gebhart/patrepeval huggingface dataset:
Bone ingrowth into porous silicon nitride (2009)

Achieving solid skeletal attachment is a requirement for the clinical success of orthopedic implants. Porous or roughened surfaces and coatings have been developed and used with mixed success to achieve attachment due to bone ingrowth. Silicon nitride is a high performance ceramic whose strength, imaging properties, and biocompatibility make it a candidate material for orthopedic implants. A porous form of silicon nitride, cancellous-structured ceramic (CSC), has been developed. CSC is a nonresorbable, partially radiolucent porous structure that can be bonded to orthopedic implants made of silicon nitride to facilitate skeletal attachment. The purpose of this study was to quantify the extent and rate of bone ingrowth into CSC in a large animal model. Cylindrical implants were placed bilaterally using staged surgeries in the medial femoral condyle of six sheep. Condyles were retrieved after 3 and 6 months in situ and prepared for examination of bone growth under SEM. Bone grew into CSC to extents and at rates similar to those reported for other titanium porous surfaces in studies involving large animals and postmortem retrievals in humans. Bone ingrowth was observed at depths of penetration greater than 3 mm in some implants after only 12 weeks in situ. Bone ingrowth into CSC is a viable method for achieving skeletal attachment.
Enhancement of La0.6Sr0.4Co0.2Fe0.8O3-δ durability and surface electrocatalytic activity by La0.85Sr0.15MnO3±δ investigated using a new test electrode platform (2011)

A carefully designed test cell platform with a new electrode structure is utilized to determine the intrinsic surface catalytic properties of an electrode. With this design, the electrocatalytic activity and stability of an La0.6Sr0.4Co0.2Fe0.8O3−δ (LSCF) cathode is enhanced by a dense thin La0.85Sr0.15MnO3±δ (LSM) coating, suggesting that an efficient electrode architecture has been demonstrated that can make effective use of desirable properties of two different materials: fast ionic and electronic transport in the backbone (LSCF) and facile surface kinetics on the thin-film coating (LSM). Theoretical analyses suggest that the enhanced electrocatalytic activity of LSM-coated LSCF is attributed possibly to surface activation under cathodic polarization due to the promotion of oxygen adsorption and/or dissociation by the surface layer and the dramatically increased oxygen vacancy population in the surface film. Further, the observed time-dependent activation over a few hundreds of hours and durability are likely associated with the formation of a favorable hybrid surface phase intermediate between LSM and LSCF. This efficient electrode architecture was successfully applied to the state-of-the-art LSCF-based cathodes by a simple solution infiltration process, achieving reduced interfacial resistance and improved stability under fuel cell operating conditions.
Suppression of mammalian 5' splice-site defects by U1 small nuclear RNAs from a distance. (1994)

One of the earliest events in the process of intron removal from mRNA precursors is the establishment of a base-pairing interaction between U1 small nuclear (sn) RNA and the 5' splice site. Mutations at the 5' splice site that prevent splicing can often be suppressed by coexpression of U1 snRNAs with compensatory changes, but in yeast, accurate splicing is not restored when the universally conserved first intron base is changed. In our mammalian system as well, such a mutation could not be suppressed, but the complementary U1 caused aberrant splicing 12 bases downstream. This result is reminiscent of observations in yeast that aberrant 5' splice sites can be activated by U1 snRNA from a distance. Using a rapid, qualitative protein expression assay, we provide evidence that 5' splice-site mutations can be suppressed in mammalian cells by U1 snRNAs with complementarity to a range of sequences upstream or downstream of the site. Our approach uncouples in vivo the commitment-activation step of mammalian splicing from the process of 5' splice-site definition and as such will facilitate the genetic characterization of both.
A low power 170 MHz discrete-time analog FIR filter (2002)

A 170 MHz analog FIR filter operating from a single 3.3 V supply is described. The design has been fabricated in the HP 1.2 /spl mu/m CMOS process and has an area of 2.35 mm by 1.97 mm including bonding pads. This 9-tap filter dissipates 70 mW when operating at 170 MHz. The multipliers are implemented using MDAC's with 6-bit resolution.
Effects of Prophylactic Antibiotic Therapy With Mezlocillin Plus Sulbactam on the Incidence and Height of Fever After Severe Acute Ischemic Stroke (2008)

In patients with acute severe stroke, prophylactic administration of mezlocillin plus sulbactam over 4 days decreases body temperature, lowers the rate of infection, and may be associated with a better clinical outcome.
Wood Deterioration and Preservation (2003)

