Query: Adiabatic charging of capacitors by Switched Capacitor Converters with multiple target voltages

Relevant passages:
Generic and unified model of Switched Capacitor Converters <=SEP=> A generic modeling methodology that analyzes the losses in Switched Capacitors Converters (SCC) was developed and verified by simulation and experiments. The proposed analytical approach is unified, covering both hard and soft switched SCC topologies. The major advantage of the proposed model is that it expresses the losses as a function of the currents passing through each flying capacitor. Since these currents are linearly proportional to the output current, the model is also applicable to SCC with multiple capacitors. The proposed model provides an insight into the expected losses in SCC and the effects of their operational conditions such as duty cycle. As such, the model can help in the optimization of SCC systems and their control to achieve desired regulations.

PERFORMANCE LIMITS OF SWITCHED-CAPACITOR DC-DC CONVERTERS <=SEP=> Theoretical performance limits of twophase switched-capacitor (SC) dc-dc converters are discussed in this paper. For a given number of capacitors k, the complete set of attainable dc conversion ratios is found. The maximum step-up or stepdown ratio is given by the k t h Fibonacci number, while the bound on the number of switches required in any SC circuit is 3k 2. Practical implications, illustrated by several SC converter examples, include savings in the number of components required for a given application, and the ability to construct SC converters that can maintain the output voltage regulation and high conversion efficiency over a wide range of input voltage variations. Limits found for the output resistance and efflciency can be used for selection and comparison of SC converters.

Analysis and Optimization of Switched-Capacitor DC–DC Converters <=SEP=> Analysis methods are developed that fully determine a switched-capacitor (SC) dc-dc converter's steady-state performance through evaluation of its output impedance. This analysis method has been verified through simulation and experimentation. The simple formulation developed permits optimization of the capacitor sizes to meet a constraint such as a total capacitance or total energy storage limit, and also permits optimization of the switch sizes subject to constraints on total switch conductances or total switch volt-ampere (V-A) products. These optimizations then permit comparison among several switched-capacitor topologies, and comparisons of SC converters with conventional magnetic-based dc-dc converter circuits, in the context of various application settings. Significantly, the performance (based on conduction loss) of a ladder-type converter is found to be superior to that of a conventional magnetic-based converter for medium to high conversion ratios.

Technical Committees Switched-Capacitor Power Electronics Circuits By <=SEP=> One of the main orientations in power electronics in the last decade has been the development of switching-mode converters without inductors and transformers. Light weight, small size and high power density are the result of using only switches and capacitors in the power stage of these converters. Thus, they serve as ideal power supplies for mobile electronic systems (e.g. cellular phones, personal digital assistants, and so forth). Switched-capacitor (SC) converters, with their large voltage conversion ratio, promise to be a response to such challenges of the 21st century as high-efficiency converters with low EMI emissions and the ability to realize steep step-down of the voltage (to 3V or even a smaller supply voltage for integrated circuits) or steep step-up of the voltage for automotive industry or internet services in the telecom industry. This paper is a tutorial of the main results in SC-converter research and design. Switched-Capacitor Converters— A Typical Power Electronics Contribution of the CAS Society From all of the research subjects in the Power Systems and Power Electronics Circuits Technical Committee—the stability of power systems, analysis of power electronics circuits (which are time-variable circuits with internally-controlled switches), chaos, modeling and simulation of converters, hardswitching and soft-switching converters, and so forth—the SCconverter is probably the most pertinent contribution of the CAS Society to power electronics. In fact, most of the researchers in this field belong to our Society’s technical committee and most of the breakthrough contributions on this subject have appeared in CAS publications. The reason for this is clear. Pursuing small size filters in the 1950’s, the circuit theory community found a solution in the elimination of bulky inductors from the structure of a passive filter. Active and then switched-capacitor filters showed the possibility of implementing the filtering function without using magnetic devices. The power electronics designer faced a similar challenge: the request for miniaturization of the power supply. This could be achieved only by eliminating the inductors and transformers. In a converter, the inductor serves the two purposes of processing energy and filtering the output voltage. The use of a switched-capacitor circuit for processing energy seemed doomed to failure because it is known that charging a capacitor from zero is achieved at a 50% efficiency. Ten years of research were needed to overcome the efficiency dilemma and to develop high-efficiency switched-capacitor energy-processing circuits. First SC Converters and Basic Principles The primary goal of any switching-mode power converter is to provide a constant (DC or AC) output voltage at its load, despite variations in the input voltage or load. A control element, therefore, has to be introduced in the process of transmitting energy so that the converter (power stage) changes its topology cyclically, and the durations of the switching topologies are adjusted for regulation purposes. The first SC converters were developed by a group of researchers from Kumamoto, Japan, who processed a DC unregulated voltage toward a DC regulated voltage [1, 2, 4]. These DC-DC converters were soon followed by AC-DC converters [3], DC-AC inverters [5] and AC-AC transformers [6]. By avoiding magnetic elements, these circuits, realized in a hybrid technology, featured a high power density (23W/inch). Figure 1(a). Basic SC step-down DC-DC converter. *A. Ioinovici is with the Department of Electrical and Electronics Engineering, Holon Academic Institute of Technology, Israel. R V S

