OpenRaiser/Intern-Atlas huggingface dataset:
Cross-lingual Candidate Search for Biomedical Concept Normalization
Venue: arXiv cs.CL 2018
Year: 2018

Biomedical concept normalization links concept mentions in texts to a semantically equivalent concept in a biomedical knowledge base. This task is challenging as concepts can have different expressions in natural languages, e.g. paraphrases, which are not necessarily all present in the knowledge base. Concept normalization of non-English biomedical text is even more challenging as non-English resources tend to be much smaller and contain less synonyms. To overcome the limitations of non-English terminologies we propose a cross-lingual candidate search for concept normalization using a character-based neural translation model trained on a multilingual biomedical terminology. Our model is trained with Spanish, French, Dutch and German versions of UMLS. The evaluation of our model is carried out on the French Quaero corpus, showing that it outperforms most teams of CLEF eHealth 2015 and 2016. Additionally, we compare performance to commercial translators on Spanish, French, Dutch and German versions of Mantra. Our model performs similarly well, but is free of charge and can be run locally. This is particularly important for clinical NLP applications as medical documents underlay strict privacy restrictions.
Encoding fairness in a synchronous concurrent program algebra: extended version with proofs
Venue: arXiv cs.LO 2018
Year: 2018

Concurrent program refinement algebra provides a suitable basis for supporting mechanised reasoning about shared-memory concurrent programs in a compositional manner, for example, it supports the rely/guarantee approach of Jones. The algebra makes use of a synchronous parallel operator motivated by Aczel's trace model of concurrency and with similarities to Milner's SCCS. This paper looks at defining a form of fairness within the program algebra. The encoding allows one to reason about the fair execution of a single process in isolation as well as define fair-parallel in terms of a base parallel operator, of which no fairness properties are assumed. An algebraic theory to support fairness and fair-parallel is developed.
eDisco: Discovering Edge Nodes Along the Path
Venue: arXiv cs.NI 2018
Year: 2018

Edge computing is seen as an enabler for upcoming applications requiring low latency offloading, such as augmented reality, and as a key building block for Internet of Things. Edge computing extends the centralized cloud computing model by distributing servers also close to the users, at the edge of the network. A key challenge for the clients remains on how to discover the nearby edge servers and how to access them. In this paper, we present eDisco, DNS-based edge discovery, that leverages existing protocols and requires no modifications to deployed infrastructure. eDisco enables service providers and clients to discover edge servers, and determine the optimal edge deployment configuration.
On the Secrecy Performance of SWIPT Receiver Architectures with Multiple Eavesdroppers
Venue: arXiv cs.IT 2018
Year: 2018

Physical layer security (PLS) has been shown to hold promise as a new paradigm for securing wireless links. In contrast with the conventional cryptographic techniques, PLS methods exploit the random fading in wireless channels to provide link security. As the channel dynamics prevent a constant rate of secure communications between the legitimate terminals, the outage probability of the achievable secrecy rate is used as a measure of the secrecy performance. This work investigates the secrecy outage probability of a simultaneous wireless information and power transfer (SWIPT) system, which operates in the presence of multiple eavesdroppers that also have the energy harvesting capability. The loss in secrecy performance due to eavesdropper collusion, i.e., information sharing between the eavesdroppers to decode the secret message, is also analyzed. We derive closed-form expressions for the secrecy outage probability for Nakagami-$m$ fading on the links and imperfect channel estimation at the receivers. Our analysis considers different combinations of the separated and the integrated SWIPT receiver architectures at the receivers. Numerical results are provided to validate our analysis.
X-Search: Revisiting Private Web Search using Intel SGX
Venue: arXiv cs.DC 2018
Year: 2018

