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Within one decade, Deep Learning overtook the dominating solution methods of countless problems of artificial intelligence. ``Deep'' refers to the deep architectures with operations in manifolds of which there are no immediate observations. For these deep architectures some kind of structure is pre-defined -- but what is this structure? With a formal definition for structures of neural networks, neural architecture search problems and solution methods can be formulated under a common framework. Both practical and theoretical questions arise from closing the gap between applied neural architecture search and learning theory. Does structure make a difference or can it be chosen arbitrarily? This work is concerned with deep structures of artificial neural networks and examines automatic construction methods under empirical principles to shed light on to the so called ``black-box models''. Our contributions include a formulation of graph-induced neural networks that is used to pose optimisation problems for neural architecture. We analyse structural properties for different neural network objectives such as correctness, robustness or energy consumption and discuss how structure affects them. Selected automation methods for neural architecture optimisation problems are discussed and empirically analysed. With the insights gained from formalising graph-induced neural networks, analysing structural properties and comparing the applicability of neural architecture search methods qualitatively and quantitatively we advance these methods in two ways. First, new predictive models are presented for replacing computationally expensive evaluation schemes, and second, new generative models for informed sampling during neural architecture search are analysed and discussed., Comment: PhD thesis

Seamless integration of virtual and physical worlds in augmented reality benefits from the system semantically "understanding" the physical environment. AR research has long focused on the potential of context awareness, demonstrating novel capabilities that leverage the semantics in the 3D environment for various object-level interactions. Meanwhile, the computer vision community has made leaps in neural vision-language understanding to enhance environment perception for autonomous tasks. In this work, we introduce a multimodal 3D object representation that unifies both semantic and linguistic knowledge with the geometric representation, enabling user-guided machine learning involving physical objects. We first present a fast multimodal 3D reconstruction pipeline that brings linguistic understanding to AR by fusing CLIP vision-language features into the environment and object models. We then propose "in-situ" machine learning, which, in conjunction with the multimodal representation, enables new tools and interfaces for users to interact with physical spaces and objects in a spatially and linguistically meaningful manner. We demonstrate the usefulness of the proposed system through two real-world AR applications on Magic Leap 2: a) spatial search in physical environments with natural language and b) an intelligent inventory system that tracks object changes over time. We also make our full implementation and demo data available at (https://github.com/cy-xu/spatially_aware_AI) to encourage further exploration and research in spatially aware AI., Comment: 10 pages, 6 figures, accepted to IEEE ISMAR 2024

Combining a precise sputter etching method with subsequent AlO$_x$ growth within an atomic layer deposition chamber enables fabrication of atomically flat lateral patterns of SiO$_2$ and AlO$_x$. The transfer of MoSe$_2$ monolayers onto these dielectrically modulated substrates results in formation of lateral heterojunctions, with the flat substrate topography leading to minimal strain across the junction. Kelvin probe force microscopy (KPFM) measurements show significant variations in the contact potential difference (CPD) across the interface, with AlO$_x$ regions inducing a 230~mV increase in CPD. Spatially resolved photoluminescence spectroscopy reveals shifts in spectral weight of neutral and charged exciton species across the different dielectric regions. On the AlO$_x$ side, the Fermi energy moves closer to the conduction band, leading to a higher trion-to-exciton ratio, indicating a bandgap shift consistent with CPD changes. In addition, transient reflection spectroscopy highlights the influence of the dielectric environment on carrier dynamics, with the SiO$_2$ side exhibiting rapid carrier decay typical of neutral exciton recombination. In contrast, the AlO$_x$ side shows slower, mixed decay behavior consistent with conversion of trions back into excitons. These results demonstrate how dielectric substrate engineering can tune the electronic and optical characteristics of proximal two-dimensional materials, allowing scalable fabrication of advanced junctions for novel (opto)electronics applications., Comment: 24 pages, 5 figures. SI: 2 pages, 3 figures. We welcome your feedback!

