zeta-alpha-ai/NanoSCIDOCS huggingface dataset:
Hierarchical Pitman-Yor Process priors are compelling methods for learning language models, outperforming point-estimate based methods. However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney countbased LM smoothing by up to 15%.
This paper describes a new language resource of events and semantic roles that characterize real-world situations. Narrative schemas contain sets of related events (edit and publish), a temporal ordering of the events (edit before publish), and the semantic roles of the participants (authors publish books). This type of world knowledge was central to early research in natural language understanding. Scripts were one of the main formalisms, representing common sequences of events that occur in the world. Unfortunately, most of this knowledge was hand-coded and time consuming to create. Current machine learning techniques, as well as a new approach to learning through coreference chains, has allowed us to automatically extract rich event structure from open domain text in the form of narrative schemas. The narrative schema resource described in this paper contains approximately 5000 unique events combined into schemas of varying sizes. We describe the resource, how it is learned, and a new evaluation of the coverage of these schemas over unseen documents.
Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure, and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.
This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
Electromigration (EM) is one of the key concerns going forward for interconnect reliability in integrated circuit (IC) design. Although analog designers have been aware of the EM problem for some time, digital circuits are also being affected now. This talk addresses basic design issues and their effects on electromigration during interconnect physical design. The intention is to increase current density limits in the interconnect by adopting electromigration-inhibiting measures, such as short-length and reservoir effects. Exploitation of these effects at the layout stage can provide partial relief of EM concerns in IC design flows in future.
BACKGROUND
Mobile apps hold promise for serving as a lifestyle intervention in public health to promote wellness and attenuate chronic conditions, yet little is known about how individuals with chronic illness use or perceive mobile apps.


OBJECTIVE
The objective of this study was to explore behaviors and perceptions about mobile phone-based apps for health among individuals with chronic conditions.


METHODS
Data were collected from a national cross-sectional survey of 1604 mobile phone users in the United States that assessed mHealth use, beliefs, and preferences. This study examined health app use, reason for download, and perceived efficacy by chronic condition.


RESULTS
Among participants, having between 1 and 5 apps was reported by 38.9% (314/807) of respondents without a condition and by 6.6% (24/364) of respondents with hypertension. Use of health apps was reported 2 times or more per day by 21.3% (172/807) of respondents without a condition, 2.7% (10/364) with hypertension, 13.1% (26/198) with obesity, 12.3% (20/163) with diabetes, 12.0% (32/267) with depression, and 16.6% (53/319) with high cholesterol. Results of the logistic regression did not indicate a significant difference in health app download between individuals with and without chronic conditions (P>.05). Compared with individuals with poor health, health app download was more likely among those with self-reported very good health (odds ratio [OR] 3.80, 95% CI 2.38-6.09, P<.001) and excellent health (OR 4.77, 95% CI 2.70-8.42, P<.001). Similarly, compared with individuals who report never or rarely engaging in physical activity, health app download was more likely among those who report exercise 1 day per week (OR 2.47, 95% CI 1.6-3.83, P<.001), 2 days per week (OR 4.77, 95% CI 3.27-6.94, P<.001), 3 to 4 days per week (OR 5.00, 95% CI 3.52-7.10, P<.001), and 5 to 7 days per week (OR 4.64, 95% CI 3.11-6.92, P<.001). All logistic regression results controlled for age, sex, and race or ethnicity.


CONCLUSIONS
Results from this study suggest that individuals with poor self-reported health and low rates of physical activity, arguably those who stand to benefit most from health apps, were least likely to report download and use these health tools.
Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m of urban area in total.
AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems
The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared. 1. AUTOMATIC TEXT ANALYSIS In the late 195Os, Luhn [l] first suggested that automatic text retrieval systems could be designed based on a comparison of content identifiers attached both to the stored texts and to the users’ information queries. Typically, certain words extracted from the texts of documents and queries would be used for content identification; alternatively, the content representations could be chosen manually by trained indexers familiar with the subject areas under consideration and with the contents of the document collections. In either case, the documents would be represented by term vectors of the form D= (ti,tj,...ytp) (1) where each tk identifies a content term assigned to some sample document D. Analogously, the information requests, or queries, would be represented either in vector form, or in the form of Boolean statements. Thus, a typical query Q might be formulated as Q = (qa,qbr.. . ,4r) (2)
This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross­modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real­ time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well­proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure­pursuit control and our local frame perception strategy, obstacle avoidance using kino­dynamic RRT path planning, U­turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. † Executive Summary This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross-modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure-pursuit control and our local frame perception strategy, obstacle avoidance using kino-dynamic RRT path planning, U-turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. DISCLAIMER: The information contained in this paper does not represent the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Additional support …
We present the analysis and design of fixed physical length, spoof Surface Plasmon Polariton based waveguides with adjustable delay at terahertz frequencies. The adjustable delay is obtained using Corrugated Planar Goubau Lines (CPGL) by changing its corrugation depth without changing the total physical length of the waveguide. Our simulation results show that electrical lengths of 237.9°, 220.6°, and 310.6° can be achieved by physical lengths of 250 μm and 200 μm at 0.25, 0.275, and 0.3 THz, respectively, for demonstration purposes. These simulations results are also consistent with our analytical calculations using the physical parameter and material properties. When we combine pairs of same length delay lines as if they are two branches of a terahertz phase shifter, we achieved an error rate of relative phase shift estimation better than 5.8%. To the best of our knowledge, this is the first-time demonstration of adjustable spoof Surface Plasmon Polariton based CPGL delay lines. The idea can be used for obtaining tunable delay lines with fixed lengths and phase shifters for the terahertz band circuitry.
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.
For the first time, a fully integrated phased array antenna with radio frequency microelectromechanical systems (RF MEMS) switches on a flexible, organic substrate is demonstrated above 10 GHz. A low noise amplifier (LNA), MEMS phase shifter, and 2 times 2 patch antenna array are integrated into a system-on-package (SOP) on a liquid crystal polymer substrate. Two antenna arrays are compared; one implemented using a single-layer SOP and the second with a multilayer SOP. Both implementations are low-loss and capable of 12deg of beam steering. The design frequency is 14 GHz and the measured return loss is greater than 12 dB for both implementations. The use of an LNA allows for a much higher radiated power level. These antennas can be customized to meet almost any size, frequency, and performance needed. This research furthers the state-of-the-art for organic SOP devices.
High-voltage-rated solid-state switches such as insulated-gate bipolar transistors (IGBTs) are commercially available up to 6.5 kV. Such voltage ratings are attractive for pulsed power and high-voltage switch-mode converter applications. However, as the IGBT voltage ratings increase, the rate of current rise and fall are generally reduced. This tradeoff is difficult to avoid as IGBTs must maintain a low resistance in the epitaxial or drift region layer. For high-voltage-rated IGBTs with thick drift regions to support the reverse voltage, the required high carrier concentrations are injected at turn on and removed at turn off, which slows the switching speed. An option for faster switching is to series multiple, lower voltage-rated IGBTs. An IGBT-stack prototype with six, 1200 V rated IGBTs in series has been experimentally tested. The six-series IGBT stack consists of individual, optically isolated, gate drivers and aluminum cooling plates for forced air cooling which results in a compact package. Each IGBT is overvoltage protected by transient voltage suppressors. The turn-on current rise time of the six-series IGBT stack and a single 6.5 kV rated IGBT has been experimentally measured in a pulsed resistive-load, capacitor discharge circuit. The IGBT stack has also been compared to two IGBT modules in series, each rated at 3.3 kV, in a boost circuit application switching at 9 kHz and producing an output of 5 kV. The six-series IGBT stack results in improved turn-on switching speed, and significantly higher power boost converter efficiency due to a reduced current tail during turn off. The experimental test parameters and the results of the comparison tests are discussed in the following paper
We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.
The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.
People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.
The  autonomous  vehicle  is  a  mobile  robot integrating  multi‐sensor  navigation  and  positioning, intelligent decision making and control  technology. This paper  presents  the  control  system  architecture  of  the autonomous vehicle, called “Intelligent Pioneer”, and the path  tracking  and  stability  of  motion  to  effectively navigate  in unknown  environments  is discussed.  In  this approach,  a  two  degree‐of‐freedom  dynamic  model  is developed to formulate the path‐tracking problem in state space format. For controlling the instantaneous path error, traditional  controllers  have  difficulty  in  guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive‐PID  controller  will  be  used.  By  using  this approach the flexibility of the vehicle control system will be  increased  and  achieving  great  advantages. Throughout,  we  provide  examples  and  results  from Intelligent Pioneer and the autonomous vehicle using this approach  competed  in  the  2010  and  2011  Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won  first position  in 2010 and third position in 2011.
The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent advancement by others dates back 8 years (error rate 0.4%). Good old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark with a single MLP and 0.31% with a committee of seven MLP. All we need to achieve this until 2011 best result are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.
Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.
This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.
