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https://www.readbyqxmd.com/read/28812886/room-temperature-ring-opening-of-quinoline-isoquinoline-and-pyridine-with-low-valent-titanium
#1
Seung-Yeol Baek, Takashi Kurogi, Dahye Kang, Masahiro Kamitani, Seongyeon Kwon, Douglas Solowey, Chun-Hsing Chen, Maren Pink, Patrick J Carroll, Daniel J Mindiola, Mu-Hyun Baik
The complex (PNP)Ti=CHtBu(CH2tBu) (PNP = N[2-PiPr2-4-methylphenyl]2-) dehydrogenates cyclohexane to cyclohexene by forming a transient low-valent titanium-alkyl species, [(PNP)Ti(CH2tBu)], which reacts with two equivalents of quinoline (Q) at room temperature to form H3CtBu and a Ti(IV) species where the less hindered C2=N1 bond of Q is ruptured and coupled to another equivalent of Q. The product isolated from this reaction is an imide with a tethered cycloamide group, (PNP)Ti=N[C18H13N] (1). Under photolytic conditions, intramolecular C-H bond activation across the imide-moiety in 1 occurs to form 2, and thermolysis reverses this process...
August 16, 2017: Journal of the American Chemical Society
https://www.readbyqxmd.com/read/28809686/salient-region-detection-using-diffusion-process-on-a-2-layer-sparse-graph
#2
Li Zhou, Zhaohui Yang, Zongtan Zhou, Dewen Hu
Diffusion-based salient region detection has recently received intense research attention. In this paper, we present some effective improvements concerning two important aspects of diffusion-based methods: the construction of the diffusion matrix and the seed vector. First, we construct a 2-layer sparse graph, which is generated by connecting each node to its neighboring nodes and the most similar node that shares common boundaries with its neighboring nodes. Compared with the most frequently used 2-layer neighborhood graph, our graph not only effectively uses local spatial relationships, but also removes dissimilar redundant nodes...
August 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28809685/human-motion-segmentation-via-robust-kernel-sparse-subspace-clustering
#3
Guiyu Xia, Huaijiang Sun, Lei Feng, Guoqing Zhang, Yazhou Liu
Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem...
August 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28809574/selectivity-control-in-ruthenium-ii-catalyzed-c-h-n-o-activation-with-alkynyl-bromides
#4
Huawen Huang, Sachiyo Nakanowatari, Lutz Ackermann
C-H/N-O activation with 1-bromoalkynes was accomplished within a chemoselective ruthenium(II) catalysis manifold by means of carboxylate assistance. The exceedingly mild reaction conditions set the stage for the positional selective annulations of bromoalkynes at an ambient reaction temperature of 25 °C, placing the organic substituent distal to nitrogen in isoquinolones with a regioselectivity that is complementary to all previous protocols.
August 15, 2017: Organic Letters
https://www.readbyqxmd.com/read/28805818/all-electric-control-of-donor-nuclear-spin-qubits-in-silicon
#5
Anthony J Sigillito, Alexei M Tyryshkin, Thomas Schenkel, Andrew A Houck, Stephen A Lyon
The electronic and nuclear spin degrees of freedom of donor impurities in silicon form ultra-coherent two-level systems that are potentially useful for applications in quantum information and are intrinsically compatible with industrial semiconductor processing. However, because of their smaller gyromagnetic ratios, nuclear spins are more difficult to manipulate than electron spins and are often considered too slow for quantum information processing. Moreover, although alternating current magnetic fields are the most natural choice to drive spin transitions and implement quantum gates, they are difficult to confine spatially to the level of a single donor, thus requiring alternative approaches...
August 14, 2017: Nature Nanotechnology
https://www.readbyqxmd.com/read/28805793/conformational-landscape-of-a-virus-by-single-particle-x-ray-scattering
#6
Ahmad Hosseinizadeh, Ghoncheh Mashayekhi, Jeremy Copperman, Peter Schwander, Ali Dashti, Reyhaneh Sepehr, Russell Fung, Marius Schmidt, Chun Hong Yoon, Brenda G Hogue, Garth J Williams, Andrew Aquila, Abbas Ourmazd
Using a manifold-based analysis of experimental diffraction snapshots from an X-ray free electron laser, we determine the three-dimensional structure and conformational landscape of the PR772 virus to a detector-limited resolution of 9 nm. Our results indicate that a single conformational coordinate controls reorganization of the genome, growth of a tubular structure from a portal vertex and release of the genome. These results demonstrate that single-particle X-ray scattering has the potential to shed light on key biological processes...
