cssi/SciSciGPT-SciSciNet huggingface dataset:
Click cross-linking improves retention and targeting of refillable alginate depots

Injectable alginate hydrogels have demonstrated utility in tissue engineering and drug delivery applications due in part to their mild gelation conditions, low host responses and chemical versatility. Recently, the potential of these gels has expanded with the introduction of refillable hydrogel depots - alginate gels chemically decorated with click chemistry groups to efficiently capture prodrug refills from the blood. Unfortunately, high degrees of click group substitution on alginate lead to poor viscoelastic properties and loss of ionic cross-linking. In this work, we introduce tetrabicyclononyne (tBCN) agents that covalently cross-link azide-modified alginate hydrogels for tissue engineering and drug delivery application in vivo. Adjusting cross-linker concentration allowed tuning the hydrogel mechanical properties for tissue-specific mechanical strength. The bioorthogonal and specific click reaction creates stable hydrogels with improved in vivo properties, including improved retention at injected sites. Azide-alginate hydrogels cross-linked with tBCN elicited minimal inflammation and maintained structural integrity over several months and efficiently captured therapeutics drug surrogates from the circulation. Taken together, azide-alginate hydrogels cross-linked with tBCN convey the benefits of alginate hydrogels for use in tissue engineering and drug delivery applications of refillable drug delivery depots. STATEMENT OF SIGNIFICANCE: Ionically cross-linked, injectable alginate biomaterials hold promise in many different clinical settings. However, adding new chemical functionality to alginate can disrupt their ionic cross-linking, limiting their utility. We have developed a "click" cross-linking strategy to improve the mechanical properties and tissue function of modified alginate biomaterials and enable them to capture small molecule drugs from the blood. We show that click cross-linked materials remain in place better than ionically cross-linked materials and efficiently capture payloads from the blood. Development of click cross-linking for refillable depots represents a crucial step toward clinical application of this promising drug delivery platform.
ViBE: Dressing for Diverse Body Shapes

Body shape plays an important role in determining what garments will best suit a given person, yet today’s clothing recommendation methods take a “one shape fits all” approach. These body-agnostic vision methods and datasets are a barrier to inclusion, ill-equipped to provide good suggestions for diverse body shapes. We introduce ViBE, a VIsual Body-aware Embedding that captures clothing’s affinity with different body shapes. Given an image of a person, the proposed embedding identifies garments that will flatter her specific body shape. We show how to learn the embedding from an online catalog displaying fashion models of various shapes and sizes wearing the products, and we devise a method to explain the algorithm’s suggestions for well-fitting garments. We apply our approach to a dataset of diverse subjects, and demonstrate its strong advantages over status quo body-agnostic recommendation, both according to automated metrics and human opinion.
DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentation

We present the Dayton Annotated LiDAR Earth Scan (DALES) data set, a new large-scale aerial LiDAR data set with over a half-billion hand-labeled points spanning 10 km 2 of area and eight object categories. Large annotated point cloud data sets have become the standard for evaluating deep learning methods. However, most of the existing data sets focus on data collected from a mobile or terrestrial scanner with few focusing on aerial data. Point cloud data collected from an Aerial Laser Scanner (ALS) presents a new set of challenges and applications in areas such as 3D urban modeling and large-scale surveillance. DALES is the most extensive publicly available ALS data set with over 400 times the number of points and six times the resolution of other currently available annotated aerial point cloud data sets. This data set gives a critical number of expert verified hand-labeled points for the evaluation of new 3D deep learning algorithms, helping to expand the focus of current algorithms to aerial data. We describe the nature of our data, annotation workflow, and provide a benchmark of current state-of-the-art algorithm performance on the DALES data set.
Recursive computation of limited lookahead supervisory controls for discrete event systems

We continue the study of limited lookahead policies in supervisory control of discrete event systems undertaken in a previous paper. On-line control of discrete event systems using limited lookahead policies requires, after the execution of each event, the calculation of the supremal controllable sublanguage of a given language with respect to another larger language. These two languages are finite and represented by their tree generators, where one tree is a subtree of the other. These trees change dynamically from step to step, where one step is the execution of one event by the system. We show in this paper how to perform this calculation in a recursive manner, in the sense that the calculation for a new pair of trees can make use of the calculation for the preceding pair, thus substantially reducing the amount of computation that has to be done on-line. In order to make such a recursive procedure possible from step to step, we show how the calculation for a single step (i.e., for a given pair of trees) can itself be performed recursively by means of a backward dynamic programming algorithm on the vertices of the larger tree. These two nested recursive procedures are also extended to the limited lookahead version of the “supervisory control problem with tolerance.”
Mapping Twitter Conversation Landscapes.

