cssi/SciSciGPT-SciSciNet huggingface dataset:
Ability Tracking, School Competition, and the Distribution of Educational Benefits

Abstract To study the effects of ability grouping on school competition, we develop a theoretical and computational model of tracking in public and private schools. We examine tracking’s consequences for the allocation of students of differing abilities and income within and between public and private schools. Private schools tend to attract the most able and wealthiest students, and rarely track in equilibrium. Public sector schools can maximize attendance by tracking students. Public schools retain a greater proportion of higher-ability students by tracking, but lose more wealthy, lower-ability students to the private sector. Consequently, socioeconomic status is a predictor of track assignment in public schools. For the entire population, public-sector tracking has small aggregate effects on achievement and welfare, but results in significant redistribution from lower- to higher-ability students.
Reconstructing the time evolution of wall-bounded turbulent flows from non-time-resolved PIV measurements

Laboratory Particle Image Velocimetry (PIV) systems are often limited in their ability to resolve broadband spatio-temporal fluctuations in turbulent flows due to hardware constraints. Here we develop linear models to reconstruct the time evolution of wall-bounded turbulent flows from non-time resolved PIV snapshots. These models are integrated forward and backward in time using the snapshots as initial conditions, and the resulting predictions fused. This retains small-scale fluctuations in the flow field and resolves velocity spectra at frequencies higher than the system Nyquist limit.
PROBLEMS AND SOLUTIONS OF THE EXTENSOR MECHANISM

A key component to the success of total knee replacement is the health and integrity of the extensor mechanism. While there are issues related to the patella, such as fracture, dislocation, subluxation, clunk due to peripatellar fibrosis and anterior knee pain, the overall integrity of the extensor mechanism is of tantamount importance in providing an excellent functional outcome. During total knee replacement it is of utmost importance to preserve the anatomic insertion of the patellar tendon on the tibial tubercle. However, after total knee replacement, a fall or extreme osteoporosis of the patella may cause a rupture of the patellar tendon, distally or proximally, and possibly the quadriceps tendon off of the proximal pole of the patella. Simple repairs of the patellar tendon avulsion may involve use of the semitendonosis and gracilis tendons along with primary repair of the tendon. Usually, patella infera develops after such a repair affecting overall strength and function. For severe disruptions of the extensor mechanism that are accompanied by a significant extensor lag, autologous tissue repair may not be possible. Thus, there are three techniques for reconstruction of this difficult problem: Extensor mechanism allograft with bone-patellar tendon-patella-quadriceps tendon, extensor mechanism allograft with os calcis-Achilles tendon construct and Marlex-mesh reconstruction for patellar tendon avulsion. The key to success of extensor mechanism allograft is proper tensioning of the allograft at full extensor and immobilisation for 6 weeks. Rosenberg9s early experience showed that the allograft works best placed at maximum tension in extension. Rubash has described the use of the os calcis-Achilles tendon which does not utilise a patellar substitute. Hansen has recently described excellent results with the use of Marlex mesh to act as a structural reinforcement to the patellar tendon when it is avulsed.
Gram Filtering and Sinogram Interpolation for Pixel-Basis in Parallel-Beam X-Ray CT Reconstruction

The key aspect of parallel-beam X-ray CT is forward and back projection, but its computational burden continues to be an obstacle for applications. We propose a method to improve the performance of related algorithms by calculating the Gram filter exactly and interpolating the sinogram signal optimally. In addition, the detector blur effect can be included in our model efficiently. The improvements in speed and quality for back projection and iterative reconstruction are shown in our experiments on both analytical phantoms and real CT images.
Spatiotemporal Antialiasing in Photoacoustic Computed Tomography

Photoacoustic computed tomography (PACT) based on a full-ring ultrasonic transducer array is widely used for small animal wholebody and human organ imaging, thanks to its high in-plane resolution and full-view fidelity. However, spatial aliasing in full-ring geometry PACT has not been studied in detail. If the spatial Nyquist criterion is not met, aliasing in spatial sampling causes artifacts in reconstructed images, even when the temporal Nyquist criterion has been satisfied. In this work, we clarified the source of spatial aliasing through spatiotemporal analysis. We demonstrated that the combination of spatial interpolation and temporal filtering can effectively mitigate artifacts caused by aliasing in either image reconstruction or spatial sampling, and we validated this method by both numerical simulations and in vivo experiments.
Probing Nuclear Structure with Future Colliders

Improved knowledge of the nucleon structure is a crucial pathway toward a deeper understanding of the fundamental nature of the QCD interaction, and will enable important future discoveries. The experimental facilities proposed for the next decade offer a tremendous opportunity to advance the precision of our theoretical predictions to unprecedented levels. In this report we briefly highlight some of the recently developed tools and techniques which, together with data from these new colliders, have the potential to revolutionize our understanding of the QCD theory in the next decade.
Examining Citations of Natural Language Processing Literature

