Query: Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

Relevant passages:
The neural correlates of maternal and romantic love <=SEP=> Romantic and maternal love are highly rewarding experiences. Both are linked to the perpetuation of the species and therefore have a closely linked biological function of crucial evolutionary importance. Yet almost nothing is known about their neural correlates in the human. We therefore used fMRI to measure brain activity in mothers while they viewed pictures of their own and of acquainted children, and of their best friend and of acquainted adults as additional controls. The activity specific to maternal attachment was compared to that associated to romantic love described in our earlier study and to the distribution of attachment-mediating neurohormones established by other studies. Both types of attachment activated regions specific to each, as well as overlapping regions in the brain's reward system that coincide with areas rich in oxytocin and vasopressin receptors. Both deactivated a common set of regions associated with negative emotions, social judgment and 'mentalizing', that is, the assessment of other people's intentions and emotions. We conclude that human attachment employs a push-pull mechanism that overcomes social distance by deactivating networks used for critical social assessment and negative emotions, while it bonds individuals through the involvement of the reward circuitry, explaining the power of love to motivate and exhilarate.

Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. <=SEP=> Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images <=SEP=> Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.

The rises and falls of disconnection syndromes. <=SEP=> In a brain composed of localized but connected specialized areas, disconnection leads to dysfunction. This simple formulation underlay a range of 19th century neurological disorders, referred to collectively as disconnection syndromes. Although disconnectionism fell out of favour with the move against localized brain theories in the early 20th century, in 1965, an American neurologist brought disconnection to the fore once more in a paper entitled, 'Disconnexion syndromes in animals and man'. In what was to become the manifesto of behavioural neurology, Norman Geschwind outlined a pure disconnectionist framework which revolutionized both clinical neurology and the neurosciences in general. For him, disconnection syndromes were higher function deficits that resulted from white matter lesions or lesions of the association cortices, the latter acting as relay stations between primary motor, sensory and limbic areas. From a clinical perspective, the work reawakened interest in single case studies by providing a useful framework for correlating lesion locations with clinical deficits. In the neurosciences, it helped develop contemporary distributed network and connectionist theories of brain function. Geschwind's general disconnectionist paradigm ruled clinical neurology for 20 years but in the late 1980s, with the re-emergence of specialized functional roles for association cortex, the orbit of its remit began to diminish and it became incorporated into more general models of higher dysfunction. By the 1990s, textbooks of neurology were devoting only a few pages to classical disconnection theory. Today, new techniques to study connections in the living human brain allow us, for the first time, to test the classical formulation directly and broaden it beyond disconnections to include disorders of hyperconnectivity. In this review, on the 40th anniversary of Geschwind's publication, we describe the changing fortunes of disconnection theory and adapt the general framework that evolved from it to encompass the entire spectrum of higher function disorders in neurology and psychiatry.

EEG-correlated fMRI of human alpha activity <=SEP=> Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to spontaneous power fluctuations in the alpha rhythm (8-12 Hz), the dominant EEG pattern during relaxed wakefulness. Thirty-two channels of EEG were recorded in 10 subjects during eyes-closed rest inside a 1.5-T magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 4 s. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data during fMRI acquisition. The average alpha power over 1-s epochs was derived at several electrode positions using a Fast Fourier Transform. The power time course was then convolved with a canonical hemodynamic response function, down-sampled, and used for statistical parametric mapping of associated signal changes in the image time series. At all electrode positions studied, a strong negative correlation of parietal and frontal cortical activity with alpha power was found. Conversely, only sparse and nonsystematic positive correlation was detected. The relevance of these findings is discussed in view of the current theories on the generation and significance of the alpha rhythm and the related functional neuroimaging findings.

