Query: Explorer Merlin : An Open Source Neural Network Speech Synthesis System

Relevant passages:
Deep neural networks employing Multi-Task Learning and stacked bottleneck features for speech synthesis <=SEP=> Deep neural networks (DNNs) use a cascade of hidden representations to enable the learning of complex mappings from input to output features. They are able to learn the complex mapping from text-based linguistic features to speech acoustic features, and so perform text-to-speech synthesis. Recent results suggest that DNNs can produce more natural synthetic speech than conventional HMM-based statistical parametric systems. In this paper, we show that the hidden representation used within a DNN can be improved through the use of Multi-Task Learning, and that stacking multiple frames of hidden layer activations (stacked bottleneck features) also leads to improvements. Experimental results confirmed the effectiveness of the proposed methods, and in listening tests we find that stacked bottleneck features in particular offer a significant improvement over both a baseline DNN and a benchmark HMM system.

Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds <=SEP=> A set of simple new procedures has been developed to enable the real-time manipulation of speech parameters. The proposed method uses pitch-adaptive spectral analysis combined with a surface reconstruction method in the time± frequency region. The method also consists of a fundamental frequency (F0) extraction using instantaneous frequency calculation based on a new concept called `fundamentalness'. The proposed procedures preserve the details of time± frequency surfaces while almost perfectly removing ®ne structures due to signal periodicity. This close-to-perfect elimination of interferences and smooth F0 trajectory allow for over 600% manipulation of such speech parameters as pitch, vocal tract length, and speaking rate, while maintaining high reproductive quality. Ó 1999 Elsevier Science B.V. All rights reserved.

Extracting deep bottleneck features using stacked auto-encoders <=SEP=> In this work, a novel training scheme for generating bottleneck features from deep neural networks is proposed. A stack of denoising auto-encoders is first trained in a layer-wise, unsupervised manner. Afterwards, the bottleneck layer and an additional layer are added and the whole network is fine-tuned to predict target phoneme states. We perform experiments on a Cantonese conversational telephone speech corpus and find that increasing the number of auto-encoders in the network produces more useful features, but requires pre-training, especially when little training data is available. Using more unlabeled data for pre-training only yields additional gains. Evaluations on larger datasets and on different system setups demonstrate the general applicability of our approach. In terms of word error rate, relative improvements of 9.2% (Cantonese, ML training), 9.3% (Tagalog, BMMI-SAT training), 12% (Tagalog, confusion network combinations with MFCCs), and 8.7% (Switchboard) are achieved.

Auto-encoder bottleneck features using deep belief networks <=SEP=> Neural network (NN) bottleneck (BN) features are typically created by training a NN with a middle bottleneck layer. Recently, an alternative structure was proposed which trains a NN with a constant number of hidden units to predict output targets, and then reduces the dimensionality of these output probabilities through an auto-encoder, to create auto-encoder bottleneck (AE-BN) features. The benefit of placing the BN after the posterior estimation network is that it avoids the loss in frame classification accuracy incurred by networks that place the BN before the softmax. In this work, we investigate the use of pre-training when creating AE-BN features. Our experiments indicate that with the AE-BN architecture, pre-trained and deeper NNs produce better AE-BN features. On a 50-hour English Broadcast News task, the AE-BN features provide over a 1% absolute improvement compared to a state-of-the-art GMM/HMM with a WER of 18.8% and pre-trained NN hybrid system with a WER of 18.4%. In addition, on a larger 430-hour Broadcast News task, AE-BN features provide a 0.5% absolute improvement over a strong GMM/HMM baseline with a WER of 16.0%. Finally, system combination with the GMM/HMM baseline and AE-BN systems provides an additional 0.5% absolute on 430 hours over the AE-BN system alone, yielding a final WER of 15.0%.

