OpenRaiser/Intern-Atlas huggingface dataset:
From Dissipativity Theory to Compositional Abstractions of Interconnected Stochastic Hybrid Systems
Venue: arXiv cs.SY 2018
Year: 2018

In this work, we derive conditions under which compositional abstractions of networks of stochastic hybrid systems can be constructed using the interconnection topology and joint dissipativity-type properties of subsystems and their abstractions. In the proposed framework, the abstraction, itself a stochastic hybrid system (possibly with a lower dimension), can be used as a substitute of the original system in the controller design process. Moreover, we derive conditions for the construction of abstractions for a class of stochastic hybrid systems involving nonlinearities satisfying an incremental quadratic inequality. In this work, unlike existing results, the stochastic noises and jumps in the concrete subsystem and its abstraction need not to be the same. We provide examples with numerical simulations to illustrate the effectiveness of the proposed dissipativity-type compositional reasoning for interconnected stochastic hybrid systems.
Unsupervised Domain Adaptation using Regularized Hyper-graph Matching
Venue: arXiv cs.CV 2018
Year: 2018

Domain adaptation (DA) addresses the real-world image classification problem of discrepancy between training (source) and testing (target) data distributions. We propose an unsupervised DA method that considers the presence of only unlabelled data in the target domain. Our approach centers on finding matches between samples of the source and target domains. The matches are obtained by treating the source and target domains as hyper-graphs and carrying out a class-regularized hyper-graph matching using first-, second- and third-order similarities between the graphs. We have also developed a computationally efficient algorithm by initially selecting a subset of the samples to construct a graph and then developing a customized optimization routine for graph-matching based on Conditional Gradient and Alternating Direction Multiplier Method. This allows the proposed method to be used widely. We also performed a set of experiments on standard object recognition datasets to validate the effectiveness of our framework over state-of-the-art approaches.
AffinityNet: semi-supervised few-shot learning for disease type prediction
Venue: arXiv cs.LG 2018
Year: 2018

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .
MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter-wave communication is a challenge in the highly mobile vehicular context. Traditional beam training is inadequate in satisfying low overheads and latency. In this paper, we propose to combine machine learning tools and situational awareness to learn the beam information (power, optimal beam index, etc) from past observations. We consider forms of situational awareness that are specific to the vehicular setting including the locations of the receiver and the surrounding vehicles. We leverage regression models to predict the received power with different beam power quantizations. The result shows that situational awareness can largely improve the prediction accuracy and the model can achieve throughput with little performance loss with almost zero overhead.
A Psychopathological Approach to Safety Engineering in AI and AGI
Venue: arXiv cs.AI 2018
Year: 2018

The complexity of dynamics in AI techniques is already approaching that of complex adaptive systems, thus curtailing the feasibility of formal controllability and reachability analysis in the context of AI safety. It follows that the envisioned instances of Artificial General Intelligence (AGI) will also suffer from challenges of complexity. To tackle such issues, we propose the modeling of deleterious behaviors in AI and AGI as psychological disorders, thereby enabling the employment of psychopathological approaches to analysis and control of misbehaviors. Accordingly, we present a discussion on the feasibility of the psychopathological approaches to AI safety, and propose general directions for research on modeling, diagnosis, and treatment of psychological disorders in AGI.
Learning Based Industrial Bin-picking Trained with Approximate Physics Simulator
Venue: arXiv cs.RO 2018
Year: 2018

In this research, we tackle the problem of picking an object from randomly stacked pile. Since complex physical phenomena of contact among objects and fingers makes it difficult to perform the bin-picking with high success rate, we consider introducing a learning based approach. For the purpose of collecting enough number of training data within a reasonable period of time, we introduce a physics simulator where approximation is used for collision checking. In this paper, we first formulate the learning based robotic bin-picking by using CNN (Convolutional Neural Network). We also obtain the optimum grasping posture of parallel jaw gripper by using CNN. Finally, we show that the effect of approximation introduced in collision checking is relaxed if we use exact 3D model to generate the depth image of the pile as an input to CNN.
Opportunistic Scheduling in Underlay Cognitive Radio based MIMO-RF/FSO Networks
Venue: arXiv cs.IT 2018
Year: 2018

