cssi/SciSciGPT-SciSciNet huggingface dataset:
Do we now collect any revenue from taxing capital income

The wide variation in effective tax rates on income from different types of capital received by different investors creates numerous tax arbitrage opportunities that result in a loss in both government revenue and economic efficiency. The objective of this chapter is to estimate the revenue and distributional effects of tax arbitrage, using tax data from 1983, by examining the effects of two tax changes that would each substantially reduce the opportunities for tax arbitrage. Our principal conclusions are as follows: 1. Taxing real rather than nominal interest income would have raised government revenue in 1983 by $25.5 billion. 2. This increase in revenue would occur mainly at the expense of those in the highest tax brackets. 3. Taxing the cash flow from real capital and exempting from tax any income from financial assets would have raised government revenue by $17.4 billion. Since this tax change eliminates all distortions to savings and investment decisions, our revenue forecast suggests that the tax l...
Estimating Reproducible Functional Networks Associated with Task Dynamics Using Unsupervised LSTMS

We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task.
Illness, Social Disadvantage, and Sexual Risk Behavior in Adolescence and the Transition to Adulthood.

This study investigated the influence of illness on sexual risk behavior in adolescence and the transition to adulthood, both directly and through moderation of the impact of social disadvantage. We hypothesized positive effects for social disadvantages and illness on sexual risk behavior, consistent with the development of faster life history strategies among young people facing greater life adversity. Using the first two waves of the National Longitudinal Study of Adolescent to Adult Health, we developed a mixed-effects multinomial logistic regression model predicting sexual risk behavior in three comparisons: risky nonmonogamous sex versus safer nonmonogamous sex, versus monogamous sex, and versus being sexually inactive, by social characteristics, illness, interactions thereof, and control covariates. Multiple imputation was used to address a modest amount of missing data. Subjects reporting higher levels of illness had lower odds of having safer nonmonogamous sex (OR = 0.84, p < .001), monogamous sex (OR = 0.82, p < .001), and being sexually inactive (OR = 0.74, p < .001) versus risky nonmonogamous sex, relative to subjects in better health. Illness significantly moderated the sex (OR = 0.88, p < .01), race/ethnicity (e.g., OR = 1.21, p < .001), and childhood SES (OR = 0.94; p < .01) effects for the sexually inactive versus risky nonmonogamous sex comparison. Substantive findings were generally robust across waves and in sensitivity analyses. These findings offer general support for the predictions of life history theory. Illness and various social disadvantages are associated with increased sexual risk behavior in adolescence and the transition to adulthood. Further, analyses indicate that the buffering effects of several protective social statuses against sexual risk-taking are substantially eroded by illness.
Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation

Upper esophageal sphincter is an important anatomical landmark of the swallowing process commonly observed through the kinematic analysis of radiographic examinations that are vulnerable to subjectivity and clinical feasibility issues. Acting as the doorway of esophagus, upper esophageal sphincter allows the transition of ingested materials from pharyngeal into esophageal stages of swallowing and a reduced duration of opening can lead to penetration/aspiration and/or pharyngeal residue. Therefore, in this study we consider a non-invasive high resolution cervical auscultation-based screening tool to approximate the human ratings of upper esophageal sphincter opening and closure. Swallows were collected from 116 patients and a deep neural network was trained to produce a mask that demarcates the duration of upper esophageal sphincter opening. The proposed method achieved more than 90% accuracy and similar values of sensitivity and specificity when compared to human ratings even when tested over swallows from an independent clinical experiment. Moreover, the predicted opening and closure moments surprisingly fell within an inter-human comparable error of their human rated counterparts which demonstrates the clinical significance of high resolution cervical auscultation in replacing ionizing radiation-based evaluation of swallowing kinematics.
Efficient and Robust Shape Correspondence via Sparsity-Enforced Quadratic Assignment

In this work, we introduce a novel local pairwise descriptor and then develop a simple, effective iterative method to solve the resulting quadratic assignment through sparsity control for shape correspondence between two approximate isometric surfaces. Our pairwise descriptor is based on the stiffness and mass matrix of finite element approximation of the Laplace-Beltrami differential operator, which is local in space, sparse to represent, and extremely easy to compute while containing global information. It allows us to deal with open surfaces, partial matching, and topological perturbations robustly. To solve the resulting quadratic assignment problem efficiently, the two key ideas of our iterative algorithm are: 1) select pairs with good (approximate) correspondence as anchor points, 2) solve a regularized quadratic assignment problem only in the neighborhood of selected anchor points through sparsity control. These two ingredients can improve and increase the number of anchor points quickly while reducing the computation cost in each quadratic assignment iteration significantly. With enough high-quality anchor points, one may use various pointwise global features with reference to these anchor points to further improve the dense shape correspondence. We use various experiments to show the efficiency, quality, and versatility of our method on large data sets, patches, and point clouds (without global meshes).
From Paris to Berlin: Discovering Fashion Style Influences Around the World

