cssi/SciSciGPT-SciSciNet huggingface dataset:
How COVID-19 Have Changed Crowdfunding: Evidence From GoFundMe

While the long-term effects of COVID-19 are yet to be determined, its immediate impact on crowdfunding is nonetheless significant. This study takes a computational approach to more deeply comprehend this change. Using a unique data set of all the campaigns published over the past two years on GoFundMe, we explore the factors that have led to the successful funding of a crowdfunding project. In particular, we study a corpus of crowdfunded projects, analyzing cover images and other variables commonly present on crowdfunding sites. Furthermore, we construct a classifier and a regression model to assess the significance of features based on XGBoost. In addition, we employ counterfactual analysis to investigate the causality between features and the success of crowdfunding. More importantly, sentiment analysis and the paired sample t-test are performed to examine the differences in crowdfunding campaigns before and after the COVID-19 outbreak that started in March 2020. First, we note that there is significant racial disparity in crowdfunding success. Second, we find that sad emotion expressed through the campaign's description became significant after the COVID-19 outbreak. Considering all these factors, our findings shed light on the impact of COVID-19 on crowdfunding campaigns.
The Elastic Behavior of Vulcanized Rubber

Abstract As a consequence of the work described the author arrives at the conclusion that in the deformation range from 0 to 270 per cent only one elastic constant is needed for a full mathematical description, and that this constant is the modulus of shear defined for infinitely small deformations. If models could be made of rubber at less expense, this material could be used for the integration of the equations of elasticity. The solutions could be easily corrected for the lack of compressibility and for the change in the common law of elasticity.
Deep Conservation: A Latent-Dynamics Model for Exact Satisfaction of Physical Conservation Laws

This work proposes an approach for latent-dynamics learning that exactly enforces physical conservation laws. The method comprises two steps. First, the method computes a low-dimensional embedding of the high-dimensional dynamical-system state using deep convolutional autoencoders. This defines a low-dimensional nonlinear manifold on which the state is subsequently enforced to evolve. Second, the method defines a latent-dynamics model that associates with the solution to a constrained optimization problem. Here, the objective function is defined as the sum of squares of conservation-law violations over control volumes within a finite-volume discretization of the problem; nonlinear equality constraints explicitly enforce conservation over prescribed subdomains of the problem. Under modest conditions, the resulting dynamics model guarantees that the time-evolution of the latent state exactly satisfies conservation laws over the prescribed subdomains.
Substructure Substitution: Structured Data Augmentation for NLP

We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks. SUB2 generates new examples by substituting substructures (e.g., subtrees or subsequences) with ones with the same label, which can be applied to many structured NLP tasks such as part-of-speech tagging and parsing. For more general tasks (e.g., text classification) which do not have explicitly annotated substructures, we present variations of SUB2 based on constituency parse trees, introducing structure-aware data augmentation methods to general NLP tasks. For most cases, training with the augmented dataset by SUB2 achieves better performance than training with the original training set. Further experiments show that SUB2 has more consistent performance than other investigated augmentation methods, across different tasks and sizes of the seed dataset.
Synthesis and Evaluation of Biotinylated Bivalent HistoBlood Group Antigens for Capturing Human Noroviruses

A panel of biotinylated bivalent H-type glycans that have been reported as binding ligands for human noroviruses were synthesized using a modular synthetic strategy. These glycoconjugates were attached to streptavidin-coated magnetic beads and used to recover human norovirus from fecal samples using a magnetic bead-based assay. The biotinylated bivalent glycans synthesized for this study exhibited similar or better capturing ability when compared to commercial biotinylated glycopolymers.
Could you give me a hint ? Generating inference graphs for defeasible reasoning

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. A commonly used method in cognitive science and logic literature is to handcraft argumentation supporting inference graphs. While humans find inference graphs very useful for reasoning, constructing them at scale is difficult. In this paper, we automatically generate such inference graphs through transfer learning from another NLP task that shares the kind of reasoning that inference graphs support. Through automated metrics and human evaluation, we find that our method generates meaningful graphs for the defeasible inference task. Human accuracy on this task improves by 20% by consulting the generated graphs. Our findings open up exciting new research avenues for cases where machine reasoning can help human reasoning. (A dataset of 230,000 influence graphs for each defeasible query is located at: this https URL.)
Opening band gaps of low-dimensional materials at the meta-GGA level of density functional approximations

