155 research outputs found
COVID-19 Vaccines: Characterizing Misinformation Campaigns and Vaccine Hesitancy on Twitter
Vaccine hesitancy and misinformation on social media has increased concerns
about COVID-19 vaccine uptake required to achieve herd immunity and overcome
the pandemic. However anti-science and political misinformation and
conspiracies have been rampant throughout the pandemic. For COVID-19 vaccines,
we investigate misinformation and conspiracy campaigns and their characteristic
behaviours. We identify whether coordinated efforts are used to promote
misinformation in vaccine related discussions, and find accounts coordinately
promoting a `Great Reset' conspiracy group promoting vaccine related
misinformation and strong anti-vaccine and anti-social messages such as boycott
vaccine passports, no lock-downs and masks. We characterize other
misinformation communities from the information diffusion structure, and study
the large anti-vaccine misinformation community and smaller anti-vaccine
communities, including a far-right anti-vaccine conspiracy group. In comparison
with the mainstream and health news, left-leaning group, which are more
pro-vaccine, the right-leaning group is influenced more by the anti-vaccine and
far-right misinformation/conspiracy communities. The misinformation communities
are more vocal either specific to the vaccine discussion or political
discussion, and we find other differences in the characteristic behaviours of
different communities. Lastly, we investigate misinformation narratives and
tactics of information distortion that can increase vaccine hesitancy, using
topic modeling and comparison with reported vaccine side-effects (VAERS)
finding rarer side-effects are more frequently discussed on social media
Chirality driven topological electronic structure of DNA-like materials
Topological aspects of the geometry of DNA and similar chiral molecules have
received a lot of attention, while the topology of their electronic structure
is less explored. Previous experiments have revealed that DNA can efficiently
filter spin-polarized electrons between metal contacts, a process called
chiral-induced spin-selectivity (CISS). However, the underlying correlation
between chiral structure and electronic spin remains elusive. In this work, we
reveal an orbital texture in the band structure, a topological characteristic
induced by the chirality. We find that this orbital texture enables the chiral
molecule to polarize the quantum orbital. This orbital polarization effect
(OPE) induces spin polarization assisted by the spin-orbit interaction from a
metal contact and leads to magnetorestistance and chiral separation. The
orbital angular momentum of photoelectrons also plays an essential role in
related photoemission experiments. Beyond CISS, we predict that OPE can induce
spin-selective phenomena even in achiral but inversion-breaking materials.Comment: 24 pages, 4 figures, and Supplementary Material
The Indirect Effects of Trading Restrictions: Evidence from a Quasi-Natural Experiment
Stock market trading restrictions directly affect stock prices and liquidity via constraints on investors’ transactions. They also have indirect effects by altering the information environment. We isolate these indirect effects by analyzing the effect of stock market restrictions on the corporate bond market. Using the staggered relaxation of the restrictions on margin trading and short selling in the Chinese stock market as a quasi-natural experiment, we find that the relaxation of these restrictions on a firm’s stock reduces the credit spread of its corporate bond. This effect is more pronounced for firms with more opaque information or lower credit ratings
Interpretable Outlier Summarization
Outlier detection is critical in real applications to prevent financial
fraud, defend network intrusions, or detecting imminent device failures. To
reduce the human effort in evaluating outlier detection results and effectively
turn the outliers into actionable insights, the users often expect a system to
automatically produce interpretable summarizations of subgroups of outlier
detection results. Unfortunately, to date no such systems exist. To fill this
gap, we propose STAIR which learns a compact set of human understandable rules
to summarize and explain the anomaly detection results. Rather than use the
classical decision tree algorithms to produce these rules, STAIR proposes a new
optimization objective to produce a small number of rules with least
complexity, hence strong interpretability, to accurately summarize the
detection results. The learning algorithm of STAIR produces a rule set by
iteratively splitting the large rules and is optimal in maximizing this
objective in each iteration. Moreover, to effectively handle high dimensional,
highly complex data sets which are hard to summarize with simple rules, we
propose a localized STAIR approach, called L-STAIR. Taking data locality into
consideration, it simultaneously partitions data and learns a set of localized
rules for each partition. Our experimental study on many outlier benchmark
datasets shows that STAIR significantly reduces the complexity of the rules
required to summarize the outlier detection results, thus more amenable for
humans to understand and evaluate, compared to the decision tree methods
Price of Stability in Quality-Aware Federated Learning
Federated Learning (FL) is a distributed machine learning scheme that enables
clients to train a shared global model without exchanging local data. The
presence of label noise can severely degrade the FL performance, and some
existing studies have focused on algorithm design for label denoising. However,
they ignored the important issue that clients may not apply costly label
denoising strategies due to them being self-interested and having heterogeneous
valuations on the FL performance. To fill this gap, we model the clients'
interactions as a novel label denoising game and characterize its equilibrium.
We also analyze the price of stability, which quantifies the difference in the
system performance (e.g., global model accuracy, social welfare) between the
equilibrium outcome and the socially optimal solution. We prove that the
equilibrium outcome always leads to a lower global model accuracy than the
socially optimal solution does. We further design an efficient algorithm to
compute the socially optimal solution. Numerical experiments on MNIST dataset
show that the price of stability increases as the clients' data become noisier,
calling for an effective incentive mechanism.Comment: Accepted to IEEE GLOBECOM 202
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