OpenRaiser/Intern-Atlas huggingface dataset:
Linear-Complexity Relaxed Word Mover's Distance with GPU Acceleration
Venue: arXiv cs.IR 2017
Year: 2017

The amount of unstructured text-based data is growing every day. Querying, clustering, and classifying this big data requires similarity computations across large sets of documents. Whereas low-complexity similarity metrics are available, attention has been shifting towards more complex methods that achieve a higher accuracy. In particular, the Word Mover's Distance (WMD) method proposed by Kusner et al. is a promising new approach, but its time complexity grows cubically with the number of unique words in the documents. The Relaxed Word Mover's Distance (RWMD) method, again proposed by Kusner et al., reduces the time complexity from qubic to quadratic and results in a limited loss in accuracy compared with WMD. Our work contributes a low-complexity implementation of the RWMD that reduces the average time complexity to linear when operating on large sets of documents. Our linear-complexity RWMD implementation, henceforth referred to as LC-RWMD, maps well onto GPUs and can be efficiently distributed across a cluster of GPUs. Our experiments on real-life datasets demonstrate 1) a performance improvement of two orders of magnitude with respect to our GPU-based distributed implementation of the quadratic RWMD, and 2) a performance improvement of three to four orders of magnitude with respect to our distributed WMD implementation that uses GPU-based RWMD for pruning.
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we first investigate why typical two-stage methods are not as fast as single-stage, fast detectors like YOLO and SSD. We find that Faster R-CNN and R-FCN perform an intensive computation after or before RoI warping. Faster R-CNN involves two fully connected layers for RoI recognition, while R-FCN produces a large score maps. Thus, the speed of these networks is slow due to the heavy-head design in the architecture. Even if we significantly reduce the base model, the computation cost cannot be largely decreased accordingly. We propose a new two-stage detector, Light-Head R-CNN, to address the shortcoming in current two-stage approaches. In our design, we make the head of network as light as possible, by using a thin feature map and a cheap R-CNN subnet (pooling and single fully-connected layer). Our ResNet-101 based light-head R-CNN outperforms state-of-art object detectors on COCO while keeping time efficiency. More importantly, simply replacing the backbone with a tiny network (e.g, Xception), our Light-Head R-CNN gets 30.7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy. Code will be made publicly available.
On the Ergodic Rate of OFDMA Cognitive Radios under Imperfect Cross-Link Information
Venue: arXiv cs.IT 2017
Year: 2017

The ergodic rate performance and limits of orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) is studied under imperfect cross-link knowledge. We propose a novel stochastic interference management and exploitation technique to mitigate and control the imposed interference by CRs on licensed users in underlay spectrum sharing. The optimum downlink channel-adaptive resource allocation (RA) algorithm is designed to maximize the CRs functionality subject to the satisfying average transmit and probabilistic peak interference power constraints. An expression of the cumulative density function (cdf) of CRs' received signal-to-interference-plus-noise ratio (SINR) is developed to evaluate the resultant ergodic rate. Simulation studies are conducted to examine the proposed RA algorithm and investigate the impact of parameters uncertainty on the overall system performance.
Robust Seed Mask Generation for Interactive Image Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist of drawing pictorial hints as an initial estimate of the object to extract. Only after this time consuming first phase, the efficient selective refinement of current segmentation results begins. Erroneously labeled seeds, especially near the border of the object, are challenging to detect and replace for a human and may substantially impact the overall segmentation quality. We propose an automatic seeding pipeline as well as a configuration based on saliency recognition, in order to skip the time-consuming initial interaction phase during segmentation. A median Dice score of 68.22% is reached before the first user interaction on the test data set with an error rate in seeding of only 0.088%.
An Enhanced Middleware for Collaborative Privacy in IPTV Recommender Services
Venue: arXiv cs.CR 2017
Year: 2017