An evaluation framework for viable business models for m-commerce in the information technology sector <=SEP=> This paper presents a study of the characteristics of viable business models in the field of Mobile Commerce (m-commerce). Mobility has given new dimensions to the way commerce works. All over the world various stakeholder organisations are consistently probing into the areas where m-commerce can be exploited and can generate revenue or value for them, even though some of those implementations are making the business environment more complex and uncertain. This paper proposes a viable business model evaluation framework, based on the VISOR model, which helps in determining the sustainability capabilities of a business model. Four individual cases were conducted with diverse organisations in the Information Technology sector. The four cases discussed dealt with mobile business models and the primary data was collected via semi structured interviews, supplemented by an extensive range of secondary data. A cross-case comparative data analysis was used to review the patterns of different viable business components across the four cases and, finally, the findings and conclusions of the study are presented.

Delving into adversarial attacks on deep policies <=SEP=> Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs. random noise. We present a novel method for reducing the number of times adversarial examples need to be injected for a successful attack, based on the value function. We further explore how re-training on random noise and FGSM perturbations affects the resilience against adversarial examples.

Columbia-IBM news video story segmentation in trecvid 2004 <=SEP=> In this technical report, we give an overview our technical developments in the story segmentation task in TRECVID 2004. Among them, we propose an information-theoretic framework, visual cue cluster construction (VC), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. When tested in TRECVID 2004 news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually; meanwhile, it achieved one of the top performances, F1=0.65, close to the highest performance, F1=0.69, by other groups. We also experiment with other promising visual features and continue investigating effective prosody features. The introduction of post-processing also provides practical improvements. Furthermore, the fusion from other modalities, such as speech prosody features and ASR-based segmentation scores are significant and have been confirmed again in this experiment.

Teachable robots: Understanding human teaching behavior to build more effective robot learners <=SEP=> While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback — possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. Given this, we made specific modifications to the simulated RL robot, and analyzed and evaluated its learning behavior in four additional experiments with human trainers. We report significant improvements on several learning measures. This work demonstrates the importance of understanding the human-teacher/robot-learner partnership in order to design algorithms that support how people want to teach while simultaneously improving the robot’s learning behavior.

Key Estimation in Electronic Dance Music <=SEP=> In this paper we study key estimation in electronic dance music, an umbrella term referring to a variety of electronic music subgenres intended for dancing at nightclubs and raves. We start by defining notions of tonality and key before outlining the basic architecture of a template-based key estimation method. Then, we report on the tonal characteristics of electronic dance music, in order to infer possible modifications of the method described. We create new key profiles combining these observations with corpus analysis, and add two pre-processing stages to the basic algorithm. We conclude by comparing our profiles to existing ones, and testing our modifications on independent datasets of pop and electronic dance music, observing interesting improvements in the performance or our algorithms, and suggesting paths for future research.

Detection and Discrimination of Open-Phase Fault in Permanent Magnet Synchronous Motor Drive System <=SEP=> Open-phase fault in permanent magnet synchronous motor (PMSM) drive system occurs as the phase winding is disconnected or one leg of the inverter bridge fails. It may generate large electromagnetic torque ripple and serious mechanical vibration. Therefore, a rapid fault detection method is greatly required to identify this fault at early stage and prevent damage to the system. This paper develops a method of the open-phase fault detection and discrimination for the PMSM drive system based on the zero-sequence voltage components, in which the discrimination of the fault types, namely internal stator winding failure and switches failure of the inverter is realized. Then, appropriate fault-tolerant measures may be taken according to the different fault types. The experimental platform is established, and the experimental results verify the effectiveness of the proposed method, showing that not only the open-phase fault can be rapidly detected, but also the fault type can be effectively discriminated.

Google App Engine <=SEP=> Cloud computing becomes more popular day by day. The “dream” of users to “to get rid” of administrators, huge investments on expensive hardware and human resources becomes true. The goal of this paper is to present a general overview of the new coming service offered by Google company Google App Engine. “What is it for?”, “What can I do with the system?” and “How does it work?” are some of the questions on which this paper will try to give an answer. In addition, brief overview and description of “cloud computing” and comparison of Google App Engine with other similar products will be provided to the reader as well.

