Query: Near or Far, Wide Range Zero-Shot Cross-Lingual Dependency Parsing

Relevant passages:
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks <=SEP=> Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.

Word Translation Without Parallel Data <=SEP=> State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.

Deep Biaffine Attention for Neural Dependency Parsing <=SEP=> Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., and Collins, M. (2016). Globally normalized transition-based neural networks. In Association for Computational Linguistics. Ballesteros, M., Goldberg, Y., Dyer, C., and Smith, N. A. (2016). Training with exploration improves a greedy stack-LSTM parser. Proceedings of the conference on empirical methods in natural language processing. Chen, D. and Manning, C. D. (2014). A fast and accurate dependency parser using neural networks. In Proceedings of the conference on empirical methods in natural language processing, pages 740–750. Cheng, H., Fang, H., He, X., Gao, J., and Deng, L. (2016). Bi-directional attention with agreement for dependency parsing. arXiv preprint arXiv:1608.02076. Hashimoto, K., Xiong, C., Tsuruoka, Y., and Socher, R. (2016). A joint many-task model: Growing a neural network for multiple nlp tasks. arXiv preprint arXiv:1611.01587. Kiperwasser, E. and Goldberg, Y. (2016). Simple and accurate dependency parsing using bidirectional LSTM feature representations. Transactions of the Association for Computational Linguistics, 4:313–327. Kuncoro, A., Ballesteros, M., Kong, L., Dyer, C., Neubig, G., and Smith, N. A. (2016). What do recurrent neural network grammars learn about syntax? CoRR, abs/1611.05774. McDonald, R. T. and Pereira, F. C. (2006). Online learning of approximate dependency parsing algorithms. In EACL. Nivre, J., Hall, J., and Nilsson, J. (2006). Maltparser: A data-driven parser-generator for dependency parsing. In Proceedings of LREC, volume 6, pages 2216–2219.

Dependency-Based Open Information Extraction <=SEP=> Building shallow semantic representations from text corpora is the first step to perform more complex tasks such as text entailment, enrichment of knowledge bases, or question answering. Open Information Extraction (OIE) is a recent unsupervised strategy to extract billions of basic assertions from massive corpora, which can be considered as being a shallow semantic representation of those corpora. In this paper, we propose a new multilingual OIE system based on robust and fast rule-based dependency parsing. It permits to extract more precise assertions (verb-based triples) from text than state of the art OIE systems, keeping a crucial property of those systems: scaling to Web-size document collections.

Constituency Parsing with a Self-Attentive Encoder <=SEP=> We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-ofthe-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-ofthe-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.

A novel wireless power transfer for in-motion EV/PHEV charging <=SEP=> Wireless power transfer (WPT) is a convenient, safe, and autonomous means for electric and plug-in hybrid electric vehicle charging that has seen rapid growth in recent years for stationary applications. WPT does not require bulky contacts, plugs, and wires, is not affected by dirt or weather conditions, and is as efficient as conventional charging systems. When applied in-motion, WPT additionally relives range anxiety, adds further convenience, reduces battery size, and may help increase the battery life through charge sustaining approach. This study summarizes some of the recent activities of Oak Ridge National Laboratory (ORNL) in WPT charging of EV and PHEV's inmotion. Laboratory experimental results that highlight the wireless transfer of power to a moving receiver coil as it passes a pair of transmit coils and investigation of results of insertion loss due to roadway surfacing materials. Some of the experimental lessons learned are also included in this study.

