gebhart/patrepeval huggingface dataset:
Characterization of Indium Segregation in Metalorganic Vapor Phase Epitaxy-Grown InGaP by Schottky Barrier Height Measurement (2011)

Surface/interface segregation of indium in InGaP layers grown by metalorganic vapor phase epitaxy has been studied. Al/InGaP Schottky barrier height (ΦB) measurement was used for the evaluation of the segregation. It is shown that ΦB of GaAs/InGaP/GaAs double heterostructure (DH) is larger than that of InGaP/GaAs single heterostructure (SH), suggesting the diffusion of indium into upper GaAs layer. It is shown that the indium segregation progresses at the initial stage of InGaP growth and saturates when the InGaP thickness reaches about 15 nm. Using the segregation model, we calculated the exchange coefficient of indium as 0.76 at 620 °C.
Control Theory and Fast Marching Techniques for Brain Connectivity Mapping (2006)

We propose a novel, fast and robust technique for the computation of anatomical connectivity in the brain. Our approach exploits the information provided by Diffusion Tensor Magnetic Resonance Imaging (or DTI) and models the white matter by using Riemannian geometry and control theory. We show that it is possible, from a region of interest, to compute the geodesic distance to any other point and the associated optimal vector field. The latter can be used to trace shortest paths coinciding with neural fiber bundles. We also demonstrate that no explicit computation of those 3D curves is necessary to assess the degree of connectivity of the region of interest with the rest of the brain. We finally introduce a general local connectivity measure whose statistics along the optimal paths may be used to evaluate the degree of connectivity of any pair of voxels. All those quantities can be computed simultaneously in a Fast Marching framework, directly yielding the connectivity maps. Apart from being extremely fast, this method has other advantages such as the strict respect of the convoluted geometry of white matter, the fact that it is parameter-free, and its robustness to noise. We illustrate our technique by showing results on real and synthetic datasets. OurGCM(Geodesic Connectivity Mapping) algorithm is implemented in C++ and will be soon available on the web.
From chip to cooling tower data center modeling: Validation of a Multi-Scale Energy Management model (2012)

Novel modeling techniques have become prominent tools in the design of data center facilities and optimization of their operating profiles. These tools provide a method for extensive analysis of data center facilities by enabling rapid assessment of numerous "what-if" scenarios. As the data center industry moves toward lower PUE, further efficiency gains can be found only in detailed understanding of the integrated IT-facility data center infrastructure. The reliance on rule-of-thumb guidelines for system operations are being superseded by more physics and empirically based models. The Multi-Scale Energy Management (MSEM) tool has hence been developed based on a compact modeling approach that maintains much of the physics of the cooling architecture. However, extensive validation of such models in the live data center environment is essential to deploy a reliable tool for data center energy optimization. Previous work by the authors in experimental assessment and validation of the MSEM model highlighted a number of lessons to be taken into further experimental validation phases. In this paper, a more rigorous validation of the Multi-Scale Energy Management model in the live data center is explored integrating both the IT and facilities infrastructure. The methodology for data center experimental analysis is presented with consideration of steady-state evaluation in a data center facility which employs an extensive uncertainty characterization of the data center's operating parameters. The experimental evaluation is verified against the compact models within the MSEM code. A chip to cooling tower validation of the MSEM model is then undertaken with this new empirical data from the test data center.
<title>New image diagnosis system with ultrathin endoscope and clinical results</title> (1991)

The characteristics of the thin silica based imagefiber have been investigated by evaluating the image thrown on the TV monitor. As a result, it has been found that the picture diameter of the imagefiber and the magnification of the eyepiece strongly relate to the image quality of the total system. On the basis of this result, the image system for the diagnosis of breast lesions with thte thin endoscope of only 0.4 mm diameter has been developed. And we have applied this system to 66 ducts and succeeded in observing 61 ducts (92.4%) and could get some clinical information, which would be useful to the early detection of cancer.
Spatiotemporally Consistent HDR Indoor Lighting Estimation (2023)

We propose a physically motivated deep learning framework to solve a general version of the challenging indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Particularly, when the input is an LDR video sequence, our framework not only progressively refines the lighting prediction as it sees more regions, but also preserves temporal consistency by keeping the refinement smooth. Our framework reconstructs a spherical Gaussian lighting volume (SGLV) through a tailored 3D encoder-decoder, which enables spatially consistent lighting prediction through volume ray tracing, a hybrid blending network for detailed environment maps, an in-network Monte Carlo rendering layer to enhance photorealism for virtual object insertion, and recurrent neural networks (RNN) to achieve temporally consistent lighting prediction with a video sequence as the input. For training, we significantly enhance the OpenRooms public dataset of photorealistic synthetic indoor scenes with around 360k HDR environment maps of much higher resolution and 38k video sequences, rendered with GPU-based path tracing. Experiments show that our framework achieves lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods, leading to photorealistic AR applications such as object insertion.
Application of quadratures and operator splitting to partial differential equations (2013)

