OpenRaiser/Intern-Atlas huggingface dataset:
Improved Deterministic Distributed Construction of Spanners
Venue: arXiv cs.DS 2017
Year: 2017

Graph spanners are fundamental graph structures with a wide range of applications in distributed networks. We consider a standard synchronous message passing model where in each round $O(\log n)$ bits can be transmitted over every edge (the CONGEST model). The state of the art of deterministic distributed spanner constructions suffers from large messages. The only exception is the work of Derbel et al. '10, which computes an optimal-sized $(2k-1)$-spanner but uses $O(n^{1-1/k})$ rounds. In this paper, we significantly improve this bound. We present a deterministic distributed algorithm that given an unweighted $n$-vertex graph $G = (V, E)$ and a parameter $k > 2$, constructs a $(2k-1)$-spanner with $O(k \cdot n^{1+1/k})$ edges within $O(2^{k} \cdot n^{1/2 - 1/k})$ rounds for every even $k$. For odd $k$, the number of rounds is $O(2^{k} \cdot n^{1/2 - 1/(2k)})$. For the weighted case, we provide the first deterministic construction of a $3$-spanner with $O(n^{3/2})$ edges that uses $O(\log n)$-size messages and $\widetilde{O}(1)$ rounds. If the nodes have IDs in $[1, Θ(n)]$, then the algorithm works in only $2$ rounds!
Collusion-Secure Watermarking for Sequential Data
Venue: arXiv cs.CR 2017
Year: 2017

In this work, we address the liability issues that may arise due to unauthorized sharing of personal data. We consider a scenario in which an individual shares his sequential data (such as genomic data or location patterns) with several service providers (SPs). In such a scenario, if his data is shared with other third parties without his consent, the individual wants to determine the service provider that is responsible for this unauthorized sharing. To provide this functionality, we propose a novel optimization-based watermarking scheme for sharing of sequential data. Thus, in the case of an unauthorized sharing of sensitive data, the proposed scheme can find the source of the leakage by checking the watermark inside the leaked data. In particular, the proposed schemes guarantees with a high probability that (i) the malicious SP that receives the data cannot understand the watermarked data points, (ii) when more than one malicious SPs aggregate their data, they still cannot determine the watermarked data points, (iii) even if the unauthorized sharing involves only a portion of the original data or modified data (to damage the watermark), the corresponding malicious SP can be kept responsible for the leakage, and (iv) the added watermark is compliant with the nature of the corresponding data. That is, if there are inherent correlations in the data, the added watermark still preserves such correlations. Watermarking typically means changing certain parts of the data, and hence it may have negative effects on data utility. The proposed scheme also minimizes such utility loss while it provides the aforementioned security guarantees. Furthermore, we conduct a case study of the proposed scheme on genomic data and show the security and utility guarantees of the proposed scheme.
Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset
Venue: arXiv cs.CL 2017
Year: 2017

We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online.
Deep MR to CT Synthesis using Unpaired Data
Venue: arXiv cs.CV 2017
Year: 2017

MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adversarial network (GAN) with unpaired MR and CT images. A GAN consisting of two synthesis convolutional neural networks (CNNs) and two discriminator CNNs was trained with cycle consistency to transform 2D brain MR image slices into 2D brain CT image slices and vice versa. Brain MR and CT images of 24 patients were analyzed. A quantitative evaluation showed that the model was able to synthesize CT images that closely approximate reference CT images, and was able to outperform a GAN model trained with paired MR and CT images.
Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification
Venue: arXiv cs.LG 2017
Year: 2017

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological response of the brain and describe the optimal parameters found that elicit a steady-state visually evoked potential (SSVEP) in certain parts of the cerebral cortex where it can be reliably perceived by the electrode of the EEG device. After that, we continue with a description of the advanced machine learning pipeline model that can perform confident classification of the collected EEG data in order to (a) reliably distinguish signal from noise (about 85% validation score) and (b) reliably distinguish between EEG records collected from different human participants (about 80% validation score). Finally, we demonstrate that the proposed method works reliably even with an inexpensive (less than $100) consumer-grade EEG sensing device and with participants who do not have previous experience with EEG technology (EEG illiterate). All this in combination opens up broad prospects for the development of new types of consumer devices, [e.g.] based on virtual reality helmets or augmented reality glasses where EEG sensor can be easily integrated. The proposed method can be used to improve an online user experience by providing [e.g.] password-less user identification for VR / AR applications. It can also find a more advanced application in intensive care units where collected EEG data can be used to classify the level of conscious awareness of patients during anesthesia or to automatically detect hardware failures by classifying the input signal as noise.
Estimating speech from lip dynamics
Venue: arXiv cs.CV 2017
Year: 2017