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from everyday images of people wearing clothes. We introduce an approach that detects which cities influence which other cities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a forecasting model that predicts the popularity of any given style at any given city into the future. Demonstrating our idea with GeoStyle—a large-scale dataset of 7.7M images covering 44 major world cities, we present the discovered influence relationships, revealing how cities exert and receive fashion influence for an array of 50 observed visual styles. Furthermore, the proposed forecasting model achieves state-of-the-art results for a challenging style forecasting task, showing the advantage of grounding visual style evolution both spatially and temporally.
Racial Disparity in COVID-19 Deaths: Seeking Economic Roots with Census Data

This note seeks the socio-economic roots of racial disparities in COVID-19 mortality, using monthly county-level mortality, economic, and demographic data from 3140 counties through December 2020. The county-level approach shows a sharp disparity affecting all minority groups in the sample, peaking in spring or summer 2020 and then dissipating by the end of autumn. The effect disappears for Asian Americans when occupation and other controls are added, but not for other minorities;for them, the racial disparity, as long as it lasts, does not seem to be due to differences in income, poverty rates, education, occupational mix, or even access to healthcare insurance, although in April public transit use explains a large part of it. This is a puzzle, but the rapid change in the disparities over the year show that they are not immutable - an important message for future pandemics. © 2021 Walter de Gruyter GmbH, Berlin/Boston 2021.
What Exactly is the Information Paradox

The black hole information paradox tells us something important about the way quantum mechanics and gravity fit together. In these lectures I try to give a pedagogical review of the essential physics leading to the paradox, using mostly pictures. Hawking’s argument is recast as a ‘theorem’: if quantum gravity effects are confined to within a given length scale and the vacuum is assumed to be unique, then there will be information loss. We conclude with a brief summary of how quantum effects in string theory violate the first condition and make the interior of the hole a ‘fuzzball’.
RORγ Structural Plasticity and Druggability

Retinoic acid receptor-related orphan receptor γ (RORγ) is a transcription factor regulating the expression of the pro-inflammatory cytokine IL-17 in human T helper 17 (Th17) cells. Activating RORγ can induce multiple IL-17-mediated autoimmune diseases but may also be useful for anticancer therapy. Its deep immunological functions make RORɣ an attractive drug target. Over 100 crystal structures have been published describing atomic interactions between RORɣ and agonists and inverse agonists. In this review, we focus on the role of dynamic properties and plasticity of the RORɣ orthosteric and allosteric binding sites by examining structural information from crystal structures and simulated models. We discuss the possible influences of allosteric ligands on the orthosteric binding site. We find that high structural plasticity favors the druggability of RORɣ, especially for allosteric ligands.
Assessing the impact of introductory programming workshops on the computational reproducibility of biomedical workflows

Author(s): Deardorff, Ariel | Abstract: AbstractIntroductionAs biomedical research becomes more data-intensive, computational reproducibility is a growing area of importance. Unfortunately, many biomedical researchers have not received formal computational training and often struggle to produce results that can be reproduced using the same data, code, and methods. Programming workshops can be a tool to teach new computational methods, but it is not always clear whether researchers are able to use their new skills to make their work more computationally reproducible.MethodsThis mixed methods study consisted of in-depth interviews with 14 biomedical researchers before and after participation in an introductory programming workshop. During the interviews, participants described their research workflows and responded to a quantitative checklist measuring reproducible behaviors. The interview data was analyzed using a thematic analysis approach, and the pre and post workshop checklist scores were compared to assess the impact of the workshop on computational reproducibility of the researchers’ workflows.ResultsPre and post scores on a checklist of reproducible behaviors did not increase in a statistically significant manner. The qualitative interviews revealed that several participants had made small changes to their workflows including switching to open source programming languages for their data cleaning, analysis, and visualization. Overall many of the participants indicated higher levels of programming literacy and an interest in further training. Factors that enabled change included supportive environments and an immediate research need, while barriers included collaborators that were resistant to new tools and a lack of time.ConclusionWhile none of the participants completely changed their workflows, many of them did incorporate new practices, tools, or methods that helped make their work more reproducible and transparent to other researchers. This indicate that programming workshops now offered by libraries and other organizations contribute to computational reproducibility training for researchers
Visual Hide and Seek

We train embodied agents to play Visual Hide and Seek to study the relationship between agent behaviors and environmental complexity. In Visual Hide and Seek, a prey must navigate in a simulated en...
Serdab: An IoT Framework for Partitioning Neural Networks Computation across Multiple Enclaves

