Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

The paper presents a spatio-temporal wind speed forecasting algorithm using Deep Learning (DL)and in particular, Recurrent Neural Networks(RNNs). Motivated by recent advances in renewable energy integration and smart grids, we apply our proposed algorithm for wind speed forecasting. Renewable energy resources (wind and solar)are random in nature and, thus, their integration is facilitated with accurate short-term forecasts. In our proposed framework, we model the spatiotemporal information by a graph whose nodes are data generating entities and its edges basically model how these nodes are interacting with each other. One of the main contributions of our work is the fact that we obtain forecasts of all nodes of the graph at the same time based on one framework. Results of a case study on recorded time series data from a collection of wind mills in the north-east of the U.S. show that the proposed DL-based forecasting algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmarks models.Comment: Accepted to the ICML 2017, Time Series Workshop. arXiv admin note: text overlap with arXiv:1503.0121

Octet and decuplet contribution to the proton self energy

Within the hadronic language of Chiral Perturbation Theory we present the full leading-order octet-baryon$-$meson and decuplet-baryon$-$meson contribution to the proton self energy and thus to its wave function renormalization factor $Z$. By Fock-expanding the physical proton state into its bare and hadron-cloud part, we show how each individual baryon-meson probability depend on the average momenta of the particles in the fluctuation. We present how the results depend on the choice of the form factor involved in the regularization (Gaussian or Besselian) and how they depend on the cut-off parameter. We also show how the results vary with respect to a variation of the decuplet coupling constant $h_A$. The momentum distributions of the fluctuations are given and the fluctuations' relative probabilities are presented. We show that for reasonable values of the cut-off parameter, the Delta-pion fluctuation is of the same strength as the nucleon-pion fluctuation.Comment: 32 pages, 10 figures, fixed Ref.-format, added a Ref., fixed a couple of irrelevant typo

On the method of likelihood-induced priors

We demonstrate that the functional form of the likelihood contains a sufficient amount of information for constructing a prior for the unknown parameters. We develop a four-step algorithm by invoking the information entropy as the measure of uncertainty and show how the information gained from coarse-graining and resolving power of the likelihood can be used to construct the likelihood-induced priors. As a consequence, we show that if the data model density belongs to the exponential family, the likelihood-induced prior is the conjugate prior to the corresponding likelihood

Opinion Dynamics in Social Networks: A Local Interaction Game with Stubborn Agents

The process by which new ideas, innovations, and behaviors spread through a large social network can be thought of as a networked interaction game: Each agent obtains information from certain number of agents in his friendship neighborhood, and adapts his idea or behavior to increase his benefit. In this paper, we are interested in how opinions, about a certain topic, form in social networks. We model opinions as continuous scalars ranging from 0 to 1 with 1(0) representing extremely positive(negative) opinion. Each agent has an initial opinion and incurs some cost depending on the opinions of his neighbors, his initial opinion, and his stubbornness about his initial opinion. Agents iteratively update their opinions based on their own initial opinions and observing the opinions of their neighbors. The iterative update of an agent can be viewed as a myopic cost-minimization response (i.e., the so-called best response) to the others' actions. We study whether an equilibrium can emerge as a result of such local interactions and how such equilibrium possibly depends on the network structure, initial opinions of the agents, and the location of stubborn agents and the extent of their stubbornness. We also study the convergence speed to such equilibrium and characterize the convergence time as a function of aforementioned factors. We also discuss the implications of such results in a few well-known graphs such as Erdos-Renyi random graphs and small-world graphs

An Improved Bound for Minimizing the Total Weighted Completion Time of Coflows in Datacenters

In data-parallel computing frameworks, intermediate parallel data is often produced at various stages which needs to be transferred among servers in the datacenter network (e.g. the shuffle phase in MapReduce). A stage often cannot start or be completed unless all the required data pieces from the preceding stage are received. \emph{Coflow} is a recently proposed networking abstraction to capture such communication patterns. We consider the problem of efficiently scheduling coflows with release dates in a shared datacenter network so as to minimize the total weighted completion time of coflows. Several heuristics have been proposed recently to address this problem, as well as a few polynomial-time approximation algorithms with provable performance guarantees. Our main result in this paper is a polynomial-time deterministic algorithm that improves the prior known results. Specifically, we propose a deterministic algorithm with approximation ratio of $5$, which improves the prior best known ratio of $12$. For the special case when all coflows are released at time zero, our deterministic algorithm obtains approximation ratio of $4$ which improves the prior best known ratio of $8$. The key ingredient of our approach is an improved linear program formulation for sorting the coflows followed by a simple list scheduling policy. Extensive simulation results, using both synthetic and real traffic traces, are presented that verify the performance of our algorithm and show improvement over the prior approaches.Comment: 12 pages, 11 figure

