Experiences with Dynamic Circuit Creation in a Regional Network Testbed

In this paper we share our experiences of enabling dynamic circuit creation in the GpENI network. GpENI is a network research testbed in the mid-west USA involving several educational institutions. University of Nebraska-Lincoln is involved in provisioning dynamic circuits across the GpENI network among its participating universities. We discuss several options investigated for deploying dynamic circuits over the GpENI network as well as our demonstration experiments at the GENI engineering conferences. UNL has also collaborated with ProtoGENI project of University of Utah and Mid-Atlantic Crossroads (MAX) facility of Washington DC to create interdomain dynamic circuits

Self-Assembly and Rheology of Block Copolymer with Polymer Additives or Particle Suspensions

Ph.DDOCTOR OF PHILOSOPH

OFFLINE OPTIMIZATION OF ADVANCE RESERVATION OF BANDWIDTH OVER DYNAMIC CIRCUIT NETWORKS

E-science projects require very high-speed and reliable networks to transfer data across various destinations in the world. Dynamic Circuit Network (DCN) is a networking service to make advance reservation of bandwidth between a source and a destination in a network. In this thesis we solve the problem of advance reservation of bandwidth in next-generation wavelength-division multiplexing (WDM) networks using a simulation based approach.We implement a greedy algorithm and a genetic algorithm in parallel, in separate threads. The request for advance reservation is processed by both but the user gets the response only from the greedy algorithm. The genetic algorithm is used for offline re-optimization where we optimize the schedule of all the future reservations thereby maximizing the number of reservations possible in the network. We evaluate the approach using trace-driven traffic and simulated traffic. We observed an improvement in the blocking probability and service blocking probability of the network using our approach

Training and Model Parameters to Defend against Tabular Leakage Attacks

Federated Learning (FL) is a privacy-preserving approach to train machine learning models on distributed datasets across different organizations. This is particularly beneficial for domains like healthcare and finance, where user data is often sensitive and tabular (e.g., hospital records and financial transactions). However, recent research like Tableak highlighted vulnerabilities that can exploit information leakage in model updates to reconstruct sensitive user data from tabular FL systems. This thesis addresses these vulnerabilities by investigating the potential of training and machine learning parameters as defensive measures against leakage attacks on tabular data. We conducted experiments to analyze how modifying these parameters within the Federated Learning training process impacts the attacker's ability to reconstruct data. Our findings demonstrate that specific parameter configurations, including data encoding techniques, batch updates, epoch adjustments, and the use of sequential Peer-to-Peer (P2P) architectures, can significantly hinder reconstruction attacks on tabular data. These results contribute significantly to the development of more robust and privacy-preserving FL systems, especially for applications relying on sensitive tabular data

Effect of chain length of PEO on the gelation and micellization of the pluronic F127 copolymer aqueous system

10.1021/la401639gLangmuir29319694-9701LANG

Effect of Chain Length of PEO on the Gelation and Micellization of the Pluronic F127 Copolymer Aqueous System

The effect of adding homopolymer poly­(ethylene oxide) (PEO) on the sol/gel behavior of amphiphilic triblock copolymer Pluronic F127 ((EO)₉₈(PO)₆₇(EO)₉₈) in aqueous media is explored. Emphasis is placed on the influence of the PEO molecular weight and concentration on micellization and gelation and the exploration of their correlation. PEO is always found to lower the critical micellization temperature modestly. However, short PEO chains promote the gelation of F127, and long chains delay or even curb gel formation. Micelle size measurements and cryo-TEM micrographs provide evidence for micellar aggregation via the bridging of long PEO chains or depletion flocculation, thereby impeding the ordering of micelles for gel formation

Enhancement approach for liver lesion diagnosis using unenhanced CT images

Hepatocellular carcinoma, the primary liver cancer and other liver‐related pathologies are diagnosed with the help of contrast enhanced computed tomography (CECT) images. The CECT imaging technology is claimed to be an invasive technique, as the intravenous contrast agent injected prior to computed tomography (CT) acquisition is harmful and is not advised for patients with pre‐existing diabetes and kidney disorders. This study presents a novel enhancement technique for the diagnosis of liver lesions from unenhanced CT images by means of fuzzy histogram equalisation in the non‐sub‐sampled contourlet transform domain followed by decorrelation stretching. The enhanced images obtained in this study substantiate that the proposed method improves the diagnostic value from the unenhanced CT images thereby providing an alternate painless solution for CT acquisition for the subset of patients mentioned above. Another major highlight of this work is the characterisation of lesions from the enhanced output for five different classes of pathology. The obtained results presented in this study demonstrate the potency of the proposed enhancement technique in achieving an appreciable performance in lesion characterisation. The images used for this research study have been obtained from Jawaharlal Institute of Medical Education and Research Puducherry, India

implementation experience and experimentation

Computer Networks 61, 51-74The Great Plains Environment for Network Innovation (GpENI) is an international program- mable network testbed centered initially in the Midwest US with the goal to provide pro- grammability across the entire protocol stack. In this paper, we present the overall GpENI framework and our implementation experience for the programmable routing environ- ment and the dynamic circuit network (DCN). GpENI is built to provide a collaborative research infrastructure enabling the research community to conduct experiments in Future Internet architecture. We present illustrative examples of our experimentation in the GpENI platform.This work is funded in part by the US National Science Foundation GENI program (GPO Contract No. 9500009441), by the EU FP7 FIRE programme ResumeNet project (Grant Agreement No. 224619), and in large part, by the participating institutions.Grant Agreement No. 22461