OpenRaiser/Intern-Atlas huggingface dataset:
The End of a Myth: Distributed Transactions Can Scale
Venue: arXiv cs.DB 2016
Year: 2016

The common wisdom is that distributed transactions do not scale. But what if distributed transactions could be made scalable using the next generation of networks and a redesign of distributed databases? There would be no need for developers anymore to worry about co-partitioning schemes to achieve decent performance. Application development would become easier as data placement would no longer determine how scalable an application is. Hardware provisioning would be simplified as the system administrator can expect a linear scale-out when adding more machines rather than some complex sub-linear function, which is highly application specific. In this paper, we present the design of our novel scalable database system NAM-DB and show that distributed transactions with the very common Snapshot Isolation guarantee can indeed scale using the next generation of RDMA-enabled network technology without any inherent bottlenecks. Our experiments with the TPC-C benchmark show that our system scales linearly to over 6.5 million new-order (14.5 million total) distributed transactions per second on 56 machines.
Robust Deep Appearance Models
Venue: arXiv cs.CV 2016
Year: 2016

This paper presents a novel Robust Deep Appearance Models to learn the non-linear correlation between shape and texture of face images. In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann Machines, can separate corrupted/occluded pixels in the texture modeling to achieve better reconstruction results. The two models are connected by Restricted Boltzmann Machines at the top layer to jointly learn and capture the variations of both facial shapes and appearances. This paper also introduces new fitting algorithms with occlusion awareness through the mask obtained from the RDBM reconstruction. The proposed approach is evaluated in various applications by using challenging face datasets, i.e. Labeled Face Parts in the Wild (LFPW), Helen, EURECOM and AR databases, to demonstrate its robustness and capabilities.
Stochastic Modeling of Hybrid Cache Systems
Venue: arXiv cs.PF 2016
Year: 2016

In recent years, there is an increasing demand of big memory systems so to perform large scale data analytics. Since DRAM memories are expensive, some researchers are suggesting to use other memory systems such as non-volatile memory (NVM) technology to build large-memory computing systems. However, whether the NVM technology can be a viable alternative (either economically and technically) to DRAM remains an open question. To answer this question, it is important to consider how to design a memory system from a "system perspective", that is, incorporating different performance characteristics and price ratios from hybrid memory devices. This paper presents an analytical model of a "hybrid page cache system" so to understand the diverse design space and performance impact of a hybrid cache system. We consider (1) various architectural choices, (2) design strategies, and (3) configuration of different memory devices. Using this model, we provide guidelines on how to design hybrid page cache to reach a good trade-off between high system throughput (in I/O per sec or IOPS) and fast cache reactivity which is defined by the time to fill the cache. We also show how one can configure the DRAM capacity and NVM capacity under a fixed budget. We pick PCM as an example for NVM and conduct numerical analysis. Our analysis indicates that incorporating PCM in a page cache system significantly improves the system performance, and it also shows larger benefit to allocate more PCM in page cache in some cases. Besides, for the common setting of performance-price ratio of PCM, "flat architecture" offers as a better choice, but "layered architecture" outperforms if PCM write performance can be significantly improved in the future.
Understanding the Abstract Dialectical Framework (Preliminary Report)
Venue: arXiv cs.AI 2016
Year: 2016

Among the most general structures extending the framework by Dung are the abstract dialectical frameworks (ADFs). They come equipped with various types of semantics, with the most prominent - the labeling-based one - analyzed in the context of computational complexity, signatures, instantiations and software support. This makes the abstract dialectical frameworks valuable tools for argumentation. However, there are fewer results available concerning the relation between the ADFs and other argumentation frameworks. In this paper we would like to address this issue by introducing a number of translations from various formalisms into ADFs. The results of our study show the similarities and differences between them, thus promoting the use and understanding of ADFs. Moreover, our analysis also proves their capability to model many of the existing frameworks, including those that go beyond the attack relation. Finally, translations allow other structures to benefit from the research on ADFs in general and from the existing software in particular.
Using the pyMIC Offload Module in PyFR
Venue: arXiv cs.MS 2016
Year: 2016

