Magnetoelectric Effect at the Ni/HfO\u3csub\u3e2\u3c/sub\u3e Interface Induced by Ferroelectric Polarization

Driven by the technological importance of the recently discovered ferroelectric HfO2, we explore a magnetoelectric effect at the HfO2-based ferroelectric-ferromagnetic interface. Using density-functionaltheory calculations of the Ni/HfO2/Ni (001) heterostructure as a model system, we predict a stable and sizable ferroelectric polarization in a few-nm-thick HfO2 layer. For the Ni/HfO2 interface with opposite polarization directions (pointing to or away from the interface), we find a sizable difference in the interfacial Ni—O bonding, resulting in dissimilar degrees of depletion of the electron density around the interface. The latter affects the relative population of the exchange-split majority and minority spin bands at the interface and thus the interfacial magnetic moments. The sizable change in the interface magnetization with ferroelectric polarization reversal of HfO2 manifests a significant ferroelectrically induced magnetoelectric effect at the Ni/HfO2 interface. Our results reveal promising prospects of ferroelectric-ferromagnetic composite multiferroics based on HfO2-based ferroelectric materials

SmartCheck: Static Analysis of Ethereum Smart Contracts

Ethereum is a major blockchain-based platform for smart contracts – Turing complete programs that are executed in a decentralized network and usually manipulate digital units of value. Solidity is the most mature high-level smart contract language. Ethereum is a hostile execution environment, where anonymous attackers exploit bugs for immediate financial gain. Developers have a very limited ability to patch deployed contracts. Hackers steal up to tens of millions of dollars from flawed contracts, a well-known example being “The DAO“, broken in June 2016. Advice on secure Ethereum programming practices is spread out across blogs, papers, and tutorials. Many sources are outdated due to a rapid pace of development in this field. Automated vulnerability detection tools, which help detect potentially problematic language constructs, are still underdeveloped in this area. We provide a comprehensive classification of code issues in Solidity and implement SmartCheck – an extensible static analysis tool that detects them. SmartCheck translates Solidity source code into an XML-based intermediate representation and checks it against XPath patterns. We evaluated our tool on a big dataset of real-world contracts and compared the results with manual audit on three contracts. Our tool reflects the current state of knowledge on Solidity vulnerabilities and shows significant improvements over alternatives. SmartCheck has its limitations, as detection of some bugs requires more sophisticated techniques such as taint analysis or even manual audit. We believe though that a static analyzer should be an essential part of contract developers’ toolbox, letting them fix simple bugs fast and allocate more effort to complex issues

The ATLAS EventIndex: a BigData catalogue for all ATLAS experiment events

The ATLAS EventIndex system comprises the catalogue of all events collected, processed or generated by the ATLAS experiment at the CERN LHC accelerator, and all associated software tools to collect, store and query this information. ATLAS records several billion particle interactions every year of operation, processes them for analysis and generates even larger simulated data samples; a global catalogue is needed to keep track of the location of each event record and be able to search and retrieve specific events for in-depth investigations. Each EventIndex record includes summary information on the event itself and the pointers to the files containing the full event. Most components of the EventIndex system are implemented using BigData open-source tools. This paper describes the architectural choices and their evolution in time, as well as the past, current and foreseen future implementations of all EventIndex components.Comment: 21 page

Tunneling Anisotropic Magnetoresistance in Ferroelectric Tunnel Junctions

Using a simple quantum-mechanical model, we explore a tunneling anisotropic magnetoresistance (TAMR) effect in ferroelectric tunnel junctions (FTJs) with a ferromagnetic electrode and a ferroelectric barrier layer, where spontaneous polarization gives rise to the Rashba and Dresselhaus spin-orbit coupling (SOC). For realistic parameters of the model, we predict sizable TAMR measurable experimentally. For asymmetric FTJs, whose electrodes have different work functions, the built-in electric field affects the SOC parameters and leads to TAMR being dependent on the ferroelectric polarization direction. The SOC change with polarization switching affects tunneling conductance, revealing an alternative mechanism of tunneling electroresistance. These results demonstrate alternative functionalities of FTJs, which can be explored experimentally and used in electronic devices

Defect-Assisted Tunneling Electroresistance in Ferroelectric Tunnel Junctions

Recent experimental results have demonstrated ferroelectricity in thin films of SrTiO3 induced by antisite TiSr defects. This opens a possibility to use SrTiO3 as a barrier layer in ferroelectric tunnel junctions (FTJs)—emerging electronic devices promising for applications in nanoelectronics. Here using density functional theory combined with quantum-transport calculations applied to a prototypical Pt/SrTiO3/Pt FTJ, we demonstrate that the localized in-gap energy states produced by the antisite TiSr defects are responsible for the enhanced electron tunneling conductance which can be controlled by ferroelectric polarization. Our tight-binding modeling, which takes into account multiple defects, shows that the predicted defect-assisted tunneling electroresistance effect is greatly amplified when the defect energy levels are brought to the Fermi energy by one of the polarization states. Our results have implications for FTJs based on conventional ferroelectric barriers with defects and can be employed for the design of new types of FTJs with enhanced performance

Classification and monomer-by-monomer annotation dataset of suprachromosomal family 1 alpha satellite higher-order repeats in hg38 human genome assembly

In the latest hg38 human genome assembly, centromeric gaps has been filled in by alpha satellite (AS) reference models (RMs) which are statistical representations of homogeneous higher-order repeat (HOR) arrays that make up the bulk of the centromeric regions. We analyzed these models to compose an atlas of human AS HORs where each monomer of a HOR was represented by a number of its polymorphic sequence variants. We combined these data and HMMER sequence analysis platform to annotate AS HORs in the assembly. This led to discovery of a new type of low copy number highly divergent HORs which were not represented by RMs. These were included in the dataset. The annotation can be viewed as UCSC Genome Browser custom track (the HOR-track) and used together with our previous annotation of AS suprachromosomal families (SFs) in the same assembly, where each AS monomer can be viewed in its genomic context together with its classification into one of the 5 major SFs (the SF-track). To catalog the diversity of AS HORs in the human genome we introduced a new naming system. Each HOR received a name which showed its SF, chromosomal location and index number. Here we present the first installment of the HOR-track covering only the 17 HORs that belong to SF1 which forms live functional centromeres in chromosomes 1, 3, 5, 6, 7, 10, 12, 16 and 19 and also a large number of minor dead HOR domains, both homogeneous and divergent. Monomer-by-monomer HOR annotation used for this dataset as opposed to annotation of whole HOR repeats provides for mapping and quantification of various structural variants of AS HORs which can be used to collect data on inter-individual polymorphism of AS

Development of the ATLAS Event Picking Server

During LHC Run 2, the ATLAS experiment collected almost 20 billion real data events and produced about three times more simulated events. During physics analysis it is often necessary to retrieve one or a few events to inspect their properties in detail and check their reconstruction parameters. Occasionally it is also necessary to select larger samples of events in RAW format to reconstruct them with enhanced code. The new Event Picking Server automates the procedure of finding the location of the events using the EventIndex and submitting the Grid jobs to retrieve the individual events from the files in which they are stored