OpenRaiser/Intern-Atlas huggingface dataset:
Complex-valued image denosing based on group-wise complex-domain sparsity
Venue: arXiv cs.CV 2017
Year: 2017

Phase imaging and wavefront reconstruction from noisy observations of complex exponent is a topic of this paper. It is a highly non-linear problem because the exponent is a 2π-periodic function of phase. The reconstruction of phase and amplitude is difficult. Even with an additive Gaussian noise in observations distributions of noisy components in phase and amplitude are signal dependent and non-Gaussian. Additional difficulties follow from a prior unknown correlation of phase and amplitude in real life scenarios. In this paper, we propose a new class of non-iterative and iterative complex domain filters based on group-wise sparsity in complex domain. This sparsity is based on the techniques implemented in Block-Matching 3D filtering (BM3D) and 3D/4D High-Order Singular Decomposition (HOSVD) exploited for spectrum design, analysis and filtering. The introduced algorithms are a generalization of the ideas used in the CD-BM3D algorithms presented in our previous publications. The algorithms are implemented as a MATLAB Toolbox. The efficiency of the algorithms is demonstrated by simulation tests.
Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making
Venue: arXiv cs.AI 2017
Year: 2017

It is often argued that an agent making decisions on behalf of two or more principals who have different utility functions should adopt a {\em Pareto-optimal} policy, i.e., a policy that cannot be improved upon for one agent without making sacrifices for another. A famous theorem of Harsanyi shows that, when the principals have a common prior on the outcome distributions of all policies, a Pareto-optimal policy for the agent is one that maximizes a fixed, weighted linear combination of the principals' utilities. In this paper, we show that Harsanyi's theorem does not hold for principals with different priors, and derive a more precise generalization which does hold, which constitutes our main result. In this more general case, the relative weight given to each principal's utility should evolve over time according to how well the agent's observations conform with that principal's prior. The result has implications for the design of contracts, treaties, joint ventures, and robots.
Successive Cancellation Soft Output Detector For Uplink MU-MIMO Systems With One-bit ADCs
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we present a successive-cancellation-soft-output (SCSO) detector for an uplink multiuser multiple-input-multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs). The proposed detector produces soft outputs (e.g., log-likelihood ratios (LLRs)) from one-bit quantized observations in a successive way: each user k's message is sequentially decoded from a channel decoder $k$ for $k=1,...,K$ in that order, and the previously decoded messages are exploited to improve the reliabilities of LLRs. Furthermore, we develop an efficient greedy algorithm to optimize a decoding order. Via simulation results, we demonstrate that the proposed ordered SCSO detector outperforms the other detectors for the coded MU-MIMO systems with one-bit ADCs.
Improving SIEM capabilities through an enhanced probe for encrypted Skype traffic detection
Venue: arXiv cs.CR 2017
Year: 2017

Nowadays, the Security Information and Event Management (SIEM) systems take on great relevance in handling security issues for critical infrastructures as Internet Service Providers. Basically, a SIEM has two main functions: i) the collection and the aggregation of log data and security information from disparate network devices (routers, firewalls, intrusion detection systems, ad hoc probes and others) and ii) the analysis of the gathered data by implementing a set of correlation rules aimed at detecting potential suspicious events as the presence of encrypted real-time traffic. In the present work, the authors propose an enhanced implementation of a SIEM where a particular focus is given to the detection of encrypted Skype traffic by using an ad-hoc developed enhanced probe (ESkyPRO) conveniently governed by the SIEM itself. Such enhanced probe, able to interact with an agent counterpart deployed into the SIEM platform, is designed by exploiting some machine learning concepts. The main purpose of the proposed ad-hoc SIEM is to correlate the information received by ESkyPRO and other types of data obtained by an Intrusion Detection System (IDS) probe in order to make the encrypted Skype traffic detection as accurate as possible.
Materials that make robots smart
Venue: arXiv cs.RO 2017
Year: 2017

We posit that embodied artificial intelligence is not only a computational, but also a materials problem. While the importance of material and structural properties in the control loop are well understood, materials can take an active role during control by tight integration of sensors, actuators, computation and communication. We envision such materials to abstract functionality, therefore making the construction of intelligent robots more straightforward and robust. For example, robots could be made of bones that measure load, muscles that move, skin that provides the robot with information about the kind and location of tactile sensations ranging from pressure, to texture and damage, eyes that extract high-level information, and brain material that provides computation in a scalable manner. Such materials will not resemble any existing engineered materials, but rather the heterogeneous components out of which their natural counterparts are made. We describe the state-of-the-art in so-called "robotic materials", their opportunities for revolutionizing applications ranging from manipulation to autonomous driving, and open challenges the robotics community needs to address in collaboration with allies, such as wireless sensor network researchers and polymer scientists.
A Review of Privacy and Consent Management in Healthcare: A Focus on Emerging Data Sources
Venue: arXiv cs.CY 2017
Year: 2017

