OpenRaiser/Intern-Atlas huggingface dataset:
Load Curtailment Estimation in Response to Extreme Events
Venue: arXiv cs.SY 2018
Year: 2018

A machine learning model is proposed in this paper to help estimate potential nodal load curtailment in response to an extreme event. This is performed through identifying which grid components will fail as a result of an extreme event, and consequently, which parts of the power system will encounter a supply interruption. The proposed model to predict component outages is based on a Support Vector Machine (SVM) model. This model considers the category and the path of historical hurricanes, as the selected extreme event in this paper, and accordingly trains the SVM. Once trained, the model is capable of classifying the grid components into two categories of outage and operational in response to imminent hurricanes. The obtained component outages are then integrated into a load curtailment minimization model to estimate the nodal load curtailments. The merits and the effectiveness of the proposed models are demonstrated using the standard IEEE 30-bus system based on various hurricane path/intensity scenarios.
A Unified View of Causal and Non-causal Feature Selection
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we are able to interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-word data.
Generalizing Bottleneck Problems
Venue: arXiv cs.IT 2018
Year: 2018

Given a pair of random variables $(X,Y)\sim P_{XY}$ and two convex functions $f_1$ and $f_2$, we introduce two bottleneck functionals as the lower and upper boundaries of the two-dimensional convex set that consists of the pairs $\left(I_{f_1}(W; X), I_{f_2}(W; Y)\right)$, where $I_f$ denotes $f$-information and $W$ varies over the set of all discrete random variables satisfying the Markov condition $W \to X \to Y$. Applying Witsenhausen and Wyner's approach, we provide an algorithm for computing boundaries of this set for $f_1$, $f_2$, and discrete $P_{XY}$. In the binary symmetric case, we fully characterize the set when (i) $f_1(t)=f_2(t)=t\log t$, (ii) $f_1(t)=f_2(t)=t^2-1$, and (iii) $f_1$ and $f_2$ are both $\ell^β$ norm function for $β\geq 2$. We then argue that upper and lower boundaries in (i) correspond to Mrs. Gerber's Lemma and its inverse (which we call Mr. Gerber's Lemma), in (ii) correspond to estimation-theoretic variants of Information Bottleneck and Privacy Funnel, and in (iii) correspond to Arimoto Information Bottleneck and Privacy Funnel.
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
Venue: arXiv cs.LG 2018
Year: 2018

Estimating causal models from observational data is a crucial task in data analysis. For continuous-valued data, Shimizu et al. have proposed a linear acyclic non-Gaussian model to understand the data generating process, and have shown that their model is identifiable when the number of data is sufficiently large. However, situations in which continuous and discrete variables coexist in the same problem are common in practice. Most existing causal discovery methods either ignore the discrete data and apply a continuous-valued algorithm or discretize all the continuous data and then apply a discrete Bayesian network approach. These methods possibly loss important information when we ignore discrete data or introduce the approximation error due to discretization. In this paper, we define a novel hybrid causal model which consists of both continuous and discrete variables. The model assumes: (1) the value of a continuous variable is a linear function of its parent variables plus a non-Gaussian noise, and (2) each discrete variable is a logistic variable whose distribution parameters depend on the values of its parent variables. In addition, we derive the BIC scoring function for model selection. The new discovery algorithm can learn causal structures from mixed continuous and discrete data without discretization. We empirically demonstrate the power of our method through thorough simulations.
Training Deep Face Recognition Systems with Synthetic Data
Venue: arXiv cs.CV 2018
Year: 2018

Recent advances in deep learning have significantly increased the performance of face recognition systems. The performance and reliability of these models depend heavily on the amount and quality of the training data. However, the collection of annotated large datasets does not scale well and the control over the quality of the data decreases with the size of the dataset. In this work, we explore how synthetically generated data can be used to decrease the number of real-world images needed for training deep face recognition systems. In particular, we make use of a 3D morphable face model for the generation of images with arbitrary amounts of facial identities and with full control over image variations, such as pose, illumination, and background. In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly. 2) Combining large-scale real-world data with synthetic data leads to an increased performance. 3) Models trained only on synthetic data with strong variations in pose, illumination, and background perform very well across different datasets even without dataset adaptation. 4) The real-to-virtual performance gap can be closed when using synthetic data for pre-training, followed by fine-tuning with real-world images. 5) There are no observable negative effects of pre-training with synthetic data. Thus, any face recognition system in our experiments benefits from using synthetic face images. The synthetic data generator, as well as all experiments, are publicly available.
Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Web Personalisation
Venue: arXiv cs.HC 2018
Year: 2018