The quasiparticle band structure can be properly described by Hedin's GW approximation (GW), at a high computational cost. For band gaps, semilocal density functionals up to the generalized gradient approximation (GGA) level cannot compete with the accuracy of hybrid-based approximations or GW. Meta-GGA density functionals with a strong dependence on the kinetic energy density ingredient can potentially give wider band gaps compared with GGAs. The recent TASK meta-GGA density functional from Aschebrock and K\"ummel [Phys. Rev. Research 1, 033082 (2019)], is constructed with an enhanced nonlocality in the generalized Kohn-Sham scheme and therefore harbors great opportunities for band gap prediction. Although this approximation was found to yield excellent band gaps of bulk solids, this accuracy cannot be straightforwardly transferred to low-dimensional materials. The reduced screening of these materials results in larger band gaps compared with their bulk counterparts, as an additional barrier to overcome. In this paper we demonstrate how the alteration of this functional affects the band gaps of monolayers and nanoribbons and present accurate band gaps competing with the revised Heyd-Scuseria-Ernzerhof (HSE06) approximation. In order to achieve this goal, we have modified the TASK functional (a) by changing the tight upper bound for one- or two-electron systems (${h}_{X}^{0}$) from 1.174 to 1.29 and (b) by changing the limit of the interpolation function ${f}_{X}(\ensuremath{\alpha}\ensuremath{\rightarrow}\ensuremath{\infty}$) of the TASK functional that interpolates the exchange enhancement factor ${F}_{X}(s,\ensuremath{\alpha})$ from $\ensuremath{\alpha}=0$ to 1. The resulting modified TASK (mTASK) was tested for various materials from three dimensions to two dimensions to one dimension (nanoribbons) and was compared with the results of the higher-level hybrid functional HSE06 or with the ${\mathrm{G}}_{0}{\mathrm{W}}_{0}$ approximation within many-body perturbation theory. We find that mTASK systematically improves the band gaps and band structures of two-dimensional (2D) and 1D systems, without significantly affecting the accuracy of the original TASK for the bulk 3D materials, when compared with the Perdew-Burke-Ernzerhof (PBE) GGA and the strongly constrained and appropriately normed (SCAN) meta-GGA. We further demonstrate the applicability of mTASK by assessing the band structures of transition metal dichalcogenide nanoribbons with respect to various bending curvatures.
An analytical and experimental investigation of flutter suppression via piezoelectric actuation

The feasibility of using piezoelectric material actuators to suppress flutter was investigated analytically and experimentally. For this purpose, a two-degree of freedom wind-tunnel model was designed, analyzed, and tested. A digital control law was designed and implemented based on a discretized model. Experiments consisted of open- and closed-loop flutter tests. It is shown that analytical predictions agree well with experimental results. The open-loop flutter velocity prediction was 3.5 percent conservative, while the closed-loop prediction was 7.6 percent conservative. Results of the analysis showed that the flutter velocity would be increased by 15.7 percent with the control law, while 20 percent improvement was observed experimentally.
Comparing the behavior of OpenMP Implementations with various Applications on two different Fujitsu A64FX platforms

The development of the A64FX processor by Fujitsu has been a massive innovation in vectorized processors and led to Fugaku: the current world’s fastest supercomputer. We use a variety of tools to analyze the behavior and performance of several OpenMP applications with different compilers, and how these applications scale on the different A64FX processors on clusters at Stony Brook University and RIKEN.
Next-Cycle Optimal Fuel Control for Cycle-to-Cycle Variability Reduction in EGR-Diluted Combustion

