cssi/SciSciGPT-SciSciNet huggingface dataset:
Private Equity and the Leverage Myth

Investors have traditionally relied on mean–variance analysis to determine a portfolio’s optimal asset mix, but they have struggled to incorporate private equity into this framework because they do not know how to estimate its risk. The observed volatility of private equity returns is unrealistically low because the recorded returns of private equity are based on appraised values, which are serially linked to each other. These linked appraisals, therefore, significantly dampen the observed volatility. As an alternative to observed volatility, some investors have argued that private equity volatility should be estimated as leveraged public equity volatility, because private equity companies are more highly levered than publicly traded companies. However, this approach yields unrealistically high values for private equity volatility, which invites the following question: Why isn’t the appropriately leveraged volatility of public companies a reasonable approximation of private equity volatility? This article offers an answer to this puzzle. TOPICS:Private equity, risk management, performance measurement Key Findings ▪ Why isn’t the appropriately leveraged volatility of public companies a reasonable approximation of private equity volatility? The authors look for clues in the public markets where they find no association between volatility and leverage, counter to what financial theory would suggest. ▪ The evidence suggests that the relationship between leverage and volatility is hopelessly obscured by a variety of confounding effects in both public and private markets. ▪ This article arrives at the counterintuitive conclusion that private equity volatility is similar to public equity volatility despite its higher leverage. The likely explanation is that privately held companies are inherently less risky and thus able to bear greater leverage.
Membrane-proximal F-actin restricts local membrane protrusions and directs cell migration

Cell migration is driven by local membrane protrusion through directed polymerization of F-actin at the front. However, F-actin next to the plasma membrane also tethers the membrane and thus resists outgoing protrusions. Here, we developed a fluorescent reporter to monitor changes in the density of membrane-proximal F-actin (MPA) during membrane protrusion and cell migration. Unlike the total F-actin concentration, which was high in the front of migrating cells, MPA density was low in the front and high in the back. Back-to-front MPA density gradients were controlled by higher cofilin-mediated turnover of F-actin in the front. Furthermore, nascent membrane protrusions selectively extended outward from areas where MPA density was reduced. Thus, locally low MPA density directs local membrane protrusions and stabilizes cell polarization during cell migration.
Long time asymptotics for homoenergetic solutions of the Boltzmann equation. Hyperbolic-dominated case

In this paper we continue the formal analysis of the long-time asymptotics of the homoenergetic solutions for the Boltzmann equation that we began by James et al (2019 J. Nonlinear Sci. 29 1943–73). They have the form where where A is a constant matrix. Homoenergetic solutions satisfy an integro-differential equation which contains, in addition to the classical Boltzmann collision operator, a linear hyperbolic term. Depending on the properties of the collision kernel the collision and the hyperbolic terms might be of the same order of magnitude as t → ∞, or the collision term could be the dominant one for large times, or the hyperbolic term could be the largest. The first case has been rigorously studied by James et al (2019 Arch. Ration. Mech. Anal. 231 787–843). Formal asymptotic expansions in the second case have been obtained by James et al (2019 J. Nonlinear Sci. 29 1943–73). All the solutions obtained in this case can be approximated by Maxwellian distributions with changing temperature. In this paper we focus in the case where the hyperbolic terms are much larger than the collision term for large times (hyperbolic-dominated behaviour). In the hyperbolic-dominated case it does not seem to be possible to describe in a simple way all the long time asymptotics of the solutions, but we discuss several physical situations and formulate precise conjectures. We give explicit formulae for the relationship between density, temperature and entropy for these solutions. These formulae differ greatly from the ones at equilibrium.
Strongly Heterogeneous Transmission of COVID-19 in Mainland China: Local and Regional Variation

Background: The outbreak of novel coronavirus disease 2019 (COVID-19) started in the city of Wuhan, China, with a period of rapid initial spread. Transmission on a regional and then national scale was promoted by intense travel during the holiday period of the Chinese New Year. We studied the variation in transmission of COVID-19, locally in Wuhan, as well as on a larger spatial scale, among different cities and even among provinces in mainland China. Methods: In addition to reported numbers of new cases, we have been able to assemble detailed contact data for some of the initial clusters of COVID-19. This enabled estimation of the serial interval for clinical cases, as well as reproduction numbers for small and large regions. Findings: We estimated the average serial interval was 4.8 days. For early transmission in Wuhan, any infectious case produced as many as four new cases, transmission outside Wuhan was less intense, with reproduction numbers below two. During the rapid growth phase of the outbreak the region of Wuhan city acted as a hot spot, generating new cases upon contact, while locally, in other provinces, transmission was low. Interpretation: COVID-19 is capable of spreading very rapidly. The sizes of outbreak in provinces of mainland China mainly depended on the numbers of cases imported from Wuhan as the local reproduction numbers were low. The COVID-19 epidemic should be controllable with appropriate interventions (suspension of public transportation, cancellation of mass gatherings, implementation of surveillance and testing, and promotion of personal hygiene and face mask use).
Clinical and Genomic Epidemiology of Carbapenem-Nonsusceptible Citrobacter spp. at a Tertiary Health Care Center over 2 Decades