In this work we study the stability of a method for the numerical solution of initial value problems, that combines finite differences with Simpson's rule. This method is applied to a one spatial dimension, convection-dominated transport problem. To solve the same problem in two spatial dimensions, the proposed method was used in combination with Strang's operator decomposition method.
Development of cardanol-bonded cellulose thermoplastics: high productivity achieved in two-step heterogeneous process (2015)

We developed a heterogeneous process with low energy consumption to synthesize novel high-strength and heat-resistant cellulose-based bioplastics: cellulose esters bonded with a short chain component (acetic acid) and a long one (3-pentadecylphenoxy acetic acid; PAA), which is a derivative of cardanol, extracted from cashew nut shells. Although we recently showed that PAA-bonded cellulose acetates (cardanol-bonded cellulose resins) exhibit highly practical properties for durable products, they were synthesized in a homogeneous process, which requires large quantities of poor solvents to isolate the resins. In the developed two-step heterogeneous process, the cardanol-bonded cellulose resins are produced without poor solvents. In the first step, cellulose is esterified with limited amounts of the long and short chain components using a selected reaction solvent and catalyst. The resultant intermediate products are adequately swollen in the solvent then efficiently recovered by filtration. In the second step, the short chain component is additionally bonded to attain good thermoplasticity of the final products, which are readily recovered only by distilling a solvent and the remaining short chain component. The solvent usage was reduced by approximately 90 % compared with a homogeneous process, which leads to a large reduction in energy. The produced cardanol-bonded cellulose resins exhibited higher thermoplasticity, elastic modulus, water resistivity, and close heat resistivity compared to that of a homogeneous process. Furthermore, the mechanical and thermal characteristics of the resins were greatly improved by adding a specific polyester, polybutylene succinate adipate, and glass fiber, reaching high-level target properties for durable products.
Heparan sulfate proteoglycan syndecan-3 is a novel receptor for GDNF, neurturin, and artemin (2011)

Glial cell line-derived neurotrophic factor (GDNF) family ligands (GFLs) are potent survival factors for dopaminergic neurons and motoneurons with therapeutic potential for Parkinson's disease. Soluble GFLs bind to a ligand-specific glycosylphosphatidylinositol-anchored coreceptor (GDNF family receptor α) and signal through the receptor tyrosine kinase RET. In this paper, we show that all immobilized matrix-bound GFLs, except persephin, use a fundamentally different receptor. They interact with syndecan-3, a transmembrane heparan sulfate (HS) proteoglycan, by binding to its HS chains with high affinity. GFL-syndecan-3 interaction mediates both cell spreading and neurite outgrowth with the involvement of Src kinase activation. GDNF promotes migration of cortical neurons in a syndecan-3-dependent manner, and in agreement, mice lacking syndecan-3 or GDNF have a reduced number of cortical γ-aminobutyric acid-releasing neurons, suggesting a central role for the two molecules in cortical development. Collectively, syndecan-3 may directly transduce GFL signals or serve as a coreceptor, presenting GFLs to the signaling receptor RET.
A sliding mode speed and rotor time constant observer for induction machines (2002)

This paper describes a new closed loop approach to estimate induction machine speed and rotor time constant from measured terminal voltages and currents for speed/torque sensorless control. For this purpose, a new state estimator, which eliminates flux information of the machine, is defined and a Lyapunov function is derived to determine speed and rotor resistance. The proposed algorithm is analyzed and verified experimentally.
Action Languages and COVID-19: Lessons Learned (2020)

We recently conducted an exercise in which we evaluated the use of RAC and action languages to formalize policies related to covid19 In this paper, we summarize the most salient lessons we learned from this exercise We believe our findings are relevant not only to this specific domain, but also to policy formalization in general and possibly even to tasks beyond policy formalization © 2020 CEUR-WS All rights reserved
Translating Adult's Focus of Attention to Elderly's (2021)

Predicting which part of a scene elderly people would pay attention to could be useful in assisting their daily activities, such as driving, walking, and searching. Many computational models for predicting focus of attention (FoA) have been developed. However, most of them focus on mimicking adult FoA and do not work well for predicting elderly's, due to age-related changes in human vision. Is it possible to leverage the prediction results made by an FoA model of general adults to accurately predict elderly's FoA, rather than training a new network from scratch? In this paper, we consider a novel problem of translating adult's FoA to elderly's and propose an approach based on deep image-to-image translation. Our model is trained by minimizing both Kullback-Leibler divergence and adversarial loss to approximate the joint probability distribution of adult and elderly FoA. Experiments on two datasets demonstrate that our model gives remarkable prediction accuracy.
Structural Characterization of GNNQQNY Amyloid Fibrils by Magic Angle Spinning NMR (2010)