In this paper we consider the neuroscientific theory of the Bayesian brain in the light of adaptive web systems and content personalisation. In particular, we elaborate on neural mechanisms of human decision-making and the origin of lacking reliability of user feedback, often denoted as noise or human uncertainty. To this end, we first introduce an adaptive model of cognitive agency in which populations of neurons provide an estimation for states of the world. Subsequently, we present various so-called decoder functions with which neuronal activity can be translated into quantitative decisions. The interplay of the underlying cognition model and the chosen decoder function leads to different model-based properties of decision processes. The goal of this paper is to promote novel user models and exploit them to naturally associate users to different clusters on the basis of their individual neural characteristics and thinking patterns. These user models might be able to turn the variability of user behaviour into additional information for improving web personalisation and its experience.
PRoST: Distributed Execution of SPARQL Queries Using Mixed Partitioning Strategies
Venue: arXiv cs.DB 2018
Year: 2018

The rapidly growing size of RDF graphs in recent years necessitates distributed storage and parallel processing strategies. To obtain efficient query processing using computer clusters a wide variety of different approaches have been proposed. Related to the approach presented in the current paper are systems built on top of Hadoop HDFS, for example using Apache Accumulo or using Apache Spark. We present a new RDF store called PRoST (Partitioned RDF on Spark Tables) based on Apache Spark. PRoST introduces an innovative strategy that combines the Vertical Partitioning approach with the Property Table, two preexisting models for storing RDF datasets. We demonstrate that our proposal outperforms state-of-the-art systems w.r.t. the runtime for a wide range of query types and without any extensive precomputing phase.
Recognizing Cuneiform Signs Using Graph Based Methods
Venue: arXiv cs.CV 2018
Year: 2018

The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already been discovered and are incrementally digitalized and made available to automated processing. As reading cuneiform script is still a manual task, we address the real-world application of recognizing cuneiform signs by two graph based methods with complementary runtime characteristics. We present a graph model for cuneiform signs together with a tailored distance measure based on the concept of the graph edit distance. We propose efficient heuristics for its computation and demonstrate its effectiveness in classification tasks experimentally. To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size. In order to overcome this issue, we propose to use CNNs adapted to graphs as an alternative approach shifting the computational cost to the training phase. We demonstrate the practicability of both approaches in an extensive experimental comparison regarding runtime and prediction accuracy. Although currently available annotated real-world data is still limited, we obtain a high accuracy using CNNs, in particular, when the training set is enriched by augmented examples.
Compositional Verification of Compiler Optimisations on Relaxed Memory
Venue: arXiv cs.PL 2018
Year: 2018

A valid compiler optimisation transforms a block in a program without introducing new observable behaviours to the program as a whole. Deciding which optimisations are valid can be difficult, and depends closely on the semantic model of the programming language. Axiomatic relaxed models, such as C++11, present particular challenges for determining validity, because such models allow subtle effects of a block transformation to be observed by the rest of the program. In this paper we present a denotational theory that captures optimisation validity on an axiomatic model corresponding to a fragment of C++11. Our theory allows verifying an optimisation compositionally, by considering only the block it transforms instead of the whole program. Using this property, we realise the theory in the first push-button tool that can verify real-world optimisations under an axiomatic memory model.
Analysis of Schema.org Usage in the Tourism Domain
Venue: arXiv cs.IR 2018
Year: 2018

Schema.org is an initiative founded in 2011 by the four-big search engine Bing, Google, Yahoo!, and Yandex. The goal of the initiative is to publish and maintain the schema.org vocabulary, in order to facilitate the publication of structured data on the web which can enable the implementation of automated agents like intelligent personal assistants and chatbots. In this paper, the usage of schema.org in tourism domain between years 2013 and 2016 is analysed. The analysis shows the adoption of schema.org, which indicates how well the tourism sector is prepared for the web that targets automated agents. The results have shown that the adoption of schema.org type and properties is grown over the years. While the US is dominating the annotation numbers, a drastic drop is observed for the proportion of the US in 2016. Poorly rated businesses are encountered more in 2016 results in comparison to previous years.
Paxos Consensus, Deconstructed and Abstracted (Extended Version)
Venue: arXiv cs.DC 2018
Year: 2018