We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers. We explore questions such as: how well cited are papers of different types (journal articles, conference papers, demo papers, etc.)? how well cited are papers from different areas of within NLP? etc. Notably, we show that only about 56% of the papers in AA are cited ten or more times. CL Journal has the most cited papers, but its citation dominance has lessened in recent years. On average, long papers get almost three times as many citations as short papers; and papers on sentiment classification, anaphora resolution, and entity recognition have the highest median citations. The analyses presented here, and the associated dataset of NLP papers mapped to citations, have a number of uses including: understanding how the field is growing and quantifying the impact of different types of papers.
Automotive-Radar-Based 50-cm Urban Positioning

Deployment of automated ground vehicles (AGVs) beyond the confines of sunny and dry climes will require sub-lane-level positioning techniques based on radio waves rather than near-visible-light radiation. Like human sight, lidar and cameras perform poorly in low-visibility conditions. This paper develops and demonstrates a novel technique for robust 50-cm-accurate urban ground positioning based on commercially-available low-cost automotive radars. The technique is computationally efficient yet obtains a globally-optimal translation and heading solution, avoiding local minima caused by repeating patterns in the urban radar environment. Performance is evaluated on an extensive and realistic urban data set. Comparison against ground truth shows that, when coupled with stable short-term odometry, the technique maintains 95-percentile errors below 50 cm in horizontal position and 1° in heading.
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization

Abstract Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effective mechanical properties. In this paper, a three-dimensional deep convolutional neural network (3D-CNN) is proposed to predict the anisotropic effective material properties for representative volume elements (RVEs) with random inclusions. The high-fidelity dataset generated by a computational homogenization approach is used for training the 3D-CNN models. The inference results of the trained networks on unseen data indicate that the network is capable of capturing the microstructural features of RVEs and produces an accurate prediction of effective stiffness and Poisson’s ratio. The benefits of the 3D-CNN over conventional finite-element-based homogenization with regard to computational efficiency, uncertainty quantification and model’s transferability are discussed in sequence. We find the salient features of the 3D-CNN approach make it a potentially suitable alternative for facilitating material design with fast product design iteration and efficient uncertainty quantification.
Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison

This paper presents a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems. Specifically, the performance of Multi-Index Stochastic Collocation (MISC) and adaptive multi-fidelity Stochastic Radial Basis Functions (SRBF) surrogates is assessed for the UQ of a roll-on/roll-off passengers ferry advancing in calm water and subject to two operational uncertainties, namely the ship speed and draught. The estimation of expected value, standard deviation, and probability density function of the (model-scale) resistance is presented and discussed; the required simulations are obtained by the in-house unsteady multi-grid Reynolds Averaged Navier-Stokes (RANS) solver $\chi$navis. Both MISC and SRBF use as multi-fidelity levels the evaluations on the different grid levels intrinsically employed by the RANS solver for multi-grid acceleration; four grid levels are used here, obtained as isotropic coarsening of the initial finest mesh. The results suggest that MISC could be preferred when only limited data sets are available. For larger data sets both MISC and SRBF represent a valid option, with a slight preference for SRBF, due to its robustness to noise.
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning

The goal of neural-symbolic computation is to integrate the connectionist and symbolist paradigms. Prior methods learn the neural-symbolic models using reinforcement learning (RL) approaches, which ignore the error propagation in the symbolic reasoning module and thus converge slowly with sparse rewards. In this paper, we address these issues and close the loop of neural-symbolic learning by (1) introducing the \textbf{grammar} model as a \textit{symbolic prior} to bridge neural perception and symbolic reasoning, and (2) proposing a novel \textbf{back-search} algorithm which mimics the top-down human-like learning procedure to propagate the error through the symbolic reasoning module efficiently. We further interpret the proposed learning framework as maximum likelihood estimation using Markov chain Monte Carlo sampling and the back-search algorithm as a Metropolis-Hastings sampler. The experiments are conducted on two weakly-supervised neural-symbolic tasks: (1) handwritten formula recognition on the newly introduced HWF dataset; (2) visual question answering on the CLEVR dataset. The results show that our approach significantly outperforms the RL methods in terms of performance, converging speed, and data efficiency. Our code and data are released at \url{this https URL}.
Pre-innervated tissue-engineered muscle promotes a pro-regenerative microenvironment following volumetric muscle loss

