Long ties accelerate noisy threshold-based contagions
By: Dean Eckles, Elchanan Mossel, M. Amin Rahimian, Subhabrata Sen
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshol... more
Network structure can affect when and how widely new ideas, products, and
behaviors are adopted. In widely-used models of biological contagion,
interventions that randomly rewire edges (generally making them "longer")
accelerate spread. However, there are other models relevant to social
contagion, such as those motivated by myopic best-response in games with
strategic complements, in which an individual's behavior is described by a
threshold number of adopting neighbors above which adoption occurs (i.e.,
complex contagions). Recent work has argued that highly clustered, rather than
random, networks facilitate spread of these complex contagions. Here we show
that minor modifications to this model, which make it more realistic, reverse
this result, thereby harmonizing qualitative facts about how network structure
affects contagion. To model the trade-off between long and short edges we
analyze the rate of spread over networks that are the union of circular
lattices and random graphs on $n$ nodes. Allowing for noise in adoption
decisions (i.e., adoptions below threshold) to occur with order $1/\sqrt{n}$
probability along some "short" cycle edges) is enough to ensure that random
rewiring accelerates spread. This conclusion continues to hold true under
partial but frequent enough rewiring and when adoption decisions are reversible
but infrequently so. Simulations illustrate the robustness of these results to
several variations on this noisy best-response behavior. Hypothetical
interventions that randomly rewire existing edges or add random edges (versus
adding "short", triad-closing edges) in hundreds of empirical social networks
reduce time to spread. This revised conclusion suggests that those wanting to
increase spread should induce formation of long ties, rather than triad-closing
ties. More generally, this highlights the importance of noise in game-theoretic
analyses of behavior.
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Resonant transport in Kekule-distorted graphene nanoribbons
By: Elias Andrade, Ramon Carrillo-Bastos, Pierre A. Pantaleón, Francisco Mireles
The formation of a superlattice in graphene can serve as a way to modify its
electronic bandstructure and thus to engineer its electronic transport
properties. Recent experiments have discovered a Kekul\'e bond ordering in
graphene deposited on top of a Copper substrate, leading to the breaking of the
valley degeneracy while preserving the highly desirable feature of linearity
and gapless character of its band dispersion. In this paper we s... more
The formation of a superlattice in graphene can serve as a way to modify its
electronic bandstructure and thus to engineer its electronic transport
properties. Recent experiments have discovered a Kekul\'e bond ordering in
graphene deposited on top of a Copper substrate, leading to the breaking of the
valley degeneracy while preserving the highly desirable feature of linearity
and gapless character of its band dispersion. In this paper we study the
effects of a Kekul\'e distortion in zigzag graphene nanoribbons in both, the
subband spectrum and on its electronic transport properties. We extend our
study to investigate also the electronic conductance in graphene nanoribbons
composed of sequentially ordered Kek-Y superlattice. We find interesting
resonances in the conductance response emerging in the otherwise energy gap
regions, which scales with the number of Kek-Y interfaces minus one. Such
features resembles the physics of resonant tunneling behavior observed in
semiconductors heterostructures. Our findings provide a possible way to measure
the strenght of Kekul\'e parameter in graphene nanoribbons.
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Metrics on End-Periodic Manifolds as Models for Dark Matter
By: Christopher L Duston
In this paper we will detail an approach to generate metrics and matter
models on end-periodic manifolds, which are used extensively in the study of
the exotic smooth structures of $\mathbb{R}^4$. We will present three distinct
examples, discuss their associated matter models by solving the Einstein
equations, and determine their physical viability by examining the energy
conditions. We will also compare one of the models directly with exis... more
In this paper we will detail an approach to generate metrics and matter
models on end-periodic manifolds, which are used extensively in the study of
the exotic smooth structures of $\mathbb{R}^4$. We will present three distinct
examples, discuss their associated matter models by solving the Einstein
equations, and determine their physical viability by examining the energy
conditions. We will also compare one of the models directly with existing
models of matter distributions in extragalactic systems, to highlight the
viability of utilizing exotic smooth structures to understand the existence and
distribution of dark matter.