Lamport's Paxos algorithm is a classic consensus protocol for state machine replication in environments that admit crash failures. Many versions of Paxos exploit the protocol's intrinsic properties for the sake of gaining better run-time performance, thus widening the gap between the original description of the algorithm, which was proven correct, and its real-world implementations. In this work, we address the challenge of specifying and verifying complex Paxos-based systems by (a) devising composable specifications for implementations of Paxos's single-decree version, and (b) engineering disciplines to reason about protocol-aware, semantics-preserving optimisations to single-decree Paxos. In a nutshell, our approach elaborates on the deconstruction of single-decree Paxos by Boichat et al. We provide novel non-deterministic specifications for each module in the deconstruction and prove that the implementations refine the corresponding specifications, such that the proofs of the modules that remain unchanged can be reused across different implementations. We further reuse this result and show how to obtain a verified implementation of Multi-Paxos from a verified implementation of single-decree Paxos, by a series of novel protocol-aware transformations of the network semantics, which we prove to be behaviour-preserving.
Information Rates and Error Exponents for Probabilistic Amplitude Shaping
Venue: arXiv cs.IT 2018
Year: 2018

Probabilistic Amplitude Shaping (PAS) is a coded-modulation scheme in which the encoder is a concatenation of a distribution matcher with a systematic Forward Error Correction (FEC) code. For reduced computational complexity the decoder can be chosen as a concatenation of a mismatched FEC decoder and dematcher. This work studies the theoretic limits of PAS. The classical joint source-channel coding (JSCC) setup is modified to include systematic FEC and the mismatched FEC decoder. At each step error exponents and achievable rates for the corresponding setup are derived.
Optimal Hybrid Full-Duplex/Half-Duplex Scheme for Buffer Aided Relay Systems
Venue: arXiv cs.IT 2018
Year: 2018

Full-duplex (FD) communication has received great interest in recent years due to the potential of doubling the spectral efficiency. However, how to alleviate the detrimental effects of the residual self-interference (RSI) incurred by the FD mode is still a challenging problem. In this paper, focusing on the statistical throughput maximization, we propose an optimal hybrid FD/half-duplex (HD) scheme for the one-way FD buffer aided relay system. To solve this problem, we divide the system into four different transmission modes and formulate the problem as a binary integer programming problem. By relaxing the binary variables to be continuous ones, we solve the problem using the Karush-Kuhn-Tucker (KKT) optimal conditions. We obtain the selection probability of each mode based on the instantaneous channel outage states. The proposed scheme not only achieves the optimal FD or HD mode selection, but also realizes adaptive source-to-relay or relay-to-destination link selection. Simulation results show that the proposed scheme offers 95% maximum gain over the HD counterparts.
Towards a Continuous Knowledge Learning Engine for Chatbots
Venue: arXiv cs.CL 2018
Year: 2018

Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they cannot learn new knowledge during the conversation process, i.e., their knowledge is fixed beforehand and cannot be expanded or updated during conversation. In this paper, we propose to build a general knowledge learning engine for chatbots to enable them to continuously and interactively learn new knowledge during conversations. As time goes by, they become more and more knowledgeable and better and better at learning and conversation. We model the task as an open-world knowledge base completion problem and propose a novel technique called lifelong interactive learning and inference (LiLi) to solve it. LiLi works by imitating how humans acquire knowledge and perform inference during an interactive conversation. Our experimental results show LiLi is highly promising.
SkyLiTE: End-to-End Design of Low-Altitude UAV Networks for Providing LTE Connectivity
Venue: arXiv cs.NI 2018
Year: 2018

