Query: Query Attention GloVe GloVe CNN Attention Flow Layer Modeling Layer Output Layer

Relevant passages:
Character-Aware Neural Language Models <=SEP=> We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long shortterm memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-ofthe-art despite having 60% fewer parameters. On languages with rich morphology (Czech, German, French, Spanish, Russian), the model consistently outperforms a Kneser-Ney baseline and word-level/morpheme-level LSTM baselines, again with far fewer parameters. Our results suggest that on many languages, character inputs are sufficient for language modeling.

A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

A Convolutional Attention Network for Extreme Summarization of Source Code <=SEP=> Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model’s attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network’s performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms.

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <=SEP=> Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.

'C and tcc: A Language and Compiler for Dynamic Code Generation <=SEP=> Dynamic code generation allows programmers to use run-time information in order to achieve performance and expressiveness superior to those of static code. The 'C(Tick C) language is a superset of ANSI C that supports efficient and high-level use of dynamic code generation. 'C provides dynamic code generation at the level of C expressions and statements and supports the composition of dynamic code at run time. These features enable programmers to add dynamic code generation to existing C code incrementally and to write important applications (such as “just-in-time” compilers) easily. The article presents many examples of how 'C can be used to solve practical problems. The tcc compiler is an efficient, portable, and freely available implementation of 'C. tcc allows programmers to trade dynamic compilation speed for dynamic code quality: in some aplications, it is most important to generate code quickly, while in others code quality matters more than compilation speed. The overhead of dynamic compilation is on the order of 100 to 600 cycles per generated instruction, depending on the level of dynamic optimizaton. Measurements show that the use of dynamic code generation can improve performance by almost an order of magnitude; two- to four-fold speedups are common. In most cases, the overhead of dynamic compilation is recovered in under 100 uses of the dynamic code; sometimes it can be recovered within one use.

The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress <=SEP=> 0747-5632/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.chb.2013.10.047 ⇑ Corresponding author. Address: Department of Business Management, National Sun Yat-sen University, No. 70, Lianhai Rd., Gushan District, Kaohsiung City 804, Taiwan, Tel.: +886 7 525 2000x4627. E-mail address: ctchang@faculty.nsysu.edu.tw (C.-T. Chang). Yu-Kang Lee, Chun-Tuan Chang ⇑, You Lin, Zhao-Hong Cheng

Fourier Lucas-Kanade Algorithm <=SEP=> In this paper, we propose a framework for both gradient descent image and object alignment in the Fourier domain. Our method centers upon the classical Lucas &#x0026; Kanade (LK) algorithm where we represent the source and template/model in the complex 2D Fourier domain rather than in the spatial 2D domain. We refer to our approach as the Fourier LK (FLK) algorithm. The FLK formulation is advantageous when one preprocesses the source image and template/model with a bank of filters (e.g., oriented edges, Gabor, etc.) as 1) it can handle substantial illumination variations, 2) the inefficient preprocessing filter bank step can be subsumed within the FLK algorithm as a sparse diagonal weighting matrix, 3) unlike traditional LK, the computational cost is invariant to the number of filters and as a result is far more efficient, and 4) this approach can be extended to the Inverse Compositional (IC) form of the LK algorithm where nearly all steps (including Fourier transform and filter bank preprocessing) can be precomputed, leading to an extremely efficient and robust approach to gradient descent image matching. Further, these computational savings translate to nonrigid object alignment tasks that are considered extensions of the LK algorithm, such as those found in Active Appearance Models (AAMs).

Sensor Reduction for Driver-Automation Shared Steering Control via an Adaptive Authority Allocation Strategy <=SEP=> This paper presents a new shared control method for lane keeping assist (LKA) systems of intelligent vehicles. The proposed method allows the LKA system to effectively share the control authority with a human driver by avoiding or minimizing the conflict situations between these two driving actors. To realize the shared control scheme, the unpredictable driver-automation interaction is explicitly taken into account in the control design via a fictive driver activity variable. This latter is judiciously introduced into the driver–road–vehicle system to represent the driver's need for assistance in accordance with his/her real-time driving activity. Using Lyapunov stability arguments, Takagi–Sugeno fuzzy model-based design conditions are derived to handle not only the time-varying driver activity variable, but also a large variation range of vehicle speed. Both simulation and hardware experiments are presented to demonstrate that the proposed control strategy together with a linear matrix inequality design formulation provide an effective tool to deal with the challenging shared steering control issue. In particular, a fuzzy output feedback control scheme is exploited to achieve the shared control goal without at least two important vehicle sensors. These physical sensors are widely employed in previous works to measure the lateral speed and the steering rate for the control design and real-time implementation. The successful results of this idea of sensor-reduction control has an obvious interest from practical viewpoint.

