gebhart/patrepeval huggingface dataset:
Metal–ligand cooperation at tethered π-ligands (2016)

Metal-ligand cooperativity in homogeneous catalysis is emerging as a powerful tool for the design of efficient transition-metal catalysts. This perspective highlights recent advances in the use of neutral π-coordinating ligands, tethered to a transition-metal center by other donor ligands, as cooperative reaction centers. The state-of-the-art organometallic complexes, including π-coordinating ligands originating from C[double bond, length as m-dash]C, C[double bond, length as m-dash]E (E = O, N) and boron containing moieties, are described here, with special attention on their specific reactivity. Geometric and electronic aspects of ligand design and their influence on the coordination mode and reactivity of the π-system are discussed.
Open-Box Spectral Clustering: Applications to Medical Image Analysis (2013)

Spectral clustering is a powerful and versatile technique, whose broad range of applications includes 3D image analysis. However, its practical use often involves a tedious and time-consuming process of tuning parameters and making application-specific choices. In the absence of training data with labeled clusters, help from a human analyst is required to decide the number of clusters, to determine whether hierarchical clustering is needed, and to define the appropriate distance measures, parameters of the underlying graph, and type of graph Laplacian. We propose to simplify this process via an open-box approach, in which an interactive system visualizes the involved mathematical quantities, suggests parameter values, and provides immediate feedback to support the required decisions. Our framework focuses on applications in 3D image analysis, and links the abstract high-dimensional feature space used in spectral clustering to the three-dimensional data space. This provides a better understanding of the technique, and helps the analyst predict how well specific parameter settings will generalize to similar tasks. In addition, our system supports filtering outliers and labeling the final clusters in such a way that user actions can be recorded and transferred to different data in which the same structures are to be found. Our system supports a wide range of inputs, including triangular meshes, regular grids, and point clouds. We use our system to develop segmentation protocols in chest CT and brain MRI that are then successfully applied to other datasets in an automated manner.
Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury (2019)

A dissociation between the absence of behavioral responses to motor commands and the evidence of brain activation in response to these commands in EEG recordings was found in 15% of patients in a consecutive series of patients with acute brain injury. (Supported by the Dana Foundation and the James S. McDonnell Foundation.).
Low-dose interleukin-2 as a regulatory immunotherapy for systemic lupus erythematosus (2016)

Accumulating evidence that interleukin-2 (IL-2) is requisite for the maintenance and expansion of regulatory T cells (Treg) has yielded low-dose IL-2 therapy for several autoimmune diseases such as type 1 diabetes and systemic lupus erythematosus (SLE) (1,2). He and colleagues recently reported in Nat Med the efficacy of low-dose IL-2 treatment in 38 active SLE patients (3). They described that IL-2 treatment significantly ameliorated the clinical severity associated with expansion of Treg and decrease of follicular helper T (T FH ) cells and IL-17-producing helper T (T H 17) cells.
Coverage Enhancement and Fundamental Performance of 5G: Analysis and Field Trial (2019)

To meet the demands of mobile networks toward 2020, 5G has emerged. Compared to 4G, the 5G network will be deployed in higher-frequency bands, such as 3.5 GHz. Meanwhile, in order to save costs, operators expect to deploy the 5G network by reusing the legacy sites of 4G networks. Note that the coverage of base stations decreases with the increase of carrier frequency, and coverage enhancement techniques are required in 5G to cope with the coverage loss. In this article, coverage enhancement techniques are first analyzed. A field trial adopting pre-commercial 5G systems is then set up to assess the coverage performance. The analysis along with the promising field trial results indicate that the coverage of the 3.5 GHz frequency band can be guaranteed in the 5G network deployed with 4G legacy sites.
Integrated Metabolo-Proteomic Approach to Decipher the Mechanisms by Which Wheat QTL (Fhb1) Contributes to Resistance against Fusarium graminearum (2012)

The present study demonstrates that the resistance in Fhb1 derived from the wheat genotype Nyubai is mainly associated with cell wall thickening due to deposition of hydroxycinnamic acid amides, phenolic glucosides and flavonoids, but not with the conversion of deoxynivalenol to less toxic deoxynivalenol 3-O-glucoside.
A radio-frequency energy harvesting scheme for use in low-power ad hoc distributed networks (2012)

