OpenRaiser/Intern-Atlas huggingface dataset:
Towards Task Understanding in Visual Settings
Venue: arXiv cs.IR 2018
Year: 2018

We consider the problem of understanding real world tasks depicted in visual images. While most existing image captioning methods excel in producing natural language descriptions of visual scenes involving human tasks, there is often the need for an understanding of the exact task being undertaken rather than a literal description of the scene. We leverage insights from real world task understanding systems, and propose a framework composed of convolutional neural networks, and an external hierarchical task ontology to produce task descriptions from input images. Detailed experiments highlight the efficacy of the extracted descriptions, which could potentially find their way in many applications, including image alt text generation.
Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET
Venue: arXiv cs.CV 2018
Year: 2018

Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI. Conventionally, attenuated events are corrected in sinogram space using attenuation maps derived from CT or MR-derived pseudo CT. Separately, scattered events are iteratively estimated by a 3D model-based simulation using down-sampled attenuation and emission sinograms. However, no studies have investigated joint correction of attenuation and scatter using DCNN in image space. Therefore, we aim to develop and optimize a DCNN model for attenuation and scatter correction (ASC) simultaneously in PET image space without additional anatomical imaging or time-consuming iterative scatter simulation. For the first time, we demonstrated the feasibility of directly producing PET images corrected for attenuation and scatter using DCNN (PET-DCNN) from noncorrected PET (PET-NC) images.
Efficient Measuring of Congruence on High Dimensional Time Series
Venue: arXiv cs.DS 2018
Year: 2018

A time series is a sequence of data items; typical examples are streams of temperature measurements, stock ticker data, or gestures recorded with modern virtual reality motion controllers. Quite some research has been devoted to comparing and indexing time series. Especially, when the comparison should not be affected by time warping, the ubiquitous Dynamic Time Warping distance function ($\texttt{DTW}$) is one of the most analyzed time series distance functions. The Dog-Keeper distance ($\texttt{DK}$) is another example for a distance function on time series which is truely invariant under time warping. For many application scenarios (e.$\,$g. motion gesture recognition in virtual reality), the invariance under isometric spatial transformations (i.$\,$e. rotation, translation, and mirroring) is as important as the invariance under time warping. Distance functions on time series which are invariant under isometric transformations can be seen as measurements for the congruency of two time series. The congruence distance ($\texttt{CD}$) is an example for such a distance function. However, it is very hard to compute and it is not invariant under time warpings. In this work, we are taking one step towards developing a feasable distance function which is invariant under isometric spatial transformations and time warping: We develop four approximations for $\texttt{CD}$. Two of these even satisfy the triangle inequality and can thus be used with metric indexing structures. We show that all approximations serve as a lower bound to $\texttt{CD}$. Our evaluation shows that they achieve remarkable tightness while providing a speedup of more than two orders of magnitude to the congruence distance.
Privacy-Preserving Aggregation of Controllable Loads to Compensate Fluctuations in Solar Power
Venue: arXiv cs.SY 2018
Year: 2018

Cybersecurity and privacy are of the utmost importance for safe, reliable operation of the electric grid. It is well known that the increased connectivity/interoperability between all stakeholders (e.g., utilities, suppliers, and consumers) will enable personal information collection. Significant advanced metering infrastructure (AMI) deployment and demand response (DR) programs across the country, while enable enhanced automation, also generate energy data on individual consumers that can potentially be used for exploiting privacy. Inspired by existing works which consider DR, battery-based perturbation, and differential privacy noise adding, we novelly consider the aggregator (cluster) level privacy issue in the DR framework of solar photovoltaic (PV) generation following. Different from most of the existing works which mainly rely on the charging/discharging scheduling of rechargeable batteries, we utilize controllable building loads to serve as virtual storage devices to absorb a large portion of the PV generation while delicately keeping desired noisy terms to satisfy the differential privacy for the raw load profiles at the aggregator level. This not only ensures differential privacy, but also improves the DR efficiency in load following since part of the noisy signal in solar PV generation has been filtered out. In particular, a mixed integer quadratic optimization problem is formulated to optimally dispatch a population of on/off controllable loads to achieve this privacy preserving DR service.
Low-Complexity Adaptive Beam and Channel Tracking for Mobile mmWave Communications
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study low-complexity algorithms for beam and channel tracking for millimeter-wave (mmWave) communications. In particular, the least mean squares (LMS) and bidirectional LMS (BiLMS) algorithms are derived for a mobile mmWave transmission scenario, where channel measurement is a nonlinear function of the unknown angle-of-arrival (AoA) and angle-of-departure (AoD). Numerical results confirm that LMS is superior to widely used Extended Kalman Filter (EKF) algorithm in tracking the mmWave beam, when the initialization of AoA/AoD and channel gains is imperfect (i.e., performed using noisy channel estimates). Moreover, BiLMS exhibits a very good mean square error (MSE) performance as compared to both LMS and EKF, which makes it a promising channel tracking algorithm for a mobile mmWave transmission scenario. We also show that LMS and BiLMS algorithms are more robust against the impairments due to the non-optimal antenna array size as compared to EKF, and show relatively faster convergence characteristic along with increasing signal-to-noise ratio (SNR).
Deep learning for pedestrians: backpropagation in CNNs
Venue: arXiv cs.LG 2018
Year: 2018