While the most ambitious polls are based on standardized interviews with a few thousand people, millions are tweeting freely and publicly in their own voices about issues they care about. This data offers a vibrant 24/7 snapshot of people's response to various events and topics. The sheer scale of the data on Twitter allows us to measure in aggregate how the various issues are rising and falling in prominence over time. However, the volume of the data also means that an intelligent tool is required to allow the users to make sense of the data. To this end, we built a novel, interactive web-based tool for mapping the conversation landscapes on Twitter. Our system utilizes recent advances in natural language processing and deep neural networks that are robust with respect to the noisy and unconventional nature of tweets, in conjunction with a scalable clustering algorithm an interactive visualization engine to allow users to tap the mine of information that is Twitter. We ran a user study with 40 participants using tweets about the 2016 US presidential election and the summer 2016 Orlando shooting, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can identify and make sense of the various conversation topics on Twitter.
Stability and Learning in Strategic Queuing Systems

Bounding the price of anarchy, which quantifies the damage to social welfare due to selfish behavior of the participants, has been an important area of research in algorithmic game theory. In this paper, we study this phenomenon in the context of a game modeling queuing systems: routers compete for servers, where packets that do not get service will be resent at future rounds, resulting in a system where the number of packets at each round depends on the success of the routers in the previous rounds. We model this as an (infinitely) repeated game, where the system holds a state (number of packets held by each queue) that arises from the results of the previous rounds. We assume that routers satisfy the no-regret condition, e.g. they use learning strategies to identify the server where their packets get the best service. Classical work on repeated games makes the strong assumption that the subsequent rounds of the repeated games are independent (beyond the influence on learning from past history). The carryover effect caused by packets remaining in this system makes learning in our context result in a highly dependent random process. We analyze this random process and find that if the capacity of the servers is high enough to allow a centralized and knowledgeable scheduler to get all packets served even when service rates are halved, and queues use no-regret learning algorithms, then the expected number of packets in the queues will remain bounded throughout time, assuming older packets have priority. This paper is the first to study the effect of selfish learning in a queuing system, where the learners compete for resources, but rounds are not all independent: the number of packets to be routed at each round depends on the success of the routers in the previous rounds.
How Much Should I Pay? An Empirical Analysis on Monetary Prize in TopCoder

It is reported that task monetary prize is one of the most important motivating factors to attract crowd workers. While using expert-based methods to price Crowdsourcing tasks is a common practice, the challenge of validating the associated prices across different tasks is a constant issue. To address this issue, three different classifications of multiple linear regression, logistic regression, and K-nearest neighbor were compared to find the most accurate predicted price, using a dataset from TopCoder website. The result of comparing chosen algorithms showed that the logistics regression model will provide the highest accuracy of 90% to predict the associated price to tasks and KNN ranked the second with an accuracy of 64% for K = 7. Also, applying PCA wouldn’t lead to any better prediction accuracy as data components are not correlated.
Ghost problems from Pauli–Villars to fourth-order quantum gravity and their resolution

We review the history of the ghost problem in quantum field theory from the Pauli–Villars regulator theory to currently popular fourth-order derivative quantum gravity theories. While these theorie...
Selective Disruption of Synaptic BMP Signaling by a Smad Mutation Adjacent to the Highly Conserved H2 Helix.

Bone morphogenetic proteins (BMPs) shape normal development and function via canonical and noncanonical signaling pathways. BMPs initiate canonical signaling by binding to transmembrane receptors that phosphorylate Smad proteins and induce their translocation into the nucleus and regulation of target genes. Phosphorylated Smads also accumulate at cellular junctions, but this noncanonical, local BMP signaling modality remains less defined. We have recently reported that phosphorylated Smad (pMad in Drosophila) accumulates at synaptic junctions in protein complexes with genetically distinct composition and regulation. Here, we examined a wide collection of Drosophila Mad alleles and searched for molecular features relevant to pMad accumulation at synaptic junctions. We found that strong Mad alleles generally disrupt both synaptic and nuclear pMad, whereas moderate Mad alleles have a wider range of phenotypes and can selectively impact different BMP signaling pathways. Interestingly, regulatory Mad mutations reveal that synaptic pMad appears to be more sensitive to a net reduction in Mad levels than nuclear pMad. Importantly, a previously uncharacterized allele, Mad8, showed markedly reduced synaptic pMad but only moderately diminished nuclear pMad. The postsynaptic composition and electrophysiological properties of Mad8 neuromuscular junctions (NMJs) were also altered. Using biochemical approaches, we examined how a single point mutation in Mad8 could influence the Mad-receptor interface and identified a key motif, the H2 helix. Our study highlights the biological relevance of Smad-dependent, synaptic BMP signaling and uncovers a highly conserved structural feature of Smads, critical for normal development and function.
Land and labor allocation under communal tenure: Theory and evidence from China

