OpenRaiser/Intern-Atlas huggingface dataset:
Computing Hypergeometric Solutions of Second Order Linear Differential Equations using Quotients of Formal Solutions and Integral Bases
Venue: arXiv cs.SC 2016
Year: 2016

We present two algorithms for computing hypergeometric solutions of second order linear differential operators with rational function coefficients. Our first algorithm searches for solutions of the form \[ \exp(\int r \, dx)\cdot{_{2}F_1}(a_1,a_2;b_1;f) \] where $r,f \in \overline{\mathbb{Q}(x)}$, and $a_1,a_2,b_1 \in \mathbb{Q}$. It uses modular reduction and Hensel lifting. Our second algorithm tries to find solutions in the form \[ \exp(\int r \, dx)\cdot \left( r_0 \cdot{_{2}F_1}(a_1,a_2;b_1;f) + r_1 \cdot{_{2}F_1}'(a_1,a_2;b_1;f) \right) \] where $r_0, r_1 \in \overline{\mathbb{Q}(x)}$, as follows: It tries to transform the input equation to another equation with solutions of the first type, and then uses the first algorithm.
Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we present an end-to-end approach to simultaneously learn spatio-temporal features and corresponding similarity metric for video-based person re-identification. Given the video sequence of a person, features from each frame that are extracted from all levels of a deep convolutional network can preserve a higher spatial resolution from which we can model finer motion patterns. These low-level visual percepts are leveraged into a variant of recurrent model to characterize the temporal variation between time-steps. Features from all time-steps are then summarized using temporal pooling to produce an overall feature representation for the complete sequence. The deep convolutional network, recurrent layer, and the temporal pooling are jointly trained to extract comparable hidden-unit representations from input pair of time series to compute their corresponding similarity value. The proposed framework combines time series modeling and metric learning to jointly learn relevant features and a good similarity measure between time sequences of person. Experiments demonstrate that our approach achieves the state-of-the-art performance for video-based person re-identification on iLIDS-VID and PRID 2011, the two primary public datasets for this purpose.
Microgrid Revenue Maximization by Charging Scheduling of EVs in Multiple Parking Stations
Venue: arXiv cs.SY 2016
Year: 2016

Nowadays, there has been a rapid growth in global usage of the electronic vehicles (EV). Despite apparent environmental and economic advantages of EVs, their high demand charging jobs pose an immense challenge to the existing electricity grid infrastructure. In microgrids, as the small-scale version of traditional power grid, however, the EV charging scheduling is more challenging. This is because, the microgrid owner, as a large electricity customer, is interested in shaving its global peak demand, i.e., the aggregated demand over multiple parking stations, to reduce total electricity cost. While the EV charging scheduling problem in single station scenario has been studied extensively in the previous research, the microgrid-level problem with multiple stations subject to a global peak constraint is not tackled. This paper aims to propose a near-optimal EV charging scheduling mechanism in a microgrid governed by a single utility provider with multiple charging stations. The goal is to maximize the total revenue while respecting both local and global peak constraints. The underlying problem, however, is a NP-hard mixed integer linear problem which is difficult to tackle and calls for approximation algorithm design. We design a primaldual scheduling algorithm which runs in polynomial time and achieves bounded approximation ratio. Moreover, the proposed global scheduling algorithm applies a valley-filling strategy to further reduce the global peak. Simulation results show that the performance of the proposed algorithm is 98% of the optimum, which is much better than the theoretical bound obtained by our approximation analysis. Our algorithm reduces the peak demand obtained by the existing alternative algorithm by 16% and simultaneously achieves better resource utilization.
A Graph Partitioning Algorithm for Leak Detection in Water Distribution Networks
Venue: arXiv cs.DS 2016
Year: 2016

Leak detection in urban water distribution networks (WDNs) is challenging given their scale, complexity, and limited instrumentation. We present an algorithm for leak detection in WDNs, which involves making additional flow measurements on-demand, and repeated use of water balance. Graph partitioning is used to determine the location of flow measurements, with the objective to minimize the measurement cost. We follow a multi-stage divide and conquer approach. In every stage, a section of the WDN identified to contain the leak is partitioned into two or more sub-networks, and water balance is used to trace the leak to one of these sub-networks. This process is recursively continued until the desired resolution is achieved. We investigate different methods for solving the arising graph partitioning problem like integer linear programming (ILP) and spectral bisection. The proposed methods are tested on large scale benchmark networks, and our results indicate that on average, less than 3% of the pipes need to be measured for finding the leak in large networks.
Classical - Quantum Arbitrarily Varying Wiretap Channel: Common Randomness Assisted Code and Continuity
Venue: arXiv cs.IT 2016
Year: 2016