The emergence of New Data Sources (NDS) in healthcare is revolutionising traditional electronic health records in terms of data availability, storage, and access. Increasingly, clinicians are using NDS to build a virtual holistic image of a patient's health condition. This research is focused on a review and analysis of the current legislation and privacy rules available for healthcare professionals. NDS in this project refers to and includes patient-generated health data, consumer device data, wearable health and fitness data, and data from social media. This project reviewed legal and regulatory requirements for New Zealand, Australia, the European Union, and the United States to establish the ground reality of existing mechanisms in place concerning the use of NDS. The outcome of our research is to recommend changes and enhancements required to better prepare for the 'tsunami' of NDS and applications in the currently evolving data-driven healthcare area and precision or personalised health initiatives such as Precision Driven Health (PDH) in New Zealand.
Deep Learning from Noisy Image Labels with Quality Embedding
Venue: arXiv cs.CV 2017
Year: 2017

There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a quality embedding model, which explicitly introduces a quality variable to represent the trustworthiness of noisy labels. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the noise effect. At the same time, the high-quality labels is still able to be applied for training. To instantiate the model, we further propose a Contrastive-Additive Noise network (CAN), which consists of two important layers: (1) the contrastive layer estimates the quality variable in the embedding space to reduce noise effect; and (2) the additive layer aggregates the prior predictions and noisy labels as the posterior to train the classifier. Moreover, to tackle the optimization difficulty, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We conduct the experimental evaluation of the proposed method over a range of noisy image datasets. Comprehensive results have demonstrated CAN outperforms the state-of-the-art deep learning approaches.
Optimal Parametric Search for Path and Tree Partitioning
Venue: arXiv cs.DS 2017
Year: 2017

We present linear-time algorithms for partitioning a path or a tree with weights on the vertices by removing $k$ edges to maximize the minimum-weight component. We also use the same framework to partition a path with weight on the vertices, removing $k$ edges to minimize the maximum-weight component. The algorithms use the parametric search paradigm, testing candidate values until an optimum is found while simultaneously reducing the running time needed for each test. For path-partitioning, the algorithm employs a synthetic weighting scheme that results in a constant fraction reduction in running time after each test. For tree-partitioning, our dual-pronged strategy makes progress no matter what the specific structure of our tree is.
Understanding and Predicting The Attractiveness of Human Action Shot
Venue: arXiv cs.CV 2017
Year: 2017

Selecting attractive photos from a human action shot sequence is quite challenging, because of the subjective nature of the "attractiveness", which is mainly a combined factor of human pose in action and the background. Prior works have actively studied high-level image attributes including interestingness, memorability, popularity, and aesthetics. However, none of them has ever studied the "attractiveness" of human action shot. In this paper, we present the first study of the "attractiveness" of human action shots by taking a systematic data-driven approach. Specifically, we create a new action-shot dataset composed of about 8000 high quality action-shot photos. We further conduct rich crowd-sourced human judge studies on Amazon Mechanical Turk(AMT) in terms of global attractiveness of a single photo, and relative attractiveness of a pair of photos. A deep Siamese network with a novel hybrid distribution matching loss was further proposed to fully exploit both types of ratings. Extensive experiments reveal that (1) the property of action shot attractiveness is subjective but predicable (2) our proposed method is both efficient and effective for predicting the attractive human action shots.
Related Fact Checks: a tool for combating fake news
Venue: arXiv cs.IR 2017
Year: 2017

The emergence of "Fake News" and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new "Related Fact Checks" service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a "Related Fact Checks" service, and present results from an evaluation of an implementation.
Spectral Methods for Immunization of Large Networks
Venue: arXiv cs.DM 2017
Year: 2017

Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbor nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint) that are immunized (vaccinated, screened, filtered) so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature. We test the performance of our algorithm on several real world graphs. Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic) and efficiency (runtime and space complexity).
3D Mobile Localization Using Distance-only Measurements
Venue: arXiv cs.SY 2017
Year: 2017

For a group of cooperating UAVs, localizing each other is often a key task. This paper studies the localization problem for a group of UAVs flying in 3D space with very limited information, i.e., when noisy distance measurements are the only type of inter-agent sensing that is available, and when only one UAV knows a global coordinate basis, the others being GPS-denied. Initially for a two-agent problem, but easily generalized to some multi-agent problems, constraints are established on the minimum number of required distance measurements required to achieve the localization. The paper also proposes an algorithm based on semidefinite programming (SDP), followed by maximum likelihood estimation using a gradient descent initialized from the SDP calculation. The efficacy of the algorithm is verified with experimental noisy flight data.
Acceleration of tensor-product operations for high-order finite element methods
Venue: arXiv cs.MS 2017
Year: 2017

This paper is devoted to GPU kernel optimization and performance analysis of three tensor-product operators arising in finite element methods. We provide a mathematical background to these operations and implementation details. Achieving close-to-the-peak performance for these operators requires extensive optimization because of the operators' properties: low arithmetic intensity, tiered structure, and the need to store intermediate results inside the kernel. We give a guided overview of optimization strategies and we present a performance model that allows us to compare the efficacy of these optimizations against an empirically calibrated roofline.
Automatic Query Image Disambiguation for Content-Based Image Retrieval
Venue: arXiv cs.CV 2017
Year: 2017