Volumetric muscle loss (VML) is the traumatic or surgical loss of skeletal muscle beyond the inherent regenerative capacity of the body, generally leading to severe functional deficit. Formation of appropriate somato-motor innervations remains one of the biggest challenges for both autologous grafts as well as tissue-engineered muscle constructs. We aim to address this challenge by developing pre-innervated tissue-engineered muscle comprised of long aligned networks of spinal motor neurons and skeletal myocytes on aligned nanofibrous scaffolds. Motor neurons led to enhanced differentiation and maturation of skeletal myocytes in vitro. These pre-innervated tissue-engineered muscle constructs when implanted in a rat VML model significantly increased satellite cell density, neuromuscular junction maintenance, graft revascularization, and muscle volume over three weeks as compared to myocyte-only constructs and nanofiber scaffolds alone. These pro-regenerative effects may enhance functional neuromuscular regeneration following VML, thereby improving the levels of functional recovery following these devastating injuries.
A Spectral Representation Method For Continuous-Time Stochastic System Estimation Based On Analog Data Records

Abstract In this paper a novel and effective maximum likelihood type method for the estimation of physically meaningful continuous-time stochastic systems from analog data records is introduced. The method utilizes the ARMAX canonical form and block-pulse function spectral representations, through which the problem is shown to be transformed into that of estimating an induced and special-form discrete stochastic system from spectral data. The proposed method is based on a number of key structural and probabilistic properties that this discrete system is shown to possess, including stationarity, invertibility, and the bijective transformation nature of its mapping relationship with the original continuous-time system. Unlike previous schemes, the proposed method utilizes analog data without depending upon estimates of signal derivatives or prefilters, avoids errors due to direct discretizations associated with instantaneous sampling, and is characterized by a linear transformation relationship between the discrete and the original continuous-time system parameters. This leads to additional important advantages, such as the elimination of sensitivity problems associated with highly non-linear mappings, the capability of incorporating a priori system information, and reduced computational complexity. The effectiveness of the method is verified via numerical experiments with a number of stochastic systems.
Methane, arsenic, selenium and the origins of the DMSO reductase family

Mononuclear molybdoenzymes of the dimethyl sulfoxide reductase (DMSOR) family catalyze a number of reactions essential to the carbon, nitrogen, sulfur, arsenic, and selenium biogeochemical cycles. These enzymes are also ancient, with many lineages likely predating the divergence of the last universal common ancestor into the Bacteria and Archaea domains. We have constructed rooted phylogenies for over 1,550 representatives of the DMSOR family using maximum likelihood methods to investigate the evolution of the arsenic biogeochemical cycle. The phylogenetic analysis provides compelling evidence that formylmethanofuran dehydrogenase B subunits, which catalyze the reduction of CO2 to formate during hydrogenotrophic methanogenesis, constitutes the most ancient lineage. Our analysis also provides robust support for selenocysteine as the ancestral ligand for the Mo/W atom. Finally, we demonstrate that anaerobic arsenite oxidase and respiratory arsenate reductase catalytic subunits represent a more ancient lineage of DMSORs compared to aerobic arsenite oxidase catalytic subunits, which evolved from the assimilatory nitrate reductase lineage. This provides substantial support for an active arsenic biogeochemical cycle on the anoxic Archean Earth. Our work emphasizes that the use of chalcophilic elements as substrates as well as the Mo/W ligand in DMSORs has indelibly shaped the diversification of these enzymes through deep time.
Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning.

Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Topt values are desired for enhanced reaction rates. Recently, a machine l...
Revisiting the Timpson Induced Earthquake Sequence: A System of Two Parallel Faults

The 17 May 2012 M4.8 Timpson earthquake is the largest known earthquake in eastern Texas. It is suspected to have been induced by wastewater injection from two nearby, high-volume wells. Its catalo...
Integrated Power and Thermal Management of Connected HEVs via Multi-Horizon MPC

In this paper, a multi-horizon model predictive controller (MH-MPC) is developed for integrated power and thermal management (iPTM) of a power-split hybrid electric vehicle (HEV). The proposed MH-MPC leverages an accurate short-horizon vehicle speed preview and an approximate forecast over a longer shrinking horizon till the end of the driving cycle. This multiple-horizon scheme is developed to cope with fast and slow dynamics associated with power and thermal responses. The main objective of the proposed MH-MPC is to minimize fuel consumption and enforce the power and thermal constraints on the battery state-of-charge and engine coolant temperature, while meeting the driving (traction) and cabin air conditioning (heating) demands. The proposed MH-MPC allows for exploiting the engine coolant as thermal energy storage, providing more flexibility for the HEV energy flow optimization. The simulation results show that the proposed MH-MPC provides near-optimal results in reference to the Dynamic Programming (DP) solution with an affordable computational cost. Moreover, compared with a more conventional MPC strategy, the MH-MPC can leverage the speed previews with different resolutions effectively to achieve the desired performance with satisfactory robustness.
Functional Characterization of Three-Dimensional Cortical Cultures for In Vitro Modeling of Brain Networks.