PyFR is an open-source high-order accurate computational fluid dynamics solver for unstructured grids. It is designed to efficiently solve the compressible Navier-Stokes equations on a range of hardware platforms, including GPUs and CPUs. In this paper we will describe how the Python Offload Infrastructure for the Intel Many Integrated Core Architecture (pyMIC) was used to enable PyFR to run with near native performance on the Intel Xeon Phi coprocessor. We will introduce the architecture of both pyMIC and PyFR and present a variety of examples showcasing the capabilities of pyMIC. Further, we will also compare the contrast pyMIC to other approaches including native execution and OpenCL. The process of adding support for pyMIC into PyFR will be described in detail. Benchmark results show that for a standard cylinder flow problem PyFR with pyMIC is able achieve 240 GFLOP/s of sustained double precision floating point performance; for a 1.85 times improvement over PyFR with C/OpenMP on a 12 core Intel Xeon E5-2697 v2 CPU.
Joint User Association and Power Control for Load Balancing in Downlink Heterogeneous Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

Instead of achievable rate in the conventional association, we utilize the effective rate to design two association schemes for load balancing in heterogeneous cellular networks (HCNs), which are both formulated as such problems with maximizing the sum of effective rates. In these two schemes, the one just considers user association, but the other introduces power control to mitigate interference and reduce energy consumption while performing user association. Since the effective rate is closely related to the load of some BS and the achievable rate of some user, it can be used as a key factor of association schemes for load balancing in HCNs. To solve the association problem without power control, we design a one-layer iterative algorithm, which converts the sum-of-ratio form of original optimization problem into a parameterized polynomial form. By combining this algorithm with power control algorithm, we propose a two-layer iterative algorithm for the association problem with power control. Specially, the outer layer performs user association using the algorithm of problem without power control, and the inner layer updates the transmit power of each BS using a power update function (PUF). At last, we give some convergence and complexity analyses for the proposed algorithms. As shown in simulation results, the proposed schemes have superior performance than the conventional association, and the scheme with joint user association and power control achieves a higher load balancing gain and energy efficiency than conventional scheme and other offloading scheme.
One- and Multi-Pass Long-Hop Routing for Wireless Network
Venue: arXiv cs.NI 2016
Year: 2016

In our paper we provide mathematical analysis of a probabilistic long-hop routing algorithms which uses as the randomizing factor the estimate of distance of a station from the previous-hop source of the message.
Observing Custom Software Modifications: A Quantitative Approach of Tracking the Evolution of Patch Stacks
Venue: arXiv cs.SE 2016
Year: 2016

Modifications to open-source software (OSS) are often provided in the form of "patch stacks" - sets of changes (patches) that modify a given body of source code. Maintaining patch stacks over extended periods of time is problematic when the underlying base project changes frequently. This necessitates a continuous and engineering-intensive adaptation of the stack. Nonetheless, long-term maintenance is an important problem for changes that are not integrated into projects, for instance when they are controversial or only of value to a limited group of users. We present and implement a methodology to systematically examine the temporal evolution of patch stacks, track non-functional properties like integrability and maintainability, and estimate the eventual economic and engineering effort required to successfully develop and maintain patch stacks. Our results provide a basis for quantitative research on patch stacks, including statistical analyses and other methods that lead to actionable advice on the construction and long-term maintenance of custom extensions to OSS.
Practical Tera-scale Walsh-Hadamard Transform
Venue: arXiv cs.DC 2016
Year: 2016

In the mid-second decade of new millennium, the development of IT has reached unprecedented new heights. As one derivative of Moore's law, the operating system evolves from the initial 16 bits, 32 bits, to the ultimate 64 bits. Most modern computing platforms are in transition to the 64-bit versions. For upcoming decades, IT industry will inevitably favor software and systems, which can efficiently utilize the new 64-bit hardware resources. In particular, with the advent of massive data outputs regularly, memory-efficient software and systems would be leading the future. In this paper, we aim at studying practical Walsh-Hadamard Transform (WHT). WHT is popular in a variety of applications in image and video coding, speech processing, data compression, digital logic design, communications, just to name a few. The power and simplicity of WHT has stimulated research efforts and interests in (noisy) sparse WHT within interdisciplinary areas including (but is not limited to) signal processing, cryptography. Loosely speaking, sparse WHT refers to the case that the number of nonzero Walsh coefficients is much smaller than the dimension; the noisy version of sparse WHT refers to the case that the number of large Walsh coefficients is much smaller than the dimension while there exists a large number of small nonzero Walsh coefficients. Clearly, general Walsh-Hadamard Transform is a first solution to the noisy sparse WHT, which can obtain all Walsh coefficients larger than a given threshold and the index positions. In this work, we study efficient implementations of very large dimensional general WHT. Our work is believed to shed light on noisy sparse WHT, which remains to be a big open challenge. Meanwhile, the main idea behind will help to study parallel data-intensive computing, which has a broad range of applications.
Amplify-and-Forward Full-Duplex Relay with Power Splitting-Based SWIPT
Venue: arXiv cs.IT 2016
Year: 2016

