55 research outputs found
QAOA with fewer qubits: a coupling framework to solve larger-scale Max-Cut problem
Maximum cut (Max-Cut) problem is one of the most important combinatorial
optimization problems because of its various applications in real life, and
recently Quantum Approximate Optimization Algorithm (QAOA) has been widely
employed to solve it. However, as the size of the problem increases, the number
of qubits required will become larger. With the aim of saving qubits, we
propose a coupling framework for designing QAOA circuits to solve larger-scale
Max-Cut problem. This framework relies on a classical algorithm that
approximately solves a certain variant of Max-Cut, and we derive an
approximation guarantee theoretically, assuming the approximation ratio of the
classical algorithm and QAOA. Furthermore we design a heuristic approach that
fits in our framework and perform sufficient numerical experiments, where we
solve Max-Cut on various -vertex Erd\H{o}s-R\'enyi graphs. Our framework
only consumes qubits and achieves approximation ratio on average,
which outperforms the previous methods showing (quantum algorithm
using the same number of qubits) and (classical algorithm). The
experimental results indicate our well-designed quantum-classical coupling
framework gives satisfactory approximation ratio while reduces the qubit cost,
which sheds light on more potential computing power of NISQ devices
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
A combined method for gas-bearing layer identification in a complex sandstone reservoir
Langgu Depression is a mature oil and gas exploration area with complicated lithological and physical properties. The varying formation fluid, low-resistivity hydrocarbon-bearing reservoirs, and non-uniform logging series greatly increase the difficulty of gas reservoir identification. The Monte Carlo method is employed to simulate the neutron–gamma logging responses to gas saturation and the influential factors. According to the result, a new gas identification chart eliminating the influence of porosity and formation water salinity is proposed to identify gas reservoirs in the old wells. At the same time, a fluid factor extracted from array acoustic logging and core measurement data is sensitive to the development of gas-bearing layers and useful for the identification of gas reservoirs in the new wells with array acoustic logging. The field examples show that the new combined method greatly improves the ability to identify gas-bearing layers and works well in old well reexamination and new well interpretation
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
Multivariable association discovery in population-scale meta-omics studies.
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2\u27s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles
PyPose v0.6: The Imperative Programming Interface for Robotics
PyPose is an open-source library for robot learning. It combines a
learning-based approach with physics-based optimization, which enables seamless
end-to-end robot learning. It has been used in many tasks due to its
meticulously designed application programming interface (API) and efficient
implementation. From its initial launch in early 2022, PyPose has experienced
significant enhancements, incorporating a wide variety of new features into its
platform. To satisfy the growing demand for understanding and utilizing the
library and reduce the learning curve of new users, we present the fundamental
design principle of the imperative programming interface, and showcase the
flexible usage of diverse functionalities and modules using an extremely simple
Dubins car example. We also demonstrate that the PyPose can be easily used to
navigate a real quadruped robot with a few lines of code
Wearable Haptic Interfaces and Systems
The past two decades have seen significant advances in how users interact with machines. Yet nowadays, people are increasingly paying attention to developing new control terminals and interfaces regarding communication between humans and robots, special equipment, or the virtual world. Wearable haptic interfaces offer more comfortable and realistic interactive experiences in human-machine touch and satisfy people’s needs beyond simply controlling objects. They are now applied in various areas, including health, education, virtual reality, object detection, etc... The passage briefly introduces some familiar wearable haptic interfaces, including hand-worn, vest-worn, and foot-worn devices. And then the advantages and disadvantages of the mentioned wearable devices will also be discussed. This passage will provide an overview of the current technology in wearable haptic interfaces and help people understand the strengths and weaknesses of the devices for different body parts
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