Phonon-driven spin-Floquet magneto-valleytronics in MoS2

Two-dimensional materials equipped with strong spin-orbit coupling can display novel electronic, spintronic, and topological properties originating from the breaking of time or inversion symmetry. A lot of interest has focused on the valley degrees of freedom that can be used to encode binary information. By performing ab initio time-dependent density functional simulation on MoS2, here we show that the spin is not only locked to the valley momenta but strongly coupled to the optical E '' phonon that lifts the lattice mirror symmetry. Once the phonon is pumped so as to break time-reversal symmetry, the resulting Floquet spectra of the phonon-dressed spins carry a net out-of-plane magnetization (approximate to 0.024 mu(B) for single-phonon quantum) even though the original system is non-magnetic. This dichroic magnetic response of the valley states is general for all 2H semiconducting transition-metal dichalcogenides and can be probed and controlled by infrared coherent laser excitation

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Vamo: Towards a fully automated malware clustering validity analysis

ABSTRACT Malware clustering is commonly applied by malware analysts to cope with the increasingly growing number of distinct malware variants collected every day from the Internet. While malware clustering systems can be useful for a variety of applications, assessing the quality of their results is intrinsically hard. In fact, clustering can be viewed as an unsupervised learning process over a dataset for which the complete ground truth is usually not available. Previous studies propose to evaluate malware clustering results by leveraging the labels assigned to the malware samples by multiple anti-virus scanners (AVs). However, the methods proposed thus far require a (semi-)manual adjustment and mapping between labels generated by different AVs, and are limited to selecting a reference sub-set of samples for which an agreement regarding their labels can be reached across a majority of AVs. This approach may bias the reference set towards "easy to cluster" malware samples, thus potentially resulting in an overoptimistic estimate of the accuracy of the malware clustering results. In this paper we propose VAMO, a system that provides a fully automated quantitative analysis of the validity of malware clustering results. Unlike previous work, VAMO does not seek a majority voting-based consensus across different AV labels, and does not discard the malware samples for which such a consensus cannot be reached. Rather, VAMO explicitly deals with the inconsistencies typical of multiple AV labels to build a more representative reference set, compared to majority voting-based approaches. Furthermore, VAMO avoids the need of a (semi-)manual mapping between AV labels from different scanners that was required in previous work. Through an extensive evaluation in a controlled setting and a real-world application, we show that VAMO outperforms majority voting-based approaches, and provides a better way for malware analysts to automatically assess the quality of their malware clustering results