Unified analysis of switched-capacitor resonant converters <=SEP=> A family of switched-capacitor resonant circuits using only two transistors is presented. The circuit operates under zero-current switching and, therefore, the switching loss is zero. It also offers a wide choice of voltage conversions including fractional as well as multiple and inverted voltage conversion ratios.

Monte Carlo Strength Evaluation: Fast and Reliable Password Checking <=SEP=> Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices and password strength evaluators failed to keep up: they are generally based on heuristic rules designed to defend against obsolete brute force attacks. Many passwords can only be guessed with significant effort, and motivated attackers may be willing to invest resources to obtain valuable passwords. However, it is eminently impractical for the defender to simulate expensive attacks against each user to accurately characterize their password strength. This paper proposes a novel method to estimate the number of guesses needed to find a password using modern attacks. The proposed method requires little resources, applies to a wide set of probabilistic models, and is characterised by highly desirable convergence properties.
 The experiments demonstrate the scalability and generality of the proposal. In particular, the experimental analysis reports evaluations on a wide range of password strengths, and of state-of-the-art attacks on very large datasets, including attacks that would have been prohibitively expensive to handle with existing simulation-based approaches.

Shape from focus system <=SEP=> 

Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus <=SEP=> Remembering experiences that lead to reward is essential for survival. The hippocampus is required for forming and storing memories of events and places, but the mechanisms that associate specific experiences with rewarding outcomes are not understood. Event memory storage is thought to depend on the reactivation of previous experiences during hippocampal sharp wave ripples (SWRs). We used a sequence switching task that allowed us to examine the interaction between SWRs and reward. We compared SWR activity after animals traversed spatial trajectories and either received or did not receive a reward. Here, we show that rat hippocampal CA3 principal cells are significantly more active during SWRs following receipt of reward. This SWR activity was further enhanced during learning and reactivated coherent elements of the paths associated with the reward location. This enhanced reactivation in response to reward could be a mechanism to bind rewarding outcomes to the experiences that precede them.

An unsupervised user identification algorithm using network embedding and scalable nearest neighbour <=SEP=> Most of the current studies on social network (SN) mainly focused on a single SN platform. Integration of SNs can provide more sufficient user behaviour data and more complete network structure, and thus is rewarding to an ocean of studies on social computing. Recognizing the identical users across SNs, or user identification, naturally bridges the SNs through users and has attracted extensive attentions. Due to the fragmentation, inconsistency and disruption of the accessible information among SNs, user identification is still an intractable problem. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions are supervised or semi-supervised and require some given identified users or seed users. In the scenarios where seed users are hard to obtain, it is laborious to label the seed users manually. In this study, we proposed an unsupervised scheme by employing the reliability and consistence of friend relationships in different SNs, termed Unsupervised Friend Relationship-based User Identification algorithm (UFRUI). The UFRUI first models the network structure and embeds the feature of each user into a vector using network embedding technique, and then converts the user identification problem into a nearest neighbour problem. Finally, the matching user is computed using the scalable nearest neighbour algorithm. Results of experiments demonstrated that UFRUI performs much better than current state-of-art network structure-based algorithm without seed users.