This paper proposes a virtual harvest-transmit model and a harvest-transmit-store model for amplify-and-forward full-duplex relay (FDR) networks with power splitting-based simultaneous wireless information and power transfer. The relay node employs a battery group consisting of two rechargeable batteries. By switching periodically between two batteries for charging and discharging in two consecutive time slots of each transmission block, all the harvested energy in each block has been applied for full duplex transmission in the virtual harvest-transmit model. By employing energy scheduling, the relay node switches among the harvesting, relaying, harvesting-relaying, and idle behaviors at a block level, so that a part of the harvested energy in a block can be scheduled for future usage in the harvest-transmit-store model. A greedy switching policy is designed to implement the harvest-transmit-store model, where the FDR node transmits when its residual energy ensures decoding at the destination. Numerical results verify the outage performance of the proposed schemes.
Best Practices for Replicability, Reproducibility and Reusability of Computer-Based Experiments Exemplified by Model Reduction Software
Venue: arXiv cs.MS 2016
Year: 2016

Over the recent years the importance of numerical experiments has gradually been more recognized. Nonetheless, sufficient documentation of how computational results have been obtained is often not available. Especially in the scientific computing and applied mathematics domain this is crucial, since numerical experiments are usually employed to verify the proposed hypothesis in a publication. This work aims to propose standards and best practices for the setup and publication of numerical experiments. Naturally, this amounts to a guideline for development, maintenance, and publication of numerical research software. Such a primer will enable the replicability and reproducibility of computer-based experiments and published results and also promote the reusability of the associated software.
Optimal control for a robotic exploration, pick-up and delivery problem
Venue: arXiv cs.SY 2016
Year: 2016

This paper addresses an optimal control problem for a robot that has to find and collect a finite number of objects and move them to a depot in minimum time. The robot has fourth-order dynamics that change instantaneously at any pick-up or drop-off of an object. The objects are modeled by point masses with a-priori unknown locations in a bounded two-dimensional space that may contain unknown obstacles. For this hybrid system, an Optimal Control Problem (OCP) is approximately solved by a receding horizon scheme, where the derived lower bound for the cost-to-go is evaluated for the worst and for a probabilistic case, assuming a uniform distribution of the objects. First, a time-driven approximate solution based on time and position space discretization and mixed integer programming is presented. Due to the high computational cost of this solution, an alternative event-driven approximate approach based on a suitable motion parameterization and gradient-based optimization is proposed. The solutions are compared in a numerical example, suggesting that the latter approach offers a significant computational advantage while yielding similar qualitative results compared to the former. The methods are particularly relevant for various robotic applications like automated cleaning, search and rescue, harvesting or manufacturing.
An extended MABAC for multi-attribute decision making using trapezoidal interval type-2 fuzzy numbers
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we attempt to extend Multi Attributive Border Approximation area Comparison (MABAC) approach for multi-attribute decision making (MADM) problems based on type-2 fuzzy sets (IT2FSs). As a special case of IT2FSs interval type-2 trapezoidal fuzzy numbers (IT2TrFNs) are adopted here to deal with uncertainties present in many practical evaluation and selection problems. A systematic description of MABAC based on IT2TrFNs is presented in the current study. The validity and feasibility of the proposed method are illustrated by a practical example of selecting the most suitable candidate for a software company which is heading to hire a system analysis engineer based on few attributes. Finally, a comparison with two other existing MADM methods is described.
New features of CitedReferencesExplorer (CRExplorer)
Venue: arXiv cs.DL 2016
Year: 2016

