cssi/SciSciGPT-SciSciNet huggingface dataset:
Accuracy of slender body theory in approximating force exerted by thin fiber on viscous fluid

We consider the accuracy of slender body theory in approximating the force exerted by a thin fiber on the surrounding viscous fluid when the fiber velocity is prescribed. We term this the slender body inverse problem, as it is known that slender body theory converges to a well-posed PDE solution when the force is prescribed and the fiber velocity is unknown. From a PDE perspective, the slender body inverse problem is simply the Dirichlet problem for the Stokes equations, but from an approximation perspective, nonlocal slender body theory exhibits high wavenumber instabilities which complicate analysis. Here we consider two methods for regularizing the slender body approximation: spectral truncation and the $\delta$-regularization of Tornberg and Shelley (2004). For a straight, periodic fiber with constant radius $\epsilon>0$, we explicitly calculate the spectrum of the operator mapping fiber velocity to force for both the PDE and the approximations. We show that the spectrum of the original slender body approximation agrees closely with the PDE solution at low wavenumbers but differs at high frequencies, allowing us to define a truncated approximation with a wavenumber cutoff $\sim1/\epsilon$. For both the truncated and $\delta$-regularized approximations, we obtain similar convergence results to the PDE solution as $\epsilon\to0$: a fiber velocity with $H^1$ regularity gives $O(\epsilon)$ convergence, while a fiber velocity with at least $H^2$ regularity yields $O(\epsilon^2)$ convergence. Moreover, we determine the dependence of the $\delta$-regularized error estimate on the regularization parameter $\delta$.
Oh, How the Mighty Have Fallen: The Bank Failures and Near Failures That Started America’s Greatest Financial Panics

This paper examines the failures or in some cases near-failures of financial institutions that started the 12 most severe peacetime financial panics in the United States, beginning with the Panic of 1819 and ending with the Panic of 2008. The following generalizations were true in most cases, although not in all. (1) Panics were triggered by a short series of failures or near-failures; (2) many of the failing institutions were what we would now call shadow banks; (3) typically, the source of trouble was an excessive investment in real estate; and (4) typically, they had outstanding reputations for trustworthiness, prudence, and financial acumen—before they failed. It appears that in these respects the Panic of 2008 was an old-school panic. [a panic] occurs when a succession of unexpected failures has created in the mercantile, and sometimes also in the non-mercantile public a general distrust in each other’s solvency; disposing every one not only to refuse fresh credit, except on very onerous terms, but to call in, if possible all credit which he has already given. —John Stuart Mill All of this has happened before, and it will all happen again. —Peter Pan
Tenant Concentration in REITs

To the extent that a property portfolio is dependent on a small number of tenants for a large proportion of rental revenue, the portfolio has a concentrated tenant base. This article investigates the impact of tenant concentration on property portfolio performance, risk, and the cost of debt. Utilizing the disclosure of major tenants by 152 Equity Real Estate Investment Trusts (REITs) from 2000 to 2017, I document a positive relation between tenant concentration and profitability. REITs with greater tenant concentration experience higher profit margins and lower expense ratios, suggesting the positive relation between tenant concentration and profitability is driven by increased operational efficiency. Although these REITs are more efficient, tenant concentration is often stated as a risk in public disclosures. Consistent with this view, I find that REITs with greater tenant concentration have greater idiosyncratic risk. Further, banks appear to price this risk into their rate setting process and penalize REITs with greater tenant concentration. Accounting for the quality of the tenant base, the positive effects of tenant concentration are prevalent in REITs with high quality tenants and the negative effects of tenant concentration are prevalent REITs with low quality tenants.
Nanoscale oxidation behavior of carbon fibers revealed with in situ gas cell STEM

Abstract Thermal protection systems are used to protect spacecraft payloads during the extreme conditions of atmospheric entry. The backbone of the composite material often used for NASA mission is carbon fiber, which oxidizes at these conditions. This study presents the direct observation of carbon oxidation using in situ Scanning Transmission Electron Microscopy (STEM). A thin section of a commercially-available carbon fiber material containing multiple structures was examined by STEM in a closed-environmental cell in which temperature was raised from 25 to 1050 °C under a steady flow of air. Results show that the random polycrystalline carbon structure oxidized more uniformly and rapidly than the single crystallite region, which oxidized more anisotropically. These findings are the first to directly observe the structural dependence of carbon oxidation rates at these length-scales while also giving important insight into the onset of pitting at various active surface sites, important pieces in fundamentally understanding of carbon oxidation.
A Formal Model For Real-Time Parallel Computation

