Query: A hybrid bug triage algorithm for developer recommendation

Relevant passages:
Improving bug triage with bug tossing graphs <=SEP=> bug report is typically assigned to a single developer who is then responsible for fixing the bug. In Mozilla and Eclipse, between 37%-44% of bug reports are "tossed" (reassigned) to other developers, for example because the bug has been assigned by accident or another developer with additional expertise is needed. In any case, tossing increases the time-to-correction for a bug.
 In this paper, we introduce a graph model based on Markov chains, which captures bug tossing history. This model has several desirable qualities. First, it reveals developer networks which can be used to discover team structures and to find suitable experts for a new task. Second, it helps to better assign developers to bug reports. In our experiments with 445,000 bug reports, our model reduced tossing events, by up to 72%. In addition, the model increased the prediction accuracy by up to 23 percentage points compared to traditional bug triaging approaches.

Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports <=SEP=> For a large and evolving software system, the project team could receive a large number of bug reports. Locating the source code files that need to be changed in order to fix the bugs is a challenging task. Once a bug report is received, it is desirable to automatically point out to the files that developers should change in order to fix the bug. In this paper, we propose BugLocator, an information retrieval based method for locating the relevant files for fixing a bug. BugLocator ranks all files based on the textual similarity between the initial bug report and the source code using a revised Vector Space Model (rVSM), taking into consideration information about similar bugs that have been fixed before. We perform large-scale experiments on four open source projects to localize more than 3,000 bugs. The results show that BugLocator can effectively locate the files where the bugs should be fixed. For example, relevant buggy files for 62.60% Eclipse 3.1 bugs are ranked in the top ten among 12,863 files. Our experiments also show that BugLocator outperforms existing state-of-the-art bug localization methods.

Automatic Bug Triage using Semi-Supervised Text Classification <=SEP=> In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy. Keywordsautomatic bug triage; expectation-maximization; semi-supervised text classification; weighted recommendation list

DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking <=SEP=> This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.

Assigning bug reports using a vocabulary-based expertise model of developers <=SEP=> For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming reports is a time consuming task. Part of the bug triage is the assignment of a report to a developer with the appropriate expertise. In this paper, we present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report. We model developer expertise using the vocabulary found in their source code contributions and compare this vocabulary to the vocabulary of bug reports. We evaluate our approach by comparing the suggested experts to the persons who eventually worked on the bug. Using eight years of Eclipse development as a case study, we achieve 33.6% top-1 precision and 71.0% top-10 recall.

Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs <=SEP=> Although business intelligence systems (BIS) adoption research has progressed considerably since its early inceptions, our understanding of the influence of BIS determinants across different adoption stages remains limited. In response, we develop and empirically test a conceptual model for assessing the determinants of BIS diffusion on the evaluation, adoption, and use stages. The model is based on two prominent, firm-level adoption concepts: the diffusion of innovation (DOI) and the technology, organization, environment (TOE) framework, extended with our previous research findings. Drawing on data from 181 small and medium enterprises (SMEs), we identify seven distinct determinants (i.e. cost, BIS is part of ERP, management support, rational decision-making culture, project champion, organizational data environment, organizational readiness) as statistically significant for different adoption stages.

The Stratosphere platform for big data analytics <=SEP=> We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.

Improving Duckworth lewis method by using machine learning <=SEP=> There has been lot of research has been done in the area of sports with prediction. Machine learning algorithm has been applied on most of the data to predict its outcome. In cricket also there are many research has been done to find out the outcome of the match. But there are a special case in the cricket where statistic plays an important role. When the cricket match is interrupted by the rain and then again it starts during the shortage of the time it is difficult to predict the run and overs remaining in the cricket match.To over come this issue Duck worth lewis method has been introduced. In this paper we are going to discuss the short come of the method of Duck worth lewis method. In this research We are going to improve the model of duck worth lewis method with machine learning models. At end we are going to compare the Duck worth lewis method, Duck worth lewis method with run rate as a variable and Improved version of duck worth lewis method.

Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties <=SEP=> This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input. The unknown physical properties is a major challenge for pushing. Push-Net overcomes the challenge by tracking a history of push interactions with an LSTM module and training an auxiliary objective function that estimates an object’s center of mass. We trained Push-Net entirely in simulation and tested it extensively on many different objects in both simulation and on two real robots, a Fetch arm and a Kinova MICO arm. Experiments suggest that Push-Net is robust and efficient. It achieved over 97% success rate in simulation on average and succeeded in all real robot experiments with a small number of pushes.

