Estimation of an open economy DSGE model for Romania. Do nominal and real frictions matter?

In this paper we use a medium scale open economy DSGE model developed by Adolfson et al. (2005). Besides authors’ observables we include also one extra observable series (CPI) in the model. Some of the parameters will be calibrated as to match sample’s mean or common values found in literature and others will be etimated on Romania’s data with the help of Bayesian techniques. Next, we specify some alternative scenarios where nominal or real rigidities will be ”turned off” and we asses their importance for the data generating process (with the help of marginal log likelihood).DSGE

A Mediated Definite Delegation Model allowing for Certified Grid Job Submission

Grid computing infrastructures need to provide traceability and accounting of their users" activity and protection against misuse and privilege escalation. A central aspect of multi-user Grid job environments is the necessary delegation of privileges in the course of a job submission. With respect to these generic requirements this document describes an improved handling of multi-user Grid jobs in the ALICE ("A Large Ion Collider Experiment") Grid Services. A security analysis of the ALICE Grid job model is presented with derived security objectives, followed by a discussion of existing approaches of unrestricted delegation based on X.509 proxy certificates and the Grid middleware gLExec. Unrestricted delegation has severe security consequences and limitations, most importantly allowing for identity theft and forgery of delegated assignments. These limitations are discussed and formulated, both in general and with respect to an adoption in line with multi-user Grid jobs. Based on the architecture of the ALICE Grid Services, a new general model of mediated definite delegation is developed and formulated, allowing a broker to assign context-sensitive user privileges to agents. The model provides strong accountability and long- term traceability. A prototype implementation allowing for certified Grid jobs is presented including a potential interaction with gLExec. The achieved improvements regarding system security, malicious job exploitation, identity protection, and accountability are emphasized, followed by a discussion of non- repudiation in the face of malicious Grid jobs

Optimising Sparse Matrix Vector multiplication for large scale FEM problems on FPGA

Sparse Matrix Vector multiplication (SpMV) is an important kernel in many scientific applications. In this work we propose an architecture and an automated customisation method to detect and optimise the architecture for block diagonal sparse matrices. We evaluate the proposed approach in the context of the spectral/hp Finite Element Method, using the local matrix assembly approach. This problem leads to a large sparse system of linear equations with block diagonal matrix which is typically solved using an iterative method such as the Preconditioned Conjugate Gradient. The efficiency of the proposed architecture combined with the effectiveness of the proposed customisation method reduces BRAM resource utilisation by as much as 10 times, while achieving identical throughput with existing state of the art designs and requiring minimal development effort from the end user. In the context of the Finite Element Method, our approach enables the solution of larger problems than previously possible, enabling the applicability of FPGAs to more interesting HPC problems

An efficient sparse conjugate gradient solver using a Beneš permutation network

© 2014 Technical University of Munich (TUM).The conjugate gradient (CG) is one of the most widely used iterative methods for solving systems of linear equations. However, parallelizing CG for large sparse systems is difficult due to the inherent irregularity in memory access pattern. We propose a novel processor architecture for the sparse conjugate gradient method. The architecture consists of multiple processing elements and memory banks, and is able to compute efficiently both sparse matrix-vector multiplication, and other dense vector operations. A Beneš permutation network with an optimised control scheme is introduced to reduce memory bank conflicts without expensive logic. We describe a heuristics for offline scheduling, the effect of which is captured in a parametric model for estimating the performance of designs generated from our approach

Design of object processing systems

Object processing systems are met rather often in every day life, in industry, tourism, commerce, etc. When designing such a system, many problems can be posed and considered, depending on the scope and purpose of design. We give here a general approach which involves graph theory, and which can have many applications. The generation of possible designs for an object processing system, known as synthesis in the engineering field, is reduced to first solving a graph embedding problem. We believe that our model could be successful and relatively easily implemented in a software tool, called Smart Synthesis Tool, so that the engineering design process will perform quicker. We propose three types of graph transformations which aid the way an object processing system can be designed. Future work will show to which extent these transformation types suffice for generating most of the layouts of the object processing systems

A Security Monitoring Framework For Virtualization Based HEP Infrastructures

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.Comment: Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco. Submitted to Journal of Physics: Conference Series (JPCS

User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution

Context aware mobile cloud services: a user experience oriented middleware for mobile cloud computing

Existing research on implementing the mobile cloud computing paradigm is typically based on offloading demanding computation from mobile devices to cloud-based servers. A continuous, high quality connection to the cloud infrastructure is normally required, with frequent high-volume data transfer, which can have a detrimental impact on the user experience of the application or service. In this paper, the Context Aware Mobile Cloud Services (CAMCS) middleware is presented as a solution that can deliver an integrated user experience of the mobile cloud to users. Such an experience respects the resource limitations of the mobile device. This is achieved by the Cloud Personal Assistant (CPA), the user’s trusted representative within CAMCS, which completes user-assigned tasks using existing cloud-based services, with an asynchronous, disconnected approach. A thin client mobile application, the CAMCS Client, allows the mobile user to send descriptions of tasks to his/her CPA, and view task results saved at the CPA, when convenient. The design and implementation of the middleware is presented, along with results of experimental evaluation on Amazon EC2. The resource usage of the CAMCS client is also studied. Analysis shows that CAMCS delivers an integrated user experience of mobile cloud applications and services