A User's Guide to Compressed Sensing for Communications Systems <=SEP=> This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control. key words: compressed sensing, sparse signal, compressible signal, 1norm, underdetermined system

The utility of low frequency heart rate variability as an index of sympathetic cardiac tone: a review with emphasis on a reanalysis of previous studies. <=SEP=> This article evaluates the suitability of low frequency (LF) heart rate variability (HRV) as an index of sympathetic cardiac control and the LF/high frequency (HF) ratio as an index of autonomic balance. It includes a comprehensive literature review and a reanalysis of some previous studies on autonomic cardiovascular regulation. The following sources of evidence are addressed: effects of manipulations affecting sympathetic and vagal activity on HRV, predictions of group differences in cardiac autonomic regulation from HRV, relationships between HRV and other cardiac parameters, and the theoretical and mathematical bases of the concept of autonomic balance. Available data challenge the interpretation of the LF and LF/HF ratio as indices of sympathetic cardiac control and autonomic balance, respectively, and suggest that the HRV power spectrum, including its LF component, is mainly determined by the parasympathetic system.

Solving ill-posed inverse problems using iterative deep neural networks <=SEP=> We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularization theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularizing functional. The method results in a gradient-like iterative scheme, where the “gradient” component is learned using a convolutional network that includes the gradients of the data discrepancy and regularizer as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512× 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Two-stream Attentive CNNs for Image Retrieval <=SEP=> In content-based image retrieval, the most challenging (and ambiguous) part is to define the similarity between images. For the human-being, such similarity can be defined with respect to where they pay attention to and what semantic attributes they understand. Inspired by this fact, this paper presents two-stream attentive CNNs for image retrieval. As the human-being does, the proposed network has two streams that simultaneously handle two tasks. The Main stream focuses on extracting discriminative visual features that are tightly correlated with semantic attributes. Meanwhile, the Auxiliary stream aims to facilitate the main stream by redirecting the feature extraction operation mainly to the image content that human may pay attention to. By fusing these two streams into the Main and Auxiliary CNNs (MAC), image similarity can be computed as the human-being does by reserving the conspicuous content and suppressing the irrelevant regions. Extensive experiments show that the proposed model achieves impressive performance in image retrieval on four public datasets.