Several human diseases are associated with the formation of amyloid aggregates, but experimental characterization of these amyloid fibrils and their oligomeric precursors has remained challenging. Experimental and computational analysis of simpler model systems has therefore been necessary, for instance, on the peptide fragment GNNQQNY7−13 of yeast prion protein Sup35p. Expanding on a previous publication, we report here a detailed structural characterization of GNNQQNY fibrils using magic angle spinning (MAS) NMR. On the basis of additional chemical shift assignments we confirm the coexistence of three distinct peptide conformations within the fibrillar samples, as reflected in substantial chemical shift differences. Backbone torsion angle measurements indicate that the basic structure of these coexisting conformers is an extended β-sheet. We structurally characterize a previously identified localized distortion of the β-strand backbone specific to one of the conformers. Intermolecular contacts are consistent with each of the conformers being present in its own parallel and in-register sheet. Overall the MAS NMR data indicate a substantial difference between the structure of the fibrillar and crystalline forms of these peptides, with a clearly increased complexity in the GNNQQNY fibril structure. These experimental data can provide guidance for future work, both experimental and theoretical, and provide insights into the distinction between fibril growth and crystal formation.
Fundamental Structural Limitations of an Industrial Energy Management Controller Architecture for Hybrid Vehicles (2009)

Energy management controllers for hybrid electric vehicles typically contain numerous parameters that must be tuned in order to arrive at a desired compromise among competing attributes, such as fuel economy and driving quality. This paper estimates the Pareto tradeoff curve of fuel economy versus driving quality for a baseline industrial controller, and compares it to the Pareto tradeoff curve of an energy management controller based on Shortest Path Stochastic Dynamic Programming (SPSDP). Previous work demonstrated important performance advantages of the SPSDP controller in comparison to the baseline industrial controller. Because the baseline industrial controller relies on manual tuning, there was always the possibility that better calibration of the algorithm could significantly improve its performance. To investigate this, a numerical search of possible controller calibrations is conducted to determine the best possible performance of the baseline industrial controller and estimate its Pareto tradeoff curve. The SPSDP and baseline controllers are causal; they do not rely on future drive cycle information. The SPSDP controllers achieve better performance (i.e., better fuel economy with equal or better driving quality) over a wide range of driving cycles due to fundamental structural limitations of the baseline controller that cannot be overcome by tuning. The message here is that any decisions that specify or restrict controller structure may limit attainable performance, even when many tunable parameters are made available to calibration engineers. The structure of the baseline algorithm and possible sources of its limitations are discussed.Copyright © 2009 by ASME
A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices (2017)

The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation. (Evaluation of Multisensor Data in Heart Failure Patients With Implanted Devices [MultiSENSE]; NCT01128166).
Electrophoretic NMR Studies of Polymer and Surfactant Systems (2006)

Abstract ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.
Circulating tumor DNA dynamics and recurrence risk in patients undergoing curative intent resection of colorectal cancer liver metastases: A prospective cohort study (2021)

ACTRN12612000345886.
Processing and Characterization of Peti Composites Fabricated by High Temperature Vartm (Section) (2011)

The use of composites as primary structures on aerospace vehicles has increased dramatically over the past decade, but so have the production costs associated with their fabrication. For certain composites, high temperature vacuum assisted resin transfer molding (HT-VARTM) can offer reduced fabrication costs compared to conventional autoclave techniques. The process has been successfully used with phenylethynyl terminated imide (PETI) resins developed by NASA Langley Research Center (LaRC). In the current study, three PETI resins have been used to make test specimens using HT-VARTM. Based on previous work at NASA LaRC, larger panels with a quasi-isotropic lay-up were fabricated. The resultant composite specimens exhibited void contents of ~ 3% by volume depending on the type of carbon fabric preform used. Mechanical properties of the panels were determined at both room and elevated temperatures. Fabric permeability characterizations and limited process modeling efforts were carried out to determine infusion times and composite panel size limitations. In addition, new PETI based resins were synthesized specifically for HT-VARTM.
Font retrieval on a large scale: An experimental study (2010)