The goal of this project is to develop a limited lip reading algorithm for a subset of the English language. We consider a scenario in which no audio information is available. The raw video is processed and the position of the lips in each frame is extracted. We then prepare the lip data for processing and classify the lips into visemes and phonemes. Hidden Markov Models are used to predict the words the speaker is saying based on the sequences of classified phonemes and visemes. The GRID audiovisual sentence corpus [10][11] database is used for our study.
Metadata in the BioSample Online Repository are Impaired by Numerous Anomalies
Venue: arXiv cs.DB 2017
Year: 2017

The metadata about scientific experiments are crucial for finding, reproducing, and reusing the data that the metadata describe. We present a study of the quality of the metadata stored in BioSample--a repository of metadata about samples used in biomedical experiments managed by the U.S. National Center for Biomedical Technology Information (NCBI). We tested whether 6.6 million BioSample metadata records are populated with values that fulfill the stated requirements for such values. Our study revealed multiple anomalies in the analyzed metadata. The BioSample metadata field names and their values are not standardized or controlled--15% of the metadata fields use field names not specified in the BioSample data dictionary. Only 9 out of 452 BioSample-specified fields ordinarily require ontology terms as values, and the quality of these controlled fields is better than that of uncontrolled ones, as even simple binary or numeric fields are often populated with inadequate values of different data types (e.g., only 27% of Boolean values are valid). Overall, the metadata in BioSample reveal that there is a lack of principled mechanisms to enforce and validate metadata requirements. The aberrancies in the metadata are likely to impede search and secondary use of the associated datasets.
MemexQA: Visual Memex Question Answering
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a new task, MemexQA: given a collection of photos or videos from a user, the goal is to automatically answer questions that help users recover their memory about events captured in the collection. Towards solving the task, we 1) present the MemexQA dataset, a large, realistic multimodal dataset consisting of real personal photos and crowd-sourced questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network architecture for image, text and video question answering. Experimental results on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines and yields the state-of-the-art on this novel and challenging task. The promising results on TextQA and VideoQA suggest MemexNet's efficacy and scalability across various QA tasks.
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
Venue: arXiv cs.DC 2017
Year: 2017

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking.
Load Balancing using Hilbert Space-filling Curves for Parallel Reservoir Simulations
Venue: arXiv cs.DC 2017
Year: 2017

The goal of load balancing (grid partitioning) is to minimize overall computations and communications, and to make sure that all processors have a similar workload. Geometric methods divide a grid by using a location of a cell while topological methods work with connectivity of cells, which is generally described as a graph. This paper introduces a Hilbert space-filling curve method. A space-filling curve is a continuous curve and defines a map between a one-dimensional space and a multi-dimensional space. A Hilbert space-filling curve is one special space-filling curve discovered by Hilbert and has many useful characteristics, such as good locality, which means that two objects that are close to each other in a multi-dimensional space are also close to each other in a one dimensional space. This property can model communications in grid-based parallel applications. The idea of the Hilbert space-filling curve method is to map a computational domain into a one-dimensional space, partition the one-dimensional space to certain intervals, and assign all cells in a same interval to a MPI. To implement a load balancing method, a mapping kernel is required to convert high-dimensional coordinates to a scalar value and an efficient one-dimensional partitioning module that divides a one-dimensional space and makes sure that all intervals have a similar workload. The Hilbert space-filling curve method is compared with ParMETIS, a famous graph partitioning package. The results show that our Hilbert space-filling curve method has good partition quality. It has been applied to grids with billions of cells, and linear scalability has been obtained on IBM Blue Gene/Q.
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning
Venue: arXiv cs.CL 2017
Year: 2017