Transformation of formants for voice conversion using artificial neural networks <=SEP=> In this paper we propose a scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker. In particular, we address the issue of transformation of the vocal tract system features from one speaker to another. Formants are used to represent the vocal tract system features and a formant vocoder is used for synthesis. The scheme consists of a formant analysis phase, followed by a learning phase in which the implicit formant transformation is captured by a neural network. The transformed formants together with the pitch contour modified to suit the average pitch of the target speaker are used to synthesize speech with the desired vocal tract system characteristics.

Burnout in the Mental Health Workforce: A Review <=SEP=> There are enormous concerns regarding the recruitment, retention, training, and performance of the behavioral health workforce. Paramount among these concerns is turnover, which causes disruption in continuity of care, diminishes access to care while a position remains vacant, and poses financial hardship on the provider organization through costs related to recruitment, orientation, and training of a new hire. There is frequent mention of burnout within the literature and among behavioral health managers as a potential cause of turnover. However, there is no recent or comprehensive review of burnout that examines the evidence surrounding its validity, utility, and potential relationship to turnover. The purpose of this paper is to provide such a review by examining the construct of burnout, methodological and measurement issues, its prevalence in the mental health workforce, correlates of burnout, and interventions to decrease it. The implications for provider organizations and recommendations for future research are identified.

Low-Temperature Sintering of Nanoscale Silver Paste for Attaching Large-Area $({>}100~{\rm mm}^{2})$ Chips <=SEP=> A low-temperature sintering technique enabled by a nanoscale silver paste has been developed for attaching large-area (>100 mm2) semiconductor chips. This development addresses the need of power device or module manufacturers who face the challenge of replacing lead-based or lead-free solders for high-temperature applications. The solder-reflow technique for attaching large chips in power electronics poses serious concern on reliability at higher junction temperatures above 125°C. Unlike the soldering process that relies on melting and solidification of solder alloys, the low-temperature sintering technique forms the joints by solid-state atomic diffusion at processing temperatures below 275°C with the sintered joints having the melting temperature of silver at 961°C. Recently, we showed that a nanoscale silver paste could be used to bond small chips at temperatures similar to soldering temperatures without any externally applied pressure. In this paper, we extend the use of the nanomaterial to attach large chips by introducing a low pressure up to 5 MPa during the densification stage. Attachment of large chips to substrates with silver, gold, and copper metallization is demonstrated. Analyses of the sintered joints by scanning acoustic imaging and electron microscopy showed that the attachment layer had a uniform microstructure with micrometer-sized porosity with the potential for high reliability under high-temperature applications.

A Type-Based Compiler for Standard ML <=SEP=> Compile-time type information should be valuable in efficient compilation of statically typed functional languages such as Standard ML. But how should type-directed compilation work in real compilers, and how much performance gain will type-based optimizations yield? In order to support more efficient data representations and gain more experience about type-directed compilation, we have implemented a new type-based middle end and back end for the Standard ML of New Jersey compiler. We describe the basic design of the new compiler, identify a number of practical issues, and then compare the performance of our new compiler with the old non-type-based compiler. Our measurement shows that a combination of several simple type-based optimizations reduces heap allocation by 36%; and improves the already-efficient code generated by the old non-type-based compiler by about 19% on a DECstation 500.

Opportunities and Limits of Remote Timing Attacks <=SEP=> Many algorithms can take a variable amount of time to complete depending on the data being processed. These timing differences can sometimes disclose confidential information. Indeed, researchers have been able to reconstruct an RSA private key purely by querying an SSL Web server and timing the results. Our work analyzes the limits of attacks based on accurately measuring network response times and jitter over a local network and across the Internet. We present the design of filters to significantly reduce the effects of jitter, allowing an attacker to measure events with 15-100μs accuracy across the Internet, and as good as 100ns over a local network. Notably, security-related algorithms on Web servers and other network servers need to be carefully engineered to avoid timing channel leaks at the accuracy demonstrated in this article.

