OpenRaiser/Intern-Atlas huggingface dataset:
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Venue: arXiv cs.CV 2018
Year: 2018

Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the application of various classification methods on explicitly extracted statistical features from vital signals. In this work, we propose to eliminate the high cost of engineering hand-crafted features from multivariate time-series of physiologic signals by learning their representation with a sequence-to-sequence auto-encoder. We then propose to hash the learned representations to enable signal similarity assessment for the prediction of critical events. We apply this methodological framework to predict Acute Hypotensive Episodes (AHE) on a large and diverse dataset of vital signal recordings. Experiments demonstrate the ability of the presented framework in accurately predicting an upcoming AHE.
Provenance-enabled Packet Path Tracing in the RPL-based Internet of Things
Venue: arXiv cs.CR 2018
Year: 2018

The interconnection of resource-constrained and globally accessible things with untrusted and unreliable Internet make them vulnerable to attacks including data forging, false data injection, and packet drop that affects applications with critical decision-making processes. For data trustworthiness, reliance on provenance is considered to be an effective mechanism that tracks both data acquisition and data transmission. However, provenance management for sensor networks introduces several challenges, such as low energy, bandwidth consumption, and efficient storage. This paper attempts to identify packet drop (either maliciously or due to network disruptions) and detect faulty or misbehaving nodes in the Routing Protocol for Low-Power and Lossy Networks (RPL) by following a bi-fold provenance-enabled packed path tracing (PPPT) approach. Firstly, a system-level ordered-provenance information encapsulates the data generating nodes and the forwarding nodes in the data packet. Secondly, to closely monitor the dropped packets, a node-level provenance in the form of the packet sequence number is enclosed as a routing entry in the routing table of each participating node. Lossless in nature, both approaches conserve the provenance size satisfying processing and storage requirements of IoT devices. Finally, we evaluate the efficacy of the proposed scheme with respect to provenance size, provenance generation time, and energy consumption.
Orthogonal Policy Gradient and Autonomous Driving Application
Venue: arXiv cs.AI 2018
Year: 2018

One less addressed issue of deep reinforcement learning is the lack of generalization capability based on new state and new target, for complex tasks, it is necessary to give the correct strategy and evaluate all possible actions for current state. Fortunately, deep reinforcement learning has enabled enormous progress in both subproblems: giving the correct strategy and evaluating all actions based on the state. In this paper we present an approach called orthogonal policy gradient descent(OPGD) that can make agent learn the policy gradient based on the current state and the actions set, by which the agent can learn a policy network with generalization capability. we evaluate the proposed method on the 3D autonomous driving enviroment TORCS compared with the baseline model, detailed analyses of experimental results and proofs are also given.
Exploiting Sentence Embedding for Medical Question Answering
Venue: arXiv cs.CL 2018
Year: 2018

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.
Face Verification and Forgery Detection for Ophthalmic Surgery Images
Venue: arXiv cs.CV 2018
Year: 2018

Although modern face verification systems are accessible and accurate, they are not always robust to pose variance and occlusions. Moreover, accurate models require a large amount of data to train. We structure our experiments to operate on small amounts of data obtained from an NGO that funds ophthalmic surgeries. We set up our face verification task as that of verifying pre-operation and post-operation images of a patient that undergoes ophthalmic surgery, and as such the post-operation images have occlusions like an eye patch. In this paper, we present a system that performs the face verification task using one-shot learning. To this end, our paper uses deep convolutional networks and compares different model architectures and loss functions. Our best model achieves 85% test accuracy. During inference time, we also attempt to detect image forgeries in addition to performing face verification. To achieve this, we use Error Level Analysis. Finally, we propose an inference pipeline that demonstrates how these techniques can be used to implement an automated face verification and forgery detection system.
SGR: Self-Supervised Spectral Graph Representation Learning
Venue: arXiv cs.SI 2018
Year: 2018

Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand. Unfortunately, a "one-size-fits-all" solution is unattainable, as different analytical tasks may require different attention to global or local graph features. We develop SGR, the first, to our knowledge, method for learning graph representations in a self-supervised manner. Grounded on spectral graph analysis, SGR seamlessly combines all aforementioned desirable properties. In extensive experiments, we show how our approach works on large graph collections, facilitates self-supervised representation learning across a variety of application domains, and performs competitively to state-of-the-art methods without re-training.
In-silico Feedback Control of a MIMO Synthetic Toggle Switch via Pulse-Width Modulation
Venue: arXiv cs.SY 2018
Year: 2018

The synthetic toggle switch, first proposed by Gardner & Collins [1] is a MIMO control system that can be controlled by varying the concentrations of two inducer molecules, aTc and IPTG, to achieve a desired level of expression of the two genes it comprises. It has been shown [2] that this can be accomplished through an open-loop external control strategy where the two inputs are selected as mutually exclusive periodic pulse waves of appropriate amplitude and duty-cycle. In this paper, we use a recently derived average model of the genetic toggle switch subject to these inputs to synthesize new feedback control approaches that adjust the inputs duty-cycle in real-time via two different possible strategies, a model based hybrid PI-PWM approach and a so-called Zero-Average dynamics (ZAD) controller. The controllers are validated in-silico via both deterministic and stochastic simulations (SSA) illustrating the advantages and limitations of each strategy
Decentralized Data Storages: Technologies of Construction
Venue: arXiv cs.DC 2018
Year: 2018

The paper presents a comparative overview of decentralized data storages of various types. It is shown that although they have a number of common properties that are typical of all peer-to-peer (P2P) networks, the problems to be solved and, accordingly, the technologies used to build different types of storages differ significantly.
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
Venue: arXiv cs.CV 2018
Year: 2018

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN. In this paper, we experimentally find that, under some circumstances, the one-to-one mapping learned by CycleGAN is just a random one within the large feasible solution space. Based on this observation, we explore to add extra constraints such that the one-to-one mapping is controllable and satisfies more properties related to specific tasks. We propose to solve an optimal transport mapping restrained by a task-specific cost function that reflects the desired properties, and use the barycenters of optimal transport mapping to serve as references for CycleGAN. Our experiments indicate that the proposed algorithm is capable of learning a one-to-one mapping with the desired properties.
Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction
Venue: arXiv cs.SI 2018
Year: 2018

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks.
Cost of selfishness in the allocation of cities in the Multiple Travelling Salesmen Problem
Venue: arXiv cs.MA 2018
Year: 2018

The decision to centralise or decentralise human organisations requires quantified evidence but little is available in the literature. We provide such data in a variant of the Multiple Travelling Salesmen Problem (MTSP) in which we study how the allocation sub-problem may be decentralised among selfish selfmen. Our contributions are (i) this modification of the MTSP in order to include selfishness, (ii) the proposition of organisations to solve this modified MTSP, and (iii) the comparison of these organisations. Our 5 organisations may be summarised as follows: (i) OptDecentr is a pure Centralised Organisation (CO) in which a Central Authority (CA) finds the best solution which could be found by a Decentralised Organisation (DO), (ii) Cluster and (iii) Auction are CO/DO hybrids, and (iv) P2P and (v) CNP are pure DO. Sixth and seventh organisations are used as benchmarks: (vi) NoRealloc is a pure DO which ignores the allocation problem, and (vii) FullCentr is a pure CO which solves a different problem, viz., the traditional MTSP. Comparing the efficiency of pairs of these mechanisms quantify the price of decentralising an organisation. In particular, our model of selfishness in OptDecentr makes the total route length 30% (respectively, 60%) longer with 5 (respectively, 9) salesmen than the traditional MTSP in FullCentr when the computation time is limited to 30 minutes. With this time limit, our results also seem to indicate that the level of coercion of the CA impacts more the total route length than the level of centralisation.
Traceability as an integral part of supply chain logistics management: an analytical review
Venue: arXiv cs.CY 2018
Year: 2018