The imposition of real-time constraints on a parallel computing environment--- specifically high-performance, cluster-computing systems--- introduces a variety of challenges with respect to the formal verification of the system's timing properties. In this paper, we briefly motivate the need for such a system, and we introduce an automaton-based method for performing such formal verification. We define the concept of a consistent parallel timing system: a hybrid system consisting of a set of timed automata (specifically, timed Buechi automata as well as a timed variant of standard finite automata), intended to model the timing properties of a well-behaved real-time parallel system. Finally, we give a brief case study to demonstrate the concepts in the paper: a parallel matrix multiplication kernel which operates within provable upper time bounds. We give the algorithm used, a corresponding consistent parallel timing system, and empirical results showing that the system operates under the specified timing constraints.
The Allure and Illusion of Partners' Interests in a Partnership

Favorable tax treatment and management flexibility make tax partnerships very popular. For starters, tax partnerships, unlike tax corporations, are not subject to entity-level taxes. Partnership taxable income flows through to the partners, and the partners report their shares of partnership taxable income on their individual tax returns. Partnership tax allocation rules determine the partners’ shares of partnership taxable income. Those rules rely upon the alluring concept of partners’ interests in a partnership. It seems intuitive that partners would know their interests in a partnership and be able to allocate partnership taxable income accordingly. This Article illustrates, however, that the concept is illusory and that it undermines the tax allocation rules, crippling the effectiveness of partnership taxation. Some partners therefore allocate partnership income to reduce their overall tax liability and unfairly deplete government revenue. The Article attributes the concept’s allure and illusion to path dependency and tax myopia—partnership tax experts expend considerable effort mastering difficult rules, which they cling to, and they focus narrowly on the tax aspects of those rules. The Article introduces three correlatives to end the myopia and improve the tax allocation rules: (1) economic items and tax items; (2) state law and tax law; and (3) economic interests and partners’ interests in a partnership. The Article illustrates that aspects of the current rules are tax-centric (i.e., economic results follow tax allocations) and illusory. The rules’ tax-centricity may create unintended legal consequences for unsuspecting partners; their illusion creates opportunities for tax abuse. After illustrating the current rules’ shortcomings, the Article recommends fundamental reform of the partnership tax allocation rules. It recommends a move to item-specific economic-centric rules that will eliminate the unintended legal and economic consequences of the current rules and curb tax abuse.
In Defense of Online Intermediary Immunity: Facilitating Communities of Modified Exceptionalism

In the ten years since its enactment, Section 230 of the Communications Decency Act of 1996 (CDA) has become perhaps the single most significant statute in the regulation of online content, and one of the most heavily criticized. Many early commentators criticized both Congress, for its apparent inability to craft the more limited statute it intended, and the courts, for interpreting the statute broadly and failing to limit its reach. Later commentators focus more clearly on policy concerns, contending that the failure to impose liability on intermediaries fails to effectuate principles of efficiency and cost avoidance. This article takes the opposing view, in defense of broad Section 230 immunity. It argues that the immunity provisions of Section 230 play a significant role in broader questions of Internet governance. Specifically, Section 230 immunity provides a means of working within the sovereign legal system to effectuate many of the goals, ideals and realities of the Internet exceptionalism, cyberlibertarian movements. By mitigating the imposition of certain external legal norms in the online environment, Section 230 helps to create the initial condition necessary for the development of a modified form exceptionalism. With the impact of external norms diminished, Web 2.0 communities, such as wikis and social networks, have emerged to facilitate a limited market in norms and values, and to provide internal enforcement mechanisms that allow new communal norms to emerge. Section 230 plays a vital role in this process of building heterogeneous communities that encourage collaborative production and communication. Efforts to reform or restrict Section 230 immunity are therefore unnecessary and unwise.
Impeachable Offenses?: Why Civil Parties in Quasi-Criminal Cases Should be Treated Like Criminal Defendants Under the Felony Impeachment Rule