We determine the secrecy capacities under common randomness assisted coding of arbitrarily varying classical-quantum wiretap channels.Furthermore, we determine the secrecy capacity of a mixed channel model which is compound from the sender to the legal receiver and varies arbitrarily from the sender to the eavesdropper. As an application we examine when the secrecy capacity is a continuous function of the system parameters and show that resources, i.e., having access to a perfect copy of the outcome of a random experiment. are helpful for channel stability.
Modular non-repeating codes for DNA storage
Venue: arXiv cs.IT 2016
Year: 2016

We describe a strategy for constructing codes for DNA-based information storage by serial composition of weighted finite-state transducers. The resulting state machines can integrate correction of substitution errors; synchronization by interleaving watermark and periodic marker signals; conversion from binary to ternary, quaternary or mixed-radix sequences via an efficient block code; encoding into a DNA sequence that avoids homopolymer, dinucleotide, or trinucleotide runs and other short local repeats; and detection/correction of errors (including local duplications, burst deletions, and substitutions) that are characteristic of DNA sequencing technologies. We present software implementing these codes, available at github.com/ihh/dnastore, with simulation results demonstrating that the generated DNA is free of short repeats and can be accurately decoded even in the presence of substitutions, short duplications and deletions.
Temporal Correlation of Interference in Bounded Mobile Ad Hoc Networks with Blockage
Venue: arXiv cs.IT 2016
Year: 2016

In mobile wireless networks with blockage, different users, and/or a single user at different time slots, may be blocked by some common obstacles. Therefore the temporal correlation of interference does not depend only on the user displacement law but also on the spatial correlation introduced by the obstacles. In this letter, we show that in mobile networks with a high density of users, blockage increases the temporal correlation of interference, while in sparse networks blockage has the opposite effect.
Atomic scale nanoelectronics for quantum neuromorphic devices: comparing different materials
Venue: arXiv cs.ET 2016
Year: 2016

I review the advancements of atomic scale nanoelectronics towards quantum neuromorphics. First, I summarize the key properties of elementary combinations of few neurons, namely long-- and short--term plasticity, spike-timing dependent plasticity (associative plasticity), quantumness and stochastic effects, and their potential computational employment. Next, I review several atomic scale device technologies developed to control electron transport at the atomic level, including single atom implantation for atomic arrays and CMOS quantum dots, single atom memories, Ag$_2$S and Cu$_2$S atomic switches, hafnium based RRAMs, organic material based transistors, Ge$_2$Sb$_2$Te$_5$ synapses. Each material/method proved successful in achieving some of the properties observed in real neurons. I compare the different methods towards the creation of a new generation of naturally inspired and biophysically meaningful artificial neurons, in order to replace the rigid CMOS based neuromorphic hardware. The most challenging aspect to address appears to obtain both the stochastic/quantum behavior and the associative plasticity, which are currently observed only below and above 20 nm length scale respectively, by employing the same material.
Consistency and Trust in Peer Data Exchange Systems
Venue: arXiv cs.DB 2016
Year: 2016

We propose and investigate a semantics for "peer data exchange systems" where different peers are related by data exchange constraints and trust relationships. These two elements plus the data at the peers' sites and their local integrity constraints are made compatible via a semantics that characterizes sets of "solution instances" for the peers. They are the intended -possibly virtual- instances for a peer that are obtained through a data repair semantics that we introduce and investigate. The semantically correct answers from a peer to a query, the so-called "peer consistent answers", are defined as those answers that are invariant under all its different solution instances. We show that solution instances can be specified as the models of logic programs with a stable model semantics. The repair semantics is based on null values as used in SQL databases, and is also of independent interest for repairs of single databases with respect to integrity constraints.
Forecasting and skipping to Reduce Transmission Energy in WSN
Venue: arXiv cs.NI 2016
Year: 2016

