gebhart/patrepeval huggingface dataset:
A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC) (2021)

Piecewise Linear Interface Construction (PLIC) is frequently used to geometrically reconstruct fluid interfaces in Computational Fluid Dynamics (CFD) modeling of two-phase flows. PLIC reconstructs interfaces from a scalar field that represents the volume fraction of each phase in each computational cell. Given the volume fraction and interface normal, the location of a linear interface is uniquely defined. For a cubic computational cell (3D), the position of the planar interface is determined by intersecting the cube with a plane, such that the volume of the resulting truncated polyhedron cell is equal to the volume fraction. Yet it is geometrically complex to find the exact position of the plane, and it involves calculations that can be a computational bottleneck of many CFD models. However, while the forward problem of 3D PLIC is challenging, the inverse problem, of finding the volume of the truncated polyhedron cell given a defined plane, is simple. In this work, we propose a deep learning model for the solution to the forward problem of PLIC by only making use of its inverse problem. The proposed model is up to several orders of magnitude faster than traditional schemes, which significantly reduces the computational bottleneck of PLIC in CFD simulations.
Feasibility study of the automated detection and localization of underground tunnel excavation using Brillouin optical time domain reflectometer (2009)

Cross-borders smuggling tunnels enable unmonitored movement of people, drugs and weapons and pose a very serious threat to homeland security. Recent advances in strain measurements using optical fibers allow the development of smart underground security fences that could detect the excavation of smuggling tunnels. This paper presents the first stages in the development of such a fence using Brillouin Optical Time Domain Reflectometry (BOTDR). In the simulation study, two different ground displacement models are used in order to evaluate the robustness of the system against imperfect modeling. In both cases, soil-fiber interaction is considered. Measurement errors, and surface disturbances (obtained from a field test) are also included in the calibration and validation stages of the system. The proposed detection system is based on wavelet decomposition of the BOTDR signal, followed by a neural network that is trained to recognize the tunnel signature in the wavelet coefficients. The results indicate that the proposed system is capable of detecting even small tunnel (0.5m diameter) as deep as 20 meter.
Synthesis of magnetic nanocomposites and alloys from platinum–iron oxide core–shell nanoparticles (2005)

This paper presents a systematic study on the generation of iron platinum-containing magnetic nanocomposites and alloys from Pt@Fe(2)O(3) core-shell nanoparticle precursors. These core-shell nanoparticles were made using a sequential synthetic approach. They could form FePt alloys and alloy-containing nanocomposites through a solid-state reaction at >400 °C. The chemical compositions of FePt alloys were controllable by using Pt@Fe(2)O(3) core-shell nanoparticles that had the designed Pt core diameter and iron oxide shell thickness. We show that face-centred tetragonal (fct) FePt@Fe core-shell nanoparticles could be made from Pt@Fe(2)O(3) core-shell nanoparticles with 5% hydrogen in argon (v/v). Furthermore, various FePt alloys and alloy-containing nanocomposites including metastable intermediate phases could be obtained. The materials were characterized by high resolution scanning transmission electron microscopy (HR-STEM), energy dispersive x-ray (EDX) spectroscopy, powder x-ray diffraction (PXRD), parallel electron energy loss spectroscopy (PEELS), and superconducting quantum interference device (SQUID) magnetometry. These materials could have potential applications as permanent hard magnets and data storage media.
Two-stage False Contour Detection Algorithm Using Re-quantization and Directional Contrast Features and Its Application to Adaptive False Contour Reduction (2006)

This paper proposes a two-stage false contour detection algorithm using re-quantization and directional contrast features, with application to false contour reduction. In the first stage of false contour detection, smooth regions are first removed by bit-depth reduction and re-quantization. In the second stage, false contours are separated from edge or texture regions using directional contrast features, yielding the false contour detection map that shows possible candidate regions of false contours. For false contour reduction, adaptive directional smoothing filtering is applied only to candidate regions that are specified by the false contour detection map. Therefore the proposed algorithm is able to preserve sharp edges and details. Computer simulation with a large number of test images shows the effectiveness of the proposed algorithm.
Vector Quantized CSI Prediction in Linear Multi-User MIMO Systems (2008)

In this paper, we discuss the methods of improving sum-rates of linear multi-user multiple-input multiple-output (MIMO) systems employing vector quantization (VQ) algorithms for encoding channel state information (CSI). We consider an approach where the mobile receiver estimates the downlink channel and chooses one of the predefined channel characterization CSI codewords whose indices are transmitted back to the base station transmitter. The algorithms described here are aimed at combating bit errors, erasures and delays occurring in CSI feedback channels. The proposed solution is to design transmitter-side CSI index prediction algorithms that allow the system to alleviate feedback channel imperfections. Moreover, the proposed techniques can be used to lower the required feedback rate, while keeping the system sum-rate at the required level. We demonstrate the performance of our algorithms in broadcast MIMO systems with and without CSI prediction. The simulation results show that significant sum-rate gains are achievable for a large range of channel Doppler rates and feedback delays.
Adaptive multi-user detection of fading CDMA channels using antenna arrays (2002)

