Query: Generalized residual vector quantization for large scale data

Relevant passages:
Optimized Product Quantization for Approximate Nearest Neighbor Search <=SEP=> Product quantization is an effective vector quantization approach to compactly encode high-dimensional vectors for fast approximate nearest neighbor (ANN) search. The essence of product quantization is to decompose the original high-dimensional space into the Cartesian product of a finite number of low-dimensional subspaces that are then quantized separately. Optimal space decomposition is important for the performance of ANN search, but still remains unaddressed. In this paper, we optimize product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks. We present two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method that is guaranteed to achieve the optimal solution if the input data follows some Gaussian distribution. We show by experiments that our optimized approach substantially improves the accuracy of product quantization for ANN search.

K-means clustering via principal component analysis <=SEP=> Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for performing unsupervised learning tasks. Here we prove that principal components are the continuous solutions to the discrete cluster membership indicators for K-means clustering. New lower bounds for K-means objective function are derived, which is the total variance minus the eigenvalues of the data covariance matrix. These results indicate that unsupervised dimension reduction is closely related to unsupervised learning. Several implications are discussed. On dimension reduction, the result provides new insights to the observed effectiveness of PCA-based data reductions, beyond the conventional noise-reduction explanation that PCA, via singular value decomposition, provides the best low-dimensional linear approximation of the data. On learning, the result suggests effective techniques for K-means data clustering. DNA gene expression and Internet newsgroups are analyzed to illustrate our results. Experiments indicate that the new bounds are within 0.5-1.5% of the optimal values.

Cartesian K-Means <=SEP=> A fundamental limitation of quantization techniques like the k-means clustering algorithm is the storage and run-time cost associated with the large numbers of clusters required to keep quantization errors small and model fidelity high. We develop new models with a compositional parameterization of cluster centers, so representational capacity increases super-linearly in the number of parameters. This allows one to effectively quantize data using billions or trillions of centers. We formulate two such models, Orthogonal k-means and Cartesian k-means. They are closely related to one another, to k-means, to methods for binary hash function optimization like ITQ (Gong and Lazebnik, 2011), and to Product Quantization for vector quantization (Jegou et al., 2011). The models are tested on large-scale ANN retrieval tasks (1M GIST, 1B SIFT features), and on codebook learning for object recognition (CIFAR-10).

Video Google: A Text Retrieval Approach to Object Matching in Videos <=SEP=> We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieval is immediate, returning a ranked list of key frames/shots in the manner of Google. The method is illustrated for matching on two full length feature films.

Aggregating local descriptors into a compact image representation <=SEP=> We address the problem of image search on a very large scale, where three constraints have to be considered jointly: the accuracy of the search, its efficiency, and the memory usage of the representation. We first propose a simple yet efficient way of aggregating local image descriptors into a vector of limited dimension, which can be viewed as a simplification of the Fisher kernel representation. We then show how to jointly optimize the dimension reduction and the indexing algorithm, so that it best preserves the quality of vector comparison. The evaluation shows that our approach significantly outperforms the state of the art: the search accuracy is comparable to the bag-of-features approach for an image representation that fits in 20 bytes. Searching a 10 million image dataset takes about 50ms.

Internet of Things Security Analysis <=SEP=> Internet of Things is an up-and-coming information and technology industry, and in such an environment of the concept and the content and the extension of Internet of Things are not very distinctive, the project of Internet of Things which is a piece of small area with small-scale and self-system obtain gratifying achievement and bright future, it can promote the development of Internet of Things in some extent. But there are some serious hidden danger and potential crisis problems. The paper focuses on the application of Internet of Things in the nation and even in the global in the future, analysing the existed security risks of the Internet of Things 's network points, transmission, finally we propose some suggestive solutions due to these problems.

Open Targets: a platform for therapeutic target identification and validation <=SEP=> We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.

3D velocity model and ray tracing of antenna array GPR <=SEP=> Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.

Active noise control: a tutorial review <=SEP=> Active noise control (ANC) is achieved by introducing a canceling “antinoise” wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.

An Enhanced PRACH Preamble Detector for Cellular IoT Communications <=SEP=> We propose an enhanced physical random access channel (PRACH) preamble detector, which efficiently identifies non-orthogonal preambles while suppressing the noise rise. The proposed PRACH preamble detector reconstructs preambles from the received signal and efficiently utilizes them to improve the detection performance. Through simulations, we verify that the proposed detector successfully detects the desired preambles while suppressing the undesired noise rise, and, thus, it can detect ten non-orthogonal preambles simultaneously with an extremely low mis-detection probability lower than <inline-formula> <tex-math notation="LaTeX">$10^{-5}$ </tex-math></inline-formula>, while achieving a false alarm probability of approximately <inline-formula> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula>.

