Long Range Intrinsic Ferromagnetism in Two Dimensional Materials and Dissipationless Future Technologies

The inherent susceptibility of low-dimensional materials to thermal fluctuations has long been expected to poses a major challenge to achieving intrinsic long-range ferromagnetic order in two-dimensional materials. The recent explosion of interest in atomically thin materials and their assembly into van der Waals heterostructures has renewed interest in two-dimensional ferromagnetism, which is interesting from a fundamental scientific point of view and also offers a missing ingredient necessary for the realization of spintronic functionality in van der Waals heterostructures. Recently several atomically thin materials have been shown to be robust ferromagnets. Such ferromagnetism is thought to be enabled by magneto crystalline anisotropy which suppresses thermal fluctuations. In this article, we review recent progress in two-dimensional ferromagnetism in detail and predict new possible two-dimensional ferromagnetic materials. We also discuss the prospects for applications of atomically thin ferromagnets in novel dissipationless electronics, spintronics, and other conventional magnetic technologies. Particularly atomically thin ferromagnets are promising to realize time reversal symmetry breaking in two-dimensional topological systems, providing a platform for electronic devices based on the quantum anomalous Hall Effect showing dissipationless transport. Our proposed directions will assist the scientific community to explore novel two-dimensional ferromagnetic families which can spawn new technologies and further improve the fundamental understanding of this fascinating area.Comment: To be appear in Applied Physics Review

Confidence-aware Non-repetitive Multimodal Transformers for TextCaps

When describing an image, reading text in the visual scene is crucial to understand the key information. Recent work explores the TextCaps task, i.e. image captioning with reading Optical Character Recognition (OCR) tokens, which requires models to read text and cover them in generated captions. Existing approaches fail to generate accurate descriptions because of their (1) poor reading ability; (2) inability to choose the crucial words among all extracted OCR tokens; (3) repetition of words in predicted captions. To this end, we propose a Confidence-aware Non-repetitive Multimodal Transformers (CNMT) to tackle the above challenges. Our CNMT consists of a reading, a reasoning and a generation modules, in which Reading Module employs better OCR systems to enhance text reading ability and a confidence embedding to select the most noteworthy tokens. To address the issue of word redundancy in captions, our Generation Module includes a repetition mask to avoid predicting repeated word in captions. Our model outperforms state-of-the-art models on TextCaps dataset, improving from 81.0 to 93.0 in CIDEr. Our source code is publicly available.Comment: 9 pages; Accepted by AAAI 202

Bis­(chloro­acetato-κO)bis(trimethyl­silylmethyl)tin(IV)

In the title complex, [Sn(C2H2ClO2)2(C4H11Si)2], the SnIV ion is coordinated in a distorted tetra­hedral environment formed by two O atoms from two monodenate chloro­acetato ligands and two C atoms from two trimethyl silyl ligands. Two further weak intra­molecular Sn⋯O contacts [2.744 (2) and 2.655 (2) Å] are formed by the chloro­acetato ligands

Proteomic analysis of immediate-early response plasma proteins after 70% and 90% partial hepatectomy

AIM: Partial hepatectomy (PH) induces robust hepatic regenerative and metabolic responses that are considered to be triggered by humoral factors. The aim of the study was to identify plasma protein factors that potentially trigger or reflect the body's immediate-early responses to liver mass reduction. METHODS: Male C57BL/6 mice were subjected to sham operation, 70% PH or 90% PH. Blood was collected from the inferior vena cava at 20, 60 and 180 min after surgery. RESULTS: Using a label-free quantitative mass spectrometry-based proteomics approach, we identified 399 proteins exhibiting significant changes in plasma expression between any two groups. Of the 399 proteins, 167 proteins had multiple unique sequences and high peptide ID confidence (>90%) and were defined as priority 1 proteins. A group of plasma proteins largely associated with metabolism is enriched after 70% PH. Among the plasma proteins that respond to 90% PH are a dominant group of proteins that are also associated with metabolism and one known cytokine (platelet factor 4). Ninety percent PH and 70% PH induces similar changes in plasma protein profile. CONCLUSION: Our findings enable us to gain insight into the immediate-early response of plasma proteins to liver mass loss. Our data support the notion that increased metabolic demands of the body after massive liver mass loss may function as a sensor that calibrates hepatic regenerative response

Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation

In this paper, we consider the network latency estimation, which has been an important metric for network performance. However, a large scale of network latency estimation requires a lot of computing time. Therefore, we propose a new method that is much faster and maintains high accuracy. The data structure of network nodes can form a matrix, and the tensor model can be formed by introducing the time dimension. Thus, the entire problem can be be summarized as a tensor completion problem. The main idea of our method is improving the tensor leverage sampling strategy and introduce tensor QR decomposition into tensor completion. To achieve faster tensor leverage sampling, we replace tensor singular decomposition (t-SVD) with tensor CSVD-QR to appoximate t-SVD. To achieve faster completion for incomplete tensor, we use the tensor $L_{2,1}$-norm rather than traditional tensor nuclear norm. Furthermore, we introduce tensor QR decomposition into alternating direction method of multipliers (ADMM) framework. Numerical experiments witness that our method is faster than state-of-art algorithms with satisfactory accuracy.Comment: 20 pages, 7 figure

Implementing universal nonadiabatic holonomic quantum gates with transmons

Geometric phases are well known to be noise-resilient in quantum evolutions/operations. Holonomic quantum gates provide us with a robust way towards universal quantum computation, as these quantum gates are actually induced by nonabelian geometric phases. Here we propose and elaborate how to efficiently implement universal nonadiabatic holonomic quantum gates on simpler superconducting circuits, with a single transmon serving as a qubit. In our proposal, an arbitrary single-qubit holonomic gate can be realized in a single-loop scenario, by varying the amplitudes and phase difference of two microwave fields resonantly coupled to a transmon, while nontrivial two-qubit holonomic gates may be generated with a transmission-line resonator being simultaneously coupled to the two target transmons in an effective resonant way. Moreover, our scenario may readily be scaled up to a two-dimensional lattice configuration, which is able to support large scalable quantum computation, paving the way for practically implementing universal nonadiabatic holonomic quantum computation with superconducting circuits.Comment: v3 Appendix added, v4 published version, v5 published version with correction

Characterizing pump line phase offset of a single-soliton Kerr comb by dual comb interferometry

We experimentally demonstrate phase retrieval of a single-soliton Kerr comb using electric field cross-correlation implemented via dual-comb interferometry. The phase profile of the Kerr comb is acquired through the heterodyne beat between the Kerr comb and a reference electro-optical comb with a pre-characterized phase profile. The soliton Kerr comb has a nearly flat phase profile, and the pump line is observed to show a phase offset which depends on the pumping parameters. The experimental results are in agreement with numerical simulations. Our all-linear approach enables rapid measurements (3.2 $\mu$s) with low input power (20 $\mu$W)