With one exception, every Federal Rule of Evidence dealing with propensity character evidence or evidence which can be misused as propensity character evidence makes it either: (a) as difficult to admit such evidence in civil trials as it is in criminal trials, or (b) more difficult to admit such evidence in civil trials than it is in criminal trials. The "mercy rule" falls into this latter category as it allows criminal defendants to inject the issue of character into their trials while a similar luxury is not afforded to civil parties. Before 2006, however, a substantial minority of courts extended the "mercy rule" to civil parties in quasi-criminal cases because they were in most respects similar to criminal cases. Congress finally shut the door to this practice based upon the serious risks of prejudice, confusion, and delay that propensity character evidence engenders.These same risks, however, support treating civil parties in quasi-criminal cases the same as criminal defendants under the felony impeachment rule. That rule, Rule 609(a)(1), makes it much more difficult for courts to exclude the felony convictions of civil parties than it is for them to exclude the felony convictions of testifying criminal defendants. It is thus the only Federal Rule of Evidence which makes it easier to admit evidence which can be misused as propensity character evidence in civil trials than it is in criminal trials. Courts should correct this anomaly by treating civil parties in quasi-criminal cases the same as criminal defendant under the Rule.
Semantic text analysis for detection of compromised accounts on social networks

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for semantic analysis of text messages coming out from an account to detect compromised accounts. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream. We study the effectiveness of the proposed semantic features using a Twitter data set. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.
Voting in a Pandemic: COVID-19 and Primary Turnout in Milwaukee, Wisconsin

We report the first study of the effect of the novel coronavirus SARS-CoV-2 (COVID-19) on polling place consolidation and voting behavior. We draw upon individual-level observations from Milwaukee ...
Modular Zwitterion-Functionalized Poly(isopropyl methacrylate) Polymers for Hosting Luminescent Lead Halide Perovskite Nanocrystals

Inorganic lead halide perovskite nanocrystals (NCs) are an exciting class of luminescent materials with high defect tolerance and broad spectral tunability, but such NCs are vulnerable to degradati...
Prestin amplifies cardiac motor functions

Cardiac cells generate and amplify force in the context of cardiac load, yet the membranous sheath enclosing the muscle fibers-the sarcolemma-does not experience displacement. That the sarcolemma sustains beat-to-beat pressure changes without experiencing significant distortion is a muscle-contraction paradox. Here, we report that an elastic element-the motor protein prestin (Slc26a5)-serves to amplify actin-myosin force generation in mouse and human cardiac myocytes, accounting partly for the nonlinear capacitance of cardiomyocytes. The functional significance of prestin is underpinned by significant alterations of cardiac contractility in Prestin-knockout mice. Prestin was previously considered exclusive to the inner ear's outer hair cells; however, our results show that prestin serves a broader cellular motor function.
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT

Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing such personalized IoT applications arises from human variability. Such variability stems from the fact that different humans exhibit different behaviors when interacting with IoT applications (intra-human variability), the same human may change the behavior over time when interacting with the same IoT application (inter-human variability), and human behavior may be affected by the behaviors of other people in the same environment (multi-human variability). To that end, we propose FaiR-IoT, a general reinforcement learning-based framework for adaptive and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three levels of reinforcement learning agents interact to continuously learn human preferences and maximize the system's performance and fairness while taking into account the intra-, inter-, and multi-human variability. We validate the proposed framework on two applications, namely (i) Human-in-the-Loop Automotive Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House. Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1.5 orders of magnitude.
Floquet Majorana bound states in voltage-biased planar Josephson junctions

The authors show that Floquet Majorana modes should emerge in a voltage-bias planar Josephson junction and explore the possibility of manipulating Majorana bound states in one-dimensional settings.
Modeling Retinal Ganglion Cell Dysfunction in Optic Neuropathies

As in glaucoma and other optic neuropathies cellular dysfunction often precedes cell death, the assessment of retinal ganglion cell (RGC) function represents a key outcome measure for neuroprotective strategies aimed at targeting distressed but still viable cells. RGC dysfunction can be assessed with the pattern electroretinogram (PERG), a sensitive measure of electrical activity of RGCs that is recorded non-invasively in human subjects and mouse models. Here, we offer a conceptual framework based on an intuitive state-transition model used for disease management in patients to identify progressive, potentially reversible stages of RGC dysfunction leading to cell death in mouse models of glaucoma and other optic neuropathies. We provide mathematical equations to describe state-transitions with a set of modifiable parameters that alter the time course and severity of state-transitions, which can be used for hypothesis testing and fitting experimental PERG data. PERG dynamics as a function of physiological stimuli are also used to differentiate phenotypic and altered RGC response dynamics, to assess susceptibility to stressors and to assess reversible dysfunction upon pharmacological treatment.
Stem cells, pluripotency and nuclear reprogramming

