OpenRaiser/Intern-Atlas huggingface dataset:
'Constant in gain Lead in phase' element - Application in precision motion control
Venue: arXiv cs.SY 2018
Year: 2018

This work presents a novel 'Constant in gain Lead in phase' (CgLp) element using nonlinear reset technique. PID is the industrial workhorse even to this day in high-tech precision positioning applications. However, Bode's gain phase relationship and waterbed effect fundamentally limit performance of PID and other linear controllers. This paper presents CgLp as a controlled nonlinear element which can be introduced within the framework of PID allowing for wide applicability and overcoming linear control limitations. Design of CgLp with generalized first order reset element (GFORE) and generalized second order reset element (GSORE) (introduced in this work) is presented using describing function analysis. A more detailed analysis of reset elements in frequency domain compared to existing literature is first carried out for this purpose. Finally, CgLp is integrated with PID and tested on one of the DOFs of a planar precision positioning stage. Performance improvement is shown in terms of tracking, steady-state precision and bandwidth.
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
Venue: arXiv cs.CV 2018
Year: 2018

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single image showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling.
The Arbitrarily Varying Gaussian Relay Channel with Sender Frequency Division
Venue: arXiv cs.IT 2018
Year: 2018

We consider the arbitrarily varying Gaussian relay channel with sender frequency division. We determine the random code capacity, and establish lower and upper bounds on the deterministic code capacity. It is observed that when the channel input is subject to a low power limit, the deterministic code capacity may be strictly lower than the random code capacity, and the gap vanishes as the input becomes less constrained. A second model addressed in this paper is the general case of primitive arbitrarily varying relay channels. We develop lower and upper bounds on the random code capacity, and give conditions under which the deterministic code capacity coincides with the random code capacity, and conditions under which it is lower. Then, we establish the capacity of the primitive counterpart of the arbitrarily varying Gaussian relay channel with sender frequency division. In this case, the deterministic and random code capacities are the same.
Minimax Learning for Remote Prediction
Venue: arXiv cs.IT 2018
Year: 2018

The classical problem of supervised learning is to infer an accurate predictor of a target variable $Y$ from a measured variable $X$ by using a finite number of labeled training samples. Motivated by the increasingly distributed nature of data and decision making, in this paper we consider a variation of this classical problem in which the prediction is performed remotely based on a rate-constrained description $M$ of $X$. Upon receiving $M$, the remote node computes an estimate $\hat Y$ of $Y$. We follow the recent minimax approach to study this learning problem and show that it corresponds to a one-shot minimax noisy source coding problem. We then establish information theoretic bounds on the risk-rate Lagrangian cost and a general method to design a near-optimal descriptor-estimator pair, which can be viewed as a rate-constrained analog to the maximum conditional entropy principle used in the classical minimax learning problem. Our results show that a naive estimate-compress scheme for rate-constrained prediction is not in general optimal.
Key Management Systems for Smart Grid Advanced Metering Infrastructure: A Survey
Venue: arXiv cs.CR 2018
Year: 2018

Smart Grids are evolving as the next generation power systems that involve changes in the traditional ways of generation, transmission and distribution of power. Advanced Metering Infrastructure (AMI) is one of the key components in smart grids. An AMI comprises of systems and networks, that collects and analyzes data received from smart meters. In addition, AMI also provides intelligent management of various power-related applications and services based on the data collected from smart meters. Thus, AMI plays a significant role in the smooth functioning of smart grids. AMI is a privileged target for security attacks as it is made up of systems that are highly vulnerable to such attacks. Providing security to AMI is necessary as adversaries can cause potential damage against infrastructures and privacy in smart grid. One of the most effective and challenging topic's identified, is the Key Management System (KMS), for sustaining the security concerns in AMI. Therefore, KMS seeks to be a promising research area for future development of AMI. This survey work highlights the key security issues of advanced metering infrastructures and focuses on how key management techniques can be utilized for safeguarding AMI. First of all, we explore the main features of advanced metering infrastructures and identify the relationship between smart grid and AMI. Then, we introduce the security issues and challenges of AMI. We also provide a classification of the existing works in literature that deal with secure key management system in AMI. Finally, we identify possible future research directions of KMS in AMI.
Damping Effect on PageRank Distribution
Venue: arXiv cs.SI 2018
Year: 2018