A Hierarchical Recurrent Neural Network for Symbolic Melody Generation <=SEP=> In recent years, neural networks have been used to generate symbolic melodies. However, the long-term structure in the melody has posed great difficulty for designing a good model. In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time. Specifically, the three subnetworks generate bar profiles, beat profiles and notes in turn, and the output of the high-level subnetworks are fed into the low-level subnetworks, serving as guidance for generating the finer time-scale melody components in low-level subnetworks. Two human behavior experiments demonstrate the advantage of this structure over the single-layer LSTM which attempts to learn all hidden structures in melodies. Compared with the state-of-the-art models MidiNet (Yang, Chou, and Yang 2017) and MusicVAE (Roberts et al. 2018), the hierarchical recurrent neural network produces better melodies evaluated by humans. Introduction Automatic music generation using neural networks has attracted much attention. There are two classes of music generation approaches, symbolic music generation (Hadjeres, Pachet, and Nielsen 2017)(Waite et al. 2016)(Yang, Chou, and Yang 2017) and audio music generation (van den Oord et al. )(Mehri et al. 2016). In this study, we focus on symbolic melody generation, which requires learning from sheet music. Many music genres such as pop music consist of melody and harmony. Since usually beautiful harmonies can be ensured by using legitimate chord progressions which have been summarized by musicians, we only focus on melody generation, similar to some recent studies (Waite et al. 2016)(Yang, Chou, and Yang 2017)(Colombo, Seeholzer, and Gerstner 2017)(Roberts et al. 2018). This greatly simplifies the melody generation problem. Melody is a linear succession of musical notes along time. It has both short time scale such as notes and long time scale such as phrases and movements, which makes the melody generation a challenging task. Existing methods generate pitches and rhythm simultaneously (Waite et al. 2016) or sequentially (Chu, Urtasun, and Fidler 2016) using Recurrent Neural Networks (RNNs), but they usually work on the note scale without explicitly modeling the larger time-scale components such as rhythmic patterns. It is difficult for them to learn long-term dependency or structure in melody. Theoretically, an RNN can learn the temporal structure of any length in the input sequence, but in reality, as the sequence gets longer it is very hard to learn long-term structure. Different RNNs have different learning capability, e.g., LSTM (Hochreiter and Schmidhuber 1997) performs much better than the simple Elman network. But any model has a limit for the length of learnable structure, and this limit depends on the complexity of the sequence to be learned. To enhance the learning capability of an RNN, one approach is to invent a new structure. In this work we take another approach: increase the granularity of the input. Since each symbol in the sequence corresponds to longer segment than the original representation, the same model would learn longer temporal structure. To implement this idea, we propose a Hierarchical Recurrent Neural Network (HRNN) for learning melody. It consists of three LSTM-based sequence generators — Bar Layer, Beat Layer and Note Layer. The Bar Layer and Beat Layer are trained to generate bar profiles and beat profiles, which are designed to represent the high-level temporal features of melody. The Note Layer is trained to generate melody conditioned on the bar profile sequence and beat profile sequence output by the Bar Layer and Beat Layer. By learning on different time scales, the HRNN can grasp the general regular patterns of human composed melodies in different granularities, and generate melody with realistic long-term structures. This method follows the general idea of granular computing (Bargiela and Pedrycz 2012), in which different resolutions of knowledge or information is extracted and represented for problem solving. With the shorter profile sequences to guide the generation of note sequence, the difficulty of generating note sequence with wellorganized structure is alleviated. Related Work Melody Generation with Neural Networks There is a long history of generating melody with RNNs. A recurrent autopredictive connectionist network called CONCERT is used to compose music (Mozer 1994). With a set of composition rules as constraints to evaluate ar X iv :1 71 2. 05 27 4v 2 [ cs .S D ] 5 S ep 2 01 8 melodies, an evolving neural network is employed to create melodies (Chen and Miikkulainen 2001). As an important form of RNN, LSTM (Hochreiter and Schmidhuber 1997) is used to capture the global music structure and improve the quality of the generated music (Eck and Schmidhuber 2002). Boulanger-Lewandowski, Bengio, and Vincent explore complex polyphonic music generation with an RNNRBM model (Boulanger-Lewandowski, Bengio, and Vincent 2012). Lookback RNN and Attention RNN are proposed to tackle the problem of creating melody’s long-term structure (Waite et al. 2016). The Lookback RNN introduces a handcrafted lookback feature that makes the model repeat sequences easier while the Attention RNN leverages an attention mechanism to learn longer-term structures. Inspired by convolution, two variants of RNN are employed to attain transposition invariance (Johnson 2017). To model the relation between rhythm and melody flow, a melody is divided into pitch sequence and duration sequence and these two sequences are processed in parallel (Colombo et al. 2016). This approach is further extended in (Colombo, Seeholzer, and Gerstner 2017). A hierarchical VAE is employed to learn the distribution of melody pieces in (Roberts et al. 2018), the decoder of which is similar to our model. The major difference is that the higher layer of its decoder uses the automatically learned representation of bars, while our higher layers use predefined representation of bars and beats which makes the learning problem easier. Generative Adversarial Networks (GANs) have also been used to generate melodies. For example, RNN-based GAN (Mogren 2016) and CNN-based GAN (Yang, Chou, and Yang 2017) are employed to generate melodies, respectively. However, the generated melodies also lack realistic long-term structures. Some models are proposed to generate multi-track music. A 4-layer LSTM is employed to produce the key, press, chord and drum of pop music seperately (Chu, Urtasun, and Fidler 2016). With pseudo-Gibbs sampling, a model can generate highly convincing chorales in the style of Bach (Colombo, Seeholzer, and Gerstner 2017). Three GANs for symbolic-domain multi-track music generation were proposed (Dong et al. 2018). An end-to-end melody and arrangement generation framework XiaoIce Band was proposed to generate a melody track with accompany tracks with RNN (Zhu et al. 2018). Hierarchical and Multiple Time Scales Networks The idea of hierarchical or multiple time scales has been used in neural network design, especially in the area of natural language processing. The Multiple Timescale Recurrent Neural Network (MTRNN) realizes the self-organization of a functional hierarchy with two types of neurons “fast” unit and “slow” unit (Yamashita and Tani 2008). Then it is shown that the MTRNN can acquire the capabilities to recognize, generate, and correct sentences in a hierarchical way: characters grouped into words, and words into sentences (Hinoshita et al. 2011). An LSTM auto-encoder is trained to preserve and reconstruct paragraphs by hierarchically building embeddings of words, sentences and paragraphs (Li, Luong, and Jurafsky 2015). To process inputs at multiple time Time signature Bar Key signature