One of the concerns users have to confronted when using IPTV system is the information overload that makes it difficult for them to find a suitable content according to their personal preferences. Recommendation service is one of the most widely adopted technologies for alleviating this problem, these services intend to provide people with referrals of items they will appreciate based on their preferences. IPTV users must ensure their sensitive preferences collected by any recommendation service are properly secured. In this work, we introduce a framework for private recommender service based on Enhanced Middleware for Collaborative Privacy (EMCP). EMCP executes a two-stage concealment process that gives the user a complete control on the privacy level of his/her profile. We utilize trust mechanism to augment the accuracy and privacy of the recommendations. Trust heuristic spot users who are trustworthy with respect to the user requesting the recommendation. Later, the neighborhood formation is calculated using proximity metrics based on these trustworthy users. Finally, Users submit their profiles in an obfuscated form without revealing any information about their data, and the computation of recommendations proceeds over the obfuscated data using secure multiparty computation protocol. We expand the obfuscation scope from single obfuscation level for all users to arbitrary obfuscation levels based on trustworthy between users. In other words, we correlate the obfuscation level with different trust levels, so the more trusted a target user is the less obfuscation copy of profile he can access. We also provide an IPTV network scenario and experimentation results. Our results and analysis show that our two-stage concealment process not only protects the privacy of users but also can maintain the recommendations accuracy.
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Venue: arXiv cs.IR 2017
Year: 2017

User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large number of users, and requirements for recommendations to be responsive to user actions and generated on demand in real-time. Here we present Pixie, a scalable graph-based real-time recommender system that we developed and deployed at Pinterest. Given a set of user-specific pins as a query, Pixie selects in real-time from billions of possible pins those that are most related to the query. To generate recommendations, we develop Pixie Random Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and 17 billion edges. Experiments show that recommendations provided by Pixie lead up to 50% higher user engagement when compared to the previous Hadoop-based production system. Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations. Last, we discuss system aspects of Pixie, where a single server executes 1,200 recommendation requests per second with 60 millisecond latency. Today, systems backed by Pixie contribute to more than 80% of all user engagement on Pinterest.
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Venue: arXiv cs.CV 2017
Year: 2017

Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus lack of rigorous mathematical principles and derivations. Several recent studies build deep structures by unrolling a particular optimization model that involves task information. Unfortunately, due to the dynamic nature of network parameters, their resultant deep propagation networks do \emph{not} possess the nice convergence property as the original optimization scheme does. This paper provides a novel proximal unrolling framework to establish deep models by integrating experimentally verified network architectures and rich cues of the tasks. More importantly, we \emph{prove in theory} that 1) the propagation generated by our unrolled deep model globally converges to a critical-point of a given variational energy, and 2) the proposed framework is still able to learn priors from training data to generate a convergent propagation even when task information is only partially available. Indeed, these theoretical results are the best we can ask for, unless stronger assumptions are enforced. Extensive experiments on various real-world applications verify the theoretical convergence and demonstrate the effectiveness of designed deep models.
Condition directed Multi-domain Adversarial Learning for Loop Closure Detection
Venue: arXiv cs.RO 2017
Year: 2017

Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are usually affected by the illumination changes and texture changes under different weather conditions. Traditional methods in LCD usually rely on handcraft features, however, such methods are unable to capture the common descriptions under different weather conditions, such as rainy, foggy and sunny. Furthermore, traditional handcraft features could not capture the highly level understanding for the local scenes. In this paper, we proposed a novel condition directed multi-domain adversarial learning method, where we use the weather condition as the direction for feature inference. Based on the generative adversarial networks (GANs) and a classification networks, the proposed method could extract the high-level weather-invariant features directly from the raw data. The only labels required here are the weather condition of each visual input. Experiments are conducted in the GTAV game simulator, which could generated lifelike outdoor scenes under different weather conditions. The performance of LCD results shows that our method outperforms the state-of-arts significantly.
Optimal Task Allocation in Near-Far Computing Enhanced C-RAN for Wireless Big Data Processing
Venue: arXiv cs.NI 2017
Year: 2017

