OpenRaiser/Intern-Atlas huggingface dataset:
A Unified Alternating Direction Method of Multipliers by Majorization Minimization
Venue: arXiv cs.NA 2016
Year: 2016

Accompanied with the rising popularity of compressed sensing, the Alternating Direction Method of Multipliers (ADMM) has become the most widely used solver for linearly constrained convex problems with separable objectives. In this work, we observe that many previous variants of ADMM update the primal variable by minimizing different majorant functions with their convergence proofs given case by case. Inspired by the principle of majorization minimization, we respectively present the unified frameworks and convergence analysis for the Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating. Our frameworks further generalize previous ADMMs to the ones capable of solving the problems with non-separable objectives by minimizing their separable majorant surrogates. We also show that the bound which measures the convergence speed of ADMMs depends on the tightness of the used majorant function. Then several techniques are introduced to improve the efficiency of ADMMs by tightening the majorant functions. In particular, we propose the Mixed Gauss-Seidel and Jacobian ADMM (M-ADMM) which alleviates the slow convergence issue of Jacobian ADMMs by absorbing merits of the Gauss-Seidel ADMMs. M-ADMM can be further improved by using backtracking, wise variable partition and fully exploiting the structure of the constraint. Beyond the guarantee in theory, numerical experiments on both synthesized and real-world data further demonstrate the superiority of our new ADMMs in practice. Finally, we release a toolbox at https://github.com/canyilu/LibADMM that implements efficient ADMMs for many problems in compressed sensing.
Network Flows Under Thermal Restrictions
Venue: arXiv cs.DM 2016
Year: 2016

We define a \emph{thermal network}, which is a network where the flow functionality of a node depends upon its temperature. This model is inspired by several types of real-life networks, and generalizes some conventional network models wherein nodes have fixed capacities and the problem is to maximize the flow through the network. In a thermal network, the temperature of a node increases as traffic moves through it, and nodes may also cool spontaneously over time, or by employing cooling packets. We analyze the problems of maximizing the flow from a source to a sink for both these cases, for a holistic view with respect to the single-source-single-sink dynamic flow problem in a thermal network. We have studied certain properties such a thermal network exhibits, and give closed-form solutions for the maximum flow that can be achieved through such a network.
Hierarchical Deep Temporal Models for Group Activity Recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem. In our approach, one LSTM model is designed to represent action dynamics of individual people in a video sequence and another LSTM model is designed to aggregate person-level information for group activity recognition. We collected a new dataset consisting of volleyball videos labeled with individual and group activities in order to evaluate our method. Experimental results on this new Volleyball Dataset and the standard benchmark Collective Activity Dataset demonstrate the efficacy of the proposed models.
Optimal Joint Power and Subcarrier Allocation for Full-Duplex Multicarrier Non-Orthogonal Multiple Access Systems
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate resource allocation algorithm design for multicarrier non-orthogonal multiple access (MC-NOMA) systems employing a full-duplex (FD) base station (BS) for serving multiple half-duplex (HD) downlink (DL) and uplink (UL) users simultaneously. The proposed algorithm is obtained from the solution of a non-convex optimization problem for the maximization of the weighted sum system throughput. We apply monotonic optimization to develop an optimal joint power and subcarrier allocation policy. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. Furthermore, a suboptimal iterative scheme based on successive convex approximation is proposed to strike a balance between computational complexity and optimality. Our simulation results reveal that the proposed suboptimal algorithm achieves a close-to-optimal performance. Besides, FD MC-NOMA systems employing the proposed resource allocation algorithms provide a substantial system throughput improvement compared to conventional HD multicarrier orthogonal multiple access (MC-OMA) systems and other baseline schemes. Also, our results unveil that the proposed FD MC-NOMA systems achieve a fairer resource allocation compared to traditional HD MC-OMA systems.
Towards an "In-the-Wild" Emotion Dataset Using a Game-based Framework
Venue: arXiv cs.CV 2016
Year: 2016

