Query: A Biologically Plausible Learning Rule for Deep Learning in the Brain

Relevant passages:
Random synaptic feedback weights support error backpropagation for deep learning <=SEP=> The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.

Getting Formal with Dopamine and Reward <=SEP=> Recent neurophysiological studies reveal that neurons in certain brain structures carry specific signals about past and future rewards. Dopamine neurons display a short-latency, phasic reward signal indicating the difference between actual and predicted rewards. The signal is useful for enhancing neuronal processing and learning behavioral reactions. It is distinctly different from dopamine's tonic enabling of numerous behavioral processes. Neurons in the striatum, frontal cortex, and amygdala also process reward information but provide more differentiated information for identifying and anticipating rewards and organizing goal-directed behavior. The different reward signals have complementary functions, and the optimal use of rewards in voluntary behavior would benefit from interactions between the signals. Addictive psychostimulant drugs may exert their action by amplifying the dopamine reward signal.

Deep Big Multilayer Perceptrons for Digit Recognition <=SEP=> The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent advancement by others dates back 8 years (error rate 0.4%). Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark with a single MLP and 0.31% with a committee of seven MLP. All we need to achieve this until 2011 best result are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.

Feature-based attention influences motion processing gain in macaque visual cortex <=SEP=> Changes in neural responses based on spatial attention have been demonstrated in many areas of visual cortex, indicating that the neural correlate of attention is an enhanced response to stimuli at an attended location and reduced responses to stimuli elsewhere. Here we demonstrate non-spatial, feature-based attentional modulation of visual motion processing, and show that attention increases the gain of direction-selective neurons in visual cortical area MT without narrowing the direction-tuning curves. These findings place important constraints on the neural mechanisms of attention and we propose to unify the effects of spatial location, direction of motion and other features of the attended stimuli in a ‘feature similarity gain model’ of attention.

Gradient-based learning applied to document recognition <=SEP=> 

Condition monitoring of helicopter drive shafts using quadratic-nonlinearity metric based on cross-bispectrum <=SEP=> Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.

Internet security architecture <=SEP=> Fear of security breaches has been a major r eason f or the business world’ s reluctance to embrace the Inter net as a viable means of communication. A widely adopted solution consists of physically separating private networks from the rest of Internet using firewalls. This paper discusses the curr ent cryptographic security measures available for the Internet infrastructur e as an alternative to physical segregation. First the IPsec ar chitecture including security protocols in the Internet Layer and the related key management pr oposals are introduced. The transport layer security protocol and security issues in the netw ork control and management are then presented. The paper is addr essed to r eaders with a basic understanding of common security mechanisms including encryption, authentication and key exchange techniques.

Client attachment, attachment to the therapist and client-therapist attachment match: how do they relate to change in psychodynamic psychotherapy? <=SEP=> OBJECTIVE
We examined the associations between client attachment, client attachment to the therapist, and symptom change, as well as the effects of client-therapist attachment match on outcome. Clients (n = 67) and their therapists (n = 27) completed the ECR to assess attachment.


METHOD
Clients completed also the Client Attachment to Therapist scale three times (early, middle, and late sessions) and the OQ-45 at intake and four times over the course of a year of psychodynamic psychotherapy.


RESULTS
Clients characterized by avoidant attachment and by avoidant attachment to their therapist showed the least improvement. A low-avoidant client-therapist attachment match led to a greater decrease in symptom distress than when a low-avoidant therapist treated a high-avoidant client.


CONCLUSIONS
These findings suggest the importance of considering client-therapist attachment matching and the need to pay attention to the special challenges involved in treating avoidant clients in order to facilitate progress in psychotherapy.

