178 research outputs found
Horizon-unbiased Investment with Ambiguity
In the presence of ambiguity on the driving force of market randomness, we
consider the dynamic portfolio choice without any predetermined investment
horizon. The investment criteria is formulated as a robust forward performance
process, reflecting an investor's dynamic preference. We show that the market
risk premium and the utility risk premium jointly determine the investors'
trading direction and the worst-case scenarios of the risky asset's mean return
and volatility. The closed-form formulas for the optimal investment strategies
are given in the special settings of the CRRA preference
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models
Camouflaged Object Detection (COD) is a challenging task in computer vision
due to the high similarity between camouflaged objects and their surroundings.
Existing COD methods primarily employ semantic segmentation, which suffers from
overconfident incorrect predictions. In this paper, we propose a new paradigm
that treats COD as a conditional mask-generation task leveraging diffusion
models. Our method, dubbed CamoDiffusion, employs the denoising process of
diffusion models to iteratively reduce the noise of the mask. Due to the
stochastic sampling process of diffusion, our model is capable of sampling
multiple possible predictions from the mask distribution, avoiding the problem
of overconfident point estimation. Moreover, we develop specialized learning
strategies that include an innovative ensemble approach for generating robust
predictions and tailored forward diffusion methods for efficient training,
specifically for the COD task. Extensive experiments on three COD datasets
attest the superior performance of our model compared to existing
state-of-the-art methods, particularly on the most challenging COD10K dataset,
where our approach achieves 0.019 in terms of MAE
HODN: Disentangling Human-Object Feature for HOI Detection
The task of Human-Object Interaction (HOI) detection is to detect humans and
their interactions with surrounding objects, where transformer-based methods
show dominant advances currently. However, these methods ignore the
relationship among humans, objects, and interactions: 1) human features are
more contributive than object ones to interaction prediction; 2) interactive
information disturbs the detection of objects but helps human detection. In
this paper, we propose a Human and Object Disentangling Network (HODN) to model
the HOI relationships explicitly, where humans and objects are first detected
by two disentangling decoders independently and then processed by an
interaction decoder. Considering that human features are more contributive to
interaction, we propose a Human-Guide Linking method to make sure the
interaction decoder focuses on the human-centric regions with human features as
the positional embeddings. To handle the opposite influences of interactions on
humans and objects, we propose a Stop-Gradient Mechanism to stop interaction
gradients from optimizing the object detection but to allow them to optimize
the human detection. Our proposed method achieves competitive performance on
both the V-COCO and the HICO-Det datasets. It can be combined with existing
methods easily for state-of-the-art results.Comment: Accepted by TMM 202
Exploring Target Representations for Masked Autoencoders
Masked autoencoders have become popular training paradigms for
self-supervised visual representation learning. These models randomly mask a
portion of the input and reconstruct the masked portion according to the target
representations. In this paper, we first show that a careful choice of the
target representation is unnecessary for learning good representations, since
different targets tend to derive similarly behaved models. Driven by this
observation, we propose a multi-stage masked distillation pipeline and use a
randomly initialized model as the teacher, enabling us to effectively train
high-capacity models without any efforts to carefully design target
representations. Interestingly, we further explore using teachers of larger
capacity, obtaining distilled students with remarkable transferring ability. On
different tasks of classification, transfer learning, object detection, and
semantic segmentation, the proposed method to perform masked knowledge
distillation with bootstrapped teachers (dBOT) outperforms previous
self-supervised methods by nontrivial margins. We hope our findings, as well as
the proposed method, could motivate people to rethink the roles of target
representations in pre-training masked autoencoders.The code and pre-trained
models are publicly available at https://github.com/liuxingbin/dbot.Comment: The first two authors contributed equall