The goal of this document is to provide a pedagogical introduction to the main concepts underpinning the training of deep neural networks using gradient descent; a process known as backpropagation. Although we focus on a very influential class of architectures called "convolutional neural networks" (CNNs) the approach is generic and useful to the machine learning community as a whole. Motivated by the observation that derivations of backpropagation are often obscured by clumsy index-heavy narratives that appear somewhat mathemagical, we aim to offer a conceptually clear, vectorized description that articulates well the higher level logic. Following the principle of "writing is nature's way of letting you know how sloppy your thinking is", we try to make the calculations meticulous, self-contained and yet as intuitive as possible. Taking nothing for granted, ample illustrations serve as visual guides and an extensive bibliography is provided for further explorations. (For the sake of clarity, long mathematical derivations and visualizations have been broken up into short "summarized views" and longer "detailed views" encoded into the PDF as optional content groups. Some figures contain animations designed to illustrate important concepts in a more engaging style. For these reasons, we advise to download the document locally and open it using Adobe Acrobat Reader. Other viewers were not tested and may not render the detailed views, animations correctly.)
Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional Neural Networks (CNNs) usually use the same activation function, such as RELU, for all convolutional layers. There are performance limitations of just using RELU. In order to achieve better classification performance, reduce training and testing times, and reduce power consumption and memory usage, a new "Compressed Multi-function CNN" is developed. Google's Inception-V4, for example, is a very deep CNN that consists of 4 Inception-A blocks, 7 Inception-B blocks, and 3 Inception-C blocks. RELU is used for all convolutional layers. A new "Compressed Multi-function Inception-V4" (CMI) that can use different activation functions is created with k Inception-A blocks, m Inception-B blocks, and n Inception-C blocks where k in {1, 2, 3, 4}, m in {1, 2, 3, 4, 5, 6, 7}, n in {1, 2, 3}, and (k+m+n)<14. For performance analysis, a dataset for classifying brain MRI images into one of the four stages of Alzheimer's disease is used to compare three CMI architectures with Inception-V4 in terms of F1-score, training and testing times (related to power consumption), and memory usage (model size). Overall, simulations show that the new CMI models can outperform both the commonly used Inception-V4 and Inception-V4 using different activation functions. In the future, other "Compressed Multi-function CNNs", such as "Compressed Multi-function ResNets and DenseNets" that have a reduced number of convolutional blocks using different activation functions, will be developed to further increase classification accuracy, reduce training and testing times, reduce computational power, and reduce memory usage (model size) for building more effective healthcare systems, such as implementing accurate and convenient disease diagnosis systems on mobile devices that have limited battery power and memory.
MOBIUS: Model-Oblivious Binarized Neural Networks
Venue: arXiv cs.CR 2018
Year: 2018

A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted prediction, has been studied in machine learning that provides prediction services. In this work, we present MOBIUS (Model-Oblivious BInary neUral networkS), a new system that combines Binarized Neural Networks (BNNs) and secure computation based on secret sharing as tools for scalable and fast privacy-preserving machine learning. BNNs improve computational performance by binarizing values in training to $-1$ and $+1$, while secure computation based on secret sharing provides fast and various computations under encrypted forms via modulo operations with a short bit length. However, combining these tools is not trivial because their operations have different algebraic structures and the use of BNNs downgrades prediction accuracy in general. MOBIUS uses improved procedures of BNNs and secure computation that have compatible algebraic structures without downgrading prediction accuracy. We created an implementation of MOBIUS in C++ using the ABY library (NDSS 2015). We then conducted experiments using the MNIST dataset, and the results show that MOBIUS can return a prediction within 0.76 seconds, which is six times faster than SecureML (IEEE S\&P 2017). MOBIUS allows a client to request for encrypted prediction and allows a trainer to obliviously publish an encrypted model to a cloud provided by a computational resource provider, i.e., without revealing the original model itself to the provider.
Networks for Nonlinear Diffusion Problems in Imaging
Venue: arXiv cs.CV 2018
Year: 2018

