OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge Acquisition: A Complex Networks Approach
Venue: arXiv cs.SI 2017
Year: 2017

Complex networks have been found to provide a good representation of the structure of knowledge, as understood in terms of discoverable concepts and their relationships. In this context, the discovery process can be modeled as agents walking in a knowledge space. Recent studies proposed more realistic dynamics, including the possibility of agents being influenced by others with higher visibility or by their own memory. However, rather than dealing with these two concepts separately, as previously approached, in this study we propose a multi-agent random walk model for knowledge acquisition that incorporates both concepts. More specifically, we employed the true self avoiding walk alongside a new dynamics based on jumps, in which agents are attracted by the influence of others. That was achieved by using a Lévy flight influenced by a field of attraction emanating from the agents. In order to evaluate our approach, we use a set of network models and two real networks, one generated from Wikipedia and another from the Web of Science. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not significantly affect the discovery dynamics. The local analysis revealed a substantial difference of performance depending on the network regions where the dynamics are occurring. In particular, the dynamics at the core of networks tend to be more effective. The choice of the dynamics parameters also had no significant impact to the acquisition performance for the considered knowledge networks, even at the local scale.
Deep Radial Kernel Networks: Approximating Radially Symmetric Functions with Deep Networks
Venue: arXiv cs.LG 2017
Year: 2017

We prove that a particular deep network architecture is more efficient at approximating radially symmetric functions than the best known 2 or 3 layer networks. We use this architecture to approximate Gaussian kernel SVMs, and subsequently improve upon them with further training. The architecture and initial weights of the Deep Radial Kernel Network are completely specified by the SVM and therefore sidesteps the problem of empirically choosing an appropriate deep network architecture.
Towards Computing Victory Margins in STV Elections
Venue: arXiv cs.GT 2017
Year: 2017

The Single Transferable Vote (STV) is a system of preferential voting employed in multi-seat elections. Each vote cast by a voter is a (potentially partial) ranking over a set of candidates. No techniques currently exist for computing the margin of victory (MOV) in STV elections. The MOV is the smallest number of vote manipulations (changes, additions, and deletions) required to bring about a change in the set of elected candidates. Knowledge of the MOV of an election gives greater insight into both how much time and money should be spent on the auditing of the election, and whether uncovered mistakes (such as ballot box losses) throw the election result into doubt---requiring a costly repeat election---or can be safely ignored. In this paper, we present algorithms for computing lower and upper bounds on the MOV in STV elections. In small instances, these algorithms are able to compute exact margins.
Real-time Perception meets Reactive Motion Generation
Venue: arXiv cs.RO 2017
Year: 2017

We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: (i) a traditional sense-plan-act approach that is still widely used, (ii) a myopic controller that only reacts to local environment dynamics and (iii) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. In 333 experiments, we quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building.
Towards Wi-Fi AP-Assisted Content Prefetching for On-Demand TV Series: A Reinforcement Learning Approach
Venue: arXiv cs.MM 2017
Year: 2017

The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge storage space, opens a new research area on how to utilize these resources at the edge network to improve users' quality of experience (QoE) (e.g., a short startup delay and smooth playback). One important research interest in this area is content prefetching, which predicts and accurately fetches contents ahead of users' requests to shift the traffic away during peak periods. However, in practice, the different video watching patterns among users, and the varying network connection status lead to the time-varying server load, which eventually makes the content prefetching problem challenging. To understand this challenge, this paper first performs a large-scale measurement study on users' AP connection and TV series watching patterns using real-traces. Then, based on the obtained insights, we formulate the content prefetching problem as a Markov Decision Process (MDP). The objective is to strike a balance between the increased prefetching&storage cost incurred by incorrect prediction and the reduced content download delay because of successful prediction. A learning-based approach is proposed to solve this problem and another three algorithms are adopted as baselines. In particular, first, we investigate the performance lower bound by using a random algorithm, and the upper bound by using an ideal offline approach. Then, we present a heuristic algorithm as another baseline. Finally, we design a reinforcement learning algorithm that is more practical to work in the online manner. Through extensive trace-based experiments, we demonstrate the performance gain of our design. Remarkably, our learning-based algorithm achieves a better precision and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to the random (resp. heuristic) algorithm.
The Efficiency Challenges of Resource Discovery in Grid Environments
Venue: arXiv cs.DC 2017
Year: 2017

