OpenRaiser/Intern-Atlas huggingface dataset:
Identifying and Categorizing Anomalies in Retinal Imaging Data
Venue: arXiv cs.LG 2016
Year: 2016

The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for the observed data. A deep convolutional autoencoder is trained on healthy retinal images. The learned model generates a new feature representation, and the distribution of healthy retinal patches is estimated by a one-class support vector machine. Results demonstrate that we can identify pathologic regions in images without using expert annotations. A subsequent clustering categorizes findings into clinically meaningful classes. In addition the learned features outperform standard embedding approaches in a classification task.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Venue: arXiv cs.CV 2016
Year: 2016

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.
Energy-efficient 8-point DCT Approximations: Theory and Hardware Architectures
Venue: arXiv cs.MM 2016
Year: 2016

Due to its remarkable energy compaction properties, the discrete cosine transform (DCT) is employed in a multitude of compression standards, such as JPEG and H.265/HEVC. Several low-complexity integer approximations for the DCT have been proposed for both 1-D and 2-D signal analysis. The increasing demand for low-complexity, energy efficient methods require algorithms with even lower computational costs. In this paper, new 8-point DCT approximations with very low arithmetic complexity are presented. The new transforms are proposed based on pruning state-of-the-art DCT approximations. The proposed algorithms were assessed in terms of arithmetic complexity, energy retention capability, and image compression performance. In addition, a metric combining performance and computational complexity measures was proposed. Results showed good performance and extremely low computational complexity. Introduced algorithms were mapped into systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45nm CMOS technology. All hardware-related metrics showed low resource consumption of the proposed pruned approximate transforms. The best proposed transform according to the introduced metric presents a reduction in power consumption of 21--25%.
Learning Operations on a Stack with Neural Turing Machines
Venue: arXiv cs.LG 2016
Year: 2016

Multiple extensions of Recurrent Neural Networks (RNNs) have been proposed recently to address the difficulty of storing information over long time periods. In this paper, we experiment with the capacity of Neural Turing Machines (NTMs) to deal with these long-term dependencies on well-balanced strings of parentheses. We show that not only does the NTM emulate a stack with its heads and learn an algorithm to recognize such words, but it is also capable of strongly generalizing to much longer sequences.
A Novel Approach for Modeling Complex Deep Futures
Venue: arXiv cs.CY 2016
Year: 2016

Many large-scale, complex systems consist of interactions between humans, human-made systems and the environment. The approach developed in this paper is to partition the problem space into two fundamental layers and identify, parameterize and model the main dimensions of each layer and interactions across and in between layers. One layer is the key actors or major organization or human decision makers who influence the state of the world. The other layer includes the domains or fields of knowledge relevant to the problem being addressed. These domains include elements such as the physical earth and its atmosphere, world demography, world economy, level of globalization, and politics. Key parameters for each of the actor types and domains will be extracted and assessed using existing data sources. Novel systems, uncertainty modeling and analysis techniques are combined with advanced computational technologies to determine a spectrum of likely future system states and conduct ifthen scenario analyses.
Analysis and Design of a Passive Switched-Capacitor Matrix Multiplier for Approximate Computing
Venue: arXiv cs.ET 2016
Year: 2016

A switched-capacitor matrix multiplier is presented for approximate computing and machine learning applications. The multiply-and-accumulate operations perform discrete-time charge-domain signal processing using passive switches and 300 aF unit capacitors. The computation is digitized with a 6 b asynchronous successive approximation register analog-to-digital converter. The analyses of incomplete charge accumulation and thermal noise are discussed. The design was fabricated in 40 nm CMOS, and experimental measurements of multiplication are illustrated using matched filtering and image convolutions to analyze noise and offset. Two applications are highlighted: 1) energy-efficient feature extraction layer performing both compression and classification in a neural network for an analog front end and 2) analog acceleration for solving optimization problems that are traditionally performed in the digital domain. The chip obtains measured efficiencies of 8.7 TOPS/W at 1 GHz for the first application and 7.7 TOPS/W at 2.5 GHz for the second application.
RecSys Challenge 2016: job recommendations based on preselection of offers and gradient boosting
Venue: arXiv cs.AI 2016
Year: 2016

We present the Mim-Solution's approach to the RecSys Challenge 2016, which ranked 2nd. The goal of the competition was to prepare job recommendations for the users of the website Xing.com. Our two phase algorithm consists of candidate selection followed by the candidate ranking. We ranked the candidates by the predicted probability that the user will positively interact with the job offer. We have used Gradient Boosting Decision Trees as the regression tool.
Ensembles of Generative Adversarial Networks
Venue: arXiv cs.CV 2016
Year: 2016

Ensembles are a popular way to improve results of discriminative CNNs. The combination of several networks trained starting from different initializations improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.
On spreading rumor in heterogeneous systems
Venue: arXiv cs.DC 2016
Year: 2016

