OpenRaiser/Intern-Atlas huggingface dataset:
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
Venue: arXiv cs.IR 2020
Year: 2020

Herb recommendation plays a crucial role in the therapeutic process of Traditional Chinese Medicine(TCM), which aims to recommend a set of herbs to treat the symptoms of a patient. While several machine learning methods have been developed for herb recommendation, they are limited in modeling only the interactions between herbs and symptoms, and ignoring the intermediate process of syndrome induction. When performing TCM diagnostics, an experienced doctor typically induces syndromes from the patient's symptoms and then suggests herbs based on the induced syndromes. As such, we believe the induction of syndromes, an overall description of the symptoms, is important for herb recommendation and should be properly handled. However, due to the ambiguity and complexity of syndrome induction, most prescriptions lack the explicit ground truth of syndromes. In this paper, we propose a new method that takes the implicit syndrome induction process into account for herb recommendation. Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes. Towards symptom embedding learning, we additionally construct a symptom-symptom graph from the input prescriptions for capturing the relations between symptoms; we then build graph convolution networks(GCNs) on both symptom-symptom and symptom-herb graphs to learn symptom embedding. Similarly, we construct a herb-herb graph and build GCNs on both herb-herb and symptom-herb graphs to learn herb embedding, which is finally interacted with the syndrome representation to predict the scores of herbs. In this way, more comprehensive representations can be obtained. We conduct extensive experiments on a public TCM dataset, showing significant improvements over state-of-the-art herb recommendation methods.
Linear-time Erasure List-decoding of Expander Codes
Venue: arXiv cs.IT 2020
Year: 2020

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let $r > 0$ be any integer. Given an inner code $C_0$ of length $d$, and a $d$-regular bipartite expander graph $G$ with $n$ vertices on each side, we give an algorithm to list-decode the expander code $C = C(G, C_0)$ of length $nd$ from approximately $δδ_r nd$ erasures in time $n \cdot \mathrm{poly}(d2^r / δ)$, where $δ$ and $δ_r$ are the relative distance and the $r$'th generalized relative distance of $C_0$, respectively. To the best of our knowledge, this is the first linear-time algorithm that can list-decode expander codes from erasures beyond their (designed) distance of approximately $δ^2 nd$. To obtain our results, we show that an approach similar to that of (Hemenway and Wootters, Information and Computation, 2018) can be used to obtain such an erasure-list-decoding algorithm with an exponentially worse dependence of the running time on $r$ and $δ$; then we show how to improve the dependence of the running time on these parameters.
A Hybrid Systems-based Hierarchical Control Architecture for Heterogeneous Field Robot Teams
Venue: arXiv cs.RO 2020
Year: 2020

Field robot systems have recently been applied to a wide range of research fields. Making such systems more automated, advanced, and activated requires cooperation among heterogeneous robots. Classic control theory is inefficient in managing large-scale complex dynamic systems. Therefore, the supervisory control theory based on discrete event system needs to be introduced to overcome this limitation. In this study, we propose a hybrid systems-based hierarchical control architecture through a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid systems and its dynamics are modeled through a formal method called hybrid automata, and the behavior specifications expressing the control objectives for cooperation are designed. Additionally, a modular supervisor that is more scalable and maintainable than a centralized supervisory controller was synthesized. The proposed hybrid systems and hierarchical control architecture were implemented, validated, and then evaluated for performance through the physics-based simulator. Experimental results confirmed that the heterogeneous field robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.
Diversity sampling is an implicit regularization for kernel methods
Venue: arXiv cs.LG 2020
Year: 2020

Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nyström method and preconditioning techniques. The Nyström approximation -- based on a subset of landmarks -- gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization. We further elaborate on the impact of sampling diverse landmarks for constructing the Nyström approximation in supervised as well as unsupervised kernel methods. By using Determinantal Point Processes for sampling, we obtain additional theoretical results concerning the interplay between diversity and regularization. Empirically, we demonstrate the advantages of training kernel methods based on subsets made of diverse points. In particular, if the dataset has a dense bulk and a sparser tail, we show that Nyström kernel regression with diverse landmarks increases the accuracy of the regression in sparser regions of the dataset, with respect to a uniform landmark sampling. A greedy heuristic is also proposed to select diverse samples of significant size within large datasets when exact DPP sampling is not practically feasible.
Equivalence Testing of Weighted Automata over Partially Commutative Monoids
Venue: arXiv cs.FL 2020
Year: 2020