Resource discovery is one of the most important services that significantly affects the efficiency of grid computing systems. The inherent dynamic and large-scale characteristics of grid environments make their resource discovery a challenging task. In recent years, different approaches have been proposed for resource discovery, attempting to tackle the challenges of grid environments and improve the efficiency. Being aware of these challenges and approaches is worthwhile in order to choose an appropriate approach according to the application in different organizations. This study reviews the most important factors that should be considered and challenges to be tackled in order to develop an efficient grid resource discovery system.
Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.
From Depth Data to Head Pose Estimation: a Siamese approach
Venue: arXiv cs.CV 2017
Year: 2017

The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss function to improve the learning of the regression network layer. The system has been tested on two public datasets, Biwi Kinect Head Pose and ICT-3DHP database. The reported results demonstrate the improvement in accuracy with respect to current state-of-the-art approaches and the real time capabilities of the overall framework.
Using STPA in Compliance with ISO 26262 for Developing a Safe Architecture for Fully Automated Vehicles
Venue: arXiv cs.SE 2017
Year: 2017

Safety has become of paramount importance in the development lifecycle of the modern automobile systems. However, the current automotive safety standard ISO 26262 does not specify clearly the methods for safety analysis. Different methods are recommended for this purpose. FTA (Fault Tree Analysis) and FMEA (Failure Mode and Effects Analysis) are used in the most recent ISO 26262 applications to identify component failures, errors and faults that lead to specific hazards (in the presence of faults). However, these methods are based on reliability theory, and they are not adequate to address new hazards caused by dysfunctional component interactions, software failure or human error. A holistic approach was developed called STPA (Systems-Theoretic Process Analysis) which addresses more types of hazards and treats safety as a dynamic control problem rather than an individual component failure. STPA also addresses types of hazardous causes in the absence of failure. Accordingly, there is a need for investigating hazard analysis techniques like STPA. In this paper, we present a concept on how to use STPA to extend the safety scope of ISO 26262 and support the Hazard Analysis and Risk Assessments (HARA) process. We applied the proposed concept to a current project of a fully automated vehicle at Continental. As a result, we identified 24 system- level accidents, 176 hazards, 27 unsafe control actions, and 129 unsafe scenarios. We conclude that STPA is an effective and efficient approach to derive detailed safety constraints. STPA can support the functional safety engineers to evaluate the architectural design of fully automated vehicles and build the functional safety concept.
Battery Degradation Maps for Power System Optimization and as a Benchmark Reference
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a novel method to describe battery degradation. We use the concept of degradation maps to model the incremental charge capacity loss as a function of discrete battery control actions and state of charge. The maps can be scaled to represent any battery system in size and power. Their convex piece-wise affine representations allow for tractable optimal control formulations and can be used in power system simulations to incorporate battery degradation. The map parameters for different battery technologies are published making them an useful basis to benchmark different battery technologies in case studies.
Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions
Venue: arXiv cs.CL 2017
Year: 2017

We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to reconsider the assumption that a preposition's lexical contribution is equivalent to the role/relation that it mediates. Our proposal is to embrace the potential for construal in adposition use, expressing such phenomena directly at the token level to manage complexity and avoid sense proliferation. We suggest a framework to represent both the scene role and the adposition's lexical function so they can be annotated at scale---supporting automatic, statistical processing of domain-general language---and sketch how this representation would inform a constructional analysis.
User Longevity and Engagement in Mobile Multiplayer Sports Management Games
Venue: arXiv cs.CY 2017
Year: 2017

Mobile games are extremely popular and engage millions of people every day. Even if they are often quite simple, their development features a high degree of difficulty and requires close attention to both achieve a high satisfaction from users and grant a return to the developers. In this paper we propose a model that analyzes users' playing session time in order to evaluate and maximize the longevity of games, even during the first phases of their development.
Memristive circuits and their Newtonian models $ϕ''=F(t,ϕ,ϕ')/m$ with memory
Venue: arXiv cs.ET 2017
Year: 2017