Summary Three-dimensional (3D) in vitro cultures recapitulate key features of the brain including morphology, cell-cell and cell-extracellular matrix interactions, gradients of factors, and mechanical properties. However, there remains a need for experimental and computational tools to investigate network functions in these 3D models. To address this need, we present an experimental system based on 3D scaffold-based cortical neuron cultures in which we expressed the genetically encoded calcium indicator GCaMP6f to record neuronal activity at the millimeter-scale. Functional neural network descriptors were computed with graph-theory-based network analysis methods, showing the formation of functional networks at 3 weeks of culture. Changes to the functional network properties upon perturbations to glutamatergic neurotransmission or GABAergic neurotransmission were quantitatively characterized. The results illustrate the applicability of our 3D experimental system for the study of brain network development, function, and disruption in a biomimetic microenvironment.
When Uncle Sam Introduced Main Street to Wall Street: Liberty Bonds and the Transformation of American Finance

Abstract We study the effects of the Liberty Bond drives of World War I on financial intermediation in the 1920s and beyond. Using panel data on US counties, and an instrument that captures differences in the approaches used to market the bonds, we find that higher Liberty Bond subscription rates led to an increase in investment banks and a contraction in commercial bank assets. We also find that in the late 1930s, individuals residing in states where Liberty Bond subscription rates had been higher were more likely to report owning stocks or bonds. Although they were conducted to support the American effort in World War I, these bond drives reshaped American finance.
What is the Value of Data? on Mathematical Methods for Data Quality Estimation

Data is one of the most important assets of the information age, and its societal impact is undisputed. Yet, rigorous methods of assessing the quality of data are lacking. In this paper, we propose a formal definition for the quality of a given dataset. We assess a dataset’s quality by a quantity we call the expected diameter, which measures the expected disagreement between two randomly chosen hypotheses that explain it, and has recently found applications in active learning. We focus on Boolean hyperplanes, and utilize a collection of Fourier analytic, algebraic, and probabilistic methods to come up with theoretical guarantees and practical solutions for the computation of the expected diameter. We also study the behaviour of the expected diameter on algebraically structured datasets, conduct experiments that validate this notion of quality, and demonstrate the feasibility of our techniques.
Realizations of rigid C*-tensor categories as bimodules over GJS C*-algebras

Given an arbitrary countably generated rigid C*-tensor category, we construct a fully faithful bi-involutive strong monoidal functor onto a subcategory of finitely generated projective bimodules over a simple, exact, separable, unital C*-algebra with a unique trace. The C*-algebras involved are built from the category using the Guionnet–Jones–Shlyakhtenko construction. Out of this category of Hilbert C*-bimodules, we construct a fully faithful bi-involutive strong monoidal functor into the category of bifinite spherical bimodules over an interpolated free group factor. The composite of these two functors recovers the functor constructed by Brothier, Hartglass, and Penneys.
Religion in the Age of Social Distancing: How COVID-19 Presents New Directions for Research

In this brief note written during a global pandemic, we consider some of the important ways this historical moment is altering the religious landscape, aiming our investigative lens at how religious institutions, congregations, and individuals are affected by the social changes produced by COVID-19. This unprecedented time prompts scholars of religion to reflect on how to strategically approach the study of religion in the time of "social distancing," as well as moving forward. Particularly important considerations include developing heuristic, innovative approaches for revealing ongoing changes to religion, as well as how religion continues to structure social life across a wide range of contexts, from the most intimate and personal to the most public and global. Although our note can only be indicative rather than exhaustive, we do suggest that the initial groundwork for reconsiderations might productively focus on several key analytical themes, including: Epidemiology, Ideology, Religious Practice, Religious Organizations and Institutions, as well as Epistemology and Methodology. In offering these considerations as a starting point, we remain aware (and hopeful) that inventive and unanticipated approaches will also emerge.
Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer's disease.

