How To Quit Planning Goals And Start Achieving Them
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How To Quit Planning Goals And Start Achieving Them
Over the past few years, I’ve made an algorithmic method of goal-making that helps me achieve anything. Here’s how it works.
Large language models use a surprisingly simple mechanism to retrieve some stored knowledge
Researchers demonstrate a technique that can be used to probe a model to see what it knows about new subjects.
Thread Hijacking: Phishes That Prey on Your Curiosity
Thread hijacking attacks. They happen when someone you know has their email account compromised, and you are suddenly dropped into an existing conversation between the sender and someone else. These missives draw on the recipient’s natural curiosity about being copied on a private discussion, which is modified to include a malicious link or attachment. Here’s the story of a thread hijacking attack in which a journalist was copied on a phishing email from the unwilling subject of a recent scoop.
Quanto Option Pricing on a Multivariate Levy Process Model with a Generative Artificial Intelligence
In this study, we discuss a machine learning technique to price exotic options with two underlying assets based on a non-Gaussian L´evy process model. We introduce a new multivariate L´evy process model named the generalized normal tempered stable (gNTS) process, which is defined by time-changed multivariate Brownian motion. Since the gNTS process does not provide a simple analytic formula for the probability density function (PDF), we use the conditional real-valued non-volume preserving (CRealNVP) model, which is a type of flow-based generative network. Then, we discuss the no-arbitrage pricing on the gNTS model for pricing the quanto option whose underlying assets consist of a foreign index and foreign exchange rate. We present the training of the CRealNVP model to learn the PDF of the gNTS process using a training set generated by Monte Carlo simulation. Next, we estimate the parameters of the gNTS model with the trained CRealNVP model using the empirical data observed in the market. Finally, we provide a method to find an equivalent martingale measure on the gNTS model and to price the quanto option using the CRealNVP model with the risk-neutral parameters of the gNTS model.
mTOR and neuroinflammation in epilepsy: implications for disease progression and treatment
Epilepsy remains a major health concern as anti-seizure medications frequently fail, and there is currently no treatment to stop or prevent epileptogenesis, the process underlying the onset and progression of epilepsy. The identification of the pathological processes underlying epileptogenesis is instrumental to the development of drugs that may prevent the generation of seizures or control pharmaco-resistant seizures, which affect about 30% of patients. mTOR signalling and neuroinflammation have been recognized as critical pathways that are activated in brain cells in epilepsy. They represent a potential node of biological convergence in structural epilepsies with either a genetic or an acquired aetiology. Interventional studies in animal models and clinical studies give strong support to the involvement of each pathway in epilepsy. In this Review, we focus on available knowledge about the pathophysiological features of mTOR signalling and the neuroinflammatory brain response, and their interactions, in epilepsy. We discuss mitigation strategies for each pathway that display therapeutic effects in experimental and clinical epilepsy. A deeper understanding of these interconnected molecular cascades could enhance our strategies for managing epilepsy. This could pave the way for new treatments to fill the gaps in the development of preventative or disease-modifying drugs, thus overcoming the limitations of current symptomatic medications.
Risk Budgeting Allocation for Dynamic Risk Measures
We define and develop an approach for risk budgeting allocation – a risk diversification portfolio strategy – where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalise the classical Euler contributions and which allow us to obtain dynamic risk contributions in a recursive manner. We prove that, for the class of coherent dynamic distortion risk measures, the risk allocation problem may be recast as a sequence of strictly convex optimisation problems. Moreover, we show that self-financing dynamic risk budgeting strategies with initial wealth of 1 are scaled versions of the solution of the sequence of convex optimisation problems. Furthermore, we develop an actor-critic approach, leveraging the elicitability of dynamic risk measures, to solve for risk budgeting strategies using deep learning.