gebhart/patrepeval huggingface dataset:
Electron-beam array lithography stitching experiments (1981)

A series of experiments to demonstrate the feasibility of the EBAL (electron-beam array lithography) concept is described, using a system of electron optics that deflects the electron beam in two stages: coarse and fine deflection. The field of view is divided up into a mosaic of smaller fields, called lenslets. It is required to stitch the lenslet fields into a complete, coherent field. A stitching theory is used [Singhal et al. (to be published)], together with a calibration plate. This procedure is demonstrated using two techniques: test patterns on resist are written and developed for examination by microscope; also electronic stitching is done, wherein test fiducial marks at lenslet boundaries are located with the electron beam, and data from adjacent lenslets are compared. This technique gives quantitative results more directly than writing on resist. We present results on 3×3 lenslet resist stitching to an accuracy of 0.2 μm, and 5×5 lenslet electronic stitching to 0.14 μm, which help to demonstrate the feasibility of EBAL for microcircuit lithography.
Word-Level Confidence Estimation for Machine Translation (2007)

This article introduces and evaluates several different word-level confidence measures for machine translation. These measures provide a method for labeling each word in an automatically generated translation as correct or incorrect. All approaches to confidence estimation presented here are based on word posterior probabilities. Different concepts of word posterior probabilities as well as different ways of calculating them will be introduced and compared. They can be divided into two categories: System-based methods that explore knowledge provided by the translation system that generated the translations, and direct methods that are independent of the translation system. The system-based techniques make use of system output, such as word graphs or N-best lists. The word posterior probability is determined by summing the probabilities of the sentences in the translation hypothesis space that contains the target word. The direct confidence measures take other knowledge sources, such as word or phrase lexica, into account. They can be applied to output from nonstatistical machine translation systems as well. Experimental assessment of the different confidence measures on various translation tasks and in several language pairs will be presented. Moreover,the application of confidence measures for rescoring of translation hypotheses will be investigated.
Active quasi-circulators using quadrature hybrids for simultaneous transmit and receive (2009)

This paper presents a new type of quasi-circulator that consists of three quadrature hybrids with broadband operation, high isolation, and suppression of noise figure (NF) for simultaneous transmit and receive (STAR) phased-array systems. The operation principal of the circulator is based on phase cancellation and combination techniques to enhance isolation and suppress noise figure (NF). The circulator is implemented using three MMIC Lange couplers and active elements to provide quasi-circulator operation. The preliminary measured data shows that it is possible to obtain 35 dB isolation* between the transmit and receive port with 1.5 GHz operational bandwidth at X-band, while the NF at the receive port is < 5.5 dB. Using an external integrated power amplifier to partially implement an active quasi circulator, the performance of the device shows 25 dB isolation with 17% bandwidth, with NF less than 4.7 dB at the receive port. The proposed architecture is suitable for MMIC integration and system-on-chip (SOC) applications. (*The isolation is defined by the S-parameter difference, in dB, between forward transmission from the transmit to receive port and from the transmit to the antenna port).
Preclinical Evidence for the Use of Sunitinib Malate in the Treatment of Plexiform Neurofibromas (2016)

Purpose

Plexiform neurofibromas (pNF) are pathognomonic nerve and soft tissue tumors of neurofibromatosis type I (NF1), which are highly resistant to conventional chemotherapy and associated with significant morbidity/mortality. Disruption of aberrant SCF/c-Kit signaling emanating from the pNF microenvironment induced the first ever objective therapeutic responses in a recent phase 2 trial. Sunitinib malate is a potent, highly selective RTK inhibitor with activity against c-Kit, PDGFR, and VEGFR, which have also been implicated in the pathogenesis of these lesions. Here, we evaluate the efficacy of sunitinib malate in a preclinical Krox20;Nf1flox/− pNF murine model.