An information theoretic approach to improve semantic similarity assessments across multiple ontologies <=SEP=> Semantic similarity has become, in recent years, the backbone of numerous knowledge-based applications dealing with textual data. From the different methods and paradigms proposed to assess semantic similarity, ontology-based measures and, more specifically, those based on quantifying the Information Content (IC) of concepts are the most widespread solutions due to their high accuracy. However, these measures were designed to exploit a single ontology. They thus cannot be leveraged in many contexts in which multiple knowledge bases are considered. In this paper, we propose a new approach to achieve accurate IC-based similarity assessments for concept pairs spread throughout several ontologies. Based on Information Theory, our method defines a strategy to accurately measure the degree of commonality between concepts belonging to different ontologies—this is the cornerstone for estimating their semantic similarity. Our approach therefore enables classic IC-based measures to be directly applied in a multiple ontology setting. An empirical evaluation, based on well-established benchmarks and ontologies related to the bio-medical domain, illustrates the accuracy of our approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works. Semantic similarity is a pillar of text understanding since it quantifies the degree of resemblance between the meanings of textual terms. In recent years, due to the marked increase in electronically available textual data, considerable effort has been focused on defining semantic measures, which have been extensively applied in various contexts such as information retrieval [54], information extraction [56], word sense disambiguation [32], data clustering [4,27], data privacy [6,45–47] and biomedicine (e.g. protein classification and interaction [19,57], chemical entity identification [18], identification of the communalities between brain data and linguistic data [15], etc.) to cite a few. Different knowledge sources have been used to facilitate the semantic similarity calculus, including: measures relying solely on textual corpora to estimate similarity from the degree of co-occurrence of terms [11], and measures involving structured knowledge bases such as ontologies [36,37,44], whereby the similarity calculus is based on the analysis of semantic relationships modelled between concepts. Compared to corpora-based approaches, ontology-based measures have a dual benefit: concept meanings can be

How to Remember What to Remember: Exploring Possibilities for Digital Reminder Systems <=SEP=> Digital reminder systems typically use time and place as triggers to remind people to perform activities. In this paper, we investigate how digital reminder systems could better support the process of remembering in a wider range of situations. We report findings from a survey and one-week diary study, which reveal that people want to remember to perform a broad spectrum of activities in the future, many of which cannot be supported by simple time- and location-based reminders. In addition to these examples of prospective memory, or ‘remembering intentions’ [53], we also find that people want support in ‘retrieving’ [53] information and details, especially those encountered through social interactions or intended for use in conversations with others. Drawing on our analysis of what people want to remember and how they try to support this, we draw implications for the design of intelligent reminder systems such as digital assistants (e.g. Microsoft’s Cortana) and smart speaker systems (e.g. Amazon Echo), and highlight the possibilities afforded by drawing on conversation and giving material form to digital reminders.

Automatic emotional speech classification <=SEP=> Our purpose is to design a useful tool which can be used in psychology to automatically classify utterances into five emotional states such as anger, happiness, neutral, sadness, and surprise. The major contribution of the paper is to rate the discriminating capability of a set of features for emotional speech recognition. A total of 87 features has been calculated over 500 utterances from the Danish Emotional Speech database. The sequential forward selection method (SFS) has been used in order to discover a set of 5 to 10 features which are able to classify the utterances in the best way. The criterion used in SFS is the cross-validated correct classification score of one of the following classifiers: nearest mean and Bayes classifier where class pdf are approximated via Parzen windows or modelled as Gaussians. After selecting the 5 best features, we reduce the dimensionality to two by applying principal component analysis. The result is a 51.6% /spl plusmn/ 3% correct classification rate at 95% confidence interval for the five aforementioned emotions, whereas a random classification would give a correct classification rate of 20%. Furthermore, we find out those two-class emotion recognition problems whose error rates contribute heavily to the average error and we indicate that a possible reduction of the error rates reported in this paper would be achieved by employing two-class classifiers and combining them.

Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System <=SEP=> Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity and scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tend to limit the range of users. This paper presents an approach that combines the advantages of these two kinds of approaches by joining the two methods. Firstly, it employs memory-based CF to fill the vacant ratings of the user-item matrix. Then, it uses the item-based CF as model-based to form the nearest neighbors of every item. At last, it produces prediction of the target user to the target item at real time. The collaborative filtering recommendation method combining memory-based CF and model-based CF can provide better recommendation than traditional collaborative filtering.

Taming verification hardness: an efficient algorithm for testing subgraph isomorphism <=SEP=> Graphs are widely used to model complicated data semantics in many applications. In this paper, we aim to develop efficient techniques to retrieve graphs, containing a given query graph, from a large set of graphs. Considering the problem of testing subgraph isomorphism is generally NP-hard, most of the existing techniques are based on the framework of filtering-and-verification to reduce the precise computation costs; consequently various novel feature-based indexes have been developed. While the existing techniques work well for small query graphs, the verification phase becomes a bottleneck when the query graph size increases. Motivated by this, in the paper we firstly propose a novel and efficient algorithm for testing subgraph isomorphism, QuickSI. Secondly, we develop a new feature-based index technique to accommodate QuickSI in the filtering phase. Our extensive experiments on real and synthetic data demonstrate the efficiency and scalability of the proposed techniques, which significantly improve the existing techniques.

