OpenRaiser/Intern-Atlas huggingface dataset:
A Study of Delay Drifts on Massive MIMO Wideband Channel Models
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study the effects of the variations of the propagation delay over large-scale antenna-arrays used in massive multiple-input multiple-output (MIMO) wideband communication systems on the statistical properties of the channel. Due to its simplicity and popularity, the Elliptical geometry-based stochastic channel model (GBSM) is employed to demonstrate new non-stationary properties of the channel in the frequency and spatial domains caused by the drift of delays. In addition, we show that the time of travel of multi-path components (MPCs) over large-scale arrays may result in overlooked frequency and spatial decorrelation effects. These are theoretically demonstrated by deriving the space-time-frequency correlation functions (STFCFs) of both narrowband and wideband Elliptical models. Closed-form expressions of the array-variant frequency correlation function (FCF), power delay profile (PDP), mean delay, and delay spread of single- and multi-confocal Elliptical models are derived when the angles of arrival (AOAs) are von Mises distributed. In such conditions, we find that the large dimensions of the antenna array may limit the narrowband characteristic of the single-ellipse model and alter the wideband characteristics (PDP and FCF) of the multi-confocal Elliptical channel model. Although we present and analyze numerical and simulation results for a particular GBSM, similar conclusions can be extended to other GBSMs.
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

Interventional cancer clinical trials are generally too restrictive, and some patients are often excluded on the basis of comorbidity, past or concomitant treatments, or the fact that they are over a certain age. The efficacy and safety of new treatments for patients with these characteristics are, therefore, not defined. In this work, we built a model to automatically predict whether short clinical statements were considered inclusion or exclusion criteria. We used protocols from cancer clinical trials that were available in public registries from the last 18 years to train word-embeddings, and we constructed a~dataset of 6M short free-texts labeled as eligible or not eligible. A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible. We additionally analyzed the semantic reasoning of the word-embedding representations obtained and were able to identify equivalent treatments for a type of tumor analogous with the drugs used to treat other tumors. We show that representation learning using {deep} neural networks can be successfully leveraged to extract the medical knowledge from clinical trial protocols for potentially assisting practitioners when prescribing treatments.
Securing Conditional Branches in the Presence of Fault Attacks
Venue: arXiv cs.CR 2018
Year: 2018

In typical software, many comparisons and subsequent branch operations are highly critical in terms of security. Examples include password checks, signature checks, secure boot, and user privilege checks. For embedded devices, these security-critical branches are a preferred target of fault attacks as a single bit flip or skipping a single instruction can lead to complete access to a system. In the past, numerous redundancy schemes have been proposed in order to provide control-flow-integrity (CFI) and to enable error detection on processed data. However, current countermeasures for general purpose software do not provide protection mechanisms for conditional branches. Hence, critical branches are in practice often simply duplicated. We present a generic approach to protect conditional branches, which links an encoding-based comparison result with the redundancy of CFI protection mechanisms. The presented approach can be used for all types of data encodings and CFI mechanisms and maintains their error-detection capabilities throughout all steps of a conditional branch. We demonstrate our approach by realizing an encoded comparison based on AN-codes, which is a frequently used encoding scheme to detect errors on data during arithmetic operations. We extended the LLVM compiler so that standard code and conditional branches can be protected automatically and analyze its security. Our design shows that the overhead in terms of size and runtime is lower than state-of-the-art duplication schemes.
Quality expectations of machine translation
Venue: arXiv cs.CL 2018
Year: 2018

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single 'gold standard' measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected 'shelf-life' of the translation itself.
Dyloc: Dynamic and Collaborative User-controlled AOA based Localizing System with your laptops
Venue: arXiv cs.NI 2018
Year: 2018

Currently, accurate localization system based on commodity WiFi devices is not broadly available yet. In the literature, the solutions are based on either network infrastructure like WiFi router, which have at least three antennas, or sacrifice accuracy with coarse grained information like RSSI. In this work, we design a new localizing system which is accurate based on AOA estimation and instantly deployable on users' devices. Dyloc is designed to be dynamically constructed with user's devices as network nodes without any network infrastructure. On the platform of laptops, our system achieve comparable localization accuracy with state-of-the-art work despite of the limitation of less number and large separation of antennas. We design multi-stage signal processing to resolve the ambiguity issue arisen in this scenario. To enable dynamic and collaborative construction, our system can accurately conduct self-localization and also eliminate the need of infrastructure anchors, which is due to the dedicated two-layer algorithm design.
Guided Image Inpainting: Replacing an Image Region by Pulling Content from Another Image
Venue: arXiv cs.CV 2018
Year: 2018

