93 research outputs found
Universal Relations in Composite Higgs Models
We initiate a phenomenological study of `universal relations' in composite
Higgs models, which are dictated by nonlinear shift symmetries acting on the
125 GeV Higgs boson. These are relations among one Higgs couplings with two
electroweak gauge bosons (HVV), two Higgses couplings with two electroweak
gauge bosons (HHVV), one Higgs couplings with three electroweak gauge bosons
(HVVV), as well as triple gauge boson couplings (TGC), which are all controlled
by a single input parameter: the decay constant of the
pseudo-Nambu-Goldstone Higgs boson. Assuming custodial invariance in strong
sector, the relation is independent of the symmetry breaking pattern in the UV,
for an arbitrary symmetric coset . The complete list of corrections to
HVV, HHVV, HVVV and TGC couplings in composite Higgs models is presented to all
orders in , and up to four-derivative level, without referring to a
particular . We then present several examples of universal relations in
ratios of coefficients which could be extracted experimentally. Measuring the
universal relation requires a precision sensitive to effects of dimension-8
operators in the effective Lagrangian and highlights the importance of
verifying the tensor structure of HHVV interactions in the standard model,
which remains untested to date.Comment: 31 pages, 6 figure
Universal Imprints of a Pseudo-Nambu-Goldstone Higgs Boson
A large class of models addressing the electroweak naturalness problem
postulates the existence of new spontaneously broken global symmetries above
the weak scale. The Higgs boson arises as a pseudo-Nambu-Goldstone boson (pNGB)
whose interactions are nonlinear due to the presence of de- generate vacua. We
argue that, once the normalization of the pNGB decay constant f is determined,
the Higgs nonlinear interactions in the gauge sector are universal in the
infrared and independent of the symmetry breaking pattern G/H, even after
integrating out heavy composite resonances. We propose a set of "universal
relations" in Higgs couplings with electroweak gauge bosons and in triple gauge
boson couplings, which are unique predictions of the universal nonlinearity.
Experimental measurements of these relations would serve as the litmus test of
a pNGB Higgs boson.Comment: 5 page
Collision of Environmental Injustice and Sea Level Rise: Assessment of Risk Inequality in Flood-induced Pollutant Dispersion from Toxic Sites in Texas
Global sea-level rise causes increasing threats of coastal flood and
subsequent pollutant dispersion. However, there are still few studies on the
disparity arising from such threats and the extent to which different
communities could be exposed to flood-induced pollution dispersion from toxic
sites under future sea level rise. To address this gap, this study selects
Texas (a U.S. state with a large number of toxic sites and significant flood
hazards) as the study area and investigates impacts of flood-induced pollutant
dispersion on different communities under current (2018) and future (2050)
flood hazard scenarios.The results show, currently, north coastline in Texas
bears higher threats and vulnerable communities (i.e., low income, minorities
and unemployed) are disproportionally exposed to these threats. In addition,
the future sea-level rise and the exacerbated flood hazards will put additional
threats on more (about 10%) Texas residents, among which vulnerable communities
will still be disproportionately exposed to the increased threats. Our study
reveals the facts that potential coastal pollutant dispersion will further
aggravate the environmental injustice issues at the intersection of toxic sites
and flood hazards for vulnerable populations and exacerbate risk inequalities.
Given the dire impacts of flood-induced pollution dispersion on public health,
the findings have important implications for specific actions from the policy
makers to mitigate the inequitable risks
Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards
Evaluating human exposure to environmental hazards is crucial for identifying
susceptible communities and devising targeted health policies. Standard
environmental hazard exposure assessment methods have been primarily based on
place of residence, an approach which neglect individuals hazard exposures due
to the daily life activities and mobility outside home neighborhood. To address
this limitation, this study proposes a novel mobility-based index for hazard
exposure evaluation. Using large-scale and fine-grained human mobility data, we
quantify the extent of population dwell time in high-environmental-hazard
places in 239 U.S. counties for three major environmental hazards: air
pollution, heat, and toxic sites. Subsequently we explore the extent to which
human mobility extends the reach of environmental hazards and also lead to the
emergence of latent exposure for populations living outside high hazard areas
with relatively considerable dwell time in high hazard areas. The findings help
quantify environmental hazard exposure more reliably, considering the role of
human mobility and activities. The interplay of spatial clustering in
high-hazard regions and human movement trends creates environmental hazard
traps intensifying exposure. Poor and ethnic minority residents
disproportionately face multiple types of environmental hazards, aggravating
potential health impacts. This data-driven evidence supports the severity of
these injustices. We also studied latent exposure arising from visits outside
residents' home areas, revealing millions population having 5% to10% of daily
activities occur in high-exposure zones. Despite living in perceived safe
areas, human mobility could expose millions of residents to different hazards.