With the increasing popularity of user equipments (UEs), the corresponding UEs' generating big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggling in processing those data and applications. This paper proposes a Near-Far Computing Enhanced C-RAN (NFC-RAN) architecture, which can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in remote radio head (RRH), which can fast respond to delay sensitive tasks from the UEs, while FEC sits next to baseband unit (BBU) pool which can do other computational intensive tasks. The task allocation between NEC or FEC is introduced in this paper. Also WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.
Optimized Visibility Functions for Revectorization-Based Shadow Mapping
Venue: arXiv cs.GR 2017
Year: 2017

High-quality shadow anti-aliasing is a challenging problem in shadow mapping. Revectorization-based shadow mapping (RBSM) minimizes shadow aliasing by revectorizing the jagged shadow edges generated with shadow mapping, keeping low memory footprint and real-time performance for the shadow computation. However, the current implementation of RBSM is not so well optimized because its visibility functions are composed of a set of 43 cases, each one of them handling a specific revectorization scenario and being implemented as a specific branch in the shader. Here, we take advantage of the shadow shape patterns to reformulate the RBSM visibility functions, simplifying the implementation of the technique and further providing an optimized version of the RBSM. Our results indicate that our implementation runs faster than the original implementation of RBSM, while keeping its same visual quality and memory consumption. Furthermore, we show GLSL source codes to ease the implementation of our technique, provide a comparison between the optimized RBSM and related work, and discuss the limitations of the shadow revectorization.
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
Venue: arXiv cs.CL 2017
Year: 2017

Sentiment analysis is attracting more and more attentions and has become a very hot research topic due to its potential applications in personalized recommendation, opinion mining, etc. Most of the existing methods are based on either textual or visual data and can not achieve satisfactory results, as it is very hard to extract sufficient information from only one single modality data. Inspired by the observation that there exists strong semantic correlation between visual and textual data in social medias, we propose an end-to-end deep fusion convolutional neural network to jointly learn textual and visual sentiment representations from training examples. The two modality information are fused together in a pooling layer and fed into fully-connected layers to predict the sentiment polarity. We evaluate the proposed approach on two widely used data sets. Results show that our method achieves promising result compared with the state-of-the-art methods which clearly demonstrate its competency.
Application of generative autoencoder in de novo molecular design
Venue: arXiv cs.LG 2017
Year: 2017

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the training set were identified.
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
Venue: arXiv cs.CV 2017
Year: 2017

The objective of this paper is 3D shape understanding from single and multiple images. To this end, we introduce a new deep-learning architecture and loss function, SilNet, that can handle multiple views in an order-agnostic manner. The architecture is fully convolutional, and for training we use a proxy task of silhouette prediction, rather than directly learning a mapping from 2D images to 3D shape as has been the target in most recent work. We demonstrate that with the SilNet architecture there is generalisation over the number of views -- for example, SilNet trained on 2 views can be used with 3 or 4 views at test-time; and performance improves with more views. We introduce two new synthetics datasets: a blobby object dataset useful for pre-training, and a challenging and realistic sculpture dataset; and demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally, we show that SilNet exceeds the state of the art on the ShapeNet benchmark dataset, and use SilNet to generate novel views of the sculpture dataset.
Would You Like to Motivate Software Testers? Ask Them How
Venue: arXiv cs.SE 2017
Year: 2017