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.
AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks
Venue: arXiv cs.CV 2017
Year: 2017

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg
Wireless Network Simplification: The Performance of Routing
Venue: arXiv cs.IT 2017
Year: 2017

Consider a wireless Gaussian network where a source wishes to communicate with a destination with the help of N full-duplex relay nodes. Most practical systems today route information from the source to the destination using the best path that connects them. In this paper, we show that routing can in the worst case result in an unbounded gap from the network capacity - or reversely, physical layer cooperation can offer unbounded gains over routing. More specifically, we show that for $N$-relay Gaussian networks with an arbitrary topology, routing can in the worst case guarantee an approximate fraction $\frac{1}{\left\lfloor N/2 \right\rfloor + 1}$ of the capacity of the full network, independently of the SNR regime. We prove that this guarantee is fundamental, i.e., it is the highest worst-case guarantee that we can provide for routing in relay networks. Next, we consider how these guarantees are refined for Gaussian layered relay networks with $L$ layers and $N_L$ relays per layer. We prove that for arbitrary $L$ and $N_L$, there always exists a route in the network that approximately achieves at least $\frac{2}{(L-1)N_L + 4}$ $\left(\mbox{resp.}\frac{2}{LN_L+2}\right)$ of the network capacity for odd $L$ (resp. even $L$), and there exist networks where the best routes exactly achieve these fractions. These results are formulated within the network simplification framework, that asks what fraction of the capacity we can achieve by using a subnetwork (in our case, a single path). A fundamental step in our proof is a simplification result for MIMO antenna selection that may also be of independent interest. To the best of our knowledge, this is the first result that characterizes, for general wireless network topologies, what is the performance of routing with respect to physical layer cooperation techniques that approximately achieve the network capacity.
Elasticutor: Rapid Elasticity for Realtime Stateful Stream Processing
Venue: arXiv cs.DB 2017
Year: 2017

Elasticity is highly desirable for stream processing systems to guarantee low latency against workload dynamics, such as surges in data arrival rate and fluctuations in data distribution. Existing systems achieve elasticity following a resource-centric approach that uses dynamic key partitioning across the parallel instances, i.e. executors, to balance the workload and scale operators. However, such operator-level key repartitioning needs global synchronization and prohibits rapid elasticity. To address this problem, we propose an executor-centric approach, whose core idea is to avoid operator-level key repartitioning while implementing each executor as the building block of elasticity. Following this new approach, we design the Elasticutor framework with two level of optimizations: i) a novel implementation of executors, i.e., elastic executors, that perform elastic multi-core execution via efficient intra-executor load balancing and executor scaling and ii) a global model-based scheduler that dynamically allocates CPU cores to executors based on the instantaneous workloads. We implemented a prototype of Elasticutor and conducted extensive experiments. Our results show that Elasticutor doubles the throughput and achieves an average processing latency up to 2 orders of magnitude lower than previous methods, for a dynamic workload of real-world applications.
Competition and Cooperation Analysis for Data Sponsored Market: A Network Effects Model
Venue: arXiv cs.GT 2017
Year: 2017

The data sponsored scheme allows the content provider to cover parts of the cellular data costs for mobile users. Thus the content service becomes appealing to more users and potentially generates more profit gain to the content provider. In this paper, we consider a sponsored data market with a monopoly network service provider, a single content provider, and multiple users. In particular, we model the interactions of three entities as a two-stage Stackelberg game, where the service provider and content provider act as the leaders determining the pricing and sponsoring strategies, respectively, in the first stage, and the users act as the followers deciding on their data demand in the second stage. We investigate the mutual interaction of the service provider and content provider in two cases: (i) competitive case, where the content provider and service provider optimize their strategies separately and competitively, each aiming at maximizing the profit and revenue, respectively; and (ii) cooperative case, where the two providers jointly optimize their strategies, with the purpose of maximizing their aggregate profits. We analyze the sub-game perfect equilibrium in both cases. Via extensive simulations, we demonstrate that the network effects significantly improve the payoff of three entities in this market, i.e., utilities of users, the profit of content provider and the revenue of service provider. In addition, it is revealed that the cooperation between the two providers is the best choice for all three entities.
Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
Venue: arXiv cs.CV 2017
Year: 2017

With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information. To the best of our knowledge, this is the first attempt to utilize LSTM structure for RGB-D based human detection. Our method achieves superior performance on two publicly available datasets.
A Rudimentary Model for Low-Latency Anonymous Communication Systems
Venue: arXiv cs.DC 2017
Year: 2017

In this paper we present a rudimentary model for low-latency anonymous communication systems. Specifically, we study distributed OR algorithm as an abstract of the system. Based on our model, we give several satisfactory lower bounds of anonymity leakage of a deterministic OR algorithm. Some of them reveal a trade-off between anonymity and communication complexity. For the randomized OR algorithm, we only give a relatively trivial but possibly tight lower bound when leaving out communication complexity. And we find the relationship between our model and some open case in the study of secret sharing scheme, if considering communication complexity.