In order to create an "in-the-wild" dataset of facial emotions with large number of balanced samples, this paper proposes a game-based data collection framework. The framework mainly include three components: a game engine, a game interface, and a data collection and evaluation module. We use a deep learning approach to build an emotion classifier as the game engine. Then a emotion web game to allow gamers to enjoy the games, while the data collection module obtains automatically-labelled emotion images. Using our game, we have collected more than 15,000 images within a month of the test run and built an emotion dataset "GaMo". To evaluate the dataset, we compared the performance of two deep learning models trained on both GaMo and CIFE. The results of our experiments show that because of being large and balanced, GaMo can be used to build a more robust emotion detector than the emotion detector trained on CIFE, which was used in the game engine to collect the face images.
Perturbation-Assisted PAPR Reduction for Large-Scale MIMO-OFDM Systems via ADMM
Venue: arXiv cs.IT 2016
Year: 2016

We consider the problem of peak-to-average power ratio (PAPR) reduction for orthogonal frequency-division multiplexing (OFDM) based large-scale multiple-input multipleoutput (MIMO) systems. A novel perturbation-assisted scheme is developed to reduce the PAPRs of the transmitted signals by exploiting the redundant degrees-of-freedom (DoFs) inherent in the large-scale antenna array. Specifically, we introduce artificial perturbation signals to the frequency-domain precoded signals, with the aim of reducing the PAPRs of their time-domain counterpart signals. Meanwhile, the additive perturbation signal associated with each tone is constrained to lie in the null-space of its associated channel matrix, such that it does not cause any multi-user inference or out-of-band radiations. Such a problem is formulated as a convex optimization problem, and an efficient algorithm is developed by resorting to the variable splitting and alterative direction method of multipliers (ADMM) techniques. Simulation results show that the proposed method has a fast convergence rate and achieves substantial PAPR reduction within only tens of iterations. In addition, unlike other precoding-based PAPR reduction methods, our proposed method which introduces perturbation signals to the precoded signals is independent of the precoding stage and thus could be more suitable for practical systems.
Learning to Sketch Human Facial Portraits using Personal Styles by Case-Based Reasoning
Venue: arXiv cs.CV 2016
Year: 2016

This paper employs case-based reasoning (CBR) to capture the personal styles of individual artists and generate the human facial portraits from photos accordingly. For each human artist to be mimicked, a series of cases are firstly built-up from her/his exemplars of source facial photo and hand-drawn sketch, and then its stylization for facial photo is transformed as a style-transferring process of iterative refinement by looking-for and applying best-fit cases in a sense of style optimization. Two models, fitness evaluation model and parameter estimation model, are learned for case retrieval and adaptation respectively from these cases. The fitness evaluation model is to decide which case is best-fitted to the sketching of current interest, and the parameter estimation model is to automate case adaptation. The resultant sketch is synthesized progressively with an iterative loop of retrieval and adaptation of candidate cases until the desired aesthetic style is achieved. To explore the effectiveness and advantages of the novel approach, we experimentally compare the sketch portraits generated by the proposed method with that of a state-of-the-art example-based facial sketch generation algorithm as well as a couple commercial software packages. The comparisons reveal that our CBR based synthesis method for facial portraits is superior both in capturing and reproducing artists' personal illustration styles to the peer methods.
Mobile Internet of Things: Can UAVs Provide an Energy-Efficient Mobile Architecture?
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, the optimal trajectory and deployment of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum energy consumption, a new approach for optimal mobility of the UAVs is proposed. First, given a fixed ground IoT network, the total transmit power of the devices is minimized by properly clustering the IoT devices with each cluster being served by one UAV. Next, to maintain energy-efficient communications in time-varying mobile IoT networks, the optimal trajectories of the UAVs are determined by exploiting the framework of optimal transport theory. Simulation results show that by using the proposed approach, the total transmit power of IoT devices for reliable uplink communications can be reduced by 56% compared to the fixed Voronoi deployment method. Moreover, our results yield the optimal paths that will be used by UAVs to serve the mobile IoT devices with a minimum energy consumption.
Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search
Venue: arXiv cs.CV 2016
Year: 2016