While RF energy harvesting has proven to be a viable power source for low-power electronics, it is still a challenge to obtain significant amounts of energy fast and efficiently from the ambiance. Available RF power is usually very weak, resulting in a weak voltage applied to a demodulator to drive it into a region of significant nonlinearity. An RF energy harvesting system consisting of a rectenna, a dc-dc voltage converter, and a novel battery cell is proposed. The rectenna is an integration of an antenna and rectifying diodes. In addition, a switched-capacitor dc-dc voltage converter is integrated on a silicon integrated circuit with energy transfer efficiency as high as 40.5%. A new battery system that can be recharged at low voltage (<; 1.2 V) is demonstrated. In this brief, all of these elements are tested as a system to achieve RF battery recharging from a commercially available hand-held communication device. The system exhibited an overall harvesting efficiency of 11.6%.
A weighted cepstral distance measure for speech recognition (1987)

A weighted cepstral distance measure is proposed and is tested in a speaker-independent isolated word recognition system using standard DTW (dynamic time warping) techniques. The measure is a statistically weighted distance measure with weights equal to the inverse variance of the cepstral coefficients. The experimental results show that the weighted cepstral distance measure works substantially better than both the Euclidean cepstral distance and the log likelihood ratio distance measures across two different databases. The recognition error rate obtained using the weighted cepstral distance measure was about 1 percent for digit recognition. This result was less than one-fourth of that obtained using the simple Euclidean cepstral distance measure and about one-third of the results using the log likelihood ratio distance measure. The most significant performance characteristic of the weighted cepstral distance was that it tended to equalize the performance of the recognizer across different talkers.
The Properties of Asphaltenes and Their Interaction with Amphiphiles (2009)

The functional groups on asphaltene surfaces of two kinds of Chinese residue oil were analyzed by X-ray photoelectron spectroscopy (XPS). The ζ potential and electrophoretic mobility of asphaltene solutions and residue solutions were measured through phase analysis light scattering (PALS) technique. The ability to stabilize asphaltenes of two typical ionic amphiphiles, dodecyl benzene sulfonic acid (DBSA) and dodecyl trimethyl ammonium bromide (DTAB), were investigated. Karamay asphaltenes contain large amount of carboxyl and calcium and are negatively charged; whereas Lungu asphaltenes are rich in nickel, vanadium, and pyrrolic structures and are positively charged. DBSA has good ability to stabilize Lungu asphaltenes but has no effect on Karamay asphaltenes. Differently, DTAB has good ability to disperse Karamay asphaltenes but has no obvious effect on Lungu asphaltenes. It is concluded from these results that the charges might derive from the dissociation of metal ions and the deprotonation of acid groups (such as COOH, OH, and SH) or basic groups (such as pyridinic groups) on asphaltene surface. The electric property of asphaltenes plays an important role in the interaction between asphaltenes and amphiphiles. The negatively charged asphaltenes tend to be dispersed by cationic amphiphiles, whereas the positively charged asphaltenes tend to be dispersed by anionic amphiphiles.
Control of double diffusion front unintentionally penetrated from a Zn doped InP layer during metalorganic vapor phase epitaxy (1998)

Unintentional Zn diffusion during metalorganic vapor phase epitaxy causes serious damages in semiconductor devices. In this work, profiles of unintentionally diffused Zn atoms from a p-InP layer to the adjoining InP substrate during growth of the p-InP layer are measured by secondary ion mass spectrometry. Zn diffusion profiles with a double diffusion front, which is composed of a shallow front with high Zn concentration and a deep front with low Zn concentration, are investigated as an approach to controlling unintentional diffusion. Diffusion depth of each front is controlled in proportion to Zn dosage, which is proposed as a value calculated as Zn concentration without regard to saturation limit. The diffusion depth for the growth time of 60 min increases in proportion to the Zn dosage as the slope of 0.16 μm/1018 cm−3 for the shallow front and that of 0.32 μm/1018 cm−3 for the deep front at a growth temperature of 600 °C. The deep front expands two times faster than the shallow front, which is normally observed as a p-n junction. Zn concentration at which unintentional Zn diffusion occurs is determined to be more than 2×1017 cm−3. Therefore, the penetration of Zn atoms into the active region of semiconductor devices should be observed when unintentional diffusion takes place. Furthermore, even at low Zn concentration, the introduction of a kick-out mechanism is proposed to explain the diffusion coefficient of the unintentional diffusion.
A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 (2012)

This paper describes the solution of National Taiwan University for track 2 of KDD Cup 2012. Track 2 of KDD Cup 2012 aims to predict the click-through rate of ads on Tencent proprietary search engine. We exploit classication, regression, ranking, and factorization models to utilize a variety of dierent signatures captured from the dataset. We then blend our individual models to boost the performance through two stages, one on an internal validation set and one on the external test set. Our solution achieves 0.8069 AUC on the public test set and 0.8089 AUC on the private test set.
Binary Linear ECCs Optimized for Bit Inversion in Memories with Asymmetric Error Probabilities (2020)