CAT:Collaborative Adversarial Training
Adversarial training can improve the robustness of neural networks. Previous
methods focus on a single adversarial training strategy and do not consider the
model property trained by different strategies. By revisiting the previous
methods, we find different adversarial training methods have distinct
robustness for sample instances. For example, a sample instance can be
correctly classified by a model trained using standard adversarial training
(AT) but not by a model trained using TRADES, and vice versa. Based on this
observation, we propose a collaborative adversarial training framework to
improve the robustness of neural networks. Specifically, we use different
adversarial training methods to train robust models and let models interact
with their knowledge during the training process. Collaborative Adversarial
Training (CAT) can improve both robustness and accuracy. Extensive experiments
on various networks and datasets validate the effectiveness of our method. CAT
achieves state-of-the-art adversarial robustness without using any additional
data on CIFAR-10 under the Auto-Attack benchmark. Code is available at
https://github.com/liuxingbin/CAT.Comment: Tech repor
Latent Feature Relation Consistency for Adversarial Robustness
Deep neural networks have been applied in many computer vision tasks and
achieved state-of-the-art performance. However, misclassification will occur
when DNN predicts adversarial examples which add human-imperceptible
adversarial noise to natural examples. This limits the application of DNN in
security-critical fields. To alleviate this problem, we first conducted an
empirical analysis of the latent features of both adversarial and natural
examples and found the similarity matrix of natural examples is more compact
than those of adversarial examples. Motivated by this observation, we propose
\textbf{L}atent \textbf{F}eature \textbf{R}elation \textbf{C}onsistency
(\textbf{LFRC}), which constrains the relation of adversarial examples in
latent space to be consistent with the natural examples. Importantly, our LFRC
is orthogonal to the previous method and can be easily combined with them to
achieve further improvement. To demonstrate the effectiveness of LFRC, we
conduct extensive experiments using different neural networks on benchmark
datasets. For instance, LFRC can bring 0.78\% further improvement compared to
AT, and 1.09\% improvement compared to TRADES, against AutoAttack on CIFAR10.
Code is available at https://github.com/liuxingbin/LFRC.Comment: Tech repor
The Exact Distribution of the Condition Number of Complex Random Matrices
Let Gm×n (m≥n) be a complex random matrix and W=Gm×nHGm×n which is the complex Wishart matrix. Let λ1>λ2>…>λn>0 and σ1>σ2>…>σn>0 denote the eigenvalues of the W and singular values of Gm×n, respectively. The 2-norm condition number of Gm×n is κ2Gm×n=λ1/λn=σ1/σn. In this paper, the exact distribution of the condition number of the complex Wishart matrices is derived. The distribution is expressed in terms of complex zonal polynomials
Computational identification of rare codons of Escherichia coli based on codon pairs preference
<p>Abstract</p> <p>Background</p> <p>Codon bias is believed to play an important role in the control of gene expression. In <it>Escherichia coli</it>, some rare codons, which can limit the expression level of exogenous protein, have been defined by gene engineering operations. Previous studies have confirmed the existence of codon pair's preference in many genomes, but the underlying cause of this bias has not been well established. Here we focus on the patterns of rarely-used synonymous codons. A novel method was introduced to identify the rare codons merely by codon pair bias in <it>Escherichia coli</it>.</p> <p>Results</p> <p>In <it>Escherichia coli</it>, we defined the "rare codon pairs" by calculating the frequency of occurrence of all codon pairs in coding sequences. Rare codons which are disliked in genes could make great contributions to forming rare codon pairs. Meanwhile our investigation showed that many of these rare codon pairs contain termination codons and the recognized sites of restriction enzymes. Furthermore, a new index (F<sub>rare</sub>) was developed. Through comparison with the classical indices we found a significant negative correlation between F<sub>rare </sub>and the indices which depend on reference datasets.</p> <p>Conclusions</p> <p>Our approach suggests that we can identify rare codons by studying the context in which a codon lies. Also, the frequency of rare codons (F<sub>rare</sub>) could be a useful index of codon bias regardless of the lack of expression abundance information.</p
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