title: Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer

abstract: Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities’ data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization.


title: A Wide-Tracking Range Clock and Data Recovery Circuit

abstract: A hybrid analog-digital quarter-rate clock and data recovery circuit (CDR) that achieves a wide-tracking range and excellent frequency and phase tracking resolution is presented in this paper. A split-tuned analog phase-locked loop (PLL) provides eight equally spaced phases needed for quarter-rate data recovery and the digital CDR loop adjusts the phase of the PLL output clocks in a precise manner to facilitate plesiochronous clocking. The CDR employs a second-order digital loop filter and combines delta-sigma modulation with the analog PLL to achieve sub-picosecond phase resolution and better than 2 ppm frequency resolution. A test chip fabricated in a 0.18 mum CMOS process achieves BER <10-12 and consumes 14 mW power while operating at 2 Gb/s. The tracking range is greater than plusmn5000 ppm and plusmn2500 ppm at 10 kHz and 20 kHz modulation frequencies, respectively, making this CDR suitable for systems employing spread-spectrum clocking.


title: Real-time Personalization using Embeddings for Search Ranking at Airbnb

abstract: Search Ranking and Recommendations are fundamental problems of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked, personalized and recommended, each marketplace has a somewhat unique challenge. Correspondingly, at Airbnb, a short-term rental marketplace, search and recommendation problems are quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this paper we describe Listing and User Embedding techniques we developed and deployed for purposes of Real-time Personalization in Search Ranking and Similar Listing Recommendations, two channels that drive 99% of conversions. The embedding models were specifically tailored for Airbnb marketplace, and are able to capture guest's short-term and long-term interests, delivering effective home listing recommendations. We conducted rigorous offline testing of the embedding models, followed by successful online tests before fully deploying them into production.


title: Learning Deep Network Representations with Adversarially Regularized Autoencoders

abstract: The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure. Most existing network embedding models, with shallow or deep architectures, learn vertex representations from the sampled vertex sequences such that the low-dimensional embeddings preserve the locality property and/or global reconstruction capability. The resultant representations, however, are difficult for model generalization due to the intrinsic sparsity of sampled sequences from the input network. As such, an ideal approach to address the problem is to generate vertex representations by learning a probability density function over the sampled sequences. However, in many cases, such a distribution in a low-dimensional manifold may not always have an analytic form. In this study, we propose to learn the network representations with adversarially regularized autoencoders (NetRA). NetRA learns smoothly regularized vertex representations that well capture the network structure through jointly considering both locality-preserving and global reconstruction constraints. The joint inference is encapsulated in a generative adversarial training process to circumvent the requirement of an explicit prior distribution, and thus obtains better generalization performance. We demonstrate empirically how well key properties of the network structure are captured and the effectiveness of NetRA on a variety of tasks, including network reconstruction, link prediction, and multi-label classification.


title: Deep Learning on Retina Images as Screening Tool for Diagnostic Decision Support

abstract: In this project, we developed a deep learning system applied to human retina images for medical diagnostic decision support. The retina images were provided by EyePACS. These images were used in the framework of a Kaggle contest, whose purpose to identify diabetic retinopathy signs through an automatic detection system. Using as inspiration one of the solutions proposed in the contest, we implemented a model that successfully detects diabetic retinopathy from retina images. After a carefully designed preprocessing, the images were used as input to a deep convolutional neural network (CNN). The CNN performed a feature extraction process followed by a classification stage, which allowed the system to differentiate between healthy and ill patients using five categories. Our model was able to identify diabetic retinopathy in the patients with an agreement rate of 76.73% with respect to the medical expert's labels for the test data.


title: Towards a Virtual Reality Cognitive Training System for Mild Cognitive Impairment and Alzheimer's Disease Patients

abstract: The rapid growth of the aged population has stimulated research directed at designing interventions to support the associated social, economic and health challenges in an elderly population. Environmental interventions, like cognitive rehabilitation, stimulation and training can significantly improve cognitive functioning, so mitigating the cognitive decline. In this area, the adoption of state-of-the-art virtual reality technologies can provide a cost-effective, flexible and comprehensive solution for realizing complex cognitive training environments. With the aim of preserving mnestic and logical-praxic functions of patients with MCI or Alzheimer's disease at the early stages, in this paper we describe our ongoing work in designing a novel, fullyequipped virtual reality cognitive training system. The system is characterized by a high degree of realism and interactivity, to provide the patient with an adequate sense of presence within the virtual environment. Moreover, it is able to monitor the patient's biomedical signals and collect quantitative data on the training sessions, so allowing the therapist to analyze and tailor the training strategies to the patient.


title: Gyrostabilized two wheeled inverted pendulum robot

abstract: The increase in air pollution has motivated the design of many electrical transportation systems. In this paper, a "two wheeled inverted pendulum" personal transporter is considered. This system is inherently unstable and requires precise torque control of the two motor wheels to maintain its balance. To assist the control system, a mechanical controller or a "gyrostabilizer" is added to this robot. It is shown that the overall system stability is improved.


title: Multi-Image Matching using Invariant Features

abstract: This thesis concerns the problems of automatic image stitching and 3D modelling from multiple views. These are basic problems of computer vision, with applications in robotics, architecture, industrial inspection, surveillance, computer graphics and film. Recent work has brought increasing automation to these tasks, but despite a large amount of progress, state-of-the-art algorithms still require some form of user input or assumptions about the image sequence. For example, the best image stitchers currently require an ordered set of input images, or user input to identify the matching images, before automatic registration can proceed. In this work we show how such tasks can be performed automatically and without any user input at all. ::: We formulate the multi-image matching problem as one of finding all matching images, subject to the constraint that they are consistent views from a perspective camera. We use invariant features as a mechanism for finding correspondences, and indexing techniques to efficiently find matches between multiple views. We then find all sets of geometrically consistent feature matches, using a probabilistic model for verification. This allows us to identify each object or scene in the dataset using only the structure already present in the data. The major contributions of this thesis are the development of a system that can automatically recognise and stitch 2D panoramas in unordered image datasets, and a new class of invariant features for this purpose.


title: Horizontal spatial representations of time: Evidence for the STEARC effect

abstract: It is well known that stimuli such as numerals (small vs large) and auditory pitches (low vs high) have spatial characteristics, and that responses to such stimuli are biased by the mental representation of their magnitude. Walsh (2003) has argued that any spatially and action-coded magnitude will yield a relationship between magnitude and space. Here we investigated the spatial representation of ‘time’ using speeded responses to the onset timing (early vs late) of a probe stimulus following periodic auditory clicks. Participants pressed one of the two response keys depending on whether the timing of a given probe was earlier or later than expected based on the preceding clicks. The results showed that left-side responses to early onset timing were faster than those to late onset timing, whereas right-side responses to late onsets were faster than those to early onsets when the response keys were aligned horizontally. Such a time–response congruity effect was not observed with the vertical alignment of responses. These results suggest that time is represented from left to right along the horizontal axis in space. The existence of a ‘mental time line’ in space and the spatial–temporal association of response codes (STEARC) effect are discussed.


title: Low-power BiCMOS circuits for high-speed interchip communication

abstract: A universal BiCMOS low-voltage-swing transceiver (driver/receiver) with low on-chip power consumption is reported. Using a 3.3 V supply, the novel transceiver can drive/receive signals from several low-voltage-swing transceivers with termination voltages ranging from 5 V down to 2 V and frequencies well above 1 GHz. Measured results of test circuits fabricated in 0.8-/spl mu/m BiCMOS technology are also presented.


title: Consistent Presentation of Interactive Virtual Objects in Real Space with 3D Markers – Interactive Virtual Interior Design –

abstract: For presenting virtual objects in a real space, mixed reality (MR) technique is very effective to simulate an overview of a scenery. In interior design, for instance, the coordinates of the arrangements and the colours of the items need to be determined based on various layout plans. We propose a new framework for interactively arranging and rendering virtual objects in real space by manipulating physical 3D markers. A 3D marker consists of 2D codes and a spherical mirror for acquiring both geometric and photometric conditions by single camera instantaneously. Experimental results showed that the proposed approach is effective for providing appropriate geometry and local lighting conditions to present each virtual object in a simple system. The proposed system allows the user to experiment arranging of virtual furniture and verify the created scene which is visually consistent with the physical space.


