gebhart/patrepeval huggingface dataset:
Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications (2019)

Abstract In this work, an attempt is made to apply the Local Lagged Adapted Generalized Method of Moments (LLGMM) to estimate state and parameters in stochastic differential dynamic models. The development of LLGMM is motivated by parameter and state estimation problems in continuous-time nonlinear and non-stationary stochastic dynamic model validation problems in biological, chemical, engineering, energy commodity markets, financial, medical, military, physical sciences and social sciences. The byproducts of this innovative approach (LLGMM) are the balance between model specification and model prescription of continuous-time dynamic process and the development of discrete-time interconnected dynamic model of local sample mean and variance statistic process (DTIDMLSMVSP). Moreover, LLGMM is a dynamic non-parametric method. The DTIDMLSMVSP is an alternative approach to the GARCH(1,1) model, and it provides an iterative scheme for updating statistic coefficients in a system of generalized method of moment/observation equations. Furthermore, applications of LLGMM to energy commodities price, U.S. Treasury Bill interest rate and the U.S.–U.K. foreign exchange rate data strongly exhibit its unique role, scope and performance, in particular, in forecasting and confidence-interval problems in applied statistics.
Precision Analysis of Automatic Pouring Machines for the Casting Industry (2008)

This paper concerns automation of pouring process in casting industry. Recently, automatic pouring machines are used in casting with greensand mold. There are various types of automatic pouring machines such as a ladle-tilt type, stopper type, pressure type and so on. In this paper, we focus on the automation technology to the ladle-tilt type automatic pouring machines which have been widely used in East Asian. The pouring process requires the molten metal is poured quickly and precisely into the mold and is not spilled over the sprue cup. Therefore, the precision of pouring control in current automatic pouring machine is clarified in this paper. Then, the precision analysis of the automatic pouring machine is performed using mathematical model of the automatic pouring process. Finally, the automation technologies required for future pouring process are discussed in the paper.
Misalignment resilient diffractive optical networks (2018)

Abstract As an optical machine learning framework, Diffractive Deep Neural Networks (D 2 NN) take advantage of data‐driven training methods used in deep learning to devise light–matter interaction in 3D for performing a desired statistical inference task. Multi‐layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving, e.g., >98% blind inference accuracy for hand‐written digit classification. The multi‐layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer‐to‐layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D 2 NN (v‐D 2 NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly‐trained hybrid (optical‐electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase.
Development of purification processes for fully human bispecific antibodies based upon modification of protein A binding avidity (2016)

There is strong interest in the design of bispecific monoclonal antibodies (bsAbs) that can simultaneously bind 2 distinct targets or epitopes to achieve novel mechanisms of action and efficacy. Multiple bispecific formats have been proposed and are currently under development. Regeneron's bispecific technology is based upon a standard fully human IgG antibody in order to minimize immunogenicity and improve the pharmacokinetic profile. A single common light chain and 2 distinct heavy chains combine to form the bispecific molecule. One of the heavy chains contains a chimeric Fc sequence form (called Fc*) that ablates binding to Protein A via the constant region. As a result of co-expression of the 2 heavy chains and the common light chain, 3 products are created, 2 of which are homodimeric for the heavy chains and one that is the desired heterodimeric bispecific product. The Fc* sequence allows selective purification of the FcFc* bispecific product on commercially available affinity columns, due to intermediate binding affinity for Protein A compared to the high avidity FcFc heavy chain homodimer, or the weakly binding Fc*Fc* homodimer. This platform requires the use of Protein A chromatography in both a capture and polishing modality. Several challenges, including variable region Protein A binding, resin selection, selective elution optimization, and impacts upon subsequent non-affinity downstream unit operations, were addressed to create a robust and selective manufacturing process.
Vapor phase epitaxy of InP using flow modulation (1986)

We report the first preparation of indium phosphide using flow modulation techniques in the hydride vapor phase epitaxial system. A conventional single barrel reactor was used. Layer growth rates as low as 25 Å/cycle were demonstrated. Room-temperature carrier concentrations for the unintentionally doped InP of 5×1015 cm−3 and electron mobilities of 3650 cm2/V s were achieved.
Room-Temperature in situ Nuclear Spin Hyperpolarization from Optically-Pumped Nitrogen Vacancy Centers in Diamond (2015)