This paper deals with the improvement of energy efficiency in wireless sensor networks (WSN).Taking into consideration the power saving problem which is of crucial importance when the sensors are supplied by a limited power source, this paper proposes a method that optimizes as much as possible the transmission power of the sensors. Under the assumption of perfect channel between the Base Station (BS) and the Sensor Nodes (SN's) and with sufficient power resources at the BS, transferring the effort of transmission power to the BS will not be a peculiar issue. This paper proposes a method that reduces the transmitted data at the SN's and compensate this by requesting the variance of the measured value form predicted values. Furthermore, a request management algorithm (RMA) is developed to reduce the amount of requested data based on consecutive successive predictions. The result of this method reveals a major impact on reducing the transmission power at the SN's. Keywords: Wireless Sensor Networks, Data Reduction, Time Series Forecasting, Artificial Neural Networks.
Intra-Slot Interference Cancellation for Collision Resolution in Irregular Repetition Slotted ALOHA
Venue: arXiv cs.IT 2016
Year: 2016

ALOHA-type protocols became a popular solution for distributed and uncoordinated multiple random access in wireless networks. However, such distributed operation of the Medium Access Control (MAC) layer leads to sub-optimal utilization of the shared channel. One of the reasons is the occurrence of collisions when more than one packet is transmitted at the same time. These packets cannot be decoded and retransmissions are necessary. However, it has been recently shown that it is possible to apply signal processing techniques with these collided packets so that useful information can be decoded. This was recently proposed in the Irregular Repetition Slotted ALOHA (IRSA), achieving a throughput $T \simeq 0.97$ for very large MAC frame lengths as long as the number of active users is smaller than the number of slots per frame. In this paper, we extend the operation of IRSA with i) an iterative physical layer decoding processing that exploits the capture effect and ii) a Successive Interference Cancellation (SIC) processing at the slot-level, named intra-slot SIC, to decode more than one colliding packet per slot. We evaluate the performance of the proposed scheme, referred to as Extended IRSA (E-IRSA), in terms of throughput and channel capacity. Computer-based simulation results show that E-IRSA protocol allows to reach the maximum theoretical achievable throughput even in scenarios where the number of active users is higher than the number of slots per frame. Results also show that E-IRSA protocol significantly improves the performance even for small MAC frame lengths used in practical scenarios.
Optimizing Point-to-Multipoint Transmissions in High Speed Packet Access Networks
Venue: arXiv cs.IT 2016
Year: 2016

In this paper an innovative Radio Resource Management (RRM) algorithm is proposed with the purpose of increasing High Speed Packet Access (HSPA) performances, in terms of system capacity and service quality, when the Multimedia Broadcast Multicast Services (MBMS) is supplied. The proposed RRM algorithm exploits channel quality indications to set up point-to-multipoint connections to subgroups of multicast users and to select the proper modulation and coding schemes on the downlink. The number of subgroups is determined through an optimization technique that also takes into account the user satisfaction. An exhaustive simulation campaign is conducted to compare the proposed algorithm with the most promising approaches in the literature. Comparisons aim to assess the capability of the proposed RRM algorithm to efficiently manage group oriented services by providing an increment in terms of user satisfaction.
Content-based Cognitive Interference Control for City Monitoring Applications in the Urban IoT
Venue: arXiv cs.NI 2016
Year: 2016

In the Urban Internet of Things devices and systems are interconnected at the city scale to provide innovative services to the citizens.However, the traffic generated by the sensing and processing systems may overload local access networks. A coexistence problem arises where concurrent applications mutually interfere and compete for available resources. This effect is further aggravated by the multiple scales involved and heterogeneity of the networks supporting the urban IoT. One of the main contributions of this paper is the introduction of the notion of content-oriented cognitive interference control in heterogeneous local access networks supporting computing and data processing in the urban IoT. A network scenario where multiple communication technologies, such as Device-to-Device and Long Term Evolution (LTE), is considered. The focus of the present paper is on city monitoring applications, where a video data stream generated by a camera system is remotely processed to detect objects. The cognitive network paradigm is extended to dynamically shape the interference pattern generated by concurrent data streams and induce a packet loss trajectory compatible with video processing algorithms. Numerical results show that the proposed cognitive transmission strategy enables a significant throughput increase of interfering applications for a target accuracy of the monitoring application.
Adaptation is Useless for Two Discrete Additive-Noise Two-Way Channels
Venue: arXiv cs.IT 2016
Year: 2016

In two-way channels, each user transmits and receives at the same time. This allows each encoder to interactively adapt the current input to its own message and all previously received signals. Such coding approach can introduce correlation between inputs of different users, since all the users' outputs are correlated by the nature of the channel. However, for some channels, such adaptation in the coding scheme and its induced correlation among users are useless in the sense that they do not help enlarge the capacity region with respect to the standard coding method (where each user encodes only based on its own message). In this paper, it is shown that adaptation is not helpful for enlarging the capacity region of two classes of two-way discrete channels: the modulo additive-noise channel with memory and the multiple access/degraded broadcast channel.
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Venue: arXiv cs.DC 2016
Year: 2016

Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.
Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system
Venue: arXiv cs.AI 2016
Year: 2016

Prior to seeking professional medical care it is increasingly common for patients to use online resources such as automated symptom checkers. Many such systems attempt to provide a differential diagnosis based on the symptoms elucidated from the user, which may lead to anxiety if life or limb-threatening conditions are part of the list, a phenomenon termed 'cyberchondria' [1]. Systems that provide advice on where to seek help, rather than a diagnosis, are equally popular, and in our view provide the most useful information. In this technical report we describe how such a triage system can be modelled computationally, how medical insights can be translated into triage flows, and how such systems can be validated and tested. We present babylon check, our commercially deployed automated triage system, as a case study, and illustrate its performance in a large, semi-naturalistic deployment study.
Adaptive Quantization Matrices for HD and UHD Display Resolutions in Scalable HEVC
Venue: arXiv cs.OH 2016
Year: 2016

HEVC contains an option to enable custom quantization matrices, which are designed based on the Human Visual System and a 2D Contrast Sensitivity Function. Visual Display Units, capable of displaying video data at High Definition and Ultra HD display resolutions, are frequently utilized on a global scale. Video compression artifacts that are present due to high levels of quantization, which are typically inconspicuous in low display resolution environments, are clearly visible on HD and UHD video data and VDUs. The default QM technique in HEVC does not take into account the video data resolution, nor does it take into consideration the associated display resolution of a VDU to determine the appropriate levels of quantization required to reduce unwanted video compression artifacts. Based on this fact, we propose a novel, adaptive quantization matrix technique for the HEVC standard, including Scalable HEVC. Our technique, which is based on a refinement of the current HVS-CSF QM approach in HEVC, takes into consideration the display resolution of the target VDU for the purpose of minimizing video compression artifacts. In SHVC SHM 9.0, and compared with anchors, the proposed technique yields important quality and coding improvements for the Random Access configuration, with a maximum of 56.5% luma BD-Rate reductions in the enhancement layer. Furthermore, compared with the default QMs and the Sony QMs, our method yields encoding time reductions of 0.75% and 1.19%, respectively.
From Constrained Delaunay Triangulations to Roadmap Graphs with Arbitrary Clearance
Venue: arXiv cs.CG 2016
Year: 2016

This work studies path planning in two-dimensional space, in the presence of polygonal obstacles. We specifically address the problem of building a roadmap graph, that is, an abstract representation of all the paths that can potentially be followed around a given set of obstacles. Our solution consists in an original refinement algorithm for constrained Delaunay triangulations, aimed at generating a roadmap graph suited for planning paths with arbitrary clearance. In other words, a minimum distance to the obstacles can be specified, and the graph does not have to be recomputed if this distance is modified. Compared to other solutions, our approach has the advantage of being simpler, as well as significantly more efficient.
Smart Massive MIMO: An Infrastructure toward 5th Generation Smart Cities Network
Venue: arXiv cs.NI 2016
Year: 2016

On the Optimizing of Wireless Networks and toward improving the future 5th Generation mobile Network Infrastructure, we propose a novel infrastructure that can be the next Smart City Network. Our proposed Infrastructure takes into consideration most future demands and challenges, includes Capacity, Reliability, Scalability, and Flexibility. To deal with this issues we propose a wireless network infrastructure that is based on latest technologies of Massive MIMO systems. We further extend our infrastructure with many smart features, to be capable of coping with Cloud Computing, Smartphones, IoT and other intelligence-based services. The proposed infrastructure uses Network Functions Virtualization (NFV), Software-Defined Networking (SDN), Virtual Antenna Arrays (VAA) and Joint Beamforming to afford flexibility. We further propose a Terminal-centric rather than a Cell-centric based Infrastructure, which optimize interference aware environment and lead to higher capacity and reliability. The new infrastructure includes multi-purpose nodes that run a Network Operating System (NOS). This node will afford a scalable and flexible cost effective and semi-distributed network resources. Other propositions that meet Power-Effective, Cost-Effective, and Scenery aware design are discussed. Keywords - Wireless Network Infrastructure, Massive MIMO, Joint Beamforming, Cloud-based Networks, NFV, SDN, Cloud Computing, Grid Computing, and Distributed Systems.