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 $f$ 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 $G/H$. The complete list of corrections to HVV, HHVV, HVVV and TGC couplings in composite Higgs models is presented to all orders in $1/f$, and up to four-derivative level, without referring to a particular $G/H$. 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 $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. 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 $n$, 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 $O(m)$, where $m$ 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