The exploitation of user search queries by search engines is at the heart of their economic model. As consequence, offering private Web search functionalities is essential to the users who care about their privacy. Nowadays, there exists no satisfactory approach to enable users to access search engines in a privacy-preserving way. Existing solutions are either too costly due to the heavy use of cryptographic mechanisms (e.g., private information retrieval protocols), subject to attacks (e.g., Tor, TrackMeNot, GooPIR) or rely on weak adversarial models (e.g., PEAS). This paper introduces X-Search , a novel private Web search mechanism building on the disruptive Software Guard Extensions (SGX) proposed by Intel. We compare X-Search to its closest competitors, Tor and PEAS, using a dataset of real web search queries. Our evaluation shows that: (1) X-Search offers stronger privacy guarantees than its competitors as it operates under a stronger adversarial model; (2) it better resists state-of-the-art re-identification attacks; and (3) from the performance perspective, X-Search outperforms its competitors both in terms of latency and throughput by orders of magnitude.
Virtual Reality Wireless Mobile Walkthrough Framework
Venue: arXiv cs.NI 2018
Year: 2018

The last years have witnessed a dramatic growth in the number as well as the variety of graphics intensive mobile applications, which allow users to interact and navigate through large scenes such as ancient places, museums and even virtual cities. These applications support many clients and impose a heavy requirement on network resources and computational resources. One key issue in the design of cost efficient mobile walkthrough applications is the data transmission between servers and mobile client devices. In this paper, we propose an effective progressive mesh transmission framework that stores and divide scene objects into different resolutions. This way, each mobile progressively receives and process the only object's details that match with its display resolution, and hence improving the user's perception and overall system's response time. A fine grained cache mechanism is used to keep the most frequently requested objects' details in the device memory and consequently reduce the network traffics. Lastly, practical experiments are carried out to illustrate the effectiveness of the proposed framework under various setting of the virtual scene and mobile devices configuration/types.
Work Stealing with latency
Venue: arXiv cs.DC 2018
Year: 2018

We study in this paper the impact of communication latency on the classical Work Stealing load balancing algorithm. Our approach considers existing performance models and the underlying algorithms. We introduce a latency parameter in the model and study its overall impact by careful observations of simulation results. Using this method we are able to derive a new expression of the expected running time of divisible load applications. This expression enables us to predict under which conditions a given run will yield acceptable performance. For instance, we can easily calibrate the maximal number of processors one should use for a given work platform combination. We also consider the impact of several algorithmic variants like simultaneous transfers of work or thresholds for avoiding useless transfers. All our results are validated through simulation on a wide range of parameters.
Power Law in Sparsified Deep Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

The power law has been observed in the degree distributions of many biological neural networks. Sparse deep neural networks, which learn an economical representation from the data, resemble biological neural networks in many ways. In this paper, we study if these artificial networks also exhibit properties of the power law. Experimental results on two popular deep learning models, namely, multilayer perceptrons and convolutional neural networks, are affirmative. The power law is also naturally related to preferential attachment. To study the dynamical properties of deep networks in continual learning, we propose an internal preferential attachment model to explain how the network topology evolves. Experimental results show that with the arrival of a new task, the new connections made follow this preferential attachment process.
Advanced local motion patterns for macro and micro facial expression recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we develop a new method that recognizes facial expressions, on the basis of an innovative local motion patterns feature, with three main contributions. The first one is the analysis of the face skin temporal elasticity and face deformations during expression. The second one is a unified approach for both macro and micro expression recognition. And, the third one is the step forward towards in-the-wild expression recognition, dealing with challenges such as various intensity and various expression activation patterns, illumination variation and small head pose variations. Our method outperforms state-of-the-art methods for micro expression recognition and positions itself among top-rank state-of-the-art methods for macro expression recognition.
Connected greedy colouring in claw-free graphs
Venue: arXiv cs.DM 2018
Year: 2018