We consider the problem of detecting a number of short-code CDMA signals that impinge (bit-) synchronously on the antenna array of a base station (reverse link). The signals are assumed to be corrupted by both multipath fading and multiple access interference (MAI). To boost performance (measured either in BER for each user or in user capacity), we are interested in using jointly for the signal detection three key signal detection technologies: (1) RAKE processing, (2) array processing, and (3) multi-user detection. The resulting space-time multi-user detectors (ST-MUDs) have been shown in previous works to offer significant improvements over previously existing techniques. In this paper we focus on a specific type of linear detector presented by Huang and Papadias (see Proc. 1998 PIMRC Conference, Boston, 1998), which has been shown to combine good performance with implementation advantages. We propose to update this linear detector with adaptive filtering algorithms, and in particular with stochastic-gradient type trained or blind algorithms. It is shown that, such simple techniques allow the receiver to approach the ST-MUD MMSE solution and achieve significant capacity gains with respect to conventional techniques.
A Discriminative Training Framework using N-Best Speech Recognition Transcriptions and Scores for Spoken Utterance Classification (2007)

In this paper, we propose a novel discriminative training approach to spoken utterance classification (SUC). The ultimate objective of the SUC task, originally developed to map a spoken speech utterance into the most appropriate semantic class, is to minimize the classification error rate (CER). Conventionally, a two-phase approach is adapted, in which the first phase is the ASR transcription phase, and the second phase is the semantic classification phase. In the proposed framework, the classification error rate is approximated as differentiable functions of the language and classifier model parameters. Furthermore, in order to exploit all the available information from the first phase, class-specific discriminant functions are defined based on score functions derived from the N-best lists. Our experimental results on the standard ATIS database indicate a notable reduction in CER from the earlier best result on the identical task. The proposed framework achieved a reduction of CER from 4.92% to 4.04%.
Access Control Requirements for LDAP (2000)

This document describes the fundamental requirements of an access control list (ACL) model for the Lightweight Directory Application Protocol (LDAP) directory service. It is intended to be a gathering place for access control requirements needed to provide authorized access to and interoperability between directories.
Twisting Lattice and Graph Techniques to Compress Transactional Ledgers (2017)

International audience
Accuracy and variability assessment of a semiautomatic technique for segmentation of the carotid arteries from three‐dimensional ultrasound images (2000)

In this paper, we report on a semiautomatic method for segmentation of three-dimensional (3D) carotid vascular ultrasound (US) images. Our method is based on a dynamic balloon model represented by a triangulated mesh. The mesh is manually placed within the interior of the carotid vessels, then is driven outward until it reaches the vessel wall by applying an inflation force to the mesh. Once the mesh is in close proximity to the vessel wall, it is further deformed using an image-based force, in order to better localize the boundary. Since the method requires manual initialization, there is inherent variability in the position and shape of the final segmented boundary. Using a 3D US image of a patient's carotids, we have examined the local variability in boundary position as the initialization position is varied throughout the interior of the carotid vessels in the 3D image. We have compared the semiautomatic segmentation method to a fully manual segmentation method, and found that the semiautomatic approach is less variable than the intraobserver variability for manual segmentation. We have furthermore examined the accuracy of the semiautomatic method by comparing the average surface to an "ideal" surface, determined by the average manually segmented surface. We have found, in general, good agreement between the semiautomatic and manual segmentation methods. For the 3D US image in question, the mean separation between the average segmented surface and the gold standard was found to be 0.35 mm. The two surfaces were determined to agree with each other, within uncertainty, at 65% of the mesh points comprising the two surfaces.
Double differential space-time block coding for time-selective fading channels (2001)

Most existing space-time coding schemes assume time-invariant fading channels and offer antenna diversity gains relying on accurate channel estimates at the receiver. Other single differential space-time block coding schemes forego channel estimation but are less effective in rapidly fading environments. Based on a diagonal unitary matrix group, a novel double differential space-time block coding approach is derived in this paper for time-selective fading channels. Without estimating the channels at the receiver, information symbols are recovered with antenna diversity gains regardless of frequency offsets. The resulting transceiver has very low complexity and is applicable to an arbitrary number of transmit and receive antennas. Approximately optimal space-time codes are also designed to minimize bit error rate. System performance is evaluated both analytically and with simulations.
Energy scavenging from acousto-elastic metamaterial using local resonance phenomenon (2015)