How Data Breaches Ruin Firm Reputation on Social Media! - Insights from a Sentiment-based Event Study <=SEP=> Data breach events are heavily discussed in social media. Data breaches, which imply the loss of personal sensitive data, have negative consequences on the affected firms such as loss of market value, loss of customers and reputational damage. In the digital era, wherein ensuring information security is extremely demanding and the dissemination of information occurs at a very high speed, protecting corporate reputation has become a challenging task. While several studies have provided empirical evidence of the financial consequences of data breaches, little attention has been dedicated to the link between data breaches and reputational risk. To address this research gap, we have measured the reputational effect of data breaches based on social media content by applying a novel approach, the sentiment-based event study. The empirical results provide strong evidence that data breach events deteriorate the reputation of companies involved in such incidents.

What Is Seen Is Who You Are: Are Cues in Selfie Pictures Related to Personality Characteristics? <=SEP=> Developments and innovation in the areas of mobile information technology, digital media and social networks foster new reflections on computer-mediated communication research, especially in the field of self-presentation. In this context, the selfie as a self-portrait photo is interesting, because as a meaningful gesture, it actively and directly relates the content of the photo to the author of the picture. From the perspective of the selfie as an image and the impression it forms, in the first part of the research we explored the distinctive characteristics of selfie pictures; moreover, from the perspective of the potential reflection of a selfie image on the personality of its author, in the second part we related the characteristics of selfie pictures to various personality constructs (e.g., Big Five personality traits narcissism and femininity-masculinity). Important aspects of selfies especially in relation to gender include the tilt of the head, the side of the face exhibited, mood and head position, later related also to the context of the selfie picture. We found no significant relations between selfie cues and personality constructs. The face-ism index was related to entitlement, and selfie availability to neuroticism.

Tagging Sentence Boundaries <=SEP=> In this paper we tackle sentence boundary disambiguation through a part-of-speech (POS) tagging framework. We describe necessary changes in text tokenization and the implementat ion of a POS tagger and provide results of an evaluation of this system on two corpora. We also describe an extension of the traditional POS tagging by combining it with the document-centered approach to proper name identification and abbreviation handling. This made the resulting system robust to domain and topic shifts. 1 I n t r o d u c t i o n Sentence boundary disambiguation (SBD) is an important aspect in developing virtually any practical text processing application syntactic parsing, Information Extraction, Machine Translation, Text Alignment, Document Summarization, etc. Segmenting text into sentences in most cases is a simple m a t t e r a period, an exclamation mark or a question mark usually signal a sentence boundary. However, there are cases when a period denotes a decimal point or is a part of an abbreviation and thus it does not signal a sentence break. Furthermore, an abbreviation itself can be the last token in a sentence, in which case its period acts at the same time as part of this abbreviation and as the end-of-sentence indicator (fullstop). The first large class of sentence boundary disambiguators uses manually built rules which are usually encoded in terms of regular expression grammars supplemented with lists of abbreviations, common words, proper names, etc. For instance, the Alembic workbench (Aberdeen et al., 1995) contains a sentence splitting module which employs over 100 regular-expression rules written in Flex. To put together a few rules which do a job is fast and easy, but to develop a good rule-based system is quite a labour consuming enterprise. Another potential shortcoming is that such systems are usually closely tailored to a particular corpus and are not easily portable across domains. Automatically trainable software is generally seen as a way of producing systems quickly re-trainable for a new corpus, domain or even for another language. Thus, the second class of SBD systems employs machine learning techniques such as decision tree classifiers (Riley, 1989), maximum entropy modeling (MAXTERMINATOR) (Reynar and Ratnaparkhi, 1997), neural networks (SATZ) (Palmer and Hearst , 1997), etc.. Machine learning systems treat the SBD task as a classification problem, using features such as word spelling, capitalization, suffix, word class, etc., found in the local context of potent im sentence breaking punctuation. There is, however, one catch all machine learning approaches to the SBD task known to us require labeled examples for training. This implies an investment in the annotat ion phase. There are two corpora normally used for evaluation and development in a number of text processing tasks and in the SBD task in particular: the Brown Corpus and the Wall Street Journal (WSJ) corpus both par t of the Penn Treebank (Marcus, Marcinkiewicz, and Santorini, 1993). Words in both these corpora are annota ted with part-ofspeech (POS) information and the text is split into documents, paragraphs and sentences. This gives all necessary information for the development of an SBD system and its evaluation. State-of-theart machine-learning and rule-based SBD systems achieve the error rate of about 0.8-1.5% measured on the Brown Corpus and the WSJ. The best performance on the WSJ was achieved by a combination of the SATZ system with the Alembic system 0.5% error rate. The best performance on the Brown Corpus, 0.2% error rate, was reported by (Riley, 1989), who trained a decision tree classifier on a 25 million word corpus. 1.1 W o r d b a s e d vs . S y n t a c t i c M e t h o d s The first source of ambiguity in end-of-sentence marking is introduced by abbreviations: if we know tha t the word which precedes a period is n o t an abbreviation, then almost certainly this period denotes a sentence break. However, if this word is an abbreviation, then it is not tha t easy to make a clear decision. The second major source of information