Reprogramming of somatic cells to a pluripotent embryonic stem cell-like state has been achieved by nuclear transplantation of a somatic nucleus into an enucleated egg and most recently by introducing defined transcription factors into somatic cells. Nuclear reprogramming is of great medical interest as it has the potential to generate a source of patient-specific cells. This short review summarizes strategies to reprogram somatic cells to a pluripotent embryonic state and discuss the implications of this technology for transplantation medicine.
Document-Level Event Argument Extraction by Conditional Generation

Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6% F1 and 5.7% F1 over the next best model on the RAMS and WikiEvents dataset respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3% F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97% of fully supervised model’s trigger extraction performance and 82% of the argument extraction performance given only access to 10 out of the 33 types on ACE.
Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.
Initial degenerations of Grassmannians

We construct closed immersions from initial degenerations of $${{\,\mathrm{Gr}\,}}_{0}(d,n)$$ —the open cell in the Grassmannian $${{\,\mathrm{Gr}\,}}(d,n)$$ given by the nonvanishing of all Plucker coordinates—to limits of thin Schubert cells associated to diagrams induced by the face poset of the corresponding tropical linear space. These are isomorphisms when (d, n) equals (2, n), (3, 6) and (3, 7). As an application we prove $${{\,\mathrm{Gr}\,}}_0(3,7)$$ is schon, and the Chow quotient of $${{\,\mathrm{Gr}\,}}(3,7)$$ by the maximal torus in $$ {\text {PGL}}(7)$$ is the log canonical compactification of the moduli space of 7 points in $${\mathbb {P}}^2$$ in linear general position, making progress on a conjecture of Hacking, Keel, and Tevelev.
Constraining the parameter space of a quantum spin liquid candidate in applied field with iterative optimization

The quantum spin liquid (QSL) state is an exotic state of matter featuring a high degree of entanglement and lack of long-range magnetic order in the zero-temperature limit. The triangular antiferromagnet ${\mathrm{YbMgGaO}}_{4}$ is a candidate QSL host, and precise determination of the Hamiltonian parameters is critical to understanding the nature of the possible ground states. However, the presence of chemical disorder has made directly measuring these parameters challenging. Here we report neutron scattering and magnetic susceptibility measurements covering a broad range of applied magnetic field at low temperature. Our data shows a field-induced crossover in ${\mathrm{YbMgGaO}}_{4}$, which we reproduce with complementary classical Monte Carlo and density matrix renormalization group simulations. Neutron scattering data above and below the crossover reveal a shift in scattering intensity from $M$ to $K$ points and, collectively, our measurements provide essential characteristics of the phase crossover that we employ to strictly constrain proposed magnetic Hamiltonian parameters despite the chemical disorder. Constrained exchange parameters further suggest the material's proximity to the QSL state in the clean limit. More broadly, our approach demonstrates a means of pursuing QSL candidates where Hamiltonian parameters might otherwise be obscured by disorder.
ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution.

Summary Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model (ENM) for deformations along global modes, followed by clustering and short molecular dynamics (MD) simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace. Availability ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy. Supplementary information Supplementary materials comprising method details, figures, table and tutorial are available at Bioinformatics online.
Energy Storage System (ESS) for Compensating Unbalanced Multi-microgrids Using Modified Reverse Droop Control

The connection of single-phase microgrids (MG) and loads to three-phase MGs creates power quality problems such as unbalanced voltage and voltage rise at the point of common coupling (PCC) of the MGs. In this paper, a modified reverse droop control (MRDC) scheme in the Energy Storage System (ESS) is proposed to improve the three-phase PCC voltage quality in multi-microgrids (MMG). The MRDC consists of a reactive power compensator (RPC) and a voltage compensator. The controller regulates the reactive power and voltage unbalance of the MMG by using the reactive power produced by the ESS. The effectiveness of this proposed scheme is verified in real-time simulation using the Opal-RT OP5600 real-time simulator. The voltage unbalance factor (VUF) at the PCC is decreased from 3.6 percent to 0.25 percent, while the reactive power is reduced significantly at the single-phase load.
QND observation of quantum jumps between Fock states: A one-electron cyclotron oscillator at 70 mK to 4.2 K