title: Determining the Entropic Index q of Tsallis Entropy in Images through Redundancy

abstract: The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have found multiple applications in many engineering and science areas. In particular, we focus our interest on their applications to image processing through information theory. We present in this article a novel numeric method to calculate the Tsallis entropic index q characteristic to a given image, considering the image as a non-extensive system. The entropic index q is calculated through q-redundancy maximization, which is a methodology that comes from information theory. We find better results in the image processing in the grayscale by using the Tsallis entropy and thresholding q instead of the Shannon entropy.


title: UnTran: Recognizing Unseen Activities with Unlabeled Data Using Transfer Learning

abstract: The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.


title: Semi-supervised Deep Domain Adaptation via Coupled Neural Networks

abstract: Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target. In this paper, we propose a semi-supervised deep domain adaptation framework, in which the multi-layer feature extractor and a multi-class classifier are jointly learned to benefit from each other. Specifically, we develop a novel semi-supervised class-wise adaptation manner to fight off the conditional distribution mismatch between two domains by assigning a probabilistic label to each target sample, i.e., multiple class labels with different probabilities. Furthermore, a multi-class classifier is simultaneously trained on labeled source and unlabeled target samples in a semi-supervised fashion. In this way, the deep structure can formally alleviate the domain divergence and enhance the feature transferability. Experimental evaluations on several standard cross-domain benchmarks verify the superiority of our proposed approach.


abstract: The mainstream direction in face alignment is now dominated by cascaded regression methods. These methods start from an image with an initial shape and build a set of shape increments based on features with respect to the current estimated shape. These shape increments move the initial shape to the desired location. Despite the advantages of the cascaded methods, they all share two major limitations: (i) shape increments are learned independently from each other in a cascaded manner, (ii) the use of standard generic computer vision features such SIFT, HOG, does not allow these methods to learn problem-specific features. In this work, we propose a novel Recurrent Convolutional Shape Regression (RCSR) method that overcomes these limitations. We formulate the standard cascaded alignment problem as a recurrent process and learn all shape increments jointly, by using a recurrent neural network with a gated recurrent unit. Importantly, by combining a convolutional neural network with a recurrent one we avoid hand-crafted features, widely adopted in the literature and thus we allow the model to learn task-specific features. Besides, we employ the convolutional gated recurrent unit which takes as input the feature tensors instead of flattened feature vectors. Therefore, the spatial structure of the features can be better preserved in the memory of the recurrent neural network. Moreover, both the convolutional and the recurrent neural networks are learned jointly. Experimental evaluation shows that the proposed method has better performance than the state-of-the-art methods, and further supports the importance of learning a single end-to-end model for face alignment.


title: Building Dual-Domain Representations for Compression Artifacts Reduction

abstract: We propose a highly accurate approach to remove artifacts of JPEG-compressed images. Our approach jointly learns a very deep convolutional network in both DCT and pixel domains. The dual-domain representation can make full use of DCT-domain prior knowledge of JPEG compression, which is usually lacking in traditional network-based approaches. At the same time, it can also benefit from the prowess and the efficiency of the deep feed-forward architecture, in comparison to capacity-limited sparse-coding-based approaches. Two simple strategies, i.e., Adam and residual learning, are adopted to train the very deep network and later proved to be a success. Extensive experiments demonstrate the large improvements of our approach over the state of the arts.


title: PCGAN: Partition-Controlled Human Image Generation

abstract: Human image generation is a very challenging task since it is affected by many factors. Many human image generation methods focus on generating human images conditioned on a given pose, while the generated backgrounds are often blurred. In this paper, we propose a novel Partition-Controlled GAN to generate human images according to target pose and background. Firstly, human poses in the given images are extracted, and foreground/background are partitioned for further use. Secondly, we extract and fuse appearance features, pose features and background features to generate the desired images. Experiments on Market-1501 and DeepFashion datasets show that our model not only generates realistic human images but also produce the human pose and background as we want. Extensive experiments on COCO and LIP datasets indicate the potential of our method.


title: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

abstract: The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is considered "de facto" standard in the framework of learning from imbalanced data. This is due to its simplicity in the design of the procedure, as well as its robustness when applied to different type of problems. Since its publication in 2002, SMOTE has proven successful in a variety of applications from several different domains. SMOTE has also inspired several approaches to counter the issue of class imbalance, and has also significantly contributed to new supervised learning paradigms, including multilabel classification, incremental learning, semi-supervised learning, multi-instance learning, among others. It is standard benchmark for learning from imbalanced data. It is also featured in a number of different software packages -- from open source to commercial. In this paper, marking the fifteen year anniversary of SMOTE, we reect on the SMOTE journey, discuss the current state of affairs with SMOTE, its applications, and also identify the next set of challenges to extend SMOTE for Big Data problems.


title: How to handle ARP in a software-defined network

abstract: The Address Resolution Protocol (ARP) enables communication between IP-speaking nodes in a local network by reconstructing the hardware (MAC) address associated with the IP address of an interface. This is not needed in a Software-Defined Network (SDN), because each device can forward packets without the need to learn this association. We tackle the interoperability problem arising between standard network devices (end systems, routers), that rely on ARP, and SDN datapaths, that do not handle ARP packets natively. In particular, we propose a general approach to handle ARP in a SDN, that is applicable in several network scenarios, is transparent for existing devices, and can coexist with any packet forwarding logic implemented in the controller. Our approach reduces ARP traffic by confining it to the edge of SDNs and requires a minimal set of flow entries in the datapaths. We argument about its applicability and confirm it with experiments performed on SDN datapaths from a range of different vendors.


title: Corrupted DNS Resolution Paths: The Rise of a Malicious Resolution Authority

abstract: We study and document an important development in how attackers are using Internet resources: the creation of malicious DNS resolution paths. In this growing form of attack, victims are forced to use rogue DNS servers for all resolution. To document the rise of this “second secret authority” on the Internet, we studied instances of aberrant DNS resolution on a university campus. We found dozens of viruses that corrupt resolution paths, and noted that hundreds of URLs discovered per week performed drive-by alterations of host DNS settings. We used the rogue servers discovered in this analysis to document numerous live incidents on the university network. To measure this problem on the larger Internet, we generated DNS requests to most of IPv4, using a unique label query for each request. We found 17 million hosts responding, and further tracked the resolution path they used to reach our NS. Unable to find plausible harmless explanations for such a large number of open recursive hosts, we queried 600,000 of these open resolvers for “phishable” domains, such as banks and anti-virus companies. We found that 2.4% of this subsample would reply with incorrect answers, which extrapolates to 291,528 hosts on the Internet performing either incorrect or malicious DNS service. With DNS resolution behavior so trivially changed, numerous malware instances in the wild, and so many other hosts providing incorrect and misleading answers, we urge the security community to consider the corruption of the resolution path as an important problem.


title: Smartphone Background Activities in the Wild: Origin, Energy Drain, and Optimization

abstract: As new iterations of more powerful and better connected smartphones emerge, their limited battery life remains a leading factor adversely affecting the mobile experience of millions of smartphone users. While it is well-known that many apps can drain battery even while running in background, there has not been any study that quantifies the extent and severity of such background energy drain for users in the wild. To extend battery life, various new features are being incorporated within the phone, one of them being preventing applications from running in background, i.e., when the screen is off, but their impact is largely unknown. This paper makes several contributions. First, we present a large-scale measurement study that performs an in-depth analysis of the activities of various apps running in background on thousands of phones in the wild. Second, we quantify the amount of battery drain by all such background activities and possible energy saving. Third, we develop a metric to measure the usefulness of background activities that is personalized to each user. Finally, we present a system called HUSH (screen-off optimizer) that monitors the metric online and automatically identifies and suppresses background activities during screen-off periods that are not useful to the user experience. In doing so, our proposed HUSH saves screen-off energy of smartphones by 15.7% on average while incurring minimal impact on the user experience with the apps.