Experimental Design

Proliferation, β-hexosaminidase release (degranulation), and Erk1/2 phosphorylation were assessed in sunitinib treated Nf1+/− mast cells and fibroblasts, respectively. Krox20;Nf1flox/- mice with established pNF were treated sunitinib or PBS-vehicle control for a duration of 12 weeks. pNF metabolic activity was monitored by serial [18F]DG-PET/CT imaging.



Results

Sunitinib suppressed multiple in vitro gain-in-functions of Nf1+/− mast cells and fibroblasts and attenuated Erk1/2 phosphorylation. Sunitinib treated Krox20;Nf1flox/− mice exhibited significant reductions in pNF size, tumor number, and FDG uptake compared to control mice. Histopathology revealed reduced tumor cellularity and infiltrating mast cells, markedly diminished collagen deposition, and increased cellular apoptosis in sunitinib treated pNF.



Conclusions

Collectively, these results demonstrate the efficacy of sunitinib in reducing tumor burden in Krox20;Nf1flox/− mice. These preclinical findings demonstrate the utility of inhibiting multiple RTKs in pNF and provide insights into the design of future clinical trials. Pediatr Blood Cancer © 2015 Wiley Periodicals, Inc.
Learning Deep Spectral Features for Hyperspectral Data Using Convolution Over Spectral Signature Shape (2021)

Deep convolutional neural networks learn the spatial image features automatically, for classifying a hyperspectral image. Learning the spectral features automatically is equally important in analyzing the hyperspectral image. However, most of the earlier work treat a hyperspectral pixel as a n dimensional vector (n = no. of bands) and a separate convolution is performed over the depth. The features so learned are stacked together with the spatial features and are used for further processing. The semantics of the learned spectral features are completely ignored and are not interpretable in these approaches. We propose a simple transformation of the hyperspectral pixel to two-dimensional spectral graph (shape) and then the convolution over the same. This results in learning the spectral features that can be interpreted using spectroscopic knowledge of the material. We compared our approach with some of the common deep learning approaches for the hyperspectral data. The improvements are evident from the experiments.
System combination and score normalization for spoken term detection (2013)

Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of alternative methods improving keyword search performance. We apply these methods to Cantonese, a language that presents some new issues in terms of reduced resources and shorter query lengths. First, we show score normalization methodology that improves in average by 20% keyword search performance. Second, we show that properly combining the outputs of diverse ASR systems performs 14% better than the best normalized ASR system.
Key elements of a low voltage, ultracompact plasma spectrometer (2016)

Abstract Taking advantage of technological developments in wafer‐scale processing over the past two decades, such as deep etching, 3‐D chip stacking, and double‐sided lithography, we have designed and fabricated the key elements of an ultracompact (1.5 cm) 3 plasma spectrometer that requires only low‐voltage power supplies, has no microchannel plates, and has a high aperture area to instrument volume ratio. The initial design of the instrument targets the measurement of charged particles in the 3–20 keV range with a highly directional field of view and a 100% duty cycle; i.e., the entire energy range is continuously measured. In addition to reducing mass, size, and voltage requirements, the new design will affect the manufacturing process of plasma spectrometers, enabling large quantities of identical instruments to be manufactured at low individual unit cost. Such a plasma spectrometer is ideal for heliophysics plasma investigations, particularly for small satellite and multispacecraft missions. Two key elements of the instrument have been fabricated: the collimator and the energy analyzer. An initial collimator transparency of 20% with 3° × 3° angular resolution was achieved. The targeted 40% collimator transparency appears readily achievable. The targeted energy analyzer scaling factor of 1875 was achieved; i.e., 20 keV electrons were selected for only a 10.7 V bias voltage in the energy analyzer.
Flexible Multilevel Converters Using Four-Switch Extended Commutation Cells (2015)