This work proposes an optimal metric for opportunistic scheduling of secondary user transmitters (SU-TXs) in underlay cognitive radio based multiple-input multiple-output radio frequency/free space optical (MIMO-RF/FSO) decode-and-forward system with fixed and proportional interference power constraints. To analyze the performance of the proposed system, the closed-form expressions are derived for the exact and asymptotic outage probabilities considering orthogonal space-time block coded transmission over Nakagami-$m$ fading RF links. Further, the FSO link between the relay and destination eNodeB is modeled as the Generalized Málaga $(\mathcal{M})$ turbulence channel with pointing errors. Finally, simulation results are presented to develop several interesting insights into the end-to-end system performance and the selection probabilities of SU-TXs. It is also shown that the proposed system outperforms the ones existing in the current literature.
Building Extraction at Scale using Convolutional Neural Network: Mapping of the United States
Venue: arXiv cs.CV 2018
Year: 2018

Establishing up-to-date large scale building maps is essential to understand urban dynamics, such as estimating population, urban planning and many other applications. Although many computer vision tasks has been successfully carried out with deep convolutional neural networks, there is a growing need to understand their large scale impact on building mapping with remote sensing imagery. Taking advantage of the scalability of CNNs and using only few areas with the abundance of building footprints, for the first time we conduct a comparative analysis of four state-of-the-art CNNs for extracting building footprints across the entire continental United States. The four CNN architectures namely: branch-out CNN, fully convolutional neural network (FCN), conditional random field as recurrent neural network (CRFasRNN), and SegNet, support semantic pixel-wise labeling and focus on capturing textural information at multi-scale. We use 1-meter resolution aerial images from National Agriculture Imagery Program (NAIP) as the test-bed, and compare the extraction results across the four methods. In addition, we propose to combine signed-distance labels with SegNet, the preferred CNN architecture identified by our extensive evaluations, to advance building extraction results to instance level. We further demonstrate the usefulness of fusing additional near IR information into the building extraction framework. Large scale experimental evaluations are conducted and reported using metrics that include: precision, recall rate, intersection over union, and the number of buildings extracted. With the improved CNN model and no requirement of further post-processing, we have generated building maps for the United States. The quality of extracted buildings and processing time demonstrated the proposed CNN-based framework fits the need of building extraction at scale.
Dictionary Learning by Dynamical Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

A dynamical neural network consists of a set of interconnected neurons that interact over time continuously. It can exhibit computational properties in the sense that the dynamical system's evolution and/or limit points in the associated state space can correspond to numerical solutions to certain mathematical optimization or learning problems. Such a computational system is particularly attractive in that it can be mapped to a massively parallel computer architecture for power and throughput efficiency, especially if each neuron can rely solely on local information (i.e., local memory). Deriving gradients from the dynamical network's various states while conforming to this last constraint, however, is challenging. We show that by combining ideas of top-down feedback and contrastive learning, a dynamical network for solving the l1-minimizing dictionary learning problem can be constructed, and the true gradients for learning are provably computable by individual neurons. Using spiking neurons to construct our dynamical network, we present a learning process, its rigorous mathematical analysis, and numerical results on several dictionary learning problems.
CNN+CNN: Convolutional Decoders for Image Captioning
Venue: arXiv cs.CV 2018
Year: 2018

Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field. However, RNNs or LSTMs cannot be calculated in parallel and ignore the underlying hierarchical structure of a sentence. In this paper, we propose a framework that only employs convolutional neural networks (CNNs) to generate captions. Owing to parallel computing, our basic model is around 3 times faster than NIC (an LSTM-based model) during training time, while also providing better results. We conduct extensive experiments on MSCOCO and investigate the influence of the model width and depth. Compared with LSTM-based models that apply similar attention mechanisms, our proposed models achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of CIDEr. We also test our model on the paragraph annotation dataset, and get higher CIDEr score compared with hierarchical LSTMs
Area-preserving parameterizations for spherical ellipses
Venue: arXiv cs.GR 2018
Year: 2018

