results for au:Xu_J in:cs

- The (ultra-)dense deployment of small-cell base stations (SBSs) endowed with cloud-like computing functionalities paves the way for pervasive mobile edge computing (MEC), enabling ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. To handle spatially uneven computation workloads in the network, cooperation among SBSs via workload peer offloading is essential to avoid large computation latency at overloaded SBSs and provide high quality of service to end users. However, performing effective peer offloading faces many unique challenges in small cell networks due to limited energy resources committed by self-interested SBS owners, uncertainties in the system dynamics and co-provisioning of radio access and computing services. This paper develops a novel online SBS peer offloading framework, called OPEN, by leveraging the Lyapunov technique, in order to maximize the long-term system performance while keeping the energy consumption of SBSs below individual long-term constraints. OPEN works online without requiring information about future system dynamics, yet provides provably near-optimal performance compared to the oracle solution that has the complete future information. In addition, this paper formulates a novel peer offloading game among SBSs, analyzes its equilibrium and efficiency loss in terms of the price of anarchy in order to thoroughly understand SBSs' strategic behaviors, thereby enabling decentralized and autonomous peer offloading decision making. Extensive simulations are carried out and show that peer offloading among SBSs dramatically improves the edge computing performance.
- Mobile edge computing (a.k.a. fog computing) has recently emerged to enable in-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in energy harvesting mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to the centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning. We prove the convergence of the proposed algorithm and analytically show that the learned policy has a simple monotone structure amenable to practical implementation. Our simulation results validate the efficacy of our algorithm, which significantly improves the edge computing performance compared to fixed or myopic optimization schemes and conventional reinforcement learning algorithms.
- Mar 16 2017 cs.RO arXiv:1703.04906v1One of the most efficient ways for a learning-based robotic arm to learn to process complex tasks as human, is to directly learn from observing how human complete those tasks, and then imitate. Our idea is based on success of Deep Q-Learning (DQN) algorithm according to reinforcement learning, and then extend to Deep Deterministic Policy Gradient (DDPG) algorithm. We developed a learning-based method, combining modified DDPG and visual imitation network. Our approach acquires frames only from a monocular camera, and no need to either construct a 3D environment or generate actual points. The result we expected during training, was that robot would be able to move as almost the same as how human hands did.
- Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.
- Inference of user context information, including user's gender, age, marital status, location and so on, has been proven to be valuable for building context aware recommender system. However, prevalent existing studies on user context inference have two shortcommings: 1. focusing on only a single data source (e.g. Internet browsing logs, or mobile call records), and 2. ignoring the interdependence of multiple user contexts (e.g. interdependence between age and marital status), which have led to poor inference performance. To solve this problem, in this paper, we first exploit tensor outer product to fuse multiple data sources in the feature space to obtain an extensional user feature representation. Following this, by taking this extensional user feature representation as input, we propose a multiple attribute probabilistic model called MulAProM to infer user contexts that can take advantage of the interdependence between them. Our study is based on large telecommunication datasets from the local mobile operator of Shanghai, China, and consists of two data sources, 4.6 million call detail records and 7.5 million data traffic records of 8,000 mobile users, collected in the course of six months. The experimental results show that our model can outperform other models in terms of \emphrecall, \emphprecision, and the \emphF1-measure.
- Cognitive inference of user demographics, such as gender and age, plays an important role in creating user profiles for adjusting marketing strategies and generating personalized recommendations because user demographic data is usually not available due to data privacy concerns. At present, users can readily express feedback regarding products or services that they have purchased. During this process, user demographics are concealed, but the data has never yet been successfully utilized to contribute to the cognitive inference of user demographics. In this paper, we investigate the inference power of user ratings data, and propose a simple yet general cognitive inference model, called rating to profile (R2P), to infer user demographics from user provided ratings. In particular, the proposed R2P model can achieve the following: 1. Correctly integrate user ratings into model training. 2.Infer multiple demographic attributes of users simultaneously, capturing the underlying relevance between different demographic attributes. 3. Train its two components, i.e. feature extractor and classifier, in an integrated manner under a supervised learning paradigm, which effectively helps to discover useful hidden patterns from highly sparse ratings data. We introduce how to incorporate user ratings data into the research field of cognitive inference of user demographic data, and detail the model development and optimization process for the proposed R2P. Extensive experiments are conducted on two real-world ratings datasets against various compared state-of-the-art methods, and the results from multiple aspects demonstrate that our proposed R2P model can significantly improve on the cognitive inference performance of user demographic data.
- Mar 09 2017 cs.DB arXiv:1703.02722v1DGCC protocol has been shown to achieve good performance on multi-core in-memory system. However, distributed transactions complicate the dependency resolution, and therefore, an effective transaction partitioning strategy is essential to reduce expensive multi-node distributed transactions. During failure recovery, log must be examined from the last checkpoint onwards and the affected transactions are re-executed based on the way they are partitioned and executed. Existing approaches treat both transaction management and recovery as two separate problems, even though recovery is dependent on the sequence in which transactions are executed. In this paper, we propose to treat the transaction management and recovery problems as one. We first propose an efficient Distributed Dependency Graph based Concurrency Control (DistDGCC) protocol for handling transactions spanning multiple nodes, and propose a new novel and efficient logging protocol called Dependency Logging that also makes use of dependency graphs for efficient logging and recovery. DistDGCC optimizes the average cost for each distributed transaction by processing transactions in batch. Moreover, it also reduces the effects of thread blocking caused by distributed transactions and consequently improves the runtime performance. Further, dependency logging exploits the same data structure that is used by DistDGCC to reduce the logging overhead, as well as the logical dependency information to improve the recovery parallelism. Extensive experiments are conducted to evaluate the performance of our proposed technique against state-of-the-art techniques. Experimental results show that DistDGCC is efficient and scalable, and dependency logging supports fast recovery with marginal runtime overhead. Hence, the overall system performance is significantly improved as a result.
- Feb 27 2017 cs.CR arXiv:1702.07588v1We present novel homomorphic encryption schemes for integer arithmetic, intended for use in secure single-party computation in the cloud. These schemes are capable of securely computing only low degree polynomials homomorphically, but this appears sufficient for most practical applications. In this setting, our schemes lead to practical key and ciphertext sizes. We present a sequence of generalisations of our basic schemes, with increasing levels of security, but decreasing practicality. We have evaluated the first four of these algorithms by computing a low-degree inner product. The timings of these computations are extremely favourable. Finally, we use our ideas to derive a fully homomorphic system, which appears impractical, but can homomorphically evaluate arbitrary Boolean circuits.