The extended commutation cell is a four-port, four-switch cell that allows bidirectional energy transport in two orthogonal directions throughout the cell. By cascading multiple cells, a multilevel converter can be constructed with a high number of levels. The voltage across each cell capacitor can be adjusted independently of the load, resulting in high flexibility in output levels. In this paper, the general theoretical analysis for this cell, including the necessary design tools, is detailed. Experimental results of a two-cell eight-level dc-ac converter are given. The outcomes are in good agreement with the analysis and simulation results.
On path selection for traffic with bandwidth guarantees (2002)

Transmission of multimedia streams imposes a minimum-bandwidth requirement on the path being used to ensure end-to-end Quality-of-Service (QoS) guarantees. While any shortest-path algorithm can be used to select a feasible path, additional constraints that limit resource consumption and balance the network load are needed to achieve efficient resource utilization. We present a systematic evaluation of four routing algorithms that offer different tradeoffs between limiting the path hop count and balancing the network load. Our evaluation considers not only the call blocking rate but also the fairness to requests for different bandwidths, robustness to inaccurate routing information, and sensitivity to the routing information update frequency. It evaluates not only the performance of these algorithms for the sessions with bandwidth guarantees, but also their impact on the lower priority best-effort sessions. Our results show that a routing algorithm that gives preference to limiting the hop count performs better when the network load is heavy, while an algorithm that gives preference to balancing the network load performs slightly better when the network load is light. We also show that the performance of using pre-computed paths with a few discrete bandwidth requests is comparable to that of computing paths on-demand, which implies feasibility of class-based routing. We observe that the routing information update interval can be set reasonably large to reduce routing overhead without sacrificing the overall performance, although an increased number of sessions can be misrouted.
Structural Basis for Dual Functionality of Isoflavonoid O-Methyltransferases in the Evolution of Plant Defense Responses (2007)

In leguminous plants such as pea (Pisum sativum), alfalfa (Medicago sativa), barrel medic (Medicago truncatula), and chickpea (Cicer arietinum), 4'-O-methylation of isoflavonoid natural products occurs early in the biosynthesis of defense chemicals known as phytoalexins. However, among these four species, only pea catalyzes 3-O-methylation that converts the pterocarpanoid isoflavonoid 6a-hydroxymaackiain to pisatin. In pea, pisatin is important for chemical resistance to the pathogenic fungus Nectria hematococca. While barrel medic does not biosynthesize 6a-hydroxymaackiain, when cell suspension cultures are fed 6a-hydroxymaackiain, they accumulate pisatin. In vitro, hydroxyisoflavanone 4'-O-methyltransferase (HI4'OMT) from barrel medic exhibits nearly identical steady state kinetic parameters for the 4'-O-methylation of the isoflavonoid intermediate 2,7,4'-trihydroxyisoflavanone and for the 3-O-methylation of the 6a-hydroxymaackiain isoflavonoid-derived pterocarpanoid intermediate found in pea. Protein x-ray crystal structures of HI4'OMT substrate complexes revealed identically bound conformations for the 2S,3R-stereoisomer of 2,7,4'-trihydroxyisoflavanone and the 6aR,11aR-stereoisomer of 6a-hydroxymaackiain. These results suggest how similar conformations intrinsic to seemingly distinct chemical substrates allowed leguminous plants to use homologous enzymes for two different biosynthetic reactions. The three-dimensional similarity of natural small molecules represents one explanation for how plants may rapidly recruit enzymes for new biosynthetic reactions in response to changing physiological and ecological pressures.
Tissue-Specific Regulation of Mouse Hepatocyte Nuclear Factor 4 Expression (1994)