This paper addresses the problem of font retrieval using a query-by-example paradigm: given a font, retrieve the the most visually similar fonts. We describe a font by (a) rendering a set of reference characters, (b) extracting a feature vector for each reference character and (c) concatenating the-level character descriptors. The similarity between two fonts is simply the similarity between the vectorial representations. Our contribution is an experimental comparison of character-level descriptors of step (b) on a large dataset of 9,000 fonts. The descriptors we chose to evaluate were drawn from the literature on typed and handwritten text analysis. An important conclusion is that the SIFT descriptor, which was shown to be state-of-the-art for object recognition in photographs and for handwriting recognition, yields the best results for font retrieval.
Doping of polyethersulfone nanofiltration membranes: antifouling effect observed at ultralow concentrations of TiO2 nanoparticles (2011)

Doping of nanofiltration membranes with TiO2 nanoparticles was studied in the ultralow concentration range, in the absence of photocatalysis. Blended polyethersulfone/TiO2 flat-sheet membranes were manufactured and investigated in terms of pure water flux, permeability, fouling resistance and solute rejection. The membranes were synthesized at four different polymer concentrations by the phase inversion method, using 1-methyl-2-pyrrolidone (NMP) and deionized water as solvent and coagulant, respectively. The influence of TiO2 addition was investigated in an unusually low concentration interval (0.035–0.375 wt%). The membrane morphology was studied by determining particle size distributions of TiO2 to explore the effect of nanoparticle aggregation. Furthermore, membranes were characterized by hydrophilicity (contact angle), morphology (SEM), porosity, mechanical strength (bursting pressure) and thermal analysis (TGA). Membrane fouling was studied with humic acids as model organic foulants. Overall, a remarkable improvement in the permeability was observed with the addition of ultralow amounts of nanoparticles to the polymer. The optimum permeability was found to be as low as 0.085 wt%, using a constant rejection of dyes as the boundary condition. It was shown that rejection of solutes is not negatively influenced by the increase in permeability. In addition, the resistance against membrane fouling was found to be above 12% for the TiO2 blended membranes.
Stem Cell Transfusion Restores Immune Function in Radiation-Induced Lymphopenic C57BL/6 Mice (2015)

Radiation-induced lymphopenia (RIL) is associated with treatment of different tumors (lung, colon, pancreas, breast, sarcomas, and glioblastoma). It is a significant clinical problem affecting the survival of cancer patients. The biologic mechanisms leading to RIL are not clearly understood. In this study, we established a mouse model of RIL representing therapeutic clinical regimen for lung cancer. Flow cytometry was used to analyze circulating levels of T and B cells and bone marrow (BM) stem cells. We found that fractionated radiation to the thorax significantly reduced circulating T and B cells as well as BM stem cells. Ex-vivo irradiation of blood and autologous reinjection to mice also significantly induced lymphopenia. Furthermore, we found that mobilization of stem cells from the BM and autologous stem cell transplant rescued RIL in mice. Overall, our results suggest that RIL has not only direct effect on circulating lymphocytes, but also has indirect effect on circulating lymphocytes as well as stem cells in the nonirradiated BM. These results open a new window for investigating the direct and indirect biologic mechanisms leading to RIL, and provide a preclinical basis to test the effect of stem cell transplantation for treatment of RIL in cancer patients.
A 60 GHz single-layer Fabry-Perot Cavity Antennas using sparse array for circularly polarized radiation (2015)

Circular Polarized (CP) antennas are useful in wireless communication since the CP waves will not be affected as they travel in different media. Moreover, the CP antennas compared to Linear Polarized (LP) antennas are less sensitive to the orientation angle between the transmitter and receiver. Unlicensed 7-GHz bandwidth at center frequency of 60 GHz is amenable for high data rate wireless communications. Due to high demand for the ISM 60GHz band to establish a secure and stable communication link, Circular Polarized high gain antennas are favorable.
Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks (2019)

This paper deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, most resilient approaches so far combine stochastic gradient descent (SGD) with different robust aggregation rules. However, the sizeable SGD-induced stochastic gradient noise makes it challenging to distinguish malicious messages sent by the Byzantine attackers from noisy stochastic gradients sent by the 'honest' workers. This motivates us to reduce the variance of stochastic gradients as a means of robustifying SGD in the presence of Byzantine attacks. To this end, the present work puts forth a Byzantine attack resilient distributed (Byrd-) SAGA approach for learning tasks involving finite-sum optimization over networks. Rather than the mean employed by distributed SAGA, the novel Byrd- SAGA relies on the geometric median to aggregate the corrected stochastic gradients sent by the workers. When less than half of the workers are Byzantine attackers, the robustness of geometric median to outliers enables Byrd-SAGA to attain provably linear convergence to a neighborhood of the optimal solution, with the asymptotic learning error determined by the number of Byzantine workers. Numerical tests corroborate the robustness to various Byzantine attacks, as well as the merits of Byrd- SAGA over Byzantine attack resilient distributed SGD.