Alzheimer’s disease (AD) is characterized by an early, asymptomatic phase (AsymAD) in which individuals exhibit amyloid-beta (Aβ) plaque accumulation in the absence of clinically detectable cognitive decline. Here we report an unbiased multiplex quantitative proteomic and phosphoproteomic analysis using tandem mass tag (TMT) isobaric labeling of human post-mortem cortex (n = 27) across pathology-free controls, AsymAD and symptomatic AD individuals. With off-line high-pH fractionation and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Orbitrap Lumos mass spectrometer, we identified 11,378 protein groups across three TMT 11-plex batches. Immobilized metal affinity chromatography (IMAC) was used to enrich for phosphopeptides from the same TMT-labeled cases and 51,736 phosphopeptides were identified. Of these, 48,992 were quantified by TMT reporter ions representing 33,652 unique phosphosites. Two reference standards in each TMT 11-plex were included to assess intra- and inter-batch variance at the protein and peptide level. This comprehensive human brain proteome and phosphoproteome dataset will serve as a valuable resource for the identification of biochemical, cellular and signaling pathways altered during AD progression. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12901817
Long-Horizon Prediction and Uncertainty Propagation with Residual Point Contact Learners

The ability to simulate and predict the outcome of contacts is paramount to the successful execution of many robotic tasks. Simulators are powerful tools for the design of robots and their behaviors, yet the discrepancy between their predictions and observed data limit their usability. In this paper, we propose a self-supervised approach to learning residual models for rigid-body simulators that exploits corrections of contact models to refine predictive performance and propagate uncertainty. We empirically evaluate the framework by predicting the outcomes of planar dice rolls and compare it’s performance to state-of-the-art techniques.
Distributed Multi-User Secret Sharing

We consider a distributed secret sharing system that consists of a dealer, n storage nodes, and m users. Each user is given access to a certain subset of storage nodes, where it can download the stored data. The dealer wants to securely convey a specific secret $\text {s}_{\text {j}}$ to user j via storage nodes, for $\text {j}=1,2, {\dots },\text {m}$ . More specifically, two secrecy conditions are considered in this multi-user context. The weak secrecy condition is that each user does not get any information about the individual secrets of other users, while the perfect secrecy condition implies that a user does not get any information about the collection of all other users’ secrets. In this system, the dealer encodes secrets into several secret shares and loads them into the storage nodes. Given a certain number of storage nodes we find the maximum number of users that can be served in such a system and construct schemes that achieve this with perfect secrecy. We further define two major properties for such distributed secret sharing systems; communication complexity is defined as the total amount of data that users need to download in order to reconstruct their secrets; and storage overhead is defined as the total size of data loaded by the dealer into the storage nodes normalized by the total size of secrets. Lower bounds on the minimum communication complexity and the storage overhead are characterized given any n and m. We construct distributed secret sharing protocols, under certain conditions on the system parameters, that attain the lower bound on the communication complexity while providing perfect secrecy. Furthermore, we construct protocols, again under certain conditions, that simultaneously attain the lower bounds on the communication complexity and the storage overhead while providing weak secrecy, thereby demonstrating schemes that are optimal in terms of both parameters. It is shown how to modify the proposed protocols in order to construct schemes with balanced storage load and communication complexity.
A Euclidean Signature Semi-Classical Program

In this article we discuss our ongoing program to extend the scope of certain, well-developed microlocal methods for the asymptotic solution of Schrodinger’s equation (for suitable ‘nonlinear oscillatory’ quantum mechanical systems) to the treatment of several physically significant, interacting quantum field theories. Our main focus is on applying these ‘Euclidean-signature semi-classical’ methods to self-interacting (real) scalar fields of renormalizable type in $2$, $3$ and $4$ spacetime dimensions and to Yang-Mills fields in $3$ and $4$ spacetime dimensions. A central argument in favor of our program is that the asymptotic methods for Schrodinger operators developed in the microlocal literature are far superior, for the quantum mechanical systems to which they naturally apply, to the conventional WKB methods of the physics literature and that these methods can be modified, by techniques drawn from the calculus of variations and the analysis of elliptic boundary value problems, to apply to certain (bosonic) quantum field theories. Unlike conventional (Rayleigh/ Schrodinger) perturbation theory these methods avoid the artificial decomposition of an interacting system into an approximating ‘unperturbed’ system and its perturbation and instead keep the nonlinearities (and, if present, gauge invariances) of an interacting system intact at every level of the analysis.
The Dawn of X-Ray Astronomy

X!rays of energy greater than several hundreds of electron volts can penetrate the interstellar gas over distances comparable to the size of our own galaxy, with greater or lesser absorption depending on the direction of the line of sight. At energies of a few kilovolts, x!rays can penetrate the entire column of galactic gas and in fact can reach us from distances comparable to the radius of the universe.
Communicative Visualizations as a Learning Problem

Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: “can the recipient accurately decode my message/insight?” However, designers are unlikely to be satisfied if the message went ‘in one ear and out the other.’ The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types.
Estimating the impact of self-management education, influenza vaccines, nebulizers, and spacers on health utilization and expenditures for Medicaid-enrolled children with asthma.

