Query: Markov logic for machine reading

Relevant passages:
A* Parsing: Fast Exact Viterbi Parse Selection <=SEP=> A PCFGparsingcandramaticallyreducethetime requiredto find theexactViterbi parseby conservatively estimatingoutsideViterbi probabilities.Wediscussvariousestimatesandgive efficientalgorithmsfor computingthem.On Penntreebanksentences, ourmostdetailedestimate reducesthetotalnumberof edgesprocessedto lessthan3%of thatrequiredby exhausti ve parsing,andevenasimplerestimatewhichcanbepre-computedin underaminutestill reduces thework by a factorof 5. ThealgorithmextendstheclassicA graphsearchprocedureto a certainhypergraphassociatedwith parsing.Unlike best-firstandfinite-beammethodsfor achieving thiskind of speed-up, theA parseris guaranteedto returnthemostlikely parse,not justanapproximation.Thealgorithmis alsocorrectfor awide rangeof parsercontrolstrategies andmaintainsaworst-casecubictime bound. A Parsing: FastExact Viterbi ParseSelection

Statistical language learning <=SEP=> The $64,000 question in computational linguistics these days is: “What should I read to learn about statistical natural language processing?” I have been asked this question over and over, and each time I have given basically the same reply: there is no text that addresses this topic directly, and the best one can do is find a good probability-theory textbook and a good information-theory textbook, and supplement those texts with an assortment of conference papers and journal articles. Understanding the disappointment this answer provoked, I was delighted to hear that someone had finally written a bookdirectly addressing this topic. However, after reading Eugene Charniak’s Statistical Language Learning, I have very mixed feelings about the impact this bookmight have on the ever-growing field of statistical NLP. The book begins with a very brief description of the classic artificial intelligence approach toNLP (chapter 1), including morphology, syntax, semantics, and pragmatics. It presents a few definitions from probability theory and information theory (chapter 2), then proceeds to introduce hidden Markov models (chapters 3–4) and probabilistic context-free grammars (chapters 5–6). The book concludes with a few chapters discussing advanced topics in statistical language learning, such as grammar induction (chapter 7), syntactic disambiguation (chapter 8), word clustering (chapter 9), andword sense disambiguation (chapter 10). To its credit, the book serves as an interesting popular discussion of statistical modeling in NLP. It is well-written and entertaining, and very accessible to the reader with a limited mathematical background. It presents a good selection of statistical NLP topics to introduce the reader to the field. And the descriptions of the forward-backward algorithm for hidden Markov models and the inside-outside algorithm for probabilistic context-free grammars are intuitive and easy to follow. However, as a resource for someone interested in entering this area of research, this book falls far short of its author’s goals. These goals are clearly stated in the preface:

The PASCAL Recognising Textual Entailment Challenge <=SEP=> This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.

Adaptive duplicate detection using learnable string similarity measures <=SEP=> The problem of identifying approximately duplicate records in databases is an essential step for data cleaning and data integration processes. Most existing approaches have relied on generic or manually tuned distance metrics for estimating the similarity of potential duplicates. In this paper, we present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field's domain. We present two learnable text similarity measures suitable for this task: an extended variant of learnable string edit distance, and a novel vector-space based measure that employs a Support Vector Machine (SVM) for training. Experimental results on a range of datasets show that our framework can improve duplicate detection accuracy over traditional techniques.

Scaling Textual Inference to the Web <=SEP=> Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the HOLMES system, which utilizes textual inference (TI) over tuples extracted from text. Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, HOLMES utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, HOLMES doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, HOLMES’s runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus—they are “approximately” functional in a well-defined sense.

Diversified Visual Attention Networks for Fine-Grained Object Classification <=SEP=> Fine-grained object classification attracts increasing attention in multimedia applications. However, it is a quite challenging problem due to the subtle interclass difference and large intraclass variation. Recently, visual attention models have been applied to automatically localize the discriminative regions of an image for better capturing critical difference, which have demonstrated promising performance. Unfortunately, without consideration of the diversity in attention process, most of existing attention models perform poorly in classifying fine-grained objects. In this paper, we propose a diversified visual attention network (DVAN) to address the problem of fine-grained object classification, which substantially relieves the dependency on strongly supervised information for learning to localize discriminative regions com-pared with attention-less models. More importantly, DVAN explicitly pursues the diversity of attention and is able to gather discriminative information to the maximal extent. Multiple attention canvases are generated to extract convolutional features for attention. An LSTM recurrent unit is employed to learn the attentiveness and discrimination of attention canvases. The proposed DVAN has the ability to attend the object from coarse to fine granularity, and a dynamic internal representation for classification is built up by incrementally combining the information from different locations and scales of the image. Extensive experiments conducted on CUB-2011, Stanford Dogs, and Stanford Cars datasets have demonstrated that the pro-posed DVAN achieves competitive performance compared to the state-of-the-art approaches, without using any prior knowledge, user interaction, or external resource in training and testing.

