Central engine afterglow of Gamma-ray Bursts

Before 2004, nearly all GRB afterglow data could be understood in the context of the external shocks model. This situation has changed in the past two years, when it became clear that some afterglow components should be attributed to the activity of the central engine; i.e., the {\it central engine afterglow}. We review here the afterglow emission that is directly related to the GRB central engine. Such an interpretation proposed by Katz, Piran & Sari, peculiar in pre-{\it Swift} era, has become generally accepted now.Comment: 4 pages including 1 figure. Presented at the conference "Astrophysics of Compact Objects" (July 1-7, 2007; Huangshan, China

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Real-world applications require the classification model to adapt to new classes without forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. Typical CIL methods tend to save representative exemplars from former classes to resist forgetting, while recent works find that storing models from history can substantially boost the performance. However, the stored models are not counted into the memory budget, which implicitly results in unfair comparisons. We find that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work, especially for the case with limited memory budgets. As a result, we need to holistically evaluate different CIL methods at different memory scales and simultaneously consider accuracy and memory size for measurement. On the other hand, we dive deeply into the construction of the memory buffer for memory efficiency. By analyzing the effect of different layers in the network, we find that shallow and deep layers have different characteristics in CIL. Motivated by this, we propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel. MEMO extends specialized layers based on the shared generalized representations, efficiently extracting diverse representations with modest cost and maintaining representative exemplars. Extensive experiments on benchmark datasets validate MEMO's competitive performance. Code is available at: https://github.com/wangkiw/ICLR23-MEMOComment: Accepted to ICLR 2023 as a Spotlight Presentation. Code is available at: https://github.com/wangkiw/ICLR23-MEM

QED effects on phase transition and Ruppeiner geometry of Euler-Heisenberg-AdS black holes

Taking the quantum electrodynamics (QED) effect into account, we study the black hole phase transition and Ruppeiner geometry for the Euler-Heisenberg anti-de Sitter black hole in the extended phase space. For negative and small positive QED parameter, we observe a small/large black hole phase transition and reentrant phase transition, respectively. While a large positive value of the QED parameter ruins the phase transition. The phase diagrams for each case are explicitly exhibited. Then we construct the Ruppeiner geometry in the thermodynamic parameter space. Different features of the corresponding scalar curvature are shown for both the small/large black hole phase transition and reentrant phase transition cases. Of particular interest is that an additional region of positive scalar curvature indicating dominated repulsive interaction among black hole microstructure is present for the black hole with a small positive QED parameter. Furthermore, the universal critical phenomena are also observed for the scalar curvature of the Ruppeiner geometry. These results indicate that the QED parameter has a crucial influence on the black hole phase transition and microstructure.Comment: 19 pages, 14 figure

Contextualizing Multiple Tasks via Learning to Decompose

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts. Those ambiguities increase the difficulty of learning a generalizable model when there exists one concept or mixed concepts in a task. We propose a general approach Learning to Decompose Network (LeadNet) for both two cases, which contextualizes a model through meta-learning multiple maps for concepts discovery -- the representations of instances are decomposed and adapted conditioned on the contexts. Through taking a holistic view over multiple latent components over instances in a sampled pseudo task, LeadNet learns to automatically select the right concept via incorporating those rich semantics inside and between objects. LeadNet demonstrates its superiority in various applications, including exploring multiple views of confusing tasks, out-of-distribution recognition, and few-shot image classification

CircRNA PDE3B regulates tumorigenicity via the miR-136-5p/MAP3K2 axis of esophageal squamous cell carcinoma