Prioritized multi-task compliance control of redundant manipulators <=SEP=> We propose a new approach for dynamic control of redundant manipulators to deal with multiple prioritized tasks at the same time by utilizing null space projection techniques. The compliance control law is based on a new representation of the dynamics wherein specific null space velocity coordinates are introduced. These allow to efficiently exploit the kinematic redundancy according to the task hierarchy and lead to a dynamics formulation with block-diagonal inertia matrix. The compensation of velocity-dependent coupling terms between the tasks by an additional passive feedback action facilitates a stability analysis for the complete hierarchy based on semi-definite Lyapunov functions. No external forces have to be measured. Finally, the performance of the control approach is evaluated in experiments on a torque-controlled robot. © 2015 Elsevier Ltd. All rights reserved.

The Concept of Flow <=SEP=> What constitutes a good life? Few questions are of more fundamental importance to a positive psychology. Flow research has yielded one answer, providing an understanding of experiences during which individuals are fully involved in the present moment. Viewed through the experiential lens of flow, a good life is one that is characterized by complete absorption in what one does. In this chapter, we describe the flow model of optimal experience and optimal development, explain how flow and related constructs have been measured, discuss recent work in this area, and identify some promising directions for future research.

Corporate social responsibility and bank customer satisfaction A research agenda <=SEP=> Purpose – The purpose of this paper is to explore the relationship between corporate social responsibility (CSR) and customer outcomes. Design/methodology/approach – This paper reviews the literature on CSR effects and satisfaction, noting gaps in the literature. Findings – A series of propositions is put forward to guide future research endeavours. Research limitations/implications – By understanding the likely impact on customer satisfaction of CSR initiatives vis-à-vis customer-centric initiatives, the academic research community can assist managers to understand how to best allocate company resources in situations of low customer satisfaction. Such endeavours are managerially relevant and topical. Researchers seeking to test the propositions put forward in this paper would be able to gain links with, and possibly attract funding from, banks to conduct their research. Such endeavours may assist researchers to redefine the stakeholder view by placing customers at the centre of a network of stakeholders. Practical implications – An understanding of how to best allocate company resources to increase the proportion of satisfied customers will allow bank marketers to reduce customer churn and hence increase market share and profits. Originality/value – Researchers have not previously conducted a comparative analysis of the effects of different CSR initiatives on customer satisfaction, nor considered whether more customer-centric initiatives are likely to be more effective in increasing the proportion of satisfied customers.

Essential Layers, Artifacts, and Dependencies of Enterprise Architecture <=SEP=> After a period where implementation speed was more important than integration, consistency and reduction of complexity, architectural considerations have become a key issue of information management in recent years again. Enterprise architecture is widely accepted as an essential mechanism for ensuring agility and consistency, compliance and efficiency. Although standards like TOGAF and FEAF have developed, however, there is no common agreement on which architecture layers, which artifact types and which dependencies constitute the essence of enterprise architecture. This paper contributes to the identification of essential elements of enterprise architecture by (1) specifying enterprise architecture as a hierarchical, multilevel system comprising aggregation hierarchies, architecture layers and views, (2) discussing enterprise architecture frameworks with regard to essential elements, (3) proposing interfacing requirements of enterprise architecture with other architecture models and (4) matching these findings with current enterprise architecture practice in several large companies.

Agent-based control for decentralised demand side management in the smart grid <=SEP=> Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).

Maxillary length at 11-14 weeks of gestation in fetuses with trisomy 21. <=SEP=> OBJECTIVE
To determine the value of measuring maxillary length at 11-14 weeks of gestation in screening for trisomy 21.


METHODS
In 970 fetuses ultrasound examination was carried out for measurement of crown-rump length (CRL), nuchal translucency and maxillary length, and to determine if the nasal bone was present or absent, immediately before chorionic villus sampling for karyotyping at 11-14 weeks of gestation. In 60 cases the maxillary length was measured twice by the same operator to calculate the intraobserver variation in measurements.


