Sequential random access codes and self-testing of quantum measurement instruments

Quantum Random Access Codes (QRACs) are key tools for a variety of protocols in quantum information theory. These are commonly studied in prepare-and-measure scenarios in which a sender prepares states and a receiver measures them. Here, we consider a three-party prepare-transform-measure scenario in which the simplest QRAC is implemented twice in sequence based on the same physical system. We derive optimal trade-off relations between the two QRACs. We apply our results to construct semi-device independent self-tests of quantum instruments, i.e. measurement channels with both a classical and quantum output. Finally, we show how sequential QRACs enable inference of upper and lower bounds on the sharpness parameter of a quantum instrument

A Simplified Approach to Recovery Conditions for Low Rank Matrices

Recovering sparse vectors and low-rank matrices from noisy linear measurements has been the focus of much recent research. Various reconstruction algorithms have been studied, including $\ell_1$ and nuclear norm minimization as well as $\ell_p$ minimization with $p<1$. These algorithms are known to succeed if certain conditions on the measurement map are satisfied. Proofs of robust recovery for matrices have so far been much more involved than in the vector case. In this paper, we show how several robust classes of recovery conditions can be extended from vectors to matrices in a simple and transparent way, leading to the best known restricted isometry and nullspace conditions for matrix recovery. Our results rely on the ability to "vectorize" matrices through the use of a key singular value inequality.Comment: 6 pages, This is a modified version of a paper submitted to ISIT 2011; Proc. Intl. Symp. Info. Theory (ISIT), Aug 201

A Training Assistant Tool for the Automated Visual Inspection System

This thesis considers the problem of assisting a human user setting up an automated Visual Inspection (VI) system. The VI system uses a stationary camera on an automobile assembly line to inspect cars as they pass by. The inspection process is intended to identify when parts have been missed or incorrect parts have been assembled. The result is reported to a human working on the assembly line who then can take corrective actions. As originally developed, the system requires a setup phase in which the human user places the camera and records a video of at least 30 minutes length to use for training the system. Training includes specifying regions of cars passing by that are to be inspected. After deployment of a number of systems, it was learned that users could benefit from being provided guidance in best practices to delineate training data. It was also learned that users could benefit from simple visual feedback to ascertain whether or not an inspection problem was suitable for a VI system or if the problem was too challenging. This thesis describes a few methods and a new software tool intended to address this need

IMMUNOBIOLOGICALS

AbstractImmunobiologicals are the biologically active agents with immunological actions that are useful for the management  of immunologically mediated diseases of infectious or non-infectious origin.Keywords :Immunobiologicals,Epitope,Interferon,Monoclonal antibodies

Flooding attacks to internet threat monitors (ITM): Modeling and counter measures using botnet and honeypots

The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms. To block the monitoring system in the internet the attackers are targeted the ITM system. In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic. We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM. Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots

Polymer/metal adhesion in hybrid cardiovascular stent

Angioplasty over the years has proven to be an excellent substitute for open heart surgery where an artery/vien, blocked by atherosclerosis, is expanded using a stent. Metallic and coated metallic stents have been used for angioplasty. Metal stents might induce blood clotting, release cytotoxic heavy metal ions which are potential inducers of allergies, clotting, immune reactions and hyperproliferation of smooth muscle cells and also lead to protein absorption which activates clotting factors. Biodegradable polymers have also been tried as stent materials, but the loss of radial strength over time is a big problem associated with them. The use of a hybrid stent, consisting of biodegradable polymer and biocompatible stainless steel, is proposed. The use of such a system would require excellent adhesion between the stent metal and the biodegradable polymer. This study presents the electrochemically induced micromechanical interlocking to enhance adhesion between 304 stainless steel and high density polyethylene. High density polyethylene was used instead of biodegradable polymer for initial investigation. Electrochemical etching on the stainless steel wire was accomplished by immersing a stainless steel wire in a sodium carbonate electrolyte while applying a known voltage through the wire. The electrochemical etching of the stainless steel wire resulted in pitting under suitable conditions. The etching time, voltage and electrolyte concentration were varied to achieve different pit sizes and pit distributions on the stainless steel wire. An image analysis was conducted using an image analysis software to find the exact pit size and pit distribution on the stainless steel wire from electrochemical etching. A statistical model based on design of engineering experiments was derived. Etched and the unetched wires were molded with high density polyethylene and a mechanical test was conducted to measure the force required to pull the wire out of the polymer and verified using calculations based on the pit size and pit distribution of the pits on the surface of the wire. Electrochemical etching produced burr free surface features. It was observed that the pH level in the electrolyte contributes to the pit size and pit distribution. The results of the statistical model were consistent with the experimental values and it was possible to optimize the electrochemical etching parameters for maximum pit size and pit distribution. It was also observed that while voltage and etching time contribute to pit size and pit distribution, the electrolyte concentration does not have significant effect on the pit size and pit distribution. The calculated pull out force and measured values were off by 22.7%. The lower value of calculated force could result from neglecting some of the smaller pits while performing the image analysis. The average adhesive strength of the etched samples was 276% higher than that of the unetched samples

Evaluating Content-centric vs User-centric Ad Affect Recognition

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation (ICMI) 201

Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201