Recent advances in Deep Neural Networks (DNN) and Edge Computing have made it possible to automatically analyze streams of videos from home/security cameras over hierarchical clusters that include edge devices, close to the video source, as well as remote cloud compute resources. However, preserving the privacy and confidentiality of users' sensitive data as it passes through different devices remains a concern to most users. Private user data is subject to attacks by malicious attackers or misuse by internal administrators who may use the data in activities that are not explicitly approved by the user. To address this challenge, we present Serdab, a distributed orchestration framework for deploying deep neural network computation across multiple secure enclaves (e.g., Intel SGX). Secure enclaves provide a guarantee on the privacy of the data/code deployed inside it. However, their limited hardware resources make them inefficient when solely running an entire deep neural network. To bridge this gap, Serdab presents a DNN partitioning strategy to distribute the layers of the neural network across multiple enclave devices or across an enclave device and other hardware accelerators. Our partitioning strategy achieves up to 4.7x speedup compared to executing the entire neural network in one enclave.
Using Conditional Generative Adversarial Networks to Boost the Performance of Machine Learning in Microbiome Datasets.

The microbiome of the human body has been shown to have profound effects on physiological regulation and disease pathogenesis. However, association analysis based on statistical modeling of microbiome data has continued to be a challenge due to inherent noise, complexity of the data, and high cost of collecting large number of samples. To address this challenge, we employed a deep learning framework to construct a data-driven simulation of microbiome data using a conditional generative adversarial network. Conditional generative adversarial networks train two models against each other while leveraging side information learn from a given dataset to compute larger simulated datasets that are representative of the original dataset. In our study, we used a cohorts of patients with inflammatory bowel disease to show that not only can the generative adversarial network generate samples representative of the original data based on multiple diversity metrics, but also that training machine learning models on the synthetic samples can improve disease prediction through data augmentation. In addition, we also show that the synthetic samples generated by this cohort can boost disease prediction of a different external cohort.
Dynamic Geometric Data Structures via Shallow Cuttings

We present new results on a number of fundamental problems about dynamic geometric data structures: (1) We describe the first fully dynamic data structures with sublinear amortized update time for maintaining (i) the number of vertices or the volume of the convex hull of a 3D point set, (ii) the largest empty circle for a 2D point set, (iii) the Hausdorff distance between two 2D point sets, (iv) the discrete 1-center of a 2D point set, (v) the number of maximal (i.e., skyline) points in a 3D point set. The update times are near $$n^{11/12}$$ for (i) and (ii), $$n^{5/6}$$ for (iii) and (iv), and $$n^{2/3}$$ for (v). Previously, sublinear bounds were known only for restricted “semi-online” settings (Chan in SIAM J. Comput. 32(3), 700–716 (2003)). (2) We slightly improve previous fully dynamic data structures for answering extreme point queries for the convex hull of a 3D point set and nearest neighbor search for a 2D point set. The query time is $$O(\log ^2\!n)$$ , and the amortized update time is $$O(\log ^4\!n)$$ instead of $$O(\log ^5\!n)$$ (Chan in J. ACM 57(3), # 16 (2010); Kaplan et al. in 28th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2495–2504. SIAM, Philadelphia (2017)). (3) We also improve previous fully dynamic data structures for maintaining the bichromatic closest pair between two 2D point sets and the diameter of a 2D point set. The amortized update time is $$O(\log ^4\!n)$$ instead of $$O(\log ^7\!n)$$ (Eppstein in Discrete Comput. Geom. 13(1), 111–122 (1995); Chan in J. ACM 57(3), # 16 (2010); Kaplan et al. in 28th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 2495–2504. SIAM, Philadelphia (2017)).
Impact of Team Size on Technological Contributions: Unpacking Disruption and Development

While single individuals and small teams used to be the primary driving forces of scientific and technological breakthroughs, large teams have become more important in recent years. This raises an ...
fault: A Python Embedded Domain-Specific Language for Metaprogramming Portable Hardware Verification Components

While hardware generators have drastically improved design productivity, they have introduced new challenges for the task of verification. To effectively cover the functionality of a sophisticated generator, verification engineers require tools that provide the flexibility of metaprogramming. However, flexibility alone is not enough; components must also be portable in order to encourage the proliferation of verification libraries as well as enable new methodologies. This paper introduces fault, a Python embedded hardware verification language that aims to empower design teams to realize the full potential of generators.
Stability for product groups and property $(\tau)$