Recognition of Affect, Judgment, and Appreciation in Text <=SEP=> The main task we address in our research is classification of text using fine-grained attitude labels. The developed @AM system relies on the compositionality principle and a novel approach based on the rules elaborated for semantically distinct verb classes. The evaluation of our method on 1000 sentences, that describe personal experiences, showed promising results: average accuracy on the finegrained level (14 labels) was 62%, on the middle level (7 labels) – 71%, and on the top level (3 labels) – 88%.

Avionic Data acquisition system using MIL STD 1553B controller with IRIG-B timecode decoder <=SEP=> Many critical parameters of rockets and satellite are obtained by means of telemetry system present onboard. For meaningful analysis of data these parameters are to be time stamped. This is achieved by means IRIG B time code proposed by Inter-range instrumentation group. The data obtained need to be transmitted to various sub systems present in ground. Bus based configuration is planned for this inter connection. This is achieved by MIL STD 1553B protocol. A data bus is used to provide a medium for the exchange of data and information between various systems. The MIL STD 1553B bus consists of Bus Controller, Remote Terminals and transmission media. The data received from telemetry system are Manchester encoded signals which needs to be decoded. The objective of the project was to design and develop MIL STD 1553B Bus controller to transmit command and data words to various other remote terminals. An IRIG B time decoder module was developed to decode the timing information transmitted from a reference so that the Data acquisition system can be used to process the data worldwide. A Manchester Data Decoder for decoding the data was developed. The system had better synchronization capability and clock independence.

Detecting Semantic Parts on Partially Occluded Objects <=SEP=> In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in the training data. So the models should be inherently robust and adaptive to occlusions instead of fitting / learning the occlusion patterns in the training data. Our approach detects semantic parts by accumulating the confidence of local visual cues. Specifically, the method uses a simple voting method, based on log-likelihood ratio tests and spatial constraints, to combine the evidence of local cues. These cues are called visual concepts, which are derived by clustering the internal states of deep networks. We evaluate our voting scheme on the VehicleSemanticPart dataset with dense part annotations. We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions. Experiments show that our algorithm outperforms several competitors in semantic part detection when occlusions are present. This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. WANG ET.AL.: DETECTING SEMANTIC PARTS ON PARTIALLY OCCLUDED OBJECTS 1 Detecting Semantic Parts on Partially Occluded Objects Jianyu Wang*1 wjyouch@gmail.com Cihang Xie*2 cihangxie306@gmail.com Zhishuai Zhang*2 zhshuai.zhang@gmail.com Jun Zhu1 zhujun.sjtu@gmail.com Lingxi Xie( )2 198808xc@gmail.com Alan L. Yuille2 alan.l.yuille@gmail.com 1 Baidu Research (USA), Sunnyvale, CA 94089 USA 2 Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 USA . . . . * This work was done when the first author was a Ph.D. student at UCLA. The first three authors contributed equally to this work.

PACRR: A Position-Aware Neural IR Model for Relevance Matching <=SEP=> In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query. While previous works have successfully captured unigram term matches, how to fully employ position-dependent information such as proximity and term dependencies has been insufficiently explored. In this work, we propose a novel neural IR model named PACRR aiming at better modeling position-dependent interactions between a query and a document. Extensive experiments on six years’ TREC Web Track data confirm that the proposed model yields better results under multiple benchmarks.

Resume Parser: Semi-structured Chinese Document Analysis <=SEP=> Semi-structured Chinese document anlysis is the most diffcult task for complex structure and Chinese semantics. According to the generic characteristics of the semi-structured document and the specific characteristics of the resume document, the paper researched on resume document block anlysis based on pattern matching, multi-level information identification and feedback control algorithms was also prompted. Based on the research, Resume Parser system was implemented for ChinaHR, which is the biggest recruitment website. It can read, analysis, retrieval and store the information automatically. According to all kinds of experienments results, the accuracy and efficiency of this system can generally satisfy the practical requirements. As the research on the processing of the semi-structured document, it will not only be as a directive of the further research on the resume analysis, but also be as the reference to other form of the semi-structured document.