Abstract This paper studies rural communities' land and labor choices under communal tenure. I develop a theoretical framework to explain why rural communities often adopt practices of labor-contingent land access and frequent land reallocations under communal tenure, and argue that, although these practices reduce cross-household income inequality and improve agricultural production efficiency under imperfect markets, they can nonetheless inefficiently trap labor in agriculture. I rely on a legal reform in 2003 that stopped land reallocations in all Chinese villages and exploit the variation in villages’ reallocation practices before the reform to test the model predictions. The results suggest that the elimination of land reallocations increased off-farm labor and household per capita net income by 7% and 6.5% respectively. However, this came at the cost of a 6% reduction in total agricultural output and a significant jump in intra-village income inequality.
When racism and sexism benefit Black and female politicians: Politicians' ideology moderates prejudice's effect more than politicians' demographic background.

Using large samples that are nationally diverse or nationally representative (total N = 44,836), this article presents evidence that citizens’ prejudice does not usually benefit or undermine politicians who are from a particular demographic group, as many past studies assumed; instead, citizens’ prejudice is associated with support for conservative politicians and opposition to liberal politicians, regardless of politicians’ demographic background. Study 1a and Study 1b show that, regardless of the race and gender of real politicians, racism and sexism negatively predict support for liberal politicians, and positively predict support for conservative politicians. This overall pattern is experimentally confirmed in Study 2 where participants evaluate a hypothetical politician. Using data collected between 1972 and 2016, Study 3 shows that, historically, the predictive effect of racism and sexism on support for politicians in general is moderated by politicians’ perceived ideology. Study 4 addresses a limitation of Study 1–3, and Study 5 extends the results for prejudice to the religious domain (i.e., prejudice toward Muslims). Together, these studies suggest that the way prejudice is related to support for a politician is primarily moderated by the politician’s political ideology, not the politician’s demographic background. Thus, this article highlights the often-overlooked role of politicians’ ideology, clarifying theories that explain how citizens’ prejudice is translated into their political preferences. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Far from Equilibrium Hydrodynamics and the Beam Energy Scan

The existence of hydrodynamic attractors in rapidly expanding relativistic systems has shed light on the success of relativistic hydrodynamics in describing heavy-ion collisions at zero chemical potential. As the search for the QCD critical point continues, it is important to investigate how out of equilibrium effects influence the trajectories on the QCD phase diagram. In this proceedings, we study a Bjorken expanding hydrodynamic system based on DMNR equations of motion with initial out of equilibrium effects and finite chemical potential in a system with a critical point. We find that the initial conditions are not unique for a specific freeze-out point, but rather the system can evolve to the same final state freeze-out point with a wide range of initial baryon chemical potential, $\mu_B$. For the same initial energy density and baryon density, depending on how far out of equilibrium the system begins, the initial $\mu_B$ can vary by $\Delta \mu_B\sim 350$ MeV. Our results indicate that knowledge of the out-of-equilibrium effects in the initial state provide vital information that influences the search for the QCD critical point.
A nonlocal transport equation modeling complex roots of polynomials under differentiation

Let $p_n:\mathbb{C} \rightarrow \mathbb{C}$ be a random complex polynomial whose roots are sampled i.i.d. from a radial distribution $u(r) r dr$ in the complex plane. A natural question is how the distribution of roots evolves under repeated (say $n/2-$times) differentiation of the polynomial. We conjecture a mean-field expansion for the evolution of $\psi(s) = u(s) s$ $$ \frac{\partial \psi}{\partial t} = \frac{\partial}{\partial x} \left( \left( \frac{1}{x} \int_{0}^{x} \psi(s) ds \right)^{-1} \psi(x) \right).$$ The evolution of $\psi(s) \equiv 1$ corresponds to the evolution of random Taylor polynomials $$ p_n(z) = \sum_{k=0}^{n}{ \gamma_k \frac{z^k}{k!}} \quad \mbox{where} \quad \gamma_k \sim \mathcal{N}_{\mathbb{C}}(0,1).$$ We discuss some numerical examples suggesting that this particular solution may be stable. We prove that the solution is linearly stable. The linear stability analysis reduces to the classical Hardy integral inequality. Many open problems are discussed.
Three puzzles in cosmology

Cosmology presents us with several puzzles that are related to the fundamental structure of quantum theory. We discuss three such puzzles, linking them to effects that arise in black hole physics. ...
LOGICAL DISPUTES AND THE A PRIORI

In this paper, I propose a general model for the rational resolution of disputes about logic, and discuss a number of its features. These include its dispensing with a traditional notion of the a priori in logic, and some objections to which this might give rise.
Learning a Control Policy for Fall Prevention on an Assistive Walking Device

