OpenRaiser/Intern-Atlas huggingface dataset:
Deep Hashing with Triplet Quantization Loss
Venue: arXiv cs.CV 2017
Year: 2017

With the explosive growth of image databases, deep hashing, which learns compact binary descriptors for images, has become critical for fast image retrieval. Many existing deep hashing methods leverage quantization loss, defined as distance between the features before and after quantization, to reduce the error from binarizing features. While minimizing the quantization loss guarantees that quantization has minimal effect on retrieval accuracy, it unfortunately significantly reduces the expressiveness of features even before the quantization. In this paper, we show that the above definition of quantization loss is too restricted and in fact not necessary for maintaining high retrieval accuracy. We therefore propose a new form of quantization loss measured in triplets. The core idea of the triplet quantization loss is to learn discriminative real-valued descriptors which lead to minimal loss on retrieval accuracy after quantization. Extensive experiments on two widely used benchmark data sets of different scales, CIFAR-10 and In-shop, demonstrate that the proposed method outperforms the state-of-the-art deep hashing methods. Moreover, we show that the compact binary descriptors obtained with triplet quantization loss lead to very small performance drop after quantization.
Optimal Traffic Splitting Policy in LTE-based Heterogeneous Network
Venue: arXiv cs.NI 2017
Year: 2017

Dual Connectivity (DC) is a technique proposed to address the problem of increased handovers in heterogeneous networks. In DC, a foreground User Equipment (UE) with multiple transceivers has a possibility to connect to a Macro eNodeB (MeNB) and a Small cell eNodeB (SeNB) simultaneously. In downlink split bearer architecture of DC, a data radio bearer at MeNB gets divided into two; one part is forwarded to the SeNB through a non-ideal backhaul link to the UE, and the other part is forwarded by the MeNB. This may lead to an increase in the total delay at the UE since different packets corresponding to a single transmission may incur varying amounts of delays in the two different paths. Since the resources in the MeNB are shared by background legacy users and foreground users, DC may increase the blocking probability of background users. Moreover, single connectivity to the small cell may increase the blocking probability of foreground users. Therefore, we target to minimize the average delay of the system subject to a constraint on the blocking probability of background and foreground users. The optimal policy is computed and observed to contain a threshold structure. The variation of average system delay is studied for changes in different system parameters.
Optimal Resource Allocation in Distributed Broadband Wireless Communication Systems
Venue: arXiv cs.NI 2017
Year: 2017

This paper is concerned with optimization of distributed broadband wireless communication (BWC) systems. BWC systems contain a distributed antenna system (DAS) connected to a base station with optical fiber. Distributed BWC systems have been proposed as a solution to the power constraint problem in traditional cellular networks. So far, the research on BWC systems have advanced on two separate tracks, the design of the system to meet the quality of service requirements (QoS) and optimization of the location of the DAS. In this paper, we consider a combined optimization of BWC systems. We consider uplink communications in distributed BWC systems with multiple levels of priority traffic with arrivals and departures forming renewal processes. We develop an analysis that determines packet delay violation probability for each priority level as a function of the outage probability of the DAS through the application of results from renewal theory. Then, we determine the optimal locations of the antennas that minimize the antenna outage probability. We also study the tradeoff between the packet delay violation probability and packet loss probability. This work will be helpful in the designing of the distributed BWC systems.
Breaking the Interference Barrier in Dense Wireless Networks with Interference Alignment
Venue: arXiv cs.NI 2017
Year: 2017

A fundamental problem arising in dense wireless networks is the high co-channel interference. Interference alignment (IA) was recently proposed as an effective way to combat interference in wireless networks. The concept of IA, though, is originated by the capacity study of interference channels and as such, its performance is mainly gauged under ideal assumptions, such as instantaneous and perfect channel state information (CSI) at all nodes, and homogeneous signal-to-noise ratio (SNR) users, i.e., each user has the same average SNR. Consequently, the performance of IA under realistic conditions has not been completely investigated yet. In this paper, we aim at filling this gap by providing a performance assessment of spatial IA in practical systems. Specifically, we derive a closed-form expression for the IA average sum-rate when CSI is acquired through training and users have heterogeneous SNR. A main insight from our analysis is that IA can indeed provide significant spectral efficiency gains over traditional approaches in a wide range of dense network scenarios. To demonstrate this, we consider the examples of linear, grid and random network topologies.
Multi-Path Cooperative Communications Networks for Augmented and Virtual Reality Transmission
Venue: arXiv cs.NI 2017
Year: 2017