As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online. Most recent research has focused on mining social media for public user opinion about external entities such as product reviews or sentiment towards political news. However, less attention has been paid to analyzing users' internalized thoughts and emotions from a mental health perspective. In this paper, we quantify the semantic difference between public Tweets and private mental health journals used in online cognitive behavioral therapy. We will use deep transfer learning techniques for analyzing the semantic gap between the two domains. We show that for the task of emotional valence prediction, social media can be successfully harnessed to create more accurate, robust, and personalized mental health models. Our results suggest that the semantic gap between public and private self-expression is small, and that utilizing the abundance of available social media is one way to overcome the small sample sizes of mental health data, which are commonly limited by availability and privacy concerns.
Research Activity Classification based on Time Series Bibliometrics
Venue: arXiv cs.DL 2017
Year: 2017

Bibliometrics such as the number of papers and times cited are often used to compare researchers based on specific criteria. The criteria, however, are different in each research domain and are set by empirical laws. Moreover, there are arguments, such that the simple sum of metric values works to the advantage of elders. Therefore, this paper attempts to constitute features from time series data of bibliometrics, and then classify the researchers according to the features. In detail, time series patterns are extracted from bibliographic data sets, and then a model to classify whether the researchers are "distinguished" or not is created by a machine learning technique. The experiments achieved an F-measure of 80.0% in the classification of 114 researchers in two research domains based on the data sets of Japan Science and Technology Agency and Elsevier's Scopus. In the future, we will conduct verification on a number of researchers in several domains, and then make use of discovering "distinguished" researchers, who are not widely known.
The All-Paths and Cycles Graph Kernel
Venue: arXiv cs.LG 2017
Year: 2017

With the recent rise in the amount of structured data available, there has been considerable interest in methods for machine learning with graphs. Many of these approaches have been kernel methods, which focus on measuring the similarity between graphs. These generally involving measuring the similarity of structural elements such as walks or paths. Borgwardt and Kriegel proposed the all-paths kernel but emphasized that it is NP-hard to compute and infeasible in practice, favouring instead the shortest-path kernel. In this paper, we introduce a new algorithm for computing the all-paths kernel which is very efficient and enrich it further by including the simple cycles as well. We demonstrate how it is feasible even on large datasets to compute all the paths and simple cycles up to a moderate length. We show how to count labelled paths/simple cycles between vertices of a graph and evaluate a labelled path and simple cycles kernel. Extensive evaluations on a variety of graph datasets demonstrate that the all-paths and cycles kernel has superior performance to the shortest-path kernel and state-of-the-art performance overall.
Boosting Metrics for Cloud Services Evaluation -- The Last Mile of Using Benchmark Suites
Venue: arXiv cs.DC 2017
Year: 2017

Benchmark suites are significant for evaluating various aspects of Cloud services from a holistic view. However, there is still a gap between using benchmark suites and achieving holistic impression of the evaluated Cloud services. Most Cloud service evaluation work intended to report individual benchmarking results without delivering summary measures. As a result, it could be still hard for customers with such evaluation reports to understand an evaluated Cloud service from a global perspective. Inspired by the boosting approaches to machine learning, we proposed the concept Boosting Metrics to represent all the potential approaches that are able to integrate a suite of benchmarking results. This paper introduces two types of preliminary boosting metrics, and demonstrates how the boosting metrics can be used to supplement primary measures of individual Cloud service features. In particular, boosting metrics can play a summary Response role in applying experimental design to Cloud services evaluation. Although the concept Boosting Metrics was refined based on our work in the Cloud Computing domain, we believe it can be easily adapted to the evaluation work of other computing paradigms.
Impact of Line-of-Sight and Unequal Spatial Correlation on Uplink MU-MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

Closed-form approximations of the expected per-terminal signal-to-interference-plus-noise-ratio (SINR) and ergodic sum spectral efficiency of a multiuser multiple-input multiple-output system are presented. Our analysis assumes spatially correlated Ricean fading channels with maximum-ratio combining on the uplink. Unlike previous studies, our model accounts for the presence of unequal correlation matrices, unequal Rice factors, as well as unequal link gains to each terminal. The derived approximations lend themselves to useful insights, special cases and demonstrate the aggregate impact of line-of-sight (LoS) and unequal correlation matrices. Numerical results show that while unequal correlation matrices enhance the expected SINR and ergodic sum spectral efficiency, the presence of strong LoS has an opposite effect. Our approximations are general and remain insensitive to changes in the system dimensions, signal-to-noise-ratios, LoS levels and unequal correlation levels.
Brain Responses During Robot-Error Observation
Venue: arXiv cs.HC 2017
Year: 2017

