211 research outputs found
Tinkertoys for the E7 Theory
We classify the class theories of type . These are four-dimensional
superconformal field theories arising from the compactification
of the theory on a punctured Riemann surface, . The
classification is given by listing all 3-punctured spheres ("fixtures"), and
connecting cylinders, which can arise in a pants-decomposition of . We find
exactly 11,000 fixtures with three regular punctures, and an additional 48 with
one "irregular puncture" (in the sense used in our previous works). To organize
this large number of theories, we have created a web application at
https://golem.ph.utexas.edu/class-S/E7/ . Among these theories, we find 10 new
ones with a simple exceptional global symmetry group, as well as a new rank-2
SCFT and several new rank-3 SCFTs. As an application, we study the
strong-coupling limit of the gauge theory with 3 hypermultiplets in the
. Using our results, we also verify recent conjectures that the
compactification of certain theories can alternatively be realized
in class as fixtures in the or theories.Comment: Fixed one entry in table of interacting fixtures with an irregular
punctur
Game of Travesty: Decoy-based Psychological Cyber Deception for Proactive Human Agents
The concept of cyber deception has been receiving emerging attention. The
development of cyber defensive deception techniques requires interdisciplinary
work, among which cognitive science plays an important role. In this work, we
adopt a signaling game framework between a defender and a human agent to
develop a cyber defensive deception protocol that takes advantage of the
cognitive biases of human decision-making using quantum decision theory to
combat insider attacks (IA). The defender deceives an inside human attacker by
luring him to access decoy sensors via generators producing perceptions of
classical signals to manipulate the human attacker's psychological state of
mind. Our results reveal that even without changing the classical traffic data,
strategically designed generators can result in a worse performance for
defending against insider attackers in identifying decoys than the ones in the
deceptive scheme without generators, which generate random information based on
input signals. The proposed framework leads to fundamental theories in
designing more effective signaling schemes
Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars
The detection and discrimination of quantum states serve a crucial role in
quantum signal processing, a discipline that studies methods and techniques to
process signals that obey the quantum mechanics frameworks. However, just like
classical detection, evasive behaviors also exist in quantum detection. In this
paper, we formulate an adversarial quantum detection scenario where the
detector is passive and does not know the quantum states have been distorted by
an attacker. We compare the performance of a passive detector with the one of a
non-adversarial detector to demonstrate how evasive behaviors can undermine the
performance of quantum detection. We use a case study of target detection with
quantum radars to corroborate our analytical results
Impacts on consumer behavior according to culture
Along with the development of market globalization, it is necessary for marketers to develop marketing strategies that can be standardized between various countries. However, as the source of peoples' desires and actions, cultural factors will moderate many aspects of consumer behavior, such as their way of thinking or reaction to a stimulation. Therefore, it is important to identify the relationship between culture and consumer behavior when producing global marketing strategies. In this study, cultural assumptions are used as the basic theory to examine how peoples' behaviors are influenced by cultural factors. We then discuss which cultural factors influence cross-cultural consumer behavior the most
Highly emissive, selective and omnidirectional thermal emitters mediated by machine learning for ultrahigh performance passive radiative cooling
Real-world passive radiative cooling requires highly emissive, selective, and
omnidirectional thermal emitters to maintain the radiative cooler at a certain
temperature below the ambient temperature while maximizing the net cooling
power. Despite various selective thermal emitters have been demonstrated, it is
still challenging to achieve these conditions simultaneously because of the
extreme complexity of controlling thermal emission of photonic structures in
multidimension. Here we demonstrated machine learning mediated hybrid
metasurface thermal emitters with a high emissivity of ~0.92 within the
atmospheric transparency window 8-13 {\mu}m, a large spectral selectivity of
~1.8 and a wide emission angle up to 80 degrees, simultaneously. This selective
and omnidirectional thermal emitter has led to a new record of temperature
reduction as large as ~15.4 degree under strong solar irradiation of ~800 W/m2,
significantly surpassing the state-of-the-art results. The designed structures
also show great potential in tackling the urban heat island effect, with
modelling results suggesting a large energy saving and deployment area
reduction. This research will make significant impact on passive radiative
cooling, thermal energy photonics and tackling global climate change
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
In this paper, we introduce a large Multi-Attribute and Language Search
dataset for text-based person retrieval, called MALS, and explore the
feasibility of performing pre-training on both attribute recognition and
image-text matching tasks in one stone. In particular, MALS contains 1,510,330
image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES,
and all images are annotated with 27 attributes. Considering the privacy
concerns and annotation costs, we leverage the off-the-shelf diffusion models
to generate the dataset. To verify the feasibility of learning from the
generated data, we develop a new joint Attribute Prompt Learning and Text
Matching Learning (APTM) framework, considering the shared knowledge between
attribute and text. As the name implies, APTM contains an attribute prompt
learning stream and a text matching learning stream. (1) The attribute prompt
learning leverages the attribute prompts for image-attribute alignment, which
enhances the text matching learning. (2) The text matching learning facilitates
the representation learning on fine-grained details, and in turn, boosts the
attribute prompt learning. Extensive experiments validate the effectiveness of
the pre-training on MALS, achieving state-of-the-art retrieval performance via
APTM on three challenging real-world benchmarks. In particular, APTM achieves a
consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on
CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively
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