Information leakage from optical emanations <=SEP=> A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required; the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack.

Recurrent neural networks for time-series prediction. <=SEP=> Recurrent neural networks have been used for time-s eri s prediction with good results. In this dissertation we compare recurrent neural networks with time-delayed feed forward networks, feed forward networks and li near regression models to see which architecture that can make the most accurate predictions. The data used in all experiments is real-world sales data containing two kinds of segments: campaign segments and non-campaign segments. The task is to make predictions of sales under campaigns and with this in mind we evaluate if we c an get more accurate predictions if we only use the campaign segments when modeling the data. Throughout the entire project the KDD process have been used to give a st ructured work-process. The results showed that recurrent network is not better han the other evaluated algorithms, in fact, the time-delayed feed forward neural network showed to give the best predictions. The results also showed that we c ould get more accurate predictions when only using information from campaign segments.

Statistical Parsing for harmonic Analysis of Jazz Chord Sequences <=SEP=> Analysing music resembles natural language parsing in requiring the derivation of structure from an unstructured and highly ambiguous sequence of elements, whether they are notes or words. Such analysis is fundamental to many music processing tasks, such as key identification and score transcription. The focus of the present paper is on harmonic analysis. We use the three-dimensional tonal harmonic space developed by [4, 13, 14] to define a theory of tonal harmonic progression, which plays a role analogous to semantics in language. Our parser applies techniques from natural language processing (NLP) to the problem of analysing harmonic progression. It uses a formal grammar of jazz chord sequences of a kind that is widely used for NLP, together with the statistically based modelling techniques standardly used in wide-coverage parsing, to map music onto underlying harmonic progressions in the tonal space. Using supervised learning over a small corpus of jazz chord sequences annotated with harmonic analyses, we show that grammar-based musical parsing using simple statistical parsing models is more accurate than a baseline Markovian model trained on the same corpus.

Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks <=SEP=> Power consumption is a critical issue in many wireless sensor network scenarios where network life expectancy is measured in months or years. Communication protocols typically rely on duty-cycle mechanisms to reduce the power usage at the cost of decreased network responsiveness and increased communication latency. A low-power radio-triggered device can be used to continuously monitor the channel and activate the node for incoming communications, allowing purely asynchronous operations. To be effective, the power consumption of this wake-up receiver must be on the order of tens of microwatts since this device is always active. This paper presents the ongoing efforts to design such a low-power receiver. Initial results indicate an average power consumption below 20 uW.

Wireless image communication system for fire-fighting robots <=SEP=> Recently, it has sometimes been impossible for fire-fighting personnel to access the site of a fire, even as the fire causes tremendous property damage and loss of human life, due to high temperatures or the presence of explosive materials. In such environments, fire-fighting robots can be useful for extinguishing a fire, and they should be controlled by remote operators. In order to help a remote operator who is located far away from the fire-fighting robot, wireless image communication systems have been investigated in this study. The developed system uses a High-Speed Downlink Packet Access (HSDPA) network to transmit images, showing the view from a fire-fighting robot. The fire-fighting robots and remote controllers are equipped with industrial computers, and image-transmitting and image-receiving programs function according to various protocols to allow communication between the robots and controllers