An ordering of the vertices of a graph is \emph{connected} if every vertex (but the first) has a neighbor among its predecessors. The greedy colouring algorithm of a graph with a connected order consists in taking the vertices in order, and assigning to each vertex the smallest available colour. A graph is \emph{good} if the greedy algorithm on every connected order gives every connected induced subgraph of it an optimal colouring. We give the characterization of good claw-free graphs in terms of minimal forbidden induced subgraphs.
Fast-converging Conditional Generative Adversarial Networks for Image Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Building on top of the success of generative adversarial networks (GANs), conditional GANs attempt to better direct the data generation process by conditioning with certain additional information. Inspired by the most recent AC-GAN, in this paper we propose a fast-converging conditional GAN (FC-GAN). In addition to the real/fake classifier used in vanilla GANs, our discriminator has an advanced auxiliary classifier which distinguishes each real class from an extra `fake' class. The `fake' class avoids mixing generated data with real data, which can potentially confuse the classification of real data as AC-GAN does, and makes the advanced auxiliary classifier behave as another real/fake classifier. As a result, FC-GAN can accelerate the process of differentiation of all classes, thus boost the convergence speed. Experimental results on image synthesis demonstrate our model is competitive in the quality of images generated while achieving a faster convergence rate.
Investigating Functional Redundancies in the Context of Vehicle Automation - A Trajectory Tracking Perspective
Venue: arXiv cs.SY 2018
Year: 2018

Level 3+ automated driving implies highest safety demands for the entire vehicle automation functionality. For the part of trajectory tracking, functional redundancies among all available actuators provide an opportunity to reduce safety requirements for single actuators. Yet, the exploitation of functional redundancies must be well argued if employed in a safety concept as physical limits can be reached. In this paper, we want to examine from a trajectory tracking perspective whether such a concept can be used. For this, we present a model predictive fault-tolerant trajectory tracking approach for over-actuated vehicles featuring wheel individual all-wheel drive, brakes, and steering. Applying this approach exemplarily demonstrates for a selected reference trajectory that degradations such as missing or undesired wheel torques as well as reduced steering dynamics can be compensated. Degradations at the physical actuator limits lead to significant deviations from the reference trajectory while small constant steering angles are partially critical.
Improved Detection Strategies for Nonlinear Frequency-Division Multiplexing
Venue: arXiv cs.IT 2018
Year: 2018

Two novel detection strategies for nonlinear Fourier transform-based transmission schemes are proposed. We show, through numerical simulations, that both strategies achieve a good performance improvement (up to 3 dB and 5 dB) with respect to conventional detection, respectively without or only moderately increasing the computational complexity of the receiver.
Learning Patient Representations from Text
Venue: arXiv cs.CL 2018
Year: 2018

Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
Venue: arXiv cs.RO 2018
Year: 2018

Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However, due to rareness of the safety-critical cases and, effectively, unlimited number of possible traffic scenarios, these on-road testing efforts have been acknowledged as tedious, costly, and risky. In this study, we propose Accelerated De- ployment framework to safely and efficiently estimate the AVs performance on public streets. We showed that by appropriately addressing the gradual accuracy improvement and adaptively selecting meaningful and safe environment under which the AV is deployed, the proposed framework yield to highly accurate estimation with much faster evaluation time, and more importantly, lower deployment risk. Our findings provide an answer to the currently heated and active discussions on how to properly test AV performance on public roads so as to achieve safe, efficient, and statistically-reliable testing framework for AV technologies.
Simple Games versus Weighted Voting Games
Venue: arXiv cs.GT 2018
Year: 2018

A simple game $(N,v)$ is given by a set $N$ of $n$ players and a partition of $2^N$ into a set $\mathcal{L}$ of losing coalitions $L$ with value $v(L)=0$ that is closed under taking subsets and a set $\mathcal{W}$ of winning coalitions $W$ with $v(W)=1$. Simple games with $α= \min_{p\geq 0}\max_{W\in {\cal W},L\in {\cal L}} \frac{p(L)}{p(W)}<1$ are known as weighted voting games. Freixas and Kurz (IJGT, 2014) conjectured that $α\leq \frac{1}{4}n$ for every simple game $(N,v)$. We confirm this conjecture for two complementary cases, namely when all minimal winning coalitions have size $3$ and when no minimal winning coalition has size $3$. As a general bound we prove that $α\leq \frac{2}{7}n$ for every simple game $(N,v)$. For complete simple games, Freixas and Kurz conjectured that $α=O(\sqrt{n})$. We prove this conjecture up to a $\ln n$ factor. We also prove that for graphic simple games, that is, simple games in which every minimal winning coalition has size 2, computing $α$ is \NP-hard, but polynomial-time solvable if the underlying graph is bipartite. Moreover, we show that for every graphic simple game, deciding if $α<a$ is polynomial-time solvable for every fixed $a>0$.
The Exact Computational Complexity of Evolutionarily Stable Strategies
Venue: arXiv cs.GT 2018
Year: 2018

