OpenRaiser/Intern-Atlas huggingface dataset:
Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow
Venue: arXiv cs.CV 2018
Year: 2018

Fourier ptychography is a recently developed imaging approach for large field-of-view and high-resolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolution neural network (CNN) and recover the complex object information in the network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolution or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured low-resolution images in one forward / backward pass. We use a popular open-source machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fourier-magnitude projection using a multiplication neural network model. Since convolution and multiplication are the two most-common operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling single-pixel imaging and structured illumination microscopy (SIM). 4-frame resolution doubling is demonstrated using a neural network for SIM. We have made our implementation code open-source for the broad research community.
Cooperative Starting Movement Detection of Cyclists Using Convolutional Neural Networks and a Boosted Stacking Ensemble
Venue: arXiv cs.CV 2018
Year: 2018

In future, vehicles and other traffic participants will be interconnected and equipped with various types of sensors, allowing for cooperation on different levels, such as situation prediction or intention detection. In this article we present a cooperative approach for starting movement detection of cyclists using a boosted stacking ensemble approach realizing feature- and decision level cooperation. We introduce a novel method based on a 3D Convolutional Neural Network (CNN) to detect starting motions on image sequences by learning spatio-temporal features. The CNN is complemented by a smart device based starting movement detection originating from smart devices carried by the cyclist. Both model outputs are combined in a stacking ensemble approach using an extreme gradient boosting classifier resulting in a fast and yet robust cooperative starting movement detector. We evaluate our cooperative approach on real-world data originating from experiments with 49 test subjects consisting of 84 starting motions.
Green LTE Broadcasting
Venue: arXiv cs.NI 2018
Year: 2018

LTE broadcast is a communication standard which is expected to revolutionize the multimedia service provision in the future. The distinctive features of this standard such as increased service capacity, high spectral efficiency, and spectral adjustability have encouraged many companies to select this standard for the future generation broadcasting. In this paper, after a complete introduction of this standard and its unique features, its application in the Heterogeneous Networks (HetNets) is investigated. In this study, application of the LTE broadcast standard under a new scenario called Super cell is examined. Based on the results, the suggested scenario can be considered as one of the best LTE broadcast scenarios. The main aim of this scenario is to decrease the total energy consumption. Therefore, the study has demonstrated that this scenario can reduce the total energy consumption and offers a green servicing by taking advantage of the small cells capability and the D2D communication technology.
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings
Venue: arXiv cs.MA 2018
Year: 2018

Increasing energy efficiency in buildings can reduce costs and emissions substantially. Historically, this has been treated as a local, or single-agent, optimization problem. However, many buildings utilize the same types of thermal equipment e.g. electric heaters and hot water vessels. During operation, occupants in these buildings interact with the equipment differently thereby driving them to diverse regions in the state-space. Reinforcement learning agents can learn from these interactions, recorded as sensor data, to optimize the overall energy efficiency. However, if these agents operate individually at a household level, they can not exploit the replicated structure in the problem. In this paper, we demonstrate that this problem can indeed benefit from multi-agent collaboration by making use of targeted exploration of the state-space allowing for better generalization. We also investigate trade-offs between integrating human knowledge and additional sensors. Results show that savings of over 40% are possible with collaborative multi-agent systems making use of either expert knowledge or additional sensors with no loss of occupant comfort. We find that such multi-agent systems comfortably outperform comparable single agent systems.
Construction of neural networks for realization of localized deep learning
Venue: arXiv cs.LG 2018
Year: 2018

The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net) approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component also designed to eliminate outliers. The main theoretical result in this paper is the order $\mathcal O\left(m^{-2s/(2s+d)}\right)$ of approximation of the regression function with regularity $s$, in terms of the number $m$ of sample points, where the (unknown) manifold dimension $d$ replaces the dimension $D$ of the sampling (Euclidean) space for shallow nets.
A Nearly Optimal Algorithm for the Geodesic Voronoi Diagram in a Simple Polygon
Venue: arXiv cs.CG 2018
Year: 2018

The geodesic Voronoi diagram of m point sites inside a simple polygon of n vertices is a subdivision of the polygon into m cells, one to each site, such that all points in a cell share the same nearest site under the geodesic distance. The best known lower bound for the construction time is Omega( n + m log m ), and a matching upper bound is a long-standing open question. The state-of-the-art construction algorithms achieve O( (n+m) log (n+m) ) and O( n+m log m log^2 n ) time, which are optimal for m=Omega(n) and m=O( n / log^3 n ), respectively. In this paper, we give a construction algorithm with O( n+m( log m+ log^2 n) ) time, and it is nearly optimal in the sense that if a single Voronoi vertex can be computed in O( log n ) time, then the construction time will become the optimal O( n+m log m ). In other words, we reduce the problem of constructing the diagram in the optimal time to the problem of computing a single Voronoi vertex in O( log n ) time.
Review of Blockchain Technology and its Expectations: Case of the Energy Sector
Venue: arXiv cs.CY 2018
Year: 2018