We study \emph{multiplicity equivalence} testing of automata over partially commutative monoids (pc monoids) and show efficient algorithms in special cases, exploiting the structure of the underlying non-commutation graph of the monoid. Specifically, if the clique cover number of the non-commutation graph (the minimum number of cliques covering the graph) of the pc monoid is a constant, we obtain a deterministic quasi-polynomial time algorithm. As a consequence, we also obtain the first deterministic quasi-polynomial time algorithms for multiplicity equivalence testing of $k$-tape automata and for equivalence testing of deterministic $k$-tape automata for constant $k$. Prior to this, a randomized polynomial-time algorithm for the above problems was shown by Worrell [ICALP 2013]. We also consider pc monoids for which the non-commutation graphs have cover consisting of at most $k$ cliques and star graphs for any constant $k$. We obtain randomized polynomial-time algorithm for multiplicity equivalence testing of automata over such monoids.
Towards Certifiable Adversarial Sample Detection
Venue: arXiv cs.LG 2020
Year: 2020

Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat. There have been various proposals to improve CNNs' adversarial robustness but these all suffer performance penalties or other limitations. In this paper, we provide a new approach in the form of a certifiable adversarial detection scheme, the Certifiable Taboo Trap (CTT). The system can provide certifiable guarantees of detection of adversarial inputs for certain $l_{\infty}$ sizes on a reasonable assumption, namely that the training data have the same distribution as the test data. We develop and evaluate several versions of CTT with a range of defense capabilities, training overheads and certifiability on adversarial samples. Against adversaries with various $l_p$ norms, CTT outperforms existing defense methods that focus purely on improving network robustness. We show that CTT has small false positive rates on clean test data, minimal compute overheads when deployed, and can support complex security policies.
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs
Venue: arXiv cs.LG 2020
Year: 2020

Nowadays Knowledge Graphs constitute a mainstream approach for the representation of relational information on big heterogeneous data, however, they may contain a big amount of imputed noise when constructed automatically. To address this problem, different error detection methodologies have been proposed, mainly focusing on path ranking and representation learning. This work presents various mainstream approaches and proposes a hybrid and modular methodology for the task. We compare different methods on two benchmarks and one real-world biomedical publications dataset, showcasing the potential of our approach and providing insights on graph embeddings when dealing with noisy Knowledge Graphs.
Two More Algorithms for Randomized Signature-Free Asynchronous Binary Byzantine Consensus with $t < n/3$ and $O(n^2)$ Messages and $O(1)$ Round Expected Termination
Venue: arXiv cs.DC 2020
Year: 2020

This work describes two randomized, asynchronous, round based, Binary Byzantine faulty tolerant consensus algorithms based on the algorithms of [25] and [26]. Like the algorithms of [25] and [26] they do not use signatures, use $O(n^2)$ messages per round (where each message is composed of a round number and a constant number of bits), tolerate up to one third failures, and have expected termination in constant number of rounds. The first, like [26], uses a weak common coin (i.e. one that can return different values at different processes with a constant probability) to ensure termination. The algorithm consists of $5$ to $7$ message broadcasts per round. An optimization is described that reduces this to $4$ to $5$ broadcasts per round for rounds following the first round. Comparatively, [26] consists of $8$ to $12$ message broadcasts per round. The second algorithm, like [25], uses a perfect common coin (i.e. one that returns the same value at all non-faulty processes) for both termination and correctness. Unlike [25], it does not require a fair scheduler to ensure termination. Furthermore, the algorithm consists of $2$ to $3$ message broadcasts for the first round and $1$ to $2$ broadcasts for the following rounds, while [29] consists of $2$ to $3$ broadcasts per round.
Support-weighted Adversarial Imitation Learning
Venue: arXiv cs.LG 2020
Year: 2020