These findings provide crucial insights for targeted policies to mitigate these
severe environmental injustice
FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation
Generating realistic human flows across regions is essential for our
understanding of urban structures and population activity patterns, enabling
important applications in the fields of urban planning and management. However,
a notable shortcoming of most existing mobility generation methodologies is
neglect of prediction fairness, which can result in underestimation of mobility
flows across regions with vulnerable population groups, potentially resulting
in inequitable resource distribution and infrastructure development. To
overcome this limitation, our study presents a novel, fairness-aware deep
learning model, FairMobi-Net, for inter-region human flow prediction. The
FairMobi-Net model uniquely incorporates fairness loss into the loss function
and employs a hybrid approach, merging binary classification and numerical
regression techniques for human flow prediction. We validate the FairMobi-Net
model using comprehensive human mobility datasets from four U.S. cities,
predicting human flow at the census-tract level. Our findings reveal that the
FairMobi-Net model outperforms state-of-the-art models (such as the DeepGravity
model) in producing more accurate and equitable human flow predictions across a
variety of region pairs, regardless of regional income differences. The model
maintains a high degree of accuracy consistently across diverse regions,
addressing the previous fairness concern. Further analysis of feature
importance elucidates the impact of physical distances and road network
structures on human flows across regions. With fairness as its touchstone, the
model and results provide researchers and practitioners across the fields of
urban sciences, transportation engineering, and computing with an effective
tool for accurate generation of human mobility flows across regions
PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
{\it SimRank} is a classic measure of the similarities of nodes in a graph.
Given a node in graph , a {\em single-source SimRank query}
returns the SimRank similarities between node and each node . This type of queries has numerous applications in web search and social
networks analysis, such as link prediction, web mining, and spam detection.
Existing methods for single-source SimRank queries, however, incur query cost
at least linear to the number of nodes , which renders them inapplicable for
real-time and interactive analysis.
{ This paper proposes \prsim, an algorithm that exploits the structure of
graphs to efficiently answer single-source SimRank queries. \prsim uses an
index of size , where is the number of edges in the graph, and
guarantees a query time that depends on the {\em reverse PageRank} distribution
of the input graph. In particular, we prove that \prsim runs in sub-linear time
if the degree distribution of the input graph follows the power-law
distribution, a property possessed by many real-world graphs. Based on the
theoretical analysis, we show that the empirical query time of all existing
SimRank algorithms also depends on the reverse PageRank distribution of the
graph.} Finally, we present the first experimental study that evaluates the
absolute errors of various SimRank algorithms on large graphs, and we show that
\prsim outperforms the state of the art in terms of query time, accuracy, index
size, and scalability.Comment: ACM SIGMOD 201
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Vision Transformers (ViTs) have revolutionized the field of computer vision,
yet their deployments on resource-constrained devices remain challenging due to
high computational demands. To expedite pre-trained ViTs, token pruning and
token merging approaches have been developed, which aim at reducing the number
of tokens involved in the computation. However, these methods still have some
limitations, such as image information loss from pruned tokens and inefficiency
in the token-matching process. In this paper, we introduce a novel Graph-based
Token Propagation (GTP) method to resolve the challenge of balancing model
efficiency and information preservation for efficient ViTs. Inspired by graph
summarization algorithms, GTP meticulously propagates less significant tokens'
information to spatially and semantically connected tokens that are of greater
importance. Consequently, the remaining few tokens serve as a summarization of
the entire token graph, allowing the method to reduce computational complexity
while preserving essential information of eliminated tokens. Combined with an
innovative token selection strategy, GTP can efficiently identify image tokens
to be propagated. Extensive experiments have validated GTP's effectiveness,
demonstrating both efficiency and performance improvements. Specifically, GTP
decreases the computational complexity of both DeiT-S and DeiT-B by up to 26%
with only a minimal 0.3% accuracy drop on ImageNet-1K without finetuning, and
remarkably surpasses the state-of-the-art token merging method on various
backbones at an even faster inference speed. The source code is available at
https://github.com/Ackesnal/GTP-ViT.Comment: Accepted to WACV2024 (Oral
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