OpenRaiser/Intern-Atlas huggingface dataset:
Mode-Division Multiplexing for Silicon Photonic Network-on-chip
Venue: arXiv cs.ET 2017
Year: 2017

Optical interconnect is a potential solution to attain the large bandwidth on-chip communications needed in high performance computers in a low power and low cost manner. Mode-division multiplexing (MDM) is an emerging technology that scales the capacity of a single wavelength carrier by the number of modes in a multimode waveguide, and is attractive as a cost-effective means for high bandwidth density on-chip communications. Advanced modulation formats with high spectral efficiency in MDM networks can further improve the data rates of the optical link. Here, we demonstrate an intra-chip MDM communications link employing advanced modulation formats with two waveguide modes. We demonstrate a compact single wavelength carrier link that is expected to support 2x100 Gb/s mode multiplexed capacity. The network comprised integrated microring modulators at the transmitter, mode multiplexers, multimode waveguide interconnect, mode demultiplexers and integrated germanium on silicon photodetectors. Each of the mode channels achieves 100 Gb/s line rate with 84 Gb/s net payload data rate at 7% overhead for hard-decision forward error correction (HD-FEC) in the OFDM/16-QAM signal transmission.
Mining Electronic Health Records: A Survey
Venue: arXiv cs.IR 2017
Year: 2017

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research. We conclude this survey with a comprehensive summary of clinical data mining applications of EHR data, as illustrated in the online supplement.
A Novel Hybrid Approach Using SMS and ROCOF for Islanding Detection of Inverter-Based DGs
Venue: arXiv cs.SY 2017
Year: 2017

This paper proposes a novel hybrid approach for islanding detection of inverter-based distributed generations (DG) based on combination of the Slip mode frequency-shift (SMS) as an active and rate of change of frequency (ROCOF) relay and over/under frequency relay as passive methods. This approach is utilized to force the DG to lose its stable operation and drift the frequency out of the allowed range of the frequency threshold. Performance of the proposed approach is evaluated under the IEEE 1547, UL 1741 and multiple-DG operation. The simulation results demonstrate the effectiveness of the proposed approach for detection of islanding, especially for loads with high quality factor. It operates accurately under the condition of load switching and does not interfere with the power system operation during normal condition. In other words, not only it holds the benefits of both SMS and ROCOF, but also it removes their drawbacks by having less non-detection zone and faster response.
A More Sensitive Context
Venue: arXiv cs.LO 2017
Year: 2017

Logic of Behaviour in Context (LBC) is a spatio-temporal logic for expressing properties of continuous-state processes, such as biochemical reaction networks. LBC builds on the existing Metric Interval Temporal Logic (MITL) and adds a "context modality" that explores the behaviour of a system when composed with an external process. LBC models are terms of the Continuous π-Calculus (cπ), a process algebra with continuous state space. Our previously published LBC model-checking technique required examining many points along the behavioural trajectory of a process; and potentially computing further trajectories branching off at every such point. This raised two difficulties: mixing temporal and spatial modalities could require computing a large number of trajectories, with costly numerical solution of differential equations; and might still fail to check intermediate values between discrete points on those trajectories. In this paper we make progress against both of these problems using techniques from signal temporal logic and from sensitivity analysis. Boolean signals aggressively compress trace information, allowing more efficient computation; and sensitivity analysis lets us reliably check formulae over a region by calculating a smaller number of sample trajectories.
A Higher-Order Abstract Syntax Approach to the Verified Compilation of Functional Programs
Venue: arXiv cs.PL 2017
Year: 2017

We argue that the implementation and verification of compilers for functional programming languages are greatly simplified by employing a higher-order representation of syntax known as Higher-Order Abstract Syntax or HOAS. The underlying idea of HOAS is to use a meta-language that provides a built-in and logical treatment of binding related notions. By embedding the meta-language within a larger programming or reasoning framework, it is possible to absorb the treatment of binding structure in the object language into the meta-theory of the system, thereby greatly simplifying the overall implementation and reasoning processes. We develop the above argument in this thesis by presenting and demonstrating the effectiveness of an approach to the verified implementation of compiler transformations for functional programs that exploits HOAS. In this approach, transformations on functional programs are first articulated in the form of rule-based relational specifications. These specifications are rendered into programs in the language lambda Prolog. On the one hand, these programs serve directly as implementations. On the other hand, they can be used as input to the Abella system which allows us to prove properties about them and thereby about the implementations. Both lambda Prolog and Abella support the use of the HOAS approach. Thus, they constitute a framework that can be used to test out the benefits of the HOAS approach in verified compilation. We use them to implement and verify a compiler for a representative functional programming language that embodies the transformations that form the core of many compilers for such languages. In both the programming and the reasoning phases, we show how the use of the HOAS approach significantly simplifies the representation, manipulation, analysis and reasoning of binding structure.
K-Dominant Skyline Join Queries: Extending the Join Paradigm to K-Dominant Skylines
Venue: arXiv cs.DB 2017
Year: 2017

