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https://www.readbyqxmd.com/read/28719206/direct-quantum-dynamics-using-grid-based-wavefunction-propagation-and-machine-learned-potential-energy-surfaces
#1
Gareth W Richings, Scott Habershon
We describe a method for performing nuclear quantum dynamics calculations using standard, grid-based algorithms, including the multi configurational time-dependent Hartree (MCTDH) method, where the potential energy surface (PES) is calculated ``on-the-fly''. The method of Gaussian process regression (GPR) is used to construct a global representation of the PES using values of the energy at points distributed in molecular configuration space during the course of the wavepacket propagation. We demonstrate this direct dynamics approach for both an analytical PES function describing 3-dimensional proton transfer dynamics in malonaldehyde, and for 2- and 6-dimensional quantum dynamics simulations of proton transfer in salicylaldimine...
July 18, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28718087/speaking-two-languages-in-america-a-semantic-space-analysis-of-how-presidential-candidates-and-their-supporters-represent-abstract-political-concepts-differently
#2
Ping Li, Benjamin Schloss, D Jake Follmer
In this article we report a computational semantic analysis of the presidential candidates' speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts...
July 17, 2017: Behavior Research Methods
https://www.readbyqxmd.com/read/28711053/representations-in-neural-network-based-empirical-potentials
#3
Ekin D Cubuk, Brad D Malone, Berk Onat, Amos Waterland, Efthimios Kaxiras
Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost...
July 14, 2017: Journal of Chemical Physics
https://www.readbyqxmd.com/read/28710800/dissociation-of-spatial-memory-systems-in-williams-syndrome
#4
Mathilde Bostelmann, Emilie Fragnière, Floriana Costanzo, Silvia Di Vara, Deny Menghini, Stefano Vicari, Pierre Lavenex, Pamela Banta Lavenex
Williams syndrome (WS), a genetic deletion syndrome, is characterized by severe visuospatial deficits affecting performance on both tabletop spatial tasks and on tasks which assess orientation and navigation. Nevertheless, previous studies of WS spatial capacities have ignored the fact that two different spatial memory systems are believed to contribute parallel spatial representations supporting navigation. The place learning system depends on the hippocampal formation and creates flexible relational representations of the environment, also known as cognitive maps...
July 15, 2017: Hippocampus
https://www.readbyqxmd.com/read/28708559/texture-characterization-using-shape-co-occurrence-patterns
#5
Gui-Song Xia, Gang Liu, Xiang Bai, Liangpei Zhang
Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector...
July 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28707639/life-in-unexpected-places-employing-visual-thinking-strategies-in-global-health-training
#6
Jill Allison, Shree Mulay, Monica Kidd
BACKGROUND: The desire to make meaning out of images, metaphor, and other representations indicates higher-order cognitive skills that can be difficult to teach, especially in the complex and unfamiliar environments like those encountered in many global health experiences. Because reflecting on art can help develop medical students' imaginative and interpretive skills, we used visual thinking strategies (VTS) during an immersive 4-week global health elective for medical students to help them construct new understanding of the social determinants of health in a low-resource setting...
January 2017: Education for Health: Change in Training & Practice
https://www.readbyqxmd.com/read/28707570/using-event-related-potentials-to-inform-the-neurocognitive-processes-underlying-knowledge-extension-through-memory-integration
#7
Nicole L Varga, Patricia J Bauer
To build a general knowledge base, it is imperative that individuals acquire, integrate, and further extend knowledge across experiences. For instance, in one episode an individual may learn that George Washington was the first president. In a separate episode he or she may then learn that Washington was the commander of the Continental Army. Integration of the information in memory may then support self-derivation of the new knowledge that the leader of the Continental Army was also the first president. Despite a considerable amount of fMRI research aimed at further elucidating the neuroanatomical regions supporting this ability, a consensus has yet to be reached with regards to the precise neurocognitive processes involved...
July 14, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/28707195/the-power-of-print-reading-comics-in-the-classroom
#8
Sabine Gabaron
Evidence from neuroscience and psychological studies supporting benefits of print reading over digital reading has recently been discussed in these columns (Perbal 2017 J. Cell Commun. Signal. 11:1-4). In the present commentary, I would like to add my perspective as a Humanities educator, and build upon the idea that print reading results in better comprehension, learning and communication. The argumentation that is presented herein is based on a study performed in a French Comics language class aimed at broadening students' knowledge and experience of graphic novels, and providing them with a cultural representation in the foreign language...
July 13, 2017: Journal of Cell Communication and Signaling
https://www.readbyqxmd.com/read/28707176/estimating-the-average-need-of-semantic-knowledge-from-distributional-semantic-models
#9
Geoff Hollis
Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111-3119, 2013). Each has been demonstrated to perform markedly better at capturing human judgments about semantic relatedness than competing models (e.g., latent semantic analysis; Landauer & Dumais, Psychological Review, 104(2), 1997 211; hyperspace analogue to language; Lund & Burgess, Behavior Research Methods, Instruments, & Computers, 28(2), 203-208, 1996)...