DB2 Design Advisor: Integrated Automatic Physical Database Design <=SEP=> The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel “hybrid” approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical design recommendations that satisfy users’ requirements.

Differential Privacy as a Mutual Information Constraint <=SEP=> Differential privacy is a precise mathematical constraint meant to ensure privacy of individual pieces of information in a database even while queries are being answered about the aggregate. Intuitively, one must come to terms with what differential privacy does and does not guarantee. For example, the definition prevents a strong adversary who knows all but one entry in the database from further inferring about the last one. This strong adversary assumption can be overlooked, resulting in misinterpretation of the privacy guarantee of differential privacy. Herein we give an equivalent definition of privacy using mutual information that makes plain some of the subtleties of differential privacy. The mutual-information differential privacy is in fact sandwiched between ε-differential privacy and (ε,δ)-differential privacy in terms of its strength. In contrast to previous works using unconditional mutual information, differential privacy is fundamentally related to conditional mutual information, accompanied by a maximization over the database distribution. The conceptual advantage of using mutual information, aside from yielding a simpler and more intuitive definition of differential privacy, is that its properties are well understood. Several properties of differential privacy are easily verified for the mutual information alternative, such as composition theorems.

Empowerment - an Introduction <=SEP=> Is it better for you to own a corkscrew or not? If asked, you as a human being would likely say " yes " , but more importantly, you are somehow able to make this decision. You are able to decide this, even if your current acute problems or task do not include opening a wine bottle. Similarly, it is also unlikely that you evaluated several possible trajectories your life could take and looked at them with and without a corkscrew, and then measured your survival or reproductive fitness in each. When you, as a human cognitive agent, made this decision, you were likely relying on a behavioural " proxy " , an internal motivation that abstracts the problem of evaluating a decision impact on your overall life, but evaluating it in regard to some simple fitness function. One example would be the idea of curiosity, urging you to act so that your experience new sensations and learn about the environment. On average, this should lead to better and richer models of the world, which give you a better chance of reaching your ultimate goals of survival and reproduction. But how about questions such as, would you rather be rich than poor, sick or healthy, imprisoned or free? While each options offers some interesting new experience , there seems to be a consensus that rich, healthy and free is a preferable choice. We think that all these examples, in addition to the question of tool ownership above, share a common element of preparedness. Everything else being equal it is preferable to be prepared, to keep ones options open or to be in a state where ones actions have the greatest influence on ones direct environment. The concept of Empowerment, in a nutshell, is an attempt at formalizing and quantifying these degrees of freedom (or options) that an organism or agent has as a proxy for " preparedness " ; preparedness, in turn, is considered a proxy for prospective fitness via the hypothesis that preparedness would be a good indicator to distinguish promising from less promising regions in the prospective fitness landscape, without actually having to evaluate the full fitness landscape.

Large-signal-modulation of high-efficiency light-emitting diodes for optical communication <=SEP=> The dynamic behavior of high-efficiency light-emitting diodes (LEDs) is investigated theoretically and experimentally. A detailed theoretical description of the switch-on and switch-off transients of LEDs is derived. In the limit of small-signal modulation, the well-established exponential behavior is obtained. However, in the case of high injection, which is easily reached for thin active layer LEDs, the small-signal time constant is found to be up to a factor of two faster than the radiative recombination lifetime. Using such quantum-well LEDs, we have demonstrated optical data transfer with wide open eye diagrams at bit rates up to 2 Gbit/s. In addition, we have combined the use of thin active layers with the concept of surface-textured thin-film LEDs, which allow a significant improvement in the light extraction efficiency. With LEDs operating at 0.5 Gbit/s and 1 Gbit/s, we have achieved external quantum efficiencies of 36% and 29%, respectively.

Highly Efficient Doherty Amplifier Based on Class-E Topology for WCDMA Applications <=SEP=> This letter reports a high-efficiency gallium nitride (GaN) high-electron mobility transistor (HEMT) Doherty power amplifier (DPA) based on the class-E topology for wideband code-division multiple-access (WCDMA) applications. The class-E topology is employed as the carrier and peaking cells of the Doherty configuration. For validations, the proposed DPA is designed and implemented with 25-W GaN HEMTs at 2.14 GHz. For the proposed DPA, the power-added efficiency (PAE) and drain efficiency of 56.1% and 61.2% are achieved at 40 dBm (6-dB backoff power from Psat) for a continuous wave. For a 1-carrier WCDMA signal, the PAE of 44.8% is obtained with an adjacent channel leakage ratio (ACLR) of -31 dBc at 37 dBm, which is an 8.9% improvement over the conventional DPA with an ACLR of -36.4 dBc.