Abstract We study the notion of permutation stability (or P-stability) for countable groups. Our main result provides a wide class of non-amenable product groups which are not P-stable. This class includes the product group Σ × Λ , whenever Σ admits a non-abelian free quotient and Λ admits an infinite cyclic quotient. In particular, we obtain that the groups F m × Z d and F m × F n are not P-stable, for any integers m , n ≥ 2 and d ≥ 1 . This implies that P-stability is not closed under the direct product construction, which answers a question of Becker, Lubotzky and Thom. The proof of our main result relies on a construction of asymptotic homomorphisms from Σ × Λ to finite symmetric groups starting from sequences of finite index subgroups in Σ and Λ with and without property (τ). Our method is sufficiently robust to show that the groups covered are not even flexibly P-stable, thus giving the first such non-amenable residually finite examples.
A symmetry principle for emergent spacetime

There are many examples where geometry and gravity are concepts that emerge from a theory of quantum mechanics without gravity. This suggests thinking of gravity as an exotic phase of matter. Quant...
Visions of international law: An interdisciplinary retrospective

Taking the opportunity of this LJIL special lecture, Professor Alter provides an interdisciplinary retrospective that explains, defends and critiques six common visions of international law: The naive political scientist’s expectations about international law as a fixed reflection of political choices; the legal formalist and structural theorist who believes that formal rules, institutions, and processes should generate similar outcomes in different parts of the world; the Western centric scholar’s notion that one can draw general lessons based on European and American experiences; the liberal internationalist who believes that multilateral processes generate consent based agreements and outcomes; the law and society scholar whose focus on the local can minimize international structural elements; and the international legal sociologist who believes that meanings and practices constitute international law. After reflecting on what each vision captures and misses about international law, Professor Alter identifies the policy stakes of residing within a vision. While we need to draw from multiple visions to understand the hybridity of international law, we also need to understand the implicit presumptions of each vision, as these presumptions generate contradictory prescriptive recommendations.
Measurement of pain

Abstract The measurement of pain is important for evaluation of analgesics as well as to know the degree of suffering in patient. Existing methods are divided into two categories.
DebriSat fragment characterization: Quality assurance

Abstract The DebriSat project is a joint NASA and DoD project to generate data to update orbital debris models. The DebriSat test article, designed and fabricated to be representative of a modern-day LEO satellite, was subjected to a laboratory hypervelocity impact (HVI) test to emulate a catastrophic on orbit event. The items generated from the HVI test were collected and transported to the University of Florida for debris fragment extraction and characterization. During the characterization process, fragments are qualitatively (material, shape, color) and quantitatively (mass and size) assessed. The quantitative assessments are performed on mass balances and imaging systems by a student technicians that vary during the academic year. To ensure quality data and system operation by a group of varying technicians, a series of modified Gage Repeatability and Reproducibility (R&R) testing methods were created and implemented. This paper summarizes the DebriSat post-HVI activities and discusses the Gage R&R testing methods implemented to ensure high quality quantitative data.
Holographic map for cosmological horizons

We propose a holographic map between Einstein gravity coupled to matter in a de Sitter background and large N quantum mechanics of a system of spins. Holography maps a spin model with a finite-dime...
GS-967 and Eleclazine Block Sodium Channels in Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

GS-967 and eleclazine (GS-6615) are novel sodium channel inhibitors exhibiting antiarrhythmic effects in various in vitro and in vivo models. The antiarrhythmic mechanism has been attributed to preferential suppression of late sodium current (INaL). Here, we took advantage of a throughput automated electrophysiology platform (SyncroPatch 768PE) to investigate the molecular pharmacology of GS-967 and eleclazine on peak sodium current (INaP) recorded from human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes. We compared GS-967 and eleclazine to the antiarrhythmic drug lidocaine, the prototype INaL inhibitor ranolazine, and the slow inactivation enhancing drug lacosamide. In human induced pluripotent stem cell-derived cardiomyocytes, GS-967 and eleclazine caused a reduction of INaP in a frequency-dependent manner consistent with use-dependent block (UDB). GS-967 and eleclazine had similar efficacy but evoked more potent UDB of INaP (IC50=0.07 and 0.6 µM, respectively) than ranolazine (7.8 µM), lidocaine (133.5 µM) and lacosamide (158.5 µM). In addition, GS-967 and eleclazine exerted more potent effects on slow inactivation and recovery from inactivation compared to the other sodium channel blocking drugs we tested. The greater UDB potency of GS-967 and eleclazine was attributed to the significantly higher association rates (KON) and moderate unbinding rate (KOFF) of these two compounds with sodium channels. We propose thatsubstantial UDB contributes to the observed antiarrhythmic efficacy of GS-967 and eleclazine. Significance Statement We investigated the molecular pharmacology of GS-967 and eleclazine on sodium channels in human induced pluripotent stem cell derived cardiomyocytes using a high throughput automated electrophysiology platform. Sodium channel inhibition by GS-967 and eleclazine has unique features including accelerating the onset of slow inactivation and impairing recovery from inactivation. These effects combined with rapid binding and moderate unbinding kinetics explain potent use-dependent block, which we propose contributes to their observed antiarrhythmic efficacy.
Symmetries of the C⁎-algebra of a vector bundle

