290 research outputs found
Towards Privacy-Preserving and Verifiable Federated Matrix Factorization
Recent years have witnessed the rapid growth of federated learning (FL), an
emerging privacy-aware machine learning paradigm that allows collaborative
learning over isolated datasets distributed across multiple participants. The
salient feature of FL is that the participants can keep their private datasets
local and only share model updates. Very recently, some research efforts have
been initiated to explore the applicability of FL for matrix factorization
(MF), a prevalent method used in modern recommendation systems and services. It
has been shown that sharing the gradient updates in federated MF entails
privacy risks on revealing users' personal ratings, posing a demand for
protecting the shared gradients. Prior art is limited in that they incur
notable accuracy loss, or rely on heavy cryptosystem, with a weak threat model
assumed. In this paper, we propose VPFedMF, a new design aimed at
privacy-preserving and verifiable federated MF. VPFedMF provides for federated
MF guarantees on the confidentiality of individual gradient updates through
lightweight and secure aggregation. Moreover, VPFedMF ambitiously and newly
supports correctness verification of the aggregation results produced by the
coordinating server in federated MF. Experiments on a real-world moving rating
dataset demonstrate the practical performance of VPFedMF in terms of
computation, communication, and accuracy
Motor expertise modulates unconscious rather than conscious executive control
Background Executive control, the ability to regulate the execution of a goal-directed task, is an important element in an athlete’s skill set. Although previous studies have shown that executive control in athletes is better than that in non-athletes, those studies were mainly confined to conscious executive control. Many recent studies have suggested that executive control can be triggered by the presentation of visual stimuli without participant’s conscious awareness. However, few studies have examined unconscious executive control in sports. Thus, the present study investigated whether, similar to conscious executive control, unconscious executive control in table tennis athletes is superior to that in non-athletes. Methods In total, 42 age-matched undergraduate students were recruited for this study; 22 nonathletic students lacking practical athletic experience comprised one group, and 20 table tennis athletes with many years of training in this sport comprised a second group. Each participant first completed an unconscious response priming task, the unconscious processing of visual-spatial information, and then completed a conscious version of this same response priming task. Results Table tennis athletes showed a significant response priming effect, whereas non-athletes did not, when participants were unable to consciously perceive the visual-spatial priming stimuli. In addition, the number of years the table tennis athletes had trained in this sport (a measure of their motor expertise) was positively correlated with the strength of the unconscious response priming effect. However, both table tennis athletes and non-athletes showed a response priming effect when the primes were unmasked and the participants were able to consciously perceive the visual-spatial priming stimuli. Conclusion Our results suggest that motor expertise modulates unconscious, rather than conscious, executive control and that motor expertise is positively correlated with unconscious executive control in table tennis athletes
Depositing boron on Cu(111): Borophene or boride?
Large-area single-crystal surface structures were successfully prepared on
Cu(111) substrate with boron deposition, which is critical for prospective
applications. However, the proposed borophene structures do not match the
scanning tunneling microscopy (STM) results very well, while the proposed
copper boride is at odds with the traditional knowledge that ordered
copper-rich borides normally do not exist due to small difference in
electronegativity and large difference in atomic size. To clarify the
controversy and elucidate the formation mechanism of the unexpected copper
boride, we conducted systematic STM, X-ray photoelectron spectroscopy and
angle-resolved photoemission spectroscopy investigations, confirming the
synthesis of two-dimensional copper boride rather than borophene on Cu(111)
after boron deposition under ultrahigh vacuum. First-principles calculations
with defective surface models further indicate that boron atoms tend to react
with Cu atoms near terrace edges or defects, which in turn shapes the
intermediate structures of copper boride and leads to the formation of stable
Cu-B monolayer via large-scale surface reconstruction eventually.Comment: 15 pages, 4 figure
TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World
Object detection is the foundation of various critical computer-vision tasks
such as segmentation, object tracking, and event detection. To train an object
detector with satisfactory accuracy, a large amount of data is required.
However, due to the intensive workforce involved with annotating large
datasets, such a data curation task is often outsourced to a third party or
relied on volunteers. This work reveals severe vulnerabilities of such data
curation pipeline. We propose MACAB that crafts clean-annotated images to
stealthily implant the backdoor into the object detectors trained on them even
when the data curator can manually audit the images. We observe that the
backdoor effect of both misclassification and the cloaking are robustly
achieved in the wild when the backdoor is activated with inconspicuously
natural physical triggers. Backdooring non-classification object detection with
clean-annotation is challenging compared to backdooring existing image
classification tasks with clean-label, owing to the complexity of having
multiple objects within each frame, including victim and non-victim objects.
The efficacy of the MACAB is ensured by constructively i abusing the
image-scaling function used by the deep learning framework, ii incorporating
the proposed adversarial clean image replica technique, and iii combining
poison data selection criteria given constrained attacking budget. Extensive
experiments demonstrate that MACAB exhibits more than 90% attack success rate
under various real-world scenes. This includes both cloaking and
misclassification backdoor effect even restricted with a small attack budget.
The poisoned samples cannot be effectively identified by state-of-the-art
detection techniques.The comprehensive video demo is at
https://youtu.be/MA7L_LpXkp4, which is based on a poison rate of 0.14% for
YOLOv4 cloaking backdoor and Faster R-CNN misclassification backdoor
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