We report bulk, room-temperature hyperpolarization of 13C nuclear spins observed via high-field nuclear magnetic resonance (NMR). The hyperpolarization is achieved by optical pumping (OP) of nitrogen vacancy defect centers in diamond accompanied by dynamic nuclear polarization (DNP). The technique harnesses the large optically-induced spin polarization of NV- centers at room temperature, which is many orders of magnitude greater than thermal equilibrium polarization and typically achievable only at sub-Kelvin temperatures. Transfer of the spin polarization to the 13C nuclear spins is accomplished via a combination of OP and microwave irradiation. The OP/DNP is performed at 420 mT, where inductive detection of NMR is feasible, in contrast to the typically exploited level anticrossing regimes at 100 mT and 50 mT. Here, we report a bulk nuclear spin polarization of 6%. This polarization was generated in situ and detected with a standard, inductive NMR probe without the need for sample shuttling or precise crystal orientation. Hyperpolarization via OP/DNP should operate at arbitrary magnetic fields, enabling orders of magnitude sensitivity enhancement for NMR of solids and liquids at ambient conditions.
One-dimensional magnetic nanoparticle temperature imaging (2015)

Magnetic nanoparticle temperature estimation has been given both theoretical and experimental proving by using magnetic nanoparticle magnetization in DC and/or AC magnetic field [1-3]. However, the approaches assessing the average temperature of the target area are single point temperature estimation methods. And MPI has been presented to assess the concentration image in vivo [4]. In this paper, a novel method to assess the temperature and concentration imaging is presented.
High Temperature Static and Dynamic Pressure Transducer for Combustion Instability Control Using Acoustic Low-Pass Filter Structures (2008)

There is a need to measure static and dynamic conditions in many gas turbine applications, in particular for combustion instabilities, such as those in the afterburner. The DC and low frequency components are typically used for conventional engine control, while the high frequency data is essential for acoustic screech and rumble diagnostics and control. This paper presents a static-dynamic piezoresistive pressure transducer that measures low amplitude, dynamic pressure perturbations superimposed on top of a high pressure through the implementation of low pass mechanical structures. The transducer, which is capable of operating at ultra-high temperatures and in harsh environments, consists of a static piezoresistive pressure transducer, which measures the large pressures on the order of 200psi and greater, and an ultrasensitive, dynamic piezoresistive pressure transducer which captures small, high frequency pressure oscillations on the order of a few psi. The heightened sensitivity in high pressure environments is achieved by filtering the measured pressure of high frequency content through an innovative low pass mechanical filter structure. The large static pressures passed by the low-pass mechanical filter structures are routed to the backside of the dynamic pressure sensor, which results in both the front and the back of the dynamic sensor being exposed to the large pressures within the environment. Therefore, the large static pressures cancel out, and the dynamic sensor only senses the low magnitude, high frequency pressure perturbations. This dual sensor, static-dynamic pressure transducer reproduces pressure signals with sensitivity far higher than any single high pressure transducer available today. The dual sensor, static-dynamic transducer meets the pressure sensing specification of numerous applications including, but not limited to, the following: the optimization of turbine operation, turbine design and testing, the detection of the onset of rotating stall and surge in turbine compressors, and combustion instabilities. This paper describes a six element model of the static-dynamic transducer’s low-pass mechanical filtering structures. The paradigm is derived from first-principles of fluid motion in acoustic ducts with viscous dissipation. A dynamic pressure source is used to verify the model and its operation. Finally, a transfer function characterization of a fully operational static-dynamic pressure transducer over a wide bandwidth is presented. Based upon the analytical and experimental results, the static-dynamic pressure transducer will make it possible for turbine users and manufacturers to implement ultra-sensitive pressure monitoring to reduce compressor and combustion instabilities [1] [2].
Adsorption and Oxidation Species on a-SiC:H Surface by Corona Exposure (1990)

