Themelio: a new blockchain paradigm

Public blockchains hold great promise in building protocols that uphold security properties like transparency and consistency based on internal, incentivized cryptoeconomic mechanisms rather than preexisting trust in participants. Yet user-facing blockchain applications beyond "internal" immediate derivatives of blockchain incentive models, like cryptocurrency and decentralized finance, have not achieved widespread development or adoption. We propose that this is not primarily due to "engineering" problems in aspects such as scaling, but due to an overall lack of transferable endogenous trust—the twofold ability to uphold strong, internally-generated security guarantees and to translate them into application-level security. Yet we argue that blockchains, due to their foundation on game-theoretic incentive models rather than trusted authorities, are uniquely suited for building transferable endogenous trust, despite their current deficiencies. We then engage in a survey of existing public blockchains and the difficulties and crises that they have faced, noting that in almost every case, problems such as governance disputes and ecosystem inflexibility stem from a lack of transferable endogenous trust. Next, we introduce Themelio, a decentralized, public blockchain designed to support a new blockchain paradigm focused on transferable endogenous trust. Here, the blockchain is used as a low-level, stable, and simple root of trust, capable of sharing this trust with applications through scalable light clients. This contrasts with current blockchains, which are either applications or application execution platforms. We present evidence that this new paradigm is crucial to achieving flexible deployment of blockchain-based trust. We then describe the Themelio blockchain in detail, focusing on three areas key to its overall theme of transferable, strong endogenous trust: a traditional yet enhanced UTXO model with features that allow powerful programmability and light-client composability, a novel proof-of-stake system with unique cryptoeconomic guarantees against collusion, and Themelio's unique cryptocurrency "mel", which achieves stablecoin-like low volatility without sacrificing decentralization and security. Finally, we explore the wide variety of novel, partly off-chain applications enabled by Themelio's decoupled blockchain paradigm. This includes Astrape, a privacy-protecting off-chain micropayment network, Bitforest, a blockchain-based PKI that combines blockchain-backed security guarantees with the performance and administration benefits of traditional systems, as well as sketches of further applications

Uncertainty Quantification of Geo-Magnetically Induced Currents in UHV Power Grid

Geo-magnetically induced currents (GICs) have attracted more attention since many Ultra-High Voltage (UHV) transmission lines have been built, or are going to be built in the world. However, when calculating GICs based on the classical model, some input parameters, such as the earth conductivity and dc resistances of the grid, are uncertain or very hard to be determined in advance. Taking this into account, the uncertainty quantification (UQ) model of the geo-electric fields and GICs is proposed in this paper. The UQ of the maximums of the geo-electric fields and GICs during storms is carried out based on the polynomial chaos (PC) method. The results of the UHV grid, 1000 kV Sanhua Grid, were presented and compared to the Monte Carlo method. The total Sobol indices are calculated by using the PC expansion coefficients. The sensitivities of geo-electric fields and GICs to the input variables are analyzed based on the total Sobol indices. Results show that the GICs and geo-electric fields can be effectively simulated by the proposed model, which may offer a better understanding of the sensitivities to input uncertain variables and further give a reasonable evaluation of the geomagnetic threat to the grid

Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing

Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal the incorrect behaviors in DNN and repair them, DNN developers often collect rich unlabeled datasets from the natural world and label them to test the DNN models. However, properly labeling a large number of unlabeled datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets. NSS leverages the internal neuron's information induced by test cases to select valuable test cases, which have high confidence in causing the model to behave incorrectly. We evaluate NSS with four widely used datasets and four well-designed DNN models compared to SOTA baseline methods. The results show that NSS performs well in assessing the test cases' probability of fault triggering and model improvement capabilities. Specifically, compared with baseline approaches, NSS obtains a higher fault detection rate~(e.g., when selecting 5\% test case from the unlabeled dataset in MNIST \& LeNet1 experiment, NSS can obtain 81.8\% fault detection rate, 20\% higher than baselines)

Chaotic Phase-Coded Waveforms with Space-Time Complementary Coding for MIMO Radar Applications

A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding (STCC) is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. Firstly, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Secondly, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method

Chaotic Phase-Coded Waveforms with Space-Time Complementary Coding for MIMO Radar Applications

A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding (STCC) is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. Firstly, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Secondly, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method