OpenRaiser/Intern-Atlas huggingface dataset:
Algorithms for Colourful Simplicial Depth and Medians in the Plane
Venue: arXiv cs.CG 2016
Year: 2016

The colourful simplicial depth of a point x in the plane relative to a configuration of n points in k colour classes is exactly the number of closed simplices (triangles) with vertices from 3 different colour classes that contain x in their convex hull. We consider the problems of efficiently computing the colourful simplicial depth of a point x, and of finding a point, called a median, that maximizes colourful simplicial depth. For computing the colourful simplicial depth of x, our algorithm runs in time O(n log(n) + k n) in general, and O(kn) if the points are sorted around x. For finding the colourful median, we get a time of O(n^4). For comparison, the running times of the best known algorithm for the monochrome version of these problems are O(n log(n)) in general, improving to O(n) if the points are sorted around x for monochrome depth, and O(n^4) for finding a monochrome median.
Design and Implementation of a TDD-Based 128-Antenna Massive MIMO Prototyping System
Venue: arXiv cs.IT 2016
Year: 2016

Spurred by the dramatic mobile IP growth and the emerging Internet of Things (IoT) and cloud-based applications, wireless networking is witnessing a paradigm shift. By fully exploiting the spatial degrees of freedom, the massive multipleinput- multiple-output (MIMO) technology promises significant gains in both data rates and link reliability. This paper presents a time-division duplex (TDD)-based 128-antenna massive MIMO prototyping system designed to operate on a 20 MHz bandwidth. Up to twelve single-antenna users can be served by the designed system at the same time. System model is provided and link-level simulation corresponding to our practical TDDbased massive MIMO prototyping system is conducted to validate our design and performance of the algorithms. Based on the system hardware design demonstrated in this paper, both uplink real-time video and downlink data transmissions are realized, and the experiment results show that 268.8 Mbps rate was achieved for eight single-antenna users using QPSK modulation. The maximum spectral efficiency of the designed system will be 80.64 bit/s/Hz by twelve single-antenna users with 256-QAM modulation.
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Venue: arXiv cs.CV 2016
Year: 2016

Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime. Especially for sparse geometric structures, volumetric representations produce a huge computational overhead. We present an efficient way to fuse range data via a variational Octree-based minimization approach by taking the actual range data geometry into account. We transform the data into Octree-based truncated signed distance fields and show how the optimization can be conducted on the newly created structures. The main challenge is to uphold speed and a low memory footprint without sacrificing the solutions' accuracy during optimization. We explain how to dynamically adjust the optimizer's geometric structure via joining/splitting of Octree nodes and how to define the operators. We evaluate on various datasets and outline the suitability in terms of performance and geometric accuracy.
Hard Negative Mining for Metric Learning Based Zero-Shot Classification
Venue: arXiv cs.LG 2016
Year: 2016

Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.
Using an epidemiological approach to maximize data survival in the internet of things
Venue: arXiv cs.DC 2016
Year: 2016

The internet of things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this paper, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.
Mechanism Design Approach for Energy Efficiency
Venue: arXiv cs.GT 2016
Year: 2016

In this work we deploy a mechanism design approach for allocating a divisible commodity (electricity in our example) among consumers. We consider each consumer with an associated personal valuation function of the energy resource during a certain time interval. We aim to select the optimal consumption profile for every user avoiding consumption peaks when the total required energy could exceed the energy production. The mechanism will be able to drive users in shifting energy consumptions in different hours of the day. We start by presenting a very basic Vickrey-Clarke-Groves mechanism, we discuss its weakness and propose several more complex variants.
Containers for portable, productive and performant scientific computing
Venue: arXiv cs.DC 2016
Year: 2016

Containers are an emerging technology that hold promise for improving productivity and code portability in scientific computing. We examine Linux container technology for the distribution of a non-trivial scientific computing software stack and its execution on a spectrum of platforms from laptop computers through to high performance computing (HPC) systems. We show on a workstation and a leadership-class HPC system that when deployed appropriately there are no performance penalties running scientific programs inside containers. For Python code run on large parallel computers, the run time is reduced inside a container due to faster library imports. The software distribution approach and data that we present will help developers and users decide on whether container technology is appropriate for them. We also provide guidance for the vendors of HPC systems that rely on proprietary libraries for performance on what they can do to make containers work seamlessly and without performance penalty.
Interacting with Massive Behavioral Data
Venue: arXiv cs.LG 2016
Year: 2016