A multitude of imaging and vision tasks have seen recently a major transformation by deep learning methods and in particular by the application of convolutional neural networks. These methods achieve impressive results, even for applications where it is not apparent that convolutions are suited to capture the underlying physics. In this work we develop a network architecture based on nonlinear diffusion processes, named DiffNet. By design, we obtain a nonlinear network architecture that is well suited for diffusion related problems in imaging. Furthermore, the performed updates are explicit, by which we obtain better interpretability and generalisability compared to classical convolutional neural network architectures. The performance of DiffNet tested on the inverse problem of nonlinear diffusion with the Perona-Malik filter on the STL-10 image dataset. We obtain competitive results to the established U-Net architecture, with a fraction of parameters and necessary training data.
Interoperability-Guided Testing of QUIC Implementations using Symbolic Execution
Venue: arXiv cs.SE 2018
Year: 2018

The main reason for the standardization of network protocols, like QUIC, is to ensure interoperability between implementations, which poses a challenging task. Manual tests are currently used to test the different existing implementations for interoperability, but given the complex nature of network protocols, it is hard to cover all possible edge cases. State-of-the-art automated software testing techniques, such as Symbolic Execution (SymEx), have proven themselves capable of analyzing complex real-world software and finding hard to detect bugs. We present a SymEx-based method for finding interoperability issues in QUIC implementations, and explore its merit in a case study that analyzes the interoperability of picoquic and QUANT. We find that, while SymEx is able to analyze deep interactions between different implementations and uncovers several bugs, in order to enable efficient interoperability testing, implementations need to provide additional information about their current protocol state.
Universal Network Representation for Heterogeneous Information Networks
Venue: arXiv cs.SI 2018
Year: 2018

Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes (entities) of a network are of the same type, e.g., papers in a citation network. In this paper, we propose a universal network representation approach (UNRA), that represents different types of nodes in heterogeneous information networks in a continuous and common vector space. The UNRA is built on our latest mutually updated neural language module, which simultaneously captures inter-relationship among homogeneous nodes and node-content correlation. Relationships between different types of nodes are also assembled and learned in a unified framework. Experiments validate that the UNRA achieves outstanding performance, compared to six other state-of-the-art algorithms, in node representation, node classification, and network visualization. In node classification, the UNRA achieves a 3\% to 132\% performance improvement in terms of accuracy.
Data-parallel distributed training of very large models beyond GPU capacity
Venue: arXiv cs.DC 2018
Year: 2018

GPUs have limited memory and it is difficult to train wide and/or deep models that cause the training process to go out of memory. It is shown in this paper how an open source tool called Large Model Support (LMS) can utilize a high bandwidth NVLink connection between CPUs and GPUs to accomplish training of deep convolutional networks. LMS performs tensor swapping between CPU memory and GPU memory such that only a minimal number of tensors required in a training step are kept in the GPU memory. It is also shown how LMS can be combined with an MPI based distributed deep learning module to train models in a data-parallel fashion across multiple GPUs, such that each GPU is utilizing the CPU memory for tensor swapping. The hardware architecture that enables the high bandwidth GPU link with the CPU is discussed as well as the associated set of software tools that are available as the PowerAI package.
AI based Safety System for Employees of Manufacturing Industries in Developing Countries
Venue: arXiv cs.CY 2018
Year: 2018

In this paper authors are going to present a Markov Decision Process (MDP) based algorithm in Industrial Internet of Things (IIoT) as a safety compliance layer for human in loop system. Though some industries are moving towards Industry 4.0 and attempting to automate the systems as much as possible by using robots, still human in loop systems are very common in developing countries like India. When ever there is a need for human machine interaction, there is a scope of health hazard. In this work we have developed a system for one such industry using MDP. The proposed algorithm used in this system learned the probability of state transition from experience as well as the system is adaptable to new changes by incorporating the concept of transfer learning. The system was evaluated on the data set obtained from 39 sensors connected to a computer numerically controlled (CNC) machine pushing data every second in a 24x7 scenario. The state changes are typically instructed by a human which subsequently lead to some intentional or unintentional mistakes and errors. The proposed system raises an alarm for the operator to warn which he may or may not overlook depending on his own perception about the present condition of the system. Repeated ignorance of the operator for a particular state transition warning guides the system to retrain the model. We observed 95.61% alarms raised by the said system are taken care of by the operator. 3.2% alarms are coming from the changes in the system which in turn used to retrain the model and 1.19% alarms are false alarms. We could not compute the error coming from the mistake performed by the human operator as there is no ground truth available for that.
Play as You Like: Timbre-enhanced Multi-modal Music Style Transfer
Venue: arXiv cs.SD 2018
Year: 2018

