OpenRaiser/Intern-Atlas huggingface dataset:
Simulation of optical flow and fuzzy based obstacle avoidance system for mobile robots
Venue: arXiv cs.CV 2015
Year: 2015

Honey bees use optical flow to avoid obstacles effectively. In this research work similar methodology was tested on a simulated mobile robot. Simulation framework was based on VRML and Simulink in a 3D world. Optical flow vectors were calculated from a video scene captured by a virtual camera which was used as inputs to a fuzzy logic controller. Fuzzy logic controller decided the locomotion of the robot. Different fuzzy logic rules were evaluated. The robot was able to navigate through complex static and dynamic environments effectively, avoiding obstacles on its path.
Redesigning Fronthaul for Next-Generation Networks: Beyond Baseband Samples and Point-to-Point Links
Venue: arXiv cs.IT 2015
Year: 2015

The fronthaul (FH) is an indispensable enabler for 5G networks. However, the classical fronthauling method demands for large bandwidth, low latency, and tightly synchronized on the transport network, and only allows for point-to-point logical topology. This greatly limits the usage of FH in many 5G scenarios. In this paper, we introduce a new perspective to understand and design FH for next-generation wireless access. We allow the renovated FH to transport information other than time-domain I/Q samples and to support logical topologies beyond point-to-point links. In this way, different function splitting scheme could be incorporated into the radio access network to satisfy the bandwidth and latency requirements of ultra-dense networks, control/data (C/D) decoupling architectures, and delay-sensitive communications. At the same time, massive cooperation and device-centric networking could be effectively enabled with point-to-multi-point FH transportation. We analyze three unique design requirements for the renovated FH, including the ability to handle various payload traffic, support different logical topology, and provide differentiated latency guarantee. Following this analysis, we propose a reference architecture for designing the renovated FH. The required functionalities are categorized into four logical layers and realized using novel technologies such as decoupled synchronization layer, packet switching, and session-based control. We also discuss some important future research issues.
Reduce the Complexity of List Decoding of Polar Codes by Tree-Pruning
Venue: arXiv cs.IT 2015
Year: 2015

Polar codes under cyclic redundancy check aided successive cancellation list (CA-SCL) decoding can outperform the turbo codes and the LDPC codes when code lengths are configured to be several kilobits. In order to reduce the decoding complexity, a novel tree-pruning scheme for the \mbox{SCL/CA-SCL} decoding algorithms is proposed in this paper. In each step of the decoding procedure, the candidate paths with metrics less than a threshold are dropped directly to avoid the unnecessary computations for the path searching on the descendant branches of them. Given a candidate path, an upper bound of the path metric of its descendants is proposed to determined whether the pruning of this candidate path would affect frame error rate (FER) performance. By utilizing this upper bounding technique and introducing a dynamic threshold, the proposed scheme deletes the redundant candidate paths as many as possible while keeping the performance deterioration in a tolerant region, thus it is much more efficient than the existing pruning scheme. With only a negligible loss of FER performance, the computational complexity of the proposed pruned decoding scheme is only about $40\%$ of the standard algorithm in the low signal-to-noise ratio (SNR) region (where the FER under CA-SCL decoding is about $0.1 \sim 0.001$), and it can be very close to that of the successive cancellation (SC) decoder in the moderate and high SNR regions.
Scalable Reliability Modelling of RAID Storage Subsystems
Venue: arXiv cs.PF 2015
Year: 2015

Reliability modelling of RAID storage systems with its various components such as RAID controllers, enclosures, expanders, interconnects and disks is important from a storage system designer's point of view. A model that can express all the failure characteristics of the whole RAID storage system can be used to evaluate design choices, perform cost reliability trade-offs and conduct sensitivity analyses. However, including such details makes the computational models of reliability quickly infeasible. We present a CTMC reliability model for RAID storage systems that scales to much larger systems than heretofore reported and we try to model all the components as accurately as possible. We use several state-space reduction techniques at the user level, such as aggregating all in-series components and hierarchical decomposition, to reduce the size of our model. To automate computation of reliability, we use the PRISM model checker as a CTMC solver where appropriate. Our modelling techniques using PRISM are more practical (in both time and effort) compared to previously reported Monte-Carlo simulation techniques. Our model for RAID storage systems (that includes, for example, disks, expanders, enclosures) uses Weibull distributions for disks and, where appropriate, correlated failure modes for disks, while we use exponential distributions with independent failure modes for all other components. To use the CTMC solver, we approximate the Weibull distribution for a disk using sum of exponentials and we confirm that this model gives results that are in reasonably good agreement with those from the sequential Monte Carlo simulation methods for RAID disk subsystems reported in literature earlier. Using a combination of scalable techniques, we are able to model and compute reliability for fairly large configurations with upto 600 disks using this model.
Multi-Comm-Core Architecture for Terabit/s Wireless
Venue: arXiv cs.NI 2015
Year: 2015