Dilute combustion using exhaust gas recirculation (EGR) is a cost-effective method for increasing engine efficiency. At high EGR levels, however, its efficiency benefits diminish as cycle-to-cycle variability (CCV) intensifies. In this simulation study, cycle-to-cycle fuel control was used to reduce CCV by injecting additional fuel in operating conditions with sporadic misfires and partial burns. An optimal control policy was proposed that utilizes 1) a physics-based model that tracks in-cylinder gas composition and 2) a one-step-ahead prediction of the combustion efficiency based on a kernel density estimator. The optimal solution, however, presents a tradeoff between the reduction in combustion CCV and the increase in fuel injection quantity required to stabilize the charge. Such a tradeoff can be adjusted by a single parameter embedded in the cost function. Simulation results indicated that combustion CCV can be reduced by as much as 65% by using at most 1% additional fuel. Although the control design presented here does not include fuel trim to maintain $A$ = 1 for three-way catalyst compatibility, it is envisioned that this approach would be implemented alongside such an external controller, and the theoretical contribution presented here provides a first insight into the feasibility of CCV control using fuel injection.
Stacked planar solid immersion mirror

A folded solid immersion mirror for heat assisted magnetic recording was designed. The key components were fabricated and tested. By separating the incident light and reflected light with a directional coupler, high numerical aperture focusing can be achieved with small side lobes and high energy efficiency.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Causal Markov Boundaries

Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model. In this paper, we focus on feature selection for post-intervention outcome prediction from pre-intervention variables. We are motivated by healthcare settings, where the goal is often to select the treatment that will maximize a specific patient's outcome; however, we do not have sufficient randomized control trial data to identify well the conditional treatment effect. We show how we can use observational data to improve feature selection and effect estimation in two cases: (a) when we know the causal graph, and (b) when we do not know the causal graph but have limited experimental data. Our paper extends the notion of Markov boundary to treatment-outcome pairs. We provide theoretical guarantees for the methods we introduce. In simulated data, we show that combining observational and experimental data improves feature selection and effect estimation.
Evaluation of substrate composition and exogenous hormone application on vegetative propagule rooting success of essential oil hemp (Cannabis sativa L.).

To support the rapidly expanding industrial hemp industry, a commercial supply of high-quality starter plants with low genetic variability from nurseries will be key to consistent and efficient cultivation efforts. Rooting success was evaluated across four propagation medias, five rooting hormones, and eight commercially available high-cannabidiol (CBD) essential oil hemp cultivars. Cuttings were placed in a climate-controlled room and assessed for rooting success 12 days after cloning. Rooting success was determined by quantifying total root number, cumulative total root length, and total root mass. Propagation media had the greatest effect on rooting success (13–80%). Rockwool had the highest rooting success resulting in 10-fold increases in rooting traits over the next highest scoring medium (Berger BM6). Hormone applications significantly improved (15- to 18-fold) rooting success compared to no hormone application, while non-statistical differences were observed across auxin hormone concentrations and application methods. Genetic variation in rooting response was observed between cultivars with ‘Cherry Wine’ outperforming all other cultivars with an approximate 20% increase in rooting success over the next highest rooting cultivar, ‘Wife’. Although the ideal combination was not specifically identified in this study, findings provide insight into how rooting hormone application and medium selection impact vegetative propagule rooting success of essential oil hemp.
Slow expanders invade by forming dented fronts in microbial colonies

Most organisms grow in space, whether they are viruses spreading within a host tissue or invasive species colonizing a new continent. Evolution typically selects for higher expansion rates during spatial growth, but it has been suggested that slower expanders can take over under certain conditions. Here, we report an experimental observation of such population dynamics. We demonstrate that the slower mutants win not only when the two types are intermixed at the front but also when they are spatially segregated into sectors. The latter was thought to be impossible because previous studies focused exclusively on the global competitions mediated by expansion velocities but overlooked the local competitions at sector boundaries. We developed a theory of sector geometry that accounts for both local and global competitions and describes all possible sector shapes. In particular, the theory predicted that a slower, but more competitive, mutant forms a dented V-shaped sector as it takes over the expansion front. Such sectors were indeed observed experimentally and their shapes matched up quantitatively with the theory. In simulations, we further explored several mechanism that could provide slow expanders with a local competitive advantage and showed that they are all well-described by our theory. Taken together, our results shed light on previously unexplored outcomes of spatial competition and establish a universal framework to understand evolutionary and ecological dynamics in expanding populations. SignificanceLiving organisms never cease to evolve, so there is a significant interest in predicting and controlling evolution in all branches of life sciences from medicine to agriculture. The most basic question is whether a trait should increase or decrease in a given environment. The answer seems to be trivial for traits such as the growth rate in a bioreactor or the expansion rate of a tumor. Yet, it has been suggested that such traits can decrease rather than increase during evolution. Here, we report a mutant that outcompeted the ancestor despite having a slower expansion velocity. To explain this observation, we developed and validated a theory that describes spatial competition between organisms with different expansion rates and arbitrary competitive interactions.
Recovery of high-field GaAs photoconductive semiconductor switches