title: Showing face in video instruction: effects on information retention, visual attention, and affect

abstract: The amount of online educational content is rapidly increasing, particularly in the form of video lectures. The goal is to design video instruction to facilitate an experience that maximizes learning and satisfaction. A widely used but understudied design element in video instruction is the overlay of a small video of the instructor over lecture slides. We conducted an experiment with eye-tracking and recall tests to investigate how adding the instructor's face to video instruction affects information retention, visual attention, and affect. Participants strongly preferred instruction with the face and perceived it as more educational. They spent about 41% of time looking at the face and switched between the face and slide every 3.7 seconds. Consistent with prior work, no significant difference in short- and medium-term recall ability was found. Including the face in video instruction is encouraged based on learners' positive affective response. More fine-grained analytics combining eye-tracking with detailed learning assessment could shed light on the mechanisms by which the face aids or hinders learning.


title: Moving objects in space: exploiting proprioception in virtual-environment interaction

abstract: Manipulation in immersive virtual environments is difficult partly because users must do without the haptic contact with real objects they rely on in the real world to orient themselves and their manipulanda. To compensate for this lack, we propose exploiting the one real object every user has in a virtual environment, his body. We present a unified framework for virtual-environment interaction based on proprioception, a person's sense of the position and orientation of his body and limbs. We describe three forms of body-relative interaction: • Direct manipulation—ways to use body sense to help control manipulation • Physical mnemonics—ways to store/recall information relative to the body • Gestural actions—ways to use body-relative actions to issue commands Automatic scaling is a way to bring objects instantly within reach so that users can manipulate them using proprioceptive cues. Several novel virtual interaction techniques based upon automatic scaling and our proposed framework of proprioception allow a user to interact with a virtual world intuitively, efficiently, precisely, and lazily. We report the results of both informal user trials and formal user studies of the usability of the body-relative interaction techniques presented. CR


title: Deanonymisation of Clients in Bitcoin P2P Network

abstract: Bitcoin is a digital currency which relies on a distributed set of miners to mint coins and on a peer-to-peer network to broadcast transactions. The identities of Bitcoin users are hidden behind pseudonyms (public keys) which are recommended to be changed frequently in order to increase transaction unlinkability. We present an efficient method to deanonymize Bitcoin users, which allows to link user pseudonyms to the IP addresses where the transactions are generated. Our techniques work for the most common and the most challenging scenario when users are behind NATs or firewalls of their ISPs. They allow to link transactions of a user behind a NAT and to distinguish connections and transactions of different users behind the same NAT. We also show that a natural countermeasure of using Tor or other anonymity services can be cut-off by abusing anti-DoS countermeasures of the Bitcoin network. Our attacks require only a few machines and have been experimentally verified. The estimated success rate is between 11% and 60% depending on how stealthy an attacker wants to be. We propose several countermeasures to mitigate these new attacks.


title: Big Data: A Survey

abstract: In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.


title: Monge-Amp\`ere Flow for Generative Modeling

abstract: We present a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Amp\`ere flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Amp\`ere equation, optimal transport, and fluid dynamics into reversible flow-based generative models.


Effect of Speech Recognition Errors on Text Understandability for People who are Deaf or Hard of Hearing


title: A novel Wilkinson power divider based on slotted microstrip cross — Junction

abstract: A novel microstrip Wilkinson power divider with separate paths for the even and odd modes is presented in this paper. The proposed divider has a single quarter wavelength impedance transformer section and a reduced dimension in the transverse direction. This is achieved by etching a longitudinal slot in the ground plane of the microstrip where the isolation resistor is added. To develop a design procedure for the proposed divider, an equivalent circuit model for the slotted microstrip cross junction is also proposed. The model is validated by implementing a two-way band-pass filter. The proposed divider has a typical Wilkinson power divider performance of 0.2 dB insertion loss within 72% fractional bandwidth (15 dB return loss) and −24 dB isolation at the operating frequency. Meanwhile, it has the advantage of short lateral dimensions compared to its counter-parts. Theoretical predictions have been verified by EM simulations and measurements.


title: Design Tradeoffs of Data Access Methods

abstract: Database researchers and practitioners have been building methods to store, access, and update data for more than five decades. Designing access methods has been a constant effort to adapt to the ever changing underlying hardware and workload requirements. The recent explosion in data system designs - including, in addition to traditional SQL systems, NoSQL, NewSQL, and other relational and non-relational systems - makes understanding the tradeoffs of designing access methods more important than ever. Access methods are at the core of any new data system. In this tutorial we survey recent developments in access method design and we place them in the design space where each approach focuses primarily on one or a subset of read performance, update performance, and memory utilization. We discuss how to utilize designs and lessons-learned from past research. In addition, we discuss new ideas on how to build access methods that have tunable behavior, as well as, what is the scenery of open research problems.


title: Spatio-temporal models for estimating click-through rate

abstract: We propose novel spatio-temporal models to estimate click-through rates in the context of content recommendation. We track article CTR at a fixed location over time through a dynamic Gamma-Poisson model and combine information from correlated locations through dynamic linear regressions, significantly improving on per-location model. Our models adjust for user fatigue through an exponential tilt to the first-view CTR (probability of click on first article exposure) that is based only on user-specific repeat-exposure features. We illustrate our approach on data obtained from a module (Today Module) published regularly on Yahoo! Front Page and demonstrate significant improvement over commonly used baseline methods. Large scale simulation experiments to study the performance of our models under different scenarios provide encouraging results. Throughout, all modeling assumptions are validated via rigorous exploratory data analysis.


title: 3D Focusing Inversion of Multi-transient EM Data in the Frequency Domain

abstract: Summary We have produced an inversion code for frequency-domain controlled source EM data based on the 3D MT focussed inversion method developed by the Consortium for Electromagnetic Modelling and Inversion (CEMI). Broad-band frequency-domain data are easily determined from a knowledge of the earth’s impulse response obtainable using the transient EM approach. A rigorous integral equation (IE) based forward modeller and regularized focussing inversion are employed. The method is efficient since Green’s tensors need to be precomputed only once and saved for multiple use on every iteration of inversion and the same precomputed Green’s tensors can be used for the Frechet derivative calculation. In addition, forward modeling is required only once for each iterative step, resulting in a relatively fast but rigorous inversion method. To obtain a stable solution for blocky geological structures, we apply a regularization method based on a focussing stabilizing functional (Zhdanov 2002, Zhdanov et al. 2008). This stabilizer helps generate a sharp and focussed image of the anomalous resistivity distribution. The methodology is tested on a synthetic marine EM data set which presents a typical offshore petroleum scenario.


title: Sentiment based Food Classification for Restaurant Business

abstract: In this paper, we address the problem of classifying the restaurant reviews into different meal courses based on the review sentiments. Based on our survey, we observe that there are no works which focus on classifying the food items into different meal courses with top $\boldsymbol{n}$ food items under each course using restaurant reviews. Most of the works in literature address the issues like predicting the ratings of the restaurant or restaurant business strategies by considering various attributes of the restaurant. Thus, in this paper, we propose a sentiment based food classification framework consisting of two tasks, namely, sentiment classification and four-course meal classification. In sentiment classification, we classify the reviews into positive and negative categories based on the sentiments of the reviews. In four-course meal classification, we categorize the reviews into four courses, namely, soups and salads, appetizers, main course and desserts. We list the top $\boldsymbol{n}$ food items liked by most of the customers in each of these courses. In order to select the suitable classification technique for addressing the identified problem, we analyze the learning curves based on the bias-variance trade-off. We observe that support vector machines (SVM) classification technique outperforms other techniques. The performance analysis of the proposed framework is carried out on the standard Yelp dataset. The top 5 food items obtained in each of the categories are soups and salads: salad, corn, coffee, cocktail, tea; appetizers: burger, pizza, bread, cheese, frie; main course: steak, meal, chicken, meat, beef; and desserts: doughnut, cream, cake, chocolate, flan.


title: BER Analysis for NOMA-Enabled Visible Light Communication Systems with M-PSK

abstract: Visible light communication (VLC) and non-orthogonal multiple access (NOMA) are deemed two promising technologies in the next generation wireless communication systems due to their capabilities in achieving high capacity and massive connectivity. This paper studies the performance of a NOMA-enabled VLC system using M-ary phase shift keying (M-PSK). In particular system level bit error rate (BER) are derived for different modulation orders. Conventional methods used for analyzing the BER under orthogonal multiple access cannot be directly applied to NOMA. We propose an analytical framework based on bitwise-decision axis and signal space to obtain the closed-form BER expressions for a NOMA-enabled VLC system using M-PSK. Simulation results demonstrate the accuracy of the theoretical analysis. The study shows that the BER gap among users decreases with the increase of the modulation order but at the cost of a higher power consumption in order to achieve a better signal to noise ratio. It is observed that 8-PSK modulation in NOMA-enabled VLC systems strikes a good tradeoff between the power cost and the achievable BER.