Botanical insecticides, deterrents, and repellents in modern agriculture and an increasingly regulated world. <=SEP=> Botanical insecticides have long been touted as attractive alternatives to synthetic chemical insecticides for pest management because botanicals reputedly pose little threat to the environment or to human health. The body of scientific literature documenting bioactivity of plant derivatives to arthropod pests continues to expand, yet only a handful of botanicals are currently used in agriculture in the industrialized world, and there are few prospects for commercial development of new botanical products. Pyrethrum and neem are well established commercially, pesticides based on plant essential oils have recently entered the marketplace, and the use of rotenone appears to be waning. A number of plant substances have been considered for use as insect antifeedants or repellents, but apart from some natural mosquito repellents, little commercial success has ensued for plant substances that modify arthropod behavior. Several factors appear to limit the success of botanicals, most notably regulatory barriers and the availability of competing products (newer synthetics, fermentation products, microbials) that are cost-effective and relatively safe compared with their predecessors. In the context of agricultural pest management, botanical insecticides are best suited for use in organic food production in industrialized countries but can play a much greater role in the production and postharvest protection of food in developing countries.

Event-based Sensing for Space Situational Awareness <=SEP=> A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatiotemporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and nighttime (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An eventbased sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.

SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems <=SEP=> Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.

Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition. <=SEP=> To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.

Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer <=SEP=> Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.

Achievements : Re ̄ ections on the Measurement of Women ' s Empowerment <=SEP=> This paper begins from the understanding that women's empowerment is about the process by which those who have been denied the ability to make strategic life choices acquire such an ability. Awide gap separates this processual understanding of empowerment from the more instrumentalist forms of advocacy which have required the measurement and quanti®cation of empowerment. The ability to exercise choice incorporates three inter-related dimensions: resources (de®ned broadly to include not only access, but also future claims, to both material and human and social resources); agency (including processes of decision making, as well as less measurable manifestations of agency such as negotiation, deception and manipulation); and achievements (well-being outcomes). A number of studies of women's empowerment are analysed to make some important methodological points about the measurement of empowerment. The paper argues that these three dimensions of choice are indivisible in determining the meaning of an indicator and hence its validity as a measure of empowerment. The notion of choice is further quali®ed by referring to the conditions of choice, its content and consequences. These quali®cations represent an attempt to incorporate the structural parameters of individual choice in the analysis of women's empowerment. CONCEPTUALIZING EMPOWERMENT

Low power LVDS transmitter with low common mode variation for 1GB/s-per pin operation <=SEP=> This paper presents the design of a low voltage differential signalling (LVDS) transmitter intended using a LCD panel interface and is capable of transmitting 1GB/s per pin data rate. The transmitter is fully LVDS standard compatible which is achieved by employing a common mode feedback circuit to meet all Ac and DC specifications over PVT variation, specifically due to the implementation of feedback circuit, the common mode variation is made very less quiescent current of 5.5mA. Further, the implementation incorporates the idea of sharing of bias blocks among different transmitters in full chip resulting in less active area. The transmitter is implemented in 3.3V, 0.35/spl mu/ CMOS technology. In this implementation the active area (excluding ESD) per transmitter is 0.039 mm square. The initial silicon data (output offset voltage of 1.20V and differential voltage of 320mV) at room temperature shows the consistency of the simulation results.

MAGIC—Memristor-Aided Logic <=SEP=> Memristors are passive components with a varying resistance that depends on the previous voltage applied across the device. While memristors are naturally used as memory, memristors can also be used for other applications, including logic circuits. In this brief, a memristor-only logic family, i.e., memristor-aided logic (MAGIC), is presented. In each MAGIC logic gate, memristors serve as an input with previously stored data, and an additional memristor serves as an output. The topology of a MAGIC nor gate is similar to the structure of a common memristor-based crossbar memory array. A MAGIC nor gate can therefore be placed within memory, providing opportunities for novel non-von Neumann computer architectures. Other MAGIC gates also exist (e.g., and, or, not, and nand) and are described in this brief.