The prediction made by L. O. Chua 45+ years ago (see: IEEE Trans. Circuit Theory (1971) 18:507-519 and also: Proc. IEEE (2012) 100:1920-1927) about the existence of a passive circuit element (called memristor) that links the charge and flux variables has been confirmed by the HP lab group in its report (see: Nature (2008) 453:80-83) on a successful construction of such an element. This sparked an enormous interest in mem-elements, analysis of their unusual dynamical properties (i.e. pinched hysteresis loops, memory effects, etc.) and construction of their emulators. Such topics are also of interest in mechanical engineering where memdampers (or memory dampers) play the role equivalent to memristors in electronic circuits. In this paper we discuss certain properties of the oscillatory memristive circuits, including those with mixed-mode oscillations. Mathematical models of such circuits can be linked to the Newton's law $ϕ''\!-\!F(t,ϕ,ϕ')/m=0$, with $ϕ$ denoting the flux or charge variables, $m$ is a positive constant and the nonlinear non-autonomous function $F(t,ϕ,ϕ')$ contains memory terms. This leads further to scalar fourth-order ODEs called the jounce Newtonian equations. The jounce equations are used to construct the $RC$+op-amp simulation circuits in SPICE. Also, the linear parallel $G$-$C$ and series $R$-$L$ circuits with sinusoidal inputs are derived to match the $rms$ values of the memristive periodic circuits.
DotDFS: A Grid-based high-throughput file transfer system
Venue: arXiv cs.DC 2017
Year: 2017

DotGrid platform is a Grid infrastructure integrated with a set of open and standard protocols recently implemented on the top of Microsoft .NET in Windows and MONO .NET in UNIX/Linux. DotGrid infrastructure along with its proposed protocols provides a right and solid approach to targeting other platforms, e.g., the native C/C++ runtime. In this paper, we propose a new file transfer protocol called DotDFS as a high-throughput distributed file transfer component for DotGrid. DotDFS introduces some open binary protocols for efficient file transfers on current Grid infrastructures. DotDFS protocol also provides mechanisms for multiple file streams to gain high-throughput file transfer similar to GridFTP protocol, but by proposing and implementing a new parallel TCP connection-oriented paradigm. In our LAN tests, we have achieved better results than Globus GridFTP implementation particularly in multiple TCP streams and directory tree transfers. Our LAN experiences in memory-to-memory tests show that DotDFS accesses to the 94% bottleneck bandwidth while GridFTP is accessing 91%. In LAN disk-to-disk tests, comparing DotDFS protocol with GridFTP protocol unveils a set of interesting and technical problems in GridFTP for both the nature of the protocol and its implementation by Globus. In the WAN experimental studies, we propose a new idea for analytical modeling of file transfer protocols like DotDFS inspired by sampling, experimentation and mathematical interpolation approaches. The cross-platform and open standard-based features of DotDFS provide a substantial framework for unifying data access and resource sharing in real heterogeneous Grid environments.
A simple Python code for computing effective properties of 2D and 3D representative volume element under periodic boundary conditions
Venue: arXiv cs.CE 2017
Year: 2017

Multiscale optimization is an attractive research field recently. For the most of optimization tools, design parameters should be updated during a close loop. Therefore, a simple Python code is programmed to obtain effective properties of Representative Volume Element (RVE) under Periodic Boundary Conditions (PBCs). It can compute the mechanical properties of a composite with a periodic structure, in two or three dimensions. The computation method is based on the Asymptotic Homogenization Theory (AHT). With simple modifications, the basic Python code may be extended to the computation of the effective properties of more complex microstructure. Moreover, the code provides a convenient platform upon the optimization for the material and geometric composite design. The user may experiment with various algorithms and tackle a wide range of problems. To verify the effectiveness and reliability of the code, a three-dimensional case is employed to illuminate the code. Finally numerical results obtained by the code agree well with the available theoretical and experimental results
Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)
Venue: arXiv cs.CL 2017
Year: 2017

