Lifeboat Foundation

The US National Institute of Standards and Technology has released Federal Information Processing Standards (FIPS) publications for three quantum-resistant cryptographic algorithms.

In a landmark announcement, the National Institute of Standards and Technology (NIST) has published its first set of post-quantum cryptography (PQC) standards. This announcement serves as an inflection point in modern cybersecurity: as the global benchmark for cryptography, the NIST standards signal to enterprises, government agencies, and supply chain vendors that the time has come to make the world’s information security systems resistant to future cryptographically relevant quantum computers.

Continue reading “NIST’s post-quantum cryptography standards are here” »

Abstract: In ecological systems, be it a garden or a galaxy, populations evolve from some initial value (say zero) up to a steady state equilibrium, when the mean number of births and deaths per unit time are equal. This equilibrium point is a function of the birth and death rates, as well as the carrying capacity of the ecological system itself. The growth curve is S-shaped, saturating at the carrying capacity for large birth-to-death rate ratios and tending to zero at the other end. We argue that our astronomical observations appear inconsistent with a cosmos saturated with ETIs, and thus SETI optimists are left presuming that the true population is somewhere along the transitional part of this S-curve. Since the birth and death rates are a-priori unbounded, we argue that this presents a fine-tuning problem. Further, we show that if the birth-to-death rate ratio is assumed to have a log-uniform prior distribution, then the probability distribution of the ecological filling fraction is bi-modal — peaking at zero and unity. Indeed, the resulting distribution is formally the classic Haldane prior, conceived to describe the prior expectation of a Bernoulli experiment, such as a technological intelligence developing (or not) on a given world. Our results formally connect the Drake Equation to the birth-death formalism, the treatment of ecological carrying capacity and their connection to the Haldane perspective.

From: David Kipping [view email].

“These results confirm that computerized tongue analysis is a secure, efficient, user-friendly and affordable method for disease screening that backs up modern methods with a centuries-old practice,”

This technology could be aah-mazing!

Researchers in Iraq and Australia say they have developed a computer algorithm that can analyze the color of a person’s tongue to detect their medical condition in real time — with 98% accuracy.

Continue reading “AI model 98% accurate in detecting diseases — just by looking at your tongue” »

Three new encryption algorithms to bolster global cybersecurity efforts against future attacks using quantum technologies were published today by the National Institute of Standards and Technology (NIST), a division of the U.S. Department of Commerce. The new standards are designed for two tasks: general encryption and digital signatures.

These new standards are the culmination of an eight-year effort from the agency to tap the best minds in cybersecurity to devise the next generation of cryptography strong enough to withstand quantum computers. Experts expect quantum computers capable of breaking current current cryptographic algorithms within a decade. The new standards, the first released by NIST’s post-quantum cryptography (PQC) standardization project, are published on the department’s website. The documents contain the algorithms’ computer code, instructions for how to implement them in products and in encryption systems, and use cases for each.

Quantum physicists have developed an algorithm that uses a single qubit to solve a problem that had previously needed thousands of them.

The Driving Training Based Optimization (DTBO) algorithm, proposed by Mohammad Dehghani, is one of the novel metaheuristic algorithms which appeared in 2022⁸⁰. This algorithm is founded on the principle of learning to drive, which unfolds in three phases: selecting an instructor from the learners, receiving instructions from the instructor on driving techniques, and practicing newly learned techniques from the learner to enhance one’s driving abilities^81,82. In this work, DTBO algorithm is used, due to its effectiveness, which was confirmed by a comparative study⁸³ with other algorithms, including particle swarm optimization⁸⁴, Gravitational Search Algorithm (GSA)⁸⁵, teaching learning-based optimization, Gray Wolf Optimization (GWO)⁸⁶, Whale Optimization Algorithm (WOA)⁸⁷, and Reptile Search Algorithm (RSA)⁸⁸. The comparative study has been done using various kinds of benchmark functions, such as constrained, nonlinear and non-convex functions.

Lyapunov-based Model Predictive Control (LMPC) is a control approach integrating Lyapunov function as constraint in the optimization problem of MPC^89,90. This technique characterizes the region of the closed-loop stability, which makes it possible to define the operating conditions that maintain the system stability^91,92. Since its appearance, the LMPC method has been utilized extensively for controlling a various nonlinear systems, such as robotic systems⁹³, electrical systems⁹⁴, chemical processes⁹⁵, and wind power generation systems⁹⁰. In contrast to the LMPC, both the regular MPC and the NMPC lack explicit stability restrictions and can’t combine stability guarantees with interpretability, even with their increased flexibility.

The proposed method, named Lyapunov-based neural network model predictive control using metaheuristic optimization approach (LNNMPC-MOA), includes Lyapunov-based constraint in the optimization problem of the neural network model predictive control (NNMPC), which is solved by the DTBO algorithm. The suggested controller consists of two parts: the first is responsible for calculating predictions using a neural network model of the feedforward type, and the second is responsible to resolve the constrained nonlinear optimization problem using the DTBO algorithm. This technique is suggested to solve the nonlinear and non-convex optimization problem of the conventional NMPC, ensure on-line optimization in reasonable time thanks to their easy implementation and guaranty the stability using the Lyapunov function-based constraint. The efficiency of the proposed controller regarding to the accuracy, quickness and robustness is assessed by taking into account the speed control of a three-phase induction motor, and its stability is mathematically ensured using the Lyapunov function-based constraint. The acquired results are compared to those of NNMPC based on DTBO algorithm (NNMPC-DTBO), NNMPC using PSO algorithm (NNMPC-PSO), Fuzzy Logic controller optimized by TLBO (FLC-TLBO) and optimized PID controller using PSO algorithm (PID-PSO)⁹⁵.

A newly published study by Sheba Medical Center, Israel’s largest and internationally ranked hospital, shows that AI analysis of medical records as patients are admitted to the ER can accurately identify those at high risk of pulmonary embolism (PE).

A pulmonary embolism is a sudden blockage in an artery in the lung caused by a blood clot, most commonly due to a dislodged clot in the leg. They are normally diagnosed during a CT scan.

Using machine learning, the researchers trained an algorithm to detect a pulmonary embolism before a patient was hospitalized, based on existing medical records.

Unlike classical encryption, which relies on mathematical algorithms, quantum encryption assures security based on physical principles. Detection of espionage or interference is guaranteed by unavoidable alteration of the quantum states involved.

New approach creates skilled robots:

MIT researchers have developed a groundbreaking algorithm that enables robots to rapidly learn and master complex tasks.