Skyline queries enable multi-criteria optimization by filtering objects that are worse in all the attributes of interest than another object. To handle the large answer set of skyline queries in high-dimensional datasets, the concept of k-dominance was proposed where an object is said to dominate another object if it is better (or equal) in at least k attributes. This relaxes the full domination criterion of normal skyline queries and, therefore, produces lesser number of skyline objects. This is called the k-dominant skyline set. Many practical applications, however, require that the preferences are applied on a joined relation. Common examples include flights having one or multiple stops, a combination of product price and shipping costs, etc. In this paper, we extend the k-dominant skyline queries to the join paradigm by enabling such queries to be asked on joined relations. We call such queries KSJQ (k-dominant skyline join queries). The number of skyline attributes, k, that an object must dominate is from the combined set of skyline attributes of the joined relation. We show how pre-processing the base relations helps in reducing the time of answering such queries over the naive method of joining the relations first and then running the k-dominant skyline computation. We also extend the query to handle cases where the skyline preference is on aggregated values in the joined relation (such as total cost of the multiple legs of the flight) which are available only after the join is performed. In addition to these problems, we devise efficient algorithms to choose the value of k based on the desired cardinality of the final skyline set. Experiments on both real and synthetic datasets demonstrate the efficiency, scalability and practicality of our algorithms.
Group Scissor: Scaling Neuromorphic Computing Design to Large Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

Synapse crossbar is an elementary structure in Neuromorphic Computing Systems (NCS). However, the limited size of crossbars and heavy routing congestion impedes the NCS implementations of big neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to big neural networks. The first step is rank clipping, which integrates low-rank approximation into the training to reduce total crossbar area. The second step is group connection deletion, which structurally prunes connections to reduce routing congestion between crossbars. Tested on convolutional neural networks of LeNet on MNIST database and ConvNet on CIFAR-10 database, our experiments show significant reduction of crossbar area and routing area in NCS designs. Without accuracy loss, rank clipping reduces total crossbar area to 13.62\% and 51.81\% in the NCS designs of LeNet and ConvNet, respectively. Following rank clipping, group connection deletion further reduces the routing area of LeNet and ConvNet to 8.1\% and 52.06\%, respectively.
Distributed Averaging With Random Network Graphs and Noises
Venue: arXiv cs.SI 2017
Year: 2017

We consider discrete-time distributed averaging algorithms over multi-agent networks with measurement noises and time-varying random graph flows. Each agent updates its state by relative states between neighbours with both additive and multiplicative measurement noises. The network structure is modeled by time-varying random digraphs, which may be spatially and temporally dependent. By developing difference inequalities of proper stochastic Lyapunov function, the algebraic graph theory and martingale convergence theory, we obtain sufficient conditions for stochastic approximation type algorithms to achieve mean square and almost sure average consensus. We prove that all states of agents converge to a common variable in mean square and almost surely if the graph flow is conditionally balanced and uniformly conditionally jointly connected. The mathematical expectation of the common variable is right the average of initial values, and the upper bound of the mean square steady-state error is given quantitatively related to the weights, the algorithm gain and the energy level of the noises.
Novel Baseband Equivalent Models of Quadrature Modulated All-Digital Transmitters
Venue: arXiv cs.SY 2017
Year: 2017

In this paper an exact baseband equivalent model of a quadrature modulated all-digital transmitter is derived. No restrictions on the number of levels of a digital switched-mode power amplifier (SMPA) driving input, nor the pulse encoding scheme employed, are made. This implies a high level of generality of the proposed model. We show that all-digital transmitter (ADT) can be represented as a series connection of the pulse encoder, discrete-time Volterra series model of fixed degree and memory depth, and a linear time-varying system with special properties. This result suggests a new analytically motivated structure of a digital predistortion (DPD) of SMPA nonlinearities in ADT. Numerical simulations in MATLAB are used to verify proposed baseband equivalent model.
On the Energy/Distortion Tradeoff in the IoT
Venue: arXiv cs.IT 2017
Year: 2017

