Violation of Bohigas-Giannoni-Schmit conjecture using an integrable many-body Floquet system

Earlier studies have given enough evidence in support of the BGS conjecture, with few exceptions violating it. Here, we provide one more counterexample using a many-body system popularly known as the model of quantum kicked top consisting of $N$ qubits with all-to-all interaction and kicking strength $k=N\pi/2$. We show that it is quantum integrable even though the corresponding semiclassical phase-space is chaotic, thus violating the BGS conjecture. We solve the cases of $N=5$ to $11$ qubits analytically, finding its eigensystem, the dynamics of the entanglement, and the unitary evolution operator. For the general case of $N>11$ qubits, we provide numerical evidence of integrability using degenerate spectrum, and the exact periodic nature of the time-evolved unitary evolution operator and the entanglement dynamics.Comment: 4.5 pages (two-column) + 25 pages (one-column) + 3 figures; Comments are welcom

Are we being trained to discriminate? Need to sensitize doctors in India on issues of gender and sexuality

Even though the struggle for LGBTQIA+ (Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, Asexual; with the + indicating myriad others) rights is an ongoing one, we have come a long way in terms of acceptance and inclusion. In spite of the progress, the LGBTQIA+ community in India still faces rampant bias in society as well as in healthcare. This is fueled by misinformation, which leads to prejudice and violence against these individuals. This paper discusses this struggle, touching upon the legal and social aspects. The focus is on the detrimental effects of stigma on health outcomes and health disparities for LGBTQIA+ individuals. The outlook of some in the medical fraternity and the deficiencies in medical training, including redundant and outdated curriculum/ textbooks, are discussed. It is implied that these factors result in biased and ill-informed doctors who are poorly equipped to meet the health needs of the LGBTQIA+ population. Correcting the deficiencies is a priority in the face of the recent ruling by the Honorable Supreme Court of India striking down Section 377 of the Indian Penal Code that previously criminalized consensual carnal intercourse among consenting adults of this community of people

Implementing Cepstral Filtering Technique using Gabor Filters

Cepstral filtering technique is applied on an interlaced image, the pattern similar to that which is found in layer IV of Primate Visual Cortex. Unless the signals from left and right eyes are placed simultaneously, the disparity cannot be detected. Therefore, it has a great significance in the sphere of stereo vision. It involves Power spectrum in computation, which is square of absolute of Fast Fourier Transform (FFT), is a complicated and hardware unfriendly. This paper shows the estimation of the Cepstral technique using a set of Gabor filters. The Ocular Dominance Column pattern analysis by the Gabor function is comparable to the perception in the human visual and makes the algorithm closer to biology. We propose an algorithm in which Gabor filters, instead of Power Spectrum, are applied to an interlaced image in the Cepstral algorithm. This scheme makes it hardware friendly as it gives the flexibility of working with modules which can be imitated in hardware. Building a FFT module is a tough task in analog circuit but determining Gabor Energy, an alternative to it, can be achieved by elementary circuits. The Phase, Energy Models and other methods use multi-lambda Gabor filters to compute disparity. The proposed method uses sum of absolute difference to choose a single Gabor filter of appropriate lambda that fits to find the disparity. The algorithm inherits the quality of both Gabor filter and Ocular Dominance Pattern and hence a biologically inspired and suitable for hardware realization. The proposed algorithm has been implemented on the test data image. A hardware scheme has also been proposed that can be used to estimate disparity and the idea can be extended in building complex modules that can perform real time - real image operations with a handful of resources as compared to employing complex digital FPGAs and CPLDs

MACHINE LEARNING ASSISTED OPTIMIZATION AND ITS APPLICATION TO HYBRID DIELECTRIC RESONATOR ANTENNA DESIGN

Machine learning assisted optimization (MLAO) has become very important for improving the antenna design process because it consumes much less time than the traditional methods. These models' accountability can be checked by the accuracy metrics, which tell about the correctness of the predicted result. Machine learning (ML) methods, such as Gaussian Process Regression, Artificial Neural Networks (ANNs), and Support Vector Machine (SVM), are used to simulate the antenna model to predict the reflection coefficient faster. This paper presents the optimization of Hybrid Dielectric Resonator Antenna (DRA) using machine learning models. Several regression models are applied to the dataset for optimization, and the best results are obtained using a random forest regression model with the accuracy of 97%. Additionally, the effectiveness of machine learning based antenna design is demonstrated through comparison with conventional design methods

XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection

Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, therefore, do not exploit the full potential of the hardware telemetry. In this paper, we propose XMD, an HMD that uses an expansive set of telemetry channels extracted from the different subsystems of SoC. XMD exploits the thread-level profiling power of the CPU-core telemetry, and the global profiling power of non-core telemetry channels, to achieve significantly better detection performance than currently used Hardware Performance Counter (HPC) based detectors. We leverage the concept of manifold hypothesis to analytically prove that adding non-core telemetry channels improves the separability of the benign and malware classes, resulting in performance gains. We train and evaluate XMD using hardware telemetries collected from 723 benign applications and 1033 malware samples on a commodity Android Operating System (OS)-based mobile device. XMD improves over currently used HPC-based detectors by 32.91% for the in-distribution test data. XMD achieves the best detection performance of 86.54% with a false positive rate of 2.9%, compared to the detection rate of 80%, offered by the best performing signature-based Anti-Virus(AV) on VirusTotal, on the same set of malware samples.Comment: Revised version based on peer review feedback. Manuscript to appear in IEEE Transactions on Information Forensics and Securit