RESULTS
The median gestation was 12 (range, 11-14) weeks. The maxilla was successfully examined in all cases. The mean difference between paired measurements of maxillary length was -0.012 mm and the 95% limits of agreement were -0.42 (95% CI, -0.47 to -0.37) to 0.40 (95% CI, 0.35 to 0.44) mm. The fetal karyotype was normal in 839 pregnancies and abnormal in 131, including 88 cases of trisomy 21. In the chromosomally normal group the maxillary length increased significantly with CRL from a mean of 4.8 mm at a CRL of 45 mm to 8.3 mm at a CRL of 84 mm. In the trisomy 21 fetuses the maxillary length was significantly shorter than normal by 0.7 mm and in the trisomy 21 fetuses with absent nasal bone the maxilla was shorter than in those with present nasal bone by 0.5 mm. In fetuses with other chromosomal defects there were no significant differences from normal in the maxillary length.


CONCLUSION
At 11-14 weeks of gestation, maxillary length in trisomy 21 fetuses is significantly shorter than in normal fetuses.

From e-commerce to social commerce: A close look at design features <=SEP=> E-commerce is undergoing an evolution through the adoption of Web 2.0 capabilities to enhance customer participation and achieve greater economic value. This new phenomenon is commonly referred to as social commerce, however it has not yet been fully understood. In addition to the lack of a stable and agreed-upon definition, there is little research on social commerce and no significant research dedicated to the design of social commerce platforms. This study offers literature review to explain the concept of social commerce, tracks its nascent state-of-the-art, and discusses relevant design features as they relate to e-commerce and Web 2.0. We propose a new model and a set of principles for guiding social commerce design. We also apply the model and guidelines to two leading social commerce platforms, Amazon and Starbucks on Facebook. The findings indicate that, for any social commerce website, it is critical to achieve a minimum set of social commerce design features. These design features must cover all the layers of the proposed model, including the individual, conversation, community and commerce levels. 2012 Elsevier B.V. All rights reserved.

A spectrogram-based audio fingerprinting system for content-based copy detection <=SEP=> This paper presents a novel audio fingerprinting method that is highly robust to a variety of audio distortions. It is based on an unconventional audio fingerprint generation scheme. The robustness is achieved by generating different versions of the spectrogram matrix of the audio signal by using a threshold based on the average of the spectral values to prune this matrix. We transform each version of this pruned spectrogram matrix into a 2-D binary image. Multiple versions of these 2-D images suppress noise to a varying degree. This varying degree of noise suppression improves likelihood of one of the images matching a reference image. To speed up matching, we convert each image into an n-dimensional vector, and perform a nearest neighbor search based on this n-dimensional vector. We give results with two different feature parameters and their combination. We test this method on TRECVID 2010 content-based copy detection evaluation dataset, and we validate the performance on TRECVID 2009 dataset also. Experimental results show the effectiveness of these features even when the audio is distorted. We compare the proposed method to two state-of-the-art audio copy detection systems, namely NN-based and Shazam systems. Our method by far outperforms Shazam system for all audio transformations (or distortions) in terms of detection performance, number of missed queries and localization accuracy. Compared to NN-based system, our approach reduces minimal Normalized Detection Cost Rate (min NDCR) by 23 % and improves localization accuracy by 24 %.

More than Just Two Sexes: The Neural Correlates of Voice Gender Perception in Gender Dysphoria <=SEP=> Gender dysphoria (also known as "transsexualism") is characterized as a discrepancy between anatomical sex and gender identity. Research points towards neurobiological influences. Due to the sexually dimorphic characteristics of the human voice, voice gender perception provides a biologically relevant function, e.g. in the context of mating selection. There is evidence for a better recognition of voices of the opposite sex and a differentiation of the sexes in its underlying functional cerebral correlates, namely the prefrontal and middle temporal areas. This fMRI study investigated the neural correlates of voice gender perception in 32 male-to-female gender dysphoric individuals (MtFs) compared to 20 non-gender dysphoric men and 19 non-gender dysphoric women. Participants indicated the sex of 240 voice stimuli modified in semitone steps in the direction to the other gender. Compared to men and women, MtFs showed differences in a neural network including the medial prefrontal gyrus, the insula, and the precuneus when responding to male vs. female voices. With increased voice morphing men recruited more prefrontal areas compared to women and MtFs, while MtFs revealed a pattern more similar to women. On a behavioral and neuronal level, our results support the feeling of MtFs reporting they cannot identify with their assigned sex.