Hepatocyte nuclear factor 4 (HNF-4) is a liver-enriched transcription factor and a member of the steroid hormone receptor superfamily. HNF-4 is required for the hepatoma-specific expression of HNF-1 alpha, another liver-enriched transcription factor, suggesting the early participation of HNF-4 in development. To prepare for further study of HNF-4 in development, the tissue-specific expression of the mouse HNF-4 gene was studied by analyzing the promoter region for required DNA elements. DNase-hypersensitive sites in the gene in liver and kidney tissues were found in regions both distal and proximal to the RNA start that were absent in tissues in which HNF-4 expression did not occur. By use of reporter constructs in transient-transfection assays and with transgenic mice, a region sufficient to drive liver-specific expression of HNF-4 was identified. While an HNF-1 binding site between bp -98 and -68 played an important role in the hepatoma-specific promoter activity of HNF-4 in transient-transfection assays, it was not sufficient for the liver-specific expression of a reporter gene in transgenic mice. Distal enhancer elements indicated by the presence of DNase I-hypersensitive sites at kb -5.5 and -6.5, while not functional in transient-transfection assays, were required for the correct expression of the mouse HNF-4 gene in animals.
Phased-array sound pickup apparatus having no unwanted response pattern (1987)

First Page
Plerixafor, a CXCR4 Antagonist, Mitigates Skin Radiation-Induced Injury in Mice (2012)

Even with modern 3D conformal treatments skin radiation injury can be an inadvertent complication associated with clinical radiotherapy particularly at tissue folds. It is also of concern in the context of a radiological terrorism incident or accident, since skin irradiation lowers the lethal dose of whole body radiation. We hypothesize that radiation-induced skin injury originates from a loss of stem and progenitor cells, accompanied by excessive ROS production and proinflammatory cytokines. Plerixafor, a CXCR-4 antagonist, is one of the most efficient bone marrow stem cell mobilizers and these studies were designed to experimentally assess the potential of Plerixafor to reduce skin radiation injury. The right hind legs of groups of C57BL/6 mice were exposed to radiation alone or in combination with Plerixafor. Plerixafor was administered intraperitoneally at a dose of 5 mg/kg given in two doses separated by two days and started either on day 0, 4, 7, 15 or 24 after irradiation. The primary end point was skin injury, which was assessed three times a week for at least 2 months using a semi-quantitative scale. Secondary end points measured at selected time points included histology (primarily H&E) and cytokine levels (TGF-β and TNF-α). The acute and late skin injury in mice receiving Plerixafor was highly dependent on the timing of administration of the drug. The maximum benefit was observed when the drug was started 1 week after radiation exposure, and earlier or later administration of the drug decreased its efficacy. Secondary damage end points (cytokine levels and histologically assessed tissue thickness) provided confirmatory observations. In an attempt to gain insight into the effect of timing of administration of the agent on the mitigation effect, the ligand to CXCR4, stromal derived factor, SDF-1, was measured as a function of time after radiation exposure. Expression of SDF-1 monitored in skin as a function of time after a 30 Gy radiation exposure suggested a strong correlation between timing of administration of Plerixafor and expression of SDF-1 in irradiated skin: optimum drug administration timing coincided with maximal SDF-1 expression in the skin of irradiated mice. This report presents the first observation that CXCR4 antagonist improves both acute and late skin response to radiation exposure.
Green phosphor-converted LED (2002)

Green phosphor-converted LEDs using a blue pump InGaN diodes have advantages over the direct green InGaN LED with regards to color stability with drive and/or temperature. Added manufacturing steps are outweighed by higher color yield, as a range of pump colors can be used without changing the final chromaticity. The conversion losses can be smaller than the decrease in wall-plug efficiency from blue towards green, which has been reported by many sources. A distinct disadvantage of the concept is due to only one color and phosphor proven - SrGa<sub>2</sub>S<sub>4</sub>:Eu<sup>2+</sup> and 535 nm peak wavelength.
<i>PAGE-1</i> , an X chromosome-linked <i>GAGE-</i> like gene that is expressed in normal and neoplastic prostate, testis, and uterus (1998)