Style transfer of polyphonic music recordings is a challenging task when considering the modeling of diverse, imaginative, and reasonable music pieces in the style different from their original one. To achieve this, learning stable multi-modal representations for both domain-variant (i.e., style) and domain-invariant (i.e., content) information of music in an unsupervised manner is critical. In this paper, we propose an unsupervised music style transfer method without the need for parallel data. Besides, to characterize the multi-modal distribution of music pieces, we employ the Multi-modal Unsupervised Image-to-Image Translation (MUNIT) framework in the proposed system. This allows one to generate diverse outputs from the learned latent distributions representing contents and styles. Moreover, to better capture the granularity of sound, such as the perceptual dimensions of timbre and the nuance in instrument-specific performance, cognitively plausible features including mel-frequency cepstral coefficients (MFCC), spectral difference, and spectral envelope, are combined with the widely-used mel-spectrogram into a timber-enhanced multi-channel input representation. The Relativistic average Generative Adversarial Networks (RaGAN) is also utilized to achieve fast convergence and high stability. We conduct experiments on bilateral style transfer tasks among three different genres, namely piano solo, guitar solo, and string quartet. Results demonstrate the advantages of the proposed method in music style transfer with improved sound quality and in allowing users to manipulate the output.
Unifying the stochastic and the adversarial Bandits with Knapsack
Venue: arXiv cs.LG 2018
Year: 2018

This paper investigates the adversarial Bandits with Knapsack (BwK) online learning problem, where a player repeatedly chooses to perform an action, pays the corresponding cost, and receives a reward associated with the action. The player is constrained by the maximum budget $B$ that can be spent to perform actions, and the rewards and the costs of the actions are assigned by an adversary. This problem has only been studied in the restricted setting where the reward of an action is greater than the cost of the action, while we provide a solution in the general setting. Namely, we propose EXP3.BwK, a novel algorithm that achieves order optimal regret. We also propose EXP3++.BwK, which is order optimal in the adversarial BwK setup, and incurs an almost optimal expected regret with an additional factor of $\log(B)$ in the stochastic BwK setup. Finally, we investigate the case of having large costs for the actions (i.e., they are comparable to the budget size $B$), and show that for the adversarial setting, achievable regret bounds can be significantly worse, compared to the case of having costs bounded by a constant, which is a common assumption within the BwK literature.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Venue: arXiv cs.CL 2018
Year: 2018

Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.
Increasing the Capability of Neural Networks for Surface Reconstruction from Noisy Point Clouds
Venue: arXiv cs.GR 2018
Year: 2018

This paper builds upon the current methods to increase their capability and automation for 3D surface construction from noisy and potentially sparse point clouds. It presents an analysis of an artificial neural network surface regression and mapping method, describing caveats, improvements and justification for the different approach.
Formally Verifying WARP-V, an Open-Source TL-Verilog RISC-V Core Generator
Venue: arXiv cs.AR 2018
Year: 2018

Timing-abstract and transaction-level design using TL-Verilog have shown significant productivity gains for logic design. In this work, we explored the natural extension of transaction-level design methodology into formal verification. WARP-V is a CPU core generator written in TL-Verilog. Our primary verification vehicle for WARP-V was a formal verification framework for RISC-V, called riscv-formal. The timing-abstract and transaction-level logic modeling techniques of TL-Verilog greatly simplified the task of creating a harness connecting the WARP-V model to the verification interface of riscv-formal. Furthermore, the same harness works across all RISC-V configurations of WARP-V.
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
Venue: arXiv cs.LG 2018
Year: 2018

One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian networks are needed to successfully capture model uncertainty. We vary the number and position of Bayesian layers in a network and compare their performance on active learning with the MNIST dataset. We found that we can fully capture the model uncertainty by using only a few Bayesian layers near the output of the network, combining the advantages of deterministic and Bayesian networks.