Fall prevention is one of the most important components in senior care. We present a technique to augment an assistive walking device with the ability to prevent falls. Given an existing walking device, our method develops a fall predictor and a recovery policy by utilizing the onboard sensors and actuators. The key component of our method is a robust human walking policy that models realistic human gait under a moderate level of perturbations. We use this human walking policy to provide training data for the fall predictor, as well as to teach the recovery policy how to best modify the person’s gait when a fall is imminent. Our evaluation shows that the human walking policy generates walking sequences similar to those reported in biomechanics literature. Our experiments in simulation show that the augmented assistive device can indeed help recover balance from a variety of external perturbations. We also provide a quantitative method to evaluate the design choices for an assistive device.
A Computational Approach to Identify Interfering Medications on Urine Drug Screening Assays without Data from Confirmatory Testing.

Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse, but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. From our institution's EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol, and salicylates. Both datasets included individuals' prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering medication independently of exposure to the assay's target drug(s). For a given assay-medication pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering medications. Applying our approach to the new data, we discovered and validated multiple compounds that can cause presumptive positives on the UDS assay for acetaminophen. Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.
Cutaneous Manifestations of COVID-19: A Systematic Review.

Objective: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently a pandemic. Although pulmonary health has been the primary focus of studies during the early days of COVID-19, development of a comprehensive understanding of this emergent disease requires knowledge of all possible disease manifestations in affected patients. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant review focuses on cutaneous manifestations reported in COVID-19 patients. Approach: Literature review was conducted using the PubMed database to examine various cutaneous manifestations related to the SARS-CoV-2 infection. Published articles (n = 56) related to search criteria from the onset of the COVID-19 pandemic to June 30, 2020, were included. The primary literature articles included in this study were mainly from France, Spain, Italy, and the United Kingdom. Results: Unique to many other symptoms of COVID-19, its cutaneous manifestations have been found in people of all age groups, including children. The cutaneous manifestations of COVID-19 are varied and include maculopapular, chilblain-like, urticarial, vesicular, livedoid, and petechial lesions. In addition, rashes are common in multisystem inflammatory syndrome in children, a new and serious health condition that shares symptoms with Kawasaki disease and is likely related to COVID-19. In addition, personal protective equipment-related skin wounds are of serious concern since broken cutaneous barriers can create an opening for potential COVID-19 infections. Innovation and Conclusion: As this virus continues to spread silently, mainly through asymptomatic carriers, an accurate and rapid identification of these cutaneous manifestations may be vital to early diagnosis and lead to possible better prognosis in COVID-19 patients. This systematic review and photo atlas provide a detailed analysis of the skin pathologies related to COVID-19. Study of these cutaneous manifestations and their pathogenesis, as well their significance in human health will help define COVID-19 in its entirety, which is a prerequisite to its effective management.
Prive-HD: Privacy-Preserved Hyperdimensional Computing

The privacy of data is a major challenge in machine learning as a trained model may expose sensitive information of the enclosed dataset. Besides, the limited computation capability and capacity of edge devices have made cloud-hosted inference inevitable. Sending private information to remote servers makes the privacy of inference also vulnerable because of susceptible communication channels or even untrustworthy hosts. In this paper, we target privacy-preserving training and inference of brain-inspired Hyperdimensional (HD) computing, a new learning algorithm that is gaining traction due to its light-weight computation and robustness particularly appealing for edge devices with tight constraints. Indeed, despite its promising attributes, HD computing has virtually no privacy due to its reversible computation. We present an accuracy-privacy trade-off method through meticulous quantization and pruning of hypervectors, the building blocks of HD, to realize a differentially private model as well as to obfuscate the information sent for cloud-hosted inference. Finally, we show how the proposed techniques can be also leveraged for efficient hardware implementation.
Deep Learning of Movement Intent and Reaction Time for EEG-informed Adaptation of Rehabilitation Robots

Mounting evidence suggests that adaptation is a crucial mechanism for rehabilitation robots in promoting motor learning. Yet, it is commonly based on robot-derived movement kinematics, which is a rather subjective measurement of performance, especially in the presence of a sensorimotor impairment. Here, we propose a deep convolutional neural network (CNN) that uses electroencephalography (EEG) as an objective measurement of two kinematics components that are typically used to assess motor learning and thereby adaptation: i) the intent to initiate a goal-directed movement, and ii) the reaction time (RT) for that movement. We evaluated our CNN on data acquired from an in-house experiment where 12 healthy subjects moved a rehabilitation robotic arm in four directions on a plane, in response to visual stimuli. Our CNN achieved average test accuracies of 80.08% and 79.82% in a binary classification of the intent (intent vs. no intent) and RT (slow vs. fast), respectively. Our results demonstrate how individual movement components implicated in distinct types of motor learning can be predicted from synchronized EEG data acquired before the start of the movement. Our approach can, therefore, inform robotic adaptation in real-time and has the potential to further improve one’s ability to perform the rehabilitation task.
On Factors that Moderate the Effect of Buyer-Supplier Experience on E-Procurement Platforms