Security and compliance challenges in complex IT outsourcing arrangements: A multi-stakeholder perspective <=SEP=> Complex IT outsourcing arrangements promise numerous benefits such as increased cost predictability and reduced costs, higher flexibility and scalability upon demand. Organizations trying to realize these benefits, however, face several security and compliance challenges. In this article, we investigate the pressure to take action with respect to such challenges and discuss avenues toward promising responses. We collected perceptions on security and compliance challenges from multiple stakeholders by means of a series of interviews and an online survey, first, to analyze the current and future relevance of the challenges as well as potential adverse effects on organizational performance and, second, to discuss the nature and scope of potential responses. The survey participants confirmed the current and future relevance of the six challenges auditing clouds, managing heterogeneity of services, coordinating involved parties, managing relationships between clients and vendors, localizing and migrating data and coping with lack of security awareness. Additionally, they perceived these challenges as affecting organizational performance adversely in case they are not properly addressed. Responses in form of organizational measures were considered more promising than technical ones concerning all challenges except localizing and migrating data, for which the opposite was true. Balancing relational and contractual governance as well as employing specific client and vendor capabilities is essential for the success of IT outsourcing arrangements, yet do not seem sufficient to overcome the investigated challenges. Innovations connecting the technical perspective of utility software with the business perspective of application software relevant for security and compliance management, however, nourish the hope that the benefits associated with complex IT outsourcing arrangements can be realized in the foreseeable future whilst addressing the security and compliance challenges. a 2013 Elsevier Ltd. All rights reserved. 61. .fraunhofer.de (D. Bachlechner), stefan.thalmann@uibk.ac.at (S. Thalmann), ronald.maier@ ier Ltd. All rights reserved. c om p u t e r s & s e c u r i t y 4 0 ( 2 0 1 4 ) 3 8e5 9 39

Detection of high variability in gene expression from single-cell RNA-seq profiling <=SEP=> The advancement of the next-generation sequencing technology enables mapping gene expression at the single-cell level, capable of tracking cell heterogeneity and determination of cell subpopulations using single-cell RNA sequencing (scRNA-seq). Unlike the objectives of conventional RNA-seq where differential expression analysis is the integral component, the most important goal of scRNA-seq is to identify highly variable genes across a population of cells, to account for the discrete nature of single-cell gene expression and uniqueness of sequencing library preparation protocol for single-cell sequencing. However, there is lack of generic expression variation model for different scRNA-seq data sets. Hence, the objective of this study is to develop a gene expression variation model (GEVM), utilizing the relationship between coefficient of variation (CV) and average expression level to address the over-dispersion of single-cell data, and its corresponding statistical significance to quantify the variably expressed genes (VEGs). We have built a simulation framework that generated scRNA-seq data with different number of cells, model parameters, and variation levels. We implemented our GEVM and demonstrated the robustness by using a set of simulated scRNA-seq data under different conditions. We evaluated the regression robustness using root-mean-square error (RMSE) and assessed the parameter estimation process by varying initial model parameters that deviated from homogeneous cell population. We also applied the GEVM on real scRNA-seq data to test the performance under distinct cases. In this paper, we proposed a gene expression variation model that can be used to determine significant variably expressed genes. Applying the model to the simulated single-cell data, we observed robust parameter estimation under different conditions with minimal root mean square errors. We also examined the model on two distinct scRNA-seq data sets using different single-cell protocols and determined the VEGs. Obtaining VEGs allowed us to observe possible subpopulations, providing further evidences of cell heterogeneity. With the GEVM, we can easily find out significant variably expressed genes in different scRNA-seq data sets.