Wireless communications along with the Internet has been the most transformative technology in the past 50 years. We expect that wireless data growth driven by new mobile applications, need to connect all humankind (not just 1/3) as well as Billions of things to the Internet will require Terabit/s shared links for ground based local area and wide area wireless access, for wireless backhaul as well as access via unmanned aerial vehicles (UAVs) and satellites. We present a new scalable radio architecture that we refer to multi-comm-core (MCC) to enable low-cost ultra-high speed wireless communications using both traditional and millimeter wave spectrum.
Dynamic Radio Cooperation for Downlink Cloud-RANs with Computing Resource Sharing
Venue: arXiv cs.IT 2015
Year: 2015

A novel dynamic radio-cooperation strategy is proposed for Cloud Radio Access Networks (C-RANs) consisting of multiple Remote Radio Heads (RRHs) connected to a central Virtual Base Station (VBS) pool. In particular, the key capabilities of C-RANs in computing-resource sharing and real-time communication among the VBSs are leveraged to design a joint dynamic radio clustering and cooperative beamforming scheme that maximizes the downlink weighted sum-rate system utility (WSRSU). Due to the combinatorial nature of the radio clustering process and the non-convexity of the cooperative beamforming design, the underlying optimization problem is NP-hard, and is extremely difficult to solve for a large network. Our approach aims for a suboptimal solution by transforming the original problem into a Mixed-Integer Second-Order Cone Program (MI-SOCP), which can be solved efficiently using a proposed iterative algorithm. Numerical simulation results show that our low-complexity algorithm provides close-to-optimal performance in terms of WSRSU while significantly outperforming conventional radio clustering and beamforming schemes. Additionally, the results also demonstrate the significant improvement in computing-resource utilization of C-RANs over traditional RANs with distributed computing resources.
Cooperative Relaying for Large Random Multihop Networks
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we propose a new relaying protocol for large multihop networks combining the concepts of cooperative diversity and opportunistic relaying. The cooperative relaying protocol is based on two diversity mechanisms, incremental redundancy combining and repetition combining. We assume that nodes in the large multihop network are modeled by a homogeneous Poisson Point Process and operate under Rayleigh fading and constant power transmission per node. The performance of the proposed relaying protocol is evaluated through the progress rate density (PRD) of the multihop network and compared to the conventional multihop relaying with no cooperation. We develop an analytic approximation to the PRD based on the concept of decoding cells. The protocol parameters are optimized to maximize the PRD of network. We show that the cooperative relaying protocol provides significant throughput improvements over conventional relaying with no cooperation in a large multihop network. It is also shown that incremental redundancy combining provides a higher gain in PRD relative to repetition combining. The gain in PRD has near constant value at all values of the path loss exponent and is monotonic in diversity order.
Fourier Sparsity of GF(2) Polynomials
Venue: arXiv cs.CC 2015
Year: 2015

We study a conjecture called "linear rank conjecture" recently raised in (Tsang et al., FOCS'13), which asserts that if many linear constraints are required to lower the degree of a GF(2) polynomial, then the Fourier sparsity (i.e. number of non-zero Fourier coefficients) of the polynomial must be large. We notice that the conjecture implies a surprising phenomenon that if the highest degree monomials of a GF(2) polynomial satisfy a certain condition, then the Fourier sparsity of the polynomial is large regardless of the monomials of lower degrees -- whose number is generally much larger than that of the highest degree monomials. We develop a new technique for proving lower bound on the Fourier sparsity of GF(2) polynomials, and apply it to certain special classes of polynomials to showcase the above phenomenon.
On the Definition and Existence of an MVU Estimator for Target Location Estimation
Venue: arXiv cs.IT 2015
Year: 2015

The problem of target localization with ideal binary detectors is considered in one dimensional space. The problem is investigated in both a censored and non-censored scheme. In the censored setting, the problem is equivalent to estimating the center of a uniform distribution by knowing samples of data. It does not admit an MVU estimator according to the previous results of Lehmann-Sheffe. However, it is proven that if the radius of detection is known and sensor deployment region is very large, both censored and non-censored cases will have an MVU estimator among the functions that are invariant to Euclidean motion. In addition, it is shown that when the radius of detection is not known, the censored case still has an MVU estimator whereas in the non-censored case, an MVU estimator does not exist, even under the assumption that the estimators are invariant to Euclidean motion.
Training Conditional Random Fields with Natural Gradient Descent
Venue: arXiv cs.LG 2015
Year: 2015