ABSTRACT Carbapenem-nonsusceptible Citrobacter spp. (CNSC) are increasingly recognized as health care-associated pathogens. Information regarding their clinical epidemiology, genetic diversity, and mechanisms of carbapenem resistance is lacking. We examined microbiology records of adult patients at the University of Pittsburgh Medical Center (UMPC) Presbyterian Hospital (PUH) from 2000 to 2018 for CNSC, as defined by ertapenem nonsusceptibility. Over this time frame, the proportion of CNSC increased from 4% to 10% (P = 0.03), as did daily defined carbapenem doses/1,000 patient days (6.52 to 34.5; R2 = 0.831; P
miRNAs and Neural Alternative Polyadenylation Specify the Virgin Behavioral State

Summary How are diverse regulatory strategies integrated to impose appropriately patterned gene expression that underlie in vivo phenotypes? Here, we reveal how coordinated miRNA regulation and neural-specific alternative polyadenylation (APA) of a single locus controls complex behaviors. Our entry was the unexpected observation that deletion of Bithorax complex (BX-C) miRNAs converts virgin female flies into a subjective post-mated behavioral state, normally induced by seminal proteins following copulation. Strikingly, this behavioral switch is directly attributable to misregulation of homothorax (hth). We localize specific CNS abdominal neurons where de-repressed Hth compromises virgin behavior in BX-C miRNA mutants. Moreover, we use genome engineering to demonstrate that precise mutation of hth 3′ UTR sites for BX-C miRNAs or deletion of its neural 3′ UTR extension containing most of these sites both induce post-mated behaviors in virgins. Thus, facilitation of miRNA-mediated repression by neural APA is required for virgin females to execute behaviors appropriate to their internal state.
Fast Noise Removal for k-Means Clustering

This paper considers $k$-means clustering in the presence of noise. It is known that $k$-means clustering is highly sensitive to noise, and thus noise should be removed to obtain a quality solution. A popular formulation of this problem is called $k$-means clustering with outliers. The goal of $k$-means clustering with outliers is to discard up to a specified number $z$ of points as noise/outliers and then find a $k$-means solution on the remaining data. The problem has received significant attention, yet current algorithms with theoretical guarantees suffer from either high running time or inherent loss in the solution quality. The main contribution of this paper is two-fold. Firstly, we develop a simple greedy algorithm that has provably strong worst case guarantees. The greedy algorithm adds a simple preprocessing step to remove noise, which can be combined with any $k$-means clustering algorithm. This algorithm gives the first pseudo-approximation-preserving reduction from $k$-means with outliers to $k$-means without outliers. Secondly, we show how to construct a coreset of size $O(k \log n)$. When combined with our greedy algorithm, we obtain a scalable, near linear time algorithm. The theoretical contributions are verified experimentally by demonstrating that the algorithm quickly removes noise and obtains a high-quality clustering.
Capacity Upper Bounds for the Relay Channel via Reverse Hypercontractivity

We revisit the primitive relay channel, introduced by Cover in 1987. Recent work derived upper bounds on the capacity of this channel that are tighter than the classical cutset bound using the concentration of measure. In this paper, we recover, generalize, and improve upon some of these upper bounds with simpler proofs using reverse hypercontractivity. To our knowledge, this is the first application of reverse hypercontractivity in proving first-order converses in network information theory.
Mutual Fund Performance and Flows During the COVID-19 Crisis

We present a comprehensive analysis of the performance and flows of U.S. actively-managed equity mutual funds during the COVID-19 crisis of 2020. We find that most active funds underperform passive benchmarks during the crisis, contradicting a popular hypothesis. Funds with high sustainability ratings perform well, as do funds with high star ratings. Fund out ows surpass pre-crisis trends, but not dramatically. Investors favor funds that apply exclusion criteria and funds with high sustainability ratings, especially environmental ones. Our finding that investors remain focused on sustainability during this major crisis suggests they view sustainability as a necessity rather than a luxury good.
Optimizing High Performance Markov Clustering for Pre-Exascale Architectures