Deep generative models have shown success in automatically synthesizing missing image regions using surrounding context. However, users cannot directly decide what content to synthesize with such approaches. We propose an end-to-end network for image inpainting that uses a different image to guide the synthesis of new content to fill the hole. A key challenge addressed by our approach is synthesizing new content in regions where the guidance image and the context of the original image are inconsistent. We conduct four studies that demonstrate our results yield more realistic image inpainting results over seven baselines.
To Edge or Not to Edge?
Venue: arXiv cs.NI 2018
Year: 2018

Edge computing caters to a wide range of use cases from latency sensitive to bandwidth constrained applications. However, the exact specifications of the edge that give the most benefit for each type of application are still unclear. We investigate the concrete conditions when the edge is feasible, i.e., when users observe performance gains from the edge while costs remain low for the providers, for an application that requires both low latency and high bandwidth: video analytics.
A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign
Venue: arXiv cs.CL 2018
Year: 2018

In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.
Group Normalization
Venue: arXiv cs.CV 2018
Year: 2018

Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries.
Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings
Venue: arXiv cs.CV 2018
Year: 2018

We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections, and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail. See video at https://youtu.be/zraPvRdl13Q
On Cayley graphs of algebraic structures
Venue: arXiv cs.DM 2018
Year: 2018

We present simple graph-theoretic characterizations of Cayley graphs for left-cancellative monoids, groups, left-quasigroups and quasigroups. We show that these characterizations are effective for the end-regular graphs of finite degree.
Frequency violations from random disturbances: an MCMC approach
Venue: arXiv cs.SY 2018
Year: 2018

The frequency stability of power systems is increasingly challenged by various types of disturbances. In particular, the increasing penetration of renewable energy sources is increasing the variability of power generation and at the same time reducing system inertia against disturbances. In this paper we are particularly interested in understanding how rate of change of frequency (RoCoF) violations could arise from unusually large power disturbances. We devise a novel specialization, named ghost sampling, of the Metropolis-Hastings Markov Chain Monte Carlo method that is tailored to efficiently sample rare power disturbances leading to nodal frequency violations. Generating a representative random sample addresses important statistical questions such as "which generator is most likely to be disconnected due to a RoCoF violation?" or "what is the probability of having simultaneous RoCoF violations, given that a violation occurs?" Our method can perform conditional sampling from any joint distribution of power disturbances including, for instance, correlated and non-Gaussian disturbances, features which have both been recently shown to be significant in security analyses.
Visible Light Communication for Wearable Computing
Venue: arXiv cs.NI 2018
Year: 2018

Visible Light Communication (VLC) is emerging as a means to network computing devices that ameliorates many hurdles of radio-frequency (RF) communications, for example, the limited available spectrum. Enabling VLC in wearable computing, however, is challenging because mobility induces unpredictable drastic changes in light conditions, for example, due to reflective surfaces and obstacles casting shadows. We experimentally demonstrate that such changes are so extreme that no single design of a VLC receiver can provide efficient performance across the board. The diversity found in current wearable devices complicates matters. Based on these observations, we present three different designs of VLC receivers that i) are individually orders of magnitude more efficient than the state-of-the-art in a subset of the possible conditions, and i) can be combined in a single unit that dynamically switches to the best performing receiver based on the light conditions. Our evaluation indicates that dynamic switching incurs minimal overhead, that we can obtain throughput in the order of MBit/s, and at energy costs lower than many RF devices.
Generalization Challenges for Neural Architectures in Audio Source Separation
Venue: arXiv cs.SD 2018
Year: 2018

Recent work has shown that recurrent neural networks can be trained to separate individual speakers in a sound mixture with high fidelity. Here we explore convolutional neural network models as an alternative and show that they achieve state-of-the-art results with an order of magnitude fewer parameters. We also characterize and compare the robustness and ability of these different approaches to generalize under three different test conditions: longer time sequences, the addition of intermittent noise, and different datasets not seen during training. For the last condition, we create a new dataset, RealTalkLibri, to test source separation in real-world environments. We show that the acoustics of the environment have significant impact on the structure of the waveform and the overall performance of neural network models, with the convolutional model showing superior ability to generalize to new environments. The code for our study is available at https://github.com/ShariqM/source_separation.
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Venue: arXiv cs.SE 2018
Year: 2018