Message Understanding Conference- 6: A Brief History <=SEP=> We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations. 1 The M U C Evaluations We have just completed the sixth in a series of Message Understanding Conferences, which have been organized by NRAD, the RDT&E division of the Naval Command, Control and Ocean Surveillance Center (formerly NOSC, the Naval Ocean Systems Center) with the support of DARPA, the Defense Advanced Research Projects Agency. This paper looks briefly at the history of these Conferences and then examines the considerations which led to the structure of MUC-6} The Message Understanding Conferences were initiated by NOSC to assess and to foster research on the automated analysis of mili tary messages containing textual information. Although called "conferences", the distinguishing characteristic of the MUCs are not the conferences themselves, but the evaluations to which participants must submit in order to be permit ted to at tend the conference. For each MUC, participating groups have been given sample messages and instructions on the type of information to be extracted, and have developed a system to process such messages. Then, shortly before the conference, participants are given a set of test messages to be run through their system (without making any changes to the system); the output of each part icipant 's system 1The full proceedings of the conference are to be distributed by Morgan Kaufmann Publishers, San Mateo, California; earlier MUC proeeedings~ for MUC-3, 4, and 5, are also available from Morgan Kaufmann. Beth Sundhe im Naval Command, Control and Ocean Surveillance Center Research, Development, Test and Evaluation Division (NRaD) Code 44208 53140 Gatchell Road San Diego, CMifornia 92152-7420 s u n d h e i m @ p o j k e . n o s c . m i l is then evaluated against a manual ly-prepared answer key. The MUCs are remarkable in part because of the degree to which these evaluations have defined a prograin of research and development. DARPA has a number of information science and technology programs which are driven in large part, by regular evaluations. The MUCs are notable, however, in that they in large par t have shaped the research program in information extraction and brought it to its current s ta te}

Minoxidil-induced trichostasis spinulosa of terminal hair. <=SEP=> depression after pressure application to the skin and a fold after stretching of the skin are clinical signs of CL. To our knowledge, the exclusive acral involvement of CL has been reported in only 7 patients, and 4 of them also had multiple myeloma amyloidosis. Unlike those 4 patients, our patient manifested elastolytic cutaneous lesions as the only clinical sign of his multiple myeloma. The histopathologic findings were the clue for the diagnosis of underlying monoclonal gammopathy. Destruction of elastic fibers could be immune mediated by deposition of abnormal proteins. Wewould like toemphasize the importanceof recognizing cutaneous laxity on fingertips associated with temporary dimpling of the skin for minutes after pressure is applied, which can be the clue for the diagnosis of a monoclonalgammopathy.Clinicopathologiccorrelationisessential; demonstration of amyloid deposits with monotypic expression of light chains and diminution of dermal elastic fibers are the histopathologic clues of these forms of acrolocalized acquired CL associated with multiple myeloma.

Sequential Deep Learning for Disaster-Related Video Classification <=SEP=> Videos serve to convey complex semantic information and ease the understanding of new knowledge. However, when mixed semantic meanings from different modalities (i.e., image, video, text) are involved, it is more difficult for a computer model to detect and classify the concepts (such as flood, storm, and animals). This paper presents a multimodal deep learning framework to improve video concept classification by leveraging recent advances in transfer learning and sequential deep learning models. Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) models are then used to obtain the sequential semantics for both audio and textual models. The proposed framework is applied to a disaster-related video dataset that includes not only disaster scenes, but also the activities that took place during the disaster event. The experimental results show the effectiveness of the proposed framework.

Treatment of Hyaluronic Acid Filler-Induced Impending Necrosis With Hyaluronidase: Consensus Recommendations. <=SEP=> Injection-induced necrosis is a rare but dreaded consequence of soft tissue augmentation with filler agents. It usually occurs as a result of injection of filler directly into an artery, but can also result from compression or injury. We provide recommendations on the use of hyaluronidase when vascular compromise is suspected. Consensus recommendations were developed by thorough discussion and debate amongst the authors at a roundtable meeting on Wednesday June 18, 2014 in Las Vegas, NV as well as significant ongoing written and verbal communications amongst the authors in the months prior to journal submission. All authors are experienced tertiary care providers. A prompt diagnosis and immediate treatment with high doses of hyaluronidase (at least 200 U) are critically important. It is not felt necessary to do a skin test in cases of impending necrosis. Some experts recommend dilution with saline to increase dispersion or lidocaine to aid vasodilation. Additional hyaluronidase should be injected if improvement is not seen within 60 minutes. A warm compress also aids vasodilation, and massage has been shown to help. Some experts advocate the use of nitroglycerin paste, although this area is controversial. Introducing an oral aspirin regimen should help prevent further clot formation due to vascular compromise. In our experience, patients who are diagnosed promptly and treated within 24 hours will usually have the best outcomes.

