Test-bed of a real time detection system for L/H and H/L transitions implemented with the ITMS platform

A basic requirement of the data acquisition systems used in long pulse fusion experiments is to detect events of interest in the acquired signals in real time. Developing such applications is usually a complex task, so it is necessary to develop a set of hardware and software tools that simplify their implementation. An example of these tools is the Intelligent Test and Measurement System (ITMS), which offers distributed data acquisition, distribution and real time processing capabilities with advanced, but easy to use, software tools that simplify application development and system setup. This paper presents the application of the ITMS platform to solve the problem of detecting L/H and H/L transitions in real time based on the use of efficient pattern recognition algorithms

Infrared (8-12 um) Dome Materials: Current Status

The 8-12 um range of infrared radiation being very significant for various electrooptic applications, various materials present themselves as candidates for use as dome (window) materialsin this range. This paper discusses various thermal, mechanical and optical properties of thesematerials. Further, trends in the development of these materials are also presented

Natural Entropy Production in an Inflationary Model for a Polarized Vacuum

Though entropy production is forbidden in standard FRW Cosmology, Berman and Som presented a simple inflationary model where entropy production by bulk viscosity, during standard inflation without ad hoc pressure terms can be accommodated with Robertson-Walker's metric, so the requirement that the early Universe be anisotropic is not essential in order to have entropy growth during inflationary phase, as we show. Entropy also grows due to shear viscosity, for the anisotropic case. The intrinsically inflationary metric that we propose can be thought of as defining a polarized vacuum, and leads directly to the desired effects without the need of introducing extra pressure terms.Comment: 7 pages including front one. Accepted to publication, Astrophysics and Space Science, subjected to a minor correction, already submitte

Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a single or multiple classes of data from an ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for the single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, doesn't put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. We will make the source code publicly available

TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Synthetic tabular data generation becomes crucial when real data is limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, and generative adversarial networks (GANs) based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this paper we propose a new universal metric, TabSynDex, for robust evaluation of synthetic data. TabSynDex assesses the similarity of synthetic data with real data through different component scores which evaluate the characteristics that are desirable for "high quality" synthetic data. Being a single score metric, TabSynDex can also be used to observe and evaluate the training of neural network based approaches. This would help in obtaining insights that was not possible earlier. Further, we present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models

Compact x-ray source based on burst-mode inverse Compton scattering at 100 kHz

A design for a compact x-ray light source (CXLS) with flux and brilliance orders of magnitude beyond existing laboratory scale sources is presented. The source is based on inverse Compton scattering of a high brightness electron bunch on a picosecond laser pulse. The accelerator is a novel high-efficiency standing-wave linac and RF photoinjector powered by a single ultrastable RF transmitter at x-band RF frequency. The high efficiency permits operation at repetition rates up to 1 kHz, which is further boosted to 100 kHz by operating with trains of 100 bunches of 100 pC charge, each separated by 5 ns. The entire accelerator is approximately 1 meter long and produces hard x-rays tunable over a wide range of photon energies. The colliding laser is a Yb:YAG solid-state amplifier producing 1030 nm, 100 mJ pulses at the same 1 kHz repetition rate as the accelerator. The laser pulse is frequency-doubled and stored for many passes in a ringdown cavity to match the linac pulse structure. At a photon energy of 12.4 keV, the predicted x-ray flux is $5 \times 10^{11}$ photons/second in a 5% bandwidth and the brilliance is $2 \times 10^{12}\mathrm{photons/(sec\ mm^2\ mrad^2\ 0.1\%)}$ in pulses with RMS pulse length of 490 fs. The nominal electron beam parameters are 18 MeV kinetic energy, 10 microamp average current, 0.5 microsecond macropulse length, resulting in average electron beam power of 180 W. Optimization of the x-ray output is presented along with design of the accelerator, laser, and x-ray optic components that are specific to the particular characteristics of the Compton scattered x-ray pulses.Comment: 25 pages, 24 figures, 54 reference