This article presents the possibility of energy scavenging (ES) utilizing the physics of acousto-elastic metamaterial (AEMM) and use them in a dual mode (Acoustic Filter and Energy Harvester), simultaneously. Concurrent wave filtering and energy harvesting mechanism is previously presented using local resonance phenomenon in phononic crystal, however energy harvesting capabilities of AEMM is not reported extensively. Traditionally acoustic metamaterials are used in filtering acoustic waves by trapping or guiding the acoustic energy, whereas this work presents that the trapped dynamic energy inside the soft constituent (matrix) of metamaterials can be significantly harvested by strategically embedding piezoelectric wafers in the matrix. With unit cell model, we asserted that at lower acoustic frequencies maximum power in the micro Watts (~36μW) range can be generated, which is significantly higher than the existing harvesters of same kind. Efficient energy scavengers at low acoustic frequencies are almost absent due to large required size relevant to the acoustic wavelength. In this work we propose sub wave length scale energy scavengers utilizing the coupled physics of local, structural and matrix resonances. Upon validation of the argument through analytical, numerical and experimental studies, a broadband energy scavenger (ES) with multi-cell model is designed with varying geometrical properties.
Improving the Efficiency of Video Coding by Using Perceptual Preprocessing Filter (2013)

We describe the design of a perceptual preprocessing filter for improving the effectiveness of video coding. This filter uses known parameters of the reproduction setup, such as viewing distance, pixel density, and contrast ratio of the screen, as well as a contrast sensitivity model of human vision to identify spatial oscillations that are invisible. By removing such oscillations the filter simplifies the video content, therefore leading to more efficient encoding without causing any visible alterations of the content. Through experiments, we demonstrate the use of our filter can yield significant bit rate savings compared to conventional encoding methods that are not tailored to specific viewing conditions.
Effect of high AC magnetic field on magnetic nanoparticles for magnetic hyperthermia and radiation/chemotherapy applications (2005)

Particularly for applications such as radiation therapy and chemotherapy, magnetically induced heating, or magnetic hyperthermia, is of great importance. This study presents a relatively simple setup that generates controlled and constant heating temperatures of 47C and 53C, well in excess of required temperatures for mild hyperthermia (42C). The heating elements used are superparamagnetic nanoparticles with Curie temperature of 47C and 53C, placed in an AC B field. The nanoparticles could be used as thermo-seeds that penetrate deep into a tumor, making the chemotherapy or radiation treatment much more effective.
Foveated, wide field-of-view imaging system using a liquid crystal spatial light modulator (2001)

The field-of-view (FOV) of a simple imaging system can be dramatically improved using a liquid crystal spatial light modulator (SLM). A SLM can be used to correct the off-axis aberrations that often limit the useful FOV of an imaging system giving near diffraction-limited performance at much larger field angles than would otherwise be possible. Foveated imaging refers to the variation in spatial resolution across the image caused by using the SLM in this application, and it is useful in reducing bandwidth requirements for data transmission.
Fast Brillouin optical time domain analysis for dynamic sensing (2012)

A new technique for the fast implementation of Brillouin Optical Time Domain Analysis (BOTDA) is proposed and demonstrated, carrying the classical BOTDA method to the dynamic sensing domain. By using a digital signal generator which enables fast switching among 100 scanning frequencies, we demonstrate a truly distributed and dynamic measurement of a 100 m long fiber with a sampling rate of ~10 kHz, limited only by the fiber length and the frequency granularity. With 10 averages the standard deviation of the measured strain was ~5 µε.
Deep Recurrent Neural Network DRNN Model for Real-Time Multistage Pumping Data (2018)

Abstract A new real-time model was developed, based on a deep recurrent neural network (DRNN), to predict response variables, such as surface pressure response, during the hydraulic fracturing process. During the stimulation process stage, fluids are inserted at the top of the wellhead, and the flow is driven by the difference between the hydrostatic pressure and reservoir pressure. The major physics and engineering aspects in this process are very complex; quite often, the measured data includes a large amount of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, the best approach uses a machine learning-based technique that can resolve both temporal and spatial non-linear variations. The approach followed in this paper provides a long short-term memory (LSTM) network-based method to predict surface pressure in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, including surface treating pressure, fluid pumping rate, and proppant rate. The prediction of a response variable, such as the surface pressure response, is important because it provides the basis for decisions made in several oil and gas applications to ensure success, including hydraulic fracturing and matrix acidizing. Currently available modeling methods are limited in that the estimates are not high resolution and cannot address a high level of non-linearity in the treatment pressure time series relationship with other variables, such as flow rate and proppant rate. In addition, these methods cannot predict subsurface variable responses based only on surface variable measurements. The method described in this paper is extended to accommodate the prediction of diverter pressure response. The model presented in this paper uses a deep learning neural network model to predict the surface pressure based on flow rate and proppant rate. This work represents the first attempt to predict (in real time) a response variable, such as surface pressure, during a pumping stage using a memory-preserving recurrent neural network (RNN) variant (for example, LSTM and gated recurrent unit (GRU)). The results show that the LSTM is capable of modeling the surface pressure in a hydraulic fracturing process well. The surface pressure predictions obtained were within 10% of the actual values. The current effort to model surface pressure can be used to simulate response variables in real time, providing engineers with an accurate representation of the conditions in the wellbore and in the reservoir. The current method can overcome the handling of complex physics to provide a reliable, stable, and accurate numerical solution throughout the pumping stages.
Applications of Shape Memory Effects (1995)