Big Scholarly Data in CiteSeerX: Information Extraction from the Web <=SEP=> We examine CiteSeerX, an intelligent system designed with the goal of automatically acquiring and organizing large-scale collections of scholarly documents from the world wide web. From the perspective of automatic information extraction and modes of alternative search, we examine various functional aspects of this complex system with an eye towards ongoing and future research developments.

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks <=SEP=> In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.stanfordvl.github.io/ntp/.

Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review <=SEP=> We enhance the autonomy of the continuous active learning method shown by Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted review, in which documents from a collection are retrieved and reviewed, using relevance feedback, until substantially all of the relevant documents have been reviewed. Autonomy is enhanced through the elimination of topic-specific and dataset-specific tuning parameters, so that the sole input required by the user is, at the outset, a short query, topic description, or single relevant document; and, throughout the review, ongoing relevance assessments of the retrieved documents. We show that our enhancements consistently yield superior results to Cormack and Grossman’s version of continuous active learning, and other methods, not only on average, but on the vast majority of topics from four separate sets of tasks: the legal datasets examined by Cormack and Grossman, the Reuters RCV1-v2 subject categories, the TREC 6 AdHoc task, and the construction of the TREC 2002 filtering test collection.

Functional reactive programming from first principles <=SEP=> Functional Reactive Programming, or FRP, is a general framework for programming hybrid systems in a high-level, declarative manner. The key ideas in FRP are its notions of behaviors and events. Behaviors are time-varying, reactive values, while events are time-ordered sequences of discrete-time event occurrences. FRP is the essence of Fran, a domain-specific language embedded in Haskell for programming reactive animations, but FRP is now also being used in vision, robotics and other control systems applications. 
In this paper we explore the formal semantics of FRP and how itrelates to an implementation based on streams that represent (and therefore only approximate) continuous behaviors. We show that, in the limit as the sampling interval goes to zero, the implementation is faithful to the formal, continuous semantics, but only when certain constraints on behaviors are observed. We explore the nature of these constraints, which vary amongst the FRP primitives. Our results show both the power and limitations of this approach to language design and implementation. As an example of a limitation, we show that streams are incapable of representing instantaneous predicate events over behaviors.

A Wide-band Circular Polarization Stacked Patch Antenna for the Wireless Communication Applications <=SEP=> A wide-band ‘corners-truncated rectangular’ stacked patch antenna for use in the circular polarization applications was proposed. The antenna proposed in this paper an axial ratio of less than 3 dB and a VSWR of less than 2 : 1 were shown to be achievable over a 25% bandwidth for use in the wireless communication applications, and this antenna can achieves higher gain, lower side lobes and wider bandwidth compared to the traditional microstrip patch antenna.

An upper limb exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint <=SEP=> This paper presents an upper limb rehabilitation exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint. Traditional designs of shoulder exoskeletons use 3R mechanisms to replicate the spherical motion of the shoulder. However, due to the exceptionally large range of motion of the human shoulder, the 3R mechanism is required to operate at a singular configuration at some point in the shoulder workspace. In this configuration, the 3R mechanism loses the ability to rotate the shoulder about one DOF. To overcome this problem, the use of a kinematically redundant 4R mechanism is proposed. The 4R mechanism has been optimized in a previous work and is used in the design of the presented exoskeleton prototype. This new shoulder mechanism allows the exoskeleton to achieve the entire human shoulder workspace without mechanical interference and while operating well away from singular configurations. Numerous features have also been included in the exoskeleton design to ensure it is safe, comfortable and easy to use. The shoulder mechanism of the exoskeleton is analyzed to demonstrate the capabilities of the 4R mechanism.

Multi-modal human-machine communication for instructing robot grasping tasks <=SEP=> A major challenge for the realization of intelligent robots is to supply them with cognitive abilities in order to allow ordinary users to program them easily and intuitively. One way of such programming is teaching work tasks by interactive demonstration. To make this effective and convenient for the user, the machine must be capable to establish a common focus of attention and be able to use and integrate spoken instructions, visual perceptions, and non-verbal clues like gestural commands. We report progress in building a hybrid architecture that combines statistical methods, neural networks, and finite state machines into an integrated system for instructing grasping tasks by man-machine interaction. The system combines the GRAVIS-robot for visual attention and gestural instruction with an intelligent interface for speech recognition and linguistic interpretation, and an modality fusion module to allow multi-modal task-oriented man-machine communication with respect to dextrous robot manipulation of objects.