The Internet of Things paradigm envisages the presence of many battery-powered sensors and this entails the design of energy-aware protocols. Source coding techniques allow to save some energy by compressing the packets sent over the network, but at the cost of a poorer accuracy in the representation of the data. This paper addresses the problem of designing efficient policies to jointly perform processing and transmission tasks. In particular, we aim at defining an optimal scheduling strategy with the twofold ultimate goal of extending the network lifetime and guaranteeing a low overall distortion of the transmitted data. We propose a Time Division Multiple Access (TDMA)-based access scheme that optimally allocates resources to heterogeneous nodes. We use realistic rate-distortion curves to quantify the impact of compression on the data quality and propose a complete energy model that includes the energy spent for processing and transmitting the data, as well as the circuitry costs. Both full knowledge and statistical knowledge of the wireless channels are considered, and optimal policies are derived for both cases. The overall problem is structured in a modular fashion and solved through convex and alternate programming techniques. Finally, we thoroughly evaluate the proposed algorithms and the influence of the design variables on the system performance adopting parameters of real sensors.
Multitask Learning with Deep Neural Networks for Community Question Answering
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we developed a deep neural network (DNN) that learns to solve simultaneously the three tasks of the cQA challenge proposed by the SemEval-2016 Task 3, i.e., question-comment similarity, question-question similarity and new question-comment similarity. The latter is the main task, which can exploit the previous two for achieving better results. Our DNN is trained jointly on all the three cQA tasks and learns to encode questions and comments into a single vector representation shared across the multiple tasks. The results on the official challenge test set show that our approach produces higher accuracy and faster convergence rates than the individual neural networks. Additionally, our method, which does not use any manual feature engineering, approaches the state of the art established with methods that make heavy use of it.
Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination
Venue: arXiv cs.NE 2017
Year: 2017

The MAP-Elites algorithm produces a set of high-performing solutions that vary according to features defined by the user. This technique has the potential to be a powerful tool for design space exploration, but is limited by the need for numerous evaluations. The Surrogate-Assisted Illumination algorithm (SAIL), introduced here, integrates approximative models and intelligent sampling of the objective function to minimize the number of evaluations required by MAP-Elites. The ability of SAIL to efficiently produce both accurate models and diverse high performing solutions is illustrated on a 2D airfoil design problem. The search space is divided into bins, each holding a design with a different combination of features. In each bin SAIL produces a better performing solution than MAP-Elites, and requires several orders of magnitude fewer evaluations. The CMA-ES algorithm was used to produce an optimal design in each bin: with the same number of evaluations required by CMA-ES to find a near-optimal solution in a single bin, SAIL finds solutions of similar quality in every bin.
A Study on the Impact of Locality in the Decoding of Binary Cyclic Codes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the impact of locality on the decoding of binary cyclic codes under two approaches, namely ordered statistics decoding (OSD) and trellis decoding. Given a binary cyclic code having locality or availability, we suitably modify the OSD to obtain gains in terms of the Signal-To-Noise ratio, for a given reliability and essentially the same level of decoder complexity. With regard to trellis decoding, we show that careful introduction of locality results in the creation of cyclic subcodes having lower maximum state complexity. We also present a simple upper-bounding technique on the state complexity profile, based on the zeros of the code. Finally, it is shown how the decoding speed can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making use of a quick-look decoder that often returns the ML codeword.
Content-Based Video Retrieval in Historical Collections of the German Broadcasting Archive
Venue: arXiv cs.DL 2017
Year: 2017

The German Broadcasting Archive (DRA) maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material stimulates a large scientific interest in the video content. In this paper, we present an automatic video analysis and retrieval system for searching in historical collections of GDR television recordings. It consists of video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The performance of the system is evaluated from a technical and an archival perspective on 2,500 hours of GDR television recordings.
On the Transport Capability of LAN Cables in All-Analog MIMO-RoC Fronthaul
Venue: arXiv cs.IT 2017
Year: 2017