Augmented and/or virtual reality (AR/VR) are emerging as one of the main applications in future fifth generation (5G) networks. To meet the requirements of lower latency and massive data transmission in AR/VR applications, a solution with software-defined networking (SDN) architecture is proposed for 5G small cell networks. On this basis, a multi-path cooperative route (MCR) scheme is proposed to facilitate the AR/VR wireless transmissions in 5G small cell networks, in which the delay of MCR scheme is analytically studied. Furthermore, a service effective energy optimal (SEEO) algorithm is developed for AR/VR wireless transmission in 5G small cell networks. Simulation results indicate that both the delay and service effective energy (SEE) of the proposed MCR scheme outperform the delay and SEE of the conventional single path route scheme in 5G small cell networks.
On the List-Decodability of Random Linear Rank-Metric Codes
Venue: arXiv cs.CC 2017
Year: 2017

The list-decodability of random linear rank-metric codes is shown to match that of random rank-metric codes. Specifically, an $\mathbb{F}_q$-linear rank-metric code over $\mathbb{F}_q^{m \times n}$ of rate $R = (1-ρ)(1-\frac{n}{m}ρ)-\varepsilon$ is shown to be (with high probability) list-decodable up to fractional radius $ρ\in (0,1)$ with lists of size at most $\frac{C_{ρ,q}}{\varepsilon}$, where $C_{ρ,q}$ is a constant depending only on $ρ$ and $q$. This matches the bound for random rank-metric codes (up to constant factors). The proof adapts the approach of Guruswami, Håstad, Kopparty (STOC 2010), who established a similar result for the Hamming metric case, to the rank-metric setting.
Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer
Venue: arXiv cs.CY 2017
Year: 2017

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.
Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks
Venue: arXiv cs.DC 2017
Year: 2017

Mixed-criticality models are an emerging paradigm for the design of real-time systems because of their significantly improved resource efficiency. However, formal mixed-criticality models have traditionally been characterized by two impractical assumptions: once \textit{any} high-criticality task overruns, \textit{all} low-criticality tasks are suspended and \textit{all other} high-criticality tasks are assumed to exhibit high-criticality behaviors at the same time. In this paper, we propose a more realistic mixed-criticality model, called the flexible mixed-criticality (FMC) model, in which these two issues are addressed in a combined manner. In this new model, only the overrun task itself is assumed to exhibit high-criticality behavior, while other high-criticality tasks remain in the same mode as before. The guaranteed service levels of low-criticality tasks are gracefully degraded with the overruns of high-criticality tasks. We derive a utilization-based technique to analyze the schedulability of this new mixed-criticality model under EDF-VD scheduling. During runtime, the proposed test condition serves an important criterion for dynamic service level tuning, by means of which the maximum available execution budget for low-criticality tasks can be directly determined with minimal overhead while guaranteeing mixed-criticality schedulability. Experiments demonstrate the effectiveness of the FMC scheme compared with state-of-the-art techniques.
Separation of Water and Fat Magnetic Resonance Imaging Signals Using Deep Learning with Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Purpose: A new method for magnetic resonance (MR) imaging water-fat separation using a convolutional neural network (ConvNet) and deep learning (DL) is presented. Feasibility of the method with complex and magnitude images is demonstrated with a series of patient studies and accuracy of predicted quantitative values is analyzed. Methods: Water-fat separation of 1200 gradient-echo acquisitions from 90 imaging sessions (normal, acute and chronic myocardial infarction) was performed using a conventional model based method with modeling of R2* and off-resonance and a multi-peak fat spectrum. A U-Net convolutional neural network for calculation of water-only, fat-only, R2* and off-resonance images was trained with 900 gradient-echo Multiple and single-echo complex and magnitude input data algorithms were studied and compared to conventional extended echo modeling. Results: The U-Net ConvNet was easily trained and provided water-fat separation results visually comparable to conventional methods. Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R2*, off-resonance, water and fat signal intensities were well correlated with conventional model based water fat separation (R2>=0.97, p<0.001). DL images had a 14% higher signal-to-noise ratio (p<0.001) when compared to the conventional method. Conclusion: Deep learning utilizing ConvNets is a feasible method for MR water-fat separationimaging with complex, magnitude and single echo image data. A trained U-Net can be efficiently used for MR water-fat separation, providing results comparable to conventional model based methods.
Statistical Modeling of FSO Fronthaul Channel for Drone-based Networks
Venue: arXiv cs.IT 2017
Year: 2017