This work extends the personalized PageRank model invented by Brin and Page to a family of PageRank models with various damping schemes. The goal with increased model variety is to capture or recognize a larger number of types of network activities, phenomena and propagation patterns. The response in PageRank distribution to variation in damping mechanism is then characterized analytically, and further estimated quantitatively on 6 large real-world link graphs. The study leads to new observation and empirical findings. It is found that the difference in the pattern of PageRank vector responding to parameter variation by each model among the 6 graphs is relatively smaller than the difference among 3 particular models used in the study on each of the graphs. This suggests the utility of model variety for differentiating network activities and propagation patterns. The quantitative analysis of the damping mechanisms over multiple damping models and parameters is facilitated by a highly efficient algorithm, which calculates all PageRank vectors at once via a commonly shared, spectrally invariant subspace. The spectral space is found to be of low dimension for each of the real-world graphs.
Training LSTM Networks with Resistive Cross-Point Devices
Venue: arXiv cs.LG 2018
Year: 2018

In our previous work we have shown that resistive cross point devices, so called Resistive Processing Unit (RPU) devices, can provide significant power and speed benefits when training deep fully connected networks as well as convolutional neural networks. In this work, we further extend the RPU concept for training recurrent neural networks (RNNs) namely LSTMs. We show that the mapping of recurrent layers is very similar to the mapping of fully connected layers and therefore the RPU concept can potentially provide large acceleration factors for RNNs as well. In addition, we study the effect of various device imperfections and system parameters on training performance. Symmetry of updates becomes even more crucial for RNNs; already a few percent asymmetry results in an increase in the test error compared to the ideal case trained with floating point numbers. Furthermore, the input signal resolution to device arrays needs to be at least 7 bits for successful training. However, we show that a stochastic rounding scheme can reduce the input signal resolution back to 5 bits. Further, we find that RPU device variations and hardware noise are enough to mitigate overfitting, so that there is less need for using dropout. We note that the models trained here are roughly 1500 times larger than the fully connected network trained on MNIST dataset in terms of the total number of multiplication and summation operations performed per epoch. Thus, here we attempt to study the validity of the RPU approach for large scale networks.
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic exploration. Inspired by this, we investigate two issues in leveraging model-based RL for sample efficiency. First we investigate how to perform strategic exploration when exact planning is not feasible and empirically show that optimistic Monte Carlo Tree Search outperforms posterior sampling methods. Second we show how to learn simple deterministic models to support fast learning using object representation. We illustrate the benefit of these ideas by introducing a novel algorithm, Strategic Object Oriented Reinforcement Learning (SOORL), that outperforms state-of-the-art algorithms in the game of Pitfall! in less than 50 episodes.
SFA-GTM: Seismic Facies Analysis Based on Generative Topographic Map and RBF
Venue: arXiv cs.CE 2018
Year: 2018

Seismic facies identification plays a significant role in reservoir characterization. It helps in identifying the various lithological and stratigraphical changes in reservoir properties. With the increase in the size of seismic data or number of attributes to be analyzed, the manual process for facies identification becomes complicated and time-consuming. Even though seismic attributes add multiple dimensions to the data, their role in reservoir characterization is very crucial. There exist different linear transformation methods that use seismic attributes for identification, characterization, and visualization of seismic facies. These linear transformation methods have been widely used for facies characterization. However, there are some limitations associated with these methods such as deciding the width parameters, number of clusters, convergence rate etc. Therefore, the present research work uses non-linear transformation approach that overcomes some of the major limitations of linear approaches. The proposed Seismic facies analysis approach based on Generative Topographic Map \& Radial Basis Function(SFA-GTM) works by calculating the set of four Gray Level Co-occurrence Matrix(GLCM) texture based attributes viz. energy, homogeneity, contrast, and dissimilarity. The Generative Topographic Map(GTM) is used for unsupervised classification of seismic facies based on the set of calculated texture attributes. Further, the present work uses Radial Basis Function(RBF) for interpolating the missing values in the data.
Asymptotic Existence of Proportionally Fair Allocations
Venue: arXiv cs.GT 2018
Year: 2018