Brain-controlled robots are a promising new type of assistive device for severely impaired persons. Little is however known about how to optimize the interaction of humans and brain-controlled robots. Information about the human's perceived correctness of robot performance might provide a useful teaching signal for adaptive control algorithms and thus help enhancing robot control. Here, we studied whether watching robots perform erroneous vs. correct action elicits differential brain responses that can be decoded from single trials of electroencephalographic (EEG) recordings, and whether brain activity during human-robot interaction is modulated by the robot's visual similarity to a human. To address these topics, we designed two experiments. In experiment I, participants watched a robot arm pour liquid into a cup. The robot performed the action either erroneously or correctly, i.e. it either spilled some liquid or not. In experiment II, participants observed two different types of robots, humanoid and non-humanoid, grabbing a ball. The robots either managed to grab the ball or not. We recorded high-resolution EEG during the observation tasks in both experiments to train a Filter Bank Common Spatial Pattern (FBCSP) pipeline on the multivariate EEG signal and decode for the correctness of the observed action, and for the type of the observed robot. Our findings show that it was possible to decode both correctness and robot type for the majority of participants significantly, although often just slightly, above chance level. Our findings suggest that non-invasive recordings of brain responses elicited when observing robots indeed contain decodable information about the correctness of the robot's action and the type of observed robot.
Better Together: Joint Reasoning for Non-rigid 3D Reconstruction with Specularities and Shading
Venue: arXiv cs.CV 2017
Year: 2017

We demonstrate the use of shape-from-shading (SfS) to improve both the quality and the robustness of 3D reconstruction of dynamic objects captured by a single camera. Unlike previous approaches that made use of SfS as a post-processing step, we offer a principled integrated approach that solves dynamic object tracking and reconstruction and SfS as a single unified cost function. Moving beyond Lambertian S f S , we propose a general approach that models both specularities and shading while simultaneously tracking and reconstructing general dynamic objects. Solving these problems jointly prevents the kinds of tracking failures which can not be recovered from by pipeline approaches. We show state-of-the-art results both qualitatively and quantitatively.
An Effective Training Method For Deep Convolutional Neural Network
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we propose the nonlinearity generation method to speed up and stabilize the training of deep convolutional neural networks. The proposed method modifies a family of activation functions as nonlinearity generators (NGs). NGs make the activation functions linear symmetric for their inputs to lower model capacity, and automatically introduce nonlinearity to enhance the capacity of the model during training. The proposed method can be considered an unusual form of regularization: the model parameters are obtained by training a relatively low-capacity model, that is relatively easy to optimize at the beginning, with only a few iterations, and these parameters are reused for the initialization of a higher-capacity model. We derive the upper and lower bounds of variance of the weight variation, and show that the initial symmetric structure of NGs helps stabilize training. We evaluate the proposed method on different frameworks of convolutional neural networks over two object recognition benchmark tasks (CIFAR-10 and CIFAR-100). Experimental results showed that the proposed method allows us to (1) speed up the convergence of training, (2) allow for less careful weight initialization, (3) improve or at least maintain the performance of the model at negligible extra computational cost, and (4) easily train a very deep model.
The tightly super 3-extra connectivity and 3-extra diagnosability of crossed cubes
Venue: arXiv cs.IT 2017
Year: 2017

Many multiprocessor systems have interconnection networks as underlying topologies and an interconnection network is usually represented by a graph where nodes represent processors and links represent communication links between processors. In 2016, Zhang et al. proposed the $g$-extra diagnosability of $G$, which restrains that every component of $G-S$ has at least $(g +1)$ vertices. As an important variant of the hypercube, the $n$-dimensional crossed cube $CQ_{n}$ has many good properties. In this paper, we prove that $CQ_{n}$ is tightly $(4n-9)$ super 3-extra connected for $n\geq 7$ and the 3-extra diagnosability of $CQ_{n}$ is $4n-6$ under the PMC model $(n\geq5)$ and MM$^*$ model $(n\geq7)$.