Query: Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator

Relevant passages:
Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Caffe: Convolutional Architecture for Fast Feature Embedding <=SEP=> Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

On the momentum term in gradient descent learning algorithms <=SEP=> A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.

Handwritten Digit Recognition with a Back-Propagation Network <=SEP=> We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1 % error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.

Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin <=SEP=> We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech—two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, resulting in a 7x speedup over our previous system [26]. Because of this efficiency, experiments that previously took weeks now run in days. This enables us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Real time contextual collective anomaly detection over multiple data streams <=SEP=> Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.

Evaluating Induced CCG Parsers on Grounded Semantic Parsing <=SEP=> We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/ sivareddyg/graph-parser.

Analysis of stairs-climbing ability for a tracked reconfigurable modular robot <=SEP=> Stairs-climbing ability is the crucial performance of mobile robot for urban environment mission such as urban search and rescue or urban reconnaissance. The track type mobile mechanism has been widely applied for its advantages such as high stability, easy to control, low terrain pressure, and continuous drive. Stairs-climbing is a complicated process for a tracked mobile robot under kinematics and dynamics constraints. In this paper, the stairs-climbing process has been divided into riser climbing, riser crossing, and nose line climbing. During each climbing process, robot's mobility has been analyzed for its kinematics and dynamics factor. The track velocity and acceleration's influences on riser climbing have been analyzed. And the semiempirical design method of the track grouser and the module length has been provided in riser crossing and nose line climbing correspondingly. Finally, stairs-climbing experiments have been made on the two-module robot in line type, and three-module robot in line type and in triangle type respectively.

An Overview of the Theory and Applications of Metasurfaces: The Two-Dimensional Equivalents of Metamaterials <=SEP=> Metamaterials are typically engineered by arranging a set of small scatterers or apertures in a regular array throughout a region of space, thus obtaining some desirable bulk electromagnetic behavior. The desired property is often one that is not normally found naturally (negative refractive index, near-zero index, etc.). Over the past ten years, metamaterials have moved from being simply a theoretical concept to a field with developed and marketed applications. Three-dimensional metamaterials can be extended by arranging electrically small scatterers or holes into a two-dimensional pattern at a surface or interface. This surface version of a metamaterial has been given the name metasurface (the term metafilm has also been employed for certain structures). For many applications, metasurfaces can be used in place of metamaterials. Metasurfaces have the advantage of taking up less physical space than do full three-dimensional metamaterial structures; consequently, metasurfaces offer the possibility of less-lossy structures. In this overview paper, we discuss the theoretical basis by which metasurfaces should be characterized, and discuss their various applications. We will see how metasurfaces are distinguished from conventional frequency-selective surfaces. Metasurfaces have a wide range of potential applications in electromagnetics (ranging from low microwave to optical frequencies), including: (1) controllable “smart” surfaces, (2) miniaturized cavity resonators, (3) novel wave-guiding structures, (4) angular-independent surfaces, (5) absorbers, (6) biomedical devices, (7) terahertz switches, and (8) fluid-tunable frequency-agile materials, to name only a few. In this review, we will see that the development in recent years of such materials and/or surfaces is bringing us closer to realizing the exciting speculations made over one hundred years ago by the work of Lamb, Schuster, and Pocklington, and later by Mandel'shtam and Veselago.

Millimeter-Wave Radar Sensor for Snow Height Measurements <=SEP=> A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Études de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.

Real-time multi-feature based fire flame detection in video <=SEP=> In this paper, we present a new approach to detect fire flame by processing and analyzing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e., chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In our approach, we adopt a novel algorithm to extract the moving region and analyze the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favorably against the state-of-art methods with higher Page 1 of 22 IET Review Copy Only IET Image Processing This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.

Melody Generation for Pop Music via Word Representation of Musical Properties <=SEP=> Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique ‘word,’ thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models. 1

PinMe: Tracking a Smartphone User around the World <=SEP=> With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.

Microphone array processing for distant speech recognition: Towards real-world deployment <=SEP=> Distant speech recognition (DSR) holds out the promise of providing a natural human computer interface in that it enables verbal interactions with computers without the necessity of donning intrusive body- or head-mounted devices. Recognizing distant speech robustly, however, remains a challenge. This paper provides a overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beamforming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a 64-channel linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Furthermore, we report the results of speech recognition experiments on data captured with a popular device, the Kinect [1]. Even for speakers at a distance of four meters from the Kinect, our DSR system achieved acceptable recognition performance on a large vocabulary task, a WER of 24.1%, beginning from a WER of 42.5% with a single array channel.