We propose an efficient approach for activity detection in video that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem. Offline, we learn a binary classifier for an activity category using positive video exemplars that are "trimmed" in time to the activity of interest. Then, given a novel \emph{untrimmed} video sequence, we decompose it into a 3D array of space-time nodes, which are weighted based on the extent to which their component features support the learned activity model. To perform detection, we then directly localize instances of the activity by solving for the maximum-weight connected subgraph in the test video's space-time graph. We show that this detection strategy permits an efficient branch-and-cut solution for the best-scoring---and possibly non-cubically shaped---portion of the video for a given activity classifier. The upshot is a fast method that can search a broader space of space-time region candidates than was previously practical, which we find often leads to more accurate detection. We demonstrate the proposed algorithm on four datasets, and we show its speed and accuracy advantages over multiple existing search strategies.
Form Follows Function -- Do algorithms and applications challenge or drag behind the hardware evolution?
Venue: arXiv cs.MS 2016
Year: 2016

We summarise some of the key statements made at the workshop Form Follows Function at ISC High Performance 2016. The summary highlights what type of co-design the presented projects experience; often in the absence of an explicit co-design agenda. Their software development picks up hardware trends but it also influences the hardware development. Observations illustrate that this cycle not always is optimal for both sides as it is not proactively steered. Key statements characterise ideas how it might be possible to integrate both hardware and software creation closer to the best of both worlds---again even without classic co-design in mind where new pieces of hardware are created. The workshop finally identified three development idioms that might help to improve software and system design with respect to emerging hardware.
Towards Real-Time Estimation of Solar Generation From Micro-Synchrophasor Measurements
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a set of methods for estimating the renewable energy generation downstream of a measurement device using real-world measurements. First, we present a generation disaggregation scheme where the only information available for estimation is the micro-synchrophasor measurements obtained at the substation or feeder head. We then propose two strategies in which we use measurements from the substation as well as a proxy solar irradiance measurement. Using these two measurement points, we first propose a multiple linear regression strategy, in which we estimate a relationship between the measured reactive power and the load active power consumption, which are then used in disaggregation. Finally, we expand this strategy to strategically manage the reconstruction errors in the estimators. We simultaneously disaggregate the solar generation and load. We show that it is possible to disaggragate the generation of a 7.5 megawatt photovoltaic site with a root-mean-squared error of approximately 450 kilowatts.
On the chemistry of typestate-oriented actors
Venue: arXiv cs.PL 2016
Year: 2016

Typestate-oriented programming is an extension of the OO paradigm in which objects are modeled not just in terms of interfaces but also in terms of their usage protocols, describing legal sequences of method calls, possibly depending on the object's internal state. We argue that the Actor Model allows typestate-OOP in an inherently distributed setting, whereby objects/actors can be accessed concurrently by several processes, and local entities cooperate to carry out a communication protocol. In this article we illustrate the approach by means of a number of examples written in Scala Akka. We show that Scala's abstractions support clean and natural typestate-oriented actor programming with the usual asynchronous and non-blocking semantics. We also show that the standard type system of Scala and a typed wrapping of usual (untyped) Akka's ActorRef are enough to provide rich forms of type safety so that well-typed actors respect their intended communication protocols. This approach draws on a solid theoretical background, consisting of a sound behavioral type system for the Join Calculus, that is a foundational calculus of distributed asynchronous processes whose semantics is based on the Chemical Abstract Machine, that unveiled its strong connections with typestate-oriented programming of both concurrent objects and actors.
Are human interactivity times lognormal?
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we are analyzing the interactivity time, defined as the duration between two consecutive tasks such as sending emails, collecting friends and followers and writing comments in online social networks (OSNs). The distributions of these times are heavy tailed and often described by a power-law distribution. However, power-law distributions usually only fit the heavy tail of empirical data and ignore the information in the smaller value range. Here, we argue that the durations between writing emails or comments, adding friends and receiving followers are likely to follow a lognormal distribution. We discuss the similarities between power-law and lognormal distributions, show that binning of data can deform a lognormal to a power-law distribution and propose an explanation for the appearance of lognormal interactivity times. The historical debate of similarities between lognormal and power-law distributions is reviewed by illustrating the resemblance of measurements in this paper with the historical problem of income and city size distributions.
BioInfoBase : A Bioinformatics Resourceome
Venue: arXiv cs.DL 2016
Year: 2016