Knowing Yourself: Improving Video Caption via In-depth Recap <=SEP=> Generating natural language descriptions for videos (a.k.a video captioning) has attracted much research attention in recent years, and a lot of models have been proposed to improve the caption performance. However, due to the rapid progress in dataset expansion and feature representation, newly proposed caption models have been evaluated on different settings, which makes it unclear about the contributions from either features or models. Therefore, in this work we aim to gain a deep understanding about "where are we" for the current development of video captioning. First, we carry out extensive experiments to identify the contribution from different components in video captioning task and make fair comparison among several state-of-the-art video caption models. Second, we discover that these state-of-the-art models are complementary so that we could benefit from "wisdom of the crowd" through ensembling and reranking. Finally, we give a preliminary answer to the question "how far are we from the human-level performance in general'' via a series of carefully designed experiments. In summary, our caption models achieve the state-of-the-art performance on the MSR-VTT 2017 challenge, and it is comparable with the average human-level performance on current caption metrics. However, our analysis also shows that we still have a long way to go, such as further improving the generalization ability of current caption models.

Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network <=SEP=> Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.

DudeTM: Building Durable Transactions with Decoupling for Persistent Memory <=SEP=> Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, it has been shown by empirical evidence that programs should access NVM directly through CPU load and store instructions, so that the overhead of a traditional file system or database can be avoided. Thus, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. However, existing durable transaction systems employ either undo logging, which requires a fence for every memory write, or redo logging, which requires intercepting all memory reads within transactions.
 This paper presents DUDETM, a crash-consistent durable transaction system that avoids the drawbacks of both undo logging and redo logging. DUDETM uses shadow DRAM to decouple the execution of a durable transaction into three fully asynchronous steps. The advantage is that only minimal fences and no memory read instrumentation are required. This design also enables an out-of-the-box transactional memory (TM) to be used as an independent component in our system. The evaluation results show that DUDETM adds durability to a TM system with only 7.4 ~ 24.6% throughput degradation. Compared to the existing durable transaction systems, DUDETM provides 1.7times to 4.4times higher throughput. Moreover, DUDETM can be implemented with existing hardware TMs with minor hardware modifications, leading to a further 1.7times speedup.

Cognitive architectures: Research issues and challenges <=SEP=> In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. 2008 Published by Elsevier B.V.

A Timing-Based Scheme for Rogue AP Detection <=SEP=> This paper considers a category of rogue access points (APs) that pretend to be legitimate APs to lure users to connect to them. We propose a practical timing-based technique that allows the user to avoid connecting to rogue APs. Our detection scheme is a client-centric approach that employs the round trip time between the user and the DNS server to independently determine whether an AP is a rogue AP without assistance from the WLAN operator. We implemented our detection technique on commercially available wireless cards to evaluate their performance. Extensive experiments have demonstrated the accuracy, effectiveness, and robustness of our approach. The algorithm achieves close to 100 percent accuracy in distinguishing rogue APs from legitimate APs in lightly loaded traffic conditions, and larger than 60 percent accuracy in heavy traffic conditions. At the same time, the detection only requires less than 1 second for lightly-loaded traffic conditions and tens of seconds for heavy traffic conditions.

Segment-aware energy-efficient management of heterogeneous memory system for ultra-low-power IoT devices <=SEP=> The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.

Genetics and Marker-assisted Selection of the Chemotype in Cannabis sativa L. <=SEP=> Cannabis sativa is an interesting crop for several industrial uses, but the legislations in Europe and USA require a tight control of cannabinoid type and content for cultivation and subsidies release. Therefore, cannabinoid survey by gas chromatography of materials under selection is an important step in hemp breeding. In this paper, a number of Cannabis accessions were examined for their cannabinoid composition. Their absolute and relative content was examined, and results are discussed in the light of both the current genetic model for cannabinoid’s inheritance, and the legislation’s requirements. In addition, the effectiveness of two different types of markers associated to the locus determining the chemotype in Cannabis was evaluated and discussed, as possible tools in marker-assisted selection in hemp, but also for possible applications in the forensic and pharmaceutical fields.