Centralized Radio Access Network (C-RAN) architecture is the only viable solution to handle the complex interference scenario generated by massive antennas and small cells deployment as required by next generation (5G) mobile networks. In conventional C-RAN, the fronthaul links used to exchange the signal between Base Band Units (BBUs) and Remote Antenna Units (RAUs) are based on digital baseband (BB) signals over optical fibers due to the huge bandwidth required. In this paper we evaluate the transport capability of copper-based all-analog fronthaul architecture called Radio over Copper (RoC) that leverages on the pre-existing LAN cables that are already deployed in buildings and enterprises. In particular, the main contribution of the paper is to evaluate the number of independent BB signals for multiple antennas system that can be transported over multi-pair Cat-5/6/7 cables under a predefined fronthauling transparency condition in terms of maximum BB signal degradation. The MIMO-RoC proves to be a complementary solution to optical fiber for the last 200m toward the RAUs, mostly to reuse the existing LAN cables and to power-supply the RAUs over the same cable.
How large is your graph?
Venue: arXiv cs.DS 2017
Year: 2017

We consider the problem of estimating the graph size, where one is given only local access to the graph. We formally define a query model in which one starts with a \emph{seed} node and is allowed to make queries about neighbours of nodes that have already been seen. In the case of undirected graphs, an estimator of Katzir et al. (2014) based on a sample from the stationary distribution $π$ uses $O\left(\frac{1}{\|π\|_2} + \text{davg}\right)$ queries, we prove that this is tight. In addition, we establish this as a lower bound even when the algorithm is allowed to crawl the graph arbitrarily, the results of Katzir et al. give an upper bound that is worse by a multiplicative factor $t_\text{mix} \cdot \log(n)$. The picture becomes significantly different in the case of directed graphs. We show that without strong assumptions on the graph structure, the number of nodes cannot be predicted to within a constant multiplicative factor without using a number of queries that are at least linear in the number of nodes, in particular, rapid mixing and small diameter, properties that most real-world networks exhibit, do not suffice. The question of interest is whether any algorithm can beat breadth-first search. We introduce a new parameter, generalising the well-studied conductance, such that if a suitable bound on it exists and is known to the algorithm, the number of queries required is sublinear in the number of edges, we show that this is tight.
Evolution-Preserving Dense Trajectory Descriptors
Venue: arXiv cs.CV 2017
Year: 2017

Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve state-of-the-art human action recognition results on a number of datasets. This paper improves their performance by applying rank pooling to each trajectory, encoding the temporal evolution of deep learning features computed along the trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a novel type of video descriptor that significantly outperforms Trajectory-pooled Deep-learning Descriptors. EPT descriptors are defined based on dense trajectories, and they provide complimentary benefits to video descriptors that are not based on trajectories. In particular, we show that the combination of EPT descriptors and VideoDarwin leads to state-of-the-art performance on Hollywood2 and UCF101 datasets.
Submodular Goal Value of Boolean Functions
Venue: arXiv cs.DM 2017
Year: 2017

Recently, Deshpande et al. introduced a new measure of the complexity of a Boolean function. We call this measure the "goal value" of the function. The goal value of $f$ is defined in terms of a monotone, submodular utility function associated with $f$. As shown by Deshpande et al., proving that a Boolean function $f$ has small goal value can lead to a good approximation algorithm for the Stochastic Boolean Function Evaluation problem for $f$. Also, if $f$ has small goal value, it indicates a close relationship between two other measures of the complexity of $f$, its average-case decision tree complexity and its average-case certificate complexity. In this paper, we explore the goal value measure in detail. We present bounds on the goal values of arbitrary and specific Boolean functions, and present results on properties of the measure. We compare the goal value measure to other, previously studied, measures of the complexity of Boolean functions. Finally, we discuss a number of open questions provoked by our work.
Crowd Capital in Governance Contexts
Venue: arXiv cs.CY 2017
Year: 2017

To begin to understand the implications of the implementation of IT-mediated Crowds for Politics and Policy purposes, this research builds the first-known dataset of IT-mediated Crowd applications currently in use in the governance context. Using Crowd Capital theory and governance theory as frameworks to organize our data collection, we undertake an exploratory data analysis of some fundamental factors defining this emerging field. Specific factors outlined and discussed include the type of actors implementing IT-mediated Crowds in the governance context, the global geographic distribution of the applications, and the nature of the Crowd-derived resources being generated for governance purposes. The findings from our dataset of 209 on-going endeavours indicates that a wide-diversity of actors are engaging IT-mediated Crowds in the governance context, both jointly and severally, that these endeavours can be found to exist on all continents, and that said actors are generating Crowd-derived resources in at least ten distinct governance sectors. We discuss the ramifications of these and our other findings in comparison to the research literature on the private-sector use of IT-mediated Crowds, while highlighting some unique future research opportunities stemming from our work.