Design and control of underactuated tendon-driven mechanisms <=SEP=> Many robotic hands or prosthetic hands have been developed in the last several decades, and many use tendon-driven mechanisms for their transmissions. Robotic hands are now built with underactuated mechanisms, which have fewer actuators than degrees of freedom, to reduce mechanical complexity or to realize a biomimetic motion such as flexion of an index finger. The design is heuristic and it is useful to develop design methods for the underactuated mechanisms. This paper classifies mechanisms driven by tendons into three classes, and proposes a design method for them. The two classes are related to underactuated tendon-driven mechanisms, and these have been used without distinction so far. An index finger robot, which has four active tendons and two passive tendons, is developed and controlled with the proposed method.

Semi-Supervised Learning with the Deep Rendering Mixture Model <=SEP=> Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a probabilistic generative model that models latent nuisance variation, and whose inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to learn from both labeled and unlabeled data. Guided by the theory of the DRMM, we introduce a novel nonnegativity constraint and a variational inference term. We report state-of-the-art performance on MNIST and SVHN and competitive results on CIFAR10. We also probe deeper into how a DRMM trained in a semi-supervised setting represents latent nuisance variation using synthetically rendered images. Taken together, our work provides a unified framework for supervised, unsupervised, and semisupervised learning.

Visual analytics of spatial interaction patterns for pandemic decision support <=SEP=> This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Population mobility, i.e. the movement and contact of individuals across geographic space, is one of the essential factors that determine the course of a pandemic disease spread. This research views both individual-based daily activities and a pandemic spread as spatial interaction problems, where locations interact with each other via the visitors that they share or the virus that is transmitted from one place to another. The research proposes a general visual analytic approach to synthesize very large spatial interaction data and discover interesting (and unknown) patterns. The proposed approach involves a suite of visual and computational techniques, including (1) a new graph partitioning method to segment a very large interaction graph into a moderate number of spatially contiguous subgraphs (regions); (2) a reorderable matrix, with regions 'optimally' ordered on the diagonal, to effectively present a holistic view of major spatial interaction patterns; and (3) a modified flow map, interactively linked to the reorderable matrix, to enable pattern interpretation in a geographical context. The implemented system is able to visualize both people's daily movements and a disease spread over space in a similar way. The discovered spatial interaction patterns provide valuable insight for designing effective pandemic mitigation strategies and supporting decision-making in time-critical situations.

On Bayesian Upper Confidence Bounds for Bandit Problems <=SEP=> Stochastic bandit problems have been analyzed from two different perspectives: a frequentist view, where the parameter is a deterministic unknown quantity, and a Bayesian approach, where the parameter is drawn from a prior distribution. We show in this paper that methods derived from this second perspective prove optimal when evaluated using the frequentist cumulated regret as a measure of performance. We give a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution. For binary bandits, we prove that the corresponding algorithm, termed BayesUCB, satisfies finite-time regret bounds that imply its asymptotic optimality. More generally, Bayes-UCB appears as an unifying framework for several variants of the UCB algorithm addressing different bandit problems (parametric multi-armed bandits, Gaussian bandits with unknown mean and variance, linear bandits). But the generality of the Bayesian approach makes it possible to address more challenging models. In particular, we show how to handle linear bandits with sparsity constraints by resorting to Gibbs sampling.

Psychometric Properties of Physical Activity and Leisure Motivation Scale in Farsi: an International Collaborative Project on Motivation for Physical Activity and Leisure. <=SEP=> BACKGROUND
Given the importance of regular physical activity, it is crucial to evaluate the factors favoring participation in physical activity. We aimed to report the psychometric analysis of the Farsi version of the Physical Activity and Leisure Motivation Scale (PALMS).


METHODS
The Farsi version of PALMS was completed by 406 healthy adult individuals to test its factor structure and concurrent validity and reliability.


RESULTS
Conducting the exploratory factor analysis revealed nine factors that accounted for 64.6% of the variances. The PALMS reliability was supported with a high internal consistency of 0.91 and a high test-retest reliability of 0.97 (95% CI: 0.97-0.98). The association between the PALMS and its previous version Recreational Exercise Motivation Measure scores was strongly significant (r= 0.86, P < 0.001).