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Seeding with Costly Network Information
By: Dean Eckles, Hossein Esfandiari, Elchanan Mossel, M. Amin Rahimian
The article addresses a fundamental problem in network science: how to target interventions in a social network to maximize the spread of, e.g., adoption of a product. The computational and algorithmic foundations of this problem are well studied when the network structure is known, but it often is not and is expensive or impractical to measure. Here we analyze the case when the network structure is not available but can be acquired through c... more
The article addresses a fundamental problem in network science: how to target interventions in a social network to maximize the spread of, e.g., adoption of a product. The computational and algorithmic foundations of this problem are well studied when the network structure is known, but it often is not and is expensive or impractical to measure. Here we analyze the case when the network structure is not available but can be acquired through costly actions. less
Flux Calibration of CHIME/FRB Intensity Data
By: Bridget C. Andersen, Chitrang Patel, Charanjot Brar, P. J. Boyle, Emmanuel Fonseca, Victoria M. Kaspi, Kiyoshi W. Masui, Juan Mena-Parra, Marcus Merryfield, Bradley W. Meyers, Ketan R. Sand, Paul Scholz, Seth R. Siegel, Saurabh Singh
Fast radio bursts (FRBs) are bright radio transients of micro-to-millisecond
duration and unknown extragalactic origin. Central to the mystery of FRBs are
their extremely high characteristic energies, which surpass the typical
energies of other radio transients of similar duration, like Galactic pulsar
and magnetar bursts, by orders of magnitude. Calibration of FRB-detecting
telescopes for burst flux and fluence determination is crucial for... more
Fast radio bursts (FRBs) are bright radio transients of micro-to-millisecond
duration and unknown extragalactic origin. Central to the mystery of FRBs are
their extremely high characteristic energies, which surpass the typical
energies of other radio transients of similar duration, like Galactic pulsar
and magnetar bursts, by orders of magnitude. Calibration of FRB-detecting
telescopes for burst flux and fluence determination is crucial for FRB science,
as these measurements enable studies of the FRB energy and brightness
distribution in comparison to progenitor theories. The Canadian Hydrogen
Intensity Mapping Experiment (CHIME) is a radio interferometer of cylindrical
design. This design leads to a high FRB detection rate but also leads to
challenges for CHIME/FRB flux calibration. This paper presents a comprehensive
review of these challenges, as well as the automated flux calibration software
pipeline that was developed to calibrate bursts detected in the first CHIME/FRB
catalog, consisting of 536 events detected between July 25th, 2018 and July
1st, 2019. We emphasize that, due to limitations in the localization of
CHIME/FRB bursts, flux and fluence measurements produced by this pipeline are
best interpreted as lower limits, with uncertainties on the limiting value.
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Topological nature of the proper spin current and the spin-Hall torque
By: Hong Liu, James H. Cullen, Dimitrie Culcer
Spin currents driven by spin-orbit coupling are key to spin torque devices, but determining the proper spin current is highly non-trivial. Here we derive a general quantum-mechanical formula for the intrinsic proper spin current showing that it is a topological quantity, and can be finite even in the gap. We determine the spin-Hall current due to the bulk states of topological insulators both deep in the bulk, where the system is unmagnetiz... more
Spin currents driven by spin-orbit coupling are key to spin torque devices, but determining the proper spin current is highly non-trivial. Here we derive a general quantum-mechanical formula for the intrinsic proper spin current showing that it is a topological quantity, and can be finite even in the gap. We determine the spin-Hall current due to the bulk states of topological insulators both deep in the bulk, where the system is unmagnetized, and near the interface, where a proximity-induced magnetization is present, as well as
for low-dimensional spin-3/2 hole systems.
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Exotic Smoothness in Four Dimensions and Euclidean Quantum Gravity
By: Christopher L Duston
In this paper we calculate the effect of the inclusion of exotic smooth
structures on typical observables in Euclidean quantum gravity. We do this in
the semiclassical regime for several gravitational free-field actions and find
that the results are similar, independent of the particular action that is
chosen. These are the first results of their kind in dimension four, which we
extend to include one-loop contributions as well. We find thes... more
In this paper we calculate the effect of the inclusion of exotic smooth
structures on typical observables in Euclidean quantum gravity. We do this in
the semiclassical regime for several gravitational free-field actions and find
that the results are similar, independent of the particular action that is
chosen. These are the first results of their kind in dimension four, which we
extend to include one-loop contributions as well. We find these topological
features can have physically significant results without the need for
additional exotic physics.
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Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering
By: Eklavya Sarkar, RaviShankar Prasad, Mathew Magimai. -Doss
Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-frequency filtering (ZFF) without making any explicit model assumptions about the speech signal. This paper investigates the p... more
Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-frequency filtering (ZFF) without making any explicit model assumptions about the speech signal. This paper investigates the potential of zero-frequency filtering for jointly modeling voice source and vocal tract system information, and proposes two approaches for VAD. The first approach demarcates voiced regions using a composite signal composed of different zero-frequency filtered signals. The second approach feeds the composite signal as input to the rVAD algorithm. These approaches are compared with other supervised and unsupervised VAD methods in the literature, and are evaluated on the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show that the proposed ZFF-based methods perform comparable to state-of-art VAD methods and are more invariant to added degradation and different channel characteristics. less
Are GAN-based Morphs Threatening Face Recognition?
By: Eklavya Sarkar, Pavel Korshunov, Laurent Colbois, Sébastien Marcel
Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. ... more
Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks. less
Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?
By: Eklavya Sarkar, Mathew Magimai. -Doss
Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is not limited to modeling human speech alone. Building on this understanding, this paper explores the cross-transferability of SSL neural representations learned from human speech to analyze bio... more
Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is not limited to modeling human speech alone. Building on this understanding, this paper explores the cross-transferability of SSL neural representations learned from human speech to analyze bio-acoustic signals. We conduct a caller discrimination analysis and a caller detection study on Marmoset vocalizations using eleven SSL models pre-trained with various pretext tasks. The results show that the embedding spaces carry meaningful caller information and can successfully distinguish the individual identities of Marmoset callers without fine-tuning. This demonstrates that representations pre-trained on human speech can be effectively applied to the bio-acoustics domain, providing valuable insights for future investigations in this field. less