Fair division has long been an important problem in the economics literature. In this note, we consider the existence of proportionally fair allocations of indivisible goods, i.e., allocations of indivisible goods in which every agent gets at least her proportionally fair share according to her own utility function. We show that when utilities are additive and utilities for individual goods are drawn independently at random from a distribution, proportionally fair allocations exist with high probability if the number of goods is a multiple of the number of agents or if the number of goods grows asymptotically faster than the number of agents.
The encodability hierarchy for PCF types
Venue: arXiv cs.LO 2018
Year: 2018

Working with the simple types over a base type of natural numbers (including product types), we consider the question of when a type $σ$ is encodable as a definable retract of $τ$: that is, when there are $λ$-terms $e:σ\rightarrowτ$ and $d:τ\rightarrowσ$ with $d \circ e = id$. In general, the answer to this question may vary according to both the choice of $λ$-calculus and the notion of equality considered; however, we shall show that the encodability relation $\preceq$ between types actually remains stable across a large class of languages and equality relations, ranging from a very basic language with infinitely many distinguishable constants $0,1,\ldots$ (but no arithmetic) considered modulo computational equality, up to the whole of Plotkin's PCF considered modulo observational equivalence. We show that $σ\preceq τ\preceq σ$ iff $σ\cong τ$ via trivial isomorphisms, and that for any $σ,τ$ we have either $σ\preceq τ$ or $τ\preceq σ$. Furthermore, we show that the induced linear order on isomorphism classes of types is actually a well-ordering of type $ε_0$, and indeed that there is a close syntactic correspondence between simple types and Cantor normal forms for ordinals below $ε_0$. This means that the relation $\preceq$ is readily decidable, and that terms witnessing a retraction $σ\lhd τ$ are readily constructible when $σ\preceq τ$ holds.
Large-Margin Classification in Hyperbolic Space
Venue: arXiv cs.LG 2018
Year: 2018

Representing data in hyperbolic space can effectively capture latent hierarchical relationships. With the goal of enabling accurate classification of points in hyperbolic space while respecting their hyperbolic geometry, we introduce hyperbolic SVM, a hyperbolic formulation of support vector machine classifiers, and elucidate through new theoretical work its connection to the Euclidean counterpart. We demonstrate the performance improvement of hyperbolic SVM for multi-class prediction tasks on real-world complex networks as well as simulated datasets. Our work allows analytic pipelines that take the inherent hyperbolic geometry of the data into account in an end-to-end fashion without resorting to ill-fitting tools developed for Euclidean space.
Table Space Designs For Implicit and Explicit Concurrent Tabled Evaluation
Venue: arXiv cs.PL 2018
Year: 2018

One of the main advantages of Prolog is its potential for the implicit exploitation of parallelism and, as a high-level language, Prolog is also often used as a means to explicitly control concurrent tasks. Tabling is a powerful implementation technique that overcomes some limitations of traditional Prolog systems in dealing with recursion and redundant sub-computations. Given these advantages, the question that arises is if tabling has also the potential for the exploitation of concurrency/parallelism. On one hand, tabling still exploits a search space as traditional Prolog but, on the other hand, the concurrent model of tabling is necessarily far more complex since it also introduces concurrency on the access to the tables. In this paper, we summarize Yap's main contributions to concurrent tabled evaluation and we describe the design and implementation challenges of several alternative table space designs for implicit and explicit concurrent tabled evaluation which represent different trade-offs between concurrency and memory usage. We also motivate for the advantages of using fixed-size and lock-free data structures, elaborate on the key role that the engine's memory allocator plays on such environments, and discuss how Yap's mode-directed tabling support can be extended to concurrent evaluation. Finally, we present our future perspectives towards an efficient and novel concurrent framework which integrates both implicit and explicit concurrent tabled evaluation in a single Prolog engine. Under consideration in Theory and Practice of Logic Programming (TPLP).
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

Adverse surgical outcomes are costly to patients and hospitals. Approaches to benchmark surgical care are often limited to gross measures across the entire procedure despite the performance of particular tasks being largely responsible for undesirable outcomes. In order to produce metrics from tasks as opposed to the whole procedure, methods to recognize automatically individual surgical tasks are needed. In this paper, we propose several approaches to recognize surgical activities in robot-assisted minimally invasive surgery using deep learning. We collected a clinical dataset of 100 robot-assisted radical prostatectomies (RARP) with 12 tasks each and propose `RP-Net', a modified version of InceptionV3 model, for image based surgical activity recognition. We achieve an average precision of 80.9% and average recall of 76.7% across all tasks using RP-Net which out-performs all other RNN and CNN based models explored in this paper. Our results suggest that automatic surgical activity recognition during RARP is feasible and can be the foundation for advanced analytics.
X-Vision: An augmented vision tool with real-time sensing ability in tagged environments
Venue: arXiv cs.HC 2018
Year: 2018

