Magnetic bistability and nucleation of magnetic bubbles in a layered 2D organic-based magnet [Fe(TCNE)(NCMe)2][FeC14]

Journal ArticleThe 2D layered organic-based magnet [Fe(TCNE)(NCMe)2][FeCl4] (TCNE ¼ tetracyanoethylene) exhibits a unique macroscopic magnetic bistability between the field-cooled and zero-field-cooled states, which cannot be explained by either superparamagnetic behavior or spin freezing due to spin glass order. This magnetic bistability is described through consideration of the ensemble of uncoupled 2D Ising layers and their magnetization reversal initiated by a field-induced nucleation of magnetic bubbles in individual layers. The bubble nucleation rate strongly depends on the external field and temperature resulting in anomalous magnetic relaxation

DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances

Recent advances in pre-trained language models have significantly improved neural response generation. However, existing methods usually view the dialogue context as a linear sequence of tokens and learn to generate the next word through token-level self-attention. Such token-level encoding hinders the exploration of discourse-level coherence among utterances. This paper presents DialogBERT, a novel conversational response generation model that enhances previous PLM-based dialogue models. DialogBERT employs a hierarchical Transformer architecture. To efficiently capture the discourse-level coherence among utterances, we propose two training objectives, including masked utterance regression and distributed utterance order ranking in analogy to the original BERT training. Experiments on three multi-turn conversation datasets show that our approach remarkably outperforms the baselines, such as BART and DialoGPT, in terms of quantitative evaluation. The human evaluation suggests that DialogBERT generates more coherent, informative, and human-like responses than the baselines with significant margins.Comment: Published as a conference paper at AAAI 202

Continuous Decomposition of Granularity for Neural Paraphrase Generation

While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior work has shown that decomposing the levels of granularity~(e.g., word, phrase, or sentence) for input tokens has produced substantial improvements, suggesting the possibility of enhancing Transformers via more fine-grained modeling of granularity. In this work, we propose a continuous decomposition of granularity for neural paraphrase generation (C-DNPG). In order to efficiently incorporate granularity into sentence encoding, C-DNPG introduces a granularity-aware attention (GA-Attention) mechanism which extends the multi-head self-attention with: 1) a granularity head that automatically infers the hierarchical structure of a sentence by neurally estimating the granularity level of each input token; and 2) two novel attention masks, namely, granularity resonance and granularity scope, to efficiently encode granularity into attention. Experiments on two benchmarks, including Quora question pairs and Twitter URLs have shown that C-DNPG outperforms baseline models by a remarkable margin and achieves state-of-the-art results in terms of many metrics. Qualitative analysis reveals that C-DNPG indeed captures fine-grained levels of granularity with effectiveness.Comment: Accepted to be published in COLING 202

Evolution of catalyst particle size during carbon single walled nanotube growth and its effect on the tube characteristics

A series of Fe catalysts, with different mean diameters, supported on alumina with different molar ratios, was studied before and after carbon single walled nanotubes growth using magnetic measurements and Raman scattering techniques (laser excitation wavelengths from 1.17 to 2.54 eV) to follow changes on catalyst particle size and composition, as well as the relationship between particle size and diameter of nanotubes grown. In all cases, an increase and redistribution of the particle size after the growth was concluded based on the blocking temperature values and Langevin function analysis. This is explained in terms of agglomeration of particles due to carbon-induced liquefaction accompanied with an increase in the catalyst mobility. For large particles no direct correlation between the catalyst size and the nanotube diameters was observed.open22

Double Glomus Tumors Originating in the Submandibular and Parotid Regions

Glomus tumors are rare neoplasms that originate from the glomus bodies, an arteriovenous anastomosis with a specialized vascular structure. The most common site for these tumors is the subungal region of the fingers. Occasionally, glomus tumors are found in the middle ear, trachea, nasal cavities, stomach, and lungs. The occurrence in the parotid regions is very rare. While multiple glomus tumors in the whole body are thought to represent only 10% of all cases, instances of multiple tumors in the neck have not yet been reported in the literature. We report a case of double glomus tumors in the submandibular and parotid regions