Privacy-Preserving Aggregation of Time-Series Data


title: An Assessment of NFC for Future Mobile Payment Systems

abstract: In this paper, we present an assessment of NFC (near field communication) for future mobile payment systems. NFC is expected to become a very trendy technology for mobile services, more specifically for mobile payments. The objective of our paper is to evaluate in a systematic manner the potential of NFC as an upcoming technology for mobile payments. In order to ensure the rigor of our research, we used a formal and structured approach based on multi-actor multi-criteria methods. Our research provides one of the first assessment of NFC and a realistic picture of the current Swiss situation as we involved numerous mobile payment experts. Our findings show that Swiss industry experts are quite enthusiastic about the future of NFC.


title: Deep Multi-fidelity Gaussian Processes

abstract: We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and deep neural networks enables us to construct a method that is immune to discontinuities. We demonstrate the effectiveness of the new technology using standard benchmark problems designed to resemble the outputs of complicated high- and low-fidelity codes.


Citation function, polarity and influence classification


title: Probabilistic Typology: Deep Generative Models of Vowel Inventories

abstract: Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most---but not all---languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.


title: Big Data in Distributed Analytics, Cybersecurity, Cyber Warfare and Digital Forensics

abstract: Big Data can reduce the processing time of large volumes of data in the distributed computing environment using Hadoop. It also can predict potential cybersecurity breaches, help stop cyber attacks, and facilitate post-breach digital forensic analysis. This paper introduces Big Data applications in distributed analytics, general cybersecurity (general cyber threats, cyber attacks, and cyber security in cloud computing, etc.), cyber warfare, cyber defense, and digital forensics. Some methods and technology progress in these cyberareas are presented. Some challenges of Big Data applications in the areas and solutions to part of the challenges are also discussed.


Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis


title: Classifying and selecting UX and usability measures

abstract: There are many different types of measures of usability and user experience (UX). The overall goal of usability from a user perspective is to obtain acceptable effectiveness, efficiency and satisfaction (Bevan, 1999, ISO 9241-11). This paper summarises the purposes of measurement (summative or formative), and the measures of usability that can be taken at the user interface level and at the system level. The paper suggests that the concept of usability at the system level can be broadened to include learnability, accessibility and safety, which contribute to the overall user experience. UX can be measured as the user’s satisfaction with achieving pragmatic and hedonic goals, and pleasure. WHY MEASURE UX/USABILITY? The most common reasons for measuring usability in product development are to obtain a more complete understanding of users’ needs and to improve the product in order to provide a better user experience. But it is also important to establish criteria for UX/usability goals at an early stage of design, and to use summative measures to evaluate whether these have been achieved during development. Summative measures


title: Information technology innovations: a classification by IT locus of impact and research approach

abstract: Research based on diffusion of innovation (DOI) theory investigates the evaluation, adoption, and implementation of innovations. Gaining understanding is difficult when tracing results on a study-by-study basis. To remove some of the barriers to comparison of results, we distinguish between innovations according to their locus of impact (IS unit, intra- or inter-organizational) and between studies according to research approach (factor or stage).A representative sample of 70 information technology (IT) related DOI research studies published by IT researchers over the last decade is evaluated using this classification scheme. From the analysis, it appears that the classification scheme may be used to provide a clearer understanding of IT innovation diffusion.Traditional DOI theory appears to be most applicable to ITs which have an intra-organizational locus of impact. ITs with an information system (IS) unit locus of impact appear to require less organizational support, and their extent of implementation appears to be related to nontraditional innovation characteristics such as functionality and efficiency. Inter-organizational locus of impact IT innovations, however, appear to be more affected by contextual and environmental variables, and their differences may be better explained by economic influence or critical mass theories.


title: Validation of direct current control in LED lamp with non-inverting buck-boost converter

abstract: The given paper deals with equipment for smart lighting systems. A hypothesis, that current fed dimmable electrical ballasts with amplitude modulation of light could improve the controllability of such systems, is brought forward. Since there are very few natural current sources it is proposed to use a combination of a voltage source, switch mode voltage-to-current converter and current regulator. The first part can be built as a buck converter, while the regulator is similar to a boost converter. Together this gives a topology known as non-inverting buck-boost converter which, however, in the given case has to be controlled as two modules of “current source” nature. The control rules suitable for such strategy are discussed in the paper. In order to verify the idea a laboratory prototype of such converter has been built and tested. The features of the converter are given in the paper below. The obtained results show that the idea works in general, but certain instability and efficiency problems have to be solved. That is why it was concluded that a more accurate control method for the voltage-to-current source part has to be developed.


title: Image segmentation using fuzzy based histogram thresholding

abstract: In this paper, a new method of image segmentation by histogram thresholding based on the concept of fuzzy measure minimization is suggested. The concept introduced here, uses extreme value type-1 distribution (Gumbel distribution) in order to define the membership function. The membership function is used to express the unique association between a pixel and its belonging region (the object or the background). The optimal threshold can be effectively determined by minimizing the measure of fuzziness of the image. The result of the proposed approach is compared with some existing methods and the efficacy can be verified over some standard images having various types of histogram.


title: Table-Top Spatially-Augmented Reality: Bringing Physical Models to Life with Projected Imagery

abstract: Despite the availability of high-quality graphics systems, architects and designers still build scaled physical models of buildings and products. These physical models have many advantages, however they are typically static in structure and surface characteristics. They are inherently lifeless. In contrast, high-quality graphics systems are tremendously flexible, allowing viewers to see alternative structures, facades, textures, cut-away views, and even dynamic effects such as changing lighting, moving automobiles, people, etc. We introduce a combination of these approaches that builds on our previously-published projector-based spatially-augmented reality techniques. The basic idea is to aim multiple ceiling-mounted light projectors inward to graphically augment table-top scaled physical models of buildings or products. This approach promises to provide very compelling hybrid visualizations that afford the benefits of both traditional physical models, and modern computer graphics, effectively "bringing to life" table-top physical models.


title: Analysis of Bayes, Neural Network and Tree Classifier of Classification Technique in Data Mining using WEKA

abstract: In today’s world, gigantic amount of data is available in science, industry, business and many other areas. This data can provide valuable information which can be used by management for making important decisions. But problem is that how can find valuable information. The answer is data mining. Data Mining is popular topic among researchers. There is lot of work that cannot be explored till now. But, this paper focuses on the fundamental concept of the Data mining i.e. Classification Techniques. In this paper BayesNet, NavieBayes, NavieBayes Uptable, Multilayer perceptron, Voted perceptron and J48 classifiers are used for the classification of data set. The performance of these classifiers analyzed with the help of Mean Absolute Error, Root Mean-Squared Error and Time Taken to build the model and the result can be shown statistical as well as graphically. For this purpose the WEKA data mining tool is used. KEY TERM’S BayesNet, J48, Mean Absolute Error, NavieBayes, Root Mean-Squared Error


title: Oil Rents , Corruption , and State Stability : Evidence from Panel Data Regressions

abstract: We examine the effects of oil rents on corruption and state stability exploiting the exogenous within-country variation of a new measure of oil rents for a panel of 30 oil-exporting countries during the period 1992–2005. We find that an increase in oil rents significantly increases corruption, significantly deteriorates political rights while at the same time leading to a significant improvement in civil liberties. We argue that these findings can be explained by the political elite having an incentive to extend civil liberties but reduce political rights in the presence of oil windfalls to evade redistribution and conflict. We support our argument documenting that there is a significant effect of oil rents on corruption in countries with a high share of state participation in oil production while no such link exists in countries where state participation in oil production is low.