HipMCL is a high-performance distributed memory implementation of the popular Markov Cluster Algorithm (MCL) and can cluster large-scale networks within hours using a few thousand CPU-equipped nodes. It relies on sparse matrix computations and heavily makes use of the sparse matrix-sparse matrix multiplication kernel (SpGEMM). The existing parallel algorithms in HipMCL are not scalable to Exascale architectures, both due to their communication costs dominating the runtime at large concurrencies and also due to their inability to take advantage of accelerators that are increasingly popular.In this work, we systematically remove scalability and performance bottlenecks of HipMCL. We enable GPUs by performing the expensive expansion phase of the MCL algorithm on GPU. We propose a CPU-GPU joint distributed SpGEMM algorithm called pipelined Sparse SUMMA and integrate a probabilistic memory requirement estimator that is fast and accurate. We develop a new merging algorithm for the incremental processing of partial results produced by the GPUs, which improves the overlap efficiency and the peak memory usage. We also integrate a recent and faster algorithm for performing SpGEMM on CPUs. We validate our new algorithms and optimizations with extensive evaluations. With the enabling of the GPUs and integration of new algorithms, HipMCL is up to 12.4x faster, being able to cluster a network with 70 million proteins and 68 billion connections just under 15 minutes using 1024 nodes of ORNL’s Summit supercomputer.
Ordered set partitions, Garsia-Procesi modules, and rank varieties

We introduce a family of ideals $I_{n,\lambda,s}$ in $\mathbb{Q}[x_1,\dots,x_n]$ for $\lambda$ a partition of $k\leq n$ and an integer $s \geq \ell(\lambda)$. This family contains both the Tanisaki ideals $I_\lambda$ and the ideals $I_{n,k}$ of Haglund-Rhoades-Shimozono as special cases. We study the corresponding quotient rings $R_{n,\lambda,s}$ as symmetric group modules. When $n=k$ and $s$ is arbitrary, we recover the Garsia-Procesi modules, and when $\lambda=(1^k)$ and $s=k$, we recover the generalized coinvariant algebras of Haglund-Rhoades-Shimozono. We give a monomial basis for $R_{n,\lambda,s}$, unifying the monomial bases studied by Garsia-Procesi and Haglund-Rhoades-Shimozono, and realize the $S_n$-module structure of $R_{n,\lambda,s}$ in terms of an action on $(n,\lambda,s)$-ordered set partitions. We also prove formulas for the Hilbert series and graded Frobenius characteristic of $R_{n,\lambda,s}$. We then connect our work with Eisenbud-Saltman rank varieties using results of Weyman. As an application of our work, we give a monomial basis, Hilbert series formula, and graded Frobenius characteristic formula for the coordinate ring of the scheme-theoretic intersection of a rank variety with diagonal matrices.
Beyond marsh drowning: The many faces of marsh loss (and gain)

Abstract The morphodynamics of tidal marshes is investigated using a novel model (MarshMorpho2D) that couples 2D tidal dynamics, wave dynamics, mass-conserving sediment transport, edge erosion, pond dynamics, and marsh upland migration. By only changing tidal range, sediment supply, and RSLR rate, the model reproduces a great variety of spatial patterns in marsh loss. For all tidal ranges, an increase in RSLR triggers channel widening, whereas a decrease in sediment supply triggers tidal flat deepening and increases wave edge erosion. Marsh loss by pond runaway expansion dominates for intermediate RSLR rates, especially for small tidal ranges and for small sediment supplies. Drowning only takes place in the microtidal setting for high RSLR rates and extremely low sediment supply. Drowning is restricted to the landward portion of the marsh, and lacks the variety of spatial scales associated with pond runaway. For a large tidal range, the amount of marsh loss by drowning and pond runaway is negligible, thus increasing the relative importance of edge erosion and channel widening. Upland migration deepens the channels, which release sediment and thus help sustaining the existing marsh. Nonetheless, marsh upland expansion compensates for existing marsh loss only for a large tidal range or for a high sediment supply, and thus microtidal marshes are likely to experience net loss. Marsh loss is highly heterogeneous, and is not well represented by the dichotomy between marsh survival and marsh loss based on vertical accretion thresholds. Marsh loss can take place by a variety of mechanisms, hindering the ability to generalize predictions about marsh loss.
Accurate Angular Inference for 802.11ad Devices Using Beam-Specific Measurements

Due to their sparsity, 60GHz channels are characterized by a few dominant paths. Knowing the angular information of their dominant paths, we can develop various applications, such as the prediction of link performance and the tracking of 802.11ad devices. Although they are equipped with phased arrays, the angular inference for 802.11ad devices is still challenging due to their limited number of RF chains and limited phase control capabilities. Considering the beam sweeping operation and the high communication bandwidth of 802.11ad devices, we propose variation-based angle estimation (VAE), called VAE-CIR, by utilizing beam-specific channel impulse responses (CIRs) measured under different beams and the directional gains of the corresponding beams to infer the angular information of dominant paths. Unlike state-of-the-arts, VAE-CIR exploits the variations between different beam-specific CIRs for angular inference and provides a performance guarantee in the high signal-to-noise-ratio region. To evaluate VAE-CIR, we generate the beam-specific CIRs by simulating the beam sweeping of 802.11ad devices with the beam patterns measured on off-the-shelf 802.11ad devices. The 60GHz channel is generated via a ray-tracing-based simulator and the CIRs are extracted via channel estimation based on Golay sequences. Through extensive experiments, VAE-CIR is shown to achieve more accurate angle estimation than existing schemes.
A robust measure of event isotropy at colliders