In this short paper, we propose the split-diffuse (SD) algorithm that takes the output of an existing word embedding algorithm, and distributes the data points uniformly across the visualization space. The result improves the perceivability and the interactability by the human. We apply the SD algorithm to analyze the user behavior through access logs within the cyber security domain. The result, named the topic grids, is a set of grids on various topics generated from the logs. On the same set of grids, different behavioral metrics can be shown on different targets over different periods of time, to provide visualization and interaction to the human experts. Analysis, investigation, and other types of interaction can be performed on the topic grids more efficiently than on the output of existing dimension reduction methods. In addition to the cyber security domain, the topic grids can be further applied to other domains like e-commerce, credit card transaction, customer service to analyze the behavior in a large scale.
TopoMan: Global Network Visibility in the Presence of Middleboxes (A Graybox Approach)
Venue: arXiv cs.NI 2016
Year: 2016

Software Defined Networks (SDN) provide vital benefits to network administrators by offering global visibility and network-wide control over the switching infrastructure of the network. It is rather much difficult to obtain the same benefits in the presence of middleboxes (MBs), due to (i) lack of a proper topology discovery mechanism in environments with a mix of forwarding devices and middleboxes. (ii) lack of generic APIs to abstract and gain control on these rigid and heterogeneous third-party middleboxes (iii) lack of a generic network infrastructure framework to monitor and verify any specific device or path connectivity status in the network. These limitations make automation of network operations such as, network-wide monitoring, policy enforcement and rule-placement much difficult to handle. Hence, there is a greater urge even from middlebox vendors, to better handle the control and visibility aspects of the network in presence of middleboxes. In this paper, we propose a Unified network infrastructure framework for gaining global network visibility, by discovering the network topology in the presence of middleboxes, along with a framework to support the end-to-end path connectivity verification, independent of SDN. We have also addressed security aspects and provided necessary APIs to support our framework.
Transceiver Design for Cooperative Non-Orthogonal Multiple Access Systems with Wireless Energy Transfer
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, an energy harvesting (EH) based cooperative non-orthogonal multiple access (NOMA) system is considered, where node S simultaneously sends independent signals to a stronger node R and a weaker node D. We focus on the scenario that the direct link between S and D is too weak to meet the quality of service (QoS) of D. Based on the NOMA principle, node R, the stronger user, has prior knowledge about the information of the weaker user, node D. To satisfy the targeted rate of D, R also serves as an EH decode-and-forward (DF) relay to forward the traffic from S to D. In the sense of equivalent cognitive radio concept, node R viewed as a secondary user assists to boost the performance of D, in exchange for receiving its own information from S. Specifically, transmitter beamforming design, power splitting ratio optimization and receiver filter design to maximize node R rate are studied with the predefined QoS constraint of D and the power constraint of S. Since the problem is non-convex, we propose an iterative approach to solve it. Moreover, to reduce the computational complexity, a zero- forcing (ZF) based solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission.
Learning Bayesian Networks with Incomplete Data by Augmentation
Venue: arXiv cs.AI 2016
Year: 2016

We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach.
Type-Directed Code Reuse using Integer Linear Programming
Venue: arXiv cs.SE 2016
Year: 2016

In many common scenarios, programmers need to implement functionality that is already provided by some third party library. This paper presents a tool called Hunter that facilitates code reuse by finding relevant methods in large code bases and automatically synthesizing any necessary wrapper code. The key technical idea underlying our approach is to use types to both improve search results and guide synthesis. Specifically, our method computes similarity metrics between types and uses this information to solve an integer linear programming (ILP) problem in which the objective is to minimize the cost of synthesis. We have implemented Hunter as an Eclipse plug-in and evaluate it by (a) comparing it against S6, a state-of-the-art code reuse tool, and (b) performing a user study. Our evaluation shows that Hunter compares favorably with S6 and significantly increases programmer productivity.
The Movie Graph Argument Revisited
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we reexamine the Movie Graph Argument, which demonstrates a basic incompatibility between computationalism and materialism. We discover that the incompatibility is only manifest in singular classical-like universes. If we accept that we live in a Multiverse, then the incompatibility goes away, but in that case another line of argument shows that with computationalism, the fundamental, or primitive materiality has no causal influence on what is observed, which must must be derivable from basic arithmetic properties.
Partially Observable Markov Decision Process for Recommender Systems
Venue: arXiv cs.AI 2016
Year: 2016