title: ThingSeek: A Crawler and Search Engine for the Internet of Things

abstract: The rapidly growing paradigm of the Internet of Things (IoT) requires new search engines, which can crawl heterogeneous data sources and search in highly dynamic contexts. Existing search engines cannot meet these requirements as they are designed for traditional Web and human users only. This is contrary to the fact that things are emerging as major producers and consumers of information. Currently, there is very little work on searching IoT and a number of works claim the unavailability of public IoT data. However, it is dismissed that a majority of real-time web-based maps are sharing data that is generated by things, directly. To shed light on this line of research, in this paper, we firstly create a set of tools to capture IoT data from a set of given data sources. We then create two types of interfaces to provide real-time searching services on dynamic IoT data for both human and machine users.


title: The next big one: Detecting earthquakes and other rare events from community-based sensors

abstract: Can one use cell phones for earthquake early warning? Detecting rare, disruptive events using community-held sensors is a promising opportunity, but also presents difficult challenges. Rare events are often difficult or impossible to model and characterize a priori, yet we wish to maximize detection performance. Further, heterogeneous, community-operated sensors may differ widely in quality and communication constraints. In this paper, we present a principled approach towards detecting rare events that learns sensor-specific decision thresholds online, in a distributed way. It maximizes anomaly detection performance at a fusion center, under constraints on the false alarm rate and number of messages per sensor. We then present an implementation of our approach in the Community Seismic Network (CSN), a community sensing system with the goal of rapidly detecting earthquakes using cell phone accelerometers, consumer USB devices and cloud-computing based sensor fusion. We experimentally evaluate our approach based on a pilot deployment of the CSN system. Our results, including data from shake table experiments, indicate the effectiveness of our approach in distinguishing seismic motion from accelerations due to normal daily manipulation. They also provide evidence of the feasibility of earthquake early warning using a dense network of cell phones.


title: A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks

abstract: FPGA-based hardware accelerator for convolutional neural networks (CNNs) has obtained great attentions due to its higher energy efficiency than GPUs. However, it has been a challenge for FPGA-based solutions to achieve a higher throughput than GPU counterparts. In this paper, we demonstrate that FPGA acceleration can be a superior solution in terms of both throughput and energy efficiency when a CNN is trained with binary constraints on weights and activations. Specifically, we propose an optimized accelerator architecture tailored for bitwise convolution and normalization that features massive spatial parallelism with deep pipeline (temporal parallelism) stages. Experiment results show that the proposed architecture running at 90 MHz on a Xilinx Virtex-7 FPGA achieves a computing throughput of 7.663 TOPS with a power consumption of 8.2 W regardless of the batch size of input data. This is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests (in small batch size). For processing static data (in large batch size), the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.


title: A Near-Optimum 13.56 MHz CMOS Active Rectifier With Circuit-Delay Real-Time Calibrations for High-Current Biomedical Implants

abstract: In this paper, a near-optimum active rectifier is proposed to achieve well-optimized power conversion efficiency (PCE) and voltage conversion ratio (VCR) under various process, voltage, temperature (PVT) and loading conditions. The near-optimum operation includes: eliminated reverse current loss and maximized conduction time achieved by the proposed sampling-based real-time calibrations with automatic circuit-delay compensation for both on- and off-time of active diodes considering PVT variations; and power stage optimizations with adaptive sizing over a wide loading range. The design is fabricated in TSMC 65 nm process with standard I/O devices. Measurement results show more than 36% and 17% improvement in PCE and VCR, respectively, by the proposed techniques. A peak PCE of 94.8% with an $80~\Omega $ loading, a peak VCR of 98.7% with 1 $\text{k}\Omega $ loading, and a maximum output power of 248.1 mW are achieved with 2.5 V input amplitude.


title: A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification

abstract: High spatial resolution (HSR) imagery scene classification has recently attracted increased attention. The bag-of-visual-words (BoVW) model is an effective method for scene classification. However, it can only extract handcrafted features, and it disregards the spatial layout information, whereas deep learning can automatically mine the intrinsic features as well as preserve the spatial location, but it may lose the characteristic information of the HSR images. Although previous methods based on the combination of BoVW and deep learning have achieved comparatively high classification accuracies, they have not explored the combination of handcrafted and deep features, and they just used the BoVW model as a feature coding method to encode the deep features. This means that the intrinsic characteristics of these models were not combined in the previous works. In this paper, to discover more discriminative semantics for HSR imagery, the deep-local-global feature fusion (DLGFF) framework is proposed for HSR imagery scene classification. Differing from the conventional scene classification methods, which utilize only handcrafted features or deep features, DLGFF establishes a framework integrating multi-level semantics from the global texture feature–based method, the BoVW model, and a pre-trained convolutional neural network (CNN). In DLGFF, two different approaches are proposed, i.e., the local and global features fused with the pooling-stretched convolutional features (LGCF) and the local and global features fused with the fully connected features (LGFF), to exploit the multi-level semantics for complex scenes. The experimental results obtained with three HSR image classification datasets confirm the effectiveness of the proposed DLGFF framework. Compared with the published results of the previous scene classification methods, the classification accuracies of the DLGFF framework on the 21-class UC Merced dataset and 12-class Google dataset of SIRI-WHU can reach 99.76%, which is superior to the current state-of-the-art methods. The classification accuracy of the DLGFF framework on the 45-class NWPU-RESISC45 dataset, 96.37 ± 0.05%, is an increase of about 6% when compared with the current state-of-the-art methods. This indicates that the fusion of the global low-level feature, the local mid-level feature, and the deep high-level feature can provide a representative description for HSR imagery.


title: Measuring Underground Economy Can Be Done, but It Is Difficult

abstract: There are at least two main ways to measure "the informal sector" of an economy, both of which entail difficulties. The effort is needed, however, because the underground economy accounts for about 13 percent of GDP in developed countries and almost three times that in developing countries.


title: Closed-Form Critical Conditions of Subharmonic Oscillations for Buck Converters

abstract: A general critical condition of subharmonic oscillation in terms of the loop gain is derived. Many closed-form critical conditions for various control schemes in terms of converter parameters are also derived. Some previously known critical conditions become special cases in the generalized framework. Given an arbitrary control scheme, a systematic procedure is proposed to derive the critical condition for that control scheme. Different control schemes share similar forms of critical conditions. For example, both V2 control and voltage mode control have the same form of critical condition. A peculiar phenomenon in average current mode control where subharmonic oscillation occurs in a window value of pole can be explained by the derived critical condition. A ripple amplitude index to predict subharmonic oscillation proposed in the past research has limited application and is shown invalid for a converter with a large pole.