We propose a novel parameter estimation procedure that works efficiently for conditional random fields (CRF). This algorithm is an extension to the maximum likelihood estimation (MLE), using loss functions defined by Bregman divergences which measure the proximity between the model expectation and the empirical mean of the feature vectors. This leads to a flexible training framework from which multiple update strategies can be derived using natural gradient descent (NGD). We carefully choose the convex function inducing the Bregman divergence so that the types of updates are reduced, while making the optimization procedure more effective by transforming the gradients of the log-likelihood loss function. The derived algorithms are very simple and can be easily implemented on top of the existing stochastic gradient descent (SGD) optimization procedure, yet it is very effective as illustrated by experimental results.
Equivalence-based Security for Querying Encrypted Databases: Theory and Application to Privacy Policy Audits
Venue: arXiv cs.CR 2015
Year: 2015

Motivated by the problem of simultaneously preserving confidentiality and usability of data outsourced to third-party clouds, we present two different database encryption schemes that largely hide data but reveal enough information to support a wide-range of relational queries. We provide a security definition for database encryption that captures confidentiality based on a notion of equivalence of databases from the adversary's perspective. As a specific application, we adapt an existing algorithm for finding violations of privacy policies to run on logs encrypted under our schemes and observe low to moderate overheads.
Support for Non-conformal Meshes in PETSc's DMPlex Interface
Venue: arXiv cs.MS 2015
Year: 2015

PETSc's DMPlex interface for unstructured meshes has been extended to support non-conformal meshes. The topological construct that DMPlex implements---the CW-complex---is by definition conformal, so representing non- conformal meshes in a way that hides complexity requires careful attention to the interface between DMPlex and numerical methods such as the finite element method. Our approach---which combines a tree structure for subset- superset relationships and a "reference tree" describing the types of non-conformal interfaces---allows finite element code written for conformal meshes to extend automatically: in particular, all "hanging-node" constraint calculations are handled behind the scenes. We give example code demonstrating the use of this extension, and use it to convert forests of quadtrees and forests of octrees from the p4est library to DMPlex meshes.
Acceleration-as-a-Service: Exploiting Virtualised GPUs for a Financial Application
Venue: arXiv cs.DC 2015
Year: 2015

'How can GPU acceleration be obtained as a service in a cluster?' This question has become increasingly significant due to the inefficiency of installing GPUs on all nodes of a cluster. The research reported in this paper is motivated to address the above question by employing rCUDA (remote CUDA), a framework that facilitates Acceleration-as-a-Service (AaaS), such that the nodes of a cluster can request the acceleration of a set of remote GPUs on demand. The rCUDA framework exploits virtualisation and ensures that multiple nodes can share the same GPU. In this paper we test the feasibility of the rCUDA framework on a real-world application employed in the financial risk industry that can benefit from AaaS in the production setting. The results confirm the feasibility of rCUDA and highlight that rCUDA achieves similar performance compared to CUDA, provides consistent results, and more importantly, allows for a single application to benefit from all the GPUs available in the cluster without loosing efficiency.
Beamforming for Magnetic Induction based Wireless Power Transfer Systems with Multiple Receivers
Venue: arXiv cs.IT 2015
Year: 2015

Magnetic induction (MI) based communication and power transfer systems have gained an increased attention in the recent years. Typical applications for these systems lie in the area of wireless charging, near-field communication, and wireless sensor networks. For an optimal system performance, the power efficiency needs to be maximized. Typically, this optimization refers to the impedance matching and tracking of the split-frequencies. However, an important role of magnitude and phase of the input signal has been mostly overlooked. Especially for the wireless power transfer systems with multiple transmitter coils, the optimization of the transmit signals can dramatically improve the power efficiency. In this work, we propose an iterative algorithm for the optimization of the transmit signals for a transmitter with three orthogonal coils and multiple single coil receivers. The proposed scheme significantly outperforms the traditional baseline algorithms in terms of power efficiency.
Answering Fuzzy Conjunctive Queries over Finitely Valued Fuzzy Ontologies
Venue: arXiv cs.LO 2015
Year: 2015

Fuzzy Description Logics (DLs) provide a means for representing vague knowledge about an application domain. In this paper, we study fuzzy extensions of conjunctive queries (CQs) over the DL $\mathcal{SROIQ}$ based on finite chains of degrees of truth. To answer such queries, we extend a well-known technique that reduces the fuzzy ontology to a classical one, and use classical DL reasoners as a black box. We improve the complexity of previous reduction techniques for finitely valued fuzzy DLs, which allows us to prove tight complexity results for answering certain kinds of fuzzy CQs. We conclude with an experimental evaluation of a prototype implementation, showing the feasibility of our approach.
The Effects of Hyperparameters on SGD Training of Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces has often been limited. In this note, I report the results of large scale experiments exploring these different parameters and their interactions.
Pseudo-gradient Based Local Voltage Control in Distribution Networks
Venue: arXiv cs.SY 2015
Year: 2015