Purpose: Supply chain has become very complex today. There are multiple stakeholders at various points. All these stakeholders need to collaborate with each other in multiple directions for its effective and efficient management. The manufacturers need proper information and data about the product location, its processing history, raw materials, etc at each point so as to control the production process. Companies need to develop global and effective strategies to sustain in the competitive market. Logistics helps companies to implement the effectiveness across the business supply chain from source to destination to achieve their strategic goals. Traceability has become one of the integrated parts of the supply chain logistics management that track and trace the product's information in upstream and downstream at each point. All the stakeholders in the supply chain have different objectives to implement the traceability solution that depends on their role (e.g. manufacturers, distributers or wholesalers have their own role). The information generated and needed from all these actors are also different and are to be shared among all to run the supply chain smoothly. But the stakeholders don't want to share all the information with other stake holders which is a major challenge to be addressed in current traceability solutions. The purpose of this research is to conduct thorough study of the literatures available on the traceability solutions, finding the gaps and recommending our views to enhance the collaborations among the stakeholders in every point of the business supply chain. The study will be helpful for further researchers to propose a traceability meta-model to address the gaps. Design/methodology/approach: The relevant literatures with keywords supply chain management, traceability, logistics management are searched from the trusted database. The scope and the objectives of the research is set based on which the papers are filtered as per the titles and abstract. Proper analyses of the screened papers are done and the recommendations are given. Findings: Traceability solution is popular among the industries since a long for their supply chain management. After proper analysis of the available literatures about the traceability solution the industries use, this research will identify the gaps based on which we give the proper recommendations and perspectives of our work.
Latecomers Help to Meet: Deterministic Anonymous Gathering in the Plane
Venue: arXiv cs.DC 2018
Year: 2018

A team of anonymous mobile agents represented by points freely moving in the plane have to gather at a single point and stop. Agents start at different points of the plane and at possibly different times chosen by the adversary. They are equipped with compasses, a common unit of distance and clocks. They execute the same deterministic algorithm and travel at speed 1. When agents are at distance at most $ε$, for some positive constant $ε$ unknown to them, they can exchange all information. Due to the anonymity of the agents and the symmetry of the plane, gathering is impossible, e.g., if agents start simultaneously at distances larger than $ε$. However, if some agents start with a delay with respect to others, gathering may become possible. In which situations such latecomers can enable gathering? To answer this question we consider initial configurations formalized as sets of pairs $\{(p_1,t_1), (p_2,t_2),\dots , (p_n,t_n)\}$, for $n\geq 2$ where $p_i$ is the starting point of the $i$-th agent and $t_i$ is its starting time. An initial configuration is gatherable if agents starting at it can be gathered by some algorithm, even dedicated to this particular configuration. We characterize all gatherable initial configurations. Is there a universal deterministic algorithm that can gather all gatherable configurations of a given size. It turns out that the answer is no. We show that all gatherable configurations can be partitioned into two sets: bad and good configurations. We show that bad gatherable configurations (even of size 2) cannot be gathered by a common gathering algorithm, and we prove that there is a universal algorithm that gathers all good configurations of a given size.
Energy Efficient Precoder in Multi-User MIMO Systems with Imperfect Channel State Information
Venue: arXiv cs.IT 2018
Year: 2018

This article is on the energy efficient precoder design in multi-user multiple-input-multiple-output (MU-MIMO) systems which is also robust with respect to the imperfect channel state information (CSI) at the transmitters. In other words, we design the precoder matrix associated with each transmitter to maximize the general energy efficiency of the network. We investigate the problem in two conventional cases. The first case considers the statistical characterization for the channel estimation error that leads to a quadratically constrained quadratic program (QCQP) with a semi-closed-form solution. Then, we turn our attention to the case which considers only the uncertainty region for the channel estimation error; this case eventually results in a semi-definite program (SDP).
Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches
Venue: arXiv cs.IT 2018
Year: 2018