A Taxi Order Dispatch Model based On Combinatorial Optimization <=SEP=> Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
 We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.

LiveNet: Improving features generalization for face liveness detection using convolution neural networks <=SEP=> Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face antispoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIAFASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. © 2018 Elsevier Ltd. All rights reserved.

Character Recognition Using Convolutional Neural Networks <=SEP=> Pattern recognition is one of the traditional uses of neural networks. When trained with gradient-based learning methods, these networks can learn the classification of input data by example. An introduction to classifiers and gradient-based learning is given. It is shown how several perceptrons can be combined and trained gradient-based. Furthermore, an overview of convolutional neural networks, as well as a real-world example, are discussed.

Inverted Files Versus Signature Files for Text Indexing <=SEP=> Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available

Fast Linear Model for Knowledge Graph Embeddings <=SEP=> This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText1.

Color nonuniformity in projection-based displays: analysis and solutions <=SEP=> Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.

Entity Synonyms for Structured Web Search <=SEP=> Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.

Rough Set Theory in analyzing the attributes of combination values for the insurance market <=SEP=> Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers’ needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing. 2005 Elsevier Ltd. All rights reserved.

Model-Driven Approach for Body Area Network Application Development <=SEP=> This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Road-marking Analysis for Autonomous Vehicle Guidance <=SEP=> Driving an automobile autonomously on rural roads requires knowledge about the geometry of the road. Furthermore, knowledge about the meaning of each lane of the road is needed in order to decide which lane should be taken and if the vehicle can do a lane change. This paper addresses the problem of extracting additional information about lanes. The information is extracted from the types of road-markings. The type of lane border markings is estimated in order to find out if a lane change is allowed. Arrows, which are painted on the road, are extracted and classified in order to determine the meaning of a lane such as a turn off lane.

Entropy-based algorithms for best basis selection <=SEP=> Adapted waveform analysis uses a library of orthonormal bases and an efficiency functional to match a basis to a given signal or family of signals. It permits efficient compression of a variety of signals such as sound and images. The predefined libraries of modulated waveforms include orthogonal wavelet-packets, and localized trigonometric functions, have reasonably well controlled time-frequency localization properties. The idea is to build out of the library functions an orthonormal basis relative to which the given signal or collection of signals has the lowest information cost. The method relies heavily on the remarkable orthogonality properties of the new libraries: all expansions in a given library conserve energy, hence are comparable. Several cost functionals are useful; one of the most attractive is Shannon entropy, which has a geometric interpretation in this context.

Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy. <=SEP=> The aim of this work was to evaluate the performance of a novel non-invasive continuous glucose-monitoring system based on impedance spectroscopy (IS) in patients with diabetes. Ten patients with type 1 diabetes (mean+/-S.D., age 28+/-8 years, BMI 24.2+/-3.2 kg/m(2) and HbA(1C) 7.3+/-1.6%) and five with type 2 diabetes (age 61+/-8 years, BMI 27.5+/-3.2 kg/m(2) and HbA(1C) 8.3+/-1.8%) took part in this study, which comprised a glucose clamp experiment followed by a 7-day outpatient evaluation. The measurements obtained by the NI-CGMD and the reference blood glucose-measuring techniques were evaluated using retrospective data evaluation procedures. Under less controlled outpatient conditions a correlation coefficient of r=0.640 and a standard error of prediction (SEP) of 45 mg dl(-1) with a total of 590 paired glucose measurements was found (versus r=0.926 and a SEP of 26 mg dl(-1) under controlled conditions). Clark error grid analyses (EGA) showed 56% of all values in zone A, 37% in B and 7% in C-E. In conclusion, these results indicate that IS in the used technical setting allows retrospective, continuous and truly non-invasive glucose monitoring under defined conditions for patients with diabetes. Technical advances and developments are needed to expand on this concept to bring the results from the outpatient study closer to those in the experimental section of the study. Further studies will not only help to evaluate the performance and limitations of using such a technique for non non-invasive glucose monitoring but also help to verify technical extensions towards a IS-based concept that offers improved performance under real life operating conditions.