We report the "Recurrent Deterioration" (RD) phenomenon observed in online recommender systems. The RD phenomenon is reflected by the trend of performance degradation when the recommendation model is always trained based on users' feedbacks of the previous recommendations. There are several reasons for the recommender systems to encounter the RD phenomenon, including the lack of negative training data and the evolution of users' interests, etc. Motivated to tackle the problems causing the RD phenomenon, we propose the POMDP-Rec framework, which is a neural-optimized Partially Observable Markov Decision Process algorithm for recommender systems. We show that the POMDP-Rec framework effectively uses the accumulated historical data from real-world recommender systems and automatically achieves comparable results with those models fine-tuned exhaustively by domain exports on public datasets.
Quantitative Analyses of Chinese Poetry of Tang and Song Dynasties: Using Changing Colors and Innovative Terms as Examples
Venue: arXiv cs.CL 2016
Year: 2016

Tang (618-907 AD) and Song (960-1279) dynasties are two very important periods in the development of Chinese literary. The most influential forms of the poetry in Tang and Song were Shi and Ci, respectively. Tang Shi and Song Ci established crucial foundations of the Chinese literature, and their influences in both literary works and daily lives of the Chinese communities last until today. We can analyze and compare the Complete Tang Shi and the Complete Song Ci from various viewpoints. In this presentation, we report our findings about the differences in their vocabularies. Interesting new words that started to appear in Song Ci and continue to be used in modern Chinese were identified. Colors are an important ingredient of the imagery in poetry, and we discuss the most frequent color words that appeared in Tang Shi and Song Ci.
Exploring Opportunistic Execution for Integrating Security into Legacy Hard Real-Time Systems
Venue: arXiv cs.CR 2016
Year: 2016

Due to physical isolation as well as use of proprietary hardware and protocols, traditional real-time systems (RTS) were considered to be invulnerable to security breaches and external attacks. However, this assumption is being challenged by recent attacks that highlight the vulnerabilities in such systems. In this paper, we focus on integrating security mechanisms into RTS (especially legacy RTS) and provide a metric to measure the effectiveness of such mechanisms. We combine opportunistic execution with hierarchical scheduling to maintain compatibility with legacy systems while still providing flexibility. The proposed approach is shown to increase the security posture of RTS systems without impacting their temporal constraints.
Online Monotone Optimization
Venue: arXiv cs.LG 2016
Year: 2016

This paper presents a new framework for analyzing and designing no-regret algorithms for dynamic (possibly adversarial) systems. The proposed framework generalizes the popular online convex optimization framework and extends it to its natural limit allowing it to capture a notion of regret that is intuitive for more general problems such as those encountered in game theory and variational inequalities. The framework hinges on a special choice of a system-wide loss function we have developed. Using this framework, we prove that a simple update scheme provides a no-regret algorithm for monotone systems. While previous results in game theory prove individual agents can enjoy unilateral no-regret guarantees, our result proves monotonicity sufficient for guaranteeing no-regret when considering the adjustments of multiple agent strategies in parallel. Furthermore, to our knowledge, this is the first framework to provide a suitable notion of regret for variational inequalities. Most importantly, our proposed framework ensures monotonicity a sufficient condition for employing multiple online learners safely in parallel.
Learning-Based Resource Allocation Scheme for TDD-Based CRAN System
Venue: arXiv cs.NI 2016
Year: 2016