We present time-resolved and CW optical spectroscopy studies of interlayer excitons (IXs) in 2$H-$ and 3$R-$stacked MoSe$_2$/WSe$_2$ heterobilayers and obtain evidence for the strong participation of hot phonons in the underlying photo-physics. Photoluminescence excitation spectroscopy reveals that excess energy associated with optical excitation of \textit{intra}-layer excitons and relaxation to IXs affects the overall IX-PL lineshape, while the spectrally narrow emission lines conventionally associated with moir\'e IXs are unaffected. A striking uniform line-spacing of the sharp emission lines is observed together with temperature and excitation level dependent spectra suggesting an entirely new picture that photo-generated phonons lead to phonon-replicas shaping the IX-emission. Excitation power and time resolved data indicate that these features are polaronic in nature. Our experimental findings modify our current understanding of the photophysics of IXs beyond current interpretations based on moir\'e-trapped IXs.

Many physical, biological, and even social systems are faced with the problem of how to efficiently harvest free energy from any of many possible environmental states, yet only have a limited number of harvesting strategies to choose among. We formalize this general situation within the framework of work extraction from nonequilibirum systems using feedback control. Specifically, we analyze the combined and separate effects of noisy measurements, memory limitations, and the access to only a limited set of possible control operations, quantifying the amount of extractable work under all such constraints. Such analysis allows us to provide a general recipe to construct repertoires of allowed control operations that minimize the expected losses during the feedback protocol, i.e., that minimize expected entropy production. Our results highlight the importance and benefits of feedback control over uninformed (random) actions beyond the information gain under control restrictions and are applied to several situations including uncertain initial distributions., Comment: 13 pages, 1 figure

We study the infimum of the spectrum, or ground state energy (g.s.e.), of a discrete Schr\"odinger operator on $\theta\mathbb{Z}^d$ parameterized by a potential $V:\mathbb{R}^d\rightarrow\mathbb{R}_{\ge 0}$ and a frequency parameter $\theta\in (0,1)$. We relate this g.s.e. to that of a corresponding continuous semiclassical Schr\"odinger operator on $\mathbb{R}^d$ with parameter $\theta$, arising from the same choice of potential. We show that: the discrete g.s.e. is at most the continuous one for continuous periodic $V$ and irrational $\theta$; the opposite inequality holds up to a factor of $1-o(1)$ as $\theta\rightarrow 0$ for sufficiently regular smooth periodic $V$; and the opposite inequality holds up to a constant factor for every bounded $V$ and $\theta$ with the property that discrete and continuous averages of $V$ on fundamental domains of $\theta \mathbb{Z}^d$ are comparable. Our proofs are elementary and rely on sampling and interpolation to map low-energy functions for the discrete operator on $\theta \mathbb{Z}^d$ to low-energy functions for the continuous operator on $\mathbb{R}^d$, and vice versa.

Neural architecture search (NAS) is a challenging problem. Hierarchical search spaces allow for cheap evaluations of neural network sub modules to serve as surrogate for architecture evaluations. Yet, sometimes the hierarchy is too restrictive or the surrogate fails to generalize. We present FaDE which uses differentiable architecture search to obtain relative performance predictions on finite regions of a hierarchical NAS space. The relative nature of these ranks calls for a memory-less, batch-wise outer search algorithm for which we use an evolutionary algorithm with pseudo-gradient descent. FaDE is especially suited on deep hierarchical, respectively multi-cell search spaces, which it can explore by linear instead of exponential cost and therefore eliminates the need for a proxy search space. Our experiments show that firstly, FaDE-ranks on finite regions of the search space correlate with corresponding architecture performances and secondly, the ranks can empower a pseudo-gradient evolutionary search on the complete neural architecture search space.