The hydrogenated amorphous silicon (a-Si:H) photoreceptor used in printing systems produces image blurring at high temperature and humidity. We have examined the problem using infrared reflective-absorption spectroscopy (IR-RAS). We exposed a hydrogenated amorphous silicon carbide (a-SiC:H) film to a corona discharge at 35°C and 80% RH. Adsorption and oxidation species on the film can be detected and determined by IR-RAS. NO 3 - , CO 3 2- , Si-OH, H-OH, and Si-O-Si are detectable in the a-SiC:H film. The amount of them increases with corona exposure time. We clarify that these specids in a-SiC:H film cause the contact angles to decrease with the corona exposure time and blur the image.
An integrated resonator-based thermal compensation for Vibrating Beam Accelerometers (2012)

Although the Vibrating Beam Accelerometer (VBA) shows excellent performances (resolution <; 1 μg), its bias drift over temperature and temperature gradient is a main performance limitation. Numerical compensation can be performed with a temperature sensor in the accelerometer package, but this approach shows two drawbacks for fast measurements: first, there is a time delay between the temperature of the vibrating beam and the temperature of the package, and second the noise level is very high. In order to overcome these limitations, a new structure comprising a second resonator in torsional mode placed in the middle of the vibrating beam was designed. It shows good sensitivity and linearity over temperature and senses the instantaneous temperature of the vibrating beam with low noise, allowing a better compensation of thermal drifts. A new accelerometer with resonator-based thermal compensation was designed, realized and tested. Experimental results confirm the excellence of the torsional mode resonator used as a temperature sensor.
Microstructure actuation and gas sensing by the Knudsen thermal force (2015)

The generation of forces and moments on structures immersed in rarefied non-isothermal gas flows has received limited practical implementation since first being discovered over a century ago. The formation of significant thermal stresses requires both large thermal gradients and characteristic dimensions which are comparable to the gas molecular mean free path. For macroscopic geometries, this necessitates impractically high temperatures and very low pressures. At the microscale, however, these conditions are easily achieved, allowing the effects to be exploited, namely, for gas-property sensing and microstructure actuation. In this letter, we introduce and experimentally evaluate performance of a microelectromechanical in-plane Knudsen radiometric actuator, a self-contained device having Knudsen thermal force generation, sensing, and tuning mechanisms integrated onto the same platform. Sensitivity to ambient pressure, temperature gradient, as well as gas composition is demonstrated. Results are presented in terms of a non-dimensional force coefficient, allowing measurements to be directly compared to the previous experimental and computational data on out-of-plane cantilevered configurations.
Cytokine-Based Log-Scale Expansion of Functional Murine Dendritic Cells (2009)

The concept of DC expansion should make a significant contribution to the progress of DC-based immunotherapy.
New Methodology for Combined Simulation of Delta-I Noise Interaction With Interconnect Noise for Wide, On-Chip Data-Buses Using Lossy Transmission-Line Power-Blocks (2006)

A new technique is described for reducing computational complexity and improving accuracy of combined power distribution and interconnect noise prediction for wide, on-chip data-buses. The methodology uses lossy transmission-line power-blocks with frequency-dependent properties needed for the multigigahertz clock frequencies. The interaction between delta-I noise, common-mode noise, and crosstalk and their effect on timing is illustrated with simulations using representative driver and receiver circuits and on-chip interconnections.
Development of high performance concrete using industrial waste materials and nano-silica (2020)

There is a concerted effort worldwide to use environment-friendly binders in the establishment of civil infrastructure. The use of such materials, as a partial or total replacement of Portland cement, leads to technical, economic and environmental benefits. The reported study was conducted to develop high performance concrete (HPC) utilizing two industrial waste materials (IWMs), namely cement kiln dust (CKD) and electric arc furnace dust (EAFD), in conjunction with nano-silica (NS). The mechanical properties, morphology and durability characteristics of the developed HPC were evaluated. The strength of concrete decreased with increasing quantity of both CKD and EAFD that were used as a partial replacement of cement. However, an increase in strength was noted due to the incorporation of NS. The chloride permeability significantly decreased due to the incorporation of 5% NS in CKD and EAFD cement concretes. A dense and uniform microstructure, with a compact interfacial transition zone, was noted in concrete specimens incorporating Portland cement, IWMs and NS. The incorporation of IWMs along with NS results in the following benefits: environmental (decreased greenhouse gas emission and solution of the disposal problem associated with the IWMs), economic (decreased overall cost of concrete) and technical (enhanced service life of structures).
New 2D TeraHertz sensor based on arrayed thermal converter coupled to infrared temperature flux measurement (2010)

