110 research outputs found
Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
In Model-based Reinforcement Learning (MBRL), model learning is critical
since an inaccurate model can bias policy learning via generating misleading
samples. However, learning an accurate model can be difficult since the policy
is continually updated and the induced distribution over visited states used
for model learning shifts accordingly. Prior methods alleviate this issue by
quantifying the uncertainty of model-generated samples. However, these methods
only quantify the uncertainty passively after the samples were generated,
rather than foreseeing the uncertainty before model trajectories fall into
those highly uncertain regions. The resulting low-quality samples can induce
unstable learning targets and hinder the optimization of the policy. Moreover,
while being learned to minimize one-step prediction errors, the model is
generally used to predict for multiple steps, leading to a mismatch between the
objectives of model learning and model usage. To this end, we propose
\emph{Plan To Predict} (P2P), an MBRL framework that treats the model rollout
process as a sequential decision making problem by reversely considering the
model as a decision maker and the current policy as the dynamics. In this way,
the model can quickly adapt to the current policy and foresee the multi-step
future uncertainty when generating trajectories. Theoretically, we show that
the performance of P2P can be guaranteed by approximately optimizing a lower
bound of the true environment return. Empirical results demonstrate that P2P
achieves state-of-the-art performance on several challenging benchmark tasks.Comment: Accepted by NeurIPS202
Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence
We study a new problem of semantic complete scene forecasting (SCSF) in this
work. Given a 4D dynamic point cloud sequence, our goal is to forecast the
complete scene corresponding to the future next frame along with its semantic
labels. To tackle this challenging problem, we properly model the synergetic
relationship between future forecasting and semantic scene completion through a
novel network named SCSFNet. SCSFNet leverages a hybrid geometric
representation for high-resolution complete scene forecasting. To leverage
multi-frame observation as well as the understanding of scene dynamics to ease
the completion task, SCSFNet introduces an attention-based skip connection
scheme. To ease the need to model occlusion variations and to better focus on
the occluded part, SCSFNet utilizes auxiliary visibility grids to guide the
forecasting task. To evaluate the effectiveness of SCSFNet, we conduct
experiments on various benchmarks including two large-scale indoor benchmarks
we contributed and the outdoor SemanticKITTI benchmark. Extensive experiments
show SCSFNet outperforms baseline methods on multiple metrics by a large
margin, and also prove the synergy between future forecasting and semantic
scene completion.Comment: AAAI 2024, see https://scsfnet.github.io
Improving Robust Generalization by Direct PAC-Bayesian Bound Minimization
Recent research in robust optimization has shown an overfitting-like
phenomenon in which models trained against adversarial attacks exhibit higher
robustness on the training set compared to the test set. Although previous work
provided theoretical explanations for this phenomenon using a robust
PAC-Bayesian bound over the adversarial test error, related algorithmic
derivations are at best only loosely connected to this bound, which implies
that there is still a gap between their empirical success and our understanding
of adversarial robustness theory. To close this gap, in this paper we consider
a different form of the robust PAC-Bayesian bound and directly minimize it with
respect to the model posterior. The derivation of the optimal solution connects
PAC-Bayesian learning to the geometry of the robust loss surface through a
Trace of Hessian (TrH) regularizer that measures the surface flatness. In
practice, we restrict the TrH regularizer to the top layer only, which results
in an analytical solution to the bound whose computational cost does not depend
on the network depth. Finally, we evaluate our TrH regularization approach over
CIFAR-10/100 and ImageNet using Vision Transformers (ViT) and compare against
baseline adversarial robustness algorithms. Experimental results show that TrH
regularization leads to improved ViT robustness that either matches or
surpasses previous state-of-the-art approaches while at the same time requires
less memory and computational cost
Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation
As next-generation implantable brain-machine interfaces become pervasive on
edge device, incrementally learning new tasks in bio-plasticity ways is
urgently demanded for Neuromorphic chips. Due to the inherent characteristics
of its structure, spiking neural networks are naturally well-suited for
BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip
learning framework. HWC selectively masks synapse modifications for previous
tasks, retaining them to store new knowledge from subsequent tasks while
preserving the old knowledge. Leveraging the bio-plasticity of dendritic
spines, the intrinsic self-organizing nature of Hebbian Weight Consolidation
aligns naturally with the incremental learning paradigm, facilitating robust
learning outcomes. By reading out spikes layer by layer and performing
back-propagation on the external micro-controller unit, MLoC can efficiently
accomplish on-chip learning. Experiments show that our HWC algorithm up to
23.19% outperforms lower bound that without incremental learning algorithm,
particularly in more challenging monkey behavior decoding scenarios. Taking
into account on-chip computing on Synsense Speck 2e chip, our proposed
algorithm exhibits an improvement of 11.06%. This study demonstrates the
feasibility of employing incremental learning for high-performance neural
signal decoding in next-generation brain-machine interfaces.Comment: 12 pages, 6 figure
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
3D-aware image synthesis aims at learning a generative model that can render
photo-realistic 2D images while capturing decent underlying 3D shapes. A
popular solution is to adopt the generative adversarial network (GAN) and
replace the generator with a 3D renderer, where volume rendering with neural
radiance field (NeRF) is commonly used. Despite the advancement of synthesis
quality, existing methods fail to obtain moderate 3D shapes. We argue that,
considering the two-player game in the formulation of GANs, only making the
generator 3D-aware is not enough. In other words, displacing the generative
mechanism only offers the capability, but not the guarantee, of producing
3D-aware images, because the supervision of the generator primarily comes from
the discriminator. To address this issue, we propose GeoD through learning a
geometry-aware discriminator to improve 3D-aware GANs. Concretely, besides
differentiating real and fake samples from the 2D image space, the
discriminator is additionally asked to derive the geometry information from the
inputs, which is then applied as the guidance of the generator. Such a simple
yet effective design facilitates learning substantially more accurate 3D
shapes. Extensive experiments on various generator architectures and training
datasets verify the superiority of GeoD over state-of-the-art alternatives.
Moreover, our approach is registered as a general framework such that a more
capable discriminator (i.e., with a third task of novel view synthesis beyond
domain classification and geometry extraction) can further assist the generator
with a better multi-view consistency.Comment: Accepted by NeurIPS 2022. Project page:
https://vivianszf.github.io/geo
MCNTs@MnO2 nanocomposite cathode integrated with soluble O2-carrier Co-salen in electrolyte for high-performance Li-air batteries
Li–air batteries (LABs) are promising because of their high energy density. However, LABs are troubled by large electrochemical polarization during discharge and charge, side reactions from both carbon cathode surface/peroxide product and electrolyte/superoxide intermediate, as well as the requirement for pure O2. Here we report the solution using multiwall carbon nanotubes (MCNTs)@MnO2 nanocomposite cathode integrated with N,N′-bis(salicylidene)ethylenediaminocobalt(II) (CoII-salen) in electrolyte for LABs. The advantage of such a combination is that on one hand, the coating layer of δ-MnO2 with about 2–3 nm on MCNTs@MnO2 nanocomposite catalyzes Li2O2 decomposition during charge and suppresses side reactions between product Li2O2 and MCNT surface. On the other hand, CoII-salen works as a mobile O2-carrier and accelerates Li2O2 formation through the reaciton of (CoIII-salen)2-O22– + 2Li+ + 2e– → 2CoII-salen + Li2O2. This reaction route overcomes the pure O2 limitation and avoids the formation of aggressive superoxide intermediate (O2– or LiO2), which easily attacks organic electrolyte. By using this double-catalyst system of Co-salen/MCNTs@MnO2, the lifetime of LABs is prolonged to 300 cycles at 500 mA g–1 (0.15 mA cm–2) with fixed capacity of 1000 mAh g–1 (0.30 mAh cm–2) in dry air (21% O2). Furthermore, we up-scale the capacity to 500 mAh (5.2 mAh cm–2) in pouch-type batteries (∼4 g, 325 Wh kg–1). This study should pave a new way for the design and construction of practical LABs
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis
This work presents an easy-to-use regularizer for GAN training, which helps
explicitly link some axes of the latent space to a set of pixels in the
synthesized image. Establishing such a connection facilitates a more convenient
local control of GAN generation, where users can alter the image content only
within a spatial area simply by partially resampling the latent code.