Multi-modal feature fusion for action recognition in RGB-D sequences <=SEP=> Microsoft Kinect's output is a multi-modal signal which gives RGB videos, depth sequences and skeleton information simultaneously. Various action recognition techniques focused on different single modalities of the signals and built their classifiers over the features extracted from one of these channels. For better recognition performance, it's desirable to fuse these multi-modal information into an integrated set of discriminative features. Most of current fusion methods merged heterogeneous features in a holistic manner and ignored the complementary properties of these modalities in finer levels. In this paper, we proposed a new hierarchical bag-of-words feature fusion technique based on multi-view structured spar-sity learning to fuse atomic features from RGB and skeletons for the task of action recognition.

Query suggestion for E-commerce sites <=SEP=> Query suggestion module is an integral part of every search engine. It helps search engine users narrow or broaden their searches. Published work on query suggestion methods has mainly focused on the web domain. But, the module is also popular in the domain of e-commerce for product search. In this paper, we discuss query suggestion and its methodologies in the context of e-commerce search engines. We show that dynamic inventory combined with long and sparse tail of query distribution poses unique challenges to build a query suggestion method for an e-commerce marketplace. We compare and contrast the design of a query suggestion system for web search engines and e-commerce search engines. Further, we discuss interesting measures to quantify the effectiveness of our query suggestion methodologies. We also describe the learning gained from exposing our query suggestion module to a vibrant community of millions of users.

Virtual Robot Experimentation Platform V-REP: A Versatile 3D Robot Simulator <=SEP=> From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a modular and decentralized architecture for robotics simulation. In contrast to centralized approaches, this balances functionality, provides more diversity, and simplifies connectivity between (independent) calculation modules. As the Virtual Robot Experimentation Platform (V-REP) demonstrates, this gives a smallfootprint 3D robot simulator that concurrently simulates control, actuation, sensing and monitoring. Its distributed and modular approach are ideal for complex scenarios in which a diversity of sensors and actuators operate asynchronously with various rates and characteristics. This allows for versatile prototyping applications including systems verification, safety/remote monitoring, rapid algorithm development, and factory automation simulation.

Detecting Clickbait in Online Social Media: You Won't Believe How We Did It <=SEP=> In this paper, we propose an approach for the detection of clickbait posts in online social media (OSM). Clickbait posts are short catchy phrases that attract a user’s attention to click to an article. The approach is based on a machine learning (ML) classifier capable of distinguishing between clickbait and legitimate posts published in OSM. The suggested classifier is based on a variety of features, including image related features, linguistic analysis, and methods for abuser detection. In order to evaluate our method, we used two datasets provided by Clickbait Challenge 2017. The best performance obtained by the ML classifier was an AUC of 0.8, accuracy of 0.812, precision of 0.819, and recall of 0.966. In addition, as opposed to previous studies, we found that clickbait post titles are statistically significant shorter than legitimate post titles. Finally, we found that counting the number of formal English words in the given contentis useful for clickbait detection.

Analyzing Behavior Specialized Acceleration <=SEP=> Hardware specialization has become a promising paradigm for overcoming the inefficiencies of general purpose microprocessors. Of significant interest are Behavioral Specialized Accelerators (BSAs), which are designed to efficiently execute code with only certain properties, but remain largely configurable or programmable. The most important strength of BSAs -- their ability to target a wide variety of codes -- also makes their interactions and analysis complex, raising the following questions: can multiple BSAs be composed synergistically, what are their interactions with the general purpose core, and what combinations favor which workloads? From a methodological standpoint, BSAs are also challenging, as they each require ISA development, compiler and assembler extensions, and either simulator or RTL models.
 To study the potential of BSAs, we propose a novel modeling technique called the Transformable Dependence Graph (TDG) - a higher level alternative to the time-consuming traditional compiler+simulator approach, while still enabling detailed microarchitectural models for both general cores and accelerators. We then propose a multi-BSA organization, called ExoCore, which we model and study using the TDG. A design space exploration reveals that an ExoCore organization can push designs beyond the established energy-performance frontiers for general purpose cores. For example, a 2-wide OOO processor with three BSAs matches the performance of a conventional 6-wide OOO core, has 40% lower area, and is 2.6x more energy efficient.

Learning hierarchical control structures for multiple tasks and changing environments <=SEP=> While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a di cult task. The bene t of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.