Predictability, Complexity, and Learning <=SEP=> We define predictive information Ipred(T) as the mutual information between the past and the future of a time series. Three qualitatively different behaviors are found in the limit of large observation times T: Ipred(T) can remain finite, grow logarithmically, or grow as a fractional power law. If the time series allows us to learn a model with a finite number of parameters, then Ipred(T) grows logarithmically with a coefficient that counts the dimensionality of the model space. In contrast, power-law growth is associated, for example, with the learning of infinite parameter (or non-parametric) models such as continuous functions with smoothness constraints. There are connections between the predictive information and measures of complexity that have been defined both in learning theory and the analysis of physical systems through statistical mechanics and dynamical systems theory. Furthermore, in the same way that entropy provides the unique measure of available information consistent with some simple and plausible conditions, we argue that the divergent part of Ipred(T) provides the unique measure for the complexity of dynamics underlying a time series. Finally, we discuss how these ideas may be useful in problems in physics, statistics, and biology.

Modeling Response Time in Digital Human Communication <=SEP=> Our daily lives increasingly involve interactions with other individuals via different communication channels, such as email, text messaging, and social media. In this paper we focus on the problem of modeling and predicting how long it takes an individual to respond to an incoming communication event, such as receiving an email or a text. In particular, we explore the effect on response times of an individual’s temporal pattern of activity, such as circadian and weekly patterns which are typically present in individual data. A probabilistic time-warping approach is used, considering linear time to be a transformation of “effective time,” where the transformation is a function of an individual’s activity rate. We apply this transformation of time to two different types of temporal event models, the first for modeling response times directly, and the second for modeling event times via a Hawkes process. We apply our approach to two different sets of real-world email histories. The experimental results clearly indicate that the transformation-based approach produces systematically better models and predictions compared to simpler methods that ignore circadian and weekly patterns. Current technology allows us to collect large quantities of time-stamped individual-level event data characterizing our “digital behavior” in contexts such as texting, email activity, microblogging, social media interactions, and more — and the volume and variety of this type of data is continually increasing. The resulting time-series of events are rich in behavioral information about our daily lives. Tools for obtaining and visualizing such information are becoming increasingly popular, such as the ability to download your entire email history for mail applications such as Gmail, and various software packages for tracking personal fitness using data from devices such as Fitbit. This paper is focused on modeling the temporal aspects of how an individual (also referred to as the “ego”) responds to others, given a sequence of timestamped events (e.g. communication messages via email or text). What can we learn from the way we respond to others? Are there systematic ∗Currently employed at Google. The research described in this paper was conducted while the author was a graduate student at UC Irvine. Copyright c © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Mon Tue Wed Thu Fri Sat Sun 0 1 a (t ) Mon Tue Wed Thu Fri Sat Sun 0 1 a (t ) Figure 1: Example of smoothed daily and weekly patterns over two separate individuals’ email histories. a(t) on the yaxis represents an individual’s typical activity as a function of time. patterns that can be extracted and used predictively? How do our daily sleep and work patterns factor in to our response patterns? These are common issues that arise when modeling communication events, as the ego’s behavior typically changes significantly over the course of a day or week. Examples of such patterns are shown in Figure 1. We propose a novel method for parameterizing these circadian (daily) and weekly patterns, allowing the time dimension to be transformed according to such patterns. This transformation of time will allow models to describe and predict behavioral patterns which are invariant to the ego’s routine patterns. We apply this approach to predicting the ego’s response time to an event. Learning such response patterns is useful not only for identifying relationships between pairs of individuals (Halpin and De Boeck 2013), but also as features for prioritization of events, e.g., the priority inbox implemented in Gmail (Aberdeen, Pacovsky, and Slater 2010). Our experimental results show clear advantages in terms of predictive power when modeling response times in the transformed time domain. τ0 τ1 τ2 t0 t1 t2 ∆0

An empirical investigation of mobile ticketing service adoption in public transportation <=SEP=> In this paper, we present results from a study of mobile ticketing service adoption in public transportation. The theoretical background of the study is based on technology adoption and trust theories, which are augmented with concepts of mobile use context and mobility. Our empirical findings from analyses of a survey data suggest that compatibility of the mobile ticketing service with consumer behavior is a major determinant of adoption. Mobility and contextual factors, including budget constraints, availability of other alternatives, and time pressure in the service use situation were also found to have a strong effect on the adoption decision. Our findings suggest that contextual and mobile service-specific features are important determinants of mobile service adoption and should thus be integrated into the traditional adoption models.