Wood-degrading fungi, insects, bacteria and marine borers cause damage resulting in billions of dollars being spent on repair and replacement of wooden structures every year. The equivalent of one-tenth of the forest products produced every year is estimated to be destroyed by these agents. Although wood degradation results in an enormous waste of resources, without wood degrading organisms our world would be buried under cellulose and lignin debris, as these organisms are among the few that efficiently recycle lignocellulosic carbon. Further, in recent years some of the mechanisms employed by microorganisms to degrade wood have been used in bioindustrial processes to benefit humans. For example, fungal-based oxidative reagents are being examined in biopulping and biobleaching processes, and microbial enzymes from wood degrading organisms have been used in systems ranging from wastewater cleanup to the production of fuels from biomass. Wood degrading agents therefore cause many problems, but also greatly benefit
Deep Active Learning for Civil Infrastructure Defect Detection and Classification (2017)

Automatic detection and classification of defects in infrastructure surface images can largely boost its maintenance efficiency. Given enough labeled images, various supervised learning methods have been investigated for this task, including decision trees and support vector machines in previous studies, and deep neural networks more recently. However, in real world applications, labels are harder to obtain than images, due to the limited labeling resources (i.e., experts). Thus we propose a deep active learning system to maximize the performance. A deep residual network is firstly designed for defect detection and classification in an image. Following our active learning strategy, this network is trained as soon as an initial batch of labeled images becomes available. It is then used to select a most informative subset of new images and query labels from experts to retrain the network. Experiments demonstrate more efficient performance improvements of our method than baselines, achieving 87.5% detection accuracy.
Stacked Kernel Network (2017)

Kernel methods are powerful tools to capture nonlinear patterns behind data. They implicitly learn high (even infinite) dimensional nonlinear features in the Reproducing Kernel Hilbert Space (RKHS) while making the computation tractable by leveraging the kernel trick. Classic kernel methods learn a single layer of nonlinear features, whose representational power may be limited. Motivated by recent success of deep neural networks (DNNs) that learn multi-layer hierarchical representations, we propose a Stacked Kernel Network (SKN) that learns a hierarchy of RKHS-based nonlinear features. SKN interleaves several layers of nonlinear transformations (from a linear space to a RKHS) and linear transformations (from a RKHS to a linear space). Similar to DNNs, a SKN is composed of multiple layers of hidden units, but each parameterized by a RKHS function rather than a finite-dimensional vector. We propose three ways to represent the RKHS functions in SKN: (1)nonparametric representation, (2)parametric representation and (3)random Fourier feature representation. Furthermore, we expand SKN into CNN architecture called Stacked Kernel Convolutional Network (SKCN). SKCN learning a hierarchy of RKHS-based nonlinear features by convolutional operation with each filter also parameterized by a RKHS function rather than a finite-dimensional matrix in CNN, which is suitable for image inputs. Experiments on various datasets demonstrate the effectiveness of SKN and SKCN, which outperform the competitive methods.
The optical projection display subsystem (1971)

article The optical projection display subsystem Share on Author: Ray Mazur View Profile Authors Info & Claims ACM SIGGRAPH Computer GraphicsVolume 5Issue 2Summer 1971 pp 21–23https://doi.org/10.1145/988626.988627Online:01 July 1971Publication History 0citation128DownloadsMetricsTotal Citations0Total Downloads128Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
CP-ORTHO (2017)

Extracting patterns and deriving insights from spatio-temporal data finds many target applications in various domains, such as in urban planning and computational sustainability. Due to their inherent capability of simultaneously modeling the spatial and temporal aspects of multiple instances, tensors have been successfully used to analyze such spatio-temporal data. However, standard tensor factorization approaches often result in components that are highly overlapping, which hinders the practitioner's ability to interpret them without advanced domain knowledge. In this work, we tackle this challenge by proposing a tensor factorization framework, called CP-ORTHO, to discover distinct and easily-interpretable patterns from multi-modal, spatio-temporal data. We evaluate our approach on real data reflecting taxi drop-off activity. CP-ORTHO provides more distinct and interpretable patterns than prior art, as measured via relevant quantitative metrics, without compromising the solution's accuracy. We observe that CP-ORTHO is fast, in that it achieves this result in 5x less time than the most accurate competing approach.
Big data fueled process management of supply risks: sensing, prediction, evaluation and mitigation (2014)

Supplier risks jeopardize on-time or complete delivery of supply in a supply chain. Traditionally, a company can merely do an ex-post evaluation of a supplier's performance, and handles emergencies in a reactive rather than a proactive way. We propose an agile process management framework to monitor and manage supply risks. The innovation is two fold - Firstly, a business process is established to make sure that the right data, the right insights, and the right decision-makers are in place at the right time. Secondly, we install a big data analytics component, a simulation component and an optimization component into the business process. The big data analytics component senses and predicts supply disruptions with internally (operational) and external (environmental) data. The simulation component supports risk evaluation to convert predicted risk severity to key performance indices (KPIs) such as cost and stockout percentage. The optimization component assists the risk-hedging decision-making.
Production of photonic universal quantum gates enhanced by machine learning (2019)

We introduce photonic architectures for universal quantum computation. The first step is to produce a resource state which is a superposition of the first four Fock states with a probability greater than or equal to ${10}^{\ensuremath{-}2}$, an increase by a factor of ${10}^{4}$ over standard sequential photon-subtraction techniques. The resource state is produced with near-perfect fidelity from a quantum gadget that uses displaced squeezed vacuum states, interferometers, and photon-number-resolving detectors. The parameters of this gadget are trained using machine learning algorithms for variational circuits. We discuss in detail various aspects of the non-Gaussian state preparation resulting from the numerical experiments. We then propose a notion of resource farms where these gadgets are stacked in parallel, to increase the success probability further. We find a trade-off between the success probability of the farm, the error tolerance, and the number of gadgets. Using the resource states in conventional gate teleportation techniques, we can then implement weak tunable cubic phase gates. The numerical tools that have been developed could potentially be useful for other applications in photonics as well.
A Compact CPW-fed UWB-MIMO Antenna with Isolation Enhancement (2017)

A new design of coplanar waveguide (CPW)-fed ultra-wideband (UWB) multiple-input-multiple-output (MIMO) antenna with miniaturized size and isolation enhancement is proposed in this letter. The presented antenna is consisted of two symmetrical single UWB-antenna elements and it is printed on a 1.6 mm-thick FR-4 substrate with a miniaturized size of 18×22 mm2 which is smaller than most tow-port UWB-MIMO antennas. This antenna has good radiation characteristics and isolation (S11 < -10 dB, S12 < -17.4 dB) in the frequency range of 3.1 GHz-12.7 GHz, and exhibits good impedance matching. By etching a 0.1 mm wide rectangular slit on the ground, a high degree of isolation (S12<20 dB) between the two antenna elements was realized over frequency band of 4 GHz-12.7 GHz. The simulation and experimental results show that this antenna is a good choice for UWB-MIMO systems.
Reduction of Crosstalk by Hole-Walled Multi-Core Fibers (2012)

Hole-walled multi-core fibers (HW-MCFs) are proposed to reduce the crosstalk with arrayed holes walling. We have fabricated a HW-MCF with an effective area larger than 130 μm2 and crosstalk of less than −30 dB/100 km.
Effect of Microcrystalline Cellulose Grade and Process Variables on Pellets Prepared by Extrusion–Spheronization (2002)

This study evaluated the effects of spheronizer load and speed on the size, circularity, microporosity, compressibility, and friability of pellets prepared by extrusion-spheronization of wet microcrystalline cellulose (MCC) masses with a water content shown by mixer torque rheometry to ensure maximum consistency. Two MCC grades with different mean particle size were used. Both gave pellets with good particle size, sphericity, and compressibility, under a wide range of spheronization conditions. Modification of pellet properties of interest (including size and porosity) was possible by adjustment of spheronization conditions and MCC grade; in particular, pellet porosity was greater with MCC of larger particle size.
Checkpointing and the modeling of program execution time (1995)

Checkpointing is a commonly used technique for reducing the execution time of longrunning programs in the presence of failures.With checkpointing, the status of the program under execution is saved intermittently. Upon the occurrence of a failure, the program execution is restarted from the most recent checkpoint rather than from the beginning. Due to its overhead, checkpointing may not always be beneficial, and, if it is, an optimal checkpointing strategy may be determined so as to minimize the expected execution time or to maximize the probability of the execution time not exceeding a critical limit. In this chapter we consider several models of checkpointing and recovery in a program in order to derive the distribution of program execution time or its expectation. We make important extensions to some existing results as well as introduce and analyze new models. It is shown that the expected execution time increases linearly (exponentially) with the processing requirement in the presence (absence) of checkpointing. Furthermore, these models can be used to compare different checkpointing strategies and to determine an optimal interval between checkpoints. Throughout the chapter, and whenever appropriate, we attempt to relate to other work in the existing literature.
Update on melatonin receptors: IUPHAR Review 20 (2016)

Melatonin receptors are seven transmembrane-spanning proteins belonging to the GPCR superfamily. In mammals, two melatonin receptor subtypes exist - MT1 and MT2 - encoded by the MTNR1A and MTNR1B genes respectively. The current review provides an update on melatonin receptors by the corresponding subcommittee of the International Union of Basic and Clinical Pharmacology. We will highlight recent developments of melatonin receptor ligands, including radioligands, and give an update on the latest phenotyping results of melatonin receptor knockout mice. The current status and perspectives of the structure of melatonin receptor will be summarized. The physiological importance of melatonin receptor dimers and biologically important and type 2 diabetes-associated genetic variants of melatonin receptors will be discussed. The role of melatonin receptors in physiology and disease will be further exemplified by their functions in the immune system and the CNS. Finally, antioxidant and free radical scavenger properties of melatonin and its relation to melatonin receptors will be critically addressed.
An Advanced Statistical Method for Wind Power Forecasting (2007)

This paper presents an advanced statistical method for wind power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of wind speed and direction interpolated at the site of the wind farm. A self-organized map is trained to classify the forecasted local wind speed provided by the meteorological services. A unique feature of the method is that following a preliminary wind power prediction, it provides an estimation of the quality of the meteorological forecasts that is subsequently used to improve predictions. The proposed method is suitable for operational planning of power systems with increased wind power penetration, i.e., forecasting horizon of 48 h ahead and for wind farm operators trading in electricity markets. Application of the forecasting method on the power production of an actual wind farm shows the validity of the method
Mixed‐effects models in psychophysiology (2000)

The current methodological policy in Psychophysiology stipulates that repeated-measures designs be analyzed using either multivariate analysis of variance (ANOVA) or repeated-measures ANOVA with the Greenhouse-Geisser or Huynh-Feldt correction. Both techniques lead to appropriate type I error probabilities under general assumptions about the variance-covariance matrix of the data. This report introduces mixed-effects models as an alternative procedure for the analysis of repeated-measures data in Psychophysiology. Mixed-effects models have many advantages over the traditional methods: They handle missing data more effectively and are more efficient, parsimonious, and flexible. We described mixed-effects modeling and illustrated its applicability with a simple example.
Customer Segmentation Using Mobile Phone Usage Data to Reveal Finance Application User’s Behavior (2021)

Data-driven customer segmentation is one of the most commonly used techniques followed by FinTech companies for its marketing, focus strategies, and customer acquisition. Understanding the Customer behavioral data can increase the customer acquisition rate, reduce cost and help to target the right customers. The promotion of Cashless payments and fully digitized bank processes help people to track their finances via phones. In day-to-day life, the amount of time users spend on mobile phones is very high due to the increased dependency on mobile banking solutions. In this paper, we propose a customer segmentation model using mobile application usage data of a customer to understand the user interest and financial needs.
Optimizing the Weight of an Aircraft Power Supply System through a +/- 270 VDC Main Voltage (2012)

This paper deals with an onboard power supply of +/- 270 V DC (HVDC, high voltage DC). Conventional aircraft grids have a main voltage level of 115 V AC with a variable frequency of 360…800 Hz. Changing to a HVDC-grid has a lot of advantages. Higher voltage means lower cable weight. Furthermore, the electrical converter architecture can be optimized. Especially the converters inside the loads can be build much lighter when using a +/- 270 V DC supply. This paper gives an overview of the electrical architecture on a modern short- and midrange aircraft and the effect of a +/- 270 V DC power grid thus focusing on the converter architecture in a modern aircraft. Streszczenie. Praca dotyczy źrodla zasilania w samolotach o parametrach +/-270 V DC (HVDC, high voltage DC- wysokie napiecie DC). Konwencjonalne samolotowe sieci zasilające mają napiecie zasilania o poziomie 115 V AC ze zmienną czestotliwością 360…800 Hz. Zmiana na siec HVDC ma wiele zalet. Wieksze napiecie oznacza mniejszą grubośc kabli. Ponadto struktura przeksztaltnika elektronicznego jest zoptymalizowana. Szczegolnie przeksztaltniki w środku obciązenia są budowane jako znacznie lzejsze gdy uzywa sie zasilania +/-270 V DC. Praca przedstawia opis elektrycznej architektury nowoczesnych malych i średnich samolotow oraz efekty źrodla zasilania +/- 270 V DC w ten sposob skupiając sie na architekturze przeksztaltnikow w nowoczesnych samolotach. (Optymalizacja wagi systemow zasilania samolotow poprzez zastosowanie napiecia typu +/- 270 V DC).