Abstract We consider C ⁎ -algebras constructed from compact group actions on complex vector bundles E → X endowed with a Hermitian metric. An action of G by isometries on E → X induces an action on the C ⁎ -correspondence Γ ( E ) over C ( X ) consisting of continuous sections, and on the associated Cuntz–Pimsner algebra O E , so we can study the crossed product O E ⋊ G . If the action is free and rank E = n , then we prove that O E ⋊ G is Morita–Rieffel equivalent to a field of Cuntz algebras O n over the orbit space X / G . If the action is fiberwise, then O E ⋊ G becomes a continuous field of crossed products O n ⋊ G . For transitive actions, we show that O E ⋊ G is Morita–Rieffel equivalent to a graph C ⁎ -algebra.
Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras

In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature. Our method learns to accomplish these tasks using multiple statically placed but uncalibrated RGB camera views without building an explicit 3D representation such as a pointcloud or voxel grid. This multi-camera approach achieves superior task performance on difficult stacking and insertion tasks compared to single-view baselines. Single view robotic agents struggle from occlusion and challenges in estimating relative poses between points of interest. While full 3D scene representations (voxels or pointclouds) are obtainable from registered output of multiple depth sensors, several challenges complicate operating off such explicit 3D representations. These challenges include imperfect camera calibration, poor depth maps due to object properties such as reflective surfaces, and slower inference speeds over 3D representations compared to 2D images. Our use of static but uncalibrated cameras does not require camera-robot or cameracamera calibration making the proposed approach easy to setup and our use of sensor dropout during training makes it resilient to the loss of camera-views after deployment.
Death and the stock market: international evidence from the Spanish Flu

The coronavirus pandemic in 2020 was the most devastating worldwide health threat since the 1918–1919 Spanish flu. Panel regression analysis for ten countries suggests that European and US stock ma...
Duality and Stability in Complex Multiagent State-Dependent Network Dynamics

Despite significant progress on stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings in address...
Windowed least-squares model reduction for dynamical systems

Abstract This work proposes a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.
Big fish in a small pond: Locally dominant firms and the business cycle

Abstract Following Gabaix (2011), we identify locally dominant firms that have a strong impact on their local macroeconomic environment, but are not among the largest 100 U.S. firms. Idiosyncratic shocks to these locally dominant firms propagate nationally and explain a significant portion of aggregate U.S. macroeconomic fluctuations. Specifically, we find that locally dominant firms exist in 13 U.S. states and productivity shocks to these firms explain almost 50% of the U.S. GDP growth.
PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR.

We present PyChain, a fully parallelized PyTorch implementation of end-to-end lattice-free maximum mutual information (LF-MMI) training for the so-called \emph{chain models} in the Kaldi automatic speech recognition (ASR) toolkit. Unlike other PyTorch and Kaldi based ASR toolkits, PyChain is designed to be as flexible and light-weight as possible so that it can be easily plugged into new ASR projects, or other existing PyTorch-based ASR tools, as exemplified respectively by a new project PyChain-example, and Espresso, an existing end-to-end ASR toolkit. PyChain's efficiency and flexibility is demonstrated through such novel features as full GPU training on numerator/denominator graphs, and support for unequal length sequences. Experiments on the WSJ dataset show that with simple neural networks and commonly used machine learning techniques, PyChain can achieve competitive results that are comparable to Kaldi and better than other end-to-end ASR systems.
Strong ill-posedness of logarithmically regularized 2D Euler equations in the borderline Sobolev space

Abstract Logarithmically regularized 2D Euler equations are active scalar equations with the non-local velocity u = ∇ ⊥ Δ − 1 T γ ω for the scalar ω. Two types of the regularizing operator T γ with a parameter γ > 0 are considered: T γ = ln − γ ⁡ ( e + | ∇ | ) and T γ = ln − γ ⁡ ( e − Δ ) . These models regularize the 2D Euler equation for the vorticity (conventionally corresponding to the γ = 0 case), which results in their local well-posedness in the borderline Sobolev space H 1 ( R 2 ) ∩ H ˙ − 1 ( R 2 ) when γ > 1 2 . In this paper, we examine the regularized models in the remaining regime γ ≤ 1 2 and establish the strong ill-posedness in the borderline space. This completely solves the well-posedness problem of the regularized models in the borderline space by closing the gap between the local well-posedness result for γ > 1 2 and the strong ill-posedness for γ = 0 .
Dynamical Collapse of Nonrotating Magnetic Molecular Cloud Cores: Evolution through Point-Mass Formation