Medical Image Watermarking using 2D-DWT with Enhanced security and capacity

Teleradiology enables medical images to be transferred over the computer networks for many purposes including clinical interpretation, diagnosis, archive, etc. In telemedicine, medical images can be manipulated while transferring. In addition, medical information security requirements are specified by the legislative rules, and concerned entities must adhere to them. In this research, we propose a new scheme based on 2-dimensional Discrete Wavelet Transform (2D DWT) to improve the robustness and authentication of medical images. In addition, the current research improves security and capacity of watermarking using encryption and compression in medical images. The evaluation is performed on the personal dataset, which contains 194 CTI and 68 MRI cases

On the Scalability of Reliable Data Transfer in High Speed Networks

This paper considers reliable data transfer in a high-speed network (HSN) in which the per-connection capacity is very large. We focus on sliding window protocols employing selective repeat for reliable data transfer and study two reliability mechanisms based on ARQ and FEC. The question we ask is which mechanism is more suitable for an HSN in which the scalability of reliable data transfer in terms of receiver's buffer requirement and achievable delay and throughput is a concern. To efficiently utilize the large bandwidth available to a connection in an HSN, sliding window protocols require a large transmission window. In this regime of large transmission windows, we show that while both mechanisms achieve the same asymptotic throughput in the presence of packet losses, their delay and buffer requirements are different. Specifically, an FEC-based mechanism has delay and receiver's buffer requirement that are asymptotically smaller than that of an ARQ-based selective repeat mechanism by a factor of log W, where W is the window size of the selective repeat mechanism. This result is then used to investigate the implications of each reliability mechanism on protocol design in an HSN in terms of throughput, delay, buffer requirement, and control overhead

A Theory of Auto-Scaling for Resource Reservation in Cloud Services

We consider a distributed server system consisting of a large number of servers, each with limited capacity on multiple resources (CPU, memory, disk, etc.). Jobs with different rewards arrive over time and require certain amounts of resources for the duration of their service. When a job arrives, the system must decide whether to admit it or reject it, and if admitted, in which server to schedule the job. The objective is to maximize the expected total reward received by the system. This problem is motivated by control of cloud computing clusters, in which, jobs are requests for Virtual Machines or Containers that reserve resources for various services, and rewards represent service priority of requests or price paid per time unit of service by clients. We study this problem in an asymptotic regime where the number of servers and jobs' arrival rates scale by a factor $L$, as $L$ becomes large. We propose a resource reservation policy that asymptotically achieves at least $1/2$, and under certain monotone property on jobs' rewards and resources, at least $1-1/e$ of the optimal expected reward. The policy automatically scales the number of VM slots for each job type as the demand changes, and decides in which servers the slots should be created in advance, without the knowledge of traffic rates. It effectively tracks a low-complexity greedy packing of existing jobs in the system while maintaining only a small number, $g(L)=\omega(\log L)$, of reserved VM slots for high priority jobs that pack well

Two globally convergent nonmonotone trust-region methods for unconstrained optimization

This paper addresses some trust-region methods equipped with nonmonotone strategies for solving nonlinear unconstrained optimization problems. More specifically, the importance of using nonmonotone techniques in nonlinear optimization is motivated, then two new nonmonotone terms are proposed, and their combinations into the traditional trust-region framework are studied. The global convergence to first- and second-order stationary points and local superlinear and quadratic convergence rates for both algorithms are established. Numerical experiments on the \textsf{CUTEst} test collection of unconstrained problems and some highly nonlinear test functions are reported, where a comparison among state-of-the-art nonmonotone trust-region methods show the efficiency of the proposed nonmonotne schemes

Towards a Theory of Anonymous Networking

The problem of anonymous networking when an eavesdropper observes packet timings in a communication network is considered. The goal is to hide the identities of source-destination nodes, and paths of information flow in the network. One way to achieve such an anonymity is to use mixers. Mixers are nodes that receive packets from multiple sources and change the timing of packets, by mixing packets at the output links, to prevent the eavesdropper from finding sources of outgoing packets. In this paper, we consider two simple but fundamental scenarios: double input-single output mixer and double input-double output mixer. For the first case, we use the information-theoretic definition of the anonymity, based on average entropy per packet, and find an optimal mixing strategy under a strict latency constraint. For the second case, perfect anonymity is considered, and maximal throughput strategies with perfect anonymity are found under a strict latency constraint and an average queue length constraint