We present a high-resolution single crystal x-ray diffraction study of kagome-superconductor \cvs, exploring its response to variations in pressure and temperature. We discover that at low temperatures, the structural modulations of the electronic superlattice, commonly associated with charge-density-wave order, undergo a transformation around $p \sim$ 0.7 GPa from the familiar $2\times2$ pattern to a long-range-ordered modulation at wavevector $q=(0, 3/8, 1/2)$. Our observations align with inferred changes in the CDW pattern from prior transport and nuclear-magnetic-resonance studies, providing new insights into these transitions. Interestingly, the pressure-induced variations in the electronic superlattice correlate with two peaks in the superconducting transition temperature as pressure changes, hinting that fluctuations within the electronic superlattice could be key to stabilizing superconductivity. However, our findings contrast with the minimal pressure dependency anticipated by ab initio calculations of the electronic structure. They also challenge prevailing scenarios based on a Peierls-like nesting mechanism involving van Hove singularities., Comment: Revised version, to appear in PRL

We study the problem of state synthesis in the DQC1 (One Clean Qubit) model of quantum computation, which provides a single pure qubit and $n$ maximally mixed qubits, and after applying any quantum circuit some subset of the qubits are measured or discarded. In the case of discarding, we show that it is impossible to prepare additional pure qubits, and that it is impossible to prepare very low-temperature Gibbs states on additional qubits. In the case of measurements, we show that the probability of synthesizing $m$ additional qubits is bounded by $2^{1-m}$, and that the probability of preparing low-temperature Gibbs states is bounded by $2^{2-m}$. As a consequence, we give a lower-bound the runtime of a recently studied class of repeated interaction quantum algorithms. The techniques used study states and circuits at the level of entries of their respective density and unitary matrices.

In this paper, we introduce a new notion of curvature on the edges of a graph that is defined in terms of effective resistances. We call this the Ricci--Foster curvature. We study the Ricci flow resulting from this curvature. We prove the existence of solutions to Ricci flow on short time intervals, and prove that Ricci flow preserves graphs with nonnegative (resp. positive) curvature., Comment: 14 pages, 8 figures, comments welcome!

Devriendt and Lambiotte recently introduced the \emph{node resistance curvature}, a notion of graph curvature based on the effective resistance matrix. In this paper, we begin the study of the behavior of the node resistance curvature under the operation of the Cartesian graph product. We study the natural question of global positivity of node resistance curvature of the Cartesian product of positively-curved graphs, and prove that, whenever $m,n\ge3$, the node resistance curvature of the interior vertices of a $m\times n$ grid is always nonpositive, while it is always nonnegative on the boundary of such grids. For completeness, we also prove a number of results on node resistance curvature in $2\times n$ grids and exhibit a counterexample to a generalization. We also give generic bounds and suggest several further questions for future study.

In this paper the author compares the impact of two proposals: one in the Shapiro administration's budget plan and one by the PA Schools Work (PASW) campaign for a down payment on fair and full funding of K-12 schools regarding how far they go in reducing the state's inequitable and inadequate school funding for school districts on the basis of class, race, and Hispanic ethnicity. The author shows that the PASW proposal, by spending more money and distributing some of that money through the Level Up program, takes a larger step forward in pursuit of the goal of giving every child the "thorough and efficient" public education required not only by basic notions of fairness and equity but by the Pennsylvania Constitution.

Online A/B testing is widely used in the internet industry to inform decisions on new feature roll-outs. For online marketplaces (such as advertising markets), standard approaches to A/B testing may lead to biased results when buyers operate under a budget constraint, as budget consumption in one arm of the experiment impacts performance of the other arm. To counteract this interference, one can use a budget-split design where the budget constraint operates on a per-arm basis and each arm receives an equal fraction of the budget, leading to ``budget-controlled A/B testing.'' Despite clear advantages of budget-controlled A/B testing, performance degrades when budget are split too small, limiting the overall throughput of such systems. In this paper, we propose a parallel budget-controlled A/B testing design where we use market segmentation to identify submarkets in the larger market, and we run parallel experiments on each submarket. Our contributions are as follows: First, we introduce and demonstrate the effectiveness of the parallel budget-controlled A/B test design with submarkets in a large online marketplace environment. Second, we formally define market interference in first-price auction markets using the first price pacing equilibrium (FPPE) framework. Third, we propose a debiased surrogate that eliminates the first-order bias of FPPE, drawing upon the principles of sensitivity analysis in mathematical programs. Fourth, we derive a plug-in estimator for the surrogate and establish its asymptotic normality. Fifth, we provide an estimation procedure for submarket parallel budget-controlled A/B tests. Finally, we present numerical examples on semi-synthetic data, confirming that the debiasing technique achieves the desired coverage properties.