Breaking NLI Systems with Sentences that Require Simple Lexical Inferences <=SEP=> We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.

Finding surprising patterns in a time series database in linear time and space <=SEP=> The problem of finding a specified pattern in a time series database (i.e. query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of "surprise", and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these limitations we introduce a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data.

In Defense of Active Part Selection for Fine-Grained Classification <=SEP=> Fine-grained classification is a recognition task where subtle differences distinguish between different classes. To tackle this classification problem, part-based classification methods are mostly used. Partbased methods learn an algorithm to detect parts of the observed object and extract local part features for the detected part regions. In this paper we show that not all extracted part features are always useful for the classification. Furthermore, given a part selection algorithm that actively selects parts for the classification we estimate the upper bound for the fine-grained recognition performance. This upper bound lies way above the current state-of-the-art recognition performances which shows the need for such an active part selection method. Though we do not present such an active part selection algorithm in this work, we propose a novel method that is required by active part selection and enables sequential part-based classification. This method uses a support vector machine (SVM) ensemble and allows to classify an image based on arbitrary number of part features. Additionally, the training time of our method does not increase with the amount of possible part features. This fact allows to extend the SVM ensemble with an active part selection component that operates on a large amount of part feature proposals without suffering from increasing training time.

Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling <=SEP=> The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with a performance gain of 27.4%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.

Failure Trends in a Large Disk Drive Population <=SEP=> It is estimated that over 90% of all new information produced in the world is being stored on magnetic media, most of it on hard disk drives. Despite their importance, there is relatively little published work on the failure patterns of disk drives, and the key factors that affect their lifetime. Most available data are either based on extrapolation from accelerated aging experiments or from relatively modest sized field studies. Moreover, larger population studies rarely have the infrastructure in place to collect health signals from components in operation, which is critical information for detailed failure analysis. We present data collected from detailed observations of a large disk drive population in a production Internet services deployment. The population observed is many times larger than that of previous studies. In addition to presenting failure statistics, we analyze the correlation between failures and several parameters generally believed to impact longevity. Our analysis identifies several parameters from the drive’s self monitoring facility (SMART) that correlate highly with failures. Despite this high correlation, we conclude that models based on SMART parameters alone are unlikely to be useful for predicting individual drive failures. Surprisingly, we found that temperature and activity levels were much less correlated with drive failures than previously reported.

SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation <=SEP=> Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique

3D-Printing Spatially Varying BRDFs <=SEP=> A new method fabricates custom surface reflectance and spatially varying bidirectional reflectance distribution functions (svBRDFs). Researchers optimize a microgeometry for a range of normal distribution functions and simulate the resulting surface's effective reflectance. Using the simulation's results, they reproduce an input svBRDF's appearance by distributing the microgeometry on the printed material's surface. This method lets people print svBRDFs on planar samples with current 3D printing technology, even with a limited set of printing materials. It extends naturally to printing svBRDFs on arbitrary shapes.

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols <=SEP=> Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.

The individual and social dynamics of knowledge sharing: an exploratory study <=SEP=> Purpose – This paper aims to examine how knowledge sharing behavior is influenced by three sets of dynamics: a rational calculus that weighs the costs and benefits of sharing; a dispositional preference that favors certain patterns of sharing outcomes; and a relational effect based on working relationships. Design/methodology/approach – Concepts from social exchange theory, social value orientation, and leader-member exchange theory are applied to analyze behavioral intentions to share knowledge. The study population consists of employees of a large pension fund in Canada. Participants answered a survey that used allocation games and situational vignettes to measure social value orientation, propensity to share knowledge, and perception of cost and benefit. Findings – The results suggest that personal preferences about the distribution of sharing outcomes, individual perceptions about costs and benefits, and structural relationship with knowledge recipients, all affect knowledge sharing behavior significantly. Notably, it was found that propensity to share knowledge is positively related to perceived benefit to the recipient, thus suggesting that evaluation of cost and benefit in social exchange is not limited to self-interest, but is also influenced by perceived recipient benefit. Moreover, it was found that the relationship with the sharing target (superior or colleague) also influenced sharing. Originality/value – Most studies emphasize the organizational benefits of knowledge sharing. This study examines knowledge sharing from the perspective of the individual who approaches knowledge sharing as a social exchange that involves perceptions of costs and benefits, preferences about sharing outcomes, and relationship with the sharing target. The study also introduces innovative methods to measure social value orientation and propensity to share knowledge.