We have used a combination of computerized database mining and experimental expression analyses to identify a gene that is preferentially expressed in normal male and female reproductive tissues, prostate, testis, fallopian tube, uterus, and placenta, as well as in prostate cancer, testicular cancer, and uterine cancer. This gene is located on the human X chromosome, and it is homologous to a family of genes encoding GAGE-like proteins. GAGE proteins are expressed in a variety of tumors and in testis. We designate the novel gene PAGE-1 because the expression pattern in the Cancer Genome Anatomy Project libraries indicates that it is predominantly expressed in normal and neoplastic prostate. Further database analysis indicates the presence of other genes with high homology to PAGE-1, which were found in cDNA libraries derived from testis, pooled libraries (with testis), and in a germ cell tumor library. The expression of PAGE-1 in normal and malignant prostate, testicular, and uterine tissues makes it a possible target for the diagnosis and possibly for the vaccine-based therapy of neoplasms of prostate, testis, and uterus.
Stationary-Tones Interference Cancellation using Adaptive Tracking (2007)

It is usual in practice that recorded sounds are contaminated by stationary tones coming from power wiring (50/60 Hz or 400 Hz and their harmonics), frame or line frequencies from a nearby TV or monitor, computer fans, hard disk drives, etc. They are mostly stationary, but their removal using a stationary noise suppressor results in notch filtering, removing the speech content at those frequencies, because the SNRs are usually low. In this paper we propose fast, real-time algorithm for removing constant tones while keeping intact the speech content. We build and adaptively update a model of the constant tones, extrapolating it for subtraction from the next frame. In our evaluations, the proposed algorithm reduces the amplitudes of such stationary tones up to 15 dB, without introducing artifacts such as nonlinear distortions or musical noises. This algorithm is applicable as pre-processor before a classic gain-based stationary noise suppressor.
Adaptive Feature Abstraction for Translating Video to Text (2016)

Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video features. However, the variable context-dependent semantics in the video may make it more appropriate to adaptively select features from the multiple CNN layers. We propose a new approach for generating adaptive spatiotemporal representations of videos for the captioning task. A novel attention mechanism is developed, that adaptively and sequentially focuses on different layers of CNN features (levels of feature "abstraction"), as well as local spatiotemporal regions of the feature maps at each layer. The proposed approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network (2018)

We explore a solution for learning disease signatures from weakly, yet easily obtainable, annotated volumetric medical imaging data by analyzing 3D volumes as a sequence of 2D images. We demonstrate the performance of our solution in the detection of emphysema in lung cancer screening low-dose CT images. Our approach utilizes convolutional long short-term memory (LSTM) to "scan" sequentially through an imaging volume for the presence of disease in a portion of scanned region. This framework allowed effective learning given only volumetric images and binary disease labels, thus enabling training from a large dataset of 6,631 un-annotated image volumes from 4,486 patients. When evaluated in a testing set of 2,163 volumes from 2,163 patients, our model distinguished emphysema with area under the receiver operating characteristic curve (AUC) of .83. This approach was found to outperform 2D convolutional neural networks (CNN) implemented with various multiple-instance learning schemes (AUC=0.69-0.76) and a 3D CNN (AUC=.77).
Wavelet transform method for downward continuation (1992)

PreviousNext No AccessSEG Technical Program Expanded Abstracts 1992Wavelet transform method for downward continuationAuthors: Ronan LeBrasGeorge MellmanM. PetersRonan LeBrasSierra Geophysics, George MellmanSierra Geophysics, and M. PetersSierra Geophysicshttps://doi.org/10.1190/1.1822247 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Permalink: https://doi.org/10.1190/1.1822247FiguresReferencesRelatedDetailsCited byMultiscale adjoint waveform-difference tomography using waveletsYanhua O. Yuan and Frederik J. Simons22 May 2014 | GEOPHYSICS, Vol. 79, No. 3 SEG Technical Program Expanded Abstracts 1992ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 1992 Pages: 1410 publication data© 1992 Copyright © 1992 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Feb 2005 CITATION INFORMATION Ronan LeBras, George Mellman, and M. Peters, (1992), "Wavelet transform method for downward continuation," SEG Technical Program Expanded Abstracts : 889-892. https://doi.org/10.1190/1.1822247 Plain-Language Summary PDF DownloadLoading ...