CONCLUSION
We have shown that the Farsi version of the PALMS appears to be a valuable instrument to measure motivation for physical activity and leisure.

An extended online purchase intention model for middle-aged online users <=SEP=> This article focuses on examining the determinants and mediators of the purchase intention of nononline purchasers between ages 31 and 60 who mostly have strong purchasing power. It propose anew online purchase intention model by integrating the technology acceptance model with additional determinants and adding habitual online usage as a new mediator. Based on a sample of more than 300 middle-aged non-online purchasers, beyond some situationally-specific predictor variables, online purchasing attitude and habitual online usage are key mediators. Personal awareness of security only affects habitual online usage, t indicating a concern of middle-aged users. Habitual online usage is a

Maximum Complex Task Assignment: Towards Tasks Correlation in Spatial Crowdsourcing <=SEP=> Spatial crowdsourcing has gained emerging interest from both research communities and industries. Most of current spatial crowdsourcing frameworks assume independent and atomic tasks. However, there could be some cases that one needs to crowdsource a spatial complex task which consists of some spatial sub-tasks (i.e., tasks related to a specific location). The spatial complex task's assignment requires assignments of all of its sub-tasks. The currently available frameworks are inapplicable to such kind of tasks. In this paper, we introduce a novel approach to crowdsource spatial complex tasks. We first formally define the Maximum Complex Task Assignment (MCTA) problem and propose alternative solutions. Subsequently, we perform various experiments using both real and synthetic datasets to investigate and verify the usability of our proposed approach.

A survey on policy search algorithms for learning robot controllers in a handful of trials <=SEP=> Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word “big-data”, we refer to this challenge as “micro-data reinforcement learning”. We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

Visual Analysis of TED Talk Topic Trends <=SEP=> TED Talks are short, powerful talks given by some of the world's brightest minds - scientists, philanthropists, businessmen, artists, and many others. Funded by members and advertising, these talks are free to access by the public on the TED website and TED YouTube channel, and many videos have become viral phenomena. In this research project, we perform a visual analysis of TED Talk videos and playlists to gain a good understanding of the trends and relationships between TED Talk topics.

Fine-grained Concept Linking using Neural Networks in Healthcare <=SEP=> To unlock the wealth of the healthcare data, we often need to link the real-world text snippets to the referred medical concepts described by the canonical descriptions. However, existing healthcare concept linking methods, such as dictionary-based and simple machine learning methods, are not effective due to the word discrepancy between the text snippet and the canonical concept description, and the overlapping concept meaning among the fine-grained concepts. To address these challenges, we propose a Neural Concept Linking (NCL) approach for accurate concept linking using systematically integrated neural networks. We call the novel neural network architecture as the COMposite AttentIonal encode-Decode neural network (COM-AID). COM-AID performs an encode-decode process that encodes a concept into a vector and decodes the vector into a text snippet with the help of two devised contexts. On the one hand, it injects the textual context into the neural network through the attention mechanism, so that the word discrepancy can be overcome from the semantic perspective. On the other hand, it incorporates the structural context into the neural network through the attention mechanism, so that minor concept meaning differences can be enlarged and effectively differentiated. Empirical studies on two real-world datasets confirm that the NCL produces accurate concept linking results and significantly outperforms state-of-the-art techniques.

Structural XML Classification in Concept Drifting Data Streams <=SEP=> Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most existing XML classifiers are capable of processing only static data, there is a need to develop new approaches dedicated for streaming environments. In this paper, we propose a new classification algorithm for XML data streams called XSC. The algorithm uses incrementally mined frequent subtrees and a tree-subtree similarity measure to classify new documents in an associative manner. The proposed approach is experimentally evaluated against eight state-of-the-art stream classifiers on real and synthetic data. The results show that XSC performs significantly better than competitive algorithms in terms of accuracy and memory usage.