We present new methods for uniformly sampling the solid angle subtended by a disk. To achieve this, we devise two novel area-preserving mappings from the unit square $[0,1]^2$ to a spherical ellipse (i.e. the projection of the disk onto the unit sphere). These mappings allow for low-variance stratified sampling of direct illumination from disk-shaped light sources. We discuss how to efficiently incorporate our methods into a production renderer and demonstrate the quality of our maps, showing significantly lower variance than previous work.
Secure Credit Reporting on the Blockchain
Venue: arXiv cs.CR 2018
Year: 2018

We present a secure approach for maintaining and reporting credit history records on the Blockchain. Our approach removes third-parties such as credit reporting agencies from the lending process and replaces them with smart contracts. This allows customers to interact directly with the lenders or banks while ensuring the integrity, unmalleability and privacy of their credit data. Most importantly, each customer is given full control over complete or selective disclosure of her credit records, eliminating the risk of privacy violations or data breaches such as the one that happened to Equifax in 2017. Moreover, our approach provides strong guarantees for the lenders as well. A lender can check both correctness and completeness of the credit data disclosed to her. This is the first approach that is able to perform all real-world credit reporting tasks without a central authority or changing the financial mechanisms.
Decidable Logics Combining Word Equations, Regular Expressions and Length Constraints
Venue: arXiv cs.LO 2018
Year: 2018

In this work, we consider the satisfiability problem in a logic that combines word equations over string variables denoting words of unbounded lengths, regular languages to which words belong and Presburger constraints on the length of words. We present a novel decision procedure over two decidable fragments that include quadratic word equations (i.e., each string variable occurs at most twice). The proposed procedure reduces the problem to solving the satisfiability in the Presburger arithmetic. The procedure combines two main components: (i) an algorithm to derive a complete set of all solutions of conjunctions of word equations and regular expressions; and (ii) two methods to precisely compute relational constraints over string lengths implied by the set of all solutions.We have implemented a prototype tool and evaluated it over a set of satisfiability problems in the logic. The experimental results show that the tool is effective and efficient.
Demographic differences in search engine use with implications for cohort selection
Venue: arXiv cs.CY 2018
Year: 2018

The correlation between the demographics of users and the text they write has been investigated through literary texts and, more recently, social media. However, differences pertaining to language use in search engines has not been thoroughly analyzed, especially for age and gender differences. Such differences are important especially due to the growing use of search engine data in the study of human health, where queries are used to identify patient populations. Using data from multiple general-purpose Internet search engines gathered over a period of one month we investigate the correlation between demography (age, gender, and income) and the text of queries submitted to search engines. Our results show that females and younger people use longer queries. This difference is such that females make approximately 25% more queries with 10 or more words. In the case of queries which identify users as having specific medical conditions we find that females make 50% more queries than expected, and that this results in patient cohorts which are highly skewed in gender and age, compared to known gender balance. Our results indicate that studies where demographic representation is important, such as in the study of health aspect of users or when search engines are evaluated for fairness, care should be taken in the selection of search engine data so as to create a representative dataset.
In-Person and Remote Participation Review at IETF: Collaborating Without Borders
Venue: arXiv cs.CY 2018
Year: 2018

The IETF has been acting as one of the main actors when discussing standardization of protocols and good practices on the Internet. Collaborating with the IETF community can be complex and distant for many researchers and industry members because of the financial aspect to travel to the meeting. However, it notes the collaboration between industry and academia is actively and progressively developing and refining standards within the IETF. One of the incentives for the increased participation in IETF meetings is because it is being transmitted in real time since 2015, allowing for voice and chat interaction of remote participants. Thus, in this paper, we have as objectives to give a brief vision about how to collaborate with the IETF and to analyze the importance of this new form of participation of the face-to-face meetings that has been growing in recent years.
OOP and its Calculated Measures in Programming Interactivity
Venue: arXiv cs.CY 2018
Year: 2018

This study examines the object oriented programming (OOP) and its calculated measures in programming interactivity in Nigeria. It focused on the existing programming languages used by programmers and examines the need for integrating programming interactivity with OOP. A survey was conducted to measure interactivity amongst professionals using certain parameters like flexibility, interactivity, speed, interoperability, scalability, dynamism, and solving real life problems. Data was gathered using questionnaire, and analysis was carried out using frequency, percentage ratio, and mean in arriving at a more proactive stand. The results revealed that the some of the parameters used are highly in support of the programming interactivity with OOP.
SymmSLIC: Symmetry Aware Superpixel Segmentation and its Applications
Venue: arXiv cs.CV 2018
Year: 2018

