) are muted, and the increasing role as Teff decreases of the mid-infrared fluxes longward of 4 μm. These changes suggest physical reasons to expect the emergence of at least one new stellar class beyond the T dwarfs. Furthermore, studies in the mid-infrared could assume a new, perhaps transformational, importance in the understanding of the coolest brown dwarfs. Our spectral models populate, with cooler brown dwarfs having progressively more planet-like features, the theoretical gap between the known T dwarfs and the known giant planets. Such objects likely inhabit the Galaxy, but their numbers are as yet unknown.
Rules class approach to scheduling algorithms

As processors on-chip gain in numbers and complexity, task scheduling has become an important concern in system design, and the related research has produced substantial and diversified knowledge. As a result, the efficient taping and management of this knowledge has become a concern in itself. In particular, it can bring new ways to improve scheduling algorithms. This paper describes a new algorithm class based on association rules mining. It serves to both increase the knowledge about a particular scheduling algorithm and show how to improve its performance. Two examples show how this new methodology can be used to improve makespan and processor use globally by optimizing the scheduling method locally.
CS 224 W Final Report : Community Detection for Distributed Optimization

Distributed optimization has a large range of applications that make use of massive amounts of data or require limited communication between computing nodes. Therefore, it is important to investigate efficient ways of performing distributed optimization. One of the more popular methods in this area is consensus optimization using the Alternating Direction Method of Multipliers (ADMM). Since a large family of AI and machine learning problems reduces to solving large-scale convex optimization problems, the ADMM algorithm plays a key role in tackling these problems. For example, many supervised learning models under the Empirical Risk Minimization paradigm such as Lasso, Group Lasso, Generalized Linear Model, Logistic Regression, and SVM require minimizing a convex objective function. Running a linear regression on billions of training examples naturally translates to solving a convex problem with billions of terms in the objective function, and applying a distributed method to perform this optimization can considerably reduce the amount of time needed for this task. Moreover, once the scale of the problem becomes too large, solving the problem on a single machine is not even feasible because of memory limitations. In this project, we propose and examine a method to split an arbitrary convex optimization problem into smaller subproblems that can be solved efficiently in a distributed manner using the ADMM algorithm.
A Multi-scale Agent-Based Distributed Simulation Framework for Groundwater Pollution Management

Groundwater is like dark matter -- we know very little apart from the fact that it is hugely important. Given the scarcity of data, mathematical modelling can come to the rescue but existing groundwater models are mainly restricted to simulate the transport and degradation of contaminants on the scale of whole contaminated field sites by averaging out the effect of spatial heterogeneity on the availability of the pollutant to the degrading organisms. These coarse-scale mean-field models therefore tend to rely on fitting to data rather than being predictive. Also, they are less suited to incorporate spatial variability and non-linear kinetics and feedbacks. We propose to solve the two mutually exacerbating problems of environmental patchiness and data scarcity by developing a flexible and robust distributed simulation framework that uses an ensemble of small scale simulations running on different processors/computers to scale-up, i.e. to feed the effect of small-scale patchiness into a concurrent site-scale simulation of the dynamics of groundwater pollutant degradation. Our scaling approach solves problem #1 by simulating dynamics also on the small scale where some of the patchiness resides, and problem #2 by enabling rigorous validation of our small-scale model and scaling approach with laboratory data, which are high quality at low cost.
Rupture of rubber. VI. Further experiments on the tear criterion

Abstract In previous papers (I to V of this series), a tear criterion for rubbers has been proposed based on an energy balance approach. This equates the energy required to form new surfaces (the tearing energy) with the loss of elastic strain energy in the test piece. The tearing energy T is assumed to be characteristic of the material and so independent of the overall shape of the test piece. It is thus the fundamental property controlling tear behavior. The correctness of this approach was investigated by making tear measurements on test pieces of different shapes but of the same material and examining the constancy of the T values obtained. The results were consistent with the theory but not wholly conclusive, due primarily to the particular tearing behavior of the materials used (natural rubber gum vulcanizates). Another limitation was that accurate T values could be obtained only if they could be calculated directly from the measured tearing forces or elongations, and the required relationships were.
Content Based Image Retrieval CBIR Confocal Laser Endomicroscopy

— Content-based Image Retrieval (CBIR) is the problem of searching for digital images by analyzing the actual contents of the image rather than the structured (metadata) or unstructured text annotations associated with the image. In medical imaging, CBIR is being applied to support educational activities, research studies and even the clinical decision making process itself. In one hand this article gives an introduction to generic content-based image retrieval, its related technologies, standards and its future directions (e.g. Cloud computing). On the other hand, the paper describes an example CBIR application in the medical domain. The design of a CBIR-based computer-aided diagnosis system for Confocal Laser Endomicroscopy (CLE) images is described.
Preserving Symmetries in the Proper Orthogonal Decomposition

