arXiv research

New coin sampling method for Bayesian inference without learning rates.

Given a mixture between two populations of coins, "positive" coins that each have -- unknown and potentially different -- bias $\geq\frac{1}{2}+Δ$ and "negative" coins with bias $\leq\frac{1}{2}-Δ$, we consider the task of estimating the fraction $ρ$ of positive coins to within additive error $ε$. We achieve an upper a…

New algorithms for sampling in constrained domains without learning rates.

Bitcoin draws the highest degree of attention among cryptocurrencies, while coin mining is one of the most important fashion of profiting in the Bitcoin ecosystem. This paper constructs fresh coin circulation networks by tracking the fresh coin transfer routes with transaction referencing in Bitcoin blockchain. This pa…

We present a new Bitcoin coin selection algorithm, "coin selection with leverage", which aims to improve upon cost savings than that of standard knapsack like approaches. Parameters to the new algorithm are available to be tuned at the users discretion to address other goals of coin selection. Our approach naturally fi…

Study reveals strong price correlations between major and alt-coins.

Paper tightens PAC-Bayes bounds using coin-betting for better estimates.

Study sets a nontrivial upper limit on return forecasting accuracy.

Study replicability in high-dimensional statistics, resolving open problems.

We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simpl…

New system resists meme coin copy trading bots.

Study reveals widespread manipulation of meme coins, leading to significant economic losses.

Automated market-making for CBDCs and stable coins on blockchain.

A decentralized online quantum cash system, called qBitcoin, is given. We design the system which has great benefits of quantization in the following sense. Firstly, quantum teleportation technology is used for coin transaction, which prevents from the owner of the coin keeping the original coin data even after sending…

Stablecoins are unstable, but some are more stable than others.

Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.

Paper explores robustness of CCS model for matrix completion.

Quantum walks blend patterns into splines when averaged.

New algorithm closes empirical gap in PFSGD performance.

Examines various types of cryptocurrencies and their economic properties.

We study capital process behavior in the fair-coin game and biased-coin games in the framework of the game-theoretic probability of Shafer and Vovk (2001). We show that if Skeptic uses a Bayesian strategy with a beta prior, the capital process is lucidly expressed in terms of the past average of Reality's moves. From t…

Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks. We introduce a simple yet effective approach to improving cGANs by measuring the discrepancy between the data distribution and…

We discuss several uses of blockchain (and, more generally, distributed ledger) technologies outside of cryptocurrencies with a pragmatic view. We mostly focus on three areas: the role of coin economies for what we refer to as data malls (specialized data marketplaces); data provenance (a historical record of data and …

We propose factor models for the cross-section of daily cryptoasset returns and provide source code for data downloads, computing risk factors and backtesting them out-of-sample. In "cryptoassets" we include all cryptocurrencies and a host of various other digital assets (coins and tokens) for which exchange market dat…

COIN++ compresses multiple data types efficiently.

Predicts cryptocurrency pump probability using sequence-based neural networks.

SFC aims to protect the Amazon with a digital currency and smart contracts.

We study multistep Bayesian betting strategies in coin-tossing games in the framework of game-theoretic probability of Shafer and Vovk (2001). We show that by a countable mixture of these strategies, a gambler or an investor can exploit arbitrary patterns of deviations of nature's moves from independent Bernoulli trial…

Repelling random walks improve graph-based sampling efficiency.

We study a coin-tossing model used by a ratings agency to justify the sale of constant proportion debt obligations (CPDOs), and prove that it was impossible for CPDOs to achieve in a finite lifetime the Cash-In event of doubling its capital. In the best-case scenario of a two-headed coin, we show that the goal of attai…

Study designs for estimating treatment effects in adaptive experiments.

This paper discusses the potential impacts of the so-called `initial coin offerings', and of several developments based on distributed ledger technology (`DLT'), on corporate governance. While many academic papers focus mainly on the legal qualification of DLT and crypto-assets, and most notably in relation to the pote…

EagleEye detects localized density anomalies in multivariate data.

We derive some results on contrarian and one-sided strategies by Skeptic for the fair-coin game in the framework of the game-theoretic probability of Shafer and Vovk \cite{sv}. In particular, concerning the rate of convergence of the strong law of large numbers (SLLN), we prove that Skeptic can force that the convergen…

Optimizes cryptocurrency trading pairs for efficiency and decentralization.

It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice. This is primarily due to the cognitive loads required for human agents t…

Maker Protocol manages Dai stable coin on Ethereum blockchain.

New algorithms learn latent variable models without tuning, outperforming existing methods.

We consider worker skill estimation for the single-coin Dawid-Skene crowdsourcing model. In practice, skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary and uncontrolled availability of workers. We formulate skill estimation as a rank-one correlation-matrix completi…

This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.

Mix2FLD improves FL accuracy with FD, reducing convergence time.

A crypto coin designed to provide a stabilization instrument backed up by minded like financial investments instruments to maintain the purchase value of savings across time, in order to construct new tools for unstable economies.

This paper explores using nonlinear control for robust logarithmic growth in coin flipping games.

Deep learning methods achieve state-of-the-art performance in many application scenarios. Yet, these methods require a significant amount of hyperparameters tuning in order to achieve the best results. In particular, tuning the learning rates in the stochastic optimization process is still one of the main bottlenecks. …

New digital currency aims for equal wealth distribution.

Study reveals RL game's embedding space is stratified, not a manifold.

In this article, I will present a paradox whose purpose is to draw your attention to an important topic in finance, concerning the non-independence of the financial returns (non-ergodic hypothesis). In this paradox, we have two people sitting at a table separated by a black sheet so that they cannot see each other and …

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step requ…