E-procurement platforms facilitate transactions between suppliers and buyers from all over the world. Over time, suppliers and buyers may develop familiarity from prior experience with earlier transactions. The literature has established that prior experience leads to better project performance. In this study, we examine the effectiveness of prior experience between buyers and suppliers in e-procurement platforms with a focus on the moderating roles of temporal distance and language difference between the buyer and the supplier as well as the effect of routine tasks in the project (termed “task routinization”). Using a unique data set from a large e-procurement platform, we first find that buyers’ prior experience with a supplier positively affects project outcomes, and temporal distance and language difference both negatively affect project outcomes. This is consistent with previous findings for emerging e-procurement platforms. More interestingly, we find that the effectiveness of prior experience is constrained by both temporal distance and language difference, such that if a greater temporal distance separates the buyer and supplier or if the two speak different languages, prior experience is less likely to be helpful. In addition, while task routinization does not directly affect a project’s success, it has a positive interaction effect with prior experience, suggesting that buyers’ prior experience with a supplier is more effective in enhancing project outcomes when a project comprises routine tasks. Our findings on prior experience, temporal distance, language difference, and task routinization contribute to a better understanding of the e-procurement platform for global outsourcing and procurement. Limitations are discussed and topics are identified for future research.
Anisotropic thermal conductivity tensor measurements using beam-offset frequency domain thermoreflectance (BO-FDTR) for materials lacking in-plane symmetry

Abstract Many materials have anisotropic thermal conductivity, with diverse applications such as transistors, thermoelectrics, and laser gain media. Yet measuring the thermal conductivity tensor of such materials remains a challenge, particularly for materials lacking in-plane symmetry (i.e., transversely anisotropic materials). This paper demonstrates thermal conductivity tensor measurements for transversely anisotropic materials, by extending beam-offset frequency-domain thermoreflectance (BO-FDTR) methods which had previously been limited to transversely isotropic materials. Extensive sensitivity analysis is used to determine an appropriate range of heating frequencies and beam offsets to extract various tensor elements. This technique is demonstrated on a model transversely anisotropic material, x-cut quartz ( α-SiO2), by combining beam offset measurements from different sample orientations to reconstruct the full in-plane thermal conductivity tensor. The technique is also validated by measurements on two transversely isotropic materials, sapphire and highly oriented pyrolytic graphite (HOPG). The anisotropic measurements demonstrated very good self-consistency in correctly identifying isotropic directions when present, with residual anisotropy errors below 4% for sapphire and 2% for HOPG and quartz. Finally, a computational case study (simulated experiment) shows how the arbitrary in-plane thermal conductivity tensor of a fictitious material with high in-plane anisotropy can in principle be obtained from only a single sample orientation, rather than multiple orientations like the experiments on x-cut quartz.
Data‐Driven Approaches to Targeting Promotion E‐mails: The Case of Delayed Incentives

This paper empirically investigates using the e‐mail channel to target customers with a delayed incentive promotion—specifically, gift card promotion—and derives data‐driven e‐mail targeting policies. Gift card promotions are popular across retailers because they incentivize customers to spend more than a fixed expenditure level on regularly priced products by rewarding customers with a gift card to be redeemed against a future purchase. The e‐mail channel provides retailers with new sources of customer‐level data, which enables better prediction of customers' responsiveness to e‐mails (e.g., clicking) and the sales promotion that comes with it (e.g., participation in the promotion). We formulate the retailer's promotion e‐mail targeting problem by maximizing two objectives—the promotion's profitability (i.e., profit‐based targeting) and e‐mail click‐through rate (i.e., CTR‐based targeting). We also take into account the retailer's promotion budget and exclusivity concerns in targeting e‐mails. We use a comprehensive dataset from a Fortune 500 luxury fashion retailer's online channel and utilize both parametric and non‐parametric methods to predict customers' response to promotion e‐mails. Our data‐driven targeting policies improve the promotion's profitability by 5.57% and e‐mail CTR by 472.57%, on average, compared to our partner retailer's current e‐mail policy. We also find that the CTR‐based targeting policy lowers the promotion profitability by, on average, 9.09% compared to the profit‐based one. However, the CTR‐based policy recuperates the short‐term losses in the long‐term and increases the long‐term profitability by 3.94%, on average, compared to the profit‐based targeting policy.
Wilson Loop Expectations in Lattice Gauge Theories with Finite Gauge Groups