A Hybrid Learning Scheme for Chinese Word Embedding


title: A Low-Jitter Low-Phase-Noise 10-GHz Sub-Harmonically Injection-Locked PLL With Self-Aligned DLL in 65-nm CMOS Technology

abstract: In this paper, we present design and analysis of an innovative low-jitter low-phase-noise 10-GHz sub-harmonically injection-locked phase-locked loop (PLL) with self-aligned delay-locked loop in 65-nm CMOS technology. With the proposed innovative topology, the phase between the injection signal and the voltage-controlled oscillator in the PLL can be dynamically aligned to minimize the jitter over the variations. A modified theoretical model of the sub-harmonically injection-locked PLL with self-aligned injection is developed for the design methodology. The in-band phase noise of the sub-harmonically injection-locked PLL can be significantly improved using the self-aligned sub-harmonically injection-locked technique. The design considerations of the locking range, loop bandwidth, and frequency division ratio are addressed. At 10 GHz, the measured phase noise of the proposed PLL with self-aligned injection at 1-MHz offset is -130.2 dBc/Hz with a root-mean-square jitter of 44 fs. The total dc power consumption is 62.7 mW. From 10 °C to 70 °C, the measured phase noise at 1-MHz offset and jitter are better than -129 dBc/Hz and 48 fs, respectively. This work demonstrates excellent performance and good robustness over the variations, and it can be compared to the previously reported state-of-the-art sub-harmonically injection-locked PLLs.


title: DSCRETE: automatic rendering of forensic information from memory images via application logic reuse

abstract: State-of-the-art memory forensics involves signature-based scanning of memory images to uncover data structure instances of interest to investigators. A largely unaddressed challenge is that investigators may not be able to interpret the content of data structure fields, even with a deep understanding of the data structure's syntax and semantics. This is very common for data structures with application-specific encoding, such as those representing images, figures, passwords, and formatted file contents. For example, an investigator may know that a buffer field is holding a photo image, but still cannot display (and hence understand) the image. We call this the data structure content reverse engineering challenge. In this paper, we present DSCRETE, a system that enables automatic interpretation and rendering of in-memory data structure contents. DSCRETE is based on the observation that the application in which a data structure is defined usually contains interpretation and rendering logic to generate human-understandable output for that data structure. Hence DSCRETE aims to identify and reuse such logic in the program's binary and create a "scanner+renderer" tool for scanning and rendering instances of the data structure in a memory image. Different from signature-based approaches, DSCRETE avoids reverse engineering data structure signatures. Our evaluation with a wide range of real-world application binaries shows that DSCRETE is able to recover a variety of application data -- e.g., images, figures, screenshots, user accounts, and formatted files and messages -- with high accuracy. The raw contents of such data would otherwise be unfathomable to human investigators.


title: Recommending citations: translating papers into references

abstract: When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5% to 10%, respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work.


title: Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval

abstract: In this article we present novel learning methods for estimating the quality of results returned by a search engine in response to a query. Estimation is based on the agreement between the top results of the full query and the top results of its sub-queries. We demonstrate the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval. Experiments on TREC data demonstrate the robustness and the effectiveness of our learning algorithms.


title: A Waveguide to Microstrip Inline Transition With Very Simple Modular Assembly

abstract: A microstrip to empty rectangular waveguide transition is presented for systems requiring a simple modular assembly with good electrical performance. The structure is based on three building blocks, involving a microstrip line to dielectric-loaded rectangular waveguide, a transition between dielectric-loaded and empty rectangular waveguides with reduced height, and a final transformer to standard waveguide. These three building blocks (segmentation based on the design point of view) are combined in three simple parts which are assembled in a straightforward modular way, without soldering. These features are very important for signal feeders requiring a large number of transitions, as the X-band phased array antenna where the proposed structure has been integrated. The concepts involved in the design and manufacturing are illustrated with a prototype showing measured return loss of 10 dB and insertion loss of 1.1 dB in the band 8.5-9.5 GHz.


title: Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path Finding through Combination of Satisfiability Modulo Theories and Conflict-based Search

abstract: We address item relocation problems in graphs in this paper. We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be moved across edges while various constraints depending on the type of relocation problem must be satisfied. We introduce a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF) and token swapping (TSWAP). In this formulation we express two new types of relocation problems derived from token swapping that we call token rotation (TROT) and token permutation (TPERM). Our solving approach for item relocation combines satisfiability modulo theory (SMT) with conflict-based search (CBS). We interpret CBS in the SMT framework where we start with the basic model and refine the model with a collision resolution constraint whenever a collision between items occurs in the current solution. The key difference between the standard CBS and our SMT-based modification of CBS (SMT-CBS) is that the standard CBS branches the search to resolve the collision while in SMT-CBS we iteratively add a single disjunctive collision resolution constraint. Experimental evaluation on several benchmarks shows that the SMT-CBS algorithm significantly outperforms the standard CBS. We also compared SMT-CBS with a modification of the SAT-based MDD-SAT solver that uses an eager modeling of item relocation in which all potential collisions are eliminated by constrains in advance. Experiments show that lazy approach in SMT-CBS produce fewer constraint than MDD-SAT and also achieves faster solving run-times.


title: Data Sharing in Data-Centric Multi-tenant Software as a Service

abstract: Cloud Computing include applications that are delivered as services using internet in the shape of infrastructure, systems or application software (SaaS) hosted at a remote computing facility. SaaS applications are deployed on a shared environment that can be accessed by its users from various client-end software by using Internet. Organizations using SaaS application do not have control over the infrastructure of the environment that is often built using multi-tenant system architecture. Usually data-centric multi-tenant applications provide a common UI for all the tenants or organizations and data of multiple tenants is saved in a single database. Core of these applications revolve around managing organizations data. For many reasons like mergers, joint marketing campaign, moving from pilot to production SaaS instance, we have to migrate data from one tenant to another. Data migration requires a very skilled and time consuming human effort and it results in data duplication. Therefore we propose a middleware for data sharing between different organizations, a design pattern for the implementation of the middleware, and extension to current multi-tenant database designs for SaaS applications which will help in eliminating human effort in data migration and avoiding data duplication. Our results shows that data can be shared between different tenants of a SaaS efficiently without making a significant change in existing architecture of SaaS.


title: A Case of Spheroidal-Cell Carcinoma (Seminoma) of the Epididymis

abstract: The following case is reported because of the extreme rarity of this type of spheroidal-cell carcinoma (seminoma) in the epididymis. ::: ::: Case Report ::: ::: The patient, Louis W., aged forty-eight, was admitted to the Sydenham Hospital (Case No. 19866), to the service of Dr. A. Lightstone,1 on Nov. 5, 1931. ::: ::: History: One year previously the patient had observed swelling and some pain in the right testicle. There had been no trauma, and no inflammation. Venereal disease was denied. Recently the swelling had increased in size. ::: ::: Physical Examination: The general condition was normal. The penis and left testicle were normal, but the right testicle showed a hard lump, the size of a hazelnut, at the upper pole, apparently involving the epididymis. It did not involve the skin, and did not transilluminate. A few “shotty” lymph nodes were palpable in the right inguinal region. ::: ::: Preoperative Diagnosis: Carcinoma of testicle or tuberculosis of the epididymis. ::: ::: Operation: On Nov. 6, 1931, under local anesthesia, the right testicle and the vas deferens as far as the internal inguinal ring were removed. The patient left the hospital Nov. 11, 1931, and returned later for deep x-ray therapy.


title: Colorful Image Colorization

abstract: 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.


title: Design Patterns for Legal Ontology Constructions

abstract: Ontology design is known to be a difficult task, requiring much more than expertise in an area; legal ontology design, due to the complexity of its domain, makes those difficulties worse. That may be partly due to poor requirement analysis in existing tools, but there is also an inherent gap between the purely logical constructs and methods that are expected to be used, and the actual competences and thought habits of domain experts. This paper presents some solutions, based on content ontology design patterns, which are intended to make life of legal ontology designers easier. An overview of the typical tasks and services for legal knowledge is presented, the notion of ontology design pattern is introduced, and some excerpts of a reference ontology (CLO) and its related patterns are included, showing their utility in a simple legal modeling case


title: Deep deformable Q-Network: an extension of deep Q-Network

abstract: The performance of Deep Reinforcement Learning (DRL) algorithms is usually constrained by instability and variability. In this work, we present an extension of Deep Q-Network (DQN) called Deep Deformable Q-Network which is based on deformable convolution mechanisms. The new algorithm can readily be built on existing models and can be easily trained end-to-end by standard back-propagation. Extensive experiments on the Atari games validate the feasibility and effectiveness of the proposed Deep Deformable Q-Network.


title: Semi-supervised deep learning by metric embedding

abstract: Deep networks are successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data. This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set. The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.


title: Understanding Intention to Explore Business Intelligence Systems: The Role of Fit and Engagement

abstract: This paper explores how user engagement affects ::: users’ intention to explore business intelligence ::: system (BIS) and how user engagement is promoted ::: by the cognitive fit between BIS interface and tasks ::: and the regulatory compatibility between BIS ::: interface and personal characteristics, such as style ::: of information processing. Results from the lab ::: experiment suggest that the cognitive fit and the ::: regulatory compatibility could both influence users’ ::: engagement experience, which in turn affected users’ ::: intention to explore BIS. This study may contribute to ::: the extant information systems (IS) literature by ::: uncovering the impacts of engagement experience on ::: intention to explore and responding to the call for ::: investigation of the BIS context where rich ::: visualizations of the systems influence users’ ::: engagement experience.