A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education <=SEP=> 0950-7051/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.knosys.2010.03.010 * Corresponding author. Address: Software Quality L and Technology, Hellenic Open University, 12–15 Tsa 26222, Greece. Tel.: +3

On Detecting Clustered Anomalies Using SCiForest <=SEP=> Detecting local clustered anomalies is an intricate problem for many existing anomaly detection methods. Distance-based and density-based methods are inherently restricted by their basic assumptions—anomalies are either far from normal points or being sparse. Clustered anomalies are able to avoid detection since they defy these assumptions by being dense and, in many cases, in close proximity to normal instances. In this paper, without using any density or distance measure, we propose a new method called SCiForest to detect clustered anomalies. SCiForest separates clustered anomalies from normal points effectively even when clustered anomalies are very close to normal points. It maintains the ability of existing methods to detect scattered anomalies, and it has superior time and space complexities against existing distance-based and density-based methods.

Online Robust Low-Rank Tensor Learning <=SEP=> The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose an Online Robust Lowrank Tensor Modeling (ORLTM) approach to address these challenges. ORLTM dynamically explores the high-order correlations across all tensor modes for low-rank structure modeling. To analyze mixture data from multiple subspaces, ORLTM introduces a new dictionary learning component. ORLTM processes data streamingly and thus requires quite low memory cost that is independent of data size. This makes ORLTM quite suitable for processing large-scale tensor data. Empirical studies have validated the effectiveness of the proposed method on both synthetic data and one practical task, i.e., video background subtraction. In addition, we provide theoretical analysis regarding computational complexity and memory cost, demonstrating the efficiency of ORLTM rigorously.

Gesture Recognition: A Survey <=SEP=> With increasing use of computers in our daily lives, lately there has been a rapid increase in the efforts to develop a better human computer interaction interface. The need of easy to use and advance types of human-computer interaction with natural interfaces is more than ever. In the present framework, the UI (User Interface) of a computer allows user to interact with electronic devices with graphical icons and visual indicators, which is still inconvenient and not suitable for working in virtual environments. An interface which allow user to communicate through gestures is the next step in the direction of advance human computer interface. In the present paper author explore different aspects of gesture recognition techniques.

Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder. <=SEP=> OBJECTIVE
Newer antipsychotic drugs have shown promise in ameliorating neurocognitive deficits in patients with schizophrenia, but few studies have compared newer antipsychotic drugs with both clozapine and conventional agents, particularly in patients who have had suboptimal response to prior treatments.


METHOD
The authors examined the effects of clozapine, olanzapine, risperidone, and haloperidol on 16 measures of neurocognitive functioning in a double-blind, 14-week trial involving 101 patients. A global score was computed along with scores in four neurocognitive domains: memory, attention, motor function, and general executive and perceptual organization.


RESULTS
Global neurocognitive function improved with olanzapine and risperidone treatment, and these improvements were superior to those seen with haloperidol. Patients treated with olanzapine exhibited improvement in the general and attention domains but not more than that observed with other treatments. Patients treated with risperidone exhibited improvement in memory that was superior to that of both clozapine and haloperidol. Clozapine yielded improvement in motor function but not more than in other groups. Average effect sizes for change were in the small to medium range. More than half of the patients treated with olanzapine and risperidone experienced "clinically significant" improvement (changes in score of at least one-half standard deviation relative to baseline). These findings did not appear to be mediated by changes in symptoms, side effects, or blood levels of medications.


CONCLUSIONS
Patients with a history of suboptimal response to conventional treatments may show cognitive benefits from newer antipsychotic drugs, and there may be differences between atypical antipsychotic drugs in their patterns of cognitive effects.