In this paper a series of new medical applications for specific behaviour of shape memory alloys is presented. Several properties are well known, like the superelasticity and the shape memory effect, but there are some other interesting features that are used very scarcely. Examples are given of the use of the extraordinary shapeability of the martensitic material, in some cases in combination with high tensile strength, which makes memory metal a very special engineering material. For applications of superelasticity a new alloy was found, that exhibiths improved stiffness and an upper plateau stress as high as 1000 MPa. Single crystals show perfect constant plateau stresses, which give a well defined force in medical tools. Examples are given of instruments and medical implants using memory metals for functions as distraction, adjustability for customization, measuring, steerability and locking/unlocking of devices.
A DR6/p75NTR complex is responsible for β-amyloid-induced cortical neuron death (2013)

The p75 neurotrophin receptor (p75(NTR)) is a known mediator of β-amyloid (Aβ)-induced neurotoxicity implicated in Alzheimer's disease (AD). Here, we demonstrate that death receptor 6 (DR6) binds to p75(NTR) and is a component of the p75(NTR) signaling complex responsible for Aβ-induced cortical neuron death. Cortical neurons isolated from either DR6 or p75(NTR) null mice are resistant to Aβ-induced neurotoxicity. Blocking DR6 function in cortical neurons by anti-DR6 antibodies that block the binding of DR6 to p75(NTR) receptor complex or by a dominant negative DR6 construct lacking the cytoplasmic signaling death domain attenuates Aβ-induced caspase 3 activation and cell death. DR6 expression is upregulated in AD cortex and correlates with elevated neuronal death. Targeting the disruption of the DR6/p75(NTR) complex to prevent Aβ cytotoxicity represents a new approach for the treatment of neurodegenerative disorders such as AD.
Calibration of a dual probe NanoCaliper AFM for CD metrology (2005)

Results are presented which demonstrate the feasibility of calibrating a critical-dimension atomic force microscope (CD-AFM) without the use of a reference artifact in such a way that high-precision critical dimensions can be generated independently of changes in probe tip shape (including effects of tip wear), and in the presence of surface force uncertainties and stage uncertainties. Experiments were conducted using a dual-probe NanoCaliper CD-AFM architecture. The results support an estimate of 0.2 nm for single-point (static) repeatability of tip-tip calibration achievable in a commercial tool. A comprehensive method developed for calibration and measurement using a dual probe system can remove other dimensional drifts that have effects similar to tip wear. We also found that three different "calibration events" can potentially be used to compute nondimensional interaction strengths that determine a surface force bias needed to compute CDs from noncontact mode scans. Verification of this predicted result will make it possible to build a dual probe system that is self-calibrating not only with respect to tip length and other dimensional drift, but also with respect to tip radii, cantilever stiffnesses and other parameters. We have also demonstrated the feasibility of a new diffraction-based method for directly measuring the cantilever-edge-to-cantilever-edge separation and showed that this method is capable of improving CD measurement precision still further.
Synthesis of Linear Poly(tetrafluoroethylene-<i>c</i><i>o</i>-vinyl acetate) in Carbon Dioxide (2000)

Tetrafluoroethylene (TFE) and vinyl acetate (VAc) were copolymerized in carbon dioxide by a free radical mechanism to yield a series of poly(TFE-co-VAc)s with weight-average molar masses, relative to polystyrene, between 110 and 209 kg mol-1. The copolymer composition was controlled by the monomer feed concentration to have between 40 and 71 mol % TFE. Hydrolysis of the copolymer's vinyl acetate groups to vinyl alcohol (VA) yielded terpolymers, poly(TFE-co-VAc-co-VA), thereby providing a reactive functional group for further modification. Previous syntheses of poly(TFE-co-VAc) in aqueous media resulted in a branched structure due to radical hydrogen abstraction from VAc followed by continued propagation during polymerization. Consequently, a 10-fold or greater decrease in molar mass was observed following hydrolysis. Interestingly, after synthesizing poly(TFE-co-VAc) in supercritical carbon dioxide, only a small decrease in molar mass was observed after hydrolysis. This suggests that, in carbon dioxide, abstraction is suppressed relative to polymer propagation, thereby yielding predominantly linear poly(TFE-co-VAc).