176 research outputs found
Simulation analysis of UAV autonomous landing system based on TECs
Aiming at the decoupling control problem of velocity and altitude in the process of unmanned aerial vehicles (UAV)
autonomous landing under visual guidance, this paper establishes the fl ight control model of fi xed wing UAV, and deduces the coupling
relationship between airspeed and altitude in the process of UAV glide, The total energy control system (TECs) is used for decoupling
control to realize the autonomous fi xed-point landing of UAV. The simulation results show that the designed control law can decouple the
airspeed and altitude of the UAV, so that the UAV can land at the predetermined place autonomously and accurately
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits
We consider the adversarial linear contextual bandit problem, where the loss
vectors are selected fully adversarially and the per-round action set (i.e. the
context) is drawn from a fixed distribution. Existing methods for this problem
either require access to a simulator to generate free i.i.d. contexts, achieve
a sub-optimal regret no better than , or are
computationally inefficient. We greatly improve these results by achieving a
regret of without a simulator, while maintaining
computational efficiency when the action set in each round is small. In the
special case of sleeping bandits with adversarial loss and stochastic arm
availability, our result answers affirmatively the open question by Saha et al.
[2020] on whether there exists a polynomial-time algorithm with
regret. Our approach naturally handles the case where the
loss is linear up to an additive misspecification error, and our regret shows
near-optimal dependence on the magnitude of the error
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
We study online reinforcement learning in linear Markov decision processes
with adversarial losses and bandit feedback, without prior knowledge on
transitions or access to simulators. We introduce two algorithms that achieve
improved regret performance compared to existing approaches. The first
algorithm, although computationally inefficient, ensures a regret of
, where is the number of
episodes. This is the first result with the optimal dependence in the
considered setting. The second algorithm, which is based on the policy
optimization framework, guarantees a regret of
and is computationally
efficient. Both our results significantly improve over the state-of-the-art: a
computationally inefficient algorithm by Kong et al. [2023] with
regret, for some problem-dependent constant that can
be arbitrarily close to zero, and a computationally efficient algorithm by
Sherman et al. [2023b] with regret
Evaluation of Individual Contribution in Blended Collaborative Learning
With the deepening of classroom teaching reform, blended collaborative learning has become a common collaborative learning method, and its significance and value has been verified by many parties. However, there is still a lack of quantitative analysis and detailed insight into the internal interaction dynamics of the group at the individual level. There are limitations in the evaluation dimensions and methods of individual contribution in collaborative learning in previous studies, so it is difficult to obtain a comprehensive evaluation of individual contribution. The purpose of this study is to build an effective evaluation model of individual contribution in blended collaborative learning. Discussion recordings and text data in collaboration were collected in a non-invasive way to validate the model. Based on evaluation model, the characteristics and rules behind the data deeply were explored, the collaborative process of the blended collaborative learning was analyzed and mined, and the characteristics of learners\u27 contribution were summarized to support the development of blended collaborative learning
Explicit Intensity Control for Accented Text-to-speech
Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). How to control the
intensity of accent in the process of TTS is a very interesting research
direction, and has attracted more and more attention. Recent work design a
speaker-adversarial loss to disentangle the speaker and accent information, and
then adjust the loss weight to control the accent intensity. However, such a
control method lacks interpretability, and there is no direct correlation
between the controlling factor and natural accent intensity. To this end, this
paper propose a new intuitive and explicit accent intensity control scheme for
accented TTS. Specifically, we first extract the posterior probability, called
as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to
quantify the phoneme accent intensity for accented speech, then design a
FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity
expression into account during speech generation. Experiments show that the our
method outperforms the baseline model in terms of accent rendering and
intensity control.Comment: 5 pages, 3 figures. Submitted to ICASSP 2023. arXiv admin note: text
overlap with arXiv:2209.1080
Learning Diverse Tone Styles for Image Retouching
Image retouching, aiming to regenerate the visually pleasing renditions of
given images, is a subjective task where the users are with different aesthetic
sensations. Most existing methods deploy a deterministic model to learn the
retouching style from a specific expert, making it less flexible to meet
diverse subjective preferences. Besides, the intrinsic diversity of an expert
due to the targeted processing on different images is also deficiently
described. To circumvent such issues, we propose to learn diverse image
retouching with normalizing flow-based architectures. Unlike current flow-based
methods which directly generate the output image, we argue that learning in a
style domain could (i) disentangle the retouching styles from the image
content, (ii) lead to a stable style presentation form, and (iii) avoid the
spatial disharmony effects. For obtaining meaningful image tone style
representations, a joint-training pipeline is delicately designed, which is
composed of a style encoder, a conditional RetouchNet, and the image tone style
normalizing flow (TSFlow) module. In particular, the style encoder predicts the
target style representation of an input image, which serves as the conditional
information in the RetouchNet for retouching, while the TSFlow maps the style
representation vector into a Gaussian distribution in the forward pass. After
training, the TSFlow can generate diverse image tone style vectors by sampling
from the Gaussian distribution. Extensive experiments on MIT-Adobe FiveK and
PPR10K datasets show that our proposed method performs favorably against
state-of-the-art methods and is effective in generating diverse results to
satisfy different human aesthetic preferences. Source code and pre-trained
models are publicly available at https://github.com/SSRHeart/TSFlow
Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities
Multimodal emotion recognition leverages complementary information across
modalities to gain performance. However, we cannot guarantee that the data of
all modalities are always present in practice. In the studies to predict the
missing data across modalities, the inherent difference between heterogeneous
modalities, namely the modality gap, presents a challenge. To address this, we
propose to use invariant features for a missing modality imagination network
(IF-MMIN) which includes two novel mechanisms: 1) an invariant feature learning
strategy that is based on the central moment discrepancy (CMD) distance under
the full-modality scenario; 2) an invariant feature based imagination module
(IF-IM) to alleviate the modality gap during the missing modalities prediction,
thus improving the robustness of multimodal joint representation. Comprehensive
experiments on the benchmark dataset IEMOCAP demonstrate that the proposed
model outperforms all baselines and invariantly improves the overall emotion
recognition performance under uncertain missing-modality conditions. We release
the code at: https://github.com/ZhuoYulang/IF-MMIN.Comment: 5 pages, 3 figures, 1 table. Submitted to ICASSP 2023. We release the
code at: https://github.com/ZhuoYulang/IF-MMI
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