What is the amygdala? <=SEP=> 'Amygdala' and 'amygdalar complex' are terms that now refer to a highly differentiated region near the temporal pole of the mammalian cerebral hemisphere. Cell groups within it appear to be differentiated parts of the traditional cortex, the claustrum, or the striatum, and these parts belong to four obvious functional systems--accessory olfactory, main olfactory, autonomic and frontotemporal cortical. In rats, the central nucleus is a specialized autonomic-projecting motor region of the striatum, whereas the lateral and anterior basolateral nuclei together are a ventromedial extension of the claustrum for major regions of the temporal and frontal lobes. The rest of the amygdala forms association parts of the olfactory system (accessory and main), with cortical, claustral and striatal parts. Terms such as 'amygdala' and 'lenticular nucleus' combine cell groups arbitrarily rather than according to the structural and functional units to which they now seem to belong. The amygdala is neither a structural nor a functional unit.

Mitigating YARN Container Overhead with Input Splits <=SEP=> We analyze YARN container overhead and present early results of reducing its overhead by dynamically adjusting the input split size. YARN is designed as a generic resource manager that decouples programming models from resource management infrastructures. We demonstrate that YARN’s generic design incurs significant overhead because each con- tainer must perform various initialization steps, including authentication. To reduce container overhead without changing the existing YARN framework significantly, we propose leverag- ing the input split, which is the logical representation of physical HDFS blocks. With input splits, we can combine multiple HDFS blocks and increase the input size of each container, thereby enabling a single map wave and reducing the number of containers and their initialization overhead. Experimental results shows that we can avoid recurring container overhead by selecting the right size for input splits and reducing the number of containers.

Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder <=SEP=> Some of the most frequently used online applications are Facebook, WhatsApp, and Twitter. These applications allow individuals to communicate with other users, to share information or pictures, and to stay in contact with friends all over the world. However, a growing number of users suffer from negative consequences due to their excessive use of these applications, which can be referred to as Internet-communication disorder. The frequent use and easy access of these applications may also trigger the individual's fear of missing out on content when not accessing these applications. Using a sample of 270 participants, a structural equation model was analyzed to investigate the role of psychopathological symptoms and the fear of missing out on expectancies towards Internet-communication applications in the development of symptoms of an Internet-communication disorder. The results suggest that psychopathological symptoms predict higher fear of missing out on the individual's Internet-communication applications and higher expectancies to use these applications as a helpful tool to escape from negative feelings. These specific cognitions mediate the effect of psychopathological symptoms on Internet-communication disorder. Our results are in line with the theoretical model by Brand et al. (2016) as they show how Internet-related cognitive bias mediates the relationship between a person's core characteristics (e.g., psychopathological symptoms) and Internet-communication disorder. However, further studies should investigate the role of the fear of missing out as a specific predisposition, as well as specific cognition in the online context.

Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources <=SEP=> The Web has accumulated a rich source of information, such as text, image, rating, etc, which represent different aspects of user preferences. However, the heterogeneous nature of this information makes it difficult for recommender systems to leverage in a unified framework to boost the performance. Recently, the rapid development of representation learning techniques provides an approach to this problem. By translating the various information sources into a unified representation space, it becomes possible to integrate heterogeneous information for informed recommendation. 
 In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation. In this framework, each type of information source (review text, product image, numerical rating, etc) is adopted to learn the corresponding user and item representations based on available (deep) representation learning architectures. Representations from different sources are integrated with an extra layer to obtain the joint representations for users and items. In the end, both the per-source and the joint representations are trained as a whole using pair-wise learning to rank for top-N recommendation. We analyze how information propagates among different information sources in a gradient-descent learning paradigm, based on which we further propose an extendable version of the JRL framework (eJRL), which is rigorously extendable to new information sources to avoid model re-training in practice. 
 By representing users and items into embeddings offline, and using a simple vector multiplication for ranking score calculation online, our framework also has the advantage of fast online prediction compared with other deep learning approaches to recommendation that learn a complex prediction network for online calculation.