We introduce a new event shape observable — event isotropy — that quantifies how close the radiation pattern of a collider event is to a uniform distribution. This observable is based on a normalized version of the energy mover’s distance, which is the minimum “work” needed to rearrange one radiation pattern into another of equal energy. We investigate the utility of event isotropy both at electron-positron colliders, where events are compared to a perfectly spherical radiation pattern, as well as at proton-proton colliders, where the natural comparison is to either cylindrical or ring-like patterns. Compared to traditional event shape observables like sphericity and thrust, event isotropy exhibits a larger dynamic range for high-multiplicity events. This enables event isotropy to not only distinguish between dijet and multijet processes but also separate uniform N-body phase space configurations for different values of N. As a key application of this new observable, we study its performance to characterize strongly-coupled new physics scenarios with isotropic collider signatures.
A Fortran-Keras Deep Learning Bridge for Scientific Computing

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way bridge connects environments where deep learning resources are plentiful with those where they are scarce. The paper describes several unique features offered by FKB, such as customizable layers, loss functions, and network ensembles. The paper concludes with a case study that applies FKB to address open questions about the robustness of an experimental approach to global climate simulation, in which subgrid physics are outsourced to deep neural network emulators. In this context, FKB enables a hyperparameter search of one hundred plus candidate models of subgrid cloud and radiation physics, initially implemented in Keras, to be transferred and used in Fortran. Such a process allows the model’s emergent behavior to be assessed, i.e., when fit imperfections are coupled to explicit planetary-scale fluid dynamics. The results reveal a previously unrecognized strong relationship between offline validation error and online performance, in which the choice of the optimizer proves unexpectedly critical. This in turn reveals many new neural network architectures that produce considerable improvements in climate model stability including some with reduced error, for an especially challenging training dataset.
The Effects of Police Violence on Inner-City Students

Nearly 1,000 officer-involved killings occur each year in the United States. This article documents the large, racially disparate effects of these events on the educational and psychological well-being of Los Angeles public high school students. Exploiting hyperlocal variation in how close students live to a killing, I find that exposure to police violence leads to persistent decreases in GPA, increased incidence of emotional disturbance, and lower rates of high school completion and college enrollment. These effects are driven entirely by black and Hispanic students in response to police killings of other minorities and are largest for incidents involving unarmed individuals.
Nanobenders: Efficient Piezoelectric Actuators for Widely Tunable Nanophotonics at CMOS-Level Voltages

We introduce a compact (∼ 10 μm2) piezoelectric actuator, and use it to tune the optical resonance wavelength of a lithium niobate photonic crystal cavity by ∼ 5 nm/V. The tuning range is 1520-1560 nm with 4 V.
On q-series identities for false theta series

Abstract We prove several infinite families of q-series identities for false theta functions and related series. These identities are motivated by considerations of characters of modules of vertex operator superalgebras and of quantum dilogarithms. We also obtain closely related modular identities of the Gollnitz-Gordon-Andrews type. As a byproduct of our identities, we establish several identities for the Rogers dilogarithm function coming from multi q-hypergeometric series with “double poles”.
Local Trajectory Stabilization for Dexterous Manipulation via Piecewise Affine Approximations

We propose a model-based approach to design feedback policies for dexterous robotic manipulation. The manipulation problem is formulated as reaching the target region from an initial state for some non-smooth nonlinear system. First, we use trajectory optimization to find a feasible trajectory. Next, we characterize the local multi-contact dynamics around the trajectory as a piecewise affine system, and build a funnel around the linearization of the nominal trajectory using polytopes. We prove that the feedback controller at the vicinity of the linearization is guaranteed to drive the nonlinear system to the target region. During online execution, we solve linear programs to track the system trajectory. We validate the algorithm on hardware, showing that even under large external disturbances, the controller is able to accomplish the task.
Tradeoff-Focused Contrastive Explanation for MDP Planning

End-users’ trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people’s lives. In real-world applications of planning, multiple optimization objectives are often involved. Thus, planning agents’ decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts. We conduct a human subjects experiment to evaluate the effectiveness of our explanation approach in a mobile robot navigation domain. The results show that our approach significantly improves the users’ understanding, and confidence in their understanding, of the tradeoff rationale of the planning agent.
Single-Unit Technique for the Use of Acellular Dermal Matrix in Immediate Expander-Based Breast Reconstruction