August 14, 2017: Nature Methods
https://www.readbyqxmd.com/read/28802947/canine-diabetes-mellitus-risk-factors-a-matched-case-control-study
#7
Alan Gomes Pöppl, Guilherme Luiz Carvalho de Carvalho, Itatiele Farias Vivian, Luis Gustavo Corbellini, Félix Hilário Díaz González
Different subtypes of canine diabetes mellitus (CDM) have been described based on their aetiopathogenesis. Therefore, manifold risk factors may be involved in CDM development. This study aims to investigate canine diabetes mellitus risk factors. Owners of 110 diabetic dogs and 136 healthy controls matched by breed, sex, and age were interviewed concerning aspects related to diet, weight, physical activity, oral health, reproductive history, pancreatitis, and exposure to exogenous glucocorticoids. Two multivariate multivariable statistical models were created: The UMod included males and females without variables related to oestrous cycle, while the FMod included only females with all analysed variables...
August 4, 2017: Research in Veterinary Science
https://www.readbyqxmd.com/read/28799347/ultrafast-%C3%AE-cc-bond-cleavage-of-acetone-upon-excitation-to-3p-and-3d-rydberg-states-by-femtosecond-time-resolved-photoelectron-imaging
#8
O Hüter, F Temps
The radiationless electronic relaxation and α -CC bond fission dynamics of jet-cooled acetone in the S1 (nπ(*)) state and in high-lying 3p and 3d Rydberg states have been investigated by femtosecond time-resolved mass spectrometry and photoelectron imaging. The S1 state was accessed by absorption of a UV pump photon at selected wavelengths between λ = 320 and 250 nm. The observed acetone mass signals and the S1 photoelectron band decayed on sub-picosecond time scales, consistent with a recently proposed ultrafast structural relaxation of the molecules in the S1 state away from the Franck-Condon probe window...
December 7, 2016: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28796607/zero-shot-learning-on-semantic-class-prototype-graph
#9
Zhenyong Fu, Tao Xiang, Elyor Kodirov, Shaogang Gong
Zero-Shot Learning (ZSL) for visual recognition is typically achieved by exploiting a semantic embedding space. In such a space, both seen and unseen class labels as well as image features can be embedded so that the similarity among them can be measured directly. In this work, we consider that the key to effective ZSL is to compute an optimal distance metric in the semantic embedding space. Existing ZSL works employ either Euclidean or cosine distances. However, in a high-dimensional space where the projected class labels (prototypes) are sparse, these distances are suboptimal, resulting in a number of problems including hubness and domain shift...
August 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28789565/qspr-studies-on-the-photoinduced-fluorescence-behaviour-of-pharmaceuticals-and-pesticides
#10
D López-Malo, J I Bueso-Bordils, M J Duart, P A Alemán-López, R V Martín-Algarra, G M Antón-Fos, L Lahuerta-Zamora, J Martínez-Calatayud
Fluorimetric analysis is still a growing line of research in the determination of a wide range of organic compounds, including pharmaceuticals and pesticides, which makes necessary the development of new strategies aimed at improving the performance of fluorescence determinations as well as the sensitivity and, especially, the selectivity of the newly developed analytical methods. In this paper are presented applications of a useful and growing tool suitable for fostering and improving research in the analytical field...
July 2017: SAR and QSAR in Environmental Research
https://www.readbyqxmd.com/read/28783644/robust-adaptive-embedded-label-propagation-with-weight-learning-for-inductive-classification
#11
Zhao Zhang, Fanzhang Li, Lei Jia, Jie Qin, Li Zhang, Shuicheng Yan
We propose a robust inductive semi-supervised label prediction model over the embedded representation, termed adaptive embedded label propagation with weight learning (AELP-WL), for classification. AELP-WL offers several properties. First, our method seamlessly integrates the robust adaptive embedded label propagation with adaptive weight learning into a unified framework. By minimizing the reconstruction errors over embedded features and embedded soft labels jointly, our AELP-WL can explicitly ensure the learned weights to be joint optimal for representation and classification, which differs from most existing LP models that perform weight learning separately by an independent step before label prediction...
August 2, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28783643/patch-alignment-manifold-matting
#12
Xuelong Li, Kang Liu, Yongsheng Dong, Dacheng Tao
Image matting is generally modeled as a space transform from the color space to the alpha space. By estimating the alpha factor of the model, the foreground of an image can be extracted. However, there is some dimensional information redundancy in the alpha space. It usually leads to the misjudgments of some pixels near the boundary between the foreground and the background. In this paper, a manifold matting framework named Patch Alignment Manifold Matting is proposed for image matting. In particular, we first propose a part modeling of color space in the local image patch...