Anatomy of a Database System


title: Error Entropy Minimization for LSTM Training

abstract: In this paper we present a new training algorithm for the Long Short-Term Memory (LSTM) recurrent neural network. This algorithm uses entropy instead of the usual mean squared error as the cost function for the weight update. More precisely we use the Error Entropy Minimization approach, were the entropy of the error is minimized after each symbol is present to the network. Our experiments show that this approach enables the convergence of the LSTM more frequently than with the traditional learning algorithm. This in turn relaxes the burden of parameter tuning since learning is achieved for a wider range of parameter values. The use of EEM also reduces, in some cases, the number of epochs needed for convergence.


title: Evolution for automatic assessment of the difficulty of sokoban boards

abstract: Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialized population of ISAc lists while the other initializes populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.


title: The virtual reality applied to biology understanding: The in virtuo experimentation

abstract: The advent of the computer and computer science, and in particular virtual reality, offers new experiment possibilities with numerical simulations and introduces a new type of investigation for the complex systems study: the in virtuo experiment. This work lies on the framework of multi-agent systems. We propose a generic model for systems biology based on reification of the interactions, on a concept of organization and on a multi-model approach. By 'reification' we understand that interactions are considered as autonomous agents. The aim has been to combine the systemic paradigm and the virtual reality to provide an application able to collect, simulate, experiment and understand the knowledge owned by different biologists working around an interdisciplinary subject. Here, we have been focused on the urticaria disease understanding. Autonomy is taken as a principle. The method permits to integrate different natures of model in the same application using chaotic asynchronous iterations and C++ library: AReVi. We have modeled biochemical reactions, molecular 3D diffusion, cell organizations and mechanical 3D interactions. It also permits to embed different expert system modeling methods like fuzzy cognitive maps. This work provides a toolbox easily adaptable to new biological studies.


title: Parkin-Independent Mitophagy Controls Chemotherapeutic Response in Cancer Cells.

abstract: Mitophagy is an evolutionarily conserved process that selectively targets impaired mitochondria for degradation. Defects in mitophagy are often associated with diverse pathologies, including cancer. Because the main known regulators of mitophagy are frequently inactivated in cancer cells, the mechanisms that regulate mitophagy in cancer cells are not fully understood. Here, we identified an E3 ubiquitin ligase (ARIH1/HHARI) that triggers mitophagy in cancer cells in a PINK1-dependent manner. We found that ARIH1/HHARI polyubiquitinates damaged mitochondria, leading to their removal via autophagy. Importantly, ARIH1 is widely expressed in cancer cells, notably in breast and lung adenocarcinomas; ARIH1 expression protects against chemotherapy-induced death. These data challenge the view that the main regulators of mitophagy are tumor suppressors, arguing instead that ARIH1-mediated mitophagy promotes therapeutic resistance.


title: Multimodal semi-supervised learning for image classification

abstract: In image categorization the goal is to decide if an image belongs to a certain category or not. A binary classifier can be learned from manually labeled images; while using more labeled examples improves performance, obtaining the image labels is a time consuming process. We are interested in how other sources of information can aid the learning process given a fixed amount of labeled images. In particular, we consider a scenario where keywords are associated with the training images, e.g. as found on photo sharing websites. The goal is to learn a classifier for images alone, but we will use the keywords associated with labeled and unlabeled images to improve the classifier using semi-supervised learning. We first learn a strong Multiple Kernel Learning (MKL) classifier using both the image content and keywords, and use it to score unlabeled images. We then learn classifiers on visual features only, either support vector machines (SVM) or least-squares regression (LSR), from the MKL output values on both the labeled and unlabeled images. In our experiments on 20 classes from the PASCAL VOC'07 set and 38 from the MIR Flickr set, we demonstrate the benefit of our semi-supervised approach over only using the labeled images. We also present results for a scenario where we do not use any manual labeling but directly learn classifiers from the image tags. The semi-supervised approach also improves classification accuracy in this case.


title: Endoplasmic Reticulum Stress, Unfolded Protein Response, and Cancer Cell Fate

abstract: Perturbation of endoplasmic reticulum (ER) homeostasis results in a stress condition termed "ER stress" determining the activation of a finely regulated program defined as unfolded protein response (UPR) and whose primary aim is to restore this organelle's physiological activity. Several physiological and pathological stimuli deregulate normal ER activity causing UPR activation, such as hypoxia, glucose shortage, genome instability, and cytotoxic compounds administration. Some of these stimuli are frequently observed during uncontrolled proliferation of transformed cells, resulting in tumor core formation and stage progression. Therefore, it is not surprising that ER stress is usually induced during solid tumor development and stage progression, becoming an hallmark of such malignancies. Several UPR components are in fact deregulated in different tumor types, and accumulating data indicate their active involvement in tumor development/progression. However, although the UPR program is primarily a pro-survival process, sustained and/or prolonged stress may result in cell death induction. Therefore, understanding the mechanism(s) regulating the cell survival/death decision under ER stress condition may be crucial in order to specifically target tumor cells and possibly circumvent or overcome tumor resistance to therapies. In this review, we discuss the role played by the UPR program in tumor initiation, progression and resistance to therapy, highlighting the recent advances that have improved our understanding of the molecular mechanisms that regulate the survival/death switch.


An Overview of Networking Infrastructure for Massively Multiplayer Online Games


title: Applying machine learning to event data in soccer

abstract: Soccer is the world’s most popular sport but published research in soccer analytics has yet to attain the same level of sophistication as analytics in other professional sports. We hope to discover new knowledge from a soccer ball-event dataset by applying different machine learning techniques. In this thesis we present three experiments that address three interesting questions in soccer that involve game prediction and team style. We approach each question by constructing features using the ball-event data, where an event is a pass, shot, etc., and applying machine learning algorithms. In the first experiment, we construct three models that use different features to predict which team won a given game, without any knowledge of goals. We achieve a top accuracy rate of 0.84 using an L2-regularized logistic regression classifier. We also investigate the feature weights to learn relationships between game events and a team’s chances of success. In the second experiment we try several classifiers to predict which team produced the sequence of ball-events that occurred during a game. Despite the relatively small number of events per game, we achieved an accuracy rate of 0.345 for a 20-team classification task when using a RBF SVM. By learning which sequences are characteristic of teams we are potentially able to discover if successful teams have a common style. We also learn the efficacy of transforming ball-events into predefined symbols. Finally, in the third experiment, we predict which team attempted a given set of passes. We first construct 2D histograms of the locations of the origins of the passes. We then use the histograms as features in a 20-team classification task and discover that teams have characteristic passing styles, by achieving an accuracy rate of 0.735 using a learned K-NN classifier. The results demonstrate that approaching soccer analytics with a machine learning framework is effective. In addition to achieving good classification performance, we are able to discover useful, potentially actionable, knowledge by investigating the models and features that we construct. Thesis Supervisor: John V. Guttag Title: Dugald C. Jackson Professor, Electrical Engineering and Computer Science


title: Sustainable water usage systems: models for the sustainable utilization of domestic water in urban areas

abstract: Present water usage of our society cannot be considered sustainable; too much water of high quality is taken from the eco-systems and to much polluted water is discharged. Part of this problem is due to domestic water usage. In the past decade a number of technical measures on appliance level have been developed to reduce domestic water consumption and the efficiency of chemicals used has been increased to enable lower dosages. But further reductions are needed. Reuse and/or recycling water within the domestic system could, in theory, lead to further reductions. Models for integration and cascading of domestic water flows that have been developed are discussed. It is shown that water usage systems with various levels of integration and cascading can lead to significant reductions in domestic water consumption and thus lower the environmental impact of domestic water usage.