Experimental results confirm four appealing properties of our regularizer,
which we call LinkGAN. (1) The latent-pixel linkage is applicable to either a
fixed region (\textit{i.e.}, same for all instances) or a particular semantic
category (i.e., varying across instances), like the sky. (2) Two or multiple
regions can be independently linked to different latent axes, which further
supports joint control. (3) Our regularizer can improve the spatial
controllability of both 2D and 3D-aware GAN models, barely sacrificing the
synthesis performance. (4) The models trained with our regularizer are
compatible with GAN inversion techniques and maintain editability on real
images
Identification and validation of ferroptosis-related genes and immune cell infiltration in thyroid associated ophthalmopathy
Thyroid associated ophthalmopathy (TAO) is an orbital autoimmune inflammatory disease that is commonly associated with thyroid dysfunction. Although the etiology of TAO is unclear, ROS accumulation and oxidative stress have been closely linked to the pathogenesis of TAO. Ferroptosis is an iron-dependent programmed cell death characterized by intracellular labile iron levels, excessive accumulation of reactive oxygen species (ROS) and lipid peroxidation. Currently, there are few reports regarding the role of ferroptosis in TAO. This article aimed to identify ferroptosis-related genes (FRGs) with diagnostic and therapeutic potential in TAO and explore their relationship with immune cells and lncRNAs. GSE58331 was downloaded from Gene Expression Omnibus (GEO) database. A total of 162 DEGs were identified between 27 TAO samples and 22 health samples from GSE58331, among which six FRGs (CYBB, CTSB, SLC38A1, TLR4, PEX3, and ABCC1) were obtained. The AUC of SLC38A1, TLR4, PEX3 in lacrimal gland tissues was greater than 80 which suggested high diagnostic value in TAO. The result of immune cell infiltrate analysis indicated increased infiltration of monocytes (p < 0.001), macrophages M0(p = 0.039), mast cells activated (p = 0.008), and neutrophils (p = 0.045) in orbital tissues from TAO patients. Meanwhile, mast cells resting (p = 0.043) and macrophages M2 (p = 0.02) showed reduced infiltration in TAO samples. There were no gender differences in immune cell infiltration in the TAO patients. Two differentially expressed lncRNAs, LINC01140 and ZFHX4-AS1, in TAO groups were identified as ferroptosis-related lncRNAs. CYBB-LINC01140-TLR4, CYBB- LINC01140- SLC38A1, TLR4- LINC01140- SLC38A1, and CTSB- ZFHX4-AS1- CYBB may be potential RNA regulatory pathways in TAO. Targeted drugs and transcription factors for differential expressed FRGs were also screened out in our study. In vitro, experiments revealed that CTSB, PEX3, ABCC1 and ZFHX4-AS1(lncRNA) were differentially expressed in orbital fibroblasts (OFs) between TAO groups and healthy controls at the transcriptional level
GenDeF: Learning Generative Deformation Field for Video Generation
We offer a new perspective on approaching the task of video generation.
Instead of directly synthesizing a sequence of frames, we propose to render a
video by warping one static image with a generative deformation field (GenDeF).
Such a pipeline enjoys three appealing advantages. First, we can sufficiently
reuse a well-trained image generator to synthesize the static image (also
called canonical image), alleviating the difficulty in producing a video and
thereby resulting in better visual quality. Second, we can easily convert a
deformation field to optical flows, making it possible to apply explicit
structural regularizations for motion modeling, leading to temporally
consistent results. Third, the disentanglement between content and motion
allows users to process a synthesized video through processing its
corresponding static image without any tuning, facilitating many applications
like video editing, keypoint tracking, and video segmentation. Both qualitative
and quantitative results on three common video generation benchmarks
demonstrate the superiority of our GenDeF method.Comment: Project page: https://aim-uofa.github.io/GenDeF
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal
for diagnosis and treatment. The challenges lie in the irregular shapes,
blurred boundaries of tumors, and the inefficiency of existing methods.
**Purpose:** We conducted a study to introduce a model, utilizing
human-guided knowledge and unique modules, to address the challenges of 3D
tumor segmentation.
**Methods:** We developed the PropNet framework, propagating radiologists'
knowledge from 2D annotations to the entire 3D space. This model consists of a
proposing stage for coarse segmentation and a refining stage for improved
segmentation, using two-way branches for enhanced performance and an up-down
strategy for efficiency.
**Results:** With 98 patient scans for training and 30 for validation, our
method achieves a significant agreement with manual annotation (Dice of 0.803)
and improves efficiency. The performance is comparable in different scenarios
and with various radiologists' annotations (Dice between 0.785 and 0.803).
Moreover, the model shows improved prognostic prediction performance (C-index
of 0.620 vs. 0.576) on an independent validation set of 42 patients with
advanced gastric cancer.
**Conclusions:** Our model generates accurate tumor segmentation efficiently
and stably, improving prognostic performance and reducing high-throughput image
reading workload. This model can accelerate the quantitative analysis of
gastric tumors and enhance downstream task performance
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