In this paper we consider a model of spreading information in heterogeneous systems wherein we have two kinds of objects. Some of them are active and others are passive. Active objects can, if they possess information, share it with an encountered passive object. We focus on a particular case such that active objects communicate independently with randomly chosen passive objects. Such model is motivated by two real-life scenarios. The first one is a very dynamic system of mobile devices distributing information among stationary devices. The second is an architecture wherein clients communicate with several servers and can leave some information learnt from other servers. The main question we investigate is how many rounds is needed to deliver the information to all objects under the assumption that at the beginning exactly one object has the information? In this paper we provide mathematical models of such process and show rigid and very precise mathematical analysis for some special cases important from practical point of view. Some mathematical results are quite surprising -- we find relation of investigated process to both coupon collector's problem as well as the birthday paradox. Additionally, we present simulations for showing behaviour for general parameters
Word Recognition with Deep Conditional Random Fields
Venue: arXiv cs.CV 2016
Year: 2016

Recognition of handwritten words continues to be an important problem in document analysis and recognition. Existing approaches extract hand-engineered features from word images--which can perform poorly with new data sets. Recently, deep learning has attracted great attention because of the ability to learn features from raw data. Moreover they have yielded state-of-the-art results in classification tasks including character recognition and scene recognition. On the other hand, word recognition is a sequential problem where we need to model the correlation between characters. In this paper, we propose using deep Conditional Random Fields (deep CRFs) for word recognition. Basically, we combine CRFs with deep learning, in which deep features are learned and sequences are labeled in a unified framework. We pre-train the deep structure with stacked restricted Boltzmann machines (RBMs) for feature learning and optimize the entire network with an online learning algorithm. The proposed model was evaluated on two datasets, and seen to perform significantly better than competitive baseline models. The source code is available at https://github.com/ganggit/deepCRFs.
Joint Visual Denoising and Classification using Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using shared representation via non-linear mapping, and model parameters can be learnt via backpropagation. Using MNIST and USPS data corrupted with structured noise, the proposed framework performs at least 20\% better in classification than separate pipelines, as well as clearer recovered images. The noise model and the reproducible source code is available at {\url{https://github.com/ganggit/jointmodel}}.
QoS-based Computing Resources Partitioning between Virtual Machines in the Cloud Architecture
Venue: arXiv cs.DC 2016
Year: 2016

Cloud services have been used very widely, but configuration of the parameters, including the efficient allocation of resources, is an important objective for the system architect. The article is devoted to solving the problem of choosing the architecture of computers based on simulation and developed program for monitoring computing resources. Techniques were developed aimed at providing the required quality of service and efficient use of resources. The article describes the monitoring program of computing resources and time efficiency of the target application functions. On the basis of this application the technique is shown and described in the experiment, designed to ensure the requirements for quality of service, by isolating one process from the others on different virtual machines inside the hypervisor.
Privacy on the Blockchain: Unique Ring Signatures
Venue: arXiv cs.CR 2016
Year: 2016

Ring signatures are cryptographic protocols designed to allow any member of a group to produce a signature on behalf of the group, without revealing the individual signer's identity. This offers group members a level of anonymity not attainable through generic digital signature schemes. We call this property 'plausible deniability', or anonymity with respect to an anonymity set. We concentrate in particular on implementing privacy on the blockchain, introducing a unique ring signature scheme that works with existing blockchain systems. We implement a unique ring signature (URS) scheme using secp256k1, creating the first implementation compatible with blockchain libraries in this way, so as for easy implementation as an Ethereum smart contract. We review the privacy and security properties offered by the scheme we have constructed, and compare its efficiency with other commonly suggested approaches to privacy on the blockchain.
Online Localization and Prediction of Actions and Interactions
Venue: arXiv cs.CV 2016
Year: 2016

This paper proposes a person-centric and online approach to the challenging problem of localization and prediction of actions and interactions in videos. Typically, localization or recognition is performed in an offline manner where all the frames in the video are processed together. This prevents timely localization and prediction of actions and interactions - an important consideration for many tasks including surveillance and human-machine interaction. In our approach, we estimate human poses at each frame and train discriminative appearance models using the superpixels inside the pose bounding boxes. Since the pose estimation per frame is inherently noisy, the conditional probability of pose hypotheses at current time-step (frame) is computed using pose estimations in the current frame and their consistency with poses in the previous frames. Next, both the superpixel and pose-based foreground likelihoods are used to infer the location of actors at each time through a Conditional Random. The issue of visual drift is handled by updating the appearance models, and refining poses using motion smoothness on joint locations, in an online manner. For online prediction of action (interaction) confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time progresses. Lastly, we quantify the performance of both detection and prediction together, and analyze how the prediction accuracy varies as a time function of observed action (interaction) at different levels of detection performance. Our experiments on several datasets suggest that despite using only a few frames to localize actions (interactions) at each time instant, we are able to obtain competitive results to state-of-the-art offline methods.
Vehicle Powertrain Connected Route Optimization for Conventional, Hybrid and Plug-in Electric Vehicles
Venue: arXiv cs.SY 2016
Year: 2016

