Importance of Tests for the Complete Lorentz Structure of the t --> W+ b vertex at Hadron Colliders

The most general Lorentz-invariant decay-density-matrix for $t\to W^{+}b\to (l^{+}\nu)b$, or for $t\to W^{+}b\to (j_{\bar d}j_u)b$, is expressed in terms of eight helicity parameters. The parameters are physically defined in terms of partial-width-intensities for polarized-final-states in $t\to W^{+}b$ decay. The parameters are the partial width, the $b$ quark's chirality parameter $\xi$, the $W^+$ polarimetry parameter $\sigma$, a "pre-SSB" test parameter $\zeta$, and four $W_{L}$-$W_{T}$ interference parameters $\eta$, $\eta^{'}$, $\omega$, $\omega^{'}$ which test for $\tilde T_{FS}$ violation. They can be used to test for non-CKM-type CP violation, anomalous $\Gamma_{L,T}$'s, top weak magnetism, weak electricity, and second-class currents. By stage-two spin-correlation techniques, percent level statistical uncertainites are typical for measurements at the Tevatron, and several mill level uncertainites are typical at the LHC.Comment: Minor clarifications. Expression for r_{+-} corrected. 19 pages LaTex + Tables + 1 Figur

Performance analysis for a class of robust adaptive beamformers

Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness. Recently, worst-case approaches which consist of protecting the array's response in an ellipsoid centered around the nominal steering vector have been proposed. They amount to generalized (i.e. non necessarily diagonal) loading of the covariance matrix. In this paper, we present a theoretical analysis of the signal to interference plus noise ratio (SINR) for this class of robust beamformers, in the presence of random steering vector errors. A closed-form expression for the SINR is derived which is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any loading matrix. It provides insights into the influence of the loading matrix and can serve as a helpful guide to select it. Finally, the analysis enables us to predict the level of uncertainties up to which robust beamformers are effective and then depart from the optimal SINR

Wavelet Domain Communication System (WDCS): Packet-Based Wavelet Spectral Estimation and M-ARY Signaling

A recently proposed Wavelet Domain Communication System (WDCS) using transform domain processing demonstrated excellent interference avoidance capability under adverse environmental conditions. This work extends previous results by: 1) incorporating a wavelet packet decomposition technique, 2) demonstrating M-Ary signaling capability, and 3) providing increased adaptivity over a larger class of interference signals. The newly proposed packet-based WDCS is modeled and its performance characterized using MATLAB®. In addition, the WDCS response to two scenarios simulating Doppler effects and physical separation of transceivers are obtained. The fundamental metric for analysis and performance evaluation is bit error rate (Pb). Relative to the previous non-packet WDCS, the proposed packet-based WDCS provides improved/comparable bit error performance in several interference scenarios single-tone, multiple-tone, swept-tone, and partial band interference is considered. Interference avoidance capability was characterized for a bit energy-to-noise power level (Eb/N0) of 4.0 dB and interference energy-to-signal energy (I/E) ratios ranging from 0.0 dB to 16.0 dB. For binary, 4-Ary, and 8-Ary CSK data modulations, the packet-based WDCS exhibited average Pb improvements of 6.7, 9.2, and 12.0 dB, respectively, for partial band and swept-tone interference. For single and multiple-tone interference, improvements of 8.0, 12.4, and 15.7 dB were realized. Furthermore, bit error sensitivity analyses indicate the WDCS communicates effectively under non-ideal real-world conditions (transceivers located in dissimilar environments) while exhibiting average Pb improvements of 5.4, 5.1, and 5.8 dB, relative to systems having no interference suppression

CG-fusion CAM: Online segmentation of laser-induced damage on large-aperture optics

Online segmentation of laser-induced damage on large-aperture optics in high-power laser facilities is challenged by complicated damage morphology, uneven illumination and stray light interference. Fully supervised semantic segmentation algorithms have achieved state-of-the-art performance, but rely on plenty of pixel-level labels, which are time-consuming and labor-consuming to produce. LayerCAM, an advanced weakly supervised semantic segmentation algorithm, can generate pixel-accurate results using only image-level labels, but its scattered and partially under-activated class activation regions degrade segmentation performance. In this paper, we propose a weakly supervised semantic segmentation method with Continuous Gradient CAM and its nonlinear multi-scale fusion (CG-fusion CAM). The method redesigns the way of back-propagating gradients and non-linearly activates the multi-scale fused heatmaps to generate more fine-grained class activation maps with appropriate activation degree for different sizes of damage sites. Experiments on our dataset show that the proposed method can achieve segmentation performance comparable to that of fully supervised algorithms

An unsupervised acoustic fall detection system using source separation for sound interference suppression

We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person’s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods

An unsupervised acoustic fall detection system using source separation for sound interference suppression

We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods

Observation of Effective Pseudospin Scattering in ZrSiS

3D Dirac semimetals are an emerging class of materials that possess topological electronic states with a Dirac dispersion in their bulk. In nodal-line Dirac semimetals, the conductance and valence bands connect along a closed path in momentum space, leading to the prediction of pseudospin vortex rings and pseudospin skyrmions. Here, we use Fourier transform scanning tunneling spectroscopy (FT-STS) at 4.5 K to resolve quasiparticle interference (QPI) patterns at single defect centers on the surface of the line nodal semimetal zirconium silicon sulfide (ZrSiS). Our QPI measurements show pseudospin conservation at energies close to the line node. In addition, we determine the Fermi velocity to be $\hbar v_F = 2.65 \pm 0.10$ eV {\AA} in the {\Gamma}-M direction ~300 meV above the Fermi energy $E_F$, and the line node to be ~140 meV above $E_F$. More importantly, we find that certain scatterers can introduce energy-dependent non-preservation of pseudospins, giving rise to effective scattering between states with opposite valley pseudospin deep inside valence and conduction bands. Further investigations of quasiparticle interference at the atomic level will aid defect engineering at the synthesis level, needed for the development of lower-power electronics via dissipationless electronic transport in the future

Declassification of Faceted Values in JavaScript

This research addresses the issues with protecting sensitive information at the language level using information flow control mechanisms (IFC). Most of the IFC mechanisms face the challenge of releasing sensitive information in a restricted or limited manner. This research uses faceted values, an IFC mechanism that has shown promising flexibility for downgrading the confidential information in a secure manner, also called declassification. In this project, we introduce the concept of first-class labels to simplify the declassification of faceted values. To validate the utility of our approach we show how the combination of faceted values and first-class labels can build various declassification mechanisms