This article suggests that the worldwide relevance of blockchain technology is motivated by the changes that it is expected to cause in: (i) the way that business is organised and (ii) regulated, as well as (iii) by the way that it changes the role of individuals within a society. The article presents an overview of the features of blockchain technology. It then takes a closer look into the developments within the energy sector across the world to gain a preliminary indication of whether the stated expectations are coming to reality. As a result of this review, we remain cautiously optimistic that blockchain technology could deliver the expected impact.
Divergence-Optimal Fixed-to-Fixed Length Distribution Matching With Shell Mapping
Venue: arXiv cs.IT 2018
Year: 2018

Distribution matching (DM) transforms independent and Bernoulli(1/2) distributed bits into a sequence of output symbols with a desired distribution. A fixed-to-fixed length, invertible DM architecture based on shell mapping is presented. It is shown that shell mapping for distribution matching (SMDM) is the optimum DM for the informational divergence metric and that finding energy optimal sequences is a special case of divergence minimization. Additionally, it is shown how to find the required shell mapping weight function to approximate arbitrary output distributions. SMDM is combined with probabilistic amplitude shaping (PAS) to operate close to the Shannon limit. SMDM exhibits excellent performance for short blocklengths as required by ultra-reliable low-latency (URLLC) applications. SMDM outperforms constant composition DM (CCDM) by 0.6 dB when used with 64-QAM at a spectral efficiency of 3 bits/channel use and a 5G low-density parity-check code with a short blocklength of 192 bits
IcoRating: A Deep-Learning System for Scam ICO Identification
Venue: arXiv cs.CL 2018
Year: 2018

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP, NEO) have been rapidly gaining ground in use, value, and understanding among the public, bringing astonishing profits to investors. Unlike other money and banking systems, most digital tokens do not require central authorities. Being decentralized poses significant challenges for credit rating. Most ICOs are currently not subject to government regulations, which makes a reliable credit rating system for ICO projects necessary and urgent. In this paper, we introduce IcoRating, the first learning--based cryptocurrency rating system. We exploit natural-language processing techniques to analyze various aspects of 2,251 digital currencies to date, such as white paper content, founding teams, Github repositories, websites, etc. Supervised learning models are used to correlate the life span and the price change of cryptocurrencies with these features. For the best setting, the proposed system is able to identify scam ICO projects with 0.83 precision. We hope this work will help investors identify scam ICOs and attract more efforts in automatically evaluating and analyzing ICO projects.
Evolutionary Architecture Search For Deep Multitask Networks
Venue: arXiv cs.NE 2018
Year: 2018

Multitask learning, i.e. learning several tasks at once with the same neural network, can improve performance in each of the tasks. Designing deep neural network architectures for multitask learning is a challenge: There are many ways to tie the tasks together, and the design choices matter. The size and complexity of this problem exceeds human design ability, making it a compelling domain for evolutionary optimization. Using the existing state of the art soft ordering architecture as the starting point, methods for evolving the modules of this architecture and for evolving the overall topology or routing between modules are evaluated in this paper. A synergetic approach of evolving custom routings with evolved, shared modules for each task is found to be very powerful, significantly improving the state of the art in the Omniglot multitask, multialphabet character recognition domain. This result demonstrates how evolution can be instrumental in advancing deep neural network and complex system design in general.
Optimum Linear Codes with Support Constraints over Small Fields
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of designing optimal linear codes (in terms of having the largest minimum distance) subject to a support constraint on the generator matrix. We show that the largest minimum distance can be achieved by a subcode of a Reed-Solomon code of small field size. As a by-product of this result, we settle the GM-MDS conjecture of Dau et. al. in the affirmative.
Kickstarting Deep Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments.
Language Identification of Bengali-English Code-Mixed data using Character & Phonetic based LSTM Models
Venue: arXiv cs.CL 2018
Year: 2018

Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language identification at the word level of low resource Bengali-English code-mixed data taken from social media. We employ two methods of word encoding, namely character based and root phone based to train our deep LSTM models. Utilizing these two models we created two ensemble models using stacking and threshold technique which gave 91.78% and 92.35% accuracies respectively on our testing data.
Probabilistic Analysis of Block Wiedemann for Leading Invariant Factors
Venue: arXiv cs.SC 2018
Year: 2018

We determine the probability, structure dependent, that the block Wiedemann algorithm correctly computes leading invariant factors. This leads to a tight lower bound for the probability, structure independent. We show, using block size slightly larger than $r$, that the leading $r$ invariant factors are computed correctly with high probability over any field. Moreover, an algorithm is provided to compute the probability bound for a given matrix size and thus to select the block size needed to obtain the desired probability. The worst case probability bound is improved, post hoc, by incorporating the partial information about the invariant factors.
Transfer Learning with Bellwethers to find Good Configurations
Venue: arXiv cs.SE 2018
Year: 2018