Less guilty by reason of adolescence: developmental immaturity, diminished responsibility, and the juvenile death penalty. <=SEP=> The authors use a developmental perspective to examine questions about the criminal culpability of juveniles and the juvenile death penalty. Under principles of criminal law, culpability is mitigated when the actor's decision-making capacity is diminished, when the criminal act was coerced, or when the act was out of character. The authors argue that juveniles should not be held to the same standards of criminal responsibility as adults, because adolescents' decision-making capacity is diminished, they are less able to resist coercive influence, and their character is still undergoing change. The uniqueness of immaturity as a mitigating condition argues for a commitment to a legal environment under which most youths are dealt with in a separate justice system and none are eligible for capital punishment.

Alzheimer disease therapy—moving from amyloid-β to tau <=SEP=> Disease-modifying treatments for Alzheimer disease (AD) have focused mainly on reducing levels of amyloid-β (Aβ) in the brain. Some compounds have achieved this goal, but none has produced clinically meaningful results. Several methodological issues relating to clinical trials of these agents might explain this failure; an additional consideration is that the amyloid cascade hypothesis—which places amyloid plaques at the heart of AD pathogenesis—does not fully integrate a large body of data relevant to the emergence of clinical AD. Importantly, amyloid deposition is not strongly correlated with cognition in multivariate analyses, unlike hyperphosphorylated tau, neurofibrillary tangles, and synaptic and neuronal loss, which are closely associated with memory deficits. Targeting tau pathology, therefore, might be more clinically effective than Aβ-directed therapies. Furthermore, numerous immunization studies in animal models indicate that reduction of intracellular levels of tau and phosphorylated tau is possible, and is associated with improved cognitive performance. Several tau-related vaccines are in advanced preclinical stages and will soon enter clinical trials. In this article, we present a critical analysis of the failure of Aβ-directed therapies, discuss limitations of the amyloid cascade hypothesis, and suggest the potential value of tau-targeted therapy for AD.

What maintains parental support for vaccination when challenged by anti-vaccination messages? A qualitative study. <=SEP=> This study sought to explore how parents respond to competing media messages about vaccine safety. Six focus groups with mothers of infants were shown television vignettes of typical pro- and anti-vaccination claims. Thematic analysis of transcripts was undertaken. Mothers expressed surprise and concern about alleged vaccine risks but quickly reinstated their support for vaccination by deference to authority figures; type-casting immunisation opponents; and notions of anticipatory regret, good parenting and social responsibility. We conclude that personal experiences, value systems and level of trust in health professionals are fundamental to parental decision making about vaccination. Vaccination advocacy should increase the focus on matters of process such as maintaining trust and public confidence, particularly in health professionals. Stories about people affected by vaccine-preventable diseases need to re-enter the public discourse.

SuggestBot: using intelligent task routing to help people find work in wikipedia <=SEP=> Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

Application-Managed Flash <=SEP=> In flash storage, an FTL is a complex piece of code that resides completely inside the storage device and is provided by the manufacturer. Its principal virtue is providing interoperability with conventional HDDs. However, this virtue is also its biggest impediment in reaching the full performance of the underlying flash storage. We propose to refactor the flash storage architecture so that it relies on a new block I/O interface which does not permit overwriting of data without intervening erasures.We demonstrate how high-level applications, in particular file systems, can deal with this restriction efficiently by employing append-only segments. This refactoring dramatically reduces flash management overhead and improves performance of applications, such as file systems and databases, by permitting them to directly manage flash storage. Our experiments on a machine with the new block I/O interface show that DRAM in the flash controller is reduced by 128X and the performance of the file system improves by 80% over conventional SSDs.