Summary form only given. A one-electron cyclotron oscillator is cooled to thermal equilibrium at temperatures between 70 mK and 4.2 K - the first time that an isolated elementary particle has been cooled below 4.2 K. Continuous quantum non-demolition (QND) measurements of the electron cyclotron energy reveal the equilibrium oscillator in a quantum Fock state, with occasional and abrupt quantum jumps to adjacent Fock states, stimulated by blackbody photons in the cavity.
Cyclic permutations: Degrees and combinatorial types

Abstract This note will give an enumeration of n-cycles in the symmetric group S n by their degree (also known as their cyclic descent number) and studies similar counting problems for the conjugacy classes of n-cycles under the action of the rotation subgroup of S n . This is achieved by relating such cycles to periodic orbits of an associated dynamical system acting on the circle. We also compute the mean and variance of the degree of a random n-cycle and show that its distribution is asymptotically normal as n → ∞ .
Small Scale Mobile Robot Auto-parking Using Deep Learning, Image Processing, and Kinematics-Based Target Prediction

Autonomous parking is a valuable feature in many mobile robot applications. As compared to self-driving automobiles, auto-parking is more challenging for a small scale robot equipped with a front camera only, due to the camera view limited by the height of robot and the narrow Field of View (FOV) provided by the inexpensive camera. In this research, auto-parking of such a small scale mobile robot is accomplished in a four-step process: identification of available parking space using transfer learning based on the AlexNet; image processing for the detection of parking space boundary lines; kinematics-based target prediction when the parking space disappears from the camera view partially or completely; and motion control on the robot navigating towards the center of the parking space. Results show that a 95% accuracy has been achieved on identification of available parking spaces. The detection of boundary lines and prediction of target have also been successfully implemented in MATLAB. The testing of motion control and image capture for deep learning is performed on a self-built small-scale mobile robot.
PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification

We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samples and guarantee $\varepsilon$-differential privacy (DP) on its derived classifiers. Our proposed framework thus consists of an AAE-based generator and a PATE-based classifier (PATE-AAE). Evaluated on the Google Speech Commands Dataset Version II, the proposed PATE-AAE improves the average classification accuracy by +$2.11\%$ and +$6.60\%$, respectively, when compared with alternative privacy-preserving solutions, namely PATE-GAN and DP-GAN, while maintaining a strong level of privacy target at $\varepsilon$=0.01 with a fixed $\delta$=10$^{-5}$.
On the approximation of vorticity fronts by the Burgers–Hilbert equation

This paper proves that the motion of small-slope vorticity fronts in the two-dimensional incompressible Euler equations is approximated on cubically nonlinear timescales by a Burgers-Hilbert equation derived by Biello and Hunter (2010) using formal asymptotic expansions. The proof uses a modified energy method to show that the contour dynamics equations for vorticity fronts in the Euler equations and the Burgers-Hilbert equation are both approximated by the same cubically nonlinear asymptotic equation. The contour dynamics equations for Euler vorticity fronts are also derived.
Performance of a geometric deep learning pipeline for HL-LHC particle tracking

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
Cryo-EM structure determination of small proteins by nanobody-binding scaffolds (Legobodies).

We describe a general method that allows structure determination of small proteins by single-particle cryo-electron microscopy (cryo-EM). The method is based on the availability of a target-binding nanobody, which is then rigidly attached to two scaffolds: 1) a Fab fragment of an antibody directed against the nanobody and 2) a nanobody-binding protein A fragment fused to maltose binding protein and Fab-binding domains. The overall ensemble of ∼120 kDa, called Legobody, does not perturb the nanobody-target interaction, is easily recognizable in EM images due to its unique shape, and facilitates particle alignment in cryo-EM image processing. The utility of the method is demonstrated for the KDEL receptor, a 23-kDa membrane protein, resulting in a map at 3.2-A overall resolution with density sufficient for de novo model building, and for the 22-kDa receptor-binding domain (RBD) of SARS-CoV-2 spike protein, resulting in a map at 3.6-A resolution that allows analysis of the binding interface to the nanobody. The Legobody approach thus overcomes the current size limitations of cryo-EM analysis.
Accelerating materials discovery with Bayesian optimization and graph deep learning