Un-manned aerial vehicle (UAVs) have the potential to change the landscape of wide-area wireless connectivity by bringing them to areas where connectivity was sparing or non-existent (e.g. rural areas) or has been compromised due to disasters. While Google's Project Loon and Facebook's Project Aquila are examples of high-altitude, long-endurance UAV-based connectivity efforts in this direction, the telecom operators (e.g. AT&T and Verizon) have been exploring low-altitude UAV-based LTE solutions for on-demand deployments. Understandably, these projects are in their early stages and face formidable challenges in their realization and deployment. The goal of this document is to expose the reader to both the challenges as well as the potential offered by these unconventional connectivity solutions. We aim to explore the end-to-end design of such UAV-based connectivity networks particularly in the context of low-altitude UAV networks providing LTE connectivity. Specifically, we aim to highlight the challenges that span across multiple layers (access, core network, and backhaul) in an inter-twined manner as well as the richness and complexity of the design space itself. To help interested readers navigate this complex design space towards a solution, we also articulate the overview of one such end-to-end design, namely SkyLiTE-- a self-organizing network of low-altitude UAVs that provide optimized LTE connectivity in a desired region.
Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
Venue: arXiv cs.CV 2018
Year: 2018

Major winning Convolutional Neural Networks (CNNs), such as VGGNet, ResNet, DenseNet, \etc, include tens to hundreds of millions of parameters, which impose considerable computation and memory overheads. This limits their practical usage in training and optimizing for real-world applications. On the contrary, light-weight architectures, such as SqueezeNet, are being proposed to address this issue. However, they mainly suffer from low accuracy, as they have compromised between the processing power and efficiency. These inefficiencies mostly stem from following an ad-hoc designing procedure. In this work, we discuss and propose several crucial design principles for an efficient architecture design and elaborate intuitions concerning different aspects of the design procedure. Furthermore, we introduce a new layer called {\it SAF-pooling} to improve the generalization power of the network while keeping it simple by choosing best features. Based on such principles, we propose a simple architecture called {\it SimpNet}. We empirically show that SimpNet provides a good trade-off between the computation/memory efficiency and the accuracy solely based on these primitive but crucial principles. SimpNet outperforms the deeper and more complex architectures such as VGGNet, ResNet, WideResidualNet \etc, on several well-known benchmarks, while having 2 to 25 times fewer number of parameters and operations. We obtain state-of-the-art results (in terms of a balance between the accuracy and the number of involved parameters) on standard datasets, such as CIFAR10, CIFAR100, MNIST and SVHN. The implementations are available at \href{url}{https://github.com/Coderx7/SimpNet}.
A New De-blurring Technique for License Plate Images with Robust Length Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Recognizing a license plate clearly while seeing a surveillance camera snapshot is often important in cases where the troublemaker vehicle(s) have to be identified. In many real world situations, these images are blurred due to fast motion of the vehicle and cannot be recognized by the human eye. For this kind of blurring, the kernel involved can be said to be a linear uniform convolution described by its angle and length. We propose a new de-blurring technique in this paper to parametrically estimate the kernel as accurately as possible with emphasis on the length estimation process. We use a technique which employs Hough transform in estimating the kernel angle. To accurately estimate the kernel length, a novel approach using the cepstral transform is introduced. We compare the de-blurred results obtained using our scheme with those of other recently introduced blind de-blurring techniques. The comparisons corroborate that our scheme can remove a large blur from the image captured by the camera to recover vital semantic information about the license plate.
Greening Geographical Power Allocation for Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

Harvesting energy from nature (solar, wind etc.) is envisioned as a key enabler for realizing green wireless networks. However, green energy sources are geographically distributed and the power amount is random which may not enough to power a base station by a single energy site. Burning brown energy sources such as coal and crude oil, though companied with carbon dioxide emission, provides stable power. In this paper, without sacrificing communication quality, we investigate how to perform green energy allocation to abate the dependence on brown energy with hybrid brown and green energy injected in power networks. We present a comprehensive framework to characterize the performance of hybrid green and brown energy empowered cellular network. Novel performance metric "bits/ton\ce{CO2}/Hz" is proposed to evaluate the greenness of the communication network. As green energy is usually generated from distributed geographical locations and is time varying, online geographical power allocation algorithm is proposed to maximize the greenness of communication network considering electricity transmission's physical laws i.e., Ohm's law and Kirchhoff's circuit laws. Simulations show that geographically distributed green energy sources complement each other by improving the communication capacity while saving brown energy consumption. Besides, the penetration of green energy can also help reduce power loss on the transmission breaches.