Summary Background The use of acellular dermal matrices (ADMs) in immediate two-stage prosthetic breast reconstruction following mastectomy is now a common practice. The procedure confers several compelling benefits, including coverage of the inferior pole, enhanced definition of the inframammary fold, and reduction of capsular contracture. However, operative techniques used to create the ADM inferolateral sling can be unwieldy in practice, typically involving the placement of the ADM followed by positioning and anchoring of the prosthetic expander. At best, this may be a relatively minor nuisance, but may potentially influence outcomes, including discrepancies in symmetry. Methods We present a novel modification that aims to streamline this procedure. Perforations are made through the allograft, through which the tissue expander tabs are brought through and sutured together ex vivo to allow the ADM and expander to be placed into the inframammary fold position as a single unit. A retrospective chart review was then performed of patients who underwent breast reconstruction utilizing this technique between July 2015 and December 2018. Outcomes including postsurgical complications such as infection, malposition, and reoperation were analyzed. Results Sixty-two patients met the inclusion criteria, corresponding to 108 breasts. The average follow-up was 18 months. The overall complication rate was 29.6% of breasts. The most commonly observed complications were mastectomy skin necrosis (9.3%) and major infection (8.3%). There was a 7.4% rate of malposition. Conclusions This simple but effective modification in ADM technique is associated with a comparable complication rate and allows for greater ease and consistency in tissue expander placement.
Compact Speaker Embedding: lrx-Vector.

Deep neural networks (DNN) have recently been widely used in speaker recognition systems, achieving state-of-the-art performance on various benchmarks. The x-vector architecture is especially popular in this research community, due to its excellent performance and manageable computational complexity. In this paper, we present the lrx-vector system, which is the low-rank factorized version of the x-vector embedding network. The primary objective of this topology is to further reduce the memory requirement of the speaker recognition system. We discuss the deployment of knowledge distillation for training the lrx-vector system and compare against low-rank factorization with SVD. On the VOiCES 2019 far-field corpus we were able to reduce the weights by 28% compared to the full-rank x-vector system while keeping the recognition rate constant (1.83% EER).
Correlation Functions of Massless Interacting Scalar Fields in de Sitter Space

In this paper, we examine the behavior of correlation functions for a massless scalar field in de Sitter space with a quartic interaction. We find that two-loop corrections are relevant, and the resummation of these corrections generates a complicated structure whereby high momentum modes stay massless while low momentum modes develop a dynamical mass.
THE ELEVEN DIMENSIONAL SUPERGRAVITY EQUATIONS ON EDGE MANIFOLDS

We study the eleven dimensional supergravity equations on B 7 × S 4 considered as an edge manifold. We compute the indicial roots of the linearized system using the Hodge decomposition, and using the edge calculus and scattering theory we prove that the moduli space of solutions, near the Freund-Rubin states, is parameterized by three pairs of data on the bounding 6-sphere. γ1γ2γ3γ4 gαβ) d ∗ F = − 1 F ∧ F dF = 0 The nonlinear supergravity operator has an edge structure in the sense of Mazzeo (Maz91), which is a natural generalization in the context of the product of a confor- mally compact manifold and a compact manifold. We consider those solutions that are sections of the edge bundles, which are rescalings of the usual form bundles. The Fredholm property of certain elliptic edge operators is related to the invert- ibility of the corresponding normal operator N(L), which is the lift of the operator to the front face of the double stretched space X 2 e. The invertibility of the normal operator is in turn related to its action on functions polyhomogeneous at the left
Electron-capture and Low-mass Iron-core-collapse Supernovae: New Neutrino-radiation-hydrodynamics Simulations

We present new 1D (spherical) and 2D (axisymmetric) simulations of electron-capture (EC) and low-mass iron-core-collapse supernovae (SN). We consider six progenitor models: the ECSN progenitor from Nomoto; two ECSN-like low-mass low-metallicity iron-core progenitors from A. Heger (2016, private communication); and the 9, 10, and 11 (zero-age main-sequence) progenitors from Sukhbold et al. We confirm that the ECSN and ESCN-like progenitors explode easily even in 1D with explosion energies of up to a 0.15 Bethes (), and are a viable mechanism for the production of very-low-mass neutron stars. However, the 9, 10, and 11 progenitors do not explode in 1D and are not even necessarily easier to explode than higher-mass progenitor stars in 2D. We study the effect of perturbations and of changes to the microphysics and we find that relatively small changes can result in qualitatively different outcomes, even in 1D, for models sufficiently close to the explosion threshold. Finally, we revisit the impact of convection below the protoneutron star (PNS) surface. We analyze 1D and 2D evolutions of PNSs subject to the same boundary conditions. We find that the impact of PNS convection has been underestimated in previous studies and could result in an increase of the neutrino luminosity by up to factors of two.
Scraping SERPs for Archival Seeds: It Matters When You Start