Adversarial Imitation Learning (AIL) is a broad family of imitation learning methods designed to mimic expert behaviors from demonstrations. While AIL has shown state-of-the-art performance on imitation learning with only small number of demonstrations, it faces several practical challenges such as potential training instability and implicit reward bias. To address the challenges, we propose Support-weighted Adversarial Imitation Learning (SAIL), a general framework that extends a given AIL algorithm with information derived from support estimation of the expert policies. SAIL improves the quality of the reinforcement signals by weighing the adversarial reward with a confidence score from support estimation of the expert policy. We also show that SAIL is always at least as efficient as the underlying AIL algorithm that SAIL uses for learning the adversarial reward. Empirically, we show that the proposed method achieves better performance and training stability than baseline methods on a wide range of benchmark control tasks.
PA-Cache: Evolving Learning-Based Popularity-Aware Content Caching in Edge Networks
Venue: arXiv cs.NI 2020
Year: 2020

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this paper, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. \rb{The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8\% performance gap when the cache percentage is 1.0\%}. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.
Contextual Equivalence for Signal Flow Graphs
Venue: arXiv cs.LO 2020
Year: 2020

We extend the signal flow calculus---a compositional account of the classical signal flow graph model of computation---to encompass affine behaviour, and furnish it with a novel operational semantics. The increased expressive power allows us to define a canonical notion of contextual equivalence, which we show to coincide with denotational equality. Finally, we characterise the realisable fragment of the calculus: those terms that express the computations of (affine) signal flow graphs.
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Venue: arXiv cs.CL 2020
Year: 2020

Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions from clinical notes using SemEHR. Data was extracted from Clinical Record Interactive Search (CRIS) system at South London and Maudsley Biomedical Research Centre (SLaM BRC) and the cohort consisted of all individuals who had received a primary or secondary diagnosis of severe mental illness between 2007 and 2018. Three pairs of annotators annotated 2403 documents with an average Cohen's Kappa of 0.757. Results show that the NLP performance varies across different diseases areas (F1 0.601 - 0.954) suggesting that the language patterns or terminologies of different condition groups entail different technical challenges to the same NLP task.
Modeling the Impact of Network Connectivity on Consensus Security of Proof-of-Work Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

Blockchain, the technology behind the popular Bitcoin, is considered a "security by design" system as it is meant to create security among a group of distrustful parties yet without a central trusted authority. The security of blockchain relies on the premise of honest-majority, namely, the blockchain system is assumed to be secure as long as the majority of consensus voting power is honest. And in the case of proof-of-work (PoW) blockchain, adversaries cannot control more than 50% of the network's gross computing power. However, this 50% threshold is based on the analysis of computing power only, with implicit and idealistic assumptions on the network and node behavior. Recent researches have alluded that factors such as network connectivity, presence of blockchain forks, and mining strategy could undermine the consensus security assured by the honest-majority, but neither concrete analysis nor quantitative evaluation is provided. In this paper we fill the gap by proposing an analytical model to assess the impact of network connectivity on the consensus security of PoW blockchain under different adversary models. We apply our analytical model to two adversarial scenarios: 1) honest-but-potentially-colluding, 2) selfish mining. For each scenario, we quantify the communication capability of nodes involved in a fork race and estimate the adversary's mining revenue and its impact on security properties of the consensus protocol. Simulation results validated our analysis. Our modeling and analysis provide a paradigm for assessing the security impact of various factors in a distributed consensus system.
Regularized Autoencoders via Relaxed Injective Probability Flow
Venue: arXiv cs.LG 2020
Year: 2020

Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the invertibility requirement restricts models to have the same latent dimensionality as the inputs. This imposes significant architectural, memory, and computational costs, making them more challenging to scale than other classes of generative models such as Variational Autoencoders (VAEs). We propose a generative model based on probability flows that does away with the bijectivity requirement on the model and only assumes injectivity. This also provides another perspective on regularized autoencoders (RAEs), with our final objectives resembling RAEs with specific regularizers that are derived by lower bounding the probability flow objective. We empirically demonstrate the promise of the proposed model, improving over VAEs and AEs in terms of sample quality.
Online high rank matrix completion
Venue: arXiv cs.LG 2020
Year: 2020