Analogy just looks like high level perception: why a domain-general approach to analogical mapping is right <=SEP=> Hofstadter and his colleagues have criticized current accounts of analogy, claiming that such accounts do not accurately capture interactions between processes of representation construction and processes of mapping. They suggest instead that analogy should be viewed as a form of high level perception that encompasses both representation building and mapping as indivisible operations within a single model. They argue speci® cally against SM E, our model of analogical matching, on the grounds that it is modular, and oŒer instead programs such as Mitchell and Hofstadter’ s Copycat as examples of the high level perception approach. In this paper we argue against this position on two grounds. First, we demonstrate that most of their speci® c arguments involving SM E and Copycat are incorrect. Second, we argue that the claim that analogy is high-level perception, while in some ways an attractive metaphor, is too vague to be useful as a technical proposal. We focus on ® ve issues : (1) how perception relates to analogy, (2) how  ̄ exibility arises in analogical processing, (3) whether analogy is a domain-general process, (4) how micro-worlds should be used in the study of analogy, and (5) how best to assess the psychological plausibility of a model of analogy. W e illustrate our discussion with examples taken from computer models embodying both views.

MICA - A new generation of versatile instruments in robotic surgery <=SEP=> Robotic surgery systems are highly complex and expensive pieces of equipment. Demands for lower cost of care can be met if these systems are employable in a flexible manner for a large variety of procedures. To protect the initial investment the capabilities of a robotic system need to be expandable as new tasks arise.

PEEK biomaterials in trauma, orthopedic, and spinal implants. <=SEP=> Since the 1980s, polyaryletherketones (PAEKs) have been increasingly employed as biomaterials for trauma, orthopedic, and spinal implants. We have synthesized the extensive polymer science literature as it relates to structure, mechanical properties, and chemical resistance of PAEK biomaterials. With this foundation, one can more readily appreciate why this family of polymers will be inherently strong, inert, and biocompatible. Due to its relative inertness, PEEK biomaterials are an attractive platform upon which to develop novel bioactive materials, and some steps have already been taken in that direction, with the blending of HA and TCP into sintered PEEK. However, to date, blended HA-PEEK composites have involved a trade-off in mechanical properties in exchange for their increased bioactivity. PEEK has had the greatest clinical impact in the field of spine implant design, and PEEK is now broadly accepted as a radiolucent alternative to metallic biomaterials in the spine community. For mature fields, such as total joint replacements and fracture fixation implants, radiolucency is an attractive but not necessarily critical material feature.

AES-128 ECB encryption on GPUs and effects of input plaintext patterns on performance <=SEP=> In the recent years, the Graphics Processing Units (GPUs) have gained popularity for general purpose applications, immensely outperforming traditional optimized CPU based implementations. A class of such applications implemented on GPUs to achieve faster execution than CPUs include cryptographic techniques like the Advanced Encryption Standard (AES) which is a widely deployed symmetric encryption/decryption scheme in various electronic communication domains. With the drastic advancements in electronic communication technology, and growth in the user space, the size of data exchanged electronically has increased substantially. So, such cryptographic techniques become a bottleneck to fast transfers of information. In this work, we implement the AES-128 ECB Encryption on two of the recent and advanced GPUs (NVIDIA Quadro FX 7000 and Tesla K20c) with different memory usage schemes and varying input plaintext sizes and patterns. We obtained a speedup of up to 87x against an advanced CPU (Intel Xeon X5690) based implementation. Moreover, our experiments reveal that the different degrees of pattern repetitions in input plaintext affect the encryption performance on GPU.