The obstacle-restriction method for robot obstacle avoidance in difficult environments <=SEP=> This paper addresses the obstacle avoidance problem in difficult scenarios that usually are dense, complex and cluttered. The proposal is a method called the obstacle-restriction. At each iteration of the control cycle, this method addresses the obstacle avoidance in two steps. First there is procedure to compute instantaneous subgoals in the obstacle structure (obtained by the sensors). The second step associates a motion restriction to each obstacle, which are managed next to compute the most promising motion direction. The advantage of this technique is that it avoids common limitations of previous obstacle avoidance methods, improving their navigation performance in difficult scenarios. Furthermore, we obtain similar results to the recent methods that achieve navigation in troublesome scenarios. However, the new method improves their behavior in open spaces. The performance of this method is illustrated with experimental results obtained with a robotic wheelchair vehicle.

Perceived aggressiveness predicts fighting performance in mixed-martial-arts fighters. <=SEP=> Accurate assessment of competitive ability is a critical component of contest behavior in animals, and it could be just as important in human competition, particularly in human ancestral populations. Here, we tested the role that facial perception plays in this assessment by investigating the association between both perceived aggressiveness and perceived fighting ability in fighters' faces and their actual fighting success. Perceived aggressiveness was positively associated with the proportion of fights won, after we controlled for the effect of weight, which also independently predicted perceived aggression. In contrast, perception of fighting ability was confounded by weight, and an association between perceived fighting ability and actual fighting success was restricted to heavyweight fighters. Shape regressions revealed that aggressive-looking faces are generally wider and have a broader chin, more prominent eyebrows, and a larger nose than less aggressive-looking faces. Our results indicate that perception of aggressiveness and fighting ability might cue different aspects of success in male-male physical confrontation.

Positive psychology : Past , present , and ( possible ) future <=SEP=> What is positive psychology? Where has it come from? Where is it going? These are the questions we address in this article. In defining positive psychology, we distinguish between the meta-psychological level, where the aim of positive psychology is to redress the imbalance in psychology research and practice, and the pragmatic level, which is concerned with what positive psychologists do, in terms of their research, practice, and areas of interest. These distinctions in how we understand positive psychology are then used to shape conceptions of possible futures for positive psychology. In conclusion, we identify several pertinent issues for the consideration of positive psychology as it moves forward. These include the need to synthesize the positive and negative, build on its historical antecedents, integrate across levels of analysis, build constituency with powerful stakeholders, and be aware of the implications of description versus prescription.

Design and Optimization of Printed Spiral Coils for Efficient Transcutaneous Inductive Power Transmission <=SEP=> The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.

Mapping distributed brain function and networks with diffuse optical tomography. <=SEP=> Mapping of human brain function has revolutionized systems neuroscience. However, traditional functional neuroimaging by positron emission tomography or functional magnetic resonance imaging cannot be used when applications require portability, or are contraindicated because of ionizing radiation (positron emission tomography) or implanted metal (functional magnetic resonance imaging). Optical neuroimaging offers a non-invasive alternative that is radiation free and compatible with implanted metal and electronic devices (for example, pacemakers). However, optical imaging technology has heretofore lacked the combination of spatial resolution and wide field of view sufficient to map distributed brain functions. Here, we present a high-density diffuse optical tomography imaging array that can map higher-order, distributed brain function. The system was tested by imaging four hierarchical language tasks and multiple resting-state networks including the dorsal attention and default mode networks. Finally, we imaged brain function in patients with Parkinson's disease and implanted deep brain stimulators that preclude functional magnetic resonance imaging.