As software systems grow in complexity, the space of possible configurations grows exponentially. Within this increasing complexity, developers, maintainers, and users cannot keep track of the interactions between all the various configuration options. Finding the optimally performing configuration of a software system for a given setting is challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough data on enough sample configurations can be very expensive since each such sample requires configuring, compiling and executing the entire system against a complex test suite. The central insight of this paper is that choosing a suitable source (a.k.a. "bellwether") to learn from, plus a simple transfer learning scheme will often outperform much more complex transfer learning methods. Using this insight, this paper proposes BEETLE, a novel bellwether based transfer learning scheme, which can identify a suitable source and use it to find near-optimal configurations of a software system. BEETLE significantly reduces the cost (in terms of the number of measurements of sample configuration) to build performance models. We evaluate our approach with 61 scenarios based on 5 software systems and demonstrate that BEETLE is beneficial in all cases. This approach offers a new highwater mark in configuring software systems. Specifically, BEETLE can find configurations that are as good or better as those found by anything else while requiring only 1/7th of the evaluations needed by the state-of-the-art.
Reliable and Real-Time End-to-End Delivery Protocol in Wireless Sensor Networks
Venue: arXiv cs.NI 2018
Year: 2018

Many routing protocols have been proposed to handle reliability and real-time routing energy efficiency for wireless sensor networks. In this paper we propose a new routing protocol with QoS based capabilities for WSNs. We used priority queues for improve real-time and non-real-time packets forwarding according to deadline of them. The protocol finds a best-cost, time-sensitive packet forwarding mechanism for real-time data with minimum consumption of the energy. In order to avoid of congestion in network our protocol drops those packets who can't reach their destination in specified time. For service quality assurance in reliability domain we used packet reception rate as an important parameter (PRR) in selecting of neighbor nodes. Simulation results show that our new approach how can provide quality of service parameters.
Approximate Range Queries for Clustering
Venue: arXiv cs.CG 2018
Year: 2018

We study the approximate range searching for three variants of the clustering problem with a set $P$ of $n$ points in $d$-dimensional Euclidean space and axis-parallel rectangular range queries: the $k$-median, $k$-means, and $k$-center range-clustering query problems. We present data structures and query algorithms that compute $(1+\varepsilon)$-approximations to the optimal clusterings of $P\cap Q$ efficiently for a query consisting of an orthogonal range $Q$, an integer $k$, and a value $\varepsilon>0$.
A Linear Algebraic Approach to Subfield Subcodes of GRS Codes
Venue: arXiv cs.IT 2018
Year: 2018

The problem of finding subfield subcodes of generalized Reed-Solomon (GRS) codes (i.e., alternant codes) is considered. A pure linear algebraic approach is taken in order to derive message constraints that generalize the well known conjugacy constraints for cyclic GRS codes and their Bose-Chaudhuri-Hocquenghem (BCH) subfield subcodes. It is shown that the presented technique can be used for finding nested subfield subcodes with increasing design distance.
Sales forecasting using WaveNet within the framework of the Kaggle competition
Venue: arXiv cs.LG 2018
Year: 2018

We took part in the Corporacion Favorita Grocery Sales Forecasting competition hosted on Kaggle and achieved the 2nd place. In this abstract paper, we present an overall analysis and solution to the underlying machine-learning problem based on time series data, where major challenges are identified and corresponding preliminary methods are proposed. Our approach is based on the adaptation of dilated convolutional neural network for time series forecasting. By applying this technique iteratively to batches of n examples, a big amount of time series data can be eventually processed with a decent speed and accuracy. We hope this paper could serve, to some extent, as a review and guideline of the time series forecasting benchmark, inspiring further attempts and researches.
Representation Learning over Dynamic Graphs
Venue: arXiv cs.LG 2018
Year: 2018

How can we effectively encode evolving information over dynamic graphs into low-dimensional representations? In this paper, we propose DyRep, an inductive deep representation learning framework that learns a set of functions to efficiently produce low-dimensional node embeddings that evolves over time. The learned embeddings drive the dynamics of two key processes namely, communication and association between nodes in dynamic graphs. These processes exhibit complex nonlinear dynamics that evolve at different time scales and subsequently contribute to the update of node embeddings. We employ a time-scale dependent multivariate point process model to capture these dynamics. We devise an efficient unsupervised learning procedure and demonstrate that our approach significantly outperforms representative baselines on two real-world datasets for the problem of dynamic link prediction and event time prediction.
Delivery Time Minimization in Cache-Assisted Broadcast-Relay Wireless Networks with Imperfect CSI
Venue: arXiv cs.IT 2018
Year: 2018

An emerging trend of next generation communication systems is to provide network edges with additional capabilities such as storage resources in the form of caches to reduce file delivery latency. To investigate the impact of this technique on latency, we study the delivery time of a cache-aided broadcast-relay wireless network consisting of one central base station, $M$ cache-equipped transceivers and $K$ receivers under finite precision channel state information (CSI). We use the normalized delivery time (NDT) to capture the worst-case per-bit latency in a file delivery. Lower and upper bounds on the NDT are derived to understand the influence of $K,M$, cache capacity and channel quality on the NDT. In particular, regimes of NDT-optimality are identified and discussed.
Full Reference Objective Quality Assessment for Reconstructed Background Images
Venue: arXiv cs.CV 2018
Year: 2018

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.