CRExplorer version 1.6.7 was released on July 5, 2016. This version includes the following new features and improvements: Scopus: Using "File" - "Import" - "Scopus", CRExplorer reads files from Scopus. The file format "CSV" (including citations, abstracts and references) should be chosen in Scopus for downloading records. Export facilities: Using "File" - "Export" - "Scopus", CRExplorer exports files in the Scopus format. Using "File" - "Export" - "Web of Science", CRExplorer exports files in the Web of Science format. These files can be imported in other bibliometric programs (e.g. VOSviewer). Space bar: Select a specific cited reference in the cited references table, press the space bar, and all bibliographic details of the CR are shown. Internal file format: Using "File" - "Save", working files are saved in the internal file format "*.cre". The files include all data including matching results and manual matching corrections. The files can be opened by using "File" - "Open".
Parallelized Structures for MIMO FBMC under Strong Channel Frequency Selectivity
Venue: arXiv cs.IT 2016
Year: 2016

A novel architecture for MIMO transmission and reception of filterbank multicarrier (FBMC) modulated signals under strong frequency selectivity is presented. The proposed system seeks to approximate an ideal frequency-selective precoder and linear receiver by Taylor expansion, exploiting the structure of the analysis and synthesis filterbanks. The resulting architecture is implemented by linearly combining conventional MIMO linear transceivers, which are applied to sequential derivatives of the original filterbank. The classical per-subcarrier precoding/linear receiver configuration is obtained as a special case of this architecture, when only one stage is fixed at both transmitter and receiver. An asymptotic expression for the resulting intersymbol/intercarrier (ISI/ICI) distortion is derived assuming that the number of subcarriers grows large. This expression can in practice be used in order to determine the number of parallel stages that need to be implemented in the proposed architecture. Performance evaluation studies confirm the substantial advantage of the proposed scheme in practical frequency-selective MIMO scenarios.
Identity of King and Flajolet & al. Formulae for LRU Miss Rate Exact Computation
Venue: arXiv cs.DM 2016
Year: 2016

This short paper gives a detailed proof of identity between two classic formulas for the computation of the exact Miss Rate of LRU caches. An extension to the identity of two formulas of the expected time of a partial collection in the coupon collector problem is also presented.
Can mobile usage predict illiteracy in a developing country?
Venue: arXiv cs.AI 2016
Year: 2016

The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geographical mapping of illiteracy is crucial to know where the illiterate people are, and where to put in resources. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than 1 trillion dollars each year. These results potentially enable costeffective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale
Resource Allocation in a MAC with and without security via Game Theoretic Learning
Venue: arXiv cs.IT 2016
Year: 2016

In this paper a $K$-user fading multiple access channel with and without security constraints is studied. First we consider a F-MAC without the security constraints. Under the assumption of individual CSI of users, we propose the problem of power allocation as a stochastic game when the receiver sends an ACK or a NACK depending on whether it was able to decode the message or not. We have used Multiplicative weight no-regret algorithm to obtain a Coarse Correlated Equilibrium (CCE). Then we consider the case when the users can decode ACK/NACK of each other. In this scenario we provide an algorithm to maximize the weighted sum-utility of all the users and obtain a Pareto optimal point. PP is socially optimal but may be unfair to individual users. Next we consider the case where the users can cooperate with each other so as to disagree with the policy which will be unfair to individual user. We then obtain a Nash bargaining solution, which in addition to being Pareto optimal, is also fair to each user. Next we study a $K$-user fading multiple access wiretap Channel with CSI of Eve available to the users. We use the previous algorithms to obtain a CCE, PP and a NBS. Next we consider the case where each user does not know the CSI of Eve but only its distribution. In that case we use secrecy outage as the criterion for the receiver to send an ACK or a NACK. Here also we use the previous algorithms to obtain a CCE, PP or a NBS. Finally we show that our algorithms can be extended to the case where a user can transmit at different rates. At the end we provide a few examples to compute different solutions and compare them under different CSI scenarios.
Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets
Venue: arXiv cs.LG 2016
Year: 2016

As machine learning is applied to an increasing variety of complex problems, which are defined by high dimensional and complex data sets, the necessity for task oriented feature learning grows in importance. With the advancement of Deep Learning algorithms, various successful feature learning techniques have evolved. In this paper, we present a novel way of learning discriminative features by training Deep Neural Nets which have Encoder or Decoder type architecture similar to an Autoencoder. We demonstrate that our approach can learn discriminative features which can perform better at pattern classification tasks when the number of training samples is relatively small in size.