- Feb 17 2017 cs.SE arXiv:1702.04872v2Android has been the most popular smartphone system, with multiple platform versions (e.g., KITKAT and Lollipop) active in the market. To manage the application's compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we make a first effort to study this modern software mechanism. Our objective is to measure the current practice of the declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the consistency between the DSDK versions and their app API calls. To this end, we perform a three-dimensional analysis. First, we parse Android documents to obtain a mapping between each API and their corresponding platform versions. We then analyze the DSDK-API consistency for over 24K apps, among which we pre-exclude 1.3K apps that provide different app binaries for different Android versions through Google Play analysis. Besides shedding light on the current DSDK practice, our study quantitatively measures the two side effects of inappropriate DSDK versions: (i) around 1.8K apps have API calls that do not exist in some declared SDK versions, which causes runtime crash bugs on those platform versions; (ii) over 400 apps, due to claiming the outdated targeted DSDK versions, are potentially exploitable by remote code execution. These results indicate the importance and difficulty of declaring correct DSDK, and our work can help developers fulfill this goal.
- Feb 16 2017 cs.CL arXiv:1702.04488v2Recent works have been shown effective in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. Thus, we propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data with the help of high-resource corpora. Finally, given that insufficient data puts forward higher requirements for feature extraction, we propose a novel neural network which improves feature learning. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets. Besides, we explore an asynchronous parallel method on neural word segmentation to speed up training. The parallel method accelerates training substantially and is almost five times faster than a serial mode.
- Integrating mobile-edge computing (MEC) and wireless power transfer (WPT) is a promising technique in the Internet of Things (IoT) era. It can provide massive lowpower mobile devices with enhanced computation capability and sustainable energy supply. In this paper, we consider a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute latency-sensitive computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of the tasks to the AP based on a time division multiple access (TDMA) protocol. Under this setup, we pursue an energy-efficient wireless powered MEC system design by jointly optimizing the transmit energy beamformer at the AP, the central processing unit (CPU) frequency and the offloaded bits at each user, as well as the time allocation among different users. In particular, we minimize the energy consumption at the AP over a particular time block subject to the computation latency and energy harvesting constraints per user. By formulating this problem into a convex framework and employing the Lagrange duality method, we obtain its optimal solution in a semi-closed form. Numerical results demonstrate the benefit of the proposed joint design over alternative benchmark schemes in terms of the achieved energy efficiency.
- Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.
- This paper proposes a totally constructive approach for the proof of Hilbert's theorem on ternary quartic forms. The main contribution is the ladder technique, with which the Hilbert's theorem is proved vividly.
- Merging Mobile Edge Computing (MEC), which is an emerging paradigm to meet the increasing computation demands from mobile devices, with the dense deployment of Base Stations (BSs), is foreseen as a key step towards the next generation mobile networks. However, new challenges arise for designing energy efficient networks since radio access resources and computing resources of BSs have to be jointly managed, and yet they are complexly coupled with traffic in both spatial and temporal domains. In this paper, we address the challenge of incorporating MEC into dense cellular networks, and propose an efficient online algorithm, called ENGINE (ENErgy constrained offloadINg and slEeping) which makes joint computation offloading and BS sleeping decisions in order to maximize the quality of service while keeping the energy consumption low. Our algorithm leverages Lyapunov optimization technique, works online and achieves a close-to-optimal performance without using future information. Our simulation results show that our algorithm can effectively reduce energy consumption without sacrificing the user quality of service.
- Merging mobile edge computing with the dense deployment of small cell base stations promises enormous benefits such as a real proximity, ultra-low latency access to cloud functionalities. However, the envisioned integration creates many new challenges and one of the most significant is mobility management, which is becoming a key bottleneck to the overall system performance. Simply applying existing solutions leads to poor performance due to the highly overlapped coverage areas of multiple base stations in the proximity of the user and the co-provisioning of radio access and computing services. In this paper, we develop a novel user-centric mobility management scheme, leveraging Lyapunov optimization and multi-armed bandits theories, in order to maximize the edge computation performance for the user while keeping the user's communication energy consumption below a constraint. The proposed scheme effectively handles the uncertainties present at multiple levels in the system and provides both short-term and long-term performance guarantee. Simulation results show that our proposed scheme can significantly improve the computation performance (compared to state of the art) while satisfying the communication energy constraint.
- Jan 25 2017 cs.LG arXiv:1701.06725v1Contextual bandit algorithms -- a class of multi-armed bandit algorithms that exploit the contextual information -- have been shown to be effective in solving sequential decision making problems under uncertainty. A common assumption adopted in the literature is that the realized (ground truth) reward by taking the selected action is observed by the learner at no cost, which, however, is not realistic in many practical scenarios. When observing the ground truth reward is costly, a key challenge for the learner is how to judiciously acquire the ground truth by assessing the benefits and costs in order to balance learning efficiency and learning cost. From the information theoretic perspective, a perhaps even more interesting question is how much efficiency might be lost due to this cost. In this paper, we design a novel contextual bandit-based learning algorithm and endow it with the active learning capability. The key feature of our algorithm is that in addition to sending a query to an annotator for the ground truth, prior information about the ground truth learned by the learner is sent together, thereby reducing the query cost. We prove that by carefully choosing the algorithm parameters, the learning regret of the proposed algorithm achieves the same order as that of conventional contextual bandit algorithms in cost-free scenarios, implying that, surprisingly, cost due to acquiring the ground truth does not increase the learning regret in the long-run. Our analysis shows that prior information about the ground truth plays a critical role in improving the system performance in scenarios where active learning is necessary.
- Jan 10 2017 cs.OH arXiv:1701.01771v1This research was to design a 2.4 GHz class E Power Amplifier (PA) for health care, with 0.18um Semiconductor Manufacturing International Corporation CMOS technology by using Cadence software. And also RF switch was designed at cadence software with power Jazz 180nm SOI process. The ultimate goal for such application is to reach high performance and low cost, and between high performance and low power consumption design. This paper introduces the design of a 2.4GHz class E power amplifier and RF switch design. PA consists of cascade stage with negative capacitance. This power amplifier can transmit 16dBm output power to a 50\Omega load. The performance of the power amplifier and switch meet the specification requirements of the desired.
- Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC^2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction methods. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.
- Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world.
- This letter studies an emerging wireless communication intervention problem at the physical layer, where a legitimate spoofer aims to spoof a malicious link from Alice to Bob, by replacing Alice's transmitted source message with its target message at Bob side. From an information-theoretic perspective, we are interested in characterizing the maximum achievable spoofing rate of this new spoofing channel, which is equivalent to the maximum achievable rate of the target message at Bob, under the condition that Bob cannot decode the source message from Alice. We propose a novel combined spoofing approach, where the spoofer sends its own target message, combined with a processed version of the source message to cancel the source message at Bob. For both cases when Bob treats interference as noise (TIN) or applies successive interference cancelation (SIC), we obtain the maximum achievable spoofing rates by optimizing the power allocation between the target and source messages at the spoofer.
- Conventional wireless security assumes wireless communications are rightful and aims to protect them against malicious eavesdropping and jamming attacks. However, emerging infrastructure-free mobile communication networks are likely to be illegally used (e.g., by criminals or terrorists) but difficult to be monitored, thus imposing new challenges on the public security. To tackle this issue, this article presents a paradigm shift of wireless security to the surveillance and intervention of infrastructure-free suspicious and malicious wireless communications, by exploiting legitimate eavesdropping and jamming jointly. In particular, \emphproactive eavesdropping (via jamming) is proposed to intercept and decode information from suspicious communication links for the purpose of inferring their intentions and deciding further measures against them. \emphCognitive jamming (via eavesdropping) is also proposed so as to disrupt, disable, and even spoof the targeted malicious wireless communications to achieve various intervention tasks.
- Dec 07 2016 cs.CV arXiv:1612.01611v1Signal-based Surveillance systems such as Closed Circuits Televisions (CCTV) have been widely installed in public places. Those systems are normally used to find the events with security interest, and play a significant role in public safety. Though such systems are still heavily reliant on human labour to monitor the captured information, there have been a number of automatic techniques proposed to analysing the data. This article provides an overview of automatic surveillance event detection techniques . Despite it's popularity in research, it is still too challenging a problem to be realised in a real world deployment. The challenges come from not only the detection techniques such as signal processing and machine learning, but also the experimental design with factors such as data collection, evaluation protocols, and ground-truth annotation. Finally, this article propose that multi-disciplinary research is the path towards a solution to this problem.
- Nov 29 2016 cs.CL arXiv:1611.08661v2The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts well. In this paper, we propose a novel deep architecture to utilize both structural and textual information of entities. Specifically, we introduce three neural models to encode the valuable information from text description of entity, among which an attentive model can select related information as needed. Then, a gating mechanism is applied to integrate representations of structure and text into a unified architecture. Experiments show that our models outperform baseline by margin on link prediction and triplet classification tasks. Source codes of this paper will be available on Github.
- In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 million sentences describing over 40 million images crawled and downloaded from publicly available Pins (i.e. an image with sentence descriptions uploaded by users) on Pinterest. This dataset is more than 200 times larger than MS COCO, the standard large-scale image dataset with sentence descriptions. In addition, we construct an evaluation dataset to directly assess the effectiveness of word embeddings in terms of finding semantically similar or related words and phrases. The word/phrase pairs in this evaluation dataset are collected from the click data with millions of users in an image search system, thus contain rich semantic relationships. Based on these datasets, we propose and compare several Recurrent Neural Networks (RNNs) based multimodal (text and image) models. Experiments show that our model benefits from incorporating the visual information into the word embeddings, and a weight sharing strategy is crucial for learning such multimodal embeddings. The project page is: http://www.stat.ucla.edu/~junhua.mao/multimodal_embedding.html
- Labelled image datasets have played a critical role in high-level image understanding. However, the process of manual labelling is both time-consuming and labor intensive. To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images. Datasets constructed by existing methods tend to have a weak domain adaptation ability, which is known as the "dataset bias problem". To address this issue, we present a novel image dataset construction framework that can be generalized well to unseen target domains. Specifically, the given queries are first expanded by searching the Google Books Ngrams Corpus to obtain a rich semantic description, from which the visually non-salient and less relevant expansions are filtered out. By treating each selected expansion as a "bag" and the retrieved images as "instances", image selection can be formulated as a multi-instance learning problem with constrained positive bags. We propose to solve the employed problems by the cutting-plane and concave-convex procedure (CCCP) algorithm. By using this approach, images from different distributions can be kept while noisy images are filtered out. To verify the effectiveness of our proposed approach, we build an image dataset with 20 categories. Extensive experiments on image classification, cross-dataset generalization, diversity comparison and object detection demonstrate the domain robustness of our dataset.
- Nov 22 2016 cs.CL arXiv:1611.06639v1Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.
- Device-to-device (D2D) computation offloading has recently been proposed to enhance mobile edge computing (MEC) performance by exploiting spare computing resources in proximity user devices, thereby alleviating computation burdens from the network infrastructure and enabling truly pervasive edge computing. A key challenge in this new mobile computing paradigm is how to provide self-interested users with incentives to participate in D2D computing. Although incentive mechanism design has been intensively studied in the literature, this paper considers a much more challenging yet much under-investigated problem in which user incentives are complexly coupled with security risks, which is extremely important since D2D-enhanced MEC systems are vulnerable to distributed attacks, such as distributed denial of service (DDoS) attacks, due to its autonomous nature. In this paper, we build a novel mathematical framework incorporating game theory and classic epidemic models to investigate the interplay between user incentives and security risks in D2D-enhanced MEC systems under infectious DDoS attacks. A key result derived from this analysis is a phase change effect between persistent and non-persistent DDoS attacks, which is significantly different in nature from classic epidemic results for non-strategic users. Based on this, we determine the optimal reward in a contract-based incentive mechanism that the network operator should offer to users in order to maximize the operator's utility. The optimal solution exhibits an interesting "Less is More" effect: although giving users a higher reward promote more participation, it may harm the operator's utility. This is because too much participation fosters persistent DDoS attack and as a result, the \textiteffective participation level does not improve. Extensive simulations are carried out to verify the analytic conclusions.
