258 research outputs found
Can crude oil serve as a hedging asset for underlying securities? - Research on the heterogenous correlation between crude oil and stock index
In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China's stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger
Unsupervised Behavior Extraction via Random Intent Priors
Reward-free data is abundant and contains rich prior knowledge of human
behaviors, but it is not well exploited by offline reinforcement learning (RL)
algorithms. In this paper, we propose UBER, an unsupervised approach to extract
useful behaviors from offline reward-free datasets via diversified rewards.
UBER assigns different pseudo-rewards sampled from a given prior distribution
to different agents to extract a diverse set of behaviors, and reuse them as
candidate policies to facilitate the learning of new tasks. Perhaps
surprisingly, we show that rewards generated from random neural networks are
sufficient to extract diverse and useful behaviors, some even close to expert
ones. We provide both empirical and theoretical evidence to justify the use of
random priors for the reward function. Experiments on multiple benchmarks
showcase UBER's ability to learn effective and diverse behavior sets that
enhance sample efficiency for online RL, outperforming existing baselines. By
reducing reliance on human supervision, UBER broadens the applicability of RL
to real-world scenarios with abundant reward-free data.Comment: Thirty-seventh Conference on Neural Information Processing System
Preliminary Study on Functional and Aesthetic Reconstruction by Using a Small Artery-only Free Medial Flap of the Second Toe for Fingertip Injuries
OBJECTIVES: This study was designed to introduce the feasibility of fingertip reconstruction by using a free medial flap of the second toe without vein anastomosis. METHODS: In total, 8 patients with fingertip injuries were treated successfully with this method. Patients who underwent reconstruction from September 2016 to October 2017 in our hospital with an artery-only free medial flap transfer of the second toe for fingertip injuries were included, and patients who underwent additional procedures that may impact the postoperative results and were followed up for less than 6 months were excluded. Clinical trial registration: ChiCTR19000021883. RESULTS: According to the Allen classification, five patients had Type 3 injuries, and three patients had Type 4 injuries. One arterial nerve and one digital nerve were repaired at the same time. No additional dissection was performed in either the donor or recipient site of the dorsal or volar vein. Postoperative venous congestion was monitored based on the color, temperature and the degree of tissue oxygen saturation. The flap size ranged from 1.20*1.0 cm2 to 1.80*1.0 cm2 . The reconstruction time was 71.86 (SD 14.75) minutes. The two-point discrimination and the monofilament results were satisfying; cold intolerance did not appear in five patients, and the other three patients had cold intolerance with grades of 4, 12 and 26, which were considered satisfactory. Moreover, leech therapy, continuous bleeding and needle sutures were not utilized in any cases. CONCLUSIONS: Reconstruction with a small artery-only free medial flap transfer of the second toe led to satisfactory sensory and motor function in the selected patients with fingertip injuries
Spiking Denoising Diffusion Probabilistic Models
Spiking neural networks (SNNs) have ultra-low energy consumption and high
biological plausibility due to their binary and bio-driven nature compared with
artificial neural networks (ANNs). While previous research has primarily
focused on enhancing the performance of SNNs in classification tasks, the
generative potential of SNNs remains relatively unexplored. In our paper, we
put forward Spiking Denoising Diffusion Probabilistic Models (SDDPM), a new
class of SNN-based generative models that achieve high sample quality. To fully
exploit the energy efficiency of SNNs, we propose a purely Spiking U-Net
architecture, which achieves comparable performance to its ANN counterpart
using only 4 time steps, resulting in significantly reduced energy consumption.
Extensive experimental results reveal that our approach achieves
state-of-the-art on the generative tasks and substantially outperforms other
SNN-based generative models, achieving up to and
improvement on the CIFAR-10 and the CelebA datasets, respectively. Moreover, we
propose a threshold-guided strategy that can further improve the performances
by 16.7% in a training-free manner. The SDDPM symbolizes a significant
advancement in the field of SNN generation, injecting new perspectives and
potential avenues of exploration.Comment: Under Revie
Data Poisoning Attacks Against Multimodal Encoders
Traditional machine learning (ML) models usually rely on large-scale labeled
datasets to achieve strong performance. However, such labeled datasets are
often challenging and expensive to obtain. Also, the predefined categories
limit the model's ability to generalize to other visual concepts as additional
labeled data is required. On the contrary, the newly emerged multimodal model,
which contains both visual and linguistic modalities, learns the concept of
images from the raw text. It is a promising way to solve the above problems as
it can use easy-to-collect image-text pairs to construct the training dataset
and the raw texts contain almost unlimited categories according to their
semantics. However, learning from a large-scale unlabeled dataset also exposes
the model to the risk of potential poisoning attacks, whereby the adversary
aims to perturb the model's training dataset to trigger malicious behaviors in
it. Previous work mainly focuses on the visual modality. In this paper, we
instead focus on answering two questions: (1) Is the linguistic modality also
vulnerable to poisoning attacks? and (2) Which modality is most vulnerable? To
answer the two questions, we conduct three types of poisoning attacks against
CLIP, the most representative multimodal contrastive learning framework.
Extensive evaluations on different datasets and model architectures show that
all three attacks can perform well on the linguistic modality with only a
relatively low poisoning rate and limited epochs. Also, we observe that the
poisoning effect differs between different modalities, i.e., with lower MinRank
in the visual modality and with higher Hit@K when K is small in the linguistic
modality. To mitigate the attacks, we propose both pre-training and
post-training defenses. We empirically show that both defenses can
significantly reduce the attack performance while preserving the model's
utility
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