Wilson loop expectations at weak coupling are computed to first order, for four dimensional lattice gauge theories with finite gauge groups which satisfy some mild additional conditions. This continues recent work of Chatterjee, which considered the case of gauge group $$\mathbb {Z}_2$$ . The main steps are (1) reducing the first order computation to a problem of Poisson approximation, and (2) using Stein’s method to carry out the Poisson approximation.
Exotic multiplications on periodic complex bordism

Victor Snaith gave a construction of periodic complex bordism by inverting the Bott element in the suspension spectrum of $BU$. This presents an $\mathbb{E}_\infty$ structure on periodic complex bordism by different means than the usual Thom spectrum definition of the $\mathbb{E}_\infty$-ring $MUP$. Here, we prove that these two $\mathbb{E}_\infty$-rings are in fact different, though the underlying $\mathbb{E}_2$-rings are equivalent. Nonetheless, we prove that both rings $\mathbb{E}_\infty$-orient $KU_2^{\wedge}$ and other forms of $K$-theory.
Toy model of boundary states with spurious topological entanglement entropy

Topological entanglement entropy has been extensively used as an indicator of topologically ordered phases. We study the conditions needed for two-dimensional topologically trivial states to exhibit spurious contributions that contaminate topological entanglement entropy. We show that, if the state at the boundary of a subregion is a stabilizer state, then it has a nonzero spurious contribution to the region if and only if the state is in a nontrivial one-dimensional G₁×G₂ symmetry-protected-topological (SPT) phase under an on-site symmetry. However, we provide a candidate of a boundary state that has a nonzero spurious contribution but does not belong to any such SPT phase.
KP solitons in shallow water

The main purpose of the paper is to provide a survey of our recent studies on soliton solutions of the Kadomtsev–Petviashvili (KP) equation. The KP equation describes weakly dispersive and small amplitude wave propagation in a quasi-two-dimensional framework. Recently, a large variety of exact soliton solutions of the KP equation has been found and classified. These solutions are localized along certain lines in a two-dimensional plane and decay exponentially everywhere else, and are called line-solitons. The classification is based on the far-field patterns of the solutions which consist of a finite number of line-solitons. Each soliton solution is then defined by a point of the totally non-negative Grassmann variety which can be parametrized by a unique derangement of the symmetric group of permutations. Our study also includes certain numerical stability problems of those soliton solutions. Numerical simulations of the initial value problems indicate that certain classes of initial waves asymptotically approach to these exact solutions of the KP equation. We discuss an application of our theory to the Mach reflection problem in shallow water. This problem describes the resonant interaction of solitary waves appearing in the reflection of an obliquely incident wave onto a vertical wall, and it predicts an extraordinary fourfold amplification of the wave at the wall. There are several numerical studies confirming the prediction, but all indicate disagreements with the KP theory. Contrary to those previous numerical studies, we find that the KP theory actually provides an excellent model to describe the Mach reflection phenomena when the higher order corrections are included in the quasi-two-dimensional approximation. We also present laboratory experiments of the Mach reflection recently carried out by Yeh and his colleagues, and show how precisely the KP theory predicts this wave behavior.
What do CFTs tell us about anti-de Sitter spacetimes?

The AdS/CFT conjecture relates quantum gravity on anti-de Sitter (AdS) space to a conformal field theory (CFT) defined on the spacetime boundary. We interpret the CFT in terms of natural analogues of the bulk S-matrix. Our first approach finds the bulk S-matrix as a limit of scattering from an AdS bubble immersed in a space admitting asymptotic states. Next, we show how the periodicity of geodesics obstructs a standard LSZ prescription for scattering within global AdS. To avoid this subtlety we partition global AdS into patches within which CFT correlators reconstruct transition amplitudes of AdS states. Finally, we use the AdS/CFT duality to propose a large N collective field theory that describes local, perturbative supergravity. Failure of locality in quantum gravity should be related to the difference between the collective 1/N expansion and genuine finite N dynamics.
Quantifying Supernovae-driven Multiphase Galactic Outflows