Explosive growth in the use of smart wireless devices has necessitated the provision of higher data rates and always-on connectivity, which are the main motivators for designing the fifth generation (5G) systems. To achieve higher system efficiency, massive antenna deployment with tight coordination is one potential strategy for designing 5G systems, but has two types of associated system overhead. First is the synchronization overhead, which can be reduced by implementing a cloud radio access network (CRAN)-based architecture design, that separates the baseband processing and radio access functionality to achieve better system synchronization. Second is the overhead for acquiring channel state information (CSI) of the users present in the system, which, however, increases tremendously when instantaneous CSI is used to serve high-mobility users. To serve a large number of users, a CRAN system with a dense deployment of remote radio heads (RRHs) is considered, such that each user has a line-of-sight (LOS) link with the corresponding RRH. Since, the trajectory of movement for high-mobility users is predictable; therefore, fairly accurate position estimates for those users can be obtained, and can be used for resource allocation to serve the considered users. The resource allocation is dependent upon various correlated system parameters, and these correlations can be learned using well-known \emph{machine learning} algorithms. This paper proposes a novel \emph{learning-based resource allocation scheme} for time division duplex (TDD) based 5G CRAN systems with dense RRH deployment, by using only the users' position estimates for resource allocation, thus avoiding the need for CSI acquisition. This reduces the overall system overhead significantly, while still achieving near-optimal system performance; thus, better (effective) system efficiency is achieved. (See the paper for full abstract)
Topical Generalization for Presentation of User Profiles
Venue: arXiv cs.IR 2016
Year: 2016

Fine-grained user profile generation approaches have made it increasingly feasible to display on a profile page in which topics a user has expertise or interest. Earlier work on topical user profiling has been directed at enhancing search and personalization functionality, but making such profiles useful for human consumption presents new challenges. With this work, we have taken a first step toward a semantic layout mode for topical user profiles. We have developed a topical generalization approach which finds coherent groups of topics and adds labels to them, based on their association with broader topics in the Wikipedia category graph. A nested layout mode, employing topical generalization, is compared with a simpler flat layout mode in our user study. The results indicate that users favor the nested structure over flat profiles, but tend to overlook the specific topics on the lower level. We propose a third layout mode to address this issue.
Event Selection Rules to Compute Explanations
Venue: arXiv cs.AI 2016
Year: 2016

Explanations have been introduced in the previous century. Their interest in reducing the search space is no longer questioned. Yet, their efficient implementation into CSP solver is still a challenge. In this paper, we introduce ESeR, an Event Selection Rules algorithm that filters events generated during propagation. This dynamic selection enables an efficient computation of explanations for intelligent backtracking al- gorithms. We show the effectiveness of our approach on the instances of the last three MiniZinc challenges
Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, nonlinear model reduction for power systems is performed by the balancing of empirical controllability and observability covariances that are calculated around the operating region. Unlike existing model reduction methods, the external system does not need to be linearized but is directly dealt with as a nonlinear system. A transformation is found to balance the controllability and observability covariances in order to determine which states have the greatest contribution to the input-output behavior. The original system model is then reduced by Galerkin projection based on this transformation. The proposed method is tested and validated on a system comprised of a 16-machine 68-bus system and an IEEE 50-machine 145-bus system. The results show that by using the proposed model reduction the calculation efficiency can be greatly improved; at the same time, the obtained state trajectories are close to those for directly simulating the whole system or partitioning the system while not performing reduction. Compared with the balanced truncation method based on a linearized model, the proposed nonlinear model reduction method can guarantee higher accuracy and similar calculation efficiency. It is shown that the proposed method is not sensitive to the choice of the matrices for calculating the empirical covariances.
Kisa Donem Uzam-Zamansal Trafik Tahmini
Venue: arXiv cs.SY 2016
Year: 2016

The studies carried out with the objective of minimizing the effects of congestion, delay and environment problems on the transportation network have gained increasing importance in the last years. Among these studies, short-term traffic flow and average vehicle speed forecasting methods have come into prominence due to their easy implementations, efficient usage on different areas and cost-effectiveness. A large number of studies have reported that these methods, in which the expected future values of link flows and average speeds are forecasted in desired points, can reduce the traffic congestion by anticipating the problems in traffic management. In this paper, a spatio-temporal approach accounted for historical traffic characteristics data collected from a large number of points is presented for average speed forecasts in a given link. The proposed approach includes an algorithm that enables to take into account the most informative data in an input set by determining them for each stage. It is aimed to increase the forecasting accuracy by using sparse matrices in the algorithm while decreasing the calculation times significantly compared to the similar methods presented in the literature.