Environmental energy is an attractive power source for low power wireless sensor networks. We present Prometheus, a system that intelligently manages energy transfer for perpetual operation without human intervention or servicing. Combining positive attributes of different energy storage elements and leveraging the intelligence of the microprocessor, we introduce an efficient multi-stage energy transfer system that reduces the common limitations of single energy storage systems to achieve near perpetual operation. We present our design choices, tradeoffs, circuit evaluations, performance analysis, and models. We discuss the relationships between system components and identify optimal hardware choices to meet an application's needs. Finally we present our implementation of a real system that uses solar energy to power Berkeley's Telos Mote. Our analysis predicts the system will operate for 43 years under 1% load, 4 years under 10% load, and 1 year under 100% load. Our implementation uses a two stage storage system consisting of supercapacitors (primary buffer) and a lithium rechargeable battery (secondary buffer). The mote has full knowledge of power levels and intelligently manages energy transfer to maximize lifetime.
AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs
A low-power low-cost highly efficient maximum power point tracker (MPPT) to be integrated into a photovoltaic (PV) panel is proposed. This can result in a 25% energy enhancement compared to a standard photovoltaic panel, while performing functions like battery voltage regulation and matching of the PV array with the load. Instead of using an externally connected MPPT, it is proposed to use an integrated MPPT converter as part of the PV panel. It is proposed that this integrated MPPT uses a simple controller in order to be cost effective. Furthermore, the converter has to be very efficient, in order to transfer more energy to the load than a directly coupled system. This is achieved by using a simple soft-switched topology. A much higher conversion efficiency at lower cost will then result, making the MPPT an affordable solution for small PV energy systems.
The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.
 In the ZebraNet system we use GPS technology to record fine-grained position data in order to track long term animal migrations [14]. The ZebraNet hardware is composed of a 16-bit TI microcontroller, 4 Mbits of off-chip flash memory, a 900 MHz radio, and a low-power GPS chip. In this paper, we discuss our techniques for devising efficient power supplies for sensor networks, methods of managing the energy consumption of the nodes, and methods of managing the peripheral devices including the radio, flash, and sensors. We conclude by evaluating the design of the ZebraNet nodes and discussing how it can be improved. Our lessons learned in developing this hardware can be useful both in designing future sensor nodes and in using them in real systems.
The evolution of Artificial Intelligence has served as the catalyst in the field of technology. We can now develop things which was once just an imagination. One of such creation is the birth of self-driving car. Days have come where one can do their work or even sleep in the car and without even touching the steering wheel, accelerator you will still be able to reach your target destination safely. This paper proposes a working model of self-driving car which is capable of driving from one location to the other or to say on different types of tracks such as curved tracks, straight tracks and straight followed by curved tracks. A camera module is mounted over the top of the car along with Raspberry Pi sends the images from real world to the Convolutional Neural Network which then predicts one of the following directions. i.e. right, left, forward or stop which is then followed by sending a signal from the Arduino to the controller of the remote controlled car and as a result of it the car moves in the desired direction without any human intervention.
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language model approaches to retrieval, however, produces unstable performance and limited improvements. In this paper, we formally discuss three intrinsic problems of the original PV model that restrict its performance in retrieval tasks. We also describe modifications to the model that make it more suitable for the IR task, and show their impact through experiments and case studies. The three issues we address are (1) the unregulated training process of PV is vulnerable to short document over-fitting that produces length bias in the final retrieval model; (2) the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words; and (3) the lack of word-context information makes PV unable to capture word substitution relationships.
Aspect Based Sentiment Analysis (ABSA) is the task of mining and summarizing opinions from text about specific entities and their aspects. This article describes two datasets for the development and testing of ABSA systems for French which comprise user reviews annotated with relevant entities, aspects and polarity values. The first dataset contains 457 restaurant reviews (2365 sentences) for training and testing ABSA systems, while the second contains 162 museum reviews (655 sentences) dedicated to out-of-domain evaluation. Both datasets were built as part of SemEval-2016 Task 5 “Aspect-Based Sentiment Analysis” where seven different languages were represented, and are publicly available for research purposes. This article provides examples and statistics by annotation type, summarizes the annotation guidelines and discusses their cross-lingual applicability. It also explains how the data was used for evaluation in the SemEval ABSA task and briefly presents the results obtained for French.
This paper evaluates the translation quality of machine translation systems for 8 language pairs: translating French, German, Spanish, and Czech to English and back. We carried out an extensive human evaluation which allowed us not only to rank the different MT systems, but also to perform higher-level analysis of the evaluation process. We measured timing and intraand inter-annotator agreement for three types of subjective evaluation. We measured the correlation of automatic evaluation metrics with human judgments. This meta-evaluation reveals surprising facts about the most commonly used methodologies.