Galactic outflows are ubiquitously observed in star-forming disk galaxies and are critical for galaxy formation. Supernovae (SNe) play the key role in driving the outflows, but there is no consensus as to how much energy, mass and metal they can launch out of the disk. We perform 3D, high-resolution hydrodynamic simulations to study SNe-driven outflows from stratified media. Assuming SN rate scales with gas surface density $\Sigma_{\rm{gas}}$ as in the Kennicutt-Schmidt (KS) relation, we find the mass loading factor, $\eta_m$, defined as the mass outflow flux divided by the star formation surface density, decreases with increasing $\Sigma_{\rm{gas}}$ as $\eta_m \propto \Sigma^{-0.61}_{\rm{gas}}$. Approximately $\Sigma_{\rm{gas}}$ $\lesssim$ 50 $M_\odot/\rm{pc}^2$ marks when $\eta_m \gtrsim$1. About 10-50\% of the energy and 40-80\% of the metals produced by SNe end up in the outflows. The tenuous hot phase ($T>3\times 10^5$ K), which fills 60-80\% of the volume at mid-plane, carries the majority of the energy and metals in outflows. We discuss how various physical processes, including vertical distribution of SNe, photoelectric heating, external gravitational field and SN rate, affect the loading efficiencies. The relative scale height of gas and SNe is a very important factor in determining the loading efficiencies.
Multivariate Bayesian Logistic Regression for Analysis of Clinical Study Safety Issues

This paper describes a method for a model-based analysis of clinical safety data called multivariate Bayesian logistic regression (MBLR). Parallel logistic regression models are fit to a set of medically related issues, or response variables, and MBLR allows information from the different issues to "borrow strength" from each other. The method is especially suited to sparse response data, as often occurs when fine-grained adverse events are collected from subjects in studies sized more for efficacy than for safety investigations. A combined anal- ysis of data from multiple studies can be performed and the method enables a search for vulnerable subgroups based on the covariates in the regression model. An example involving 10 medically related issues from a pool of 8 studies is presented, as well as simulations showing distributional properties of the method.
THE GLOBULAR CLUSTER POPULATION OF NGC 7457: CLUES TO THE EVOLUTION OF FIELD S0 GALAXIES

In this paper, we present the results of a wide-field imaging study of the globular cluster (GC) system of the field S0 galaxy NGC 7457. To derive the global properties of the GC system, we obtained deep BVR images with the WIYN 3.5 m telescope and Minimosaic Imager and studied the GC population of NGC 7457 to a projected radius of ~30 kpc. Our ground-based data were combined with archival and published Hubble Space Telescope data to probe the properties of the GC system close to the galaxy center and reduce contamination in the GC candidate sample from foreground stars and background galaxies. We performed surface photometry of NGC 7457 and compared the galaxy's surface brightness profile with the surface density profile of the GC system. The profiles have similar shapes in the inner 1' (3.9 kpc), but the GC system profile appears to flatten relative to the galaxy light at larger radii. The GC system of NGC 7457 is noticeably elliptical in our images; we measure = 0.66 ± 0.14 for the GC distribution, which is consistent with our measured ellipticity of the galaxy light. We integrated the radial surface density profile of the GC system to derive a total number of GCs, N GC = 210 ± 30. The GC specific frequencies normalized by the galaxy luminosity and mass are SN = 3.1 ± 0.7 and T = 4.8 ± 1.1, respectively. Comparing the derived GC system properties and other empirical data for NGC 7457 to S0 formation scenarios suggests that this field S0 galaxy may have formed in an unequal-mass merger.
Solutions of the Maxwell equations and photon wave functions

Properties of six-component electromagnetic field solutions of a matrix form of the Maxwell equations, analogous to the four-component solutions of the Dirac equation, are described. It is shown that the six-component equation, including sources, is invariant under Lorentz transformations. Complete sets of eigenfunctions of the Hamiltonian for the electromagnetic fields, which may be interpreted as photon wave functions, are given both for plane waves and for angular-momentum eigenstates. Rotationally invariant projection operators are used to identify transverse or longitudinal electric and magnetic fields. For plane waves, the velocity transformed transverse wave functions are also transverse, and the velocity transformed longitudinal wave functions include both longitudinal and transverse components. A suitable sum over these eigenfunctions provides a Green function for the matrix Maxwell equation, which can be expressed in the same covariant form as the Green function for the Dirac equation. Radiation from a dipole source and from a Dirac atomic transition current are calculated to illustrate applications of the Maxwell Green function.
Properties of the donor impurity band in mixed valence insulators

In traditional semiconductors with large effective Bohr radius, an electron donor creates a hydrogen-like bound state just below the conduction band edge. The properties of the impurity band arising from such hydrogenic impurities have been studied extensively during the last 70 years. In this paper we consider whether a similar bound state and a similar impurity band can exist in mixed valence insulators, where the gap arises at low temperature due to strong electron-electron interactions. We find that the structure of the hybridized conduction band leads to an unusual bound state that can be described using the physics of the one-dimensional hydrogen atom. The properties of the resulting impurity band are also modified in a number of ways relative to the traditional semiconductor case; most notably, the impurity band can hold a much larger concentration without inducing an insulator-to-metal transition. We estimate the critical doping associated with this transition, and then proceed to calculate the DC and AC conductivities and the specific heat. We discuss our results in light of recent measurements on the mixed valence insulator ${\mathrm{SmB}}_{6}$ and find them to be consistent with the experiments.
On the Multiplicity One Conjecture in min-max theory

