Related papers: PNPCoin: Distributed Computing on Bitcoin infrastr…
Cryptocurrency mining processes always lead to a high energy consumption at considerably high production cost, which is nearly one-third of cryptocurrency (e.g. Bitcoin) price itself. As the core of mining process is based on SHA-256…
We argue that the current POW based consensus algorithm of the Bitcoin network suffers from a fundamental economic discrepancy between the real world transaction (txn) costs incurred by miners and the wealth that is being transacted. Put…
Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid…
Deep Neural Networks have now achieved state-of-the-art results in a wide range of tasks including image classification, object detection and so on. However, they are both computation consuming and memory intensive, making them difficult to…
Blockchain uses the idea of storing transaction data in the form of a distributed ledger wherein each node in the network stores a current copy of the sequence of transactions in the form of a hash chain. This requirement of storing the…
Bitcoin is the world's first decentralized digital currency. Its main technical innovation is the use of a blockchain and hash-based proof of work to synchronize transactions and prevent double-spending the currency. While the qualitative…
A hard-fork reconfiguration of the peer to peer Bitcoin network is described that substitutes tamper-evident logs and proof-of-stake consensus for proof-of-work consensus. The block creation rewards and transaction fees are reallocated to…
The mining of bitcoin is modeled using system dynamics, showing that the past evolution of the network hash rate can be explained to a large extent by an efficient market hypothesis applied to the mining of blocks. The possibility of a…
Bitcoin is a "crypto currency", a decentralized electronic payment scheme based on cryptography which has recently gained excessive popularity. Scientific research on bitcoin is less abundant. A paper at Financial Cryptography 2012…
Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously revealed phenomenon that the binary kernels in successful BNNs…
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. Neural architecture searches, hyperparameter sweeps, and rapid prototyping consume immense resources that can prevent…
This work proposes a dual-functional blockchain framework named BagChain for bagging-based decentralized learning. BagChain integrates blockchain with distributed machine learning by replacing the computationally costly hash operations in…
Blockchain is one of the most discussed and highly accepted technologies, primarily due to its application in almost every field where third parties are needed for trust. Blockchain technology relies on distributed consensus for trust,…
This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market…
Forks in the Bitcoin network result from the natural competition in the blockchain's Proof-of-Work consensus protocol. Their frequency is a critical indicator for the efficiency of a distributed ledger as they can contribute to resource…
Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature:…
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for…
Network super point is a kind of special host which plays an important role in network management and security. For a core network, detecting super points in real time is a burden task because it requires plenty computing resources to keep…
Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datasets and substantial…
Hardware compute power has been growing at an unprecedented rate in recent years. The utilization of such advancements plays a key role in producing better results in less time -- both in academia and industry. However, merging the existing…