Over the past decade there has been a significant growth in bioinformatics databases, tools and resources. Although, bioinformatics is becoming more specific, increasing the number of bioinformatics-wares has made it difficult for researchers to find the most appropriate databases, tools or methods which match their needs. Our coordinated effort has been planned to establish a reference website in Bioinformatics as a public repository of tools, databases, directories and resources annotated with contextual information and organized by functional relevance. Within the first phase of BioInfoBase development, 22 experts in different fields of molecular biology contributed and more than 2500 records were registered, which are increasing daily. For each record submitted to the database of website almost all related data (40 features) has been extracted. These include information from the biological category and subcategory to the scientific article and developer information. Searching the query keyword(s) returns links containing the entered keyword(s) found within the different features of the records with more weights on the title, abstract and application fields. The search results simply provide the users with the most informative features of the records to select the most suitable ones. The usefulness of the returned results is ranked according to the matching score based on the Term Frequency-Inverse Document Frequency (TF-IDF) methods. Therefore, this search engine will screen a comprehensive index of bioinformatics tools, databases and resources and provide the best suited records (links) to the researchers need. The BioInfoBase resource is available at www.bioinfobase.info.
Learning a metric for class-conditional KNN
Venue: arXiv cs.LG 2016
Year: 2016

Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g.~class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g.~SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose "Class Conditional" metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.
Kernel-based methods for bandit convex optimization
Venue: arXiv cs.LG 2016
Year: 2016

We consider the adversarial convex bandit problem and we build the first $\mathrm{poly}(T)$-time algorithm with $\mathrm{poly}(n) \sqrt{T}$-regret for this problem. To do so we introduce three new ideas in the derivative-free optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, and (iii) a new annealing schedule for exponential weights (with increasing learning rate). The basic version of our algorithm achieves $\tilde{O}(n^{9.5} \sqrt{T})$-regret, and we show that a simple variant of this algorithm can be run in $\mathrm{poly}(n \log(T))$-time per step at the cost of an additional $\mathrm{poly}(n) T^{o(1)}$ factor in the regret. These results improve upon the $\tilde{O}(n^{11} \sqrt{T})$-regret and $\exp(\mathrm{poly}(T))$-time result of the first two authors, and the $\log(T)^{\mathrm{poly}(n)} \sqrt{T}$-regret and $\log(T)^{\mathrm{poly}(n)}$-time result of Hazan and Li. Furthermore we conjecture that another variant of the algorithm could achieve $\tilde{O}(n^{1.5} \sqrt{T})$-regret, and moreover that this regret is unimprovable (the current best lower bound being $Ω(n \sqrt{T})$ and it is achieved with linear functions). For the simpler situation of zeroth order stochastic convex optimization this corresponds to the conjecture that the optimal query complexity is of order $n^3 / ε^2$.
Joint Source-Relay Design for Full--Duplex MIMO AF Relay Systems
Venue: arXiv cs.IT 2016
Year: 2016

The performance of full-duplex (FD) relay systems can be greatly impacted by the self-interference (SI) at relays. By exploiting multi-antenna in FD relay systems, the spectral efficiency of FD relay systems can be enhanced through spatial SI mitigation. This paper studies joint source transmit beamforming and relay processing to achieve rate maximization for FD MIMO amplify-and-forward (AF) relay systems with consideration of relay processing delay. The problem is difficult to solve due mainly to the SI constraint induced by the relay processing delay. In this paper, we first present a sufficient condition under which the relay amplification matrix has rank one structure. Then, for the case of rank one amplification matrix, the rate maximization problem is equivalently simplified into an unconstrained problem which can be locally solved using gradient ascent method. Next, we propose a penalty-based algorithmic framework, called P-BSUM, for a class of constrained optimization problems which have difficult equality constraints in addition to some convex constraints. By rewriting the rate maximization problem with a set of auxiliary variables, we apply the P-BSUM algorithm to the rate maximization problem in the general case. Finally, numerical results validate the efficiency of the proposed algorithms and show that the joint source-relay design approach under the rank one assumption could be strictly suboptimal as compared to the P-BSUM-based joint source-relay design approach.