Three categories of circuits were explored to induce fast recovery, after lock-on by temporarily reducing the field across the switch. Measurements of recovery times from 35 to 80 ns, multiple monopolar and bipolar bursts at 5-40 MHz, and hold-off fields ranging from 5-44 kV/cm (corresponding to 15-66 kV across individual switches) are presented. A comparison is made of the different circuits used to induce recovery from lock-on, and the various factors that influence the recovery are discussed. >
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace

The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices-the multiple adjacency spectral embedding-leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.
Hospital nurses' moral distress and mental health during COVID-19.

Aims To explore factors associated with nurses' moral distress during the first COVID-19 surge and their longer-term mental health. Design Cross-sectional, correlational survey study. Methods Registered nurses were surveyed in September 2020 about their experiences during the first peak month of COVID-19 using the new, validated, COVID-19 Moral Distress Scale for Nurses. Nurses' mental health was measured by recently experienced symptoms. Analyses included descriptive statistics and regression analysis. Outcome variables were moral distress and mental health. Explanatory variables were frequency of COVID-19 patients, leadership communication and personal protective equipment/cleaning supplies access. The sample comprised 307 nurses (43% response rate) from two academic medical centres. Results Many respondents had difficulty accessing personal protective equipment. Most nurses reported that hospital leadership communication was transparent, effective and timely. The most distressing situations were the transmission risk to nurses' family members, caring for patients without family members present, and caring for patients dying without family or clergy present. These occurred occasionally with moderate distress. Nurses reported 2.5 days each in the past week of feeling anxiety, withdrawn and having difficulty sleeping. Moral distress decreased with effective communication and access to personal protective equipment. Moral distress was associated with longer-term mental health. Conclusion Pandemic patient care situations are the greatest sources of nurses' moral distress. Effective leadership communication, fewer COVID-19 patients, and access to protective equipment decrease moral distress, which influences longer-term mental health. Impact Little was known about the impact of COVID-19 on nurses' moral distress. We found that nurses' moral distress was associated with the volume of care for infected patients, access to personal protective equipment, and communication from leaders. We found that moral distress was associated with longer-term mental health. Leaders should communicate transparently to decrease nurses' moral distress and the negative effects of global crises on nurses' longer-term mental health.
Perception of soft materials relies on physics-based object representations: Behavioral and computational evidence

Author(s): Bi, Wenyan; Shah, Aalap D; Wong, Kimberly W; Scholl, Brian; Yildirim, Ilker | Abstract: When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.
THE GARCETTI VIRUS

In an era where corporate malfeasance has imposed staggering costs on society, ranging from the largest oil spill in recorded history to the largest government bailout of Wall Street, one would think that those who uncover corporate wrongdoing before it causes significant harm should receive awards. Employees are particularly well-placed to uncover such wrongdoing within companies. However, rather than reward these employees, employers tend to fire or marginalize them. While there are statutory protections for whistleblowers, a disturbing new trend appears to be developing: courts are excluding from the protection of whistleblowing statutes employees who report wrongdoing as part of their jobs. Under this doctrine, the internal safety inspector who uncovers illegal behavior while performing his duties and reports it to his boss can legally be fired for blowing the whistle.This Article explains how this doctrine, the job duties exclusion, was developed by the Federal Circuit over a decade ago to limit claims brought by federal employees under the federal Whistleblower Protection Act. Flawed at its inception, the doctrine languished, with no states adopting it until the Supreme Court’s decision in Garcetti v. Ceballos. In Garcetti, the Court applied the job duties exclusion to a claim brought under the First Amendment. Even though Garcetti involved a constitutional, not a statutory, claim, the decision has given new life to