Abstract Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform “DFT-free” relaxations of crystal structures. Using this approach to significantly improve the accuracy of ML-predicted formation energies and elastic moduli of hypothetical crystals, two novel ultra-incompressible hard MoWC2 ( P 6 3 / mmc ) and ReWB ( Pca 2 1 ) were identified and successfully synthesized via in situ reactive spark plasma sintering from screening 399,960 transition metal borides and carbides. This work addresses a critical bottleneck to accurate property predictions for hypothetical materials, paving the way to ML-accelerated discovery of new materials with exceptional properties.
Data minimization for GDPR compliance in machine learning models

The EU General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) mandate the principle of data minimization, which requires that only data necessary to fulfill a certain purpose be collected. However, it can often be difficult to determine the minimal amount of data required, especially in complex machine learning models such as deep neural networks. We present a first-of-a-kind method to reduce the amount of personal data needed to perform predictions with a machine learning model, by removing or generalizing some of the input features of the runtime data. Our method makes use of the knowledge encoded within the model to produce a generalization that has little to no impact on its accuracy, based on knowledge distillation approaches. We show that, in some cases, less data may be collected while preserving the exact same level of model accuracy as before, and if a small deviation in accuracy is allowed, even more generalizations of the input features may be performed. We also demonstrate that when collecting the features dynamically, the generalizations may be even further improved. This method enables organizations to truly minimize the amount of data collected, thus fulfilling the data minimization requirement set out in the regulations.
DFTTK: Density Functional Theory ToolKit for high-throughput lattice dynamics calculations

Abstract In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory ToolKit). DFTTK is based on the atomate package and integrates our experiences in the last decades on the development of theoretical methods and computational softwares. It includes task submissions on all major operating systems and task executions on high-performance computing environments. The distribution of the DFTTK package comes with examples of calculations of phonon density of states, heat capacity, entropy, enthalpy, and free energy under the quasi-harmonic phonon scheme for the stoichiometric phases of Al, Ni, Al3Ni, AlNi, AlNi3, Al3Ni4, and Al3Ni5, and the fcc solution phases treated using the special quasirandom structures at the compositions of Al3Ni, AlNi, and AlNi3.
Epidermal Stratification Requires Retromer-Mediated Desmoglein-1 Recycling

Sorting and trafficking transmembrane cargo is essential for tissue development and homeostasis. However, the importance of intracellular trafficking in the development and regeneration of stratified epidermis has not been investigated. Here we identify the interaction between VPS35, an essential component of the retromer endosomal trafficking complex, and the desmosomal cadherin, Desmoglein 1 (Dsg1). Dsg1 is specifically expressed in stratified tissues and when properly localized on the plasma membrane, promotes epidermal stratification. We show that the retromer drives Dsg1 recycling from the endolysosomal system to the plasma membrane to support keratinocyte stratification and differentiation. The retromer enhancing chaperone, R55 promotes the plasma membrane localization of Dsg1 and a Dsg1 mutant associated with Severe dermatitis, multiple Allergies, and Metabolic wasting (SAM) syndrome, enhancing the ability of SAM-Dsg1 to promote stratification. Our work provides the first evidence for retromer function in epidermal regeneration and identifies it as a potential therapeutic skin disease target.
Polarization Selectivity of Aloof-Beam Electron Energy-Loss Spectroscopy in One-Dimensional ZnO Nanorods

Orientation-dependent electronic properties of wurtzite zinc oxide nanorods are characterized by aloof-beam electron energy-loss spectroscopy (EELS) carried out in a scanning transmission electron microscope (STEM). The two key crystal orientation differentiating transitions specific to the in-plane (13.0 eV) and out-of-plane (11.2 eV) directions with respect to the wurtzite structure are examined by first-principles density-functional theory calculations. We note some degree of orientation dependence at the onset of direct band gap transition near 3.4 eV. We demonstrate that good polarization selectivity can be achieved by placing the electron probe at different locations around the specimen with increasing impact parameter while keeping the beam-specimen orientation fixed. The observed results are qualitatively elucidated in terms of the perpendicular electric fields generated by the fast electron (60 kV) used in the microscope. The fact that good polarization selectivity can be achieved by aloof-beam EELS without the requirement of sample reorientation is an attractive aspect from the characterization method point of view in the STEM-EELS community.
Building 3D Morphable Models From a Single Scan