Oversampled adaptive sensing (OAS) is a recently proposed Bayesian framework which sequentially adapts the sensing basis. In OAS, estimation quality is, in each step, measured by conditional mean squared errors (MSEs), and the basis for the next sensing step is adapted accordingly. For given average sensing time, OAS reduces the MSE compared to non-adaptive schemes, when the signal is sparse. This paper studies the asymptotic performance of Bayesian OAS, for unitarily invariant random projections. For sparse signals, it is shown that OAS with Bayesian recovery and hard adaptation significantly outperforms the minimum MSE bound for non-adaptive sensing. To address implementational aspects, two computationally tractable algorithms are proposed, and their performances are compared against the state-of-the-art non-adaptive algorithms via numerical simulations. Investigations depict that these low-complexity OAS algorithms, despite their suboptimality, outperform well-known non-adaptive schemes for sparse recovery, such as LASSO, with rather small oversampling factors. This gain grows, as the compression rate increases.
Mathematical Analysis of Adversarial Attacks
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to classify any given image into any prescribed class. For a special two-layer neural network: a linear layer followed by the softmax output activation, we show that the CW-L2 attack increases the ratio of the classification probability between the target and ground truth classes. Moreover, we provide numerical results to verify all our theoretical results.
A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback
Venue: arXiv cs.GT 2018
Year: 2018

Computing Nash equilibria for strategic multi-agent systems is challenging for expensive black box systems. Motivated by the ubiquity of games involving exploitation of common resources, this paper considers the above problem for potential games. We use the Bayesian optimization framework to obtain novel algorithms to solve finite (discrete action spaces) and infinite (real interval action spaces) potential games, utilizing the structure of potential games. Numerical results illustrate the efficiency of the approach in computing the Nash equilibria of static potential games and linear Nash equilibria of dynamic potential games.
Stable Tensor Neural Networks for Rapid Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

We propose a tensor neural network ($t$-NN) framework that offers an exciting new paradigm for designing neural networks with multidimensional (tensor) data. Our network architecture is based on the $t$-product (Kilmer and Martin, 2011), an algebraic formulation to multiply tensors via circulant convolution. In this $t$-product algebra, we interpret tensors as $t$-linear operators analogous to matrices as linear operators, and hence our framework inherits mimetic matrix properties. To exemplify the elegant, matrix-mimetic algebraic structure of our $t$-NNs, we expand on recent work (Haber and Ruthotto, 2017) which interprets deep neural networks as discretizations of non-linear differential equations and introduces stable neural networks which promote superior generalization. Motivated by this dynamic framework, we introduce a stable $t$-NN which facilitates more rapid learning because of its reduced, more powerful parameterization. Through our high-dimensional design, we create a more compact parameter space and extract multidimensional correlations otherwise latent in traditional algorithms. We further generalize our $t$-NN framework to a family of tensor-tensor products (Kernfeld, Kilmer, and Aeron, 2015) which still induce a matrix-mimetic algebraic structure. Through numerical experiments on the MNIST and CIFAR-10 datasets, we demonstrate the more powerful parameterizations and improved generalizability of stable $t$-NNs.
Information Theoretic Limits for Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we analyze the information theoretic lower bound on the necessary number of samples needed for recovering a sparse signal under different compressed sensing settings. We focus on the weighted graph model, a model-based framework proposed by Hegde et al. (2015), for standard compressed sensing as well as for one-bit compressed sensing. We study both the noisy and noiseless regimes. Our analysis is general in the sense that it applies to any algorithm used to recover the signal. We carefully construct restricted ensembles for different settings and then apply Fano's inequality to establish the lower bound on the necessary number of samples. Furthermore, we show that our bound is tight for one-bit compressed sensing, while for standard compressed sensing, our bound is tight up to a logarithmic factor of the number of non-zero entries in the signal.