Adaptive block truncation filter for MVC depth image enhancement <=SEP=> In Multiview Video plus Depth (MVD) format, virtual views are generated from decoded texture videos with decoded depth images through Depth Image based Rendering (DIBR). 3DV-ATM is a reference model for H.264/AVC based Multiview Video Coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding of 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized views. This paper presents a low complexity adaptive block truncation filter to recover the sharp object boundaries of depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results show that sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.

The price of power: power seeking and backlash against female politicians. <=SEP=> Two experimental studies examined the effect of power-seeking intentions on backlash toward women in political office. It was hypothesized that a female politician's career progress may be hindered by the belief that she seeks power, as this desire may violate prescribed communal expectations for women and thereby elicit interpersonal penalties. Results suggested that voting preferences for female candidates were negatively influenced by her power-seeking intentions (actual or perceived) but that preferences for male candidates were unaffected by power-seeking intentions. These differential reactions were partly explained by the perceived lack of communality implied by women's power-seeking intentions, resulting in lower perceived competence and feelings of moral outrage. The presence of moral-emotional reactions suggests that backlash arises from the violation of communal prescriptions rather than normative deviations more generally. These findings illuminate one potential source of gender bias in politics.

Recognition of Car Makes and Models From a Single Traffic-Camera Image <=SEP=> This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.

The turing test track of the 2012 Mario AI Championship: Entries and evaluation <=SEP=> The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.

A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks <=SEP=> This paper describes digital circuit architectures for automatic speech recognition (ASR) and voice activity detection (VAD) with improved accuracy, programmability, and scalability. Our ASR architecture is designed to minimize off-chip memory bandwidth, which is the main driver of system power consumption. A SIMD processor with 32 parallel execution units efficiently evaluates feed-forward deep neural networks (NNs) for ASR, limiting memory usage with a sparse quantized weight matrix format. We argue that VADs should prioritize accuracy over area and power, and introduce a VAD circuit that uses an NN to classify modulation frequency features with 22.3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption. The 65-nm test chip is shown to perform a variety of ASR tasks in real time, with vocabularies ranging from 11 words to 145 000 words and full-chip power consumption ranging from 172 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> to 7.78 mW.

A cross-national profile of bullying and victimization among adolescents in 40 countries <=SEP=> (1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.

The Flipped Classroom : An Opportunity To Engage Millennial Students Through Active Learning Strategies <=SEP=> "Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.

A stepped-carrier 77-GHz OFDM MIMO radar system with 4 GHz bandwidth <=SEP=> This paper presents a 77-GHz orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar system with 4 GHz bandwidth. Due to the limited bandwidth of the analog-to-digital and digital-to-analog converters a stepped-carrier approach is used. OFDM radar measurements with a bandwidth of 200MHz at 20 different carrier frequencies are combined in signal processing to achieve a high range resolution OFDM MIMO radar. Spectrally-interleaved OFDM signals are used to allow for simultaneous transmission and thus MIMO operation. The proposed approach is verified in practical measurements of a static target scenario and a slowly moving target scenario. Therefore a self-developed software defined radar evaluation platform with four transmitters and four receivers is used. The expected high range resolution could be achieved and range/angle/velocity measurements show the suitability for detecting slowly moving targets like pedestrians.

Machine Learning Paradigms for Speech Recognition: An Overview <=SEP=> Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem - for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.

