73 research outputs found
Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning
Catastrophic forgetting remains a critical challenge in the field of
continual learning, where neural networks struggle to retain prior knowledge
while assimilating new information. Most existing studies emphasize mitigating
this issue only when encountering new tasks, overlooking the significance of
the pre-task phase. Therefore, we shift the attention to the current task
learning stage, presenting a novel framework, C&F (Create and Find Flatness),
which builds a flat training space for each task in advance. Specifically,
during the learning of the current task, our framework adaptively creates a
flat region around the minimum in the loss landscape. Subsequently, it finds
the parameters' importance to the current task based on their flatness degrees.
When adapting the model to a new task, constraints are applied according to the
flatness and a flat space is simultaneously prepared for the impending task. We
theoretically demonstrate the consistency between the created and found
flatness. In this manner, our framework not only accommodates ample parameter
space for learning new tasks but also preserves the preceding knowledge of
earlier tasks. Experimental results exhibit C&F's state-of-the-art performance
as a standalone continual learning approach and its efficacy as a framework
incorporating other methods. Our work is available at
https://github.com/Eric8932/Create-and-Find-Flatness.Comment: 10pages, ECAI2023 conferenc
Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread
Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research
Simulation is an essential tool to develop and benchmark autonomous vehicle
planning software in a safe and cost-effective manner. However, realistic
simulation requires accurate modeling of nuanced and complex multi-agent
interactive behaviors. To address these challenges, we introduce Waymax, a new
data-driven simulator for autonomous driving in multi-agent scenes, designed
for large-scale simulation and testing. Waymax uses publicly-released,
real-world driving data (e.g., the Waymo Open Motion Dataset) to initialize or
play back a diverse set of multi-agent simulated scenarios. It runs entirely on
hardware accelerators such as TPUs/GPUs and supports in-graph simulation for
training, making it suitable for modern large-scale, distributed machine
learning workflows. To support online training and evaluation, Waymax includes
several learned and hard-coded behavior models that allow for realistic
interaction within simulation. To supplement Waymax, we benchmark a suite of
popular imitation and reinforcement learning algorithms with ablation studies
on different design decisions, where we highlight the effectiveness of routes
as guidance for planning agents and the ability of RL to overfit against
simulated agents
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
Recently, the success of pre-training in text domain has been fully extended
to vision, audio, and cross-modal scenarios. The proposed pre-training models
of different modalities are showing a rising trend of homogeneity in their
model structures, which brings the opportunity to implement different
pre-training models within a uniform framework. In this paper, we present
TencentPretrain, a toolkit supporting pre-training models of different
modalities. The core feature of TencentPretrain is the modular design. The
toolkit uniformly divides pre-training models into 5 components: embedding,
encoder, target embedding, decoder, and target. As almost all of common modules
are provided in each component, users can choose the desired modules from
different components to build a complete pre-training model. The modular design
enables users to efficiently reproduce existing pre-training models or build
brand-new one. We test the toolkit on text, vision, and audio benchmarks and
show that it can match the performance of the original implementations
Loss of Angiopoietin-like 7 diminishes the regeneration capacity of hematopoietic stem and progenitor cells
© 2015 Xiao et al.; licensee Biomed Central. Successful expansion of hematopoietic stem cells (HSCs) would benefit the use of HSC transplants in the clinic. Angiopoietin-like 7 promotes the expansion of hematopoietic stem and progenitor cells (HSPC) in vitro and ex vivo. However, the impact of loss of Angptl7 on HSPCs in vivo has not been characterized. Here, we generated Angptl7-deficient mice by TALEN-mediated gene targeting and found that HSC compartments in Angptl7-null mice were compromised. In addition, wild type (WT) HSPCs failed to repopulate in the BM of Angptl7-null mice after serial transplantations while the engraftment of Angptl7-deficient HSPCs in WT mice was not impaired. These results suggest that Angptl7 is required for HSPCs repopulation in a non-cell autonomous manner.Link_to_subscribed_fulltex
Solar Ring Mission: Building a Panorama of the Sun and Inner-heliosphere
Solar Ring (SOR) is a proposed space science mission to monitor and study the
Sun and inner heliosphere from a full 360{\deg} perspective in the ecliptic
plane. It will deploy three 120{\deg}-separated spacecraft on the 1-AU orbit.
The first spacecraft, S1, locates 30{\deg} upstream of the Earth, the second,
S2, 90{\deg} downstream, and the third, S3, completes the configuration. This
design with necessary science instruments, e.g., the Doppler-velocity and
vector magnetic field imager, wide-angle coronagraph, and in-situ instruments,
will allow us to establish many unprecedented capabilities: (1) provide
simultaneous Doppler-velocity observations of the whole solar surface to
understand the deep interior, (2) provide vector magnetograms of the whole
photosphere - the inner boundary of the solar atmosphere and heliosphere, (3)
provide the information of the whole lifetime evolution of solar featured
structures, and (4) provide the whole view of solar transients and space
weather in the inner heliosphere. With these capabilities, Solar Ring mission
aims to address outstanding questions about the origin of solar cycle, the
origin of solar eruptions and the origin of extreme space weather events. The
successful accomplishment of the mission will construct a panorama of the Sun
and inner-heliosphere, and therefore advance our understanding of the star and
the space environment that holds our life.Comment: 41 pages, 6 figures, 1 table, to be published in Advances in Space
Researc
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