- Nov 03 2016 cs.CE arXiv:1611.00616v1We introduce a symplectic dual quaternion variational integrator(DQVI) for simulating single rigid body motion in all six degrees of freedom. Dual quaternion is used to represent rigid body kinematics and one-step Lie group variational integrator is used to conserve the geometric structure, energy and momentum of the system during the simulation. The combination of these two becomes the first Lie group variational integrator for rigid body simulation without decoupling translations and rotations. Newton-Raphson method is used to solve the recursive dynamic equation. This method is suitable for real-time rigid body simulations with high precision under large time step. DQVI respects the symplectic structure of the system with excellent long-term conservation of geometry structure, momentum and energy. It also allows the reference point and 6-by-6 inertia matrix to be arbitrarily defined, which is very convenient for a variety of engineering problems.
- Oct 19 2016 cs.NI arXiv:1610.05431v1With the seamless coverage of wireless cellular networks in modern society, it is interesting to consider the shape of wireless cellular coverage. Is the shape a regular hexagon, an irregular polygon, or another complex geometrical shape? Based on fractal theory, the statistical characteristic of the wireless cellular coverage boundary is determined by the measured wireless cellular data collected from Shanghai, China. The measured results indicate that the wireless cellular coverage boundary presents an extremely irregular geometrical shape, which is also called a statistical fractal shape. Moreover, the statistical fractal characteristics of the wireless cellular coverage boundary have been validated by values of the Hurst parameter estimated in angular scales. The statistical fractal characteristics of the wireless cellular coverage boundary can be used to evaluate and design the handoff scheme of mobile user terminals in wireless cellular networks.
- In animal monitoring applications, both animal detection and their movement prediction are major tasks. While a variety of animal monitoring strategies exist, most of them rely on mounting devices. However, in real world, it is difficult to find these animals and install mounting devices. In this paper, we propose an animal monitoring application by utilizing wireless sensor networks (WSNs) and unmanned aerial vehicle (UAV). The objective of the application is to detect locations of endangered species in large-scale wildlife areas and monitor movement of animals without any attached devices. In this application, sensors deployed throughout the observation area are responsible for gathering animal information. The UAV flies above the observation area and collects the information from sensors. To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model. The UAV receives a certain amount of reward from an area if some animals are detected at that location. We solve the MDP using Q-learning such that the UAV prefers going to those areas that animals are detected before. Meanwhile, the UAV explores other areas as well to cover the entire network and detects changes in the animal positions. We first define the mathematical model underlying the animal monitoring problem in terms of the value of information (VoI) and rewards. We propose a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. Then, one MDP-based path planning approach is designed to maximize the VoI while reducing message delays. The effectiveness of the proposed approach is evaluated using two real-world movement datasets of zebras and leopard. Simulation results show that our approach outperforms greedy, random heuristics and the path planning based on the traveling salesman problem.
- Recently, neural networks have achieved great success on sentiment classification due to their ability to alleviate feature engineering. However, one of the remaining challenges is to model long texts in document-level sentiment classification under a recurrent architecture because of the deficiency of the memory unit. To address this problem, we present a Cached Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic information in long texts. CLSTM introduces a cache mechanism, which divides memory into several groups with different forgetting rates and thus enables the network to keep sentiment information better within a recurrent unit. The proposed CLSTM outperforms the state-of-the-art models on three publicly available document-level sentiment analysis datasets.
- This paper proposes a recursive diffeomorphism based regression method for one-dimensional generalized mode decomposition problem that aims at extracting generalized modes $\alpha_k(t)s_k(2\pi N_k\phi_k(t))$ from their superposition $\sum_{k=1}^K \alpha_k(t)s_k(2\pi N_k\phi_k(t))$. First, a one-dimensional synchrosqueezed transform is applied to estimate instantaneous information, e.g., $\alpha_k(t)$ and $N_k\phi_k(t)$. Second, a novel approach based on diffeomorphisms and nonparametric regression is proposed to estimate wave shape functions $s_k(t)$. These two methods lead to a framework for the generalized mode decomposition problem under a weak well-separation condition. Numerical examples of synthetic and real data are provided to demonstrate the fruitful applications of these methods.
- In this paper, with respect to multichannel synthetic aperture radars (SAR), we first formulate the problems of Doppler ambiguities on the radial velocity (RV) estimation of a ground moving target in range-compressed domain, range-Doppler domain and image domain, respectively. It is revealed that in these problems, a cascaded time-space Doppler ambiguity (CTSDA) may encounter, i.e., time domain Doppler ambiguity (TDDA) in each channel arises first and then spatial domain Doppler ambiguity (SDDA) among multi-channels arises second. Accordingly, the multichannel SAR systems with different parameters are investigated in three different cases with diverse Doppler ambiguity properties, and a multi-frequency SAR is then proposed to obtain the RV estimation by solving the ambiguity problem based on Chinese remainder theorem (CRT). In the first two cases, the ambiguity problem can be solved by the existing closed-form robust CRT. In the third case, it is found that the problem is different from the conventional CRT problems and we call it a double remaindering problem in this paper. We then propose a sufficient condition under which the double remaindering problem, i.e., the CTSDA, can also be solved by the closed-form robust CRT. When the sufficient condition is not satisfied for a multi-channel SAR, a searching based method is proposed. Finally, some results of numerical experiments are provided to demonstrate the effectiveness of the proposed methods.
- Sep 30 2016 cs.CV arXiv:1609.09270v2This paper presents a method of estimating the geometry of a room and the 3D pose of objects from a single 360-degree panorama image. Assuming Manhattan World geometry, we formulate the task as a Bayesian inference problem in which we estimate positions and orientations of walls and objects. The method combines surface normal estimation, 2D object detection and 3D object pose estimation. Quantitative results are presented on a dataset of synthetically generated 3D rooms containing objects, as well as on a subset of hand-labeled images from the public SUN360 dataset.
- Recently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps on the memory. However, memory networks conduct the reasoning on sentence-level memory to output coarse semantic vectors and do not further take any attention mechanism to focus on words, which may lead to the model lose some detail information, especially when the answers are rare or unknown words. In this paper, we propose a novel Hierarchical Memory Networks, dubbed HMN. First, we encode the past facts into sentence-level memory and word-level memory respectively. Then, (k)-max pooling is exploited following reasoning module on the sentence-level memory to sample the (k) most relevant sentences to a question and feed these sentences into attention mechanism on the word-level memory to focus the words in the selected sentences. Finally, the prediction is jointly learned over the outputs of the sentence-level reasoning module and the word-level attention mechanism. The experimental results demonstrate that our approach successfully conducts answer selection on unknown words and achieves a better performance than memory networks.