On the Performance of Intel SGX <=SEP=> As cloud computing is widely used in various fields, more and more individuals and organizations are considering outsourcing data to the public cloud. However, the security of the cloud data has become the most prominent concern of many customers, especially those who possess a large volume of valuable and sensitive data. Although some technologies like Homomorphic Encryption were proposed to solve the problem of secure data, the result is still not satisfying. With the advent of Intel SGX processor, which aims to thoroughly eliminate the security concern of cloud environment in a hardware-assisted approach, it brings us a number of questions on its features and its practicability for the current cloud platform. To evaluate the potential impact of Intel SGX, we analyzed the current SGX programming mode and inferred some possible factors that may arise the overhead. To verify our performance hypothesis, we conducted a systematic study on SGX performance by a series of benchmark experiments. After analyzing the experiment result, we performed a workload characterization to help programmer better exploit the current availability of Intel SGX and identify feasible research directions.

A Generative Modeling Approach to Limited Channel ECG Classification <=SEP=> Processing temporal sequences is central to a variety of applications in health care, and in particular multichannel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.

On the use of windows for harmonic analysis with the discrete Fourier transform <=SEP=> This paper makes available a concise review of data windows and their affect on the detection of harmonic signals in the presence of broad-band noise, and in the presence of nearby strong harmonic interference. We also call attention to a number of common errors in the application of windows when used with the fast Fourier transform. This paper includes a comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared. Finally, an example demonstrates the use and value of windows to resolve closely spaced harmonic signals characterized by large differences in amplitude.

A Boosted Bayesian Multiresolution Classifier for Prostate Cancer Detection From Digitized Needle Biopsies <=SEP=> Diagnosis of prostate cancer (CaP) currently involves examining tissue samples for CaP presence and extent via a microscope, a time-consuming and subjective process. With the advent of digital pathology, computer-aided algorithms can now be applied to disease detection on digitized glass slides. The size of these digitized histology images (hundreds of millions of pixels) presents a formidable challenge for any computerized image analysis program. In this paper, we present a boosted Bayesian multiresolution (BBMR) system to identify regions of CaP on digital biopsy slides. Such a system would serve as an important preceding step to a Gleason grading algorithm, where the objective would be to score the invasiveness and severity of the disease. In the first step, our algorithm decomposes the whole-slide image into an image pyramid comprising multiple resolution levels. Regions identified as cancer via a Bayesian classifier at lower resolution levels are subsequently examined in greater detail at higher resolution levels, thereby allowing for rapid and efficient analysis of large images. At each resolution level, ten image features are chosen from a pool of over 900 first-order statistical, second-order co-occurrence, and Gabor filter features using an AdaBoost ensemble method. The BBMR scheme, operating on 100 images obtained from 58 patients, yielded: 1) areas under the receiver operating characteristic curve (AUC) of 0.84, 0.83, and 0.76, respectively, at the lowest, intermediate, and highest resolution levels and 2) an eightfold savings in terms of computational time compared to running the algorithm directly at full (highest) resolution. The BBMR model outperformed (in terms of AUC): 1) individual features (no ensemble) and 2) a random forest classifier ensemble obtained by bagging multiple decision tree classifiers. The apparent drop-off in AUC at higher image resolutions is due to lack of fine detail in the expert annotation of CaP and is not an artifact of the classifier. The implicit feature selection done via the AdaBoost component of the BBMR classifier reveals that different classes and types of image features become more relevant for discriminating between CaP and benign areas at different image resolutions.

Innovation and practice of continuous auditing <=SEP=> Article history: Received 27 August 2010 Received in revised form 23 December 2010 Accepted 6 January 2011 The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers. © 2011 Elsevier Inc. All rights reserved.

Severity Analyses of Single-Vehicle Crashes Based on Rough Set Theory <=SEP=> A single-vehicle crash is a typical pattern of traffic accidents and tends to cause heavy loss. The purpose of this study is to identify the factors significantly influencing single-vehicle crash injury severity, using a data selected from Beijing city for a 4-year period. Rough set theory was applied to complete the injury severity analysis, and followed by applying cross-validation method to estimate the prediction accuracy of extraction rules. Results show that it is effective for analyzing the severity of Single-vehicle crashes with rough set theory.