We defined the Terahertz Thermal Converters concept to design a device for 2D THz imaging. Coupled to an infrared temperature flux sensors, we demonstrate an absolute auto-calibrated Terahertz power-meter (>10μW) enable to characterized THz spatial beam profile of CW sources.
Visualizing frequent patterns in large multivariate time series (2011)

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.
Graphite-Diamond Transformation Induced by High-Intensity KrF Laser Target Irradiation (1998)

Explosives or explosive-accelerated plane flyers can pressurize the examined material up to hundreds kilobars, but it is heated significantly by a shock wave. If a phase transition occurs during the sample loading, a high post-shock temperature will influence a new phase 1 . Laser irradiation of targets may produce ablation pressures in a multi-megabar range with temperature control by means of time profiling of a laser pulse or using an appropriate target design. This offers new opportunities in investigations of equation of state and phase transformations in compressed materials, in particular, diamond production by laser compression of graphite targets 2 .
Syndrome based check node processing of high order NB-LDPC decoders (2015)

Non-binary low-density parity-check codes have superior communications performance compared to their binary counterparts. However, to be an option for future standards, efficient hardware architectures must be developed. State-of-the-art decoding algorithms lead to architectures suffering from low throughput and high latency. The check node function accounts for the largest part of the decoders overall complexity. In this paper a new, hardware aware check node algorithm is proposed. It has state-of-the-art communications performance while reducing the decoding complexity. Moreover the presented algorithm allows for partially or even fully parallel processing of the check node operations which is not applicable with currently used algorithms. It is therefore an excellent candidate for future high throughput hardware implementations.
Model to Understand Neural Interplay Involving Proprioceptive Adaptation in Lower Limb During Dual Task Paradigm (2018)

The aim of this paper is to understand how lower limb proprioception effects a dual task paradigm, while maintaining postural stability. A dual task paradigm involving collision avoidance and maintaining stability in single limb stance have been designed as a Virtual Reality game, using Kinect and played with `Google Virtual Reality' device. Five healthy subjects played the game for four consecutive days. Neural interplay and effect of proprioceptive adaptation were modeled in terms of muscle co-activation, sway of Centre of Mass, collision rate and a smoothness function of knee joint trajectory. Findings from this study would help to better understand the dual task paradigm for lower limb, task prioritization and effect of proprioceptive adaptation on motor task.
Performance and Stability of Carbonate Fuel Cell Electrodes (2010)

Polarization studies and post analysis have been carried out for characterizing the electrochemical performance and processes affecting life and stability of carbonate fuel cell electrodes. Based on this understanding optimized electrodes design and new electrolyte composition were developed to improve cell performance and achieve the useful life of &gt;5 years. The anode performance and stability were improved by developing an optimized microstructure with better electrolyte retention capabilities. Tests with an advanced electrolyte composition showed 25 mV improvement at 650°C and more than 45 mV at low temperatures compared to baseline. The NiO dissolution in advanced electrolyte is reduced by &gt;60% compared to baseline electrolyte. Anode and cathode electrodes showed stable mechanical strength and stable electrochemical performance.
A novel startup scheme of stator-flux-oriented vector-controlled induction motor drive without torque jerk (2003)

This paper proposes a novel zero-speed startup scheme of a stator-flux-oriented speed-sensorless vector-controlled induction motor drive that does not generate any torque jerk. A programmable cascaded three-stage low-pass filter (LPF) method is used for flux vector estimation. Usually, a torque jerk is generated at the vector control transition due to time delay in developing the stator flux by the three-stage LPF. At standstill condition, an algorithm is derived that calculates the stator flux using only the stator currents. A feedforward control strategy of the stator flux is developed to eliminate the torque jerk during transition from the standstill mode to the vector control mode. The performance of the newly developed startup scheme has been verified by simulation and then experimentally on a 3-hp induction motor drive, where the control was implemented by a 32-bit TMS320C30-type digital signal processor. The performance of the drive was found to be excellent in both simulation and experiment.