- Sep 19 2016 cs.DC arXiv:1609.05087v1Mobile edge computing (a.k.a. fog computing) has recently emerged to enable \emphin-situ processing of delay-sensitive applications at the edge of mobile networks. Providing grid power supply in support of mobile edge computing, however, is costly and even infeasible (in certain rugged or under-developed areas), thus mandating on-site renewable energy as a major or even sole power supply in increasingly many scenarios. Nonetheless, the high intermittency and unpredictability of renewable energy make it very challenging to deliver a high quality of service to users in renewable-powered mobile edge computing systems. In this paper, we address the challenge of incorporating renewables into mobile edge computing and propose an efficient reinforcement learning-based resource management algorithm, which learns on-the-fly the optimal policy of dynamic workload offloading (to centralized cloud) and edge server provisioning to minimize the long-term system cost (including both service delay and operational cost). Our online learning algorithm uses a decomposition of the (offline) value iteration and (online) reinforcement learning, thus achieving a significant improvement of learning rate and run-time performance when compared to standard reinforcement learning algorithms such as Q-learning.
- Despite the ubiquity of transportation data, statistical inference methods alone are not able to explain mechanistic relations within a network. Inverse optimization methods fulfill this gap, but they are designed to take observations of the same model to learn the parameters of that model. New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers' route optimization such that the value of shared network resources (e.g. link capacity dual prices) can be inferred. The inferred values are internally consistent with each agent's optimization program. We prove that the method can obtain unique dual prices for a network shared by these agents, in polynomial time. Three experiments are conducted. The first one, conducted on a 4-node network, verifies the methodology to obtain heterogeneous link cost parameters even when a mixed logit model cannot provide meaningful results. The second is a parameter recovery test on the Nguyen-Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The last test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method.
- Sep 09 2016 cs.CR arXiv:1609.02234v1Nowadays, auto insurance companies set personalized insurance rate based on data gathered directly from their customers' cars. In this paper, we show such a personalized insurance mechanism -- wildly adopted by many auto insurance companies -- is vulnerable to exploit. In particular, we demonstrate that an adversary can leverage off-the-shelf hardware to manipulate the data to the device that collects drivers' habits for insurance rate customization and obtain a fraudulent insurance discount. In response to this type of attack, we also propose a defense mechanism that escalates the protection for insurers' data collection. The main idea of this mechanism is to augment the insurer's data collection device with the ability to gather unforgeable data acquired from the physical world, and then leverage these data to identify manipulated data points. Our defense mechanism leveraged a statistical model built on unmanipulated data and is robust to manipulation methods that are not foreseen previously. We have implemented this defense mechanism as a proof-of-concept prototype and tested its effectiveness in the real world. Our evaluation shows that our defense mechanism exhibits a false positive rate of 0.032 and a false negative rate of 0.013.
- Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks. This deep neural network allows us to model very complex sequence-contact relationship as well as long-range inter-contact correlation. Our method greatly outperforms existing contact prediction methods and leads to much more accurate contact-assisted protein folding. Tested on three datasets of 579 proteins, the average top L long-range prediction accuracy obtained our method, the representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints can yield correct folds (i.e., TMscore>0.6) for 203 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 proteins, respectively. Further, our contact-assisted models have much better quality than template-based models. Using our predicted contacts as restraints, we can (ab initio) fold 208 of the 398 membrane proteins with TMscore>0.5. By contrast, when the training proteins of our method are used as templates, homology modeling can only do so for 10 of them. One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction. Finally, in recent blind CAMEO benchmark our method successfully folded 5 test proteins with a novel fold.
- Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximum likelihood principle, is generally only guaranteed to find stationary points of the likelihood objective, and these points may be far from any maximizer. This article addresses this disconnect between the statistical principles behind EM and its algorithmic properties. Specifically, it provides a global analysis of EM for specific models in which the observations comprise an i.i.d. sample from a mixture of two Gaussians. This is achieved by (i) studying the sequence of parameters from idealized execution of EM in the infinite sample limit, and fully characterizing the limit points of the sequence in terms of the initial parameters; and then (ii) based on this convergence analysis, establishing statistical consistency (or lack thereof) for the actual sequence of parameters produced by EM.
- With recent developments of wireless communication technologies, malicious users can use them to commit crimes or launch terror attacks, thus imposing new threats on the public security. To quickly respond to defend these attacks, authorized parities (e.g., the National Security Agency of the USA) need to intervene in the malicious communication links over the air. This paper investigates this emerging wireless communication intervention problem at the physical layer. Unlike prior studies using jamming to disrupt or disable the targeted wireless communications, we propose a new physical-layer spoofing approach to change their communicated information. Consider a fundamental three-node system over additive white Gaussian noise (AWGN) channels, in which an intermediary legitimate spoofer aims to spoof a malicious communication link from Alice to Bob, such that the received message at Bob is changed from Alice's originally sent message to the one desired by the spoofer. We propose a new symbol-level spoofing scheme, where the spoofer designs the spoofing signal via exploiting the symbol-level relationship between each original constellation point of Alice and the desirable one of the spoofer. In particular, the spoofer aims to minimize the average spoofing-symbol-error-rate (SSER), which is defined as the average probability that the symbols decoded by Bob fail to be changed or spoofed, by designing its spoofing signals over symbols subject to the average transmit power constraint. By considering two cases when Alice employs the widely-used binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulations, we obtain the respective optimal solutions to the two average SSER minimization problems. Numerical results show that the symbol-level spoofing scheme with optimized transmission achieves a much lower average SSER, as compared to other benchmark schemes.
- Understanding cascading failures or epidemics in networks is crucial for developing effective defensive mechanisms for many critical systems and infrastructures (e.g. biological, social and cyber networks). Most of the existing works treat the network topology as being exogenously given and study under what conditions an epidemic breaks out and/or extinguishes. However, if agents are able to strategically decide their connections according to their own self-interest, the network will instead be endogenously formed and evolving. In such systems, the epidemic, agents' strategic decisions and the network structure become complexly coupled and co-evolve. As a result, existing knowledge may no longer be applicable. Built on a continuous time Susceptible-Infected-Susceptible epidemic model with strong mixing, this paper studies stochastic epidemic networks consisting of strategic agents, who decide the number of links to form based on a careful evaluation of its current obtainable benefit and the potential future cost due to infection by forming links. A game theoretical framework is developed to analyze such networks and a number of important insights are obtained. One key result is that whereas an epidemic eventually dies out if the effective spreading rate is sufficiently low in exogenously given networks, it never dies out when agents are strategic regardless of the effective spreading rate. This property leads to reduced achievable system efficiency and considerably different optimal protection mechanisms. Without understanding the strategic behavior of agents, significant security cost may incur.