A new computing environment for collective privacy protection from constrained healthcare devices to IoT cloud services <=SEP=> The Internet of healthcare things is essentially a new model that changes the way of the delivery and management of healthcare services. It utilizes digital sensors and cloud computing to present a quality healthcare service outside of the classical hospital environment. This resulted in the emergence of a new class of online web 4.0 services, which are termed “cloud healthcare services”. Cloud healthcare services offer a straightforward opportunity for patients to communicate with healthcare professionals and utilize their personal IoHT devices to obtain timely and accurate medical guidance and decisions. The personal IoHT devices integrate sensed health data at a central cloud healthcare service to extract useful health insights for wellness and preventive care strategies. However, the present practices for cloud healthcare services rely on a centralized approach, where patients’ health data are collected and stored on servers, located at remote locations, which might be functioning under data privacy laws somewhat different from the ones applied where the service is running. Promoting a privacy respecting cloud services encourages patients to actively participate in these healthcare services and to routinely provide an accurate and precious health data about themselves. With the emergence of fog computing paradigm, privacy protection can now be enforced at the edge of the patient’s network regardless of the location of service providers. In this paper, a framework for cloud healthcare recommender service is presented. We depicted the personal gateways at the patients’ side act as intermediate nodes (called fog nodes) between IoHT devices and cloud healthcare services. A fog-based middleware will be hosted on these fog nodes for an efficient aggregation of patients generated health data while maintaining the privacy and the confidentiality of their health profiles. The proposed middleware executes a two-stage concealment process that utilizes the hierarchical nature of IoHT devices. This will unburden the constrained IoHT devices from performing intensive privacy preserving processes. At that, the patients will be empowered with a tool to control the privacy of their health data by enabling them to release their health data in a concealed form. The further processing at the cloud healthcare service continues over the concealed data by applying the proposed protocols. The proposed solution was integrated into a scenario related to preserving the privacy of the patients’ health data when utilized by a cloud healthcare recommender service to generate health insights. Our approach induces a straightforward solution with accurate results, which are beneficial to both patients and service providers.

Transfer Learning for Text Mining <=SEP=> Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed to extract the useful knowledge from different but related auxiliary domains. In its connection to text mining, transfer learning has found novel and useful applications. In this chapter, we will review some most recent developments in transfer learning for text mining, explain related algorithms in detail, and project future developments of this field. We focus on two important topics: cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images.

Sensorimotor training in virtual reality: a review. <=SEP=> Recent experimental evidence suggests that rapid advancement of virtual reality (VR) technologies has great potential for the development of novel strategies for sensorimotor training in neurorehabilitation. We discuss what the adaptive and engaging virtual environments can provide for massive and intensive sensorimotor stimulation needed to induce brain reorganization.Second, discrepancies between the veridical and virtual feedback can be introduced in VR to facilitate activation of targeted brain networks, which in turn can potentially speed up the recovery process. Here we review the existing experimental evidence regarding the beneficial effects of training in virtual environments on the recovery of function in the areas of gait,upper extremity function and balance, in various patient populations. We also discuss possible mechanisms underlying these effects. We feel that future research in the area of virtual rehabilitation should follow several important paths. Imaging studies to evaluate the effects of sensory manipulation on brain activation patterns and the effect of various training parameters on long term changes in brain function are needed to guide future clinical inquiry. Larger clinical studies are also needed to establish the efficacy of sensorimotor rehabilitation using VR in various clinical populations and most importantly, to identify VR training parameters that are associated with optimal transfer to real-world functional improvements.

Sources of mathematical thinking: behavioral and brain-imaging evidence. <=SEP=> Does the human capacity for mathematical intuition depend on linguistic competence or on visuo-spatial representations? A series of behavioral and brain-imaging experiments provides evidence for both sources. Exact arithmetic is acquired in a language-specific format, transfers poorly to a different language or to novel facts, and recruits networks involved in word-association processes. In contrast, approximate arithmetic shows language independence, relies on a sense of numerical magnitudes, and recruits bilateral areas of the parietal lobes involved in visuo-spatial processing. Mathematical intuition may emerge from the interplay of these brain systems.

NON-UNIFORM TRANSMISSION LINE TRANSFORMERS AND THEIR APPLICATION IN THE DESIGN OF COMPACT MULTI-BAND BAGLEY POWER DIVIDERS WITH HARMONICS SUPPRESSION <=SEP=> In this paper, the application of compact non-uniform transmission line transformers (NTLTs) in suppressing and controlling the odd harmonics of the fundamental frequency is presented. A design example showing the complete suppression of the odd harmonics of the fundamental frequency is given. In addition, several compact NTLTs are designed showing the possibility of controlling the existence of a fundamental frequency’s odd harmonics. Moreover, multi-band operation using NTLTs is investigated. Specifically, a design example of a miniaturized triple-frequency NTLT is introduced. Based on these compact NTLTs, a 3-way triple-frequency modified Bagley power divider (BPD) with a size reduction of 50%, and a 5-way modified BPD with harmonics suppression and size reduction of 34%, are designed. For verification purposes, both dividers are simulated using the two full-wave simulators IE3D and HFSS. Moreover, the modified 5-way BPD with harmonics suppression is fabricated and measured. Both the simulation and measurement results validate the design approach.