Most navigation systems use data from satellites to provide drivers with the shortest-distance, shortest-time or highway-preferred paths. However, when the routing decisions are made for advanced vehicles, there are other factors affecting cost, such as vehicle powertrain type, battery state of charge (SOC) and the change of component efficiencies under traffic conditions, which are not considered by traditional routing systems. The impact of the trade-off between distance and traffic on the cost of the trip might change with the type of vehicle technology and component dynamics. As a result, the least-cost paths might be different from the shortest-distance or shortest-time paths. In this work, a novel routing strategy has been proposed where the decision-making process benefits from the aforementioned information to result in a least-cost path for drivers. We integrate vehicle powertrain dynamics into route optimization and call this strategy as Vehicle Powertrain Connected Route Optimization (VPCRO). We find that the optimal paths might change significantly for all types of vehicle powertrains when VPCRO is used instead of shortest-distance strategy. About 81% and 58% of trips were replaced by different optimal paths with VPCRO when the vehicle type was Conventional Vehicle (CV) and Electrified Vehicle (EV), respectively. Changed routes had reduced travel costs on an average of 15% up to a maximum of 60% for CVs and on an average of 6% up to a maximum of 30% for EVs. Moreover, it was observed that 3% and 10% of trips had different optimal paths for a plug-in hybrid electric vehicle, when initial battery SOC changed from 90% to 60% and 40%, respectively. Paper shows that using sensory information from vehicle powertrain for route optimization plays an important role to minimize travel costs.
Empirical Research Plan: Effects of Sketching on Program Comprehension
Venue: arXiv cs.SE 2016
Year: 2016

Sketching is an important means of communication in software engineering practice. Yet, there is little research investigating the use of sketches. We want to contribute a better understanding of sketching, in particular its use during program comprehension. We propose a controlled experiment to investigate the effectiveness and efficiency of program comprehension with the support of sketches as well as what sketches are used in what way.
Diagnostic Prediction Using Discomfort Drawings
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we extend a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
Implicit Modeling -- A Generalization of Discriminative and Generative Approaches
Venue: arXiv cs.LG 2016
Year: 2016

We propose a new modeling approach that is a generalization of generative and discriminative models. The core idea is to use an implicit parameterization of a joint probability distribution by specifying only the conditional distributions. The proposed scheme combines the advantages of both worlds -- it can use powerful complex discriminative models as its parts, having at the same time better generalization capabilities. We thoroughly evaluate the proposed method for a simple classification task with artificial data and illustrate its advantages for real-word scenarios on a semantic image segmentation problem.
Mapping the Dialog Act Annotations of the LEGO Corpus into the Communicative Functions of ISO 24617-2
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we present strategies for mapping the dialog act annotations of the LEGO corpus into the communicative functions of the ISO 24617-2 standard. Using these strategies, we obtained an additional 347 dialogs annotated according to the standard. This is particularly important given the reduced amount of existing data in those conditions due to the recency of the standard. Furthermore, these are dialogs from a widely explored corpus for dialog related tasks. However, its dialog annotations have been neglected due to their high domain-dependency, which renders them unuseful outside the context of the corpus. Thus, through our mapping process, we both obtain more data annotated according to a recent standard and provide useful dialog act annotations for a widely explored corpus in the context of dialog research.
Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework with Base Station ON/OFF Switching
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, the problem of energy efficiency in cellular heterogeneous networks (HetNets) is investigated using radio resource and power management combined with the base station (BS) ON/OFF switching. The objective is to minimize the total power consumption of the network while satisfying the quality of service (QoS) requirements of each connected user. We consider the case of co-existing macrocell BS, small cell BSs, and private femtocell access points (FAPs). Three different network scenarios are investigated, depending on the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs, and HetNets with semi-closed FAPs. A unified framework is proposed to simultaneously allocate spectrum resources to users in an energy efficient manner and switch off redundant small cell BSs. The high complexity dual decomposition technique is employed to achieve optimal solutions for the problem. A low complexity iterative algorithm is also proposed and its performances are compared to those of the optimal technique. The particularly interesting case of semi-closed FAPs, in which the FAPs accept to serve external users, achieves the highest energy efficiency due to increased degrees of freedom. In this paper, a cooperation scheme between FAPs and mobile operator is also investigated. The incentives for FAPs, e.g., renewable energy sharing and roaming prices, enabling cooperation are discussed to be considered as a useful guideline for inter-operator agreements.