We present a numerical simulation of the dynamical collapse of a nonrotating, magnetic molecular cloud core and follow the core's evolution through the formation of a central point mass and its subsequent growth into a 1 M? protostar. The epoch of point-mass formation (PMF) is investigated by a self-consistent extension of previously presented models of core formation and contraction in axisymmetric, self-gravitating, isothermal, magnetically supported interstellar molecular clouds. Prior to PMF, the core is dynamically contracting and is not well approximated by a quasi-static equilibrium model. Ambipolar diffusion, which plays a key role in the early evolution of the core, is unimportant during the dynamical pre-PMF collapse phase. However, the appearance of a central mass, through its effect on the gravitational field in the inner core regions, leads to a revitalization of ambipolar diffusion in the weakly ionized gas surrounding the central protostar. This process is so efficient that it leads to a decoupling of the field from the matter and results in an outward-propagating hydromagnetic C-type shock. The existence of an ambipolar diffusion-mediated shock of this type was predicted by Li & McKee, and we find that the basic shock structure given by their analytic model is well reproduced by our more accurate numerical results. Our calculation also demonstrates that ambipolar diffusion, rather than Ohmic diffusivity operating in the innermost core region, is the main field-decoupling mechanism responsible for driving the shock after PMF. The passage of the shock leads to a substantial redistribution, by ambipolar diffusion but possibly also by magnetic interchange, of the mass contained within the magnetic flux tubes in the inner core. In particular, ambipolar diffusion reduces the flux initially threading a collapsing ~1 M? core by a factor 103 by the time this mass accumulates within the inner radius (7.3 AU) of our computational grid. This reduction, which occurs primarily during the post-PMF phase of the collapse, represents a significant step toward the resolution of the protostellar magnetic flux problem. Our calculations indicate that a 1 M? protostar forms in ~1.5 ? 105 yr for typical cloud parameters. The mass-accretion rate is time dependent, in part because of the C-shock that decelerates the infalling matter as it propagates outward: the accretion rate rises to 9.4 M? Myr-1 early on and decreases to 5.6 M? Myr-1 by the time a solar-mass protostar is formed. The infalling gas disk surrounding the protostar has a mass ~10-2 M? at radii r 500 AU. A distinguishing prediction of our model is that the rapid ambipolar diffusion after the formation of a protostar should give rise to large (1 km s-1), and potentially measurable, ion-neutral drift speeds on scales r 200 AU. The main features of our simulation, including the C-shock formation after PMF, are captured by a similarity solution that incorporates the effects of ambipolar diffusion.
Overview of the spectrometer optical fiber feed for the habitable-zone planet finder

The Habitable-zone Planet Finder (HPF) is a highly stabilized fiber fed precision radial velocity (RV) spec- trograph working in the Near Infrared (NIR): 810 – 1280 nm. In this paper we present an overview of the preparation of the optical fibers for HPF. The entire fiber train from the telescope focus down to the cryostat is detailed. We also discuss the fiber polishing, splicing and its integration into the instrument using a fused silica puck. HPF was designed to be able to operate in two modes, High Resolution (HR- the only mode mode currently commissioned) and High Efficiency (HE). We discuss these fiber heads and the procedure we adopted to attach the slit on to the HR fibers.
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction

Abstract Haplotype assembly and viral quasispecies reconstruction are challenging tasks concerned with analysis of genomic mixtures using sequencing data. High-throughput sequencing technologies generate enormous amounts of short fragments (reads) which essentially oversample components of a mixture; the representation redundancy enables reconstruction of the components (haplotypes, viral strains). The reconstruction problem, known to be NP-hard, boils down to grouping together reads originating from the same component in a mixture. Existing methods struggle to solve this problem with required level of accuracy and low runtimes; the problem is becoming increasingly more challenging as the number and length of the components increase. This paper proposes a read clustering method based on a convolutional auto-encoder designed to first project sequenced fragments to a low-dimensional space and then estimate the probability of the read origin using learned embedded features. The components are reconstructed by finding consensus sequences that agglomerate reads from the same origin. Mini-batch stochastic gradient descent and dimension reduction of reads allow the proposed method to efficiently deal with massive numbers of long reads. Experiments on simulated, semi-experimental and experimental data demonstrate the ability of the proposed method to accurately reconstruct haplotypes and viral quasispecies, often demonstrating superior performance compared to state-of-the-art methods.
Quantization of non-polynomial field theories

We re-examine the quantization of a class of non-polynomial scalar field theories which interpolates continuously from a free one to 4 theory. The quantization of such theories is problematic because the Feynman rules may not be directly obtained. We give a means for calculating the correlation functions in this theory. The Feynman rules developed here shall enable further progress in the understanding of the triviality of 4 theory in four dimensions.
ANOTHER LOOK AT THE FAST ITERATIVE SHRINKAGE/THRESHOLDING ALGORITHM (FISTA).

