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  1. Free, publicly-accessible full text available April 1, 2025
  2. Free, publicly-accessible full text available October 7, 2024
  3. On-chip assets, such as cryptographic keys, intermediate cipher computations, obfuscation keys, and hardware security primitive outputs, are usually stored in volatile memories, e.g., registers and SRAMs. Such volatile memories could be read out using active physical attacks, such laser-assisted side-channels. One way to protect assets stored in volatile memories can be the employment of sensors that detect active physical attacks and trigger complete zeroization of sensitive data. However, hundreds or thousands of clock cycles are often needed to accomplish this. Further, the sensing and self-destruction mechanisms are decoupled from the sensitive circuitry and can be disabled separately by an adversary. Moreover, defensive actions (e.g., zeroization) may be disabled by bringing the CPU/SoC into an inoperable condition, while registers may still hold their data, making them susceptible. This paper proposes a self-destructive latch to protect sensitive data from active side-channel attacks, which require supply voltage manipulations.Our proposed latch senses supply voltage interference required during such attacks, and reacts instantaneously by entering a forbidden data state, erasing its stored data. The design uses a NULL convention logic (NCL)- based polymorphic NOR/NAND gate, which changes its functionality with supply voltage. Our results show that the latch is stable across temperature and process variation reacting to attacks with 91% confidence. Even for the 9% where data is not destroyed, in 3.33% of cases data flips its state which makes reliable extraction difficult for an attacker. The polymorphic latch is straightforward to implement due to its NCL implementation and the voltage for the self-destructive behavior is easily altered by resizing only two transistors. Further, this self-destructive behavior extends to registers which are built out of latches. 
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    Free, publicly-accessible full text available November 1, 2024
  4. Soot or black carbons are combustion-generated carbonaceous nanoparticles formed during the incomplete combustion of hydrocarbon fuels. The complexity of hydrocarbon systems often makes it difficult to investigate the fundamentals of soot formation experimentally. To address this, this study uses reactive molecular dynamics simulations with reactive force field (ReaxFF) potentials. The current work focuses on the formation and evolution of soot during acetylene pyrolysis. The analysis provides insights into the physicochemical aspects of soot formation and the maturation of incipient soot particles. In this work, we focus on the evolution and interdependence of features such as the number of carbon atoms, number of aromatic rings, mass, C/H ratio, the radius of gyration, atomic fractal dimension, surface area, volume, and density. Based on the physicochemical features, two distinct classes of nascent soot can be observed. These are termed type-1 and type-2 particles. The type-1 particles show significant morphological evolution, while the type-2 particles show chemical restructuring without significantly changing the morphology. Qualitative correlations of various degrees are also observed between some of these morphological features. 
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  5. Deep learning (DL) models have demonstrated state-of-the-art performance in the classification of diagnostic imaging in oncology. However, DL models for medical images can be compromised by adversarial images, where pixel values of input images are manipulated to deceive the DL model. To address this limitation, our study investigates the detectability of adversarial images in oncology using multiple detection schemes. Experiments were conducted on thoracic computed tomography (CT) scans, mammography, and brain magnetic resonance imaging (MRI). For each dataset we trained a convolutional neural network to classify the presence or absence of malignancy. We trained five DL and machine learning (ML)-based detection models and tested their performance in detecting adversarial images. Adversarial images generated using projected gradient descent (PGD) with a perturbation size of 0.004 were detected by the ResNet detection model with an accuracy of 100% for CT, 100% for mammogram, and 90.0% for MRI. Overall, adversarial images were detected with high accuracy in settings where adversarial perturbation was above set thresholds. Adversarial detection should be considered alongside adversarial training as a defense technique to protect DL models for cancer imaging classification from the threat of adversarial images. 
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    Free, publicly-accessible full text available March 1, 2024

    In turbulence, non-linear terms drive energy transfer from large-scale eddies into small scales through the so-called energy cascade. Turbulence often relaxes toward states that minimize energy; typically these states are considered globally. However, turbulence can also relax toward local quasi-equilibrium states, creating patches or cells where the magnitude of non-linearity is reduced and the energy cascade is impaired. We show, using data from the Magnetospheric Multiscale (MMS) mission, and for the first time, compelling observational evidence that this ‘cellularization’ of turbulence can occur due to local relaxation in a strongly turbulent natural environment such as the Earth’s magnetosheath.

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  7. Outsourcing semiconductor device fabrication can result in malicious insertions and overbuilding of integrated circuits (ICs) by untrusted foundries without the IP owner’s knowledge. Active hardware metering methods attempt to combat IC piracy by requiring fabs to perform an activation protocol with the IP owner for each chip created. In this paper, we have taken a closer look at the IC metering through bus scrambling protocol mentioned in Maes et al., 2009 and we investigate alternatives which employ 1-out of 2 oblivious transfer (OT). Our focus is on Bellare Micali OT and Naor Pinkas OT, which, under certain assumptions, guarantee protection against malicious adversaries. Using OT as an alternative helps with the need to protect the integrity of the private input generated by the chip. Thus, the security of the protocol reduces to the Decisional Diffie Hellman sense. Finally, we discuss possible attacks and show how the proposed protocols could prevent them. 
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  8. Abstract

    Despite decades of study of high-temperature weakly collisional plasmas, a complete understanding of how energy is transferred between particles and fields in turbulent plasmas remains elusive. Two major questions in this regard are how fluid-scale energy transfer rates, associated with turbulence, connect with kinetic-scale dissipation, and what controls the fraction of dissipation on different charged species. Although the rate of cascade has long been recognized as a limiting factor in the heating rate at kinetic scales, there has not been direct evidence correlating the heating rate with MHD-scale cascade rates. Using kinetic simulations and in situ spacecraft data, we show that the fluid-scale energy flux indeed accounts for the total energy dissipated at kinetic scales. A phenomenology, based on disruption of proton gyromotion by fluctuating electric fields that are produced in turbulence at proton scales, argues that the proton versus electron heating is controlled by the ratio of the nonlinear timescale to the proton cyclotron time and by the plasma beta. The proposed scalings are supported by the simulations and observations.

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