The reduction of graphene oxide <=SEP=> Graphene has attracted great interest for its excellent mechanical, electrical, thermal and optical properties. It can be produced by micro-mechanical exfoliation of highly ordered pyrolytic graphite, epitaxial growth, chemical vapor deposition, and the reduction of graphene oxide (GO). The first three methods can produce graphene with a relatively perfect structure and excellent properties, while in comparison, GO has two important characteristics: (1) it can be produced using inexpensive graphite as raw material by cost-effective chemical methods with a high yield, and (2) it is highly hydrophilic and can form stable aqueous colloids to facilitate the assembly of macroscopic structures by simple and cheap solution processes, both of which are important to the large-scale uses of graphene. A key topic in the research and applications of GO is the reduction, which partly restores the structure and properties of graphene. Different reduction processes result in different properties of reduced GO (rGO), which in turn affect the final performance of materials or devices composed of rGO. In this contribution, we review the state-of-art status of the reduction of GO on both techniques and mechanisms. The development in this field will speed the applications of graphene. 2011 Elsevier Ltd. All rights reserved.

For Whom Is a Picture Worth a Thousand Words ? Extensions of a Dual-Coding Theory of Multimedia Learning <=SEP=> In 2 experiments, highand low-spatial ability students viewed a computer-generated animation and listened simultaneously (concurrent group) or successively (successive group) to a narration that explained the workings either of a bicycle tire pump (Experiment 1) or of the human respiratory system (Experiment 2). The concurrent group generated more creative solutions to subsequent transfer problems than did the successive group; this contiguity effect was strong for highbut not for low-spatial ability students. Consistent with a dual-coding theory, spatial ability allows high-spatial learners to devote more cognitive resources to building referential connections between visual and verbal representations of the presented material, whereas low-spatial ability learners must devote more cognitive resources to building representation connections between visually presented material and its visual representation.

Sparse Code Filtering for Action Pattern Mining <=SEP=> Action recognition has received increasing attention during the last decade. Various approaches have been proposed to encode the videos that contain actions, among which self-similarity matrices (SSMs) have shown very good performance by encoding the dynamics of the video. However, SSMs become sensitive when there is a very large view change. In this paper, we tackle the multi-view action recognition problem by proposing a sparse code filtering (SCF) framework which can mine the action patterns. First, a class-wise sparse coding method is proposed to make the sparse codes of the between-class data lie close by. Then we integrate the classifiers and the class-wise sparse coding process into a collaborative filtering (CF) framework to mine the discriminative sparse codes and classifiers jointly. The experimental results on several public multi-view action recognition datasets demonstrate that the presented SCF framework outperforms other state-of-the-art methods.

Aging: a theory based on free radical and radiation chemistry. <=SEP=> The phenomenon of growth, decline and death—aging—has been the source of considerable speculation (1, 8, 10). This cycle seems to be a more or less direct function of the metabolic rate and this in turn depends on the species (animal or plant) on which are superimposed the factors of heredity and the effects of the stresses and strains of life—which alter the metabolic activity. The universality of this phenomenon suggests that the reactions which cause it are basically the same in all living things. Viewing this process in the light of present day free radical and radiation chemistry and of radiobiology, it seems possible that one factor in aging may be related to deleterious side attacks of free radicals (which are normally produced in the course of cellular metabolism) on cell constituents.* Irradiation of living things induces mutation, cancer, and aging (9). Inasmuch as these also arise spontaneously in nature, it is natural to inquire if the processes might not be similar. It is believed that one mechanism of irradiation effect is through liberation of OH and HO2 radicals (12). There is evidence, although indirect, that these two highly active free radicals are produced normally in living systems. In the first place, free radicals are present in living cells; this was recently demonstrated in vivo by a paramagnetic resonance absorption method (3). Further, it was shown that the concentration of free radicals increased with increasing metabolic activity in conformity with the postulates set forth some years ago that free radicals were involved in biologic oxidation-reduction reactions (11, 13). Are some of these free radicals OH and/or HO2, or radicals of a similar high order of reactivity, and where might they arise in the cell?