Recent advances in matrix completion enable data imputation in full-rank matrices by exploiting low dimensional (nonlinear) latent structure. In this paper, we develop a new model for high rank matrix completion (HRMC), together with batch and online methods to fit the model and out-of-sample extension to complete new data. The method works by (implicitly) mapping the data into a high dimensional polynomial feature space using the kernel trick; importantly, the data occupies a low dimensional subspace in this feature space, even when the original data matrix is of full-rank. We introduce an explicit parametrization of this low dimensional subspace, and an online fitting procedure, to reduce computational complexity compared to the state of the art. The online method can also handle streaming or sequential data and adapt to non-stationary latent structure. We provide guidance on the sampling rate required these methods to succeed. Experimental results on synthetic data and motion capture data validate the performance of the proposed methods.
Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
Venue: arXiv cs.CV 2020
Year: 2020

Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networks (DNNs). For these, large parts of the traditional safety processes and requirements are not fully applicable or sufficient. The aim of this paper is to present a framework for the description and mitigation of DNN insufficiencies and the derivation of relevant safety mechanisms to increase the safety of DNNs. To assess the effectiveness of these safety mechanisms, we present a categorization scheme for evaluation metrics.
A Model-Based, Decision-Theoretic Perspective on Automated Cyber Response
Venue: arXiv cs.AI 2020
Year: 2020

Cyber-attacks can occur at machine speeds that are far too fast for human-in-the-loop (or sometimes on-the-loop) decision making to be a viable option. Although human inputs are still important, a defensive Artificial Intelligence (AI) system must have considerable autonomy in these circumstances. When the AI system is model-based, its behavior responses can be aligned with risk-aware cost/benefit tradeoffs that are defined by user-supplied preferences that capture the key aspects of how human operators understand the system, the adversary and the mission. This paper describes an approach to automated cyber response that is designed along these lines. We combine a simulation of the system to be defended with an anytime online planner to solve cyber defense problems characterized as partially observable Markov decision problems (POMDPs).
Complete Endomorphisms in Computer Vision
Venue: arXiv cs.CV 2020
Year: 2020

Correspondences between k-tuples of points are key in multiple view geometry and motion analysis. Regular transformations are posed by homographies between two projective planes that serves as structural models for images. Such transformations can not include degenerate situations. Fundamental or essential matrices expand homographies with structural information by using degenerate bilinear maps. The projectivization of the endomorphisms of a three-dimensional vector space includes all of them. Hence, they are able to explain a wider range of eventually degenerate transformations between arbitrary pairs of views. To include these degenerate situations, this paper introduces a completion of bilinear maps between spaces given by an equivariant compactification of regular transformations. This completion is extensible to the varieties of fundamental and essential matrices, where most methods based on regular transformations fail. The construction of complete endomorphisms manages degenerate projection maps using a simultaneous action on source and target spaces. In such way, this mathematical construction provides a robust framework to relate corresponding views in multiple view geometry.
Audio-video Emotion Recognition in the Wild using Deep Hybrid Networks
Venue: arXiv cs.CV 2020
Year: 2020

This paper presents an audiovisual-based emotion recognition hybrid network. While most of the previous work focuses either on using deep models or hand-engineered features extracted from images, we explore multiple deep models built on both images and audio signals. Specifically, in addition to convolutional neural networks (CNN) and recurrent neutral networks (RNN) trained on facial images, the hybrid network also contains one SVM classifier trained on holistic acoustic feature vectors, one long short-term memory network (LSTM) trained on short-term feature sequences extracted from segmented audio clips, and one Inception(v2)-LSTM network trained on image-like maps, which are built based on short-term acoustic feature sequences. Experimental results show that the proposed hybrid network outperforms the baseline method by a large margin.