222 research outputs found
A Study on the Hydrophobicity of Organosilane
Control of zeolite external surface hydrophobicity was achieved by covalently bonding organosilane molecules of varying alkyl chain length. The hydrophobicity was characterized by measurement of static contact angle, finding that zeolite hydrophobicity increased after silanization. To quantify the effect, gravimetric measurements were performed to estimate the mass of coating acquired by the zeolites during silanization. When combined with external surface area measurements,the finding was that the modest coatings (\u3e10 mg/cm2) transformed the zeolite surface hydrophobic; increasing the coating density beyond this critical loading had little effect. This work provides a rational basis for determining the optimal coating density required for a given application
Cross Contrastive Feature Perturbation for Domain Generalization
Domain generalization (DG) aims to learn a robust model from source domains
that generalize well on unseen target domains. Recent studies focus on
generating novel domain samples or features to diversify distributions
complementary to source domains. Yet, these approaches can hardly deal with the
restriction that the samples synthesized from various domains can cause
semantic distortion. In this paper, we propose an online one-stage Cross
Contrasting Feature Perturbation (CCFP) framework to simulate domain shift by
generating perturbed features in the latent space while regularizing the model
prediction against domain shift. Different from the previous fixed synthesizing
strategy, we design modules with learnable feature perturbations and semantic
consistency constraints. In contrast to prior work, our method does not use any
generative-based models or domain labels. We conduct extensive experiments on a
standard DomainBed benchmark with a strict evaluation protocol for a fair
comparison. Comprehensive experiments show that our method outperforms the
previous state-of-the-art, and quantitative analyses illustrate that our
approach can alleviate the domain shift problem in out-of-distribution (OOD)
scenarios
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
Interactive reinforcement learning has shown promise in learning complex
robotic tasks. However, the process can be human-intensive due to the
requirement of large amount of interactive feedback. This paper presents a new
method that uses scores provided by humans, instead of pairwise preferences, to
improve the feedback efficiency of interactive reinforcement learning. Our key
insight is that scores can yield significantly more data than pairwise
preferences. Specifically, we require a teacher to interactively score the full
trajectories of an agent to train a behavioral policy in a sparse reward
environment. To avoid unstable scores given by human negatively impact the
training process, we propose an adaptive learning scheme. This enables the
learning paradigm to be insensitive to imperfect or unreliable scores. We
extensively evaluate our method on robotic locomotion and manipulation tasks.
The results show that the proposed method can efficiently learn near-optimal
policies by adaptive learning from scores, while requiring less feedback
compared to pairwise preference learning methods. The source codes are publicly
available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2023
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning
The standard definition generation task requires to automatically produce
mono-lingual definitions (e.g., English definitions for English words), but
ignores that the generated definitions may also consist of unfamiliar words for
language learners. In this work, we propose a novel task of Trans-Lingual
Definition Generation (TLDG), which aims to generate definitions in another
language, i.e., the native speaker's language. Initially, we explore the
unsupervised manner of this task and build up a simple implementation of
fine-tuning the multi-lingual machine translation model. Then, we develop two
novel methods, Prompt Combination and Contrastive Prompt Learning, for further
enhancing the quality of the generation. Our methods are evaluated against the
baseline Pipeline method in both rich- and low-resource settings, and we
empirically establish its superiority in generating higher-quality
trans-lingual definitions.Comment: Accepted by ACL-BEA worksho
Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
Unsupervised image semantic segmentation(UISS) aims to match low-level visual
features with semantic-level representations without outer supervision. In this
paper, we address the critical properties from the view of feature alignments
and feature uniformity for UISS models. We also make a comparison between UISS
and image-wise representation learning. Based on the analysis, we argue that
the existing MI-based methods in UISS suffer from representation collapse. By
this, we proposed a robust network called Semantic Attention Network(SAN), in
which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise
and semantic features dynamically. Experimental results on multiple semantic
segmentation benchmarks show that our unsupervised segmentation framework
specializes in catching semantic representations, which outperforms all the
unpretrained and even several pretrained methods.Comment: AAAI2
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization
Domain Generalization (DG) is a fundamental challenge for machine learning
models, which aims to improve model generalization on various domains. Previous
methods focus on generating domain invariant features from various source
domains. However, we argue that the domain variantions also contain useful
information, ie, classification-aware information, for downstream tasks, which
has been largely ignored. Different from learning domain invariant features
from source domains, we decouple the input images into Domain Expert Features
and noise. The proposed domain expert features lie in a learned latent space
where the images in each domain can be classified independently, enabling the
implicit use of classification-aware domain variations. Based on the analysis,
we proposed a novel paradigm called Domain Disentanglement Network (DDN) to
disentangle the domain expert features from the source domain images and
aggregate the source domain expert features for representing the target test
domain. We also propound a new contrastive learning method to guide the domain
expert features to form a more balanced and separable feature space.
Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet,
and TerraIncognita demonstrate the competitive performance of our method
compared to the recently proposed alternatives
SharpContour: A Contour-based Boundary Refinement Approach for Efficient and Accurate Instance Segmentation
Excellent performance has been achieved on instance segmentation but the
quality on the boundary area remains unsatisfactory, which leads to a rising
attention on boundary refinement. For practical use, an ideal post-processing
refinement scheme are required to be accurate, generic and efficient. However,
most of existing approaches propose pixel-wise refinement, which either
introduce a massive computation cost or design specifically for different
backbone models. Contour-based models are efficient and generic to be
incorporated with any existing segmentation methods, but they often generate
over-smoothed contour and tend to fail on corner areas. In this paper, we
propose an efficient contour-based boundary refinement approach, named
SharpContour, to tackle the segmentation of boundary area. We design a novel
contour evolution process together with an Instance-aware Point Classifier. Our
method deforms the contour iteratively by updating offsets in a discrete
manner. Differing from existing contour evolution methods, SharpContour
estimates each offset more independently so that it predicts much sharper and
accurate contours. Notably, our method is generic to seamlessly work with
diverse existing models with a small computational cost. Experiments show that
SharpContour achieves competitive gains whilst preserving high efficiencyComment: 10pages, 5 figures, accepted by CVPR 2022, project page: see this
https://xyzhang17.github.io/SharpContour
A physically based surface resistance model for evaporation from bare soils
The resistance to vapor transfer across the soil-air interface, termed surface resistance, plays an important role in determining the evaporation rate from unsaturated bare soils. A physically based analytical model is developed to describe the surface resistance under varying liquid water saturation. When the vaporization plane remains in the topmost soil layer (TSL), the model considers the vapor transport through the external diffusive layer (EDL), and the hydraulic connection between the capillary water in the TSL and underneath water source for evaporation. When the vaporization plane develops below the TSL, the model predicts the surface resistance by taking into account the development of the dry soil layer, the major barrier for vapor transport at the soil-drying stage. With the consideration of the soil pore size distribution, the model is applicable to different soil types. The model was validated against six sets of laboratory experiments on the drying process of initially water-saturated soil columns under nonisothermal conditions. These experiments were conducted using different soil types and/or heat intensities above the soil surface. The model was found to perform well over intermediate and low liquid water saturation ranges while underestimating the surface resistance for the high liquid water saturation range. The results suggest that the model overall represents reasonably well the processes underlying the vapor transfer across the soil-air interface. Future model improvement may be gained by considering the hydraulic connection between the capillary water and film water in the TSL
Enhance Connectivity of Promising Regions for Sampling-based Path Planning
Sampling-based path planning algorithms usually implement uniform sampling
methods to search the state space. However, uniform sampling may lead to
unnecessary exploration in many scenarios, such as the environment with a few
dead ends. Our previous work proposes to use the promising region to guide the
sampling process to address the issue. However, the predicted promising regions
are often disconnected, which means they cannot connect the start and goal
state, resulting in a lack of probabilistic completeness. This work focuses on
enhancing the connectivity of predicted promising regions. Our proposed method
regresses the connectivity probability of the edges in the x and y directions.
In addition, it calculates the weight of the promising edges in loss to guide
the neural network to pay more attention to the connectivity of the promising
regions. We conduct a series of simulation experiments, and the results show
that the connectivity of promising regions improves significantly. Furthermore,
we analyze the effect of connectivity on sampling-based path planning
algorithms and conclude that connectivity plays an essential role in
maintaining algorithm performance.Comment: Accepted in Transactions on Automation Science and Engineering, 202
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