Context. Considering the importance of software testing to the development of high quality and reliable software systems, this paper aims to investigate how can work-related factors influence the motivation of software testers. Method. We applied a questionnaire that was developed using a previous theory of motivation and satisfaction of software engineers to conduct a survey-based study to explore and understand how professional software testers perceive and value work-related factors that could influence their motivation at work. Results. With a sample of 80 software testers we observed that software testers are strongly motivated by variety of work, creative tasks, recognition for their work, and activities that allow them to acquire new knowledge, but in general the social impact of this activity has low influence on their motivation. Conclusion. This study discusses the difference of opinions among software testers, regarding work-related factors that could impact their motivation, which can be relevant for managers and leaders in software engineering practice.
Asymptotic Close To Optimal Joint Resource Allocation and Power Control in the Uplink of Two-cell Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we investigate joint resource allocation and power control mechanisms for two-cell networks, where each cell has some sub-channels which should be allocated to some users. The main goal persuaded in the current work is finding the best power and sub-channel assignment strategies so that the associated sum-rate of network is maximized, while a minimum rate constraint is maintained by each user. The underlying optimization problem is a highly non-convex mixed integer and non-linear problem which does not yield a trivial solution. In this regard, to tackle the problem, using an approximate function which is quite tight at moderate to high signal to interference plus noise ratio (SINR) region, the problem is divided into two disjoint sub-channel assignment and power allocation problems. It is shown that having fixed the allocated power of each user, the subchannel assignment can be thought as a well-known assignment problem which can be effectively solved using the so-called Hungarian method. Then, the power allocation is analytically derived. Furthermore, it is shown that the power can be chosen from two extremal points of the maximum available power or the minimum power satisfying the rate constraint. Numerical results demonstrate the superiority of the proposed approach over the random selection strategy as well as the method proposed in [3] which is regarded as the best known method addressed in the literature.
MemJam: A False Dependency Attack against Constant-Time Crypto Implementations
Venue: arXiv cs.CR 2017
Year: 2017

Cache attacks exploit memory access patterns of cryptographic implementations. Constant-Time implementation techniques have become an indispensable tool in fighting cache timing attacks. These techniques engineer the memory accesses of cryptographic operations to follow a uniform key independent pattern. However, the constant-time behavior is dependent on the underlying architecture, which can be highly complex and often incorporates unpublished features. CacheBleed attack targets cache bank conflicts and thereby invalidates the assumption that microarchitectural side-channel adversaries can only observe memory with cache line granularity. In this work, we propose MemJam, a side-channel attack that exploits false dependency of memory read-after-write and provides a high quality intra cache level timing channel. As a proof of concept, we demonstrate the first key recovery attacks on a constant-time implementation of AES, and a SM4 implementation with cache protection in the current Intel Integrated Performance Primitives (Intel IPP) cryptographic library. Further, we demonstrate the first intra cache level timing attack on SGX by reproducing the AES key recovery results on an enclave that performs encryption using the aforementioned constant-time implementation of AES. Our results show that we can not only use this side channel to efficiently attack memory dependent cryptographic operations but also to bypass proposed protections. Compared to CacheBleed, which is limited to older processor generations, MemJam is the first intra cache level attack applicable to all major Intel processors including the latest generations that support the SGX extension.
SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis
Venue: arXiv cs.LG 2017
Year: 2017

Motivation: How do we integratively analyze large-scale multi-platform genomic data that are high dimensional and sparse? Furthermore, how can we incorporate prior knowledge, such as the association between genes, in the analysis systematically? Method: To solve this problem, we propose a Scalable Network Constrained Tucker decomposition method we call SNeCT. SNeCT adopts parallel stochastic gradient descent approach on the proposed parallelizable network constrained optimization function. SNeCT decomposition is applied to tensor constructed from large scale multi-platform multi-cohort cancer data, PanCan12, constrained on a network built from PathwayCommons database. Results: The decomposed factor matrices are applied to stratify cancers, to search for top-k similar patients, and to illustrate how the matrices can be used for personalized interpretation. In the stratification test, combined twelve-cohort data is clustered to form thirteen subclasses. The thirteen subclasses have a high correlation to tissue of origin in addition to other interesting observations, such as clear separation of OV cancers to two groups, and high clinical correlation within subclusters formed in cohorts BRCA and UCEC. In the top-k search, a new patient's genomic profile is generated and searched against existing patients based on the factor matrices. The similarity of the top-k patient to the query is high for 23 clinical features, including estrogen/progesterone receptor statuses of BRCA patients with average precision value ranges from 0.72 to 0.86 and from 0.68 to 0.86, respectively. We also provide an illustration of how the factor matrices can be used for interpretable personalized analysis of each patient.