Guidance for the evaluation and treatment of hereditary and acquired thrombophilia <=SEP=> Thrombophilias are hereditary and/or acquired conditions that predispose patients to thrombosis. Testing for thrombophilia is commonly performed in patients with venous thrombosis and their relatives; however such testing usually does not provide information that impacts management and may result in harm. This manuscript, initiated by the Anticoagulation Forum, provides clinical guidance for thrombophilia testing in five clinical situations: following 1) provoked venous thromboembolism, 2) unprovoked venous thromboembolism; 3) in relatives of patients with thrombosis, 4) in female relatives of patients with thrombosis considering estrogen use; and 5) in female relatives of patients with thrombosis who are considering pregnancy. Additionally, guidance is provided regarding the timing of thrombophilia testing. The role of thrombophilia testing in arterial thrombosis and for evaluation of recurrent pregnancy loss is not addressed. Statements are based on existing guidelines and consensus expert opinion where guidelines are lacking. We recommend that thrombophilia testing not be performed in most situations. When performed, it should be used in a highly selective manner, and only in circumstances where the information obtained will influence a decision important to the patient, and outweigh the potential risks of testing. Testing should not be performed during acute thrombosis or during the initial (3-month) period of anticoagulation.

Understanding payment card fraud through knowledge extraction from neural networks using large-scale datasets <=SEP=> 

Four common anatomic variants that predispose to unfavorable rhinoplasty results: a study based on 150 consecutive secondary rhinoplasties. <=SEP=> A retrospective study was conducted of 150 consecutive secondary rhinoplasty patients operated on by the author before February of 1999, to test the hypothesis that four anatomic variants (low radix/low dorsum, narrow middle vault, inadequate tip projection, and alar cartilage malposition) strongly predispose to unfavorable rhinoplasty results. The incidences of each variant were compared with those in 50 consecutive primary rhinoplasty patients. Photographs before any surgery were available in 61 percent of the secondary patients; diagnosis in the remaining individuals was made from operative reports, physical diagnosis, or patient history. Low radix/low dorsum was present in 93 percent of the secondary patients and 32 percent of the primary patients; narrow middle vault was present in 87 percent of the secondary patients and 38 percent of the primary patients; inadequate tip projection was present in 80 percent of the secondary patients and 31 percent of the primary patients; and alar cartilage malposition was present in 42 percent of the secondary patients and 18 percent of the primary patients. In the 150-patient secondary group, the most common combination was the triad of low radix, narrow middle vault, and inadequate tip projection (40 percent of patients). The second largest group (27 percent) had shared all four anatomic points before their primary rhinoplasties. Seventy-eight percent of the secondary patients had three or all four anatomic variants in some combination; each secondary patient had at least one of the four traits; 99 percent had two or more. Seventy-eight percent of the primary patients had at least two variants, and 58 percent had three or more. Twenty-two percent of the primary patients had none of the variants and therefore would presumably not be predisposed to unfavorable results following traditional reduction rhinoplasty. This study supports the contention that four common anatomic variants, if unrecognized, are strongly associated with unfavorable results following primary rhinoplasty. It is important for all surgeons performing rhinoplasty to recognize these anatomic variants to avoid the unsatisfactory functional and aesthetic sequelae that they may produce by making their correction a deliberate part of each preoperative surgical plan.

A whispered Mandarin corpus for speech technology applications <=SEP=> Whispered speech is a natural mode of speech in which voicing is absent – its acoustics differ significantly from normally spoken speech or so-called neutral speech, such that it is challenging to use only neutral speech to build speech processing and automatic recognition systems that can deal effectively with whisper. At the same time, humans can naturally produce and perceive whispered speech without explicit training. Tonal languages such as Mandarin present an interesting dilemma – tone is primarily encoded by pitch tracks which are absent during whispered speech, but humans can still tell tones apart. How humans manage to process whispered speech well without explicit training on it, whereas machine algorithms fail, is presently an unresolved question which could prove fruitful with study. This, however, is hindered by the lack of suitable, systematically collected corpora. We present iWhisper-Mandarin, a 25-hour parallel corpus of neutral and whispered Mandarin, designed to support research in linguistics and speech technology. We demonstrate and verify that earlier techniques applied to whispered speech from non-tonal languages also work with Mandarin, and present some preliminary studies on voice activity detection and whispered Mandarin speech recognition.

Deploying Deep Neural Networks in the Embedded Space <=SEP=> Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.

Smart manufacturing, manufacturing intelligence and demand-dynamic performance <=SEP=> Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. It responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance; demand-driven supply chain services; and broad-based workforce involvement. ITenabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.

Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition <=SEP=> Recurrent neural network language models RNNLMs are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units GPUs and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure F-RNNLM is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.

Learning under Feature Drifts in Textual Streams <=SEP=> Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.