We propose a method for constructing generative models of 3D objects from a single 3D mesh. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. We define the shape deformations in physical (3D) space and the albedo deformations as a combination of physical-space and color-space deformations. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. We demonstrate the utility of these models in the domain of face modeling through inverse rendering and registration tasks. Specifically, we show that our approach can be used to perform face recognition using only a single 3D scan (one scan total, not one per person), and further demonstrate how multiple scans can be incorporated to improve performance without requiring dense correspondence. Our approach enables the synthesis of 3D morphable models for 3D object categories where dense correspondence between multiple scans is unavailable. We demonstrate this by constructing additional 3D morphable models for fish and birds and use them to perform simple inverse rendering tasks.
On a Sparse Shortcut Topology of Artificial Neural Networks

In the established network architectures, shortcut connections are often used to take the outputs of earlier layers as additional inputs to later layers. Despite the extraordinary effectiveness of shortcuts, there remain open questions on the mechanism and characteristics. For example, why are shortcuts powerful Why do shortcuts generalize well In this paper, we investigate the expressivity and generalizability of a novel sparse shortcut topology. First, we demonstrate that this topology can empower a one-neuron-wide deep network to approximate any univariate continuous function. Then, we present a novel width-bounded universal approximator in contrast to depth-bounded universal approximators and extend the approximation result to a family of equally competent networks. Furthermore, with generalization bound theory we show that the proposed shortcut topology enjoys excellent generalizability. Finally, we corroborate our theoretical analyses by comparing the proposed topology with popular architectures including ResNet and DenseNet on well-known benchmarks and perform a saliency map analysis to interpret the proposed topology. Our work helps enhance the understanding of the role of shortcuts and suggests further opportunities to innovate neural architectures.
NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM

Natural Language Processing (NLP) has recently achieved success by using huge pre-trained Transformer networks. However, these models often contain hundreds of millions or even billions of parameters, bringing challenges to online deployment due to latency constraints. Recently, hardware manufacturers have introduced dedicated hardware for NxM sparsity to provide the flexibility of unstructured pruning with the runtime efficiency of structured approaches. NxM sparsity permits arbitrarily selecting M parameters to retain from a contiguous group of N in the dense representation. However, due to the extremely high complexity of pre-trained models, the standard sparse fine-tuning techniques often fail to generalize well on downstream tasks, which have limited data resources. To address such an issue in a principled manner, we introduce a new learning framework, called NxMTransformer, to induce NxM semi-structured sparsity on pretrained language models for natural language understanding to obtain better performance. In particular, we propose to formulate the NxM sparsity as a constrained optimization problem and use Alternating Direction Method of Multipliers (ADMM) to optimize the downstream tasks while taking the underlying hardware constraints into consideration. ADMM decomposes the NxM sparsification problem into two sub-problems that can be solved sequentially, generating sparsified Transformer networks that achieve high accuracy while being able to effectively execute on newly released hardware. We apply our approach to a wide range of NLP tasks, and our proposed method is able to achieve 1.7 points higher accuracy in GLUE score than current practices. Moreover, we perform detailed analysis on our approach and shed light on how ADMM affects fine-tuning accuracy for downstream tasks. Finally, we illustrate how NxMTransformer achieves performance improvement with knowledge distillation.
Multi-Dimensional Scaling on Groups

Leveraging the intrinsic symmetries in data for clear and efficient analysis is an important theme in signal processing and other data-driven sciences. A basic example of this is the ubiquity of the discrete Fourier transform which arises from translational symmetry (i.e. time-delay/phase-shift). Particularly important in this area is understanding how symmetries inform the algorithms that we apply to our data. In this paper we explore the behavior of the dimensionality reduction algorithm multi-dimensional scaling (MDS) in the presence of symmetry. We show that understanding the properties of the underlying symmetry group allows us to make strong statements about the output of MDS even before applying the algorithm itself. In analogy to Fourier theory, we show that in some cases only a handful of fundamental "frequencies" (irreducible representations derived from the corresponding group) contribute information for the MDS Euclidean embedding.
Atomic Data for Stellar Astrophysics: from the UV to the IR