Changes in women's choice of dress across the ovulatory cycle: naturalistic and laboratory task-based evidence. <=SEP=> The authors tested the prediction that women prefer clothing that is more revealing and sexy when fertility is highest within the ovulatory cycle. Eighty-eight women reported to the lab twice: once on a low-fertility day of the cycle and once on a high-fertility day (confirmed using hormone tests). In each session, participants posed for full-body photographs in the clothing they wore to the lab, and they drew illustrations to indicate an outfit they would wear to a social event that evening. Although each data source supported the prediction, the authors found the most dramatic changes in clothing choice in the illustrations. Ovulatory shifts in clothing choice were moderated by sociosexuality, attractiveness, relationship status, and relationship satisfaction. Sexually unrestricted women, for example, showed greater shifts in preference for revealing clothing worn to the laboratory near ovulation. The authors suggest that clothing preference shifts could reflect an increase in female-female competition near ovulation.

Generic SPWM technique for multilevel inverter <=SEP=> Multilevel inverters generate stepped AC signals and thus overcome many of the limitations with the classical inverter circuit. Multilevel inverters use many number of switches and generate staircase type output through controlled switching of various switches. The sequence of switching and the duration of each step are very vital in minimizing the THD. The methods of generating gating signals are not unique. Many methods that are proposed in literatures make use of SPWM technique. Sinusoidal Pulse Width Modulation (SPWM) is a popular control method widely used in power electronic inverter circuit. It has advantages like low switching losses, the output has fewer harmonic and method is easy to implement. The different SPWM methods are: (A) Phase Disposition PWM (PDPWM), (B) Phase Opposition Disposition PWM (PODPWM), (C) Alternative Opposition and Disposition PWM (APODPWM), (D) Phase Shift PWM (PSPWM), (E) Carrier Overlapping PWM (COPWM) and (F) Multi Carrier Sinusoidal Pulse Width Modulation with Variable Frequency (MCSPWMVF). All the above methods make use of many triangular signals, that are level shifted or phase shifted, and compare with a single sine wave to generate gating signals for the respective switches. The intersection of the sine signal with the various triangular signals will generate the gating signals for the respective switches. In the present investigation a generalized gating signal generation method is proposed using only one modulating and one carrier signal. The signal so generated is steered into various switches through pulse steering circuit developed in the present work.

Document Page Segmentation and Layout Analysis Using Soft Ordering <=SEP=> A document image is composed of a variety of physical entities or regions such as text blocks, lines, words, figures, tables, and background. We could also assign functional or logical labels such as sentences, titles, captions, author names, and addresses to some of these regions. The process of document structure and layout analysis tries to decompose a given document image into its component regions and understand their functional roles and relationships. The processing is carried out in multiple steps, such as preprocessing, page decomposition, structure understanding, etc. We will look into each of these steps in detail in the following sections. Document images are often generated from physical documents by digitization using scanners or digital cameras. Many documents, such as newspapers, magazines and brochures, contain very complex layout due to the placement of figures, titles, and captions, complex backgrounds, artistic text formatting, etc. (see Figure 1). A human reader uses a variety of additional cues such as context, conventions and information about language/script, along with a complex reasoning process to decipher the contents of a document. Automatic analysis of an arbitrary document with complex layout is an extremely difficult task and is beyond the capabilities of the state-of-the-art document structure and layout analysis systems. This is interesting since documents are designed to be effective and clear to human interpretation unlike natural images.

Learning Dynamic Guidance for Depth Image Enhancement <=SEP=> The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.

Intracranial EEG Correlates of Expectancy and Memory Formation in the Human Hippocampus and Nucleus Accumbens <=SEP=> The human brain is adept at anticipating upcoming events, but in a rapidly changing world, it is essential to detect and encode events that violate these expectancies. Unexpected events are more likely to be remembered than predictable events, but the underlying neural mechanisms for these effects remain unclear. We report intracranial EEG recordings from the hippocampus of epilepsy patients, and from the nucleus accumbens of depression patients. We found that unexpected stimuli enhance an early (187 ms) and a late (482 ms) hippocampal potential, and that the late potential is associated with successful memory encoding for these stimuli. Recordings from the nucleus accumbens revealed a late potential (peak at 475 ms), which increases in magnitude during unexpected items, but no subsequent memory effect and no early component. These results are consistent with the hypothesis that activity in a loop involving the hippocampus and the nucleus accumbens promotes encoding of unexpected events.