Over-segmentation of an image into superpixels has become a useful tool for solving various problems in image processing and computer vision. Reflection symmetry is quite prevalent in both natural and man-made objects and is an essential cue in understanding and grouping the objects in natural scenes. Existing algorithms for estimating superpixels do not preserve the reflection symmetry of an object which leads to different sizes and shapes of superpixels across the symmetry axis. In this work, we propose an algorithm to over-segment an image through the propagation of reflection symmetry evident at the pixel level to superpixel boundaries. In order to achieve this goal, we first find the reflection symmetry in the image and represent it by a set of pairs of pixels which are mirror reflections of each other. We partition the image into superpixels while preserving this reflection symmetry through an iterative algorithm. We compare the proposed method with state-of-the-art superpixel generation methods and show the effectiveness in preserving the size and shape of superpixel boundaries across the reflection symmetry axes. We also present two applications, symmetry axes detection and unsupervised symmetric object segmentation, to illustrate the effectiveness of the proposed approach.
Multi-User Cooperative Mobile Video Streaming: Performance Analysis and Online Mechanism Design
Venue: arXiv cs.NI 2018
Year: 2018

Adaptive bitrate streaming enables video users to adapt their playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user based bitrate adaptation methods may fail to provide the desirable QoE, due to lack of consideration of multi-user interactions (such as the multi-user interferences and network congestion). In this work, we propose a novel user cooperation framework based on user-provided networking for multi-user mobile video streaming over wireless cellular networks. The framework enables nearby mobile video users to crowdsource their cellular links and resources for cooperative video streaming. We first analyze the social welfare performance bound of the proposed cooperative streaming system by introducing a virtual time-slotted system. Then, we design a low complexity Lyapunov-based online algorithm, which can be implemented in an online and distributed manner without the complete future and global network information. Numerical results show that the proposed online algorithm achieves an average 97% of the theoretical maximum social welfare. We further conduct experiments with real data traces, to compare our proposed online algorithm with the existing online algorithms in the literature. Experiment results show that our algorithm outperforms the existing algorithms in terms of both the achievable bitrate (with an average gain of 20% - 30%) and social welfare (with an average gain of 10% - 50%).
Umbrella: A Unified Software Defined Development Framework
Venue: arXiv cs.NI 2018
Year: 2018

The Northbound (NB) APIs that SDN controllers provide differ in terms of architecture, syntax, naming convention, data resources, and usage. Using NB APIs to write SDN applications makes each application dependent on the API of a specific controller. To bring NB APIs from different vendors under one umbrella and make programming of SDN applications independent of specific controllers, we propose a unified software defined development framework that we call Umbrella. This paper presents the key components of the software and reports some preliminary results.
Fog Assisted Cloud Models for Smart Grid Architectures- Comparison Study and Optimal Deployment
Venue: arXiv cs.NI 2018
Year: 2018

Cloud Computing (CC) serves to be a key driver for fulfilling the store and compute requirements of a modern Smart Grid (SG). However, since the datacenters are deployed in concentrated and far remote areas, it fails to guarantee the quality of experience (QoE) attributes for the SG services, viz. latency, bandwidth, energy consumption, and network cost. Fog Computing (FC) extends the processing capabilities into the edge of the network, offering location-awareness, low latency, and latency-sensitive analytics for mission critical requirements of SG. In this work, we first examine the current state of cloud based SG architectures and highlight the motivation(s) for adopting FC as technology enabler for sustainable and real-time SG analytics. Then we present a hierarchical FC architecture for supporting integration of massive number of IoT devices into future SG. Following this architecture we proposed a cost optimization framework that jointly investigates data consumer association, workload distribution, virtual machine placement and QoS constraints towards viable deployment of FC model over SG networks. The formulated MINLP problem is then solved using Modified Differential Evolution (MDE) algorithm. Comprehensive evaluation of proposed framework on real world parameters shows that for an infrastructure with nearly 50% applications requesting real-time services, the overall service latency for fog computing get reduced to almost half of that of generic cloud paradigm. It is also observed that the fog assisted cloud framework lowers the aggregated electricity consumption of the pure cloud computing paradigm by more than 40%.