July 13, 2017: Memory & Cognition
https://www.readbyqxmd.com/read/28703919/cingulate-and-cerebellar-beta-oscillations-are-engaged-in-the-acquisition-of-auditory-motor-sequences
#10
María Herrojo Ruiz, Burkhard Maess, Eckart Altenmüller, Gabriel Curio, Vadim V Nikulin
Singing, music performance, and speech rely on the retrieval of complex sounds, which are generated by the corresponding actions and are organized into sequences. It is crucial in these forms of behavior that the serial organization (i.e., order) of both the actions and associated sounds be monitored and learned. To investigate the neural processes involved in the monitoring of serial order during the initial learning of sensorimotor sequences, we performed magnetoencephalographic recordings while participants explicitly learned short piano sequences under the effect of occasional alterations of auditory feedback (AAF)...
July 13, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28701276/medical-image-classification-via-multiscale-representation-learning
#11
Qiling Tang, Yangyang Liu, Haihua Liu
Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide range of observation scales would be useful for medical imaging measurements. The present work proposes a multiscale representation learning method via sparse autoencoder networks to capture the intrinsic scales in medical images for the classification task. We obtain the multiscale feature detectors by the sparse autoencoders with different receptive field sizes, and then generate the feature maps by the convolution operation...
June 29, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28700672/prediction-of-clinical-depression-scores-and-detection-of-changes-in-whole-brain-using-resting-state-functional-mri-data-with-partial-least-squares-regression
#12
Kosuke Yoshida, Yu Shimizu, Junichiro Yoshimoto, Masahiro Takamura, Go Okada, Yasumasa Okamoto, Shigeto Yamawaki, Kenji Doya
In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression...
2017: PloS One
https://www.readbyqxmd.com/read/28699566/entity-recognition-from-clinical-texts-via-recurrent-neural-network
#13
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, Hua Xu
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts. Recognizing these entities has become a hot topic in clinical natural language processing (NLP), and a large number of traditional machine learning methods, such as support vector machine and conditional random field, have been deployed to recognize entities from clinical texts in the past few years...
July 5, 2017: BMC Medical Informatics and Decision Making
https://www.readbyqxmd.com/read/28698606/reading-induced-shifts-of-perceptual-speech-representations-in-auditory-cortex
#14
Milene Bonte, Joao M Correia, Mirjam Keetels, Jean Vroomen, Elia Formisano
Learning to read requires the formation of efficient neural associations between written and spoken language. Whether these associations influence the auditory cortical representation of speech remains unknown. Here we address this question by combining multivariate functional MRI analysis and a newly-developed 'text-based recalibration' paradigm. In this paradigm, the pairing of visual text and ambiguous speech sounds shifts (i.e. recalibrates) the perceptual interpretation of the ambiguous sounds in subsequent auditory-only trials...
July 11, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28697777/enhancing-the-use-of-research-in-health-promoting-anti-racism-policy
#15
Angeline S Ferdinand, Yin Paradies, Margaret Kelaher
BACKGROUND: The Localities Embracing and Accepting Diversity (LEAD) programme was established to improve the health of ethnic minority communities through the reduction of racial discrimination. Local governments in the state of Victoria, Australia, were at the forefront of LEAD implementation in collaboration with leading state and national organisations. Key aims included expanding the available evidence regarding effective anti-racism interventions and facilitating the uptake of this evidence in organisational policies and practices...
July 11, 2017: Health Research Policy and Systems
https://www.readbyqxmd.com/read/28696688/convolutional-embedding-of-attributed-molecular-graphs-for-physical-property-prediction
#16
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, Klavs F Jensen
The task of learning an expressive molecular representation is central to developing quantitative structure-activity and property relationships. Traditional approaches rely on group additivity rules, empirical measurements or parameters, or generation of thousands of descriptors. In this paper, we employ a convolutional neural network for this embedding task by treating molecules as undirected graphs with attributed nodes and edges. Simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii...
July 11, 2017: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/28694337/targeted-memory-reactivation-during-sleep-adaptively-promotes-the-strengthening-or-weakening-of-overlapping-memories
#17
J P Oyarzún, J Morís, D Luque, R de Diego-Balaguer, L Fuentemilla
System memory consolidation is conceptualized as an active process whereby newly encoded memory representations are strengthened through selective memory reactivation during sleep. However, our learning experience is highly overlapping in content (i.e., shares common elements), and memories of these events are organized in an intricate network of overlapping associated events. It remains to be explored whether and how selective memory reactivation during sleep has an impact on these overlapping memories acquired during awake time...
July 10, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28694066/disease-gene-classification-with-metagraph-representations
#18
Sezin Ata Kircali, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao
Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes...
July 7, 2017: Methods: a Companion to Methods in Enzymology
https://www.readbyqxmd.com/read/28693004/key-frame-extraction-in-the-summary-space
#19
Xuelong Li, Bin Zhao, Xiaoqiang Lu
Key frame extraction is an efficient way to create the video summary which helps users obtain a quick comprehension of the video content. Generally, the key frames should be representative of the video content, meanwhile, diverse to reduce the redundancy. Based on the assumption that the video data are near a subspace of a high-dimensional space, a new approach, named as key frame extraction in the summary space, is proposed for key frame extraction in this paper. The proposed approach aims to find the representative frames of the video and filter out similar frames from the representative frame set...
July 4, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28692990/discriminative-block-diagonal-representation-learning-for-image-recognition
#20
Zheng Zhang, Yong Xu, Ling Shao, Jian Yang
Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR...
July 4, 2017: IEEE Transactions on Neural Networks and Learning Systems
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