We consider a drone-based communication network, where several drones hover above an area and serve as mobile remote radio heads for a large number of mobile users. We assume that the drones employ free space optical (FSO) links for fronthauling of the users' data to a central unit. The main focus of this paper is to quantify the geometric loss of the FSO channel arising from random fluctuation of the position and orientation of the drones. In particular, we derive upper and lower bounds, corresponding approximate expressions, and a closed-form statistical model for the geometric loss. Simulation results validate our derivations and quantify the FSO channel quality as a function of the drone's instability, i.e., the variation of its position and orientation.
Diffusive Molecular Communications with Reactive Signaling
Venue: arXiv cs.IT 2017
Year: 2017

This paper focuses on molecular communication (MC) systems where the signaling molecules may participate in a reversible bimolecular reaction in the channel. The motivation for studying these MC systems is that they can realize the concept of constructive and destructive signal superposition, which leads to favorable properties such as inter-symbol interference (ISI) reduction and avoiding environmental contamination due to continuous release of molecules into the channel. This work first derives the maximum likelihood (ML) detector for a binary MC system with reactive signaling molecules under the assumption that the detector has perfect knowledge of the ISI. The performance of this genie-aided ML detector yields an upper bound on the performance of any practical detector. In addition, two suboptimal detectors of different complexity are proposed. The proposed ML detector as well as one of the suboptimal detectors require the channel response (CR) of the considered MC system. Moreover, the CR is needed for the performance evaluation of all proposed detectors. However, analyzing MC with reactive signaling is challenging since the underlying partial differential equations that describe the reaction-diffusion mechanism are coupled and non-linear. Therefore, an algorithm is developed in this paper for efficient computation of the CR to any arbitrary transmit symbol sequence. The accuracy of this algorithm is validated via particle-based simulation. Simulation results using the developed CR algorithm show that the performance of the proposed suboptimal detectors can approach that of the genie- aided ML detector. Moreover, these results show that MC systems with reactive signaling have superior performance relative to those with non-reactive signaling due to the reduction of ISI enabled by the chemical reactions.
A multitask deep learning model for real-time deployment in embedded systems
Venue: arXiv cs.CV 2017
Year: 2017

We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems. We develop a multitask model for both Object Detection and Semantic Segmentation and analyze the challenges that appear during its training. Our multitask network is 1.6x faster, lighter and uses less memory than deploying the single-task models in parallel. We conclude that MTL has the potential to give superior performance in exchange of a more complex training process that introduces challenges not present in single-task models.
Neural Wikipedian: Generating Textual Summaries from Knowledge Base Triples
Venue: arXiv cs.CL 2017
Year: 2017

Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.
A generalized concatenation construction for q-ary 1-perfect codes
Venue: arXiv cs.DM 2017
Year: 2017

We consider perfect 1-error correcting codes over a finite field with $q$ elements (briefly $q$-ary 1-perfect codes). In this paper, a generalized concatenation construction for $q$-ary 1-perfect codes is presented that allows us to construct $q$-ary 1-perfect codes of length $(q - 1)nm + n + m$ from the given $q$-ary 1-perfect codes of length $n =(q^{s_1} - 1) / (q - 1)$ and $m = (q^{s_2} - 1) / (q - 1)$, where $s_1, s_2$ are natural numbers not less than two. This construction allows us to also construct $q$-ary codes with parameters $(q^{s_1 + s_2}, q^{q^{s_1 + s_2} - (s_1 + s_2) - 1}, 3)_q$ and can be regarded as a $q$-ary analogue of the well-known Phelps construction.
Learning deep features for source color laser printer identification based on cascaded learning
Venue: arXiv cs.CV 2017
Year: 2017

