Metamagnetic transitions and anomalous magnetoresistance in EuAg4_4As2_2 single crystal

In this paper, the magnetic and transport properties were systematically studied for EuAg$_4$As$_2$ single crystals, crystallizing in a centrosymmetric trigonal CaCu$_4$P$_2$ type structure. It was confirmed that two magnetic transitions occur at $\textit{T}$$_{N1}$ = 10 K and $\textit{T}$$_{N2}$ = 15 K, respectively. With the increasing field, the two transitions are noticeably driven to lower temperature. At low temperatures, applying a magnetic field in the $\textit{ab}$ plane induces two successive metamagnetic transitions. For both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$, EuAg$_4$As$_2$ shows a positive, unexpected large magnetoresistance (up to 202\%) at low fields below 10 K, and a large negative magnetoresistance (up to -78\%) at high fields/intermediate temperatures. Such anomalous field dependence of magnetoresistance may have potential application in the future magnetic sensors. Finally, the magnetic phase diagrams of EuAg$_{4}$As$_{2}$ were constructed for both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$

New Ni Amidinate Source for ALD/CVD of NiNx, NiO and Ni

Ni materials in the form of NiNx, NiO or NiSi have been found to be particularly important in memory as well as logic applications. Nickel silicide (NiSi) is emerging as the choice material for contact applications in semiconductor devices with 45nm technology node and beyond.$^{1}$ Recent research shows that the resistance switching characteristics of NiO thin film, in combinations with a metal-insulator-metal (MIM) structure, offer potential applications for the next generation nonvolatile resistive random access memory devices.$^{2}$ As the feature sizes of microelectronic circuits are shrinking, more complex structures are going to be adopted by the industry. Atomic layer deposition (ALD) is the preferred technique that can produce ultra-thin conformal layers (<10 nm). Nickel amidinate (Ni-AMD) has been demonstrated as an excellent precursor for both ALD and CVD Ni thin films due to its greater thermal stability and high reactivity. $^{3}$ We report our results on deposition of NiO, NiNx and its conversion into NiSi using Ni-AMD, and discuss the chemistry of forming $NiO$, $NiN_x$ and $NiSi$ films with vapor depletion and direct liquid injection (DLI) using various organic solvents that enhance the deposition process.Chemistry and Chemical BiologyEngineering and Applied Science

Drosophila Perlecan Regulates Intestinal Stem Cell Activity via Cell-Matrix Attachment

SummaryStem cells require specialized local microenvironments, termed niches, for normal retention, proliferation, and multipotency. Niches are composed of cells together with their associated extracellular matrix (ECM). Currently, the roles of ECM in regulating niche functions are poorly understood. Here, we demonstrate that Perlecan (Pcan), a highly conserved ECM component, controls intestinal stem cell (ISC) activities and ISC-ECM attachment in Drosophila adult posterior midgut. Loss of Pcan from ISCs, but not other surrounding cells, causes ISCs to detach from underlying ECM, lose their identity, and fail to proliferate. These defects are not a result of a loss of epidermal growth factor receptor (EGFR) or Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling activity but partially depend on integrin signaling activity. We propose that Pcan secreted by ISCs confers niche properties to the adjacent ECM that is required for ISC maintenance of stem cell identity, activity, and anchorage to the niche

A tomato HD-Zip homeobox protein, LeHB-1, plays an important role in floral organogenesis and ripening

Ethylene is required for climacteric fruit ripening. Inhibition of ethylene biosynthesis genes, 1-aminocyclopropane-1-carboxylate (ACC) synthase and ACC oxidase, prevents or delays ripening, but it is not known how these genes are modulated during normal development. LeHB-1, a previously uncharacterized tomato homeobox protein, was shown by gel retardation assay to interact with the promoter of LeACO1, an ACC oxidase gene expressed during ripening. Inhibition of LeHB-1 mRNA accumulation in tomato fruit, using virus-induced gene silencing, greatly reduced LeACO1 mRNA levels, and inhibited ripening. Conversely, ectopic overexpression of LeHB-1 by viral delivery to developing flowers elsewhere on injected plants triggered altered floral organ morphology, including production of multiple flowers within one sepal whorl, fusion of sepals and petals, and conversion of sepals into carpel-like structures that grew into fruits and ripened. Our findings suggest that LeHB-1 is not only involved in the control of ripening but also plays a critical role in floral organogenesis

A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends

As more and more Arabic texts emerged on the Internet, extracting important information from these Arabic texts is especially useful. As a fundamental technology, Named entity recognition (NER) serves as the core component in information extraction technology, while also playing a critical role in many other Natural Language Processing (NLP) systems, such as question answering and knowledge graph building. In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model. Specifically, we first introduce the background of Arabic NER, including the characteristics of Arabic and existing resources for Arabic NER. Then, we systematically review the development of Arabic NER methods. Traditional Arabic NER systems focus on feature engineering and designing domain-specific rules. In recent years, deep learning methods achieve significant progress by representing texts via continuous vector representations. With the growth of pre-trained language model, Arabic NER yields better performance. Finally, we conclude the method gap between Arabic NER and NER methods from other languages, which helps outline future directions for Arabic NER.Comment: Accepted by IEEE TKD

Robust estimation of similarity transformation for visual object tracking

National Research Foundation (NRF) Singapore under its AI Singapore Programm

How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension through pattern recognition? This study seeks to explore this question through the lens of syntax, a crucial component of sentence comprehension. Adopting a natural language question-answering (Q&A) scheme, we craft questions targeting nine syntactic knowledge points that are most closely related to sentence comprehension. Experiments conducted on 24 LLMs suggest that most have a limited grasp of syntactic knowledge, exhibiting notable discrepancies across different syntactic knowledge points. In particular, questions involving prepositional phrase attachment pose the greatest challenge, whereas those concerning adjectival modifier and indirect object are relatively easier for LLMs to handle. Furthermore, a case study on the training dynamics of the LLMs reveals that the majority of syntactic knowledge is learned during the initial stages of training, hinting that simply increasing the number of training tokens may not be the `silver bullet' for improving the comprehension ability of LLMs.Comment: 20 pages, 6 figure

Mining Word Boundaries in Speech as Naturally Annotated Word Segmentation Data

Inspired by early research on exploring naturally annotated data for Chinese word segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to mine word boundaries from parallel speech/text data. First we collect parallel speech/text data from two Internet sources that are related with CWS data used in our experiments. Then, we obtain character-level alignments and design simple heuristic rules for determining word boundaries according to pause duration between adjacent characters. Finally, we present an effective complete-then-train strategy that can better utilize extra naturally annotated data for model training. Experiments demonstrate our approach can significantly boost CWS performance in both cross-domain and low-resource scenarios.Comment: latest versio