Supply Chain Management ( SCM ) and Organizational Key Factors for Successful Implementation of Enterprise Resource Planning ( ERP ) Systems <=SEP=> This paper reports some findings of an on-going research into ERP implementation issues. A part of the research, which this paper reports, consists of reviewing several cases of successful implementations of ERP systems. The analysis of all these cases revealed that the critical factors for successful implementation of ERP systems fall into two main categories: technological and organizational factors. However, the organizational factors seem to be more important than the technological ones as far as the successful implementation of ERP systems under SCM is concerned.

Citation analysis as a tool in journal evaluation. <=SEP=> As a communications system, the network of journals that play a paramount role in the exchange of scientific and technical information is little understood. Periodically since 1927, when Gross and Gross published their study (1) of references in 1 year’s issues of the Journal of the American Chemical Socie/y, pieces of the network have been illuminated by the work of Bradford (2), Allen (3), Gross and Woodford (4), Hooker (5), Henkle (6), Fussier (7), Brown (8), and others (9). Nevertheless, there is still no map of the journal network as a whok. To date, studies of the network and of the interrelation of its components have been limited in the number of journak, the areas of scientific study, and the periods of time their authors were able to consider, Such shortcomings have not been due to any lack of purpose, insight, or energy on the part of investigators, but to the practical difficulty of compiling and manipulating manually the enormous amount of necessary data. A solution to this problem of data is available in the data base used to produce the Science Citation Index ( SCI ) (10). The coverage of the SCI is international and multidisciplinary; it has grown from 600 journals in 1964 to 2400 journals in 1972, and now includes the world’s most important scientific and technical journals in mow disciplines. The SCI is published quarterly and is cumulated annually and quinquennially, but the data base from which the volumes are compiled is maintained on magnetic tape and is updated weekly. At the end of 1971, this data base contained more than 27 mi[tion references to about 10 million different published items. These references appeared over the past decade in the footnotes and bibliographies of more than 2 million journal articles, communications, letters, and so on. The data base is, thus, not only multidisciplinary, it covers a substantial period of time and, being in machine-readable form, is amenable to extensive manipulation by computer. In 1971, the Institute for Scientfic Information (1S1) decided to undertake a systematic analysis of journal citation patterns across the whole of science and technology. It began by extracting from the data base all references pobIished during the last quarter of 1969 in the 2200 journals then covered by the SCL The resultant sample was about 1 million citations of journals, books, reports, theses, and so forth. To test whether this 3-month sample was representative of the year as a whole, it was matched against another sample made by selecting every 27th reference from the approximately 4 million references collected over the entire year. The two samples were similar enough in scope (number of diflerent items cited) and detail (relative frequency of their citation by different journals) to

Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval <=SEP=> Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.

The Complementarity of Information Technology Infrastructure and E-Commerce Capability: A Resource-Based Assessment of Their Business Value <=SEP=> This study seeks to assess the business value of e-commerce capability and information technology (IT) infrastructure in the context of electronic business at the firm level. Grounded in the IT business-value literature and enhanced by the resource-based theory of the firm, we developed a research framework in which both the main effects and the interaction effects of e-commerce and IT on firm performance were tested. Within this theoretical framework, we formulated several hypotheses. We then developed a multidimensional e-commerce capability construct, and after establishing its validity and reliability, tested the hypotheses with empirical data from 114 companies in the retail industry. Controlling for variations of firm size and subindustry effects, our empirical analysis found a strong positive interaction effect between IT infrastructure and e-commerce capability. This suggests that their complementarity positively contributes to firm performance in terms of sales per employee, inventory turnover, and cost reduction. The results are consistent with the resource-based theory, and provide empirical evidence to the complementary synergy between front-end e-commerce capability and back-end IT infrastructure. Combined together, they become more effective in producing business value. Yet the value of this synergy has not been recognized in the IT payoff literature. The “productivity paradox” observed in various studies has been attributed to variation in methods and measures, yet we offer an additional explanation: ignoring complementarities in business value measurement implies that the impact of IT was seriously underestimated. Our results emphasized the integration of resources as a feasible path to e-commerce value—companies need to enhance the integration between front-end e-commerce capability and back-end IT infrastructure in order to reap the benefits of e-commerce investments.