Background. CircRNA has a covalently closed circular conformation and a stable structure. However, the exact role of circRNA in esophageal squamous cell carcinoma (ESCC) remains uncertain. The purpose of this study was to explore the role of hsa_circ_0000277 (circ_PDE3B) in ESCC. Methods. The expression levels of circ_PDE3B, miR-136-5p and mitogen-activated protein kinase kinase kinase 2 (MAP3K2) in ESCC tissues and cells were detected by quantitative real-time polymerase chain reaction (qRT-PCR) or western blot. The proliferation ability of EC9706 and KYSE30 cells was detected by clonal formation, 5-ethynyl-2’-deoxyuridine (EdU) and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-Htetrazolium bromide (MTT) assays. Flow cytometry was used to detect the apoptosis rate of cells. Transwell assay was used to detect the invasion ability of EC9706 and KYSE3 cells. The relationship between miR-136-5p and circ_PDE3B or MAP3K2 was verified by dual-luciferase reporter assay and RNA pull-down, and the effect of circ_PDE3B on tumor growth in vivo was explored through tumor transplantation experiment. Immunohistochemistry (IHC) assay was used to detect MAP3K2 and Ki67 expression in mice tumor tissues. Results. The results showed that circ_PDE3B was highly expressed in ESCC tissues and cells. Downregulated circ_PDE3B expression in ESCC cells significantly reduced cell proliferation, migration and invasion. Circ_PDE3B served as a sponge for miR-136- 5p, and miR-136-5p inhibition reversed the roles of circ_PDE3B knockdown in ESCC cells. MAP3K2 was a direct target of miR-136-5p, and miR-136-5p targeted MAP3K2 to inhibit the malignant behaviors of ESCC cells. Furthermore, circ_PDE3B regulated MAP3K2 expression by sponging miR-136-5p. Importantly, circ_PDE3B knockdown inhibited tumor growth in vivo. Conclusions. In conclusion, circ_PDE3B acted as oncogenic circRNA in ESCC and accelerated ESCC progression by adsorption of miR-136-5p and activation of MAP3K2, supporting circ_PDE3B as a potential therapeutic target for ESCC

PILOT: A Pre-Trained Model-Based Continual Learning Toolbox

While traditional machine learning can effectively tackle a wide range of problems, it primarily operates within a closed-world setting, which presents limitations when dealing with streaming data. As a solution, incremental learning emerges to address real-world scenarios involving new data's arrival. Recently, pre-training has made significant advancements and garnered the attention of numerous researchers. The strong performance of these pre-trained models (PTMs) presents a promising avenue for developing continual learning algorithms that can effectively adapt to real-world scenarios. Consequently, exploring the utilization of PTMs in incremental learning has become essential. This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT also fits typical class-incremental learning algorithms (e.g., DER, FOSTER, and MEMO) within the context of pre-trained models to evaluate their effectiveness.Comment: Code is available at https://github.com/sun-hailong/LAMDA-PILO

Streaming CTR Prediction: Rethinking Recommendation Task for Real-World Streaming Data

The Click-Through Rate (CTR) prediction task is critical in industrial recommender systems, where models are usually deployed on dynamic streaming data in practical applications. Such streaming data in real-world recommender systems face many challenges, such as distribution shift, temporal non-stationarity, and systematic biases, which bring difficulties to the training and utilizing of recommendation models. However, most existing studies approach the CTR prediction as a classification task on static datasets, assuming that the train and test sets are independent and identically distributed (a.k.a, i.i.d. assumption). To bridge this gap, we formulate the CTR prediction problem in streaming scenarios as a Streaming CTR Prediction task. Accordingly, we propose dedicated benchmark settings and metrics to evaluate and analyze the performance of the models in streaming data. To better understand the differences compared to traditional CTR prediction tasks, we delve into the factors that may affect the model performance, such as parameter scale, normalization, regularization, etc. The results reveal the existence of the ''streaming learning dilemma'', whereby the same factor may have different effects on model performance in the static and streaming scenarios. Based on the findings, we propose two simple but inspiring methods (i.e., tuning key parameters and exemplar replay) that significantly improve the effectiveness of the CTR models in the new streaming scenario. We hope our work will inspire further research on streaming CTR prediction and help improve the robustness and adaptability of recommender systems