The progenitors of type Ia supernovae in the semidetached binaries with red giant donors

Context. The companions of the exploding carbon-oxygen white dwarfs (CO WDs) for producing type Ia supernovae (SNe Ia) are still not conclusively confirmed. A red-giant (RG) star has been suggested to be the mass donor of the exploding WD, named as the symbiotic channel. However, previous studies on the this channel gave a relatively low rate of SNe Ia. Aims. We aim to systematically investigate the parameter space, Galactic rates and delay time distributions of SNe Ia from the symbiotic channel by employing a revised mass-transfer prescription. Methods. We adopted an integrated mass-transfer prescription to calculate the mass-transfer process from a RG star onto the WD. In this prescription, the mass-transfer rate varies with the local material states. Results. We evolved a large number of WD+RG systems, and found that the parameter space of WD+RG systems for producing SNe Ia is significantly enlarged. This channel could produce SNe Ia with intermediate and old ages, contributing to at most 5% of all SNe Ia in the Galaxy. Our model increases the SN Ia rate from this channel by a factor of 5. We suggest that the symbiotic systems RS Oph and T CrB are strong candidates for the progenitors of SNe Ia.Comment: 8 pages, 6 figure

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST) aims to keep track of users’ intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods

An optimized parameter design method of SiC/Si hybrid switch considering turn-off current spike

In order to reduce the switching loss of SiC MOSFET/Si IGBT (SiC/Si) hybrid switch, the switching mode that turn off the Si IGBT prior to the SiC MOSFET is generally adopted to achieved the zero-voltage switching operation of IGBT. The minority carrier in N-base region of the IGBT are recombined in the form of exponential attenuation due to the conductivity modulation effect. When the SiC MOSFET is turned off, if the carrier recombination process of the IGBT is not finished, it needs to bear a large collector–emitter voltage change rate, resulting in apparent current spike. This current spike will increase the current stress of the device and produce additional turn-off loss. The equivalent model of double pulse test circuit of SiC/Si hybrid switch considering parasitic parameters is established, and the turn-off transient process is given analytically. The influence of turn-off delay time, circuit parameters and working conditions on current spike are analysed quantitatively. Combined with the consideration of device stress and comprehensive turn-off loss, an optimized circuit design method of SiC/Si hybrid switch considering turn-off current peak is proposed, which provides theoretical and design guidance for high reliability and high efficiency SiC/Si-based converters

Turn-Level Active Learning for Dialogue State Tracking

Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems. However, collecting a large amount of turn-by-turn annotated dialogue data is costly and inefficient. In this paper, we propose a novel turn-level active learning framework for DST to actively select turns in dialogues to annotate. Given the limited labelling budget, experimental results demonstrate the effectiveness of selective annotation of dialogue turns. Additionally, our approach can effectively achieve comparable DST performance to traditional training approaches with significantly less annotated data, which provides a more efficient way to annotate new dialogue data.Comment: EMNLP 2023 Main Conferenc

Dicylopenta­dien­yl[4-(4-vinyl­benz­yloxy)pyridine-2,6-dicarboxyl­ato]titanium(IV) monohydrate

The title compound, [Ti(C5H5)2(C16H11NO5)]·H2O, exhibits a titanocene unit coordinated to a styrene-substituted pyridine-2,6-dicarboxyl­ate ligand synthesized for use as a monomer for polymerization or copolymerization yielding metallocene-containing polymers. The compound crystallized as a monohydrate and the solvent water mol­ecule forms strong O—H⋯O hydrogen bonds with the carboxyl­ate O atoms of the Ti complex, which play an important role in the connection of adjacent mol­ecules. In addition, weak inter­molecular C—H⋯O hydrogen bonds also contribute to the crystal packing arrangement

Lending Interaction Wings to Recommender Systems with Conversational Agents

Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, we propose CORE, a new offline-training and online-checking paradigm that bridges a COnversational agent and REcommender systems via a unified uncertainty minimization framework. It can benefit any recommendation platform in a plug-and-play style. Here, CORE treats a recommender system as an offline relevance score estimator to produce an estimated relevance score for each item; while a conversational agent is regarded as an online relevance score checker to check these estimated scores in each session. We define uncertainty as the summation of unchecked relevance scores. In this regard, the conversational agent acts to minimize uncertainty via querying either attributes or items. Based on the uncertainty minimization framework, we derive the expected certainty gain of querying each attribute and item, and develop a novel online decision tree algorithm to decide what to query at each turn. Experimental results on 8 industrial datasets show that CORE could be seamlessly employed on 9 popular recommendation approaches. We further demonstrate that our conversational agent could communicate as a human if empowered by a pre-trained large language model.Comment: NeurIPS 202