Color laser printers have fast printing speed and high resolution, and forgeries using color laser printers can cause significant harm to society. A source printer identification technique can be employed as a countermeasure to those forgeries. This paper presents a color laser printer identification method based on cascaded learning of deep neural networks. The refiner network is trained by adversarial training to refine the synthetic dataset for halftone color decomposition. The halftone color decomposing ConvNet is trained with the refined dataset, and the trained knowledge is transferred to the printer identifying ConvNet to enhance the accuracy. The robustness about rotation and scaling is considered in training process, which is not considered in existing methods. Experiments are performed on eight color laser printers, and the performance is compared with several existing methods. The experimental results clearly show that the proposed method outperforms existing source color laser printer identification methods.
From Attention to Participation: Reviewing and Modelling Engagement with Computers
Venue: arXiv cs.CY 2017
Year: 2017

Over the last decades, the Internet and mobile technology have consolidated the digital as a public sphere of life. Designers are asked to create engaging digital experiences. However, in some cases engagement is seen as a psychological state, while in others it emphasizes a participative vein. In this paper, I review and discuss both and propose a new definition to clarify the concept engagement with computers. Thus, engagement is a quality of an active connection between a user and a computing product, either a website or a mobile phone app. Studying it requires understanding a set of aspects like the user's affect, motivation and attention, as well as the product's design, content and composition. Finally, I propose explaining these concepts aligned with engagement and integrate them into a preliminary model to measure the manifestations.
Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision
Venue: arXiv cs.LG 2017
Year: 2017

Training deep neural networks requires massive amounts of training data, but for many tasks only limited labeled data is available. This makes weak supervision attractive, using weak or noisy signals like the output of heuristic methods or user click-through data for training. In a semi-supervised setting, we can use a large set of data with weak labels to pretrain a neural network and then fine-tune the parameters with a small amount of data with true labels. This feels intuitively sub-optimal as these two independent stages leave the model unaware about the varying label quality. What if we could somehow inform the model about the label quality? In this paper, we propose a semi-supervised learning method where we train two neural networks in a multi-task fashion: a "target network" and a "confidence network". The target network is optimized to perform a given task and is trained using a large set of unlabeled data that are weakly annotated. We propose to weight the gradient updates to the target network using the scores provided by the second confidence network, which is trained on a small amount of supervised data. Thus we avoid that the weight updates computed from noisy labels harm the quality of the target network model. We evaluate our learning strategy on two different tasks: document ranking and sentiment classification. The results demonstrate that our approach not only enhances the performance compared to the baselines but also speeds up the learning process from weak labels.
Robust Saliency Detection via Fusing Foreground and Background Priors
Venue: arXiv cs.CV 2017
Year: 2017

Automatic Salient object detection has received tremendous attention from research community and has been an increasingly important tool in many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background cues. First, A series of background and foreground seeds are selected from an image reliably, and then used for calculation of saliency map separately. Next, a combination of foreground and background saliency map is performed. Last, a refinement step based on geodesic distance is utilized to enhance salient regions, thus deriving the final saliency map. Particularly we provide a robust scheme for seeds selection which contributes a lot to accuracy improvement in saliency detection. Extensive experimental evaluations demonstrate the effectiveness of our proposed method against other outstanding methods.
Non Uniform On Chip Power Delivery Network Synthesis Methodology
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, we proposed a non-uniform power delivery network (PDN) synthesis methodology. It first constructs initial PDN using uniform approach. Then preliminary power integrity analysis is performed to derive IR-safe candidate window. Congestion map is obtained based global route congestion estimation. A self-adaptive non-uniform PDN synthesis is then performed to globally and locally optimize PDN over selected regions. The PDN synthesis is congestion-driven and IR- guarded. Experimental results show significant timing important in trade-off small PDN length reduction with no EM/IR impact. We further explored potential power savings using our non-uniform PDN synthesis methodology.
Widening siamese architectures for stereo matching
Venue: arXiv cs.CV 2017
Year: 2017

Computational stereo is one of the classical problems in computer vision. Numerous algorithms and solutions have been reported in recent years focusing on developing methods for computing similarity, aggregating it to obtain spatial support and finally optimizing an energy function to find the final disparity. In this paper, we focus on the feature extraction component of stereo matching architecture and we show standard CNNs operation can be used to improve the quality of the features used to find point correspondences. Furthermore, we propose a simple space aggregation that hugely simplifies the correlation learning problem. Our results on benchmark data are compelling and show promising potential even without refining the solution.