While the computational complexity of many game-theoretic solution concepts, notably Nash equilibrium, has now been settled, the question of determining the exact complexity of computing an evolutionarily stable strategy has resisted solution since attention was drawn to it in 2004. In this paper, I settle this question by proving that deciding the existence of an evolutionarily stable strategy is $Σ_2^P$-complete.
Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines, and K-Nearest Neighbor Classifiers
Venue: arXiv cs.LG 2018
Year: 2018

Neural networks in many varieties are touted as very powerful machine learning tools because of their ability to distill large amounts of information from different forms of data, extracting complex features and enabling powerful classification abilities. In this study, we use neural networks to extract features from both images and numeric data and use these extracted features as inputs for other machine learning models, namely support vector machines (SVMs) and k-nearest neighbor classifiers (KNNs), in order to see if neural-network-extracted features enhance the capabilities of these models. We tested 7 different neural network architectures in this manner, 4 for images and 3 for numeric data, training each for varying lengths of time and then comparing the results of the neural network independently to those of an SVM and KNN on the data, and finally comparing these results to models of SVM and KNN trained using features extracted via the neural network architecture. This process was repeated on 3 different image datasets and 2 different numeric datasets. The results show that, in many cases, the features extracted using the neural network significantly improve the capabilities of SVMs and KNNs compared to running these algorithms on the raw features, and in some cases also surpass the performance of the neural network alone. This in turn suggests that it may be a reasonable practice to use neural networks as a means to extract features for classification by other machine learning models for some datasets.
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-distinct images, known as classes, to a new domain within which new classes can potentially be isolated and identified. Our target application in this paper is the Gravity Spy Project, which is an effort to characterize transient, non-Gaussian noise present in data from the Advanced Laser Interferometer Gravitational-wave Observatory, or LIGO. Accumulating large, labeled sets of noise features and identifying of new classes of noise lead to a better understanding of their origin, which makes their removal from the data and/or detectors possible.
Exploiting Physical-Layer Security for Multiuser Multicarrier Computation Offloading
Venue: arXiv cs.IT 2018
Year: 2018

This letter considers a mobile edge computing (MEC) system with one access point (AP) serving multiple users over a multicarrier channel, in the presence of a malicious eavesdropper. In this system, each user can execute the respective computation tasks by partitioning them into two parts, which are computed locally and offloaded to AP, respectively. We exploit the physical-layer security to secure the multiuser computation offloading from being overheard by the eavesdropper. Under this setup, we minimize the weighted sum-energy consumption for these users, subject to the newly imposed secrecy offloading rate constraints and the computation latency constraints, by jointly optimizing their computation and communication resource allocations. We propose an efficient algorithm to solve this problem.
Sharp Attention Network via Adaptive Sampling for Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present novel sharp attention networks by adaptively sampling feature maps from convolutional neural networks (CNNs) for person re-identification (re-ID) problem. Due to the introduction of sampling-based attention models, the proposed approach can adaptively generate sharper attention-aware feature masks. This greatly differs from the gating-based attention mechanism that relies soft gating functions to select the relevant features for person re-ID. In contrast, the proposed sampling-based attention mechanism allows us to effectively trim irrelevant features by enforcing the resultant feature masks to focus on the most discriminative features. It can produce sharper attentions that are more assertive in localizing subtle features relevant to re-identifying people across cameras. For this purpose, a differentiable Gumbel-Softmax sampler is employed to approximate the Bernoulli sampling to train the sharp attention networks. Extensive experimental evaluations demonstrate the superiority of this new sharp attention model for person re-ID over the other state-of-the-art methods on three challenging benchmarks including CUHK03, Market-1501, and DukeMTMC-reID.
Consistency and differences between centrality measures across distinct classes of networks
Venue: arXiv cs.SI 2018
Year: 2018

The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and such whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.