- Jul 19 2016 math.ST cond-mat.dis-nn cond-mat.stat-mech cs.IT math.IT math.PR stat.TH arXiv:1607.05222v2We study the problem of detecting a structured, low-rank signal matrix corrupted with additive Gaussian noise. This includes clustering in a Gaussian mixture model, sparse PCA, and submatrix localization. Each of these problems is conjectured to exhibit a sharp information-theoretic threshold, below which the signal is too weak for any algorithm to detect. We derive upper and lower bounds on these thresholds by applying the first and second moment methods to the likelihood ratio between these "planted models" and null models where the signal matrix is zero. Our bounds differ by at most a factor of root two when the rank is large (in the clustering and submatrix localization problems, when the number of clusters or blocks is large) or the signal matrix is very sparse. Moreover, our upper bounds show that for each of these problems there is a significant regime where reliable detection is information- theoretically possible but where known algorithms such as PCA fail completely, since the spectrum of the observed matrix is uninformative. This regime is analogous to the conjectured 'hard but detectable' regime for community detection in sparse graphs.
- This paper studies a full-duplex filter-and-forward (FD-FF) relay system in frequency-selective channels. Conventionally, the loop-back signal at the FD relay is treated as harmful self-interference and needs to be significantly suppressed via both analog- and digital-domain cancellation. However, the performance of the conventional self-interference cancellation approach is fundamentally limited due to the quantization error induced by the analog-to-digital converter (ADC) with limited dynamic range. In this paper, we consider an analog filter-and-forward design to help avoid the quantization error, and surprisingly show that the maximum achievable rate of such an FD-FF relay system is in fact regardless of the loop-back channel at the FD relay. We characterize the maximum achievable rate of this channel by jointly optimizing the transmit power allocation over frequency at the source and the frequency response of the filter at the relay, subject to their individual power constraints. Although this problem is non-convex, we obtain its optimal solution by applying the Lagrange duality method. By simulations it is shown that the proposed joint source and relay optimization achieves rate gains over other heuristic designs, and is also advantageous over the conventional approach by cancelling the relay loop-back signal as self-interference, especially when the residual self-interference after cancellation is still significant.
- Jun 22 2016 cs.NI arXiv:1606.06316v1Recent years with the popularity of mobile devices have witnessed an explosive growth of mobile multimedia contents which dominate more than 50\% of mobile data traffic. This significant growth poses a severe challenge for future cellular networks. As a promising approach to overcome the challenge, we advocate Content Retrieval At the Edge, a content-centric cooperative service paradigm via device-to-device (D2D) communications to reduce cellular traffic volume in mobile networks. By leveraging the Named Data Networking (NDN) principle, we propose sNDN, a social-aware named data framework to achieve efficient cooperative content retrieval. Specifically, sNDN introduces Friendship Circle by grouping a user with her close friends of both high mobility similarity and high content similarity. We construct NDN routing tables conditioned on Friendship Circle encounter frequency to navigate a content request and a content reply packet between Friendship Circles, and leverage social properties in Friendship Circle to search for the final target as inner-Friendship Circle routing. The evaluation results demonstrate that sNDN can save cellular capacity greatly and outperform other content retrieval schemes significantly.
- Jun 16 2016 cs.IR arXiv:1606.04648v1Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.
- The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex combination between the total variation functional and the high-order total variation as the regularization term to obtain the original clustering data. In order to deal with non-smooth regularization term, we solve this model by employing the alternating split Bregman method. Then, in the second step, the segmentation can be obtained by thresholding this clustering data into different phases, where the thresholds can be given by using the K-means clustering method. Numerical comparisons show that our proposed model can provide more efficient segmentation results dealing with the noise image and blurring image.
- May 27 2016 cs.MM arXiv:1605.08308v2Traditional intra prediction usually utilizes the nearest reference line to generate the predicted block when considering strong spatial correlation. However, this kind of single line-based method does not always work well due to at least two issues. One is the incoherence caused by the signal noise or the texture of other object, where this texture deviates from the inherent texture of the current block. The other reason is that the nearest reference line usually has worse reconstruction quality in block-based video coding. Due to these two issues, this paper proposes an efficient multiple line-based intra prediction scheme to improve coding efficiency. Besides the nearest reference line, further reference lines are also utilized. The further reference lines with relatively higher quality can provide potential better prediction. At the same time, the residue compensation is introduced to calibrate the prediction of boundary regions in a block when we utilize further reference lines. To speed up the encoding process, this paper designs several fast algorithms. Experimental results show that, compared with HM-16.9, the proposed fast search method achieves 2.0% bit saving on average and up to 3.7%, with increasing the encoding time by 112%.
- This paper attacks the challenging problem of violence detection in videos. Different from existing works focusing on combining multi-modal features, we go one step further by adding and exploiting subclasses visually related to violence. We enrich the MediaEval 2015 violence dataset by \emphmanually labeling violence videos with respect to the subclasses. Such fine-grained annotations not only help understand what have impeded previous efforts on learning to fuse the multi-modal features, but also enhance the generalization ability of the learned fusion to novel test data. The new subclass based solution, with AP of 0.303 and P100 of 0.55 on the MediaEval 2015 test set, outperforms several state-of-the-art alternatives. Notice that our solution does not require fine-grained annotations on the test set, so it can be directly applied on novel and fully unlabeled videos. Interestingly, our study shows that motion related features, though being essential part in previous systems, are dispensable.
- Apr 22 2016 cs.IR arXiv:1604.06270v1The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent space models have been proven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning a latent matching model, using not only click-through data but also semantic knowledge. The semantic knowledge can be categories of queries and documents as well as synonyms of words, manually or automatically created. Specifically, we incorporate semantic knowledge into the objective function by including regularization terms. We develop two methods to solve the learning task on the basis of coordinate descent and gradient descent respectively, which can be employed in different settings. Experimental results on two datasets from an app search engine demonstrate that our model can make effective use of semantic knowledge, and thus can significantly enhance the accuracies of latent matching models, particularly for tail queries.
- Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.
- Motivation: High-throughput experimental techniques have been producing more and more protein-protein interaction (PPI) data. PPI network alignment greatly benefits the understanding of evolutionary relationship among species, helps identify conserved sub-networks and provides extra information for functional annotations. Although a few methods have been developed for multiple PPI network alignment, the alignment quality is still far away from perfect and thus, new network alignment methods are needed. Result: In this paper, we present a novel method, denoted as ConvexAlign, for joint alignment of multiple PPI networks by convex optimization of a scoring function composed of sequence similarity, topological score and interaction conservation score. In contrast to existing methods that generate multiple alignments in a greedy or progressive manner, our convex method optimizes alignments globally and enforces consistency among all pairwise alignments, resulting in much better alignment quality. Tested on both synthetic and real data, our experimental results show that ConvexAlign outperforms several popular methods in producing functionally coherent alignments. ConvexAlign even has a larger advantage over the others in aligning real PPI networks. ConvexAlign also finds a few conserved complexes among 5 species which cannot be detected by the other methods.
- For an interconnection network $G$, the \it $\omega$-wide diameter $d_\omega(G)$ is the least $\ell$ such that any two vertices are joined by $\omega$ internally-disjoint paths of length at most $\ell$, and the \it $(\omega-1)$-fault diameter $D_{\omega}(G)$ is the maximum diameter of a subgraph obtained by deleting fewer than $\omega$ vertices of $G$. The enhanced hypercube $Q_{n,k}$ is a variant of the well-known hypercube. Yang, Chang, Pai, and Chan gave an upper bound for $d_{n+1}(Q_{n,k})$ and $D_{n+1}(Q_{n,k})$ and posed the problem of finding the wide diameters and fault diameters of $Q_{n,k}$. By constructing internally disjoint paths between any two vertices in the enhanced hypercube, for $n\ge3$ and $2\le k\le n$ we prove $$ D_\omega(Q_n,k)=d_\omega(Q_n,k)=\begincases d(Q_n,k) & \textrmfor $1 \leq \omega < n-\lfloor\frac{k}{2}\rfloor$;\{d(Q_n,k)+1 & \textrmfor $n-\lfloor\frac{k}{2}\rfloor \leq \omega \leq n+1$. \endcases $$ where $d(Q_{n,k})$ is the diameter of $Q_{n,k}$. These results mean that interconnection networks modelled by enhanced hypercubes are extremely robust.
- We consider the estimation of a n-dimensional vector x from the knowledge of noisy and possibility non-linear element-wise measurements of xxT , a very generic problem that contains, e.g. stochastic 2-block model, submatrix localization or the spike perturbation of random matrices. We use an interpolation method proposed by Guerra and later refined by Korada and Macris. We prove that the Bethe mutual information (related to the Bethe free energy and conjectured to be exact by Lesieur et al. on the basis of the non-rigorous cavity method) always yields an upper bound to the exact mutual information. We also provide a lower bound using a similar technique. For concreteness, we illustrate our findings on the sparse PCA problem, and observe that (a) our bounds match for a large region of parameters and (b) that it exists a phase transition in a region where the spectum remains uninformative. While we present only the case of rank-one symmetric matrix estimation, our proof technique is readily extendable to low-rank symmetric matrix or low-rank symmetric tensor estimation
- In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching $\tau^{+}\tau^{-}$ channel of Higgs bosons in high energy physics. DEFE can be viewed as a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations. This is achieved by adopting an implicit neural controller (not involved in feedforward compuation) that directly controls and distributes gradient flows from higher level deep prediction network. Such model-independent controller results in that every single local feature learned are used in the feature-to-output mapping stage, avoiding the blind averaging of features. DEFE makes the ensembles 'deep' in the sense that it allows deep post-process of these features that tries to learn to select and abstract the ensemble of neural feature learners. With the application of this model, a selection regions full of signal process can be obtained through the training of a miniature collision events set. In comparison of the Classic Deep Neural Network, DEFE shows a state-of-the-art performance: the error rate has decreased by about 37\%, the accuracy has broken through 90\% for the first time, along with the discovery significance has reached a standard deviation of 6.0 $\sigma$. Experimental data shows that, DEFE is able to train an ensemble of discriminative feature learners that boosts the overperformance of final prediction.
- Mar 25 2016 cs.CL arXiv:1603.07603v1Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear algebra between words. Since many different methods have been proposed for learning document representations, it is natural to ask whether there is also linear structure in these learned representations to allow similar reasoning at document level. To answer this question, we design a new document analogy task for testing the semantic regularities in document representations, and conduct empirical evaluations over several state-of-the-art document representation models. The results reveal that neural embedding based document representations work better on this analogy task than conventional methods, and we provide some preliminary explanations over these observations.
- Feb 26 2016 cs.CC arXiv:1602.07796v2A novel computing model, called \emphProbe Machine, is proposed in this paper. Different from Turing Machine, Probe Machine is a fully-parallel computing model in the sense that it can simultaneously process multiple pairs of data, rather than sequentially process every pair of linearly-adjacent data. In this paper, we establish the mathematical model of Probe Machine as a 9-tuple consisting of data library, probe library, data controller, probe controller, probe operation, computing platform, detector, true solution storage, and residue collector. We analyze the computation capability of the Probe Machine model, and in particular we show that Turing Machine is a special case of Probe Machine. We revisit two NP-complete problems---i.e., the Graph Coloring and Hamilton Cycle problems, and devise two algorithms on basis of the established Probe Machine model, which can enumerate all solutions to each of these problems by only one probe operation. Furthermore, we show that Probe Machine can be implemented by leveraging the nano-DNA probe technologies. The computational power of an electronic computer based on Turing Machine is known far more than that of the human brain. A question naturally arises: will a future computer based on Probe Machine outperform the human brain in more ways beyond the computational power?
- Feb 24 2016 cs.SI physics.soc-ph arXiv:1602.07048v1Understanding the ways in which local network structures are formed and organized is a fundamental problem in network science. A widely recognized organizing principle is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures formed by common neighbors have on link formation is much less well understood. To explore this problem, we begin by formally defining the structural diversity of common neighborhoods. Using a collection of 116 large-scale networks---the biggest with over 60 million nodes and 1.8 billion edges---we then leverage this definition to develop a unique network signature, which we use to uncover several distinct network superfamilies not discoverable by conventional methods. We demonstrate that structural diversity has a significant impact on link existence, and we discover striking cases where it violates the principle of homophily. Our findings suggest that structural diversity is an intrinsic network property, giving rise to potential advances in the pursuit of theories of link formation and network evolution.
- Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.
- We study a semidefinite programming (SDP) relaxation of the maximum likelihood estimation for exactly recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i,j$, $A_{ij} \sim P$ if $i, j$ are both in the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$. We identify a sufficient condition and a necessary condition for the success of SDP for the general model. For both the Bernoulli case ($P={{\rm Bern}}(p)$ and $Q={{\rm Bern}}(q)$ with $p>q$) and the Gaussian case ($P=\mathcal{N}(\mu,1)$ and $Q=\mathcal{N}(0,1)$ with $\mu>0$), which correspond to the problem of planted dense subgraph recovery and submatrix localization respectively, the general results lead to the following findings: (1) If $K=\omega( n /\log n)$, SDP attains the information-theoretic recovery limits with sharp constants; (2) If $K=\Theta(n/\log n)$, SDP is order-wise optimal, but strictly suboptimal by a constant factor; (3) If $K=o(n/\log n)$ and $K \to \infty$, SDP is order-wise suboptimal. The same critical scaling for $K$ is found to hold, up to constant factors, for the performance of SDP on the stochastic block model of $n$ vertices partitioned into multiple communities of equal size $K$. A key ingredient in the proof of the necessary condition is a construction of a primal feasible solution based on random perturbation of the true cluster matrix.
- The entanglement-assisted stabilizer formalism provides a useful framework for constructing quantum error-correcting codes (QECC), which can transform arbitrary classical linear codes into entanglement-assisted quantum error correcting codes (EAQECCs) by using pre-shared entanglement between the sender and the receiver. In this paper, we construct five classes of entanglement-assisted quantum MDS (EAQMDS) codes based on classical MDS codes by exploiting one or more pre-shared maximally entangled states. We show that these EAQMDS codes have much larger minimum distance than the standard quantum MDS (QMDS) codes of the same length, and three classes of these EAQMDS codes consume only one pair of maximally entangled states.
- We propose a novel method for network inference from partially observed edges using a node-specific degree prior. The degree prior is derived from observed edges in the network to be inferred, and its hyper-parameters are determined by cross validation. Then we formulate network inference as a matrix completion problem regularized by our degree prior. Our theoretical analysis indicates that this prior favors a network following the learned degree distribution, and may lead to improved network recovery error bound than previous work. Experimental results on both simulated and real biological networks demonstrate the superior performance of our method in various settings.
- Jan 26 2016 cs.SI physics.soc-ph arXiv:1601.06357v1In our empirical works, we find that there exists the equivalency of the cumulative degree distribution and edge-cumulative distribution. Furthermore, we employ three network models of the recursive graphs, Sierpinksi networks and Apollonian networks to verify our conjecture: \emphBoth the cumulative degree distribution and the edge-cumulative distribution are equivalent to each other in deterministic network models
- The dynamical phenomena of complex networks are very difficult to predict from local information due to the rich microstructures and corresponding complex dynamics. On the other hands, it is a horrible job to compute some stochastic parameters of a large network having thousand and thousand nodes. We design several recursive algorithms for finding spanning trees having maximal leaves (MLS-trees) in investigation of topological structures of Sierpinski growing network models, and use MLS-trees to determine the kernels, dominating and balanced sets of the models. We propose a new stochastic method for the models, called the edge-cumulative distribution, and show that it obeys a power law distribution.
- Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multi-armed bandits, an algorithm is developed that can optimally recommend a course sequence that both reduces the time to graduate while also increasing the overall GPA of the student. The algorithm dynamically learns how students with different contextual backgrounds perform for given course sequences and then recommends an optimal course sequence for new students. Using real-world student data from the UCLA Mechanical and Aerospace Engineering department, we illustrate how the proposed algorithms outperform other methods that do not include student contextual information when making course sequence recommendations.
- In this paper we define some new labellings for trees, called the in-improper and out-improper odd-graceful labellings such that some trees labelled with the new labellings can induce graceful graphs having at least a cycle. We, next, apply the new labellings to construct large scale of graphs having improper graceful/odd-graceful labellings or having graceful/odd-graceful labellings.
- We focus on constructing the domi-join model by doing the join operation based on two smallest dominating sets of two network models and analysis the properties of domi-join model, such as power law distribution, small world. Besides, we will import two class of edge-bound growing network models to explain the process of domi-join model. Then we compute the average degree, clustering coefficient, power law distribution of the domi-join model. Finally, we discuss an impressive method for cutting down redundant operation of domi-join model.
- The stochastic block model (SBM) is a popular framework for studying community detection in networks. This model is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees. The degree-corrected stochastic block model (DCSBM) is a natural extension of SBM that allows for degree heterogeneity within communities. This paper proposes a convexified modularity maximization approach for estimating the hidden communities under DCSBM. Our approach is based on a convex programming relaxation of the classical (generalized) modularity maximization formulation, followed by a novel doubly-weighted $ \ell_1 $-norm $ k $-median procedure. We establish non-asymptotic theoretical guarantees for both approximate clustering and perfect clustering. Our approximate clustering results are insensitive to the minimum degree, and hold even in sparse regime with bounded average degrees. In the special case of SBM, these theoretical results match the best-known performance guarantees of computationally feasible algorithms. Numerically, we provide an efficient implementation of our algorithm, which is applied to both synthetic and real-world networks. Experiment results show that our method enjoys competitive performance compared to the state of the art in the literature.
- To enhance the national security, there is a growing need for government agencies to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario where a legitimate monitor aims to intercept a suspicious wireless communication link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. In practice, such legitimate eavesdropping is particularly challenging, since the legitimate monitor may be far away from the suspicious transmitter and cannot eavesdrop efficiently. To overcome this issue, we propose a new approach, namely proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. In particular, we consider delay-sensitive and delay-tolerant applications for the suspicious data communications, under which the legitimate monitor maximizes the eavesdropping non-outage probability for event-based monitoring and the relative eavesdropping rate for content analysis, respectively, by optimizing its jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms the conventional passive eavesdropping without jamming and the proactive eavesdropping with constant-power jamming.
- Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.