We report temperature-dependent spectroscopy on the layered (n=4) two-dimensional (2D) Ruddlesden-Popper perovskite (BA)(MA)PbI. Helicity-resolved steady-state photoluminescence (PL) reveals no optical degree of polarization. Time-resolved PL shows a photocarrier lifetime on the order of nanoseconds. From simultaneaously recorded time-resolved differential reflectivity (TR$\Delta$R) and time-resolved Kerr ellipticity (TRKE), a photocarrier lifetime of a few nanoseconds and a spin dephasing time on the order of picoseconds was found. This stark contrast in lifetimes clearly explains the lack of spin polarization in steady-state PL. While we observe clear temperature-dependent effects on the PL dynamics that can be related to structural dynamics, the spin dephasing is nearly T-independent. Our results highlight that spin dephasing in 2D (BA)(MA)PbI occurs at time scales faster than the exciton recombination time, which poses a bottleneck for applications aimingto utilize this degree of freedom.

We theoretically analyze the properties of a geodesic random walk on the Euclidean $d$-sphere. Specifically, we prove that the random walk's transition kernel is Wasserstein contractive with a contraction rate which can be bounded from above independently of the dimension $d$. Our result is of particular interest due to its implications regarding the potential for dimension-independent performance of both geodesic slice sampling on the sphere and Gibbsian polar slice sampling, which are Markov chain Monte Carlo methods for approximate sampling from essentially arbitrary distributions on their respective state spaces., Comment: 11 pages, 1 figure

We report a new dark exciton in monolayer MoS$_2$ using second harmonic generation spectroscopy. Hereby, the spectrally dependent second harmonic generation intensity splits into two branches, and an anticrossing is observed at $\sim$ 25 meV blue detuned from the bright neutral exciton. These observations are indicative of coherent quantum interference arising from strong two-photon light-matter interaction with an excitonic state that is dark for single photon interaction. The existence of the dark state is supported by engineering its relaxation to bright localized excitons, mediated by vibrational modes of a proximal nanobeam cavity. We show that two-photon light-matter interaction involving dark states has the potential to control relaxation pathways induced by nanostructuring the local environment. Moreover, our results indicate that dark excitons have significant potential for nonlinear quantum devices based on their nontrivial excitonic photophysics.

We provide a unified framework to obtain numerically certain quantities, such as the distribution function, absolute moments and prices of financial options, from the characteristic function of some (unknown) probability density function using the Fourier-cosine expansion (COS) method. The classical COS method is numerically very efficient in one-dimension, but it cannot deal very well with certain integrands in general dimensions. Therefore, we introduce the damped COS method, which can handle a large class of integrands very efficiently. We prove the convergence of the (damped) COS method and study its order of convergence. The method converges exponentially if the characteristic function decays exponentially. To apply the (damped) COS method, one has to specify two parameters: a truncation range for the multivariate density and the number of terms to approximate the truncated density by a cosine series. We provide an explicit formula for the truncation range and an implicit formula for the number of terms. Numerical experiments up to five dimensions confirm the theoretical results.

A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating graphs has theoretical challenges, such as isomorphic representations -- evaluating how well a generative model performs is difficult. Which model to choose depending on the application domain? We extensively study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space. Manifold-based metrics outperform kernel-based metrics in embedding space. We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings. It shows the superiority of GRAN over GraphRNN - further, our proposed adaptation of GraphRNN with a depth-first search ordering is effective for small-sized graphs. A guideline on good practices regarding dataset selection and node feature initialization is provided. Our work is accompanied by open-source code and reproducible experiments., Comment: Preprint for The 9th International Conference on machine Learning, Optimization and Data science - LOD 2023

Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining parameters in expensive climate models by comparing model outputs with observations. Using the C3.ai Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with two weeks' worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every three hours and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that, within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods., Comment: 4 figures, 2 tables, submitted to Environmental Data Science

Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency. However, the reconstructed geometry, typically represented as a 3D truncated signed distance function (TSDF), is often coarse without fine geometric details. To address this problem, we propose three effective solutions for improving the fidelity of inference-based 3D reconstructions. We first present a resolution-agnostic TSDF supervision strategy to provide the network with a more accurate learning signal during training, avoiding the pitfalls of TSDF interpolation seen in previous work. We then introduce a depth guidance strategy using multi-view depth estimates to enhance the scene representation and recover more accurate surfaces. Finally, we develop a novel architecture for the final layers of the network, conditioning the output TSDF prediction on high-resolution image features in addition to coarse voxel features, enabling sharper reconstruction of fine details. Our method, FineRecon, produces smooth and highly accurate reconstructions, showing significant improvements across multiple depth and 3D reconstruction metrics., Comment: ICCV 2023

Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates. This assumption has endured, even as recent works have increasingly focused on real-time methods for mobile devices. However, the assumption of a fixed pose for each image does not hold for online execution: poses from real-time SLAM are dynamic and may be updated following events such as bundle adjustment and loop closure. This has been addressed in the RGB-D setting, by de-integrating past views and re-integrating them with updated poses, but it remains largely untreated in the RGB-only setting. We formalize this problem to define the new task of dense online reconstruction from dynamically-posed images. To support further research, we introduce a dataset called LivePose containing the dynamic poses from a SLAM system running on ScanNet. We select three recent reconstruction systems and apply a framework based on de-integration to adapt each one to the dynamic-pose setting. In addition, we propose a novel, non-linear de-integration module that learns to remove stale scene content. We show that responding to pose updates is critical for high-quality reconstruction, and that our de-integration framework is an effective solution., Comment: ICCV 2023

The electron-hole exchange interaction is a fundamental mechanism that drives valley depolarization via intervalley exciton hopping in semiconductor multi-valley systems. Here, we report polarization-resolved photoluminescence spectroscopy of neutral excitons and negatively charged trions in monolayer MoSe$_2$ and WSe$_2$ under biaxial strain. We observe a marked enhancement(reduction) on the WSe$_2$ triplet trion valley polarization with compressive(tensile) strain while the trion in MoSe$_2$ is unaffected. The origin of this effect is shown to be a strain dependent tuning of the electron-hole exchange interaction. A combined analysis of the strain dependent polarization degree using ab initio calculations and rate equations shows that strain affects intervalley scattering beyond what is expected from strain dependent bandgap modulations. The results evidence how strain can be used to tune valley physics in energetically degenerate multi-valley systems.

We introduce and derive the Fourier-enhanced 3D electrostatic field solver of the gyrokinetic full-f PIC code PICLS. The solver makes use of a Fourier representation in one periodic direction of the domain to make the solving of the system easily parallelizable and thus save run time. The presented solver is then verified using two different approaches of manufactured solutions. The test setup used for this effort is a pinch geometry with ITG-like electric potential, containing one non-periodic and two periodic directions, one of which will be discrete Fourier transformed. The results of these tests show that in all three dimensions the L2-error decreases with a constant rate close to the ideal prediction, depending on the degree of the chosen basis functions., Comment: 30 pages, 10 figures

Quantum signal processing allows for quantum eigenvalue transformation with Hermitian matrices, in which each eigenspace component of an input vector gets transformed according to its eigenvalue. In this work, we introduce the multivariate quantum eigenvalue transformation for functions of commuting Hermitian matrices. We then present a framework for working with polynomial matrix functions in which we may solve MQET, and give the application of computing functions of normal matrices using a quantum computer.

We report lasing of moir\'e trapped interlayer excitons (IXs) by integrating a pristine hBN-encapsulated MoSe$_2$/WSe$_2$ heterobilayer into a high-$Q$ ($>10^4$) nanophotonic cavity. We control the cavity-IX detuning using a magnetic field and measure their dipolar coupling strength to be $78 \pm 4\ \mathrm{\mu eV}$, fully consistent with the 82 $\mathrm{\mu eV}$ predicted by theory. The emission from the cavity mode shows clear threshold-like behavior as the transition is tuned into resonance with the cavity. We observe a superlinear power dependence accompanied by a narrowing of the linewidth as the distinct features of lasing. The onset and prominence of these threshold-like behaviors are pronounced at resonance while weak off-resonance. Our results show that a lasing transition can be induced in interacting moir\'e IXs with macroscopic coherence extending over the length scale of the cavity mode. Such systems raise interesting perspectives for low-power switching and synaptic nanophotonic devices using two-dimensional materials.