Galvanometric optical laser beam steering system for microfactory application <=SEP=> This article presents a kinematic model and control of a galvanometric laser beam steering system for high precision marking, welding or soldering applications as a microfactory module. Galvo systems are capable of scanning laser beam with relatively high frequencies that makes them suitable for fast processing applications. For the sake of flexibility and ease of use 2D reference shapes to be processed are provided as CAD drawings. Drawings are parsed and interpolated to x - y reference data points on MATLAB then stored as arrays in C code. C header file is further included as reference data points to be used by the system. Theoretical kinematic model of the system is derived and model parameters are tuned for practical implementation and validated with respect to measured positions on rotation space with optical position sensor and image field with position sensitive device. Machining with material removal requires high power laser to be employed that makes position measurement on image field unfeasible. Therefore for closed loop applications optical position sensor embedded in galvo motors is used for position feedback. Since the model approved to be approximately linear in the range of interest by simulations, a PI controller is used for precise positioning of the galvo motors. Experimental results for tracking circular and rectangular shape references are proved to be precise with errors of less than 2%.

Local potassium signaling couples neuronal activity to vasodilation in the brain <=SEP=> The mechanisms by which active neurons, via astrocytes, rapidly signal intracerebral arterioles to dilate remain obscure. Here we show that modest elevation of extracellular potassium (K+) activated inward rectifier K+ (Kir) channels and caused membrane potential hyperpolarization in smooth muscle cells (SMCs) of intracerebral arterioles and, in cortical brain slices, induced Kir-dependent vasodilation and suppression of SMC intracellular calcium (Ca2+) oscillations. Neuronal activation induced a rapid (<2 s latency) vasodilation that was greatly reduced by Kir channel blockade and completely abrogated by concurrent cyclooxygenase inhibition. Astrocytic endfeet exhibited large-conductance, Ca2+-sensitive K+ (BK) channel currents that could be activated by neuronal stimulation. Blocking BK channels or ablating the gene encoding these channels prevented neuronally induced vasodilation and suppression of arteriolar SMC Ca2+, without affecting the astrocytic Ca2+ elevation. These results support the concept of intercellular K+ channel–to–K+ channel signaling, through which neuronal activity in the form of an astrocytic Ca2+ signal is decoded by astrocytic BK channels, which locally release K+ into the perivascular space to activate SMC Kir channels and cause vasodilation.

Winnowing: Local Algorithms for Document Fingerprinting <=SEP=> Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents.We introduce the class of local document fingerprinting algorithms, which seems to capture an essential property of any finger-printing technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing's performance is within 33% of the lower bound. Finally, we also give experimental results on Web data, and report experience with MOSS, a widely-used plagiarism detection service.

Analysis of Metastability Errors in Conventional, LSB-First, and Asynchronous SAR ADCs <=SEP=> A practical model for characterizing comparator metastability errors in SAR ADCs is presented, and is used to analyze not only the conventional SAR but also LSB-first and asynchronous versions. This work makes three main contributions: first, it is shown that for characterizing metastability it is more reasonable to use input signals with normal or Laplace distributions. Previous work used uniformly-distributed signals in the interest of making derivations easier, but this simplifying assumption overestimated SMR by as much as 18 dB compared to the more reasonable analysis presented here. Second, this work shows that LSB-first SAR ADCs achieve SMR performance equal to or better than conventional SARs with the same metastability window, depending on bandwidth. Finally, the analysis is used to develop a framework for calculating the maximum effective sample rate for asynchronous SAR ADCs, and in doing so demonstrates that proximity detectors are not effective solutions to improving metastability performance.

PixelNet: Representation of the pixels, by the pixels, and for the pixels <=SEP=> We explore design principles for general pixel-level prediction problems, from low-level edge detection to midlevel surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fullyconvolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., “from scratch”) for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.

Culture Wires the Brain: A Cognitive Neuroscience Perspective. <=SEP=> There is clear evidence that sustained experiences may affect both brain structure and function. Thus, it is quite reasonable to posit that sustained exposure to a set of cultural experiences and behavioral practices will affect neural structure and function. The burgeoning field of cultural psychology has often demonstrated the subtle differences in the way individuals process information-differences that appear to be a product of cultural experiences. We review evidence that the collectivistic and individualistic biases of East Asian and Western cultures, respectively, affect neural structure and function. We conclude that there is limited evidence that cultural experiences affect brain structure and considerably more evidence that neural function is affected by culture, particularly activations in ventral visual cortex-areas associated with perceptual processing.

Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial <=SEP=> The state machine approach is a general method for implementing fault-tolerant services in distributed systems. This paper reviews the approach and describes protocols for two different failure models—Byzantine and fail stop. Systems reconfiguration techniques for removing faulty components and integrating repaired components are also discussed.