New York\u27s Plain English Law

New York was the first state to pass a law requiring that contracts governing consumer transactions be written in plain English, as opposed to legalese. This Note examines the effect of New York\u27s Plain English Law on consumer transactions as well as the Law\u27s reception by lawyers and consumers

Is China Systematically Buying Up Key Technologies? Chinese M & A transactions in Germany in the context of “Made in China 2025”. Bertelsmann Stiftung GED Study 2018

“Made in China 2025” (MIC 2025) is the Chinese central government’s main industrial policy strategy aimed at turning China into the global leader of the fourth industrial revolution. Chinese M & A transactions abroad explicitly belong to the instruments for implementing MIC 2025. Germany is an attractive location for Chinese M & A transactions and offers tailor-made know-how for MIC 2025 due to its large number of “hidden champions”, i. e. technological world market leaders in highly specialized niches. 64 percent or 112 of the 175 analyzed Chinese M & A transactions with a share of at least ten percent in German companies between 2014 and 2017 percent can be assigned to one of the ten key sectors in which China aims to assume global technology leadership with the help of MIC 2025. On the one hand, there is a clear focus on the MIC 2025 sectors of “energy-saving and new-energy vehicles”, “electrical equipment” and “high-end numerical control machinery and robotics” – i. e. sectors in which Germany can in part demonstrate significant competitive technological advantages. Even before the introduction of MIC 2025 in 2015, however, these sectors were already a focus of interest for Chinese investors in Germany. On the other hand, key sectors that played little or no role for Chinese M & A transactions in Germany have also become increasingly important since the introduction of MIC 2025. This is particularly evident in the MIC 2025 sector of “biomedicine and high-performance medical devices”. The majority of the 112 Chinese M & A transactions (just under 60 percent) that are relevant for MIC 2025 are distributed across only three German states: Baden-Württemberg (26), North Rhine-Westphalia (22) and Bavaria (18) – the very regions in which the majority of the German “hidden champions” are located. State-owned investors make up 18 percent of the Chinese M & A transactions examined, and are therefore a minority. However, taking into account only the M & A transactions that can be assigned to the MIC 2025 sectors, their share rises to around 22 percent – a possible indication of state stakeholders’ greater interest in acquiring know-how abroad for the implementation of MIC 2025. However, the formal type of ownership of Chinese companies does not show the full picture of potential state influence due to the complex interplay between the state and companies in China. Therefore, the great challenge for Germany consists in the forms of state influence that are not or only insufficiently reflected in the majority ownership type of Chinese investors

FASTM: a log-based hardware transactional memory with fast abort recovery

Version management, one of the key design dimensions of Hardware Transactional Memory (HTM) systems, defines where and how transactional modifications are stored. Current HTM systems use either eager or lazy version management. Eager systems that keep new values in-place while they hold old values in a software log, suffer long delays when aborts are frequent because the pre-transactional state is recovered by software. Lazy systems that buffer new values in specialized hardware offer complex and inefficient solutions to handle hardware overflows, which are common in applications with coarse-grain transactions. In this paper, we present FASTM, an eager log-based HTM that takes advantage of the processor’s cache hierarchy to provide fast abort recovery. FASTM uses a novel coherence protocol to buffer the transactional modifications in the first level cache and to keep the non-speculative values in the higher levels of the memory hierarchy. This mechanism allows fast abort recovery of transactions that do not overflow the first level cache resources. Contrary to lazy HTM systems, committing transactions do not have to perform any actions in order to make their results visible to the rest of the system. FASTM keeps the pre-transactional state in a software-managed log as well, which permits the eviction of speculative values and enables transparent execution even in the case of cache overflow. This approach simplifies eviction policies without degrading performance, because it only falls back to a software abort recovery for transactions whose modified state has overflowed the cache. Simulation results show that FASTM achieves a speed-up of 43% compared to LogTM-SE, improving the scalability of applications with coarse-grain transactions and obtaining similar performance to an ideal eager HTM with zero-cost abort recovery.Peer ReviewedPostprint (published version

HaTS: Hardware-Assisted Transaction Scheduler

In this paper we present HaTS, a Hardware-assisted Transaction Scheduler. HaTS improves performance of concurrent applications by classifying the executions of their atomic blocks (or in-memory transactions) into scheduling queues, according to their so called conflict indicators. The goal is to group those transactions that are conflicting while letting non-conflicting transactions proceed in parallel. Two core innovations characterize HaTS. First, HaTS does not assume the availability of precise information associated with incoming transactions in order to proceed with the classification. It relaxes this assumption by exploiting the inherent conflict resolution provided by Hardware Transactional Memory (HTM). Second, HaTS dynamically adjusts the number of the scheduling queues in order to capture the actual application contention level. Performance results using the STAMP benchmark suite show up to 2x improvement over state-of-the-art HTM-based scheduling techniques

The helping hand, the lazy hand, or the grabbing hand? Central vs. local government shareholders in publicly listed firms in China

We analyze related party transactions between Chinese publicly listed firms and their stateowned enterprise (SOEs) shareholders to examine whether companies benefit from the presence of government shareholders and politically connected directors appointed by the government. We find that related party transactions between firms and their government shareholders seem to result in expropriation of the minority shareholders in firms controlled by local government SOEs or with a large proportion of local government affiliated directors on their board, and in provinces where local government bureaucrats are less likely to be prosecuted for misappropriation of state funds. On the other hand, firms controlled by the central government (or with a large proportion of central government affiliated directors) are benefited in their related party transactions with their central government SOEs.Law and economics, Government ownership, China, State-Owned Enterprises (SOE), Related party transactions, Political connections

A true concurrent model of smart contracts executions

The development of blockchain technologies has enabled the trustless execution of so-called smart contracts, i.e. programs that regulate the exchange of assets (e.g., cryptocurrency) between users. In a decentralized blockchain, the state of smart contracts is collaboratively maintained by a peer-to-peer network of mutually untrusted nodes, which collect from users a set of transactions (representing the required actions on contracts), and execute them in some order. Once this sequence of transactions is appended to the blockchain, the other nodes validate it, re-executing the transactions in the same order. The serial execution of transactions does not take advantage of the multi-core architecture of modern processors, so contributing to limit the throughput. In this paper we propose a true concurrent model of smart contract execution. Based on this, we show how static analysis of smart contracts can be exploited to parallelize the execution of transactions.Comment: Full version of the paper presented at COORDINATION 202

Multiple perspectives HMM-based feature engineering for credit card fraud detection

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud? (ii) Is the sequence obtained by fixing the card-holder or the payment terminal? (iii) Is it a sequence of spent amount or of elapsed time between the current and previous transactions? Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sets is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. This multiple perspectives HMM-based approach enables an automatic feature engineering in order to model the sequential properties of the dataset with respect to the classification task. This strategy allows for a 15% increase in the precision-recall AUC compared to the state of the art feature engineering strategy for credit card fraud detection.Comment: Presented as a poster in the conference SAC 2019: 34th ACM/SIGAPP Symposium on Applied Computing in April 201