Expected Stock Returns and Volatility <=SEP=> This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility. Disciplines Finance | Finance and Financial Management Comments At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania. This journal article is available at ScholarlyCommons: http://repository.upenn.edu/fnce_papers/363 Expected Stock Returns and Volatility by Kenneth R. French* G. William Schwert** Robert F. Starnbaugh*

Rated aspect summarization of short comments <=SEP=> Web 2.0 technologies have enabled more and more people to freely comment on different kinds of entities (e.g. sellers, products, services). The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments comes with an overall rating. In this paper, we study the problem of generating a ``rated aspect summary'' of short comments, which is a decomposed view of the overall ratings for the major aspects so that a user could gain different perspectives towards the target entity. We formally define the problem and decompose the solution into three steps. We demonstrate the effectiveness of our methods by using eBay sellers' feedback comments. We also quantitatively evaluate each step of our methods and study how well human agree on such a summarization task. The proposed methods are quite general and can be used to generate rated aspect summary automatically given any collection of short comments each associated with an overall rating.

Deep Learning as Feature Encoding for Emotion Recognition <=SEP=> Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of lowlevel descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.

Camera/Laser/GPS Fusion Method for Vehicle Positioning Under Extended NIS-Based Sensor Validation <=SEP=> Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.

mOS: A Reusable Networking Stack for Flow Monitoring Middleboxes <=SEP=> Stateful middleboxes, such as intrusion detection systems and application-level firewalls, have provided key functionalities in operating modern IP networks. However, designing an efficient middlebox is challenging due to the lack of networking stack abstraction for TCP flow processing. Thus, middlebox developers often write the complex flow management logic from scratch, which is not only prone to errors, but also wastes efforts for similar functionalities across applications. This paper presents the design and implementation of mOS, a reusable networking stack for stateful flow processing in middlebox applications. Our API allows developers to focus on the core application logic instead of dealing with low-level packet/flow processing themselves. Under the hood, it implements an efficient event system that scales to monitoring millions of concurrent flow events. Our evaluation demonstrates that mOS enables modular development of stateful middleboxes, often significantly reducing development efforts represented by the source lines of code, while introducing little performance overhead in multi-10Gbps network environments.

Interactive Neural Translation Assistance for Human Translators <=SEP=> We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.

An architecture of diversity for commonsense reasoning <=SEP=> Although computers excel at certain bounded tasks that are difficult for humans, such as solving integrals, they have difficulty performing commonsense tasks that are easy for humans, such as understanding stories. In this Technical Forum contribution, we discuss commonsense reasoning and what makes it difficult for computers. We contend that commonsense reasoning is too hard a problem to solve using any single artificial intelligence technique. We propose a multilevel architecture consisting of diverse reasoning and representation techniques that collaborate and reflect in order to allow the best techniques to be used for the many situations that arise in commonsense reasoning. We present story understanding—specifically, understanding and answering questions about progressively harder children’s texts—as a task for evaluating and scaling up a commonsense reasoning system.

The role of satellites in 5G <=SEP=> The next generation of mobile radio communication systems - so called 5G - will provide some major changes to those generations to date. The ability to cope with huge increases in data traffic at reduced latencies and improved quality of user experience together with major reduction in energy usage are big challenges. In addition future systems will need to embody connections to billions of objects - the so called Internet of Things (IoT) which raise new challenges. Visions of 5G are now available from regions across the World and research is ongoing towards new standards. The consensus is a flatter architecture that adds a dense network of small cells operating in the millimetre wave bands and which are adaptable and software controlled. But what place for satellites in such a vision? The paper examines several potential roles for satellite including coverage extension, content distribution, providing resilience, improved spectrum utilisation and integrated signalling systems.