Event-based collections are often started with a web search, but the search results you find on Day 1 may not be the same as those you find on Day 7. In this paper, we consider collections that originate from extracting URIs (Uniform Resource Identifiers) from Search Engine Result Pages (SERPs). Specifically, we seek to provide insight about the retrievability of URIs of news stories found on Google, and to answer two main questions: first, can one "refind" the same URI of a news story (for the same query) from Google after a given time? Second, what is the probability of finding a story on Google over a given period of time? To answer these questions, we issued seven queries to Google every day for over seven months (2017-05-25 to 2018-01-12) and collected links from the first five SERPs to generate seven collections for each query. The queries represent public interest stories: "healthcare bill," "manchester bombing," "london terrorism," "trump russia," "travel ban," "hurricane harvey," and "hurricane irma." We tracked each URI in all collections over time to estimate the discoverability of URIs from the first five SERPs. Our results showed that the daily average rate at which stories were replaced on the default Google SERP ranged from 0.21 - 0.54, and a weekly rate of 0.39 - 0.79, suggesting the fast replacement of older stories by newer stories. The probability of finding the same URI of a news story after one day from the initial appearance on the SERP ranged from 0.34 - 0.44. After a week, the probability of finding the same news stories diminishes rapidly to 0.01 - 0.11. In addition to the reporting of these probabilities, we also provide two predictive models for estimating the probability of finding the URI of an arbitrary news story on SERPs as a function of time. The web archiving community considers link rot and content drift important reasons for collection building. Similarly, our findings suggest that due to the difficulty in retrieving the URIs of news stories from Google, collection building that originates from search engines should begin as soon as possible in order to capture the first stages of events, and should persist in order to capture the evolution of the events, because it becomes more difficult to find the same news stories with the same queries on Google, as time progresses.
Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data

Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation patterns and the other to explore influential patterns. Propagation patterns reveal common cascading forms of geospatial entities in a region. Influential patterns demonstrate the impact of temporally long-term geospatial entities on their neighborhood. We apply this framework on a large dataset of traffic and weather data at countrywide scale, collected for the contiguous United States over two years. Our important findings include the identification of 90 common propagation patterns of traffic and weather entities (e.g., rain --> accident --> congestion), which results in identification of four categories of states within the US; and interesting influential patterns with respect to the "location", "duration", and "type" of long-term entities (e.g., a major construction --> more traffic incidents). These patterns and the categorization of the states provide useful insights on the driving habits and infrastructure characteristics of different regions in the US, and could be of significant value for applications such as urban planning and personalized insurance.
Was the Milky Way Bulge Formed from the Buckling Disk Instability, Hierarchical Collapse, Accretion of Clumps, or All of the Above?

The assembly of the Milky Way bulge is an old topic in astronomy, one now in a period of renewed and rapid development. That is due to tremendous advances in observations of bulge stars, motivating observations of both local and high-redshift galaxies, and increasingly sophisticated simulations. The dominant scenario for bulge formation is that of the Milky Way as a nearly pure disk galaxy, with the inner disk having formed a bar and buckled. This can potentially explain virtually all bulge stars with [Fe/H] ≳ −1.0, which comprise 95% of the stellar population. The evidence is the incredible success in N-body models of this type in making non-trivial, non-generic predictions, such as the rotation curve and velocity dispersion measured from radial velocities, and the spatial morphologies of the peanut/X-shape and the long bar. The classical bulge scenario, whereby the bulge formed from early dissipative collapse and mergers, remains viable for stars with [Fe/H] ≲ −1.0 and potentially a minority of the other stars. A classical bulge is expected from Λ-CDM cosmological simulations, can accentuate the properties of an existing bar in a hybrid system, and is most consistent with the bulge abundance trends such as [Mg/Fe], which are elevated relative to both the thin and thick disks. Finally, the clumpy-galaxy scenario is considered, as it is the correct description of most Milky Way precursors given observations of high-redshift galaxies. Simulations predict that these star-forming clumps will sometimes migrate to the centres of galaxies where they may form a bulge, and galaxies often include a bulge clump as well. They will possibly form a bar with properties consistent with those of the Milky Way, such as the exponential profile and metallicity gradient. Given the relative successes of these scenarios, the Milky Way bulge is plausibly of composite origin, with a classical bulge and/or inner halo numerically dominant for stars with [Fe/H] ≲ −1.0, a buckling thick disk for stars with − 1.0 ≲ [Fe/H]] ≲ -0.50 perhaps descended from the clumpy-galaxy phase, and a buckling thin disk for stars with [Fe/H] ≳ −0.50. Overlaps from these scenarios are uncertain throughout.
Efficient Absorption Enhancement Approaches for AlInAsSb Avalanche Photodiodes for 2-μm Applications