We examine Memory Networks for the task of question answering (QA), under common real world scenario where training examples are scarce and under weakly supervised scenario, that is only extrinsic labels are available for training. We propose extensions for the Dynamic Memory Network (DMN), specifically within the attention mechanism, we call the resulting Neural Architecture as Dynamic Memory Tensor Network (DMTN). Ultimately, we see that our proposed extensions results in over 80% improvement in the number of task passed against the baselined standard DMN and 20% more task passed compared to state-of-the-art End-to-End Memory Network for Facebook's single task weakly trained 1K bAbi dataset.
On Solving Travelling Salesman Problem with Vertex Requisitions
Venue: arXiv cs.DS 2017
Year: 2017

We consider the Travelling Salesman Problem with Vertex Requisitions, where for each position of the tour at most two possible vertices are given. It is known that the problem is strongly NP-hard. The proposed algorithm for this problem has less time complexity compared to the previously known one. In particular, almost all feasible instances of the problem are solvable in O(n) time using the new algorithm, where n is the number of vertices. The developed approach also helps in fast enumeration of a neighborhood in the local search and yields an integer programming model with O(n) binary variables for the problem.
Capacity Enhancement with Meta-Multiplexing
Venue: arXiv cs.IT 2017
Year: 2017

Multiplexing services as a key communication technique to effectively combine multiple signals into one signal and transmit over a shared medium. Multiplexing can increase the channel capacity by requiring more resources on the transmission medium. For instance, the space-division multiplexing accomplished through the multiple-input multiple-output (MIMO) scheme achieves significant capacity increase by the realized parallel channel, but it requires expensive hardware resources. Here, we present a novel multiplexing methodology, named meta-multiplexing, which allows ordinary modulated signals overlap together to form a set of "artificial" parallel channels, meanwhile, it only requires similar resources as ordinary modulation schemes. We prove the capacity law for the meta-multiplexing system and disclose that under broad conditions, the capacity of a single channel increases linearly with the signal to noise ratio (SNR), which breaks the conventional logarithmic growth of the capacity over SNR. Numerous simulation studies verify the capacity law and demonstrate the high efficiency of meta-multiplexing. Through proof-of-concept hardware experiments, we tested the proposed method in communication practices and achieved a spectral efficiency of 81.7 bits/s/Hz over a single channel, which is significantly higher than the efficiency of any existing communication system.
The Accuracy of Confidence Intervals for Field Normalised Indicators
Venue: arXiv cs.DL 2017
Year: 2017

When comparing the average citation impact of research groups, universities and countries, field normalisation reduces the influence of discipline and time. Confidence intervals for these indicators can help with attempts to infer whether differences between sets of publications are due to chance factors. Although both bootstrapping and formulae have been proposed for these, their accuracy is unknown. In response, this article uses simulated data to systematically compare the accuracy of confidence limits in the simplest possible case, a single field and year. The results suggest that the MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe, whereas bootstrap MNLCS confidence intervals tend to be accurate but can be unsafe for smaller world or group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes.
Resource Allocation for a Full-Duplex Base Station Aided OFDMA System
Venue: arXiv cs.IT 2017
Year: 2017

Exploiting full-duplex (FD) technology on base stations (BSs) is a promising solution to enhancing the system performance. Motivated by this, we revisit a full-duplex base station (FD-BS) aided OFDMA system, which consists of one BS, several uplink/downlink users and multiple subcarriers. A joint 3-dimensional (3D) mapping scheme among subcarriers, down-link users (DUEs), uplink users (UUEs) is considered as well as an associated power allocation optimization. In detail, we first decompose the complex 3D mapping problem into three 2-dimensional sub ones and solve them by using the iterative Hungarian method, respectively. Then based on the Lagrange dual method, we sequentially solve the power allocation and 3- dimensional mapping problem by fixing a dual point. Finally, the optimal solution can be obtained by utilizing the sub-gradient method. Unlike existing work that only solves either 3D mapping or power allocation problem but with a high computation complexity, we tackle both of them and have successfully reduced computation complexity from exponential to polynomial order. Numerical simulations are conducted to verify the proposed scheme.