August 1, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28783635/out-of-sample-extension-for-dimensionality-reduction-of-noisy-time-series
#13
Hamid Dadkhahi, Marco F Duarte, Benjamin M Marlin
This paper proposes an out-of-sample extension framework for a global manifold learning algorithm (Isomap) that uses temporal information in out-of-sample points in order to make the embedding more robust to noise and artifacts. Given a set of noise-free training data and its embedding, the proposed framework extends the embedding for a noisy time series. This is achieved by adding a spatiotemporal compactness term to the optimization objective of the embedding. To the best of our knowledge, this is the first method for out-of-sample extension of manifold embeddings that leverages timing information available for the extension set...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783633/towards-optimal-manifold-hashing-via-discrete-locally-linear-embedding
#14
Rongrong Ji, Hong Liu, Liujuan Cao, Di Liu, Yongjian Wu, Feiyue Huang
Binary code learning, a.k.a. hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it needs first to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes...
August 2, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28783631/wide-baseline-foreground-object-interpolation-using-silhouette-shape-prior
#15
Cedric Verleysen, Thomas Maugey, Pascal Frossard, Christophe De Vleeschouwer
We consider the synthesis of intermediate views of an object captured by two widely spaced and calibrated cameras. This problem is challenging because foreshortening effects and occlusions induce significant differences between the reference images when the cameras are far apart. That makes the association or disappearance/appearance of their pixels difficult to estimate. Our main contribution lies in disambiguating this illposed problem by making the interpolated views consistent with a plausible transformation of the object silhouette between the reference views...
July 31, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28777721/infinite-von-mises-fisher-mixture-modeling-of-whole-brain-fmri-data
#16
Rasmus E Røge, Kristoffer H Madsen, Mikkel N Schmidt, Morten Mørup
Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling...
August 4, 2017: Neural Computation
https://www.readbyqxmd.com/read/28777717/nonnegative-matrix-factorization-with-rank-regularization-and-hard-constraint
#17
Ronghua Shang, Chiyang Liu, Yang Meng, Licheng Jiao, Rustam Stolkin
Nonnegative matrix factorization (NMF) is well known to be an effective tool for dimensionality reduction in problems involving big data. For this reason, it frequently appears in many areas of scientific and engineering literature. This letter proposes a novel semisupervised NMF algorithm for overcoming a variety of problems associated with NMF algorithms, including poor use of prior information, negative impact on manifold structure of the sparse constraint, and inaccurate graph construction. Our proposed algorithm, nonnegative matrix factorization with rank regularization and hard constraint (NMFRC), incorporates label information into data representation as a hard constraint, which makes full use of prior information...
August 4, 2017: Neural Computation
https://www.readbyqxmd.com/read/28772977/mechanical-properties-of-nonwoven-reinforced-thermoplastic-polyurethane-composites
#18
Muhammad Tausif, Achilles Pliakas, Tom O'Haire, Parikshit Goswami, Stephen J Russell
Reinforcement of flexible fibre reinforced plastic (FRP) composites with standard textile fibres is a potential low cost solution to less critical loading applications. The mechanical behaviour of FRPs based on mechanically bonded nonwoven preforms composed of either low or high modulus fibres in a thermoplastic polyurethane (TPU) matrix were compared following compression moulding. Nonwoven preform fibre compositions were selected from lyocell, polyethylene terephthalate (PET), polyamide (PA) as well as para-aramid fibres (polyphenylene terephthalamide; PPTA)...
June 5, 2017: Materials
https://www.readbyqxmd.com/read/28772943/fault-diagnosis-for-rolling-bearings-under-variable-conditions-based-on-visual-cognition
#19
Yujie Cheng, Bo Zhou, Chen Lu, Chao Yang
Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc...
May 25, 2017: Materials
https://www.readbyqxmd.com/read/28772601/structure-dependent-spectroscopic-properties-of-yb-3-doped-phosphosilicate-glasses-modified-by-sio%C3%A2
#20
Ling Wang, Huidan Zeng, Bin Yang, Feng Ye, Jianding Chen, Guorong Chen, Andew T Smith, Luyi Sun
Yb(3+)-doped phosphate glasses containing different amounts of SiO₂ were successfully synthesized by the conventional melt-quenching method. The influence mechanism of SiO₂ on the structural and spectroscopic properties was investigated systematically using the micro-Raman technique. It was worth noting that the glass with 26.7 mol % SiO₂ possessed the longest fluorescence lifetime (1.51 ms), the highest gain coefficient (1.10 ms·pm²), the maximum Stark splitting manifold of ²F7/2 level (781 cm(-1)), and the largest scalar crystal-field NJ and Yb(3+) asymmetry degree...
February 28, 2017: Materials
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