The proper orthogonal decomposition (POD) (also called Karhunen–Loeve expansion) has been recently used in turbulence to derive optimally fast converging bases of spatial functions, leading to efficient finite truncations. Whether a finite number of these modes can be used in numerical simulations to derive an “accurate” finite set of ordinary differential equations, over a certain range of bifurcation parameter values, still remains an open question. It is shown here that a necessary condition for achieving this goal is that the truncated system inherit the symmetry properties of the original infinite-dimensional system. In most cases, this leads to a systematic involvement of the symmetry group in deriving a new expansion basis called the symmetric POD basis. The Kuramoto–Sivashinsky equation with periodic boundary conditions is used as a paradigm to illustrate this point of view. However, the conclusion is general and can be applied to other equations, such as the Navier–Stokes equations, the complex G.
Semantic Expansion of Hashtags for Enhanced Event Detection in Twitter

this work, we present an event detection method in Twitter based on clustering of hashtags and introduce an enhancement technique by using the semantic similarities between the hashtags. To this aim, we devised two methods for tweet vector generation and evaluated their effect on clustering and event detection performance in comparison to word-based vector generation methods. By analyzing the contexts of hashtags and their co-occurrence statistics with other words, we identify their paradigmatic relationships and similarities. We make use of this information while applying a lexico-semantic expansion on tweet contents before clustering the tweets based on their similarities. Our aim is to tolerate spelling errors and capture statements which actually refer to the same concepts. We evaluate our enhancement solution on a three-day dataset of tweets with Turkish content. In our evaluations, we observe clearer clusters, improvements in accuracy, and earlier event detection times.
Ago2 Immunoprecipitation Identifies Predicted MicroRNAs in Human Embryonic Stem Cells and Neural Precursors

Background MicroRNAs are required for maintenance of pluripotency as well as differentiation, but since more microRNAs have been computationally predicted in genome than have been found, there are likely to be undiscovered microRNAs expressed early in stem cell differentiation. Methodology/Principal Findings SOLiD ultra-deep sequencing identified >107 unique small RNAs from human embryonic stem cells (hESC) and neural-restricted precursors that were fit to a model of microRNA biogenesis to computationally predict 818 new microRNA genes. These predicted genomic loci are associated with chromatin patterns of modified histones that are predictive of regulated gene expression. 146 of the predicted microRNAs were enriched in Ago2-containing complexes along with 609 known microRNAs, demonstrating association with a functional RISC complex. This Ago2 IP-selected subset was consistently expressed in four independent hESC lines and exhibited complex patterns of regulation over development similar to previously-known microRNAs, including pluripotency-specific expression in both hESC and iPS cells. More than 30% of the Ago2 IP-enriched predicted microRNAs are new members of existing families since they share seed sequences with known microRNAs. Conclusions/Significance Extending the classic definition of microRNAs, this large number of new microRNA genes, the majority of which are less conserved than their canonical counterparts, likely represent evolutionarily recent regulators of early differentiation. The enrichment in Ago2 containing complexes, the presence of chromatin marks indicative of regulated gene expression, and differential expression over development all support the identification of 146 new microRNAs active during early hESC differentiation.
Prediction of elastic—plastic displacements of tubular joints under combined loading using an energy-based approach

Abstract A series of 24 geometrically and materially non-linear finite element analyses of a simply supported YT tubular joint, with axial loads on the T- and Y-brace ends, was carried out to collapse, using solid three-dimensional element models. The analyses all have proportional and monotonic loading histories (i.e. radial load paths) and each analysis has a different T-brace to Y-brace load ratio so that the series ranges over all four quadrants of the two-dimensional load space. The results of the analyses are processed and combined by means of an energy approach, specifically a generalization of Castigliano's theorem on displacements using the concept of complementary work. A prediction method is thus developed to determine the non-linear, elastic-plastic, force-displacement responses of the joint for arbitrary (in-plane) radial load paths in the two-dimensional load space of the structure. One immediate application envisaged is the development of an elastic-plastic ‘joint finite element’ for use in the collapse prediction of tubular structures. The method is potentially applicable to a range of structures and loading scenarios, including other tubular joint types, both uniplanar and multiplanar, with bending moments and/or torques as well as transverse and axial loads.
Framework for distributed autonomic self‐healing in fixed IPv6 networks

