4,511 research outputs found
Bis(nitrato-κO)[(S)-2-(pyrrolidin-2-yl)-1H-benzimidazole]cadmium(II)
The title compound, [Cd(NO3)2(C11H13N3)2], was synthesized by hydroÂthermal reaction of Cd(NO3)2 and S-2-(pyrrolidin-2-yl)-1H-1,3-benzimidazole. The Cd atom lies on an inversion centre. The distorted octaÂhedral Cd environment contains two planar trans-related N,N-chelating S-2-(pyrrolidin-2-yl)-1H-1,3-benzimidazole ligands in one plane and two monodentate nitrate ligands. N—H⋯O hydrogen bonds involving a nitrate O atom build up an infinite chain parallel to the a axis
Dibromido[(S)-2-(pyrrolidin-2-yl)-1H-benzimidazole]zinc(II)
The title compound, [ZnBr2(C11H13N3)], was synthesized by hydroÂthermal reaction of ZnBr2 and (S)-2-(pyrrolidin-2-yl)-1H-benzimidazole. The ZnII atom has a distorted tetraÂhedral geometry and is coordinated by two N atoms from the chelating organic ligand and two terminal Br− anions. In the crystal structure, molÂecules are linked into a chain along the [101] direction by N—H⋯Br and C—H⋯Br hydrogen bonds
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
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