We describe two approaches to enhance the quantum efficiency of AlInAsSb APDs for 2-μm detection. Finite-difference time-domain simulations show the absorption at 2 µm can be enhanced by more than 100 % with a photonic crystal, and 400 % with a metal grating.
Solving large scale structure in ten easy steps with COLA

We present the COmoving Lagrangian Acceleration (COLA) method: an N-body method for solving for Large Scale Structure (LSS) in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory (LPT). Unlike standard N-body methods, the COLA method can straightforwardly trade accuracy at small-scales in order to gain computational speed without sacrificing accuracy at large scales. This is especially useful for cheaply generating large ensembles of accurate mock halo catalogs required to study galaxy clustering and weak lensing, as those catalogs are essential for performing detailed error analysis for ongoing and future surveys of LSS. As an illustration, we ran a COLA-based N-body code on a box of size 100 Mpc/h with particles of mass ≈ 5 × 109M⊙/h. Running the code with only 10 timesteps was sufficient to obtain an accurate description of halo statistics down to halo masses of at least 1011M⊙/h. This is only at a modest speed penalty when compared to mocks obtained with LPT. A standard detailed N-body run is orders of magnitude slower than our COLA-based code. The speed-up we obtain with COLA is due to the fact that we calculate the large-scale dynamics exactly using LPT, while letting the N-body code solve for the small scales, without requiring it to capture exactly the internal dynamics of halos. Achieving a similar level of accuracy in halo statistics without the COLA method requires at least 3 times more timesteps than when COLA is employed.
Characterizing the Value of Information in Medical Notes

Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about the quality of medical notes. We use two prediction tasks, readmission prediction and in-hospital mortality prediction, to characterize the value of information in medical notes. We show that as a whole, medical notes only provide additional predictive power over structured information in readmission prediction. We further propose a probing framework to select parts of notes that enable more accurate predictions than using all notes, despite that the selected information leads to a distribution shift from the training data (“all notes”). Finally, we demonstrate that models trained on the selected valuable information achieve even better predictive performance, with only 6.8%of all the tokens for readmission prediction.
Nonclassical assembly pathways of anisotropic particles

Advances in synthetic methods have spawned an array of nanoparticles and bio-inspired molecules of diverse shapes and interaction geometries. Recent experiments indicate that such anisotropic particles exhibit a variety of nonclassical self-assembly pathways, forming ordered assemblies via intermediates that do not share the architecture of the bulk material. Here we apply mean field theory to a prototypical model of interacting anisotropic particles, and find a clear thermodynamic impetus for nonclassical ordering in certain regimes of parameter space. In other parameter regimes, by contrast, assembly pathways are selected by dynamics. This approach suggests a means of predicting when anisotropic particles might assemble in a manner more complicated than that assumed by classical nucleation theory.
The habitable-zone planet finder calibration system

We present the design concept of the wavelength calibration system for the Habitable-zone Planet Finder instrument (HPF), a precision radial velocity (RV) spectrograph designed to detect terrestrial-mass planets around M-dwarfs. HPF is a stabilized, fiber-fed, R~50,000 spectrograph operating in the near-infrared (NIR) z/Y/J bands from 0.84 to 1.3 microns. For HPF to achieve 1 m s-1 or better measurement precision, a unique calibration system, stable to several times better precision, will be needed to accurately remove instrumental effects at an unprecedented level in the NIR. The primary wavelength calibration source is a laser frequency comb (LFC), currently in development at NIST Boulder, discussed separately in these proceedings. The LFC will be supplemented by a stabilized single-mode fiber Fabry-Perot interferometer reference source and Uranium-Neon lamp. The HPF calibration system will combine several other new technologies developed by the Penn State Optical-Infrared instrumentation group to improve RV measurement precision including a dynamic optical coupling system that significantly reduces modal noise effects. Each component has been thoroughly tested in the laboratory and has demonstrated significant performance gains over previous NIR calibration systems.
Semilinear wave equations on asymptotically de Sitter, Kerr–de Sitter and Minkowski spacetimes

In this paper we show the small data solvability of suitable semilinear wave and Klein-Gordon equations on geometric classes of spaces, which include so-called asymptotically de Sitter and Kerr-de Sitter spaces, as well as asymptotically Minkowski spaces. These spaces allow general infinities, called conformal infinity in the asymptotically de Sitter setting; the Minkowski type setting is that of non-trapping Lorentzian scattering metrics introduced by Baskin, Vasy and Wunsch. Our results are obtained by showing the global Fredholm property, and indeed invertibility, of the underlying linear operator on suitable L2-based function spaces, which also possess appropriate algebra or more complicated multiplicative properties. The linear framework is based on the b-analysis, in the sense of Melrose, introduced in this context by Vasy to describe the asymptotic behavior of solutions of linear equations. An interesting feature of the analysis is that resonances, namely poles of the inverse of the Mellin transformed b-normal operator, which are ‘quantum’ (not purely symbolic) objects, play an important role.
Bounds for mixing time of quantum walks on finite graphs