Viewing Real-World Faces in 3D <=SEP=> We present a data-driven method for estimating the 3D shapes of faces viewed in single, unconstrained photos (aka "in-the-wild"). Our method was designed with an emphasis on robustness and efficiency - with the explicit goal of deployment in real-world applications which reconstruct and display faces in 3D. Our key observation is that for many practical applications, warping the shape of a reference face to match the appearance of a query, is enough to produce realistic impressions of the query's 3D shape. Doing so, however, requires matching visual features between the (possibly very different) query and reference images, while ensuring that a plausible face shape is produced. To this end, we describe an optimization process which seeks to maximize the similarity of appearances and depths, jointly, to those of a reference model. We describe our system for monocular face shape reconstruction and present both qualitative and quantitative experiments, comparing our method against alternative systems, and demonstrating its capabilities. Finally, as a testament to its suitability for real-world applications, we offer an open, on-line implementation of our system, providing unique means of instant 3D viewing of faces appearing in web photos.

Sensorized Garments and Textrode-Enabled Measurement Instrumentation for Ambulatory Assessment of the Autonomic Nervous System Response in the ATREC Project <=SEP=> Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth <=SEP=> Parallel computing using accelerators has gained widespre ad research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. Howev er, accelerator based computing has significantly relegated the role of CPUs in computation. As CPUs evo lv and also offer matching computational resources, it is important to also include CPUs in the comput ation. We call this thehybrid computing model. Indeed, most computer systems of the present age offe r a d gree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 20 10). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+ GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, spars e matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-c ore Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CP U with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good ad vantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 9 0% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale yst ms with significant performance gains and resource efficiency to the large scale user community.

Research on data augmentation for image classification based on convolution neural networks <=SEP=> The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.

Ricci Flow for 3D Shape Analysis <=SEP=> Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either Euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmuller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.

DARPA's explainable artificial intelligence (XAI) program <=SEP=> The DARPA's Explainable Artificial Intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. This talk will summarize the XAI program and present highlights from these Phase 1 evaluations.

Automatic Identification of Retinal Arteries and Veins in Fundus Images using Local Binary Patterns <=SEP=> Artery and vein (AV) classification of retinal images is a key to necessary tasks, such as automated measurement of arteriolar-to-venular diameter ratio (AVR). This paper comprehensively reviews the state-of-the art in AV classification methods. To improve on previous methods, a new Local Binary Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust against low contrast and low quality fundus images; and it helps the process by including additional AV texture and shape information. Experimental results compare the performance of the new method with the state-of-the art; and also methods with different feature extraction and classification schemas.

Improving code review effectiveness through reviewer recommendations <=SEP=> Effectively performing code review increases the quality of software and reduces occurrence of defects. However, this requires reviewers with experiences and deep understandings of system code. Manual selection of such reviewers can be a costly and time-consuming task. To reduce this cost, we propose a reviewer recommendation algorithm determining file path similarity called FPS algorithm. Using three OSS projects as case studies, FPS algorithm was accurate up to 77.97%, which significantly outperformed the previous approach.

Side-channel leakage aware instruction scheduling <=SEP=> Speed-optimized side-channel protected software implementations of block ciphers are important for the security of embedded IoT devices based on general-purpose microcontrollers. The recent work of Schwabe et al. published at SAC 2016 introduced a bit-sliced implementation of AES and a first-order Boolean-masked version of it, targeting ARM Cortex-M CPU cores. The authors claim to be secure against timing as well as first-order power and electromagnetic side-channel attacks. However, the author's security claims are not taking the actual leakage characteristics of the underlying CPU architecture into account, hence making the scheme potentially vulnerable to first-order attacks in practice. In this work we show indeed that such a masking scheme can be attacked very easily by first-order electromagnetic side-channel attacks. In order to fix the issue and provide practical first-order security, we provide a strategy to schedule program instructions in way that the specific leakage of the CPU does not impair the side-channel countermeasure.

Rationalizing Sentiment Analysis in Tensorflow <=SEP=> Sentiment analysis using deep learning models is a leading subject of interest in Natural Language Processing that is as powerful as it is opaque. Current state-ofthe-art models can produce accurate predictions, but they provide little insight as to why the model predicted this sentiment. Businesses relying on these models might be less likely to act on insight given the lack of evidence for predictions. These people would be more likely to trust such predictions if a brief explanation of the outcome is provided. Recent work by Lei et al [4]. has set forth a framework for a multi-aspect sentiment analysis concurrently providing text rationalization with each prediction. This framework sets forth a two-part approach, which summarizes a review and predicts a sentiment. In this paper, we explore the performance of this framework, seeking to recreate and improve upon it in TensorFlow.

Grid Connected PV Systems with Smart Grid functionality <=SEP=> ..................................................................................................................................... ii Declaration ................................................................................................................................ iii Acknowledgements ................................................................................................................... iv Table of