This paper provides a new way of developing the fast iterative shrinkage/thresh-olding algorithm (FISTA) [A. Beck and M. Teboulle, SIAM J. Imaging Sci., 2 (2009), pp. 183--202] that is widely used for minimizing composite convex functions with a nonsmooth term such as the $\ell_1$ regularizer. In particular, this paper shows that FISTA corresponds to an optimized approach to accelerating the proximal gradient method with respect to a worst-case bound of the cost function. This paper then proposes a new algorithm that is derived by instead optimizing the step coefficients of the proximal gradient method with respect to a worst-case bound of the composite gradient mapping. The proof is based on the worst-case analysis called the performance estimation problem in [Y. Drori and M. Teboulle, Math. Program., 145 (2014), pp. 451--482].
First demonstration of a cryocooler conduction cooled superconducting radiofrequency cavity operating at practical cw accelerating gradients

We demonstrate practical accelerating gradients on a superconducting radiofrequency (SRF) accelerator cavity with cryocooler conduction cooling, a cooling technique that does not involve the complexities of the conventional liquid helium bath. A design is first presented that enables conduction cooling an elliptical-cell SRF cavity. Implementing this design, a single cell 650 MHz Nb3Sn cavity coupled using high purity aluminum thermal links to a 4 K pulse tube cryocooler generated accelerating gradients up to 6.6 MV m−1 at 100% duty cycle. The experiments were carried out with the cavity-cryocooler assembly in a simple vacuum vessel, completely free of circulating liquid cryogens. We anticipate that this cryocooling technique will make the SRF technology accessible to interested accelerator researchers who lack access to full-stack helium cryogenic systems. Furthermore, the technique can lead to SRF based compact sources of high average power electron beams for environmental protection and industrial applications. A concept of such an SRF compact accelerator is presented.
Hopf algebra actions in tensor categories

We prove that commutative algebras in braided tensor categories do not admit faithful Hopf algebra actions unless they come from group actions. We also show that a group action allows us to see the algebra as the regular algebra in the representation category of the acting group.
BARYCENTRIC STRAIGHTENING AND BOUNDED COHOMOLOGY

We study the barycentric straightening of simplices in irr educible symmetric spaces of non-compact type. We show that the p-Jacobian has uniformly bounded norm, as soon as p is larger than thrice the rank. As a consequence, for a non-compact, connected, semisimple real Lie group G, in degrees greater than thrice theR-rank of G, every cohomology class has a bounded representative. This gives, in high degree, a complete answer to an old problem of Dupont. Along the way, we provide a simplification and generalization of some estimates of Connell and Farb.
THE EXTENSION OF THE TRANSITION TEMPERATURE PLASMA INTO THE LOWER GALACTIC HALO

Column densities for H I, Al III, Si IV, C IV, and O VI toward 109 stars and 30 extragalactic objects have been assembled to study the extensions of these species away from the Galactic plane into the Galactic halo. H I and Al III mostly trace the warm neutral and warm ionized medium, respectively, while Si IV, C IV, and O VI trace a combination of warm photoionized and collisionally ionized plasmas. The much larger object sample compared to previous studies allows us to consider and correct for the effects of the sample bias that has affected earlier but smaller surveys of the gas distributions. We find that Si IV and C IV have similar exponential scale heights of 3.2(+1.0, –0.6) and 3.6(+1.0, –0.8) kpc. The scale height of O VI is marginally smaller with h = 2.6 ± 0.6 kpc. The transition temperature gas is ~3 times more extended than the warm ionized medium traced by Al III with h = 0.90(+0.62, –0.33) kpc and ~12 times more extended than the warm neutral medium traced by H I with h = 0.24 ± 0.06 kpc. There is a factor of 2 decrease in the dispersion of the log of the column density ratios for transition temperature gas for lines of sight in the Galactic disk compared to extragalactic lines of sight through the entire halo. The observations are compared to the predictions of the various models for the production of the transition temperature gas in the halo. The appendix presents a revision to the electron scale height of Gaensler et al.'s 2008 study based on electron dispersion measures.
On the construction of Nadel multiplier ideal sheaves and the limiting behavior of the Ricci flow

