122 research outputs found
The Asymmetric Overnight Return Anomaly in the Chinese Stock Market
Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that overnight return is significantly negative, contradicts the risk–return trade-off. We find that this anomaly is asymmetrical, as the overnight return is significantly negative after a negative daytime return, whereas the anomaly does not occur following a positive daytime return. We explain this anomaly from the perspective of investor attention. We show that the attention of individual investors behaves asymmetrically such that they draw more attention on negative daytime returns, and play an essential role in explaining the overnight return puzzle
SmartIntentNN: Towards Smart Contract Intent Detection
Researchers currently have been focusing on smart contract vulnerability
detection, but we find that developers' intent to write smart contracts is a
more noteworthy security concern because smart contracts with malicious intent
have caused significant financial loss to users. A more unfortunate fact is
that we can only rely on manual audits to check for unfriendly smart contracts.
In this paper, we propose \textsc{SmartIntentNN}, Smart Contract Intent Neural
Network, a deep learning-based tool that aims to automate the process of
developers' intent detection in smart contracts, saving human resources and
overhead.
The demo video is available on \url{https://youtu.be/ho1SMtYm-wI}.Comment: 4 pages, 3 figures, conference tool track. arXiv admin note:
substantial text overlap with arXiv:2211.1072
Deep Smart Contract Intent Detection
Nowadays, security activities in smart contracts concentrate on vulnerability
detection. Despite early success, we find that developers' intent to write
smart contracts is a more noteworthy security concern because smart contracts
with malicious intent have caused significant users' financial loss.
Unfortunately, current approaches to identify the aforementioned malicious
smart contracts rely on smart contract security audits, which entail huge
manpower consumption and financial expenditure. To resolve this issue, we
propose a novel deep learning-based approach, SmartIntentNN, to conduct
automated smart contract intent detection. SmartIntentNN consists of three
primary parts: a pre-trained sentence encoder to generate the contextual
representations of smart contracts, a K-means clustering method to highlight
intent-related representations, and a bidirectional LSTM-based (long-short term
memory) multi-label classification network to predict the intents in smart
contracts. To evaluate the performance of SmartIntentNN, we collect more than
40,000 real smart contracts and perform a series of comparison experiments with
our selected baseline approaches. The experimental results demonstrate that
SmartIntentNN outperforms all baselines by up to 0.8212 in terms of the
f1-score metric.Comment: 12 pages, 9 figures, conferenc
Large Norms of CNN Layers Do Not Hurt Adversarial Robustness
Since the Lipschitz properties of CNN are widely considered to be related to
adversarial robustness, we theoretically characterize the norm and
norm of 2D multi-channel convolutional layers and provide
efficient methods to compute the exact norm and norm.
Based on our theorem, we propose a novel regularization method termed norm
decay, which can effectively reduce the norms of convolutional layers and
fully-connected layers. Experiments show that norm-regularization methods,
including norm decay, weight decay, and singular value clipping, can improve
generalization of CNNs. However, they can slightly hurt adversarial robustness.
Observing this unexpected phenomenon, we compute the norms of layers in the
CNNs trained with three different adversarial training frameworks and
surprisingly find that adversarially robust CNNs have comparable or even larger
layer norms than their non-adversarially robust counterparts. Furthermore, we
prove that under a mild assumption, adversarially robust classifiers can be
achieved, and can have an arbitrarily large Lipschitz constant. For this
reason, enforcing small norms on CNN layers may be neither necessary nor
effective in achieving adversarial robustness. The code is available at
https://github.com/youweiliang/norm_robustness.Comment: 15 pages, 4 figures; v5: corrected typ
Selling vertically differentiated products under one channel or two? A quality segmentation model for differentiated distribution channels
Many manufacturers, including Lenovo, Sony, Procter & Gamble, and Buckle, have adopted differentiated distribution channels to market vertically differentiated products. However, there is scant literature addressing the issue of quality differentiation in the presence of differentiated distribution channel policies. To fill this void, we examine whether (how) differentiated channel policies affect manufacturers’ quality differentiation and all parties’ performance. Specifically, we consider a manufacturer who produces two vertically differentiated products (high- and low-tier ) together, but withtwo marketing options: (1) distributing both products through one retailer (Model O, One-channel policy), or (2) providing high-quality products through one channel but low-tier products through another (Model T, Two-channel policy). Our results show that the manufacturer is more likely to decrease the level of quality differentiation in Model T than in Model O. Moreover, contrary to popular belief, we show that “quality distortion” is not limited to low-tier products but can occur with high-tier products. Among other results, we find that the one-channel policy benefits the retailer but hurts both the manufacturer and the total supply chain. To test the robustness of the results, we also comment on how the additional horizontal consumer heterogeneity affects our results and the implications of the competition at the manufacturer level
SoK: MEV Countermeasures: Theory and Practice
Blockchains offer strong security guarantees, but they cannot protect the
ordering of transactions. Powerful players, such as miners, sequencers, and
sophisticated bots, can reap significant profits by selectively including,
excluding, or re-ordering user transactions. Such profits are called
Miner/Maximal Extractable Value or MEV. MEV bears profound implications for
blockchain security and decentralization. While numerous countermeasures have
been proposed, there is no agreement on the best solution. Moreover, solutions
developed in academic literature differ quite drastically from what is widely
adopted by practitioners. For these reasons, this paper systematizes the
knowledge of the theory and practice of MEV countermeasures. The contribution
is twofold. First, we present a comprehensive taxonomy of 28 proposed MEV
countermeasures, covering four different technical directions. Secondly, we
empirically studied the most popular MEV- auction-based solution with rich
blockchain and mempool data. In addition to gaining insights into MEV auction
platforms' real-world operations, our study shed light on the prevalent
censorship by MEV auction platforms as a result of the recent OFAC sanction,
and its implication on blockchain properties
Experiments on bright field and dark field high energy electron imaging with thick target material
Using a high energy electron beam for the imaging of high density matter with
both high spatial-temporal and areal density resolution under extreme states of
temperature and pressure is one of the critical challenges in high energy
density physics . When a charged particle beam passes through an opaque target,
the beam will be scattered with a distribution that depends on the thickness of
the material. By collecting the scattered beam either near or off axis,
so-called bright field or dark field images can be obtained. Here we report on
an electron radiography experiment using 45 MeV electrons from an S-band
photo-injector, where scattered electrons, after interacting with a sample, are
collected and imaged by a quadrupole imaging system. We achieved a few
micrometers (about 4 micrometers) spatial resolution and about 10 micrometers
thickness resolution for a silicon target of 300-600 micron thickness. With
addition of dark field images that are captured by selecting electrons with
large scattering angle, we show that more useful information in determining
external details such as outlines, boundaries and defects can be obtained.Comment: 7pages, 7 figure
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