Spell Checking Techniques in NLP: A Survey <=SEP=> Spell checkers in Indian languages are the basic tools that need to be developed. A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Spell checkers can be combined with other applications or they can be distributed individually. In this paper the authors are discussing both the approaches and their roles in various applications.

2CAuth: A New Two Factor Authentication Scheme Using QR-Code <=SEP=> Password based schemes has been the standard means of authentication over decades. Enhancements use entities like ownership (something one possess), knowledge (something one knows), and inherence (something one is) as first factor and mobile phones as token less second factor, in combinations, to offer different levels of security assurances, trading off usability. In this paper we present ‘2CAuth’ a new two factor authentication scheme that enhances secure usage of application information and preserves usability, without sacrificing user’s privacy. A significant feature of the scheme is that it DOES NOT call for any synchronization between Mobile Network Operator (MNO) and users. The analysis of the scheme clearly brings out its effectiveness in terms of its usability even at times of peak loads on mobile networks. The scheme has the dual advantage of carrying out the verification of transactions which involve the physical presence of the user as well as those to be done in his absence.

Ultraflat Interleaved Triangular Current Mode (TCM) Single-Phase PFC Rectifier <=SEP=> This paper presents the analysis and realization of a topology suitable to realize a power factor correction (PFC) rectifier with a thickness of only a few millimeters. The low height of the converter requires all components to be integrated into the printed circuit board (PCB). Still reasonable dimensions of the converter PCB are feasible (221 mm × 157 mm for a 200 W PFC rectifier), since PCB-integrated inductors and capacitors allow for high energy densities due to their large surface area which facilitates a low thermal resistance to ambient. A multicell totem-pole PFC rectifier employing a soft-switching modulation scheme over the complete mains period is identified as an adequate topology. The mode of operation is entitled triangular current mode (TCM) due to the triangular-shaped inductor currents. The modulation technique requires a reliable description of the switching transition of a half-bridge in order to provide accurate timing parameters. For this purpose, a simplified model of the nonlinear MOSFETs' output capacitances facilitates closed-form analytical expressions for duty cycle and switching frequency. Furthermore, this paper details the control of three interleaved converter cells which yields a reduction of the input current ripple. A 200 W TCM PFC rectifier with a low height of 5 mm has been realized and measurement results are provided in order to validate the theoretical considerations. The presented TCM PFC rectifier achieves an efficiency of 94.6% and a power factor of 99.3% at nominal power.

What Size Net Gives Valid Generalization? <=SEP=> We address the question of when a network can be expected to generalize from m random training examples chosen from some arbitrary probability distribution, assuming that future test examples are drawn from the same distribution. Among our results are the following bounds on appropriate sample vs. network size. Assume 0 < ∊ 1/8. We show that if m O(W/∊ log N/∊) random examples can be loaded on a feedforward network of linear threshold functions with N nodes and W weights, so that at least a fraction 1 ∊/2 of the examples are correctly classified, then one has confidence approaching certainty that the network will correctly classify a fraction 1 ∊ of future test examples drawn from the same distribution. Conversely, for fully-connected feedforward nets with one hidden layer, any learning algorithm using fewer than (W/∊) random training examples will, for some distributions of examples consistent with an appropriate weight choice, fail at least some fixed fraction of the time to find a weight choice that will correctly classify more than a 1 ∊ fraction of the future test examples.

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.

Detecting False Data Injection Attacks on DC State Estimation <=SEP=> • Y. Liu, M. K. Reiter, and P. Ning. False Data Injection Attacks Against State Estimation in Electric Power Grids. In 16th ACM Conference on Computer and Communications Security (CCS ’09). Defense Against False Data Injection Attacks on DC State Estimation Miao Lu, University of Illinois at Urbana-Champaign ADVISORS: Rakesh Bobba, Kate Morrow I T I S U M M E R U N D E R G R A D U A T E I N T E R N P R O G R A M, 2 0 1 1

EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals <=SEP=> Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably upand down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.