We prove that in a closed manifold of dimension between 3 and 7 with a bumpy metric, the min-max minimal hypersurfaces associated with the volume spectrum introduced by Gromov, Guth, Marques-Neves, are two-sided and have multiplicity one. This confirms a conjecture by Marques-Neves. We prove that in a bumpy metric each volume spectrum is realized by the min-max value of certain relative homotopy class of sweepouts of boundaries of Caccioppoli sets. The main result follows by approximating such min-max value using the min-max theory for hypersurfaces with prescribed mean curvature established by the author with Zhu.
Domain of Attraction for Maps Tangent to the Identity in \mathbb {C} ^2 with Characteristic Direction of Higher Degree

We study holomorphic fixed point germs in two complex variables that are tangent to the identity and have a degenerate characteristic direction. We show that if that characteristic direction is also a characteristic direction for higher degree terms, is non-degenerate for a higher degree term, and satisfies some additional properties, then there is a domain of attraction on which points converge to the origin along that direction.
Correlations and Pairing between Zeros and Critical Points of Gaussian Random Polynomials

Let pN be a degree N random polynomial in a complex variable. We obtain an explicit asymptotic formula for the covariance between the counting measures of its zeros and critical points, which we denote CovN(z, w). This formula shows that the correlation between a zero at z and a critical point at w is short range, decaying like e−N|z−w| 2 . With |z− w| on the order of N−1/2, however, CovN(z, w) is sharply peaked near z=w, causing zeros and critical points to appear in pairs. We prove bounds on the expected distance and angular dependence between a critical point and its paired zero.
Gravitons and pions

Both gravitons and pions are described by non-linear and non-renormalizable actions at low energies. These are most usefully treated by effective field theory, which is a full quantum field theoretic approach that relies only on the low-energy degrees of freedom and their interactions. The gravitational case is particularly clean because of the masslessness of the graviton and the wide separation of scales. This essay provides an overview of this approach.
Water Fractions in Extrasolar Planetesimals

With the goal of using externally polluted white dwarfs to investigate the water fractions of extrasolar planetesimals, we assemble from the literature a sample that we estimate to be more than 60% complete of DB white dwarfs warmer than 13,000 K, more luminous than 3 × 10–3 L ☉, and within 80 pc of the Sun. When considering all the stars together, we find that the summed mass accretion rate of heavy atoms exceeds that of hydrogen by over a factor of 1000. If so, this sub-population of extrasolar asteroids treated as an ensemble has little water and is at least a factor of 20 drier than CI chondrites, the most primitive meteorites. Furthermore, while an apparent excess of oxygen in a single DB can be interpreted as evidence that the accreted material originated in a water-rich parent body, we show that at least in some cases, there can be sufficient uncertainties in the time history of the accretion rate that such an argument may be ambiguous. Regardless of the difficulty associated with interpreting the results from an individual object, our analysis of the population of polluted DBs provides indirect observational support for the theoretical view that a snow line is important in disks where rocky planetesimals form.
Brane configurations for branes at conifolds

We study the T duality between a set of type IIB D3 branes at non-orbifold threefold singularities, and type IIA configurations of D4 branes stretched between relatively rotated NS fivebranes. The four-dimensional = 1 field theories on the D3 brane world-volume can be easily described using the IIA brane configuration. These models include families of chiral theories continuously connected to the theories appearing in brane box models (or D3 branes at orbifold singularities). We propose that flop transitions between topologically different small resolutions of the singularities are related to the crossing of rotated NS fivebranes in the T dual picture, and thus to Seiberg's duality in one of the gauge factors. We also comment on the inclusion of orientifold planes in the IIA brane picture.
What Produced the Ultraluminous Supernova Remnant in NGC 6946

The ultraluminous supernova remnant (SNR) in NGC 6946 is the brightest known SNR in X-rays, about 1000 times brighter than Cas A. To probe the nature of this remnant and its progenitor, we have obtained high-dispersion optical echelle spectra. The echelle spectra detect Hα, [N II], and [O III] lines and resolve these lines into a narrow (FWHM ~20–40 km s-1) component from unshocked material and a broad (FWHM ~250 km s-1) component from shocked material. Both narrow and broad components have unusually high [N II]/Hα ratios, about 1. Using the echelle observation, archival Hubble Space Telescope images, and archival ROSAT X-ray observations, we conclude that the SNR was produced by a normal supernova whose progenitor was a massive star, either a WN star or a luminous blue variable. The high luminosity of the remnant is caused by the supernova ejecta expanding into a dense, nitrogen-rich circumstellar nebula created by the progenitor.