Voltage regulation is critical for power grids. However, it has become a much more challenging problem as distributed energy resources (DERs) such as photovoltaic and wind generators are increasingly deployed, causing rapid voltage fluctuations beyond what can be handled by the traditional voltage regulation methods. In this paper, motivated by two previously proposed inverter-based local volt/var control algorithms, we propose a pseudo-gradient based voltage control algorithm for the distribution network that does not constrain the allowable control functions and has low implementation complexity. We characterize the convergence of the proposed voltage control scheme, and compare it against the two previous algorithms in terms of the convergence condition as well as the convergence rate.
What is Holding Back Convnets for Detection?
Venue: arXiv cs.CV 2015
Year: 2015

Convolutional neural networks have recently shown excellent results in general object detection and many other tasks. Albeit very effective, they involve many user-defined design choices. In this paper we want to better understand these choices by inspecting two key aspects "what did the network learn?", and "what can the network learn?". We exploit new annotations (Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite common belief, our results indicate that existing state-of-the-art convnet architectures are not invariant to various appearance factors. In fact, all considered networks have similar weak points which cannot be mitigated by simply increasing the training data (architectural changes are needed). We show that overall performance can improve when using image renderings for data augmentation. We report the best known results on the Pascal3D+ detection and view-point estimation tasks.
On interval edge-colorings of bipartite graphs of small order
Venue: arXiv cs.DM 2015
Year: 2015

An edge-coloring of a graph $G$ with colors $1,\ldots,t$ is an interval $t$-coloring if all colors are used, and the colors of edges incident to each vertex of $G$ are distinct and form an interval of integers. A graph $G$ is interval colorable if it has an interval $t$-coloring for some positive integer $t$. The problem of deciding whether a bipartite graph is interval colorable is NP-complete. The smallest known examples of interval non-colorable bipartite graphs have $19$ vertices. On the other hand it is known that the bipartite graphs on at most $14$ vertices are interval colorable. In this work we observe that several classes of bipartite graphs of small order have an interval coloring. In particular, we show that all bipartite graphs on $15$ vertices are interval colorable.
Modeling and Analysis of Walking Pattern for a Biped Robot
Venue: arXiv cs.RO 2015
Year: 2015

This paper addresses the design and development of an autonomous biped robot using master and worker combination of controllers. In addition, the bot is wirelessly controllable. The work presented here explains the walking pattern, system control and actuator control techniques for 10 Degree of Freedom (DOF) biped humanoid. Bi-pedal robots have better mobility than conventional wheeled robots, but they tend to topple easily. In order to walk stably in various environments, such as on rough terrain, up and down slopes, or in regions containing obstacles, it is necessary, that robot should adapt to the ground conditions with a foot motion, as well as maintain its stability with a torso motion. It is desirable to select a walking pattern that requires small torque and velocity of the joint actuators. The work proposed a low cost solution using open source hardware-software and application. The work extends to develop and implement new algorithms by adding gyroscope and accelerometer to further the research in the field of biped robots.
Towards Real-time Customer Experience Prediction for Telecommunication Operators
Venue: arXiv cs.CY 2015
Year: 2015

Telecommunications operators (telcos) traditional sources of income, voice and SMS, are shrinking due to customers using over-the-top (OTT) applications such as WhatsApp or Viber. In this challenging environment it is critical for telcos to maintain or grow their market share, by providing users with as good an experience as possible on their network. But the task of extracting customer insights from the vast amounts of data collected by telcos is growing in complexity and scale everey day. How can we measure and predict the quality of a user's experience on a telco network in real-time? That is the problem that we address in this paper. We present an approach to capture, in (near) real-time, the mobile customer experience in order to assess which conditions lead the user to place a call to a telco's customer care center. To this end, we follow a supervised learning approach for prediction and train our 'Restricted Random Forest' model using, as a proxy for bad experience, the observed customer transactions in the telco data feed before the user places a call to a customer care center. We evaluate our approach using a rich dataset provided by a major African telecommunication's company and a novel big data architecture for both the training and scoring of predictive models. Our empirical study shows our solution to be effective at predicting user experience by inferring if a customer will place a call based on his current context. These promising results open new possibilities for improved customer service, which will help telcos to reduce churn rates and improve customer experience, both factors that directly impact their revenue growth.