Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe very late-stage merger IC 4200:pre-elliptical or pre-spiral?
76
0
0.0
(
0
)
Authors
P. Serra
Ask ChatGPT about the research
No Arabic abstract
Recent observations have revealed a class of unusually HI-rich early-type galaxies. By combining observations of their morphology, stellar populations and neutral hydrogen we aim to understand how these galaxies fit into the hierarchical formation paradigm. Here we present the result of our radio and optical observations of a test case galaxy, the E/S0 IC 4200.
rate research
Read More
Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pre-trained models require large amounts of training data and have huge model sizes, which make them difficult to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train the model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT- S), which has only 45.9% parameters of the original LXMERT model and 11.76% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task.
We present the result of radio and optical observations of the S0 galaxy IC 4200. We find that the galaxy hosts 8.5 billion solar masses of HI rotating on a ~90 deg warped disk extended out to 60 kpc from the centre of the galaxy. Optical spectroscopy reveals a simple-stellar-population-equivalent age of 1.5 Gyr in the centre of the galaxy and V- and R-band images show stellar shells. Ionised gas is observed within the stellar body and is kinematically decoupled from the stars and characterised by LINER-like line ratios.We interpret these observational results as evidence for a major merger origin of IC 4200, and date the merger back to 1-3 Gyr ago.
Fissioning nuclei and fission fragments, nuclear fragments emerging from energetic collisions, or nuclei probed with various external fields can emit one or more pre-equilibrium neutrons, protons, and potentially other heavier nuclear fragments. I describe a formalism which can be used to evaluate the pre-equilibrium neutron emission probabilities and the excitation energies of the remnant fragments.
Clusters of galaxies are the largest known gravitationally-bound structures in the Universe. When clusters collide, they create merger shocks on cosmological scales, which transform most of the kinetic energy carried by the cluster gaseous halos into heat. Observations of merger shocks provide key information of the merger dynamics, and enable insights into the formation and thermal history of the large-scale structures. Nearly all of the merger shocks are found in systems where the clusters have already collided, knowledge of shocks in the pre-merger phase is a crucial missing ingredient. Here we report on the discovery of a unique shock in a cluster pair 1E 2216 and 1E 2215. The two clusters are observed at an early phase of major merger. Contrary to all the known merger shocks observed ubiquitously on merger axes, the new shock propagates outward along the equatorial plane of the merger. This discovery uncovers an important epoch in the formation of massive clusters, when the rapid approach of the cluster pair leads to strong compression of gas along the merger axis. Current theoretical models predict that the bulk of the shock energy might be dissipated outside the clusters, and eventually turn into heat of the pristine gas in the circum-cluster space.
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain pre-training works more optimally. We therefore suggest that task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