Several inequalities are proved for the mixing time of discrete-time quantum walks on finite graphs. The mixing time is defined differently than in Aharonov et al (2002 Proc. 33rd STOC (2001) (New York: ACM) pp 50–9 arXiv:quant-ph/0012090v2) and it is found that for particular examples of walks on a cycle, a hypercube and a complete graph, quantum walks provide no speedup in mixing over the classical counterparts. In addition, non-unitary quantum walks (i.e. walks with decoherence) are considered and a criterion for their convergence to the unique stationary distribution is derived.
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding

We look into the task of generalizing word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, without extra contextual information. We rely solely on the spellings of words and propose a model, along with an efficient algorithm, that simultaneously models subword segmentation and computes subword-based compositional word embedding. We call the model probabilistic bag-of-subwords (PBoS), as it applies bag-of-subwords for all possible segmentations based on their likelihood. Inspections and affix prediction experiment show that PBoS is able to produce meaningful subword segmentations and subword rankings without any source of explicit morphological knowledge. Word similarity and POS tagging experiments show clear advantages of PBoS over previous subword-level models in the quality of generated word embeddings across languages.
Learning Boolean functions with concentrated spectra

This paper discusses the theory and application of learning Boolean functions that are concentrated in the Fourier domain. We first estimate the VC dimension of this function class in order to establish a small sample complexity of learning in this case. Next, we propose a computationally efficient method of empirical risk minimization, and we apply this method to the MNIST database of handwritten digits. These results demonstrate the effectiveness of our model for modern classification tasks. We conclude with a list of open problems for future investigation.
Asymptotics of QCD factorization in exclusive hadronic decays of B mesons

Abstract Using the renormalon calculus, we study the asymptotic behavior of the perturbative expansion of the hard-scattering kernels entering the QCD factorization formula for the nonleptonic weak decays B 0 →D (∗)+ M − , where M is a light meson. In the “large- β 0 limit”, the kernels are infrared finite and free of endpoint singularities to all orders of perturbation theory. The leading infrared renormalon singularity corresponding to a power correction of order Λ QCD / m b vanishes if the light meson has a symmetric light-cone distribution amplitude. We calculate the Borel transforms and the corresponding momentum distribution functions of the hard-scattering kernels, and resum the series of O ( β 0 n −1 α s n ) corrections to explore the numerical significance of higher-order perturbative and power corrections. We also derive explicit expressions for the O(β 0 α s 2 ) contributions to the kernels, and for the renormalon singularities corresponding to power corrections of order ( Λ QCD / m b ) 2 . Finally, we study the limit m c →0 relevant to charmless hadronic decays such as B → ππ .
Conditional inferential models: combining information for prior‐free probabilistic inference

Summary The inferential model (IM) framework provides valid prior-free probabilistic inference by focusing on predicting unobserved auxiliary variables. But, efficient IM-based inference can be challenging when the auxiliary variable is of higher dimension than the parameter. Here we show that features of the auxiliary variable are often fully observed and, in such cases, a simultaneous dimension reduction and information aggregation can be achieved by conditioning. This proposed conditioning strategy leads to efficient IM inference and casts new light on Fisher's notions of sufficiency, conditioning and also Bayesian inference. A differential-equation-driven selection of a conditional association is developed, and validity of the conditional IM is proved under some conditions. For problems that do not admit a conditional IM of the standard form, we propose a more flexible class of conditional IMs based on localization. Examples of local conditional IMs in a bivariate normal model and a normal variance components model are also given.
Dense Super-Resolution Imaging of Molecular Orientation Via Joint Sparse Basis Deconvolution and Spatial Pooling

In single-molecule super-resolution microscopy, engineered point-spread functions (PSFs) are designed to efficiently encode new molecular properties, such as 3D orientation, into complex spatial features captured by a camera. To fully benefit from their optimality, algorithms must estimate multi-dimensional parameters such as molecular position and orientation in the presence of PSF overlap and model-experiment mismatches. Here, we present a novel joint sparse deconvo-lution algorithm based on the decomposition of fluorescence images into six basis images that characterize molecular orientation. The proposed algorithm exploits a group-sparsity structure across these basis images and applies a pooling strategy on corresponding spatial features for robust simultaneous estimates of the number, brightness, 2D position, and 3D orientation of fluorescent molecules. We demonstrate this method by imaging DNA transiently labeled with the intercalating dye YOYO-1. Imaging the position and orientation of each molecule reveals orientational order and disorder within DNA with nanoscale spatial precision.