Separate Hydrolysis and Fermentation of Pretreated Spruce Josefin Axelsson <=SEP=> 

Please Biofeed the Zombies: Enhancing the Gameplay and Display of a Horror Game Using Biofeedback <=SEP=> This paper describes an investigation into how real-time but low-cost biometric information can be interpreted by computer games to enhance gameplay without fundamentally changing it. We adapted a cheap sensor, (the Lightstone mediation sensor device by Wild Divine), to record and transfer biometric information about the player (via sensors that clip over their fingers) into a commercial game engine, Half-Life 2. During game play, the computer game was dynamically modified by the player’s biometric information to increase the cinematically augmented “horror” affordances. These included dynamic changes in the game shaders, screen shake, and the creation of new spawning points for the game’s non-playing characters (zombies), all these features were driven by the player’s biometric data. To evaluate the usefulness of this biofeedback device, we compared it against a control group of players who also had sensors clipped on their fingers, but for the second group the gameplay was not modified by the biometric information of the players. While the evaluation results indicate biometric data can improve the situated feeling of horror, there are many design issues that will need to be investigated by future research, and the judicious selection of theme and appropriate interaction is vital. Author

Psychophysiological contributions to phantom limbs. <=SEP=> Recent studies of amputees reveal a remarkable diversity in the qualities of experiences that define the phantom limb, whether painless or painful. This paper selectively reviews evidence of peripheral, central and psychological processes that trigger or modulate a variety of phantom limb experiences. The data show that pain experienced prior to amputation may persist in the form of a somatosensory memory in the phantom limb. It is suggested that the length and size of the phantom limb may be a perceptual marker of the extent to which sensory input from the amputation stump have re-occupied deprived cortical regions originally subserving the amputated limb. A peripheral mechanism involving a sympathetic-efferent somatic-afferent cycle is presented to explain fluctuations in the intensity of paresthesias referred to the phantom limb. While phantom pain and other sensations are frequently triggered by thoughts and feelings, there is no evidence that the painful or painless phantom limb is a symptom of a psychological disorder. It is concluded that the experience of a phantom limb is determined by a complex interaction of inputs from the periphery and widespread regions of the brain subserving sensory, cognitive, and emotional processes.

Compacting Neural Network Classifiers via Dropout Training <=SEP=> We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per unit dropout retention probability so that the optimizer can effectively prune hidden units during training. By changing the prior hyperparameters, we can control the size of the resulting network. We performed a systematic comparison of dropout compaction and competing methods on several realworld speech recognition tasks and found that dropout compaction achieved comparable accuracy with fewer than 50% of the hidden units, translating to a 2.5x speedup in run-time.

Online search of overlapping communities <=SEP=> A great deal of research has been conducted on modeling and discovering communities in complex networks. In most real life networks, an object often participates in multiple overlapping communities. In view of this, recent research has focused on mining overlapping communities in complex networks. The algorithms essentially materialize a snapshot of the overlapping communities in the network. This approach has three drawbacks, however. First, the mining algorithm uses the same global criterion to decide whether a subgraph qualifies as a community. In other words, the criterion is fixed and predetermined. But in reality, communities for different vertices may have very different characteristics. Second, it is costly, time consuming, and often unnecessary to find communities for an entire network. Third, the approach does not support dynamically evolving networks. In this paper, we focus on online search of overlapping communities, that is, given a query vertex, we find meaningful overlapping communities the vertex belongs to in an online manner. In doing so, each search can use community criterion tailored for the vertex in the search. To support this approach, we introduce a novel model for overlapping communities, and we provide theoretical guidelines for tuning the model. We present several algorithms for online overlapping community search and we conduct comprehensive experiments to demonstrate the effectiveness of the model and the algorithms. We also suggest many potential applications of our model and algorithms.