Circularly polarized single-layer U-slot microstrip patch antenna has been proposed. The suggested asymmetrical U-slot can generate the two orthogonal modes for circular polarization without chamfering any corner of the probe-fed square patch microstrip antenna. A parametric study has been carried out to investigate the effects caused by different arm lengths of the U-slot. The thickness of the foam substrate is about 8.5% of the wavelength at the operating frequency. The 3 dB axial ratio bandwidth of the antenna is 4%. Both experimental and theoretical results of the antenna have been presented and discussed. Circular polarization, printed antennas, U-slot.
In this letter, a wideband compact circularly polarized (CP) patch antenna is proposed. This patch antenna consists of a printed meandering probe (M-probe) and truncated patches that excite orthogonal resonant modes to generate a wideband CP operation. The stacked patch is employed to further improve the axial-ratio (AR) bandwidth to fit the 5G Wi-Fi application. The proposed antenna achieves 42.3% impedance bandwidth and 16.8% AR bandwidth, respectively. The average gain within the AR bandwidth is 6.6 dBic with less than 0.5 dB variation. This work demonstrates a bandwidth broadening technique of an M-probe fed CP patch antenna. It is the first study to investigate and exhibit the M-probe could also provide the wideband characteristics in the dielectric loaded patch antenna. The potential applications of the antenna are 5G Wi-Fi and satellite communication systems.
In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.
The personal stories that people write in their Internet weblogs include a substantial amount of information about the causal relationships between everyday events. In this paper we describe our efforts to use millions of these stories for automated commonsense causal reasoning. Casting the commonsense causal reasoning problem as a Choice of Plausible Alternatives, we describe four experiments that compare various statistical and information retrieval approaches to exploit causal information in story corpora. The top performing system in these experiments uses a simple co-occurrence statistic between words in the causal antecedent and consequent, calculated as the Pointwise Mutual Information between words in a corpus of millions of personal stories.
Case research has commanded respect in the information systems (IS) discipline for at least a decade. Notwithstanding the relevance and potential value of case studies, this methodological approach was once considered to be one of the least systematic. Toward the end of the 1980s, the issue of whether IS case research was rigorously conducted was first raised. Researchers from our field (e.g., Benbasat et al. 1987; Lee 1989) and from other disciplines (e.g., Eisenhardt 1989; Yin 1994) called for more rigor in case research and, through theirrecommendations, contributed to the advancement of the case study methodology. Considering these contributions, the present study seeks to determine the extent to which the field of IS has advanced in its operational use of case study method. Precisely, it investigates the level of methodological rigor in positivist IS case research conducted over the past decade. To fulfill this objective, we identified and coded 183 case articles from seven major IS journals. Evaluation attributes or criteria considered in the present review focus on three main areas, namely, design issues, data collection, and data analysis. While the level of methodological rigor has experienced modest progress with respect to some specific attributes, the overall assessed rigor is somewhat equivocal and there are still significant areas for improvement. One of the keys is to include better documentation particularly regarding issues related to the data collection and
Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper we introduce an approach that extracts simultaneously orientation and frequency of local ridge in the fingerprint image by Gabor wavelet filter bank and use them in Gabor Filtering of image. Furthermore, we describes a robust approach to fingerprint image enhancement, which is based on integration of Gabor Filters and Directional Median Filter(DMF). In fact, Gaussian-distributed noises are reduced effectively by Gabor Filters and impulse noises by DMF. the proposed DMF not only can finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. Experimental results show our method to be superior to those described in the literature.
ÐToday's globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal
A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.
This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University 's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.
State-of-the-art question answering (QA) systems employ term-density ranking to retrieve answer passages. Such methods often retrieve incorrect passages as relationships among question terms are not considered. Previous studies attempted to address this problem by matching dependency relations between questions and answers. They used strict matching, which fails when semantically equivalent relationships are phrased differently. We propose fuzzy relation matching based on statistical models. We present two methods for learning relation mapping scores from past QA pairs: one based on mutual information and the other on expectation maximization. Experimental results show that our method significantly outperforms state-of-the-art density-based passage retrieval methods by up to 78% in mean reciprocal rank. Relation matching also brings about a 50% improvement in a system enhanced by query expansion.
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.
We propose a new fast purely discriminative algorithm for natural language parsing, based on a “deep” recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of “levels”, the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features which leverage word representations from Collobert and Weston (2008), we show similar performance (in F1 score) to existing pure discriminative parsers and existing “benchmark” parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.
Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi-relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.