239 research outputs found
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
In the field of multi-objective optimization algorithms, multi-objective
Bayesian Global Optimization (MOBGO) is an important branch, in addition to
evolutionary multi-objective optimization algorithms (EMOAs). MOBGO utilizes
Gaussian Process models learned from previous objective function evaluations to
decide the next evaluation site by maximizing or minimizing an infill
criterion. A common criterion in MOBGO is the Expected Hypervolume Improvement
(EHVI), which shows a good performance on a wide range of problems, with
respect to exploration and exploitation. However, so far it has been a
challenge to calculate exact EHVI values efficiently. In this paper, an
efficient algorithm for the computation of the exact EHVI for a generic case is
proposed. This efficient algorithm is based on partitioning the integration
volume into a set of axis-parallel slices. Theoretically, the upper bound time
complexities are improved from previously and , for two- and
three-objective problems respectively, to , which is
asymptotically optimal. This article generalizes the scheme in higher
dimensional case by utilizing a new hyperbox decomposition technique, which was
proposed by D{\"a}chert et al, EJOR, 2017. It also utilizes a generalization of
the multilayered integration scheme that scales linearly in the number of
hyperboxes of the decomposition. The speed comparison shows that the proposed
algorithm in this paper significantly reduces computation time. Finally, this
decomposition technique is applied in the calculation of the Probability of
Improvement (PoI)
Auto-Encoding Adversarial Imitation Learning
Reinforcement learning (RL) provides a powerful framework for
decision-making, but its application in practice often requires a carefully
designed reward function. Adversarial Imitation Learning (AIL) sheds light on
automatic policy acquisition without access to the reward signal from the
environment. In this work, we propose Auto-Encoding Adversarial Imitation
Learning (AEAIL), a robust and scalable AIL framework. To induce expert
policies from demonstrations, AEAIL utilizes the reconstruction error of an
auto-encoder as a reward signal, which provides more information for optimizing
policies than the prior discriminator-based ones. Subsequently, we use the
derived objective functions to train the auto-encoder and the agent policy.
Experiments show that our AEAIL performs superior compared to state-of-the-art
methods on both state and image based environments. More importantly, AEAIL
shows much better robustness when the expert demonstrations are noisy.Comment: 15 page
Semantic-Aware Fine-Grained Correspondence
Establishing visual correspondence across images is a challenging and
essential task. Recently, an influx of self-supervised methods have been
proposed to better learn representations for visual correspondence. However, we
find that these methods often fail to leverage semantic information and
over-rely on the matching of low-level features. In contrast, human vision is
capable of distinguishing between distinct objects as a pretext to tracking.
Inspired by this paradigm, we propose to learn semantic-aware fine-grained
correspondence. Firstly, we demonstrate that semantic correspondence is
implicitly available through a rich set of image-level self-supervised methods.
We further design a pixel-level self-supervised learning objective which
specifically targets fine-grained correspondence. For downstream tasks, we fuse
these two kinds of complementary correspondence representations together,
demonstrating that they boost performance synergistically. Our method surpasses
previous state-of-the-art self-supervised methods using convolutional networks
on a variety of visual correspondence tasks, including video object
segmentation, human pose tracking, and human part tracking.Comment: 26 page
Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild
While 6D object pose estimation has wide applications across computer vision
and robotics, it remains far from being solved due to the lack of annotations.
The problem becomes even more challenging when moving to category-level 6D
pose, which requires generalization to unseen instances. Current approaches are
restricted by leveraging annotations from simulation or collected from humans.
