A two-phase model of galaxy formation: II. The size-mass relation of dynamically hot galaxies

In Paper-I we developed a two-phase model to connect dynamically hot galaxies (such as ellipticals and bulges) with the formation of self-gravitating, turbulent gas clouds (SGC) associated with the fast assembly of dark matter halos. Here we explore the implications of the model for the size-stellar mass relation of dynamically hot galaxies. Star-forming sub-clouds produced by the fragmentation of the SGC inherit its spatial structure and dynamical hotness, which produces a tight and 'homologous' relation, $r_{\rm f}\approx\, 100 r_{\rm bulge}$, between the size of a dynamically hot galaxy ($r_{\rm bulge}$) and that of its host halo assembled in the fast assembly regime ($r_{\rm f}$), independent of redshift and halo mass. This relation is preserved by the 'dry' expansion driven by dynamical heating when a galaxy becomes gas-poor due to inefficient cooling, and is frozen during the slow assembly regime when the bulge stops growing in mass and dynamical heating is no longer effective. The size-stellar mass relation is thus a simple combination of the galaxy-halo homology and the non-linear relation between stellar mass and halo mass. Using a set of halo assembly histories we demonstrate that this model can reproduce all properties in the observed size-mass relation of dynamically hot galaxies, including the flattening of the relation in the low-mass end and the upturn in the very massive end. The predicted evolution of this relation matches observational data currently available to redshift $z \approx 4$, and can be tested in the future at higher $z$. Our results indicate that the sizes of dynamically hot galaxies are produced by the dissipation and collapse of gas in dark matter halos to establish self-gravitating systems of sub-clouds in which stars form.Comment: 9 pages, 4 figures, 1 table; submitted to MNRA

Multimodal Learning For Hateful Memes Detection

Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting hateful memes can help reduce their harmful social impact. Compared to the conventional multimodal tasks, where the visual and textual information is semantically aligned, hateful memes detection is a more challenging task since the image and text in memes are weakly aligned or even irrelevant. Thus, it requires the model to have a deep understanding of the content and perform reasoning over multiple modalities. This paper focuses on multimodal hateful memes detection and proposes a novel method incorporating the image captioning process into the meme\u27s detection process. We conduct extensive experiments on multimodal meme datasets and illustrate the effectiveness of our approach. Our model achieves promising results on the Hateful Memes Detection Challenge. Our code is made publicly available at GitHub

Federated Few-shot Learning

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients may only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as \emph{federated few-shot learning}. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines. Our code is provided\footnote{\href{https://github.com/SongW-SW/F2L}{https://github.com/SongW-SW/F2L}}.Comment: SIGKDD 202

(Sr3La2O5)(Zn1-xMnx)2As2: A Bulk Form Diluted Magnetic Semiconductor isostructural to the "32522" Fe-based Superconductors

A new diluted magnetic semiconductor system, (Sr3La2O5)(Zn1-xMnx)2As2, has been synthesized and characterized. 10% Mn substitution for Zn in bulk form (Sr3La2O5)Zn2As2 results in a ferromagnetic ordering below Curie temperature, TC ~ 40 K. (Sr3La2O5)(Zn1-xMnx)2As2 has a layered crystal structure identical to that of 32522-type Fe based superconductors, and represents the fifth DMS family that has a direct counterpart among the FeAs high temperature superconductor families.Comment: Accepted for publication in EP

The suppression of Curie temperature by Sr doping in diluted ferromagnetic semiconductor (La1-xSrx)(Zn1-yMny)AsO

(La1-xSrx)(Zn1-yMny)AsO is a two dimensional diluted ferromagnetic semiconductor that has the advantage of decoupled charge and spin doping. The substitution of Sr2+ for La3+ and Mn2+ for Zn2+ into the parent semiconductor LaZnAsO introduces hole carriers and spins, respectively. This advantage enables us to investigate the influence of carrier doping on the ferromagnetic ordered state through the control of Sr concentrations in (La1-xSrx)(Zn0.9Mn0.1)AsO. 10 % Sr doping results in a ferromagnetic ordering below TC ~ 30 K. Increasing Sr concentration up to 30 % heavily suppresses the Curie temperature and saturation moments. Neutron scattering measurements indicate that no structural transition occurs for (La0.9Sr0.1)(Zn0.9Mn0.1)AsO below 300 K.Comment: Submitted to EP