9 research outputs found
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
We study contextual linear bandit problems under uncertainty on features;
they are noisy with missing entries. To address the challenges from the noise,
we analyze Bayesian oracles given observed noisy features. Our Bayesian
analysis finds that the optimal hypothesis can be far from the underlying
realizability function, depending on noise characteristics, which is highly
non-intuitive and does not occur for classical noiseless setups. This implies
that classical approaches cannot guarantee a non-trivial regret bound. We thus
propose an algorithm aiming at the Bayesian oracle from observed information
under this model, achieving regret bound with respect to
feature dimension and time horizon . We demonstrate the proposed
algorithm using synthetic and real-world datasets.Comment: 30 page
Efficient Parallel Audio Generation using Group Masked Language Modeling
We present a fast and high-quality codec language model for parallel audio
generation. While SoundStorm, a state-of-the-art parallel audio generation
model, accelerates inference speed compared to autoregressive models, it still
suffers from slow inference due to iterative sampling. To resolve this problem,
we propose Group-Masked Language Modeling~(G-MLM) and Group Iterative Parallel
Decoding~(G-IPD) for efficient parallel audio generation. Both the training and
sampling schemes enable the model to synthesize high-quality audio with a small
number of iterations by effectively modeling the group-wise conditional
dependencies. In addition, our model employs a cross-attention-based
architecture to capture the speaker style of the prompt voice and improves
computational efficiency. Experimental results demonstrate that our proposed
model outperforms the baselines in prompt-based audio generation.Comment: This work has been submitted to the IEEE for possible publication.
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High methane combustion activity of PdO/CeO2–ZrO2–NiO/γ-Al2O3 catalysts
PdO/CeO2–ZrO2–NiO/γ-Al2O3 catalysts were prepared for the combustion of methane at moderate temperatures. The introduction of a small amount of NiO within the cubic fluorite CeO2–ZrO2 structure as a promoter effectively enhanced the oxygen release and storage abilities of the catalysts, thereby achieving the complete oxidation of methane. The catalyst with the highest activity for methane combustion was 11.3 mass% PdO/20 mass% Ce0.64Zr0.16Ni0.2O1.9/γ-Al2O3, and efficient combustion was realized at a temperature as low as 300 °C
Preservation of Global Knowledge by Not-True Distillation in Federated Learning
In federated learning, a strong global model is collaboratively learned by
aggregating clients' locally trained models. Although this precludes the need
to access clients' data directly, the global model's convergence often suffers
from data heterogeneity. This study starts from an analogy to continual
learning and suggests that forgetting could be the bottleneck of federated
learning. We observe that the global model forgets the knowledge from previous
rounds, and the local training induces forgetting the knowledge outside of the
local distribution. Based on our findings, we hypothesize that tackling down
forgetting will relieve the data heterogeneity problem. To this end, we propose
a novel and effective algorithm, Federated Not-True Distillation (FedNTD),
which preserves the global perspective on locally available data only for the
not-true classes. In the experiments, FedNTD shows state-of-the-art performance
on various setups without compromising data privacy or incurring additional
communication costs.Comment: Under revie
Expression of ATP/GTP Binding Protein 1 Has Prognostic Value for the Clinical Outcomes in Non-Small Cell Lung Carcinoma
ATP/GTP binding protein 1 (AGTPBP1) encodes a crucial protein, cytosolic carboxypeptidase 1 (CCP1), which plays a role in modulating the polyglutamylation of tubulin and has been studied in degenerative diseases. However, the role of AGTPBP1 in malignancy has not been completely studied yet. In this study, we examined the role of AGTPBP1 in cancer progression, its association with patient survival, and related mechanisms in lung cancer, using the A549 cell line and lung cancer gene expression datasets. AGTPBP1 knockdown increased the proliferation, migration, sphere formation, and drug resistance of A549 cells. Lung cancer datasets revealed significantly lower mRNA and protein expression levels of AGTPBP1 in lung cancer tissues, as compared to those in normal tissues. Importantly, AGTPBP1 expression positively correlated with patient survival. Analysis of co-expressed genes revealed that AGTPBP1 expression positively correlated with immune infiltration in lung cancer. Our results conclusively suggested that AGTPBP1 expression was correlated with cancer progression and immune infiltration in lung cancer