The valley Zeeman physics of excitons in monolayer transition metal dichalcogenides provides valuable insight into the spin and orbital degrees of freedom inherent to these materials. Being atomically-thin materials, these degrees of freedom can be influenced by the presence of adjacent layers, due to proximity interactions that arise from wave function overlap across the 2D interface. Here, we report 60 T magnetoreflection spectroscopy of the A- and B- excitons in monolayer WS$_2$, systematically encapsulated in monolayer graphene. While the observed variations of the valley Zeeman effect for the A- exciton are qualitatively in accord with expectations from the bandgap reduction and modification of the exciton binding energy due to the graphene-induced dielectric screening, the valley Zeeman effect for the B- exciton behaves markedly different. We investigate prototypical WS$_2$/graphene stacks employing first-principles calculations and find that the lower conduction band of WS$_2$ at the $K/K'$ valleys (the $CB^-$ band) is strongly influenced by the graphene layer on the orbital level. This leads to variations in the valley Zeeman physics of the B- exciton, consistent with the experimental observations. Our detailed microscopic analysis reveals that the conduction band at the $Q$ point of WS$_2$ mediates the coupling between $CB^-$ and graphene due to resonant energy conditions and strong coupling to the Dirac cone. Our results therefore expand the consequences of proximity effects in multilayer semiconductor stacks, showing that wave function hybridization can be a multi-step process with different bands mediating the interlayer interactions. Such effects can be exploited to resonantly engineer the spin-valley degrees of freedom in van der Waals and moir\'e heterostructures., Comment: 14 pages, 6 figures, 3 tables

Spin defects in hexagonal Boron Nitride (hBN) attract increasing interest for quantum technology since they represent optically-addressable qubits in a van der Waals material. In particular, negatively-charged boron vacancy centers (${V_B}^-$) in hBN have shown promise as sensors of temperature, pressure, and static magnetic fields. However, the short spin coherence time of this defect currently limits its scope for quantum technology. Here, we apply dynamical decoupling techniques to suppress magnetic noise and extend the spin coherence time by nearly two orders of magnitude, approaching the fundamental $T_1$ relaxation limit. Based on this improvement, we demonstrate advanced spin control and a set of quantum sensing protocols to detect electromagnetic signals in the MHz range with sub-Hz resolution. This work lays the foundation for nanoscale sensing using spin defects in an exfoliable material and opens a promising path to quantum sensors and quantum networks integrated into ultra-thin structures.

Sequential updating (SU) decompositions are a well-known technique for creating profit and loss (P&L) attributions, e.g., for a bond portfolio, by dividing the time horizon into subintervals and sequentially editing each risk factor in each subinterval, e.g., FX, IR or CS. We show that SU decompositions converge for increasing number of subintervals if the P&L attribution can be expressed by a smooth function of the risk factors. We further consider average SU decompositions, which are invariant with respect to the order or labeling of the risk factors. The averaging is numerically expensive, and we discuss several ways in which the computational complexity of average SU decompositions can be significantly reduced.

Homophily and heterophobia, the tendency for people with similar characteristics to preferentially interact with (or avoid) each other are pervasive in human social networks. Here, we develop an extension of the mathematical theory of urban scaling which describes the effects of homophily and heterophobia on social interactions and resulting economic outputs of cities. Empirical tests of our model show that increased residential racial heterophobia and segregation in U.S. cities are associated with reduced economic outputs and that the strength of this relationship increased throughout the 2010s. Our findings provide the means for the formal incorporation of general homophilic and heterophobic effects into theories of modern urban science and suggest that racial segregation is increasingly and adversely impacting the economic performance and connectivity of urban societies in the U.S.