Statistical language models are powerful tools which have been used for many tasks within natural language processing. Recently, they have been used for other sequential data such as source code.(Ray et al., 2015) showed that it is possible train an n-gram source code language mode, and use it to predict buggy lines in code by determining "unnatural" lines via entropy with respect to the language model. In this work, we propose using a more advanced language modeling technique, Long Short-term Memory recurrent neural networks, to model source code and classify buggy lines based on entropy. We show that our method slightly outperforms an n-gram model in the buggy line classification task using AUC.
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Venue: arXiv cs.CV 2018
Year: 2018

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video-registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. Methods: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video-registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive Image Quality Assessment (IQA) that takes into account different quality scores, including a Mean Opinion Score (MOS). Results: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. Conclusion: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Venue: arXiv cs.DC 2018
Year: 2018

Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks. Given the recent practical focus on distributed machine learning, significant work has been dedicated to the convergence properties of this algorithm under the inconsistent and noisy updates arising from execution in a distributed environment. However, surprisingly, the convergence properties of this classic algorithm in the standard shared-memory model are still not well-understood. In this work, we address this gap, and provide new convergence bounds for lock-free concurrent stochastic gradient descent, executing in the classic asynchronous shared memory model, against a strong adaptive adversary. Our results give improved upper and lower bounds on the "price of asynchrony" when executing the fundamental SGD algorithm in a concurrent setting. They show that this classic optimization tool can converge faster and with a wider range of parameters than previously known under asynchronous iterations. At the same time, we exhibit a fundamental trade-off between the maximum delay in the system and the rate at which SGD can converge, which governs the set of parameters under which this algorithm can still work efficiently.
Twitter and the Press: an Ego-Centred Analysis
Venue: arXiv cs.SI 2018
Year: 2018

Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, whereby individuals cannot maintain meaningful interactions with more than 150 people, on average. In this work, we focus on the ego networks of journalists on Twitter, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users. Their ego network structure is very aligned with reference models derived from the social brain hypothesis and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter than with other social interaction means. Moreover, the ego networks of journalists are much stabler than those of politicians and generic users, and the ego-alter ties are often information-driven.
Broad Learning for Healthcare
Venue: arXiv cs.LG 2018
Year: 2018

A broad spectrum of data from different modalities are generated in the healthcare domain every day, including scalar data (e.g., clinical measures collected at hospitals), tensor data (e.g., neuroimages analyzed by research institutes), graph data (e.g., brain connectivity networks), and sequence data (e.g., digital footprints recorded on smart sensors). Capability for modeling information from these heterogeneous data sources is potentially transformative for investigating disease mechanisms and for informing therapeutic interventions. Our works in this thesis attempt to facilitate healthcare applications in the setting of broad learning which focuses on fusing heterogeneous data sources for a variety of synergistic knowledge discovery and machine learning tasks. We are generally interested in computer-aided diagnosis, precision medicine, and mobile health by creating accurate user profiles which include important biomarkers, brain connectivity patterns, and latent representations. In particular, our works involve four different data mining problems with application to the healthcare domain: multi-view feature selection, subgraph pattern mining, brain network embedding, and multi-view sequence prediction.
A unified framework of fully distributed adaptive output time-varying formation control for linear multi-agent systems: an observer viewpoint
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a unified framework of time-varying formation (TVF) design for general linear multi-agent systems (MAS) based on an observer viewpoint from undirected to directed topology, from stabilization to tracking and from a leader without input to a one with bounded input. The followers can form a TVF shape which is specified by piecewise continuously differential vectors. The leader's trajectory, which is available to only a subset of followers, is also time-varying. For the undirected formation tracking and directed formation stabilization cases, only the relative output measurements of neighbors are required to design control protocols; for the directed formation tracking case, the agents need to be introspective (i.e. agents have partial knowledge of their own states) and the output measurements are required. Furthermore, considering the real applications, the leader with bounded input case is studied. One main contribution of this paper is that fully distributed adaptive output protocols, which require no global information of communication topology and do not need the absolute or relative state information, are proposed to solve the TVF control problem. Numerical simulations including an application to nonholonomic mobile vehicles are provided to verify the theoretical results.