The study of stars and stellar evolution relies heavily on the analysis of stellar spectra. The need for atomic line data from the ultraviolet (UV) to the infrared (lR) regions is greater now than ever. In the past twenty years, the time since the launch of the Hubble Space Telescope, great progress has been made in acquiring atomic data for UV transitions. The optical wavelength region, now expanded by progress in detector technology, continues to provide motivation for new atomic data. In addition, investments in new instrumentation for ground-based and space observatories has lead to the availability of high-quality spectra at IR wavelengths, where the need for atomic data is most critical. In this review, examples are provided of the progress made in generating atomic data for stellar studies, with a look to the future for addressing the accuracy and completeness of atomic data for anticipated needs.
Assessment of patient function after limb-sparing surgery

Cancer rehabilitation is becoming more of a focus for the field of physiatry due to increased longevity and the side effects of treatment. In order to investigate the rehabilitation needs of patients undergoing limb-sparing procedures, chart analysis was conducted on 17 children treated for primary bone tumors by resection and an expandable endoprosthetic replacement. Each patient underwent a course of postoperative inpatient and outpatient physical therapy and was followed over an average of 2.5 years. Gait training was relatively straightforward and in seven patients required neither orthosis nor ambulatory aid. The other ten patients walked with a knee orthosis, axillary crutches, or both. Until the time came for reoperation to lengthen the implant, a shoe lift of 1in maximum was added to compensate for the limb length discrepancy. These findings compare favorably with the more complex requirements of high proximal amputees with external prostheses, including more difficult gait training and the need for frequent adjustments, as well as prosthetic replacement as the children grow. It is clear that children undergoing limb-sparing surgery have special needs that should be addressed, including early mobilization, gait training, adjustment to repeated brief hospitalizations for lengthening, and continued follow-up to monitor their activity restriction.
Translimbal Laser Photocoagulation to the Trabecular Meshwork as a Model of Glaucoma in Rats

PURPOSE: To develop and characterize a model of pressure-induced optic neuropathy in rats. METHODS: Experimental glaucoma was induced unilaterally in 174 Wistar rats, using a diode laser with wavelength of 532 nm aimed at the trabecular meshwork and episcleral veins (combination treatment group) or only at the trabecular meshwork (trabecular group) through the external limbus. Intraocular pressure (IOP) was measured by a tonometer in rats under ketamine-xylazine anesthesia. Possible retinal vascular compromise was evaluated by repeated fundus examinations and by histology. The degree of retinal ganglion cell (RGC) loss was assessed by a masked, semiautomated counting of optic nerve axons. Effects of laser treatment on anterior ocular structures and retina were judged by light microscopy. RESULTS: After the laser treatment, IOP was increased in all eyes to higher than the normal mean IOP of 19.4 +/- 2.1 mm Hg (270 eyes). Peak IOP was 49.0 +/- 6.1 mm Hg (n = 108) in the combination group that was treated by a laser setting of 0.7 seconds and 0.4 W and 34.0 +/- 5.7 mm Hg (n = 46) in the trabecular group. Mean IOP after 6 weeks was 25.5 +/- 2.9 mm Hg in glaucomatous eyes in the combination group compared with 22.0 +/- 1.8 mm Hg in the trabecular group. IOP in the glaucomatous eyes was typically higher than in the control eyes for at least 3 weeks. In the combination group, RGC loss was 16.1% +/- 14.4% at 1 week (n = 8, P = 0.01), 59.7% +/- 25.7% at 6 weeks (n = 88, P < 0.001), and 70.9% +/- 23.6% at 9 weeks (n = 12, P < 0.001). The trabecular group had mean axonal loss of 19.1% +/- 14.0% at 3 weeks (n = 9, P = 0.004) and 24.3% +/- 20.2% at 6 weeks (n = 25, P < 0.001), increasing to 48.4% +/- 32.8% at 9 weeks (n = 12, P < 0.001). Laser treatment led to closure of intertrabecular spaces and the major outflow channel. The retina and choroid were normal by ophthalmoscopy at all times after treatment. Light microscopic examination showed only loss of RGCs and their nerve fibers. CONCLUSIONS: Increased IOP caused by a laser injury to the trabecular meshwork represents a useful and efficient model of experimental glaucoma in rats.