We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-e...
PD-L1 Glycosylation and Its Impact on Binding to Clinical Antibodies

Immune checkpoint inhibitors, including PD-L1/PD-1, are key regulators of the immune response and promising targets in cancer immunotherapy. N-glycosylation of PD-L1 affects its interaction with PD-1, but little is known about the distribution of glycoforms at its four NXS/T sequons. We optimized LC-MS/MS methods using collision energy modulation for the site-specific resolution of specific glycan motifs. We demonstrate that PD-L1 on the surface of breast cancer cell line carries mostly complex glycans with a high proportion of polyLacNAc structures at the N219 sequon. Contrary to the full-length protein, the secreted form of PD-L1 expressed in breast MDA-MB-231 or HEK293 cells demonstrated minimum N219 occupancy and low contribution of the polyLacNAc structures. Molecular modeling of PD-L1/PD-1 interaction with N-glycans suggests that glycans at the N219 site of PD-L1 and N74 and N116 of PD-1 may be involved in glycan-glycan interactions, but the impact of this potential interaction on the protein function remains at this point unknown. The interaction of PD-L1 with clinical antibodies is also affected by glycosylation. In conclusion, PD-L1 expressed in the MDA-MB-231 breast cancer cell line carries polyLacNAc glycans mostly at the N219 sequon, which displays the highest variability in occupancy and is most likely to influence the interaction with PD-1.
Mobile app privacy in software engineering research: A systematic mapping study

Abstract Context: Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional privacy concerns among app users. Objective: The research on mobile app privacy has experienced rapid growth over the past decade. This line of research is aimed at systematically exposing the privacy practices of apps and proposing solutions to protect the privacy of mobile app users. In this paper, we conduct a systematic mapping study of this body of research. Our objectives are to a) explore trends in SE app privacy research, b) categorize existing evidence, and c) identify potential directions for future research. Method: A systematic mapping study of 59 Software Engineering (SE) primary studies on mobile app privacy. Our scope is studies published in software engineering venues between 2008 and 2018. Results: Our results show that existing literature can be divided into four main categories: privacy policy, requirements, user perspective, and leak detection. Furthermore, our survey reveals an imbalance between these categories—the majority of existing research focuses on proposing tools for detecting privacy leaks, with fewer studies targeting privacy requirements and policy and even fewer on user perspective. Conclusions: Our survey exposes several gaps in existing research and suggests areas for improvement.
IL-12 signaling promotes TET2-mediated DNA demethylation during CD8 T cell effector differentiation

CD8 T cell memory differentiation endows T cells with an ability to rapidly induce effector functions upon pathogen re-encounter. While it is well established that substantial epigenetic remodeling occurs during the effector stage of the immune response, the signaling events that imprint CD8 T cells with these stable epigenetic programs are not well-defined. To gain insight into the signaling determinants of effector-associated epigenetic programming among CD8 T cells, we explored the role of IL-12 in the imprinting of IFNg expression during human CD8 T cell priming. We observed that TCR-mediated stimulation of human naive CD8 T cells is not sufficient to induce substantial demethylation of the IFNg promotor. However, TCR stimulation in the presence of the inflammatory cytokine, IL-12, resulted in significant and stable demethylation of the IFNg locus that was commensurate with an increase in IFNg expression. We further show that IL-12-associated demethylation of the IFNg locus is coupled to cell division through TET2-dependent passive demethylation in an ex vivo human CAR T cell model system and an in vivo immunologically competent murine system. Collectively, these data illustrate that IL-12 signaling promotes TET2-mediated effector epigenetic programming in CD8 T cells during the primary immune response and serve as proof of concept that signal 3 cytokines can be used to guide the induction of epigenetically regulated traits among T cells used for adoptive immunotherapies.
H-principles for regular Lagrangians

We prove an existence h-principle for regular Lagrangians with Legendrian boundary in arbitrary Weinstein domains of dimension at least six; this extends a previous result of Eliashberg, Ganatra, and the author for Lagrangians in flexible domains. Furthermore, we show that all regular Lagrangians come from our construction and describe some related decomposition results. We also prove a regular version of Eliashberg and Murphy's h-principle for Lagrangian caps with loose negative end. As an application, we give a new construction of infinitely many regular Lagrangian disks in the standard Weinstein ball.
Quantum Quench in $c=1$ Matrix Model and Emergent Space-times