Many memory types are asymmetric with respect to the error vulnerability of stored 0's and 1's. For instance, DRAM, STT-MRAM and NAND flash memories may suffer from asymmetric error rates. A recently proposed error-protection scheme consists in the inversion of the memory words with too many vulnerable values before they are stored in an asymmetric memory. In this paper, a method is proposed for the optimization of systematic binary linear block error-correcting codes in order to maximize their impact when combined with memory word inversion.
Learning to Segment and Track in RGBD (2013)

We consider the problem of segmenting and tracking deformable objects in color video with depth (RGBD) data available from commodity sensors such as the Asus Xtion Pro Live or Microsoft Kinect. We frame this problem with very few assumptions-no prior object model, no stationary sensor, and no prior 3-D map-thus making a solution potentially useful for a large number of applications, including semi-supervised learning, 3-D model capture, and object recognition. Our approach makes use of a rich feature set, including local image appearance, depth discontinuities, optical flow, and surface normals to inform the segmentation decision in a conditional random field model. In contrast to previous work in this field, the proposed method learns how to best make use of these features from ground-truth segmented sequences. We provide qualitative and quantitative analyses which demonstrate substantial improvement over the state of the art. This paper is an extended version of our previous work. Building on our previous work, we show that it is possible to achieve an order of magnitude speedup and thus real-time performance ( ~ 20 FPS) on a laptop computer by applying simple algorithmic optimizations to the original work. This speedup comes at only a minor cost in overall accuracy and thus makes this approach applicable to a broader range of tasks. We demonstrate one such task: real-time, online, interactive segmentation to efficiently collect training data for an off-the-shelf object detector.
Armadillo C++ Linear Algebra Library (2016)

C++ library for linear algebra (matrix maths), aiming towards a good balance between speed and ease of use. Its syntax is deliberately similar to Matlab. Useful for algorithm development directly in C++, or conversion of research code into production environments.
An Online Semi-supervised Active Learning Algorithm with Self-organiing Incremental Neural Network (2007)

An online semi-supervised active learning algorithm is proposed, which is based on self-organizing incremental neural network (SOINN). The proposed method do not need any priori knowledge such as number of nodes or number of classes; it can automatically learn number of nodes and teacher vectors needed by the current task; It can realize online incremental learning even life-long learning. The experiments for artificial data and real world data show that the proposed method works efficiently.
BDNF–TrkB signaling in the nucleus accumbens shell of mice has key role in methamphetamine withdrawal symptoms (2015)

Abstract Depression is a core symptom of methamphetamine (METH) withdrawal during the first several weeks of abstinence. However, the precise mechanisms underlying METH withdrawal symptoms remain unknown. Brain-derived neurotrophic factor (BDNF) and its specific receptor, tropomyosin-related kinase (TrkB), have a role the in pathophysiology of depression. In this study, we examined the role of BDNF–TrkB signaling in different brain regions of male mice with METH withdrawal symptoms. Repeated METH (3 mg kg −1 per day for 5 days) administration to mice caused a long-lasting depression-like behavior including anhedonia. Western blot analysis showed that BDNF levels in the nucleus accumbens (NAc) of METH-treated mice were significantly higher than those of control mice whereas BDNF levels in other regions, including the prefrontal cortex and hippocampus, were not altered. METH-induced depression-like behavior, behavioral sensitization and dendritic changes in the NAc shell were improved by subsequent subchronic administration of TrkB antagonist ANA-12 (0.5 mg kg −1 per day for 14 days), but not TrkB agonist 7,8-dihydroxyflavone (10 mg kg −1 per day for 14 days). In vivo microdialysis showed that METH (1 mg kg −1 )-induced dopamine release in NAc shell of METH-treated mice was attenuated after subsequent subchronic ANA-12 administration. Interestingly, a single bilateral infusion of ANA-12 into the NAc shell, but not NAc core, showed a rapid and long-lasting therapeutic effect. However, ketamine and paroxetine had no effect. These findings suggest that increased BDNF–TrkB signaling in the NAc shell has an important role in the behavioral abnormalities after withdrawal from repeated METH administration, and that TrkB antagonists are potential therapeutic drugs for withdrawal symptoms in METH abusers.
Feature Pyramid Network for Multi-class Land Segmentation (2018)

Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.
FEATURE LINE BASED BUILDING DETECTION AND RECONSTRUCTION FROM OBLIQUE AIRBORNE IMAGERY (2015)

Abstract. In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.
Circular Hough Transform Assisted CNN Based Vehicle Axle Detection and Classification (2019)

In this paper we introduce a method to detect vehicle axles in an image using CNN network which is assisted by circular Hough transform and classify vehicles based on the axle count. Previous work has failed to address the issue of detecting axles for the wide field of view. To tackle this problem, we first do multi-object detection and then further process them to detect wheels. We complement a traditional Hough-based wheel detection algorithm with CNN network to reduce the computational load and the search space, further improving the efficiency. We integrate spatial and contextual filters for systematic removal of false positive wheel detection. We used MIO data set for training our vehicle detection network. We sampled 2500 images from 20 videos to create data set for our wheel detection network. We did holdout cross validation to check vehicle and wheel detection accuracy. The pipeline is simple and robust for wheel detection in wide field of view, and computationally efficient. We test the method over 5 axle classes and got average accuracy of 89.4%.
The Effect of Chemical Additives on the Light Reversion of Mechanical Pulp. (1993)

The effects of UV absorber and anti-oxidant additives on the light reversion of thermomechanical pulp (TMP) were tested. Among various additives tested, 2, 4, 6-triphenylphenol, benzylidine acetone, triallyl-s-triazine-2, 4, 6-(1 H, 3 H, 5 H)-trione, and uracil were more effective. When UV absorber and anti-oxidant were combined and applied to hand sheets, synergistic effects were observed and resulted in improvement of more than 70% RI Index in some cases.
Robust heart rate estimation from noisy phonocardiograms (2014)

Accurate heart rate estimation is a fundamental process when analysing phonocardiograms (PCGs). While this is trivial in noise-free recordings, it becomes a difficult task in PCGs corrupted by various noise sources. While numerous PCG-based heart rate estimation techniques have been explored in the literature, no comparison between these techniques has been performed to identify the best-performing method in noisy recordings. This paper evaluates various denoising, normalisation, envelope extraction and heart rate estimation techniques on 585 noisy recordings made using four different devices. The best-performing algorithm correctly estimated the heart rate in 471 (80.5%) of these PCGs, while correctly estimating the heart rate in 86% of the PCGS from a separate (publicly available) test dataset.
HIV-1 Viral Protein R (VPR) and its Interactions with Host Cell (2009)

Human immunodeficiency virus type 1 (HIV-1) is engaged in dynamic and antagonistic interactions with host cells. Once infected by HIV-1, host cells initiate various antiviral strategies, such as innate antiviral defense mechanisms, to counteract viral invasion. In contrast, the virus has different strategies to suppress these host responses to infection. The final balance between these interactions determines the outcome of the viral infection and disease progression. Recent findings suggest that HIV-1 viral protein R (Vpr) interacts with some of the host innate antiviral factors, such as heat shock proteins, and plays an active role as a viral pathogenic factor. Cellular heat stress response factors counteract Vpr activities and inhibit HIV replication. However, Vpr overcomes these heat-stress-like responses by preventing heat shock factor-1 (HSF-1)-mediated activation of heat shock proteins. In this review, we will focus on the virus-host interactions involving Vpr. In addition to heat stress response proteins, we will discuss interactions of Vpr with other proteins, such as EF2 and Skp1/GSK3, their involvements in cellular responses to Vpr, as well as strategies to develop novel antiviral therapies aimed at enhancing anti-Vpr responses of the host cell.
iHIVARNA phase IIa, a randomized, placebo-controlled, double-blinded trial to evaluate the safety and immunogenicity of iHIVARNA-01 in chronically HIV-infected patients under stable combined antiretroviral therapy (2019)

EudraCT 2016-002724-83 (22 September 2016); ClinicalTrials.gov, ID: NCT02888756 . Registered on 23 August 2016.
Abuse potential and psychoactive effects of δ-9-tetrahydrocannabinol and cannabidiol oromucosal spray (Sativex), a new cannabinoid medicine (2011)

In clinical trials, intoxication scores have been low and euphoria reported by only 2.2% of patients. Tolerance has not occurred, abrupt withdrawal has not resulted in a formal withdrawal syndrome, and no cases of abuse or diversion have been reported to date. A formal abuse liability study of Sativex in experienced cannabis smokers showed some abuse potential in comparison with placebo at higher doses, but scores were consistently lower than equivalent doses of THC. Evidence to date suggests that abuse or dependence on Sativex is likely to occur in only a very small proportion of recipients.