1,669 research outputs found
Bifunctional enzyme provides absolute concentration robustness in multisite covalent modification networks
Biochemical covalent modification networks exhibit a remarkable suite of
steady state and dynamical properties such as multistationarity, oscillations,
ultrasensitivity and absolute concentration robustness. This paper focuses on
conditions required for a network to have a species with absolute concentration
robustness. We find that the robustness in a substrate is endowed by its
interaction with a bifunctional enzyme, which is an enzyme that has different
roles when isolated versus when bound as a substrate-enzyme complex. When
isolated, the bifunctional enzyme promotes production of more molecules of the
robust species while when bound, the same enzyme facilitates degradation of the
robust species. These dual actions produce robustness in the large class of
covalent modification networks. For each network of this type, we find the
network conditions for the presence of robustness, the species that has
robustness, and its robustness value. The unified approach of simultaneously
analyzing a large class of networks for a single property, i.e. absolute
concentration robustness, reveals the underlying mechanism of the action of
bifunctional enzyme while simultaneously providing a precise mathematical
description of bifunctionality.Comment: 28 page
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model
Developing an agent in reinforcement learning (RL) that is capable of
performing complex control tasks directly from high-dimensional observation
such as raw pixels is yet a challenge as efforts are made towards improving
sample efficiency and generalization. This paper considers a learning framework
for Curiosity Contrastive Forward Dynamics Model (CCFDM) in achieving a more
sample-efficient RL based directly on raw pixels. CCFDM incorporates a forward
dynamics model (FDM) and performs contrastive learning to train its deep
convolutional neural network-based image encoder (IE) to extract conducive
spatial and temporal information for achieving a more sample efficiency for RL.
In addition, during training, CCFDM provides intrinsic rewards, produced based
on FDM prediction error, encourages the curiosity of the RL agent to improve
exploration. The diverge and less-repetitive observations provide by both our
exploration strategy and data augmentation available in contrastive learning
improve not only the sample efficiency but also the generalization. Performance
of existing model-free RL methods such as Soft Actor-Critic built on top of
CCFDM outperforms prior state-of-the-art pixel-based RL methods on the DeepMind
Control Suite benchmark
A Cosine Similarity-based Method for Out-of-Distribution Detection
The ability to detect OOD data is a crucial aspect of practical machine
learning applications. In this work, we show that cosine similarity between the
test feature and the typical ID feature is a good indicator of OOD data. We
propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that
uses a cosine similarity scoring function. Extensive experiments on multiple
benchmarks show that CTM outperforms existing post hoc OOD detection methods.Comment: Accepted paper at ICML 2023 Workshop on Spurious Correlations,
Invariance, and Stability. 10 pages (4 main + appendix
SoftGroup++: Scalable 3D Instance Segmentation with Octree Pyramid Grouping
Existing state-of-the-art 3D point cloud instance segmentation methods rely
on a grouping-based approach that groups points to obtain object instances.
Despite improvement in producing accurate segmentation results, these methods
lack scalability and commonly require dividing large input into multiple parts.
To process a scene with millions of points, the existing fastest method
SoftGroup \cite{vu2022softgroup} requires tens of seconds, which is under
satisfaction. Our finding is that -Nearest Neighbor (-NN), which serves
as the prerequisite of grouping, is a computational bottleneck. This bottleneck
severely worsens the inference time in the scene with a large number of points.
This paper proposes SoftGroup++ to address this computational bottleneck and
further optimize the inference speed of the whole network. SoftGroup++ is built
upon SoftGroup, which differs in three important aspects: (1) performs octree
-NN instead of vanilla -NN to reduce time complexity from
to , (2) performs pyramid scaling
that adaptively downsamples backbone outputs to reduce search space for -NN
and grouping, and (3) performs late devoxelization that delays the conversion
from voxels to points towards the end of the model such that intermediate
components operate at a low computational cost. Extensive experiments on
various indoor and outdoor datasets demonstrate the efficacy of the proposed
SoftGroup++. Notably, SoftGroup++ processes large scenes of millions of points
by a single forward without dividing the input into multiple parts, thus
enriching contextual information. Especially, SoftGroup++ achieves 2.4 points
AP improvement while nearly faster than the existing fastest
method on S3DIS dataset. The code and trained models will be made publicly
available.Comment: Technical repor
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