We consider quantum quench in large-N singlet sector quantum mechanics of a single hermitian matrix in the double scaling limit. The time dependent parameter is the self-coupling of the matrix. We find exact classical solutions of the collective field theory of the eigenvalue density with abrupt and smooth quench profiles which asymptote to constant couplings at early and late times, and with the system initially in its ground state. With adiabatic initial conditions we find that adiabaticity is always broken regardless of the quench speed. In a class of quench profiles the saddle point solution for the collective field diverges at a finite time, and a further time evolution becomes ambiguous. However the underlying matrix model expressed in terms of fermions predict a smooth time evolution across this point. By studying fluctuations around the saddle point solution we interpret the emergent space-times. They generically have spacelike boundaries where the couplings of the fluctuations diverge and the semi-classical description fails. Only for very finely tuned quench profiles, the space-time is normal.
Exactly solvable models: The road towards a rigorous treatment of phase transitions in finite systems

The exact analytical solutions of a variety of statistical models recently obtained for finite systems are thoroughly discussed. Among them are a constrained version of the statistical multifragmentation model, the Gas of Bags Model, and the Hills and Dales Model of surface partition. The finite volume analytical solutions of these models were obtained by a novel powerful mathematical method, the Laplace-Fourier transform. Thus, the Laplace-Fourier transform allows one to study the nuclear matter equation of state, the equation of state of hadronic matter and quark gluon plasma, and the surface entropy of large clusters on the same footing. A complete analysis of the isobaric partition singularities of these models is done for finite systems. The developed formalism allows us, for the first time, to exactly define the finite volume analogs of gaseous, liquid, and mixed phases of these models from the first principles of statistical mechanics, and to demonstrate the pitfalls of earlier works. The found solutions may be used for building up a new theoretical apparatus to rigorously study phase transitions in finite systems. The strategic directions of future research opened by these exact results are also discussed.
Exact Virasoro blocks from Wilson lines and background-independent operators

Aspects of black hole thermodynamics and information loss can be derived as a consequence of Virasoro symmetry. To bolster the connection between Virasoro conformal blocks and AdS3 quantum gravity, we study sl(2) Chern-Simons Wilson line networks and revisit the idea that they compute a variety of CFT2 observables, including Virasoro OPE blocks, exactly. We verify this in the semiclassical large central charge limit and to low orders in a perturbative 1/c expansion. Wilson lines connecting the boundary to points in the bulk play a natural role in bulk reconstruction. Because quantum gravity in AdS3 is rigidly fixed by Virasoro symmetry, we argue that sl(2) Wilson lines provide building blocks for background independent bulk reconstruction. In particular, we show explicitly that they automatically ‘know’ about the uniformizing coordinates appropriate to any background state.
EQUILATERAL SETS IN UNIFORMLY SMOOTH BANACH SPACES

Let $X$ be an infinite dimensional uniformly smooth Banach space. We prove that $X$ contains an infinite equilateral set. That is, there exists a constant $\lambda>0$ and an infinite sequence $(x_i)_{i=1}^\infty\subset X$ such that $\|x_i-x_j\|=\lambda$ for all $i\neq j$.
Extended Source Diffraction Effects near Gravitational Lens Fold Caustics

Calculations are presented detailing the gravitational lens diffraction due to the steep brightness gradient of the limb of a stellar source. The lensing case studied is the fold caustic crossing. The limb diffraction signal greatly exceeds that due to the disk as a whole and should be detectable with existing telescopes for white dwarf sources in our Galaxy and its satellites. Detection of this diffraction signal would provide an additional mathematical constraint, reducing the degeneracy among models of the lensing geometry. The diffraction pattern provides picoarcsecond resolution of the limb profile.
Kaluza-Klein Theory with Torsion Confined to the Extra-Dimension

Here we consider a variant of the five-dimensional Kaluza–Klein (KK) theory within the framework of Einstein–Cartan formalism that includes torsion. By imposing a set of constraints on torsion and Ricci rotation coefficients, we show that the torsion components are completely expressed in terms of the metric. Moreover, the Ricci tensor in 5D corresponds exactly to what one would obtain from torsion-free general relativity on a 4D hypersurface. The contributions of the scalar and vector fields of the standard KK theory to the Ricci tensor and the affine connections are completely nullified by the contributions from torsion. As a consequence, geodesic motions do not distinguish the torsion free 4D spacetime from a hypersurface of 5D spacetime with torsion satisfying the constraints. Since torsion is not an independent dynamical variable in this formalism, the modified Einstein equations are different from those in the general Einstein–Cartan theory. This leads to important cosmological consequences such as the emergence of cosmic acceleration.
Spectral approximations by the HDG method

We consider the numerical approximation of the spectrum of a secondorder elliptic eigenvalue problem by the hybridizable discontinuous Galerkin (HDG) method. We show for problems with smooth eigenfunctions that the approximate eigenvalues and eigenfunctions converge at the rate 2k + 1 and k + 1, respectively. Here k is the degree of the polynomials used to approximate the solution, its flux, and the numerical traces. Our numerical studies show that a Rayleigh quotient-like formula applied to certain locally postprocessed approximations can yield eigenvalues that converge faster at the rate 2k + 2 for the HDG method as well as for the BrezziDouglas-Marini (BDM) method. We also derive and study a condensed nonlinear eigenproblem for the numerical traces obtained by eliminating all the other variables.
On the Statistical Analysis of X-Ray Polarization Measurements