We present the concept of X-Vision, an enhanced Augmented Reality (AR)-based visualization tool, with the real-time sensing capability in a tagged environment. We envision that this type of a tool will enhance the user-environment interaction and improve the productivity in factories, smart-spaces, home & office environments, maintenance/facility rooms and operation theatres, etc. In this paper, we describe the design of this visualization system built upon combining the object's pose information estimated by the depth camera and the object's ID & physical attributes captured by the RFID tags. We built a physical prototype of the system demonstrating the projection of 3D holograms of the objects encoded with sensed information like water-level and temperature of common office/household objects. The paper also discusses the quality metrics used to compare the pose estimation algorithms for robust reconstruction of the object's 3D data.
DAQN: Deep Auto-encoder and Q-Network
Venue: arXiv cs.CV 2018
Year: 2018

The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, and executing a lot of actions on the real robot also becomes a serious problem. Therefore, we propose an extended deep reinforcement learning method that is applied a generative model to initialize the network for reducing the number of training trials. In this paper, we used a deep q-network method as the deep reinforcement learning method and a deep auto-encoder as the generative model. We conducted experiments on three different tasks: a cart-pole game, an atari game, and a real-game with an actual robot. The proposed method trained efficiently on all tasks than the previous method, especially 2.5 times faster on a task with real environment images.
Quality-Assured Synchronized Task Assignment in Crowdsourcing
Venue: arXiv cs.DB 2018
Year: 2018

With the rapid development of crowdsourcing platforms that aggregate the intelligence of Internet workers, crowdsourcing has been widely utilized to address problems that require human cognitive abilities. Considering great dynamics of worker arrival and departure, it is of vital importance to design a task assignment scheme to adaptively select the most beneficial tasks for the available workers. In this paper, in order to make the most efficient utilization of the worker labor and balance the accuracy of answers and the overall latency, we a) develop a parameter estimation model that assists in estimating worker expertise, question easiness and answer confidence; b) propose a \textit{quality-assured synchronized task assignment scheme} that executes in batches and maximizes the number of potentially completed questions (MCQ) within each batch. We prove that MCQ problem is NP-hard and present two greedy approximation solutions to address the problem. The effectiveness and efficiency of the approximation solutions are further evaluated through extensive experiments on synthetic and real datasets. The experimental results show that the accuracy and the overall latency of the MCQ approaches outperform the existing online task assignment algorithms in the synchronized task assignment scenario.
On Minrank and Forbidden Subgraphs
Venue: arXiv cs.DS 2018
Year: 2018

The minrank over a field $\mathbb{F}$ of a graph $G$ on the vertex set $\{1,2,\ldots,n\}$ is the minimum possible rank of a matrix $M \in \mathbb{F}^{n \times n}$ such that $M_{i,i} \neq 0$ for every $i$, and $M_{i,j}=0$ for every distinct non-adjacent vertices $i$ and $j$ in $G$. For an integer $n$, a graph $H$, and a field $\mathbb{F}$, let $g(n,H,\mathbb{F})$ denote the maximum possible minrank over $\mathbb{F}$ of an $n$-vertex graph whose complement contains no copy of $H$. In this paper we study this quantity for various graphs $H$ and fields $\mathbb{F}$. For finite fields, we prove by a probabilistic argument a general lower bound on $g(n,H,\mathbb{F})$, which yields a nearly tight bound of $Ω(\sqrt{n}/\log n)$ for the triangle $H=K_3$. For the real field, we prove by an explicit construction that for every non-bipartite graph $H$, $g(n,H,\mathbb{R}) \geq n^δ$ for some $δ= δ(H)>0$. As a by-product of this construction, we disprove a conjecture of Codenotti, Pudlák, and Resta. The results are motivated by questions in information theory, circuit complexity, and geometry.