In this paper, we overcome this barrier by introducing a self-supervised
learning approach trained directly on large-scale real-world object videos for
category-level 6D pose estimation in the wild. Our framework reconstructs the
canonical 3D shape of an object category and learns dense correspondences
between input images and the canonical shape via surface embedding. For
training, we propose novel geometrical cycle-consistency losses which construct
cycles across 2D-3D spaces, across different instances and different time
steps. The learned correspondence can be applied for 6D pose estimation and
other downstream tasks such as keypoint transfer. Surprisingly, our method,
without any human annotations or simulators, can achieve on-par or even better
performance than previous supervised or semi-supervised methods on in-the-wild
images. Our project page is: https://kywind.github.io/self-pose .Comment: Project page: https://kywind.github.io/self-pos
Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives
Reasoning about causal and temporal event relations in videos is a new
destination of Video Question Answering (VideoQA).The major stumbling block to
achieve this purpose is the semantic gap between language and video since they
are at different levels of abstraction. Existing efforts mainly focus on
designing sophisticated architectures while utilizing frame- or object-level
visual representations. In this paper, we reconsider the multi-modal alignment
problem in VideoQA from feature and sample perspectives to achieve better
performance. From the view of feature,we break down the video into trajectories
and first leverage trajectory feature in VideoQA to enhance the alignment
between two modalities. Moreover, we adopt a heterogeneous graph architecture
and design a hierarchical framework to align both trajectory-level and
frame-level visual feature with language feature. In addition, we found that
VideoQA models are largely dependent on language priors and always neglect
visual-language interactions. Thus, two effective yet portable training
augmentation strategies are designed to strengthen the cross-modal
correspondence ability of our model from the view of sample. Extensive results
show that our method outperforms all the state-of-the-art models on the
challenging NExT-QA benchmark, which demonstrates the effectiveness of the
proposed method
Molecular epidemiology and antimicrobial resistance patterns of carbapenem-resistant Acinetobacter baumannii isolates from patients admitted at ICUs of a teaching hospital in Zunyi, China
BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) has emerged as a predominant strain of healthcare-associated infections worldwide, particularly in intensive care units (ICUs). Therefore, it is imperative to study the molecular epidemiology of CRAB in the ICUs using multiple molecular typing methods to lay the foundation for the development of infection prevention and control strategies. This study aimed to determine the antimicrobial susceptibility profile, the molecular epidemiology and conduct homology analysis on CRAB strains isolated from ICUs.MethodsThe sensitivity to various antimicrobials was determined using the minimum inhibitory concentration (MIC) method, Kirby-Bauer disk diffusion (KBDD), and E-test assays. Resistance genes were identified by polymerase chain reaction (PCR). Molecular typing was performed using multilocus sequence typing (MLST) and multiple-locus variable-number tandem repeat analysis (MLVA).ResultsAmong the 79 isolates collected, they exhibited high resistance to various antimicrobials but showed low resistance to levofloxacin, trimethoprim-sulfamethoxazole, and tetracyclines. Notably, all isolates of A. baumannii were identified as multidrug-resistant A. baumannii (MDR-AB). The blaOXA-51-like, adeJ, and adeG genes were all detected, while the detection rates of blaOXA-23-like (97.5%), adeB (93.67%), blaADC (93.67%), qacEΔ1-sul1 (84.81%) were higher; most of the Ambler class A and class B genes were not detected. MLST analysis on the 79 isolates identified five sequence types (STs), which belonged to group 3 clonal complexes 369. ST1145Ox was the most frequently observed ST with a count of 56 out of 79 isolates (70.89%). MLST analysis for non-sensitive tigecycline isolates, which were revealed ST1145Ox and ST1417Ox as well. By using the MLVA assay, the 79 isolates could be grouped into a total of 64 distinct MTs with eleven clusters identified in them. Minimum spanning tree analysis defined seven different MLVA complexes (MCs) labeled MC1 to MC6 along with twenty singletons. The locus MLVA-AB_2396 demonstrated the highest Simpson’s diversity index value at 0.829 among all loci tested in this study while also having one of the highest variety of tandem repeat species.ConclusionThe molecular diversity and clonal affinities within the genomes of the CRAB strains were clearly evident, with the identification of ST1144Ox, ST1658Ox, and ST1646Oxqaq representing novel findings
Challenges of ELA-guided Function Evolution using Genetic Programming
Within the optimization community, the question of how to generate new
optimization problems has been gaining traction in recent years. Within topics
such as instance space analysis (ISA), the generation of new problems can
provide new benchmarks which are not yet explored in existing research. Beyond
that, this function generation can also be exploited for solving complex
real-world optimization problems. By generating functions with similar
properties to the target problem, we can create a robust test set for algorithm
selection and configuration.
However, the generation of functions with specific target properties remains
challenging. While features exist to capture low-level landscape properties,
they might not always capture the intended high-level features. We show that a
genetic programming (GP) approach guided by these exploratory landscape
analysis (ELA) properties is not always able to find satisfying functions. Our
results suggest that careful considerations of the weighting of landscape
properties, as well as the distance measure used, might be required to evolve
functions that are sufficiently representative to the target landscape
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