In many polarimetry applications, including observations in the X-ray band, the measurement of a polarization signal can be reduced to the detection and quantification of a deviation from uniformity of a distribution of measured angles of the form alpha plus beta cosine (exp 2)(phi - phi(sub 0) (0 (is) less than phi is less than pi). We explore the statistics of such polarization measurements using both Monte Carlo simulations as well as analytic calculations based on the appropriate probability distributions. We derive relations for the number of counts required to reach a given detection level (parameterized by beta the "number of sigma's" of the measurement) appropriate for measuring the modulation amplitude alpha by itself (single interesting parameter case) or jointly with the position angle phi (two interesting parameters case). We show that for the former case when the intrinsic amplitude is equal to the well known minimum detectable polarization (MDP) it is, on average, detected at the 3sigma level. For the latter case, when one requires a joint measurement at the same confidence level, then more counts are needed, by a factor of approximately equal to 2.2, than that required to achieve the MDP level. We find that the position angle uncertainty at 1sigma confidence is well described by the relation sigma(sub pi) equals 28.5(degrees) divided by beta.
Platonic solids back in the sky: Icosahedral inflation

We generalize the model of solid inflation to an anisotropic cosmic solid. Barring fine tunings, the observed isotropy of the cosmological background and of the scalar two-point function isolate the icosahedral group as the only possible symmetry group of such a solid. In such a case, higher-point correlation functions—starting with the three-point one—are naturally maximally anisotropic, which makes the standard detection strategies highly inefficient and calls for a dedicated analysis of CMB data. The tensor two-point function can also be highly anisotropic, but only in the presence of sizable higher-derivative couplings.
Remarks on the Emergence of Weak Euler Solutions in the Vanishing Viscosity Limit

We prove that if the local second-order structure function exponents in the inertial range remain positive uniformly in viscosity, then any spacetime $$L^2$$ weak limit of Leray–Hopf weak solutions of the Navier–Stokes equations on any bounded domain $$\Omega \subset \mathbb {R}^d$$ , $$d=2,3$$ is a weak solution of the Euler equations. This holds for both no-slip and Navier friction conditions with viscosity-dependent slip length. The result allows for the emergence of non-unique, possibly dissipative, limiting weak solutions of the Euler equations.
The HI Content of Galaxies in Groups and Clusters as Measured by ALFALFA

We present the HI content of galaxies in nearby groups and clusters as measured by the 70% complete Arecibo Legacy Fast-ALFA (ALFALFA) survey, including constraints from ALFALFA detection limits. Our sample includes 22 systems at distances between 70-160 Mpc over the mass range 12.5<log M/M_sun<15.0, for a total of 1986 late-type galaxies. We find that late-type galaxies in the centers of groups lack HI at fixed stellar mass relative to the regions surrounding them. Larger groups show evidence of a stronger dependence of HI properties on environment, despite a similar dependence of color on environment at fixed stellar mass. We compare several environment variables to determine which is the best predictor of galaxy properties; group-centric distance r and r/R_200 are similarly effective predictors, while local density is slightly more effective and group size and halo mass are slightly less effective. While both central and satellite galaxies in the blue cloud exhibit a significant dependence of HI content on local density, only centrals show a strong dependence on stellar mass, and only satellites show a strong dependence on halo mass. Finally, we see evidence that HI is deficient for blue cloud galaxies in denser environments even when both stellar mass and color are fixed. This is consistent with a picture where HI is removed or destroyed, followed by reddening within the blue cloud. Our results support the existence of pre-processing in isolated groups, along with an additional rapid mechanism for gas removal within larger groups and clusters, perhaps ram-pressure stripping.
Main-Belt Comets as Tracers of Ice in the Inner Solar System

As a recently recognized class of objects exhibiting apparently cometary (sublimation-driven) activity yet orbiting completely within the main asteroid belt, main-belt comets (MBCs) have revealed the existence of present-day ice in small bodies in the inner solar system and offer an opportunity to better understand the thermal and compositional history of our solar system, and by extension, those of other planetary systems as well. Achieving these overall goals, however, will require meeting various intermediate research objectives, including discovering many more MBCs than the currently known seven objects in order to ascertain the population's true abundance and distribution, confirming that water ice sublimation is in fact the driver of activity in these objects, and improving our understanding of the physical, dynamical, and thermal evolutionary processes that have acted on this population over the age of the solar system.