In this note we construct Nadel multiplier ideal sheaves using the Ricci flow on Fano manifolds. This extends a result of Phong, Sesum, and Sturm. These sheaves, like their counterparts constructed by Nadel for the continuity method, can be used to obtain an existence criterion for Kahler-Einstein metrics.
The characteristic polynomial of the Adams operators on graded connected Hopf algebras

The Adams operators Ψn on a Hopf algebra H are the convolution powers of the identity of H. They are also called Hopf powers or Sweedler powers. We study the Adams operators when H is graded connected. The main result is a complete description of the characteristic polynomial — both eigenvalues and their multiplicities — for the action of the operator Ψn on each homogeneous component of H. The eigenvalues are powers of n. The multiplicities are independent of n, and in fact only depend on the dimension sequence of H. These results apply in particular to the antipode of H, as the case n=−1. We obtain closed forms for the generating function of the sequence of traces of the Adams operators. In the case of the antipode, the generating function bears a particularly simple relationship to the one for the dimension sequence. In the case where H is cofree, we give an alternative description for the characteristic polynomial and the trace of the antipode in terms of certain palindromic words. We discuss parallel results that hold for Hopf monoids in species and for q-Hopf algebras.
Molecular Lines as Diagnostics of High-Redshift Objects

Models are presented for CO rotational line emission by high-redshift starburst galaxies. The influence of the cosmic microwave background on the thermal balance and the level populations of atomic and molecular species is explicitly included. Predictions are made for the observability of starburst galaxies through line and continuum emission between z=5 and z=30. It is found that the Millimeter Array could detect a starburst galaxy with ~105 Orion regions, corresponding to a star formation rate of about 30 M☉ yr-1, equally well at z=5 or z=30 because of the increasing cosmic microwave background temperature with redshift. Line emission is a potentially more powerful probe than dust continuum emission of very high redshift objects.
Perturbative, non-supersymmetric completions of the little Higgs

The little Higgs mechanism produces a light 100 GeV Higgs while raising the natural cutoff from 1 TeV to 10 TeV. We attempt an iterative little Higgs mechanism to produce multiple factors of 10 between the cutoff and the 100 GeV Higgs mass in a perturbative theory. In the renormalizable sector of the theory, all quantum corrections to the Higgs mass proportional to mass scales greater than 1 TeV are absent – this includes quadratically divergent, log-divergent, and finite loops at all orders. However, even loops proportional to scales just a factor of 10 above the Higgs (or any other scalar) mass come with large numerical factors that reintroduce fine-tuning. Top loops, for example, produce an expansion parameter of not 1/(4π) but 1/5. The geometric increase in the number of fields at higher energies simply exacerbates this problem. We build a complete two-stage model up to 100 TeV, show that direct sensitivity of the electroweak scale to the cutoff is erased, and estimate the tuning due to large numerical factors. We then discuss the possibility, in a toy model with only scalar and gauge fields, of generating a tower of little Higgs theories and show that the theory quickly becomes a large-N gauge theory with ~ N fundamental scalars. We find evidence that at least this toy model could successfully generate light scalars with an exponentially large cutoff in the absence of supersymmetry or strong dynamics. The fine-tuning is not completely eliminated, but evidence suggests that this result is model dependent. We then speculate as to how one might marry a working tower of fields of this type at high scales to a realistic theory at the weak scale.
On the Possibility of Habitable Moons in the System of HD 23079: Results from Orbital Stability Studies

The aim of our study is to investigate the possibility of habitable moons orbiting the giant planet HD 23079b, a Jupiter-mass planet, which follows a low-eccentricity orbit in the outer region of HD 23079’s habitable zone. We show that HD 23079b is able to host habitable moons in prograde and retrograde orbits, as expected, noting that the outer stability limit for retrograde orbits is increased by nearly 90% compared with that of prograde orbits, a result consistent with previous generalised studies. For the targeted parameter space, it was found that the outer stability limit for habitable moons varies between 0.05236 and 0.06955 AU (prograde orbits) and between 0.1023 and 0.1190 AU (retrograde orbits), depending on the orbital parameters of the Jupiter-type planet if a minimum mass is assumed. These intervals correspond to 0.306 and 0.345 (prograde orbits) and 0.583 and 0.611 (retrograde orbits) of the planet's Hill radius. Larger stability limits are obtained if an increased value for the planetary mass mp is considered; they are consistent with the theoretically deduced relationship of m 1/3 p . Finally, we compare our results with the statistical formulae of Domingos, Winter, & Yokoyama, indicating both concurrence and limitations.
Recent Advances in Maximum Entropy Biasing Techniques for Molecular Dynamics

This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods which incorporate...