A main requirement for the Future Internet is to enable self‐management behaviors facilitating the network to adapt to changing conditions and self‐heal in the face of erroneous states. On the basis of Autonomic Fault‐Management principles, this paper proposes a framework consisting of a set of components operating inside the network elements and allowing the devices to collaboratively realize self‐healing. In that context, Autonomic Fault‐Management is intuitively constituted by the detection of the presence of faulty conditions, followed in turn by the self‐diagnosis and identification of the corresponding root causes, and completed consequently by the removal of the identified root causes and their effects. The proposed framework implements a distributed control loop that interacts with the network operations personnel in case the current erroneous state is not resolvable by means of Autonomic Fault‐Management. We argue that there are a number of mutual benefits between our proposed framework and the IPv6 protocol suite. This is demonstrated by a case study that illustrates these benefits and shows how the capabilities of IPv6 can be enhanced through the self‐healing mechanisms of the proposed framework. Finally, the prototype implementation used for the case study is analyzed in terms of scalability and overhead produced in the network nodes. Copyright © 2013 John Wiley & Sons, Ltd.
Theoretical resolution of contact microspherical nanoscopy

The mechanisms of super-resolution imaging by contact microspherical (or microcylindrical) nanoscopy remain an enigmatic question since these lenses neither have an ability to amplify the near fields like in the case of far-field superlenses, nor they have a hyperbolic dispersion similar to hyperlenses. In this work we study theoretically and numerically the resolution of point sources which are imaged through microcylinders and microspheres. Our approach is based on a rigorous solution of the Maxwell equations for the dipole emission near dielectric microparticles. This allows us to account for various physical effects that may differentiate the regime of microsphere-based imaging from the standard microscopy. These effects include the near-field coupling to microspheres, excitation of microsphere resonances (whispering gallery modes), coherence and polarization of dipole emission. Their role in resolution will be discussed.
Systematic Lossy Error Protection of Video Signals

This paper proposes a scheme called systematic lossy error protection (SLEP) for robust transmission of video signals over packet erasure channels. The systematic portion of the transmission consists of a conventionally encoded video bit stream which is transmitted without channel coding. An additional bit stream generated by Wyner-Ziv encoding of the video signal is transmitted for error resilience. In the event of packet loss, this supplementary bit stream is decoded and allows the recovery of a coarsely quantized video signal, which is displayed in lieu of the lost portions of the primary video signal. The quantization mismatch results in a small, controlled loss in picture quality, but a drastic reduction in picture quality is avoided. An implementation of the SLEP system using the state-of-the-art H.264/AVC standard codec is described. Specifically, H.264/AVC redundant slices are used in conjunction with Reed-Solomon coding to generate the Wyner-Ziv bit stream. The received video quality is modeled as a function of the bit rates of the primary and redundant descriptions and the error resilience bit rate. The model is used to optimize the video quality delivered by SLEP. Via theoretical analysis and experimental simulation, it is shown that SLEP provides a flexible tradeoff between error resilience and decoded picture quality. By allowing the quality to degrade gracefully over a wider range of packet loss rates, SLEP mitigates the precipitous drop in picture quality suffered by traditional FEC-based systems.
Molecular conformations, interactions, and properties associated with drug efficiency and clinical performance among VEGFR TK inhibitors

Analyses of compounds in clinical development have shown that ligand efficient-molecules with privileged physical properties and low dose are less likely to fail in the various stages of clinical testing, have fewer postapproval withdrawals, and are less likely to receive black box safety warnings. However, detailed side-by-side examination of molecular interactions and properties within single drug classes are lacking. As a class, VEGF receptor tyrosine kinase inhibitors (VEGFR TKIs) have changed the landscape of how cancer is treated, particularly in clear cell renal cell carcinoma, which is molecularly linked to the VEGF signaling axis. Despite the clear role of the molecular target, member molecules of this validated drug class exhibit distinct clinical efficacy and safety profiles in comparable renal cell carcinoma clinical studies. The first head-to-head randomized phase III comparative study between active VEGFR TKIs has confirmed significant differences in clinical performance [Rini BI, et al. (2011) Lancet 378:193–1939]. To elucidate how fundamental drug potency–efficiency is achieved and impacts differentiation within the VEGFR TKI class, we determined potencies, time dependence, selectivities, and X-ray structures of the drug–kinase complexes using a VEGFR2 TK construct inclusive of the important juxtamembrane domain. Collectively, the studies elucidate unique drug–kinase interactions that are dependent on distinct juxtamembrane domain conformations, resulting in significant potency and ligand efficiency differences. The identified structural trends are consistent with in vitro measurements, which translate well to clinical performance, underscoring a principle that may be broadly applicable to prospective drug design for optimal in vivo performance.