title: A three-component scattering model for polarimetric SAR data

abstract: An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory's (JPLs) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.


title: Integration of independent component analysis and neural networks for ECG beat classification

abstract: In this paper, we propose a scheme to integrate independent component analysis (ICA) and neural networks for electrocardiogram (ECG) beat classification. The ICA is used to decompose ECG signals into weighted sum of basic components that are statistically mutual independent. The projections on these components, together with the RR interval, then constitute a feature vector for the following classifier. Two neural networks, including a probabilistic neural network (PNN) and a back-propagation neural network (BPNN), are employed as classifiers. ECG samples attributing to eight different beat types were sampled from the MIT-BIH arrhythmia database for experiments. The results show high classification accuracy of over 98% with either of the two classifiers. Between them, the PNN shows a slightly better performance than BPNN in terms of accuracy and robustness to the number of ICA-bases. The impressive results prove that the integration of independent component analysis and neural networks, especially PNN, is a promising scheme for the computer-aided diagnosis of heart diseases based on ECG.


title: Pulmonary nodule classification with deep residual networks

abstract: PURPOSE : Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules. ::: ::: ::: METHODS ::: We evaluate the effectiveness of very deep convolutional neural networks at the task of expert-level lung nodule malignancy classification. Using the state-of-the-art ResNet architecture as our basis, we explore the effect of curriculum learning, transfer learning, and varying network depth on the accuracy of malignancy classification. ::: ::: ::: RESULTS ::: Due to a lack of public datasets with standardized problem definitions and train/test splits, studies in this area tend to not compare directly against other existing work. This makes it hard to know the relative improvement in the new solution. In contrast, we directly compare our system against two state-of-the-art deep learning systems for nodule classification on the LIDC/IDRI dataset using the same experimental setup and data set. The results show that our system achieves the highest performance in terms of all metrics measured including sensitivity, specificity, precision, AUROC, and accuracy. ::: ::: ::: CONCLUSIONS ::: The proposed method of combining deep residual learning, curriculum learning, and transfer learning translates to high nodule classification accuracy. This reveals a promising new direction for effective pulmonary nodule CAD systems that mirrors the success of recent deep learning advances in other image-based application domains.


title: Asymmetrical Voltage-Cancellation control for LCL resonant converters in Inductive Power Transfer systems

abstract: This paper presents the first conceptual description of a fixed frequency soft switched LCL load-resonant converter for Inductive Power Transfer (IPT) systems. By using Asymmetric Voltage Cancellation (AVC) control to avoid diode reverse recovery loss (DRRL), ZVS operation over a wide load range becomes possible. The circuit was solved using a time domain analysis method and the optimal control trajectory was determined using a greedy salesperson algorithm. Experiments on a 60W prototype were carried out to validate the regions for which DRRL is avoided and were used to compare the improved performance against previous fixed frequency control strategies. It has been shown that the efficiency can be improved up to 5% and the THD can be reduced up to a factor of 7 compared to phase shift and Asymmetric Duty Cycle control, respectively.


title: Blinkering Surveillance: Enabling Video Privacy through Computer Vision

abstract: In this paper we describe a technology for protecting privacy in video systems. The paper presents a review of privacy in video surveillance and describes how a computer vision approach to understanding the video can be used to represent “just enough” of the information contained in a video stream to allow video-based tasks (including both surveillance and other “person aware” applications) to be accomplished, while hiding superfluous details, particularly identity, that can contain privacyintrusive information. The technology has been implemented in the form of a privacy console that manages operator access to different versions of the video-derived data according to access control lists. We have also built PrivacyCam—a smart camera that produces a video stream with the privacy-intrusive information already removed.


title: Adaptive and Scalable Optimizations for High Performance SR-IOV

abstract: High performance networking interfaces, such as 10-Gigabit Ethernet (10GE), are now widely deployed in commercial Cloud computing environments. Virtualization is a standard technique for these environments, one of whose key challenges is to achieve highly efficient and scalable I/O virtualization. Single Root I/O Virtualization (SR-IOV) eliminates the overhead of redundant data copies and the virtual network switch through direct I/O, but needs more work on performance and scalability. In this paper, we first study the defects of SR-IOV with 10GE networking and find two major challenges. Due to multiplexing of traffic from different virtual machines, SR-IOV may generate redundant interrupts unexpectedly and thus result in high CPU overhead. SR-IOV also suffers from single-threaded NAPI which prevents it from fully utilizing multi-core machines. Then we propose two optimizations for enhancing the SR-IOV performance. The first uses adaptive interrupt rate control (AIRC) to reduce CPU overhead caused by excessive interrupts. The second is a multi-threaded network driver (MTND) which allows SR-IOV to make full use of multi-core resources. We implement these optimizations and carry out a detailed performance evaluation. The results show that AIRC can reduce CPU overhead by up to 143% and MTND can improve SR-IOV performance by up to 38%.


title: CPU ray tracing large particle data with balanced P-k-d trees

abstract: We present a novel approach to rendering large particle data sets from molecular dynamics, astrophysics and other sources. We employ a new data structure adapted from the original balanced k-d tree, which allows for representation of data with trivial or no overhead. In the OSPRay visualization framework, we have developed an efficient CPU algorithm for traversing, classifying and ray tracing these data. Our approach is able to render up to billions of particles on a typical workstation, purely on the CPU, without any approximations or level-of-detail techniques, and optionally with attribute-based color mapping, dynamic range query, and advanced lighting models such as ambient occlusion and path tracing.


title: A Survey on Real-World Implementations of Mobile Ad-Hoc Networks

abstract: Simulation and emulation are valuable techniques for the evaluation of algorithms and protocols used in mobile ad-hoc networks. However, these techniques always require the simplification of real-world properties such as radio characteristics or node mobility. It has been shown that this may lead to results and conclusions which do not reflect the behavior of ad-hoc networks in the real world. Various prototype implementations demonstrate that even simple protocols such as flooding do not behave as it was predicted by earlier simulation. To overcome this problem, real-world experiments are required. In this paper, we present a survey on existing real-world implementations of mobile ad-hoc networks. We report on the technology used for the implementations as well as on key findings from experiments conducted with these implementations.


title: Modeling the effects of palm-house proximity on the theoretical risk of Chagas disease transmission in a rural locality of the Orinoco basin, Colombia

abstract: BackgroundChagas disease is a major public health concern in Latin America and it is transmitted by insects of the subfamily Triatominae, including Rhodnius spp. Since palm trees are ubiquitous in Colombia and a habitat for Rhodnius spp., the presence of palms near villages could increase contact rates between vectors and humans. Therefore, knowing whether a relationship exists between the proximity of palms to villages and the abundance and distribution of vectors therein, may be critical for Chagas disease prevention programs. Adapting a mathematical model for R. prolixus population dynamics in a small village, we model the implications of changing distances between palms and dwellings, to the risk of Chagas disease infection.MethodsWe implemented a mathematical model that reflects R. prolixus population dynamics in a small village located in the department of Casanare (Colombia) to study the role of palm-house proximity. We varied the distance between palms and houses by monitoring the network global efficiency metric. We constructed 1,000 hypothetical villages varying distances and each one was run 100 times.ResultsAccording to the model, as palm-house proximity increases, houses were more likely to be visited by triatomine bugs. The number of bugs per unit time increased progressively in a non-linear fashion with high variability. We stress the importance of village configuration on the model output.ConclusionsFrom a theoretical perspective, palm-house proximity may have a positive effect on the incidence of Chagas disease. The model predicts a 1% increase in new human cases per year when houses and palms are brought closer by 75%.