gebhart/patrepeval huggingface dataset:
An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization (2021)

Nowadays, there are various optimization problems that exact mathematical methods are not applicable. Metaheuristics are considered as efficient approaches for finding the solutions. Yet there are many real-world problems that consist of different properties. For instance, financial portfolio optimization may contain many dimensions for different sets of assets, which suggests the need of a more adaptive metaheuristic method for tackling such problems. However, few existing metaheuristics can achieve robust performance across these variable problems even though they may obtain impressive results in specific benchmark problems. In this paper, a metaheuristic named the Adaptive Multi-Population Optimization (AMPO) is proposed for continuous optimization. The algorithm hybridizes yet modifies several useful operations like mutation and memory retention from evolutionary algorithms and swarm intelligence (SI) techniques in a multi-population manner. Furthermore, the diverse control on multiple populations, solution cloning and reset operation are designed. Compared with other metaheuristics, the AMPO can attain an adaptive balance between the capabilities of exploration and exploitation for various optimization problems. To demonstrate its effectiveness, the AMPO is evaluated on 28 well-known benchmark functions. Also, the parameter sensitivity analysis and search behavior study are conducted. Finally, the AMPO is validated on its applicability through a portfolio optimization problem as a challenging example of real-world applications. The benchmark results show that the AMPO achieves a better performance than those of nine state-of-the-art metaheuristics including the IEEE CEC winning algorithms, recent SI and multi-population/hybrid metaheuristics. Besides, the AMPO can consistently produce a good performance in portfolio optimization.
Vision Guided Motion Control of a Biomimetic Quadruped Robot: RoboCat (2011)

This paper presents the vision system and visual processing for a biomimetic elastic cable-driven quadruped robot—RoboCat. The paper is geared towards selection of appropriate visual servoing techniques for RoboCat such as vision algorithms, high-level cognition algorithms, software architecture and hardware implementation. The system uses two video cameras for stereo vision data acquisition and a SUMIT-ism form factor embedded computer for vision data processing. The vision system employs a color based target recognition algorithm, a neural network based shape recognition algorithm and a Color and Zernike moment based face detection algorithm. The paper presents the vision algorithms, vision guidance and motion tracking algorithms, rule-based decision making algorithms and the open architecture of the autonomous vision tracking system. Experimental testing results (including video clips) are also presented.
Crystal structure of trehalose‐6‐phosphate phosphatase–related protein: Biochemical and biological implications (2006)

We report here the crystal structure of a trehalose-6-phosphate phosphatase-related protein (T6PP) from Thermoplasma acidophilum, TA1209, determined by the dual-wavelength anomalous diffraction (DAD) method. T6PP is a member of the haloacid dehalogenase (HAD) superfamily with significant sequence homology with trehalose-6-phosphate phosphatase, phosphoserine phosphatase, P-type ATPases and other members of the family. T6PP possesses a core domain of known alpha/beta-hydrolase fold, characteristic of the HAD family, and a cap domain, with a tertiary fold consisting of a four-stranded beta-sheet with two alpha-helices on one side of the sheet. An active-site magnesium ion and a glycerol molecule bound at the interface between the two domains provide insight into the mode of substrate binding by T6PP. A trehalose-6-phosphate molecule modeled into a cage formed by the two domains makes favorable interactions with the protein molecule. We have confirmed that T6PP is a trehalose phosphatase from amino acid sequence, three-dimensional structure, and biochemical assays.
A Conceptual Model for Determining Factors Influencing Online Purchasing Behavior (2017)

The world over, with the increase use of the internet, online commerce is also witnessing a huge jump. It is mainly because online retailing offers a lot of advantages for the consumer in terms of convenience, options, anytime, easy accessibility and so. However, it does suffer from some disadvantages in the form of say the physical absence of the product, security risks, privacy concern, non-delivery risks and so on. But still then, online retailing is growing at an unstoppable speed. And with this understanding of factors influencing online purchase behavior becomes more and more serious matter. Â  Researchers have been exploring online consumer behavior for many years and two widely accepted views stand out in the e-commerce literatures: consumer-oriented and technology oriented view. The consumer oriented view places focus on consumersâ€™ salient beliefs about online shopping, whereas the technology-oriented view studies the impact of website design and usability on consumersâ€™ behavior. Most of the researches are based on Technology Acceptance Model (TAM) and the Theory of Planned Behavior to understand the behavior of online consumers. Â  However, India, a country where internet penetration is still low, becomes a different case altogether. Though the number of people using the internet is increasing, e-commerce is not witnessing a proportionate increase. The question in the case of Indian customers is of Adoption of online retailing i.e. what are the factors that influences consumers to shop or not to shop in Internet.Â  Thus, in that sense it can be seen as just an issue of New Product Adoption process, considering online retailing as just a new product for offering services to the consumers.Â  Â  This study is done to incorporate the product adoption process model with the research models of TAM/TPB and others, to provide a comprehensive roadmap for further studies. The model â€˜Online Shopping Adoption Modelâ€™, suggested here incorporates the major findings of studies done on consumer behavior in India and abroad.
Integration of Engineering & Geomechanics Lead to a Successful Sidetrack, Drilling, and Completion (2022)

Abstract A landing section of a well in newly developed area of a field had three challenges: the well trajectory cuts through shale intervals at high inclination; the well plan was limited to a certain trajectory window where weak bedding planes failure could not be ruled out by changing the plan; the previous section's casing was short landed due to wellbore instability challenges. An effort to initiate a sidetrack twice was not successful due to wellbore collapse, therefore a new sidetrack depth had to be identified. Geomechanics group collaborated with Drilling Engineering, and Drilling fluid services to successfully initiate the sidetrack, drill the well and land the liner casing safely to bottom. A geomechanics model was built based on wireline logging data from offset wells. First, stress azimuth was constrained from oriented multi arm caliper of multiple wells around the field. Then, rock properties were derived from acoustic logging data, and the model was calibrated against drilling events and caliper enlargements. Finally, Geomechanical and petrophysical parameters were utilized to design fluids parameters and additives. A starting mud weight window was defined, and a sidetrack point was selected in strong rock interval. The well was monitored in real time where Logging while drilling, acoustic caliper, and azimuthal density image were utilized for breakouts onset monitoring. Furthermore, drilling parameters were monitored for hole cleaning and mud weight control. The stress azimuth around the field demonstrates a rotation of 15° from the north toward the east which introduced uncertainty in defining MW in the well trajectory that could be affected by rotation. The sidetrack was successfully initiated as expected, the hole was drilled to bottom, and liner landed successfully. However, a significant increase of MW from its starting value was required, and the model was subsequently adjusted based on LWD data. Maintenance of high concentration of bridging while continuously nourishing the mud helped in stabilizing the wellbore. Unlike previous sidetracks, non-productive time was eliminated with significant cost reduction.
Occult B infection in the Brazilian northeastern region: a preliminary report (2008)

A sentinel study on viral hepatitis is currently being carried out in the village of Cavunge in a semiarid rural region of the state of Bahia, northeastern Brazil. This study has identified individuals in whom anti-HBc IgG was the only serological marker for hepatitis B virus (HBV). This serological pattern may constitute evidence of occult HBV infection. This study Investigated the possibility of occult hepatitis B virus infection in individuals in a rural community who tested positive for anti-HBc IgG alone. A cross-sectional population-based study. ELISA III was performed on serum samples to test for serological viral markers, and ultrasensitive PCR (US-PCR) was used to assess viremia. Among the 1,536 serum samples, 3.6% (n=55) were positive for anti-HBc alone. Four years after this first serological survey, 31 of those 55 individuals (56.3%) were retested, and 11 (35.5%) remained anti-HBc positive alone. Two of these 31 (6.5%) were HBV-DNA positive based on US-PCR, with normal aminotransferase levels in both cases. Cases of occult hepatitis B infection were identified in this semiarid rural community of northeastern Brazil, where endemicity of HBV is moderate.
SFDR considerations for current steering high-speed digital to analog converters (2012)

In this paper the spurious free dynamic range (SFDR) of a current steering digital-to-analog converter (DAC) is related to the process parameters used for its implementation. It is shown that the realization of such DACs in advanced processes provides power and area reduction combined with faster signaling. However, it is very challenging to improve the SFDR at a certain given output swing and sampling frequency. It is demonstrated that the SFDR can be doubled by using an optimized cascode switch. It is also concluded that compared to MOSFET based approaches higher sampling frequencies can be achieved by bipolar transistor based DACs if the SFDR and transition frequency are fixed. The reason is the higher Early voltage of bipolar transistors.
Segmentation of 3D image of a rock sample supervised by 2D mineralogical image (2015)

We present an approach for 2D-to-3D mineralogical information propagation and preliminary results of its implementation. An X-ray microtomography (microCT) scanner is used to obtain a 3D microstructural image of the sample. A scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) detector provides a supervising 2D mineral map of a rock sample cross section. The 2D and 3D images are then registered using a surface-based algorithm, naturally taking into account specifics of said data. The overlapping area of the images is then used as a training set for supervised mineralogical segmentation of the full 3D sample.
Diagnostic erroné : préjudice de perte de chance de négocier une réduction du prix de vente (2020)

International audience
Context-Aware Photography Learning for Smart Mobile Devices (2015)

In this work we have developed a photography model based on machine learning which can assist a user in capturing high quality photographs. As scene composition and camera parameters play a vital role in aesthetics of a captured image, the proposed method addresses the problem of learning photographic composition and camera parameters. Further, we observe that context is an important factor from a photography perspective, we therefore augment the learning with associated contextual information. The proposed method utilizes publicly available photographs along with social media cues and associated metainformation in photography learning. We define context features based on factors such as time, geolocation, environmental conditions and type of image, which have an impact on photography. We also propose the idea of computing the photographic composition basis, eigenrules and baserules , to support our composition learning. The proposed system can be used to provide feedback to the user regarding scene composition and camera parameters while the scene is being captured. It can also recommend position in the frame where people should stand for better composition. Moreover, it also provides camera motion guidance for pan, tilt and zoom to the user for improving scene composition.
Systematic evaluation and validation of reference and library selection methods for deconvolution of cord blood DNA methylation data (2019)

Abstract Background Umbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age appropriate cell-specific DNA-methylation profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450K and 850K EPIC arrays. These datasets are biologically and technically different, and currently there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution. Results We first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n=191) with matched FACS measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes . Conclusion These results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.
Relationship between laser-induced gratings and vibrational properties of Eu-doped glasses (1987)

Four-wave-mixing techniques have been shown previously to produce permanent refractive index gratings in Eu-doped glasses by resonant excitation of the ${\mathrm{Eu}}^{3+}$ ions. A variety of glass hosts were studied and it was found that permanent holographic gratings could be established in some of these but not in others. We report here an extension of our previous work which includes investigations of new materials, attempts to form gratings with different excitation wavelengths, and a comparison of the Raman and resonant Raman spectra of glasses which do and do not exhibit permanent holographic gratings. It was found that direct excitation into the $^{5}\mathrm{D}_{0}$ level did not produce permanent gratings. It was also found that glasses which do exhibit permanent gratings have high-frequency vibrational modes which couple strongly to the ${\mathrm{Eu}}^{3+}$ ions. These results help to verify the model proposed previously to explain the origin of the holographic grating in terms of structural changes caused by thermal effects arising from radiationless relaxation through high-frequency local modes.
Comparison of long term results after Ho:YAG and diode laser treatment of hyperplastic inferior nasal turbinates (2007)

Both Ho:YAG- and diode laser treatment, which are performed as an outpatient procedure under local anesthesia, show promising long-term results. It has therefore, developed to become a time and cost-effective treatment modality in endonasal laser surgery at our institution. While short-term differences concerning the healing process after Ho:YAG- compared to diode-laser treatment could be ascertained according to the subjective and objective evaluation, no significant long-term differences between the two investigated groups could be observed.
The dose‐related effects of paracetamol on hyperalgesia and nociception in the rat (1996)

1. We have studied the effects of 3 low doses of paracetamol (25, 50 and 100 mg kg-1 p.o.) on inflammatory hyperalgesia, inflammatory oedema, and nociceptive thresholds in rats. 2. At the lower dose (25 mg kg-1), paracetamol reduces only central hyperalgesia. 3. At the doses of 50 and 100 mg kg-1, paracetamol reduces also peripheral hyperalgesia; moreover, it enhances nociceptive thresholds to a mechanical stimulus in the non-inflamed paws. 4. Neither paw inflammatory oedema nor tail nociceptive thresholds to a thermal stimulus were modified by paracetamol administration. 5. Our results suggest that paracetamol can reduce hyperalgesia without affecting physiological nociception and inflammation.
Development of Nanoemulsion Formulations of Ginger Extract (2013)

The aim of the study was to enhance the stability of 6-gingerol, a major constituent in ginger extract, using nanoemulsion formulations. The effects of extraction techniques and solvents on the content of 6-gingerol in ginger powders were investigated. Assessments of three commonly used extraction techniques (maceration, sonication and soxhlet extraction) with two different extraction solvents (methanol and acetone) were used in this study. The highest of 6-gingerol content was obtained by using soxhlet extraction with acetone. Nanoemulsions were prepared from a mixture of oil phase (coconut oil and squalene), Cremophor® ELP, acetone and ginger extract by spontaneous emulsification method. Effect of coconut oil and squalene mass ratio on nanoemulsion formations were evaluated for the further optimization of the system, which characterized by droplet size, polydispersity index (PI), zeta potential and Oswald ripening. Stability studies at 4°C and ambient temperature for 3 months were performed. The formulation containing the mass of coconut oil to squalene ratio of 0.8:0.2 was stable and had the required droplet size (122.2+2.2 nm) in relation with PI of 0.18+0.07, zeta potential of -20.8+0.4 mV and Ostwald ripening of 0.11 nm.day-1. Negative-staining transmission electron microscopy (TEM) was used to image the emulsion droplets. Stability test under heating-cooling cycle also performed on optimized nanoemulsion. Then, the sample was analysed for 6-gingerol content. The percentage of the remaining 6-gingerol of optimized nanoemulsion was 90.89. Hence, it was concluded that the stability of 6-gingerol could be enhanced by using nanoemulsion formulation.
Trajectory Prediction of Vehicles Based on Deep Learning (2019)

In order to safely and efficiently drive through the complex traffic scenarios, predicting the trajectory of the forward vehicle accurately is important for intelligent vehicles. Accurate and realtime trajectory prediction can make the intelligent vehicles adjust their maneuvers according to the running state of the vehicles in front of them. In recent years, deep-learning-based methods have been applied as novel alternatives for trajectory prediction with the development of the machine learning. But which kind of deep neural networks is the most suitable model for trajectory prediction is uncertain. In this paper, we design three kinds of deep neural networks: Long Short Term Memory (LSTM), Gated Recurrent Units (GRU), and Stacked Autoencoders (SAEs) to predict the position and the velocity of the forward vehicles. We verify the performance of these three network models on the NGSIM I-80 dataset which consists of real trajectories of vehicles on multi-lanes. What's more, we use Savitzky-Golay filter to filter noise in order to reduce the effect of noise on the training models. Our results demonstrate that in the three deep neural networks that we designed, the LSTM model perform better than GRU model and SAEs model in the area of trajectory prediction. The results of our works will have certain guiding significance for choosing the model of neural network to predict the vehicle trajectories.
Automatic Transcription of English Broadcast News (1998)

In this paper the Philips Broadcast News transcription system is described. The Broadcast News task aims at the recognition of &amp;quot;found&amp;quot; speech in radio and television broadcasts without any additional side information (e.g. speaking style, background conditions). The system was derived from the Philips continuous mixture density crossword HMM system, using MFCC features and Laplacian densities. A segmentation was performed to obtain sentence-like partitions of the broadcasts. Using data-driven clustering, the obtained segments were grouped into clusters with similar acoustic conditions for adaptation purposes. Gender independent wordinternal and crossword triphone models were trained on 70 hours of the HUB4 training data. No focus condition specific training was applied. Channel and speaker normalization was done by mean and variance normalization as well as VTN and MLLR. The transcription was produced by an adaptive multiple pass decoder starting with phrase-bigram decoding using word-...
Hydrogen Peroxide Triggered Prochelator Activation, Subsequent Metal Chelation, and Attenuation of the Fenton Reaction (2007)

Fenton antidote: The prochelator 2-boronobenzaldehyde isonicotinoyl hydrazone (SIH-B) reacts with H2O2 to yield the active chelator salicylaldehyde isonicotinoyl hydrazone (SIH), which sequesters copper and iron ions (see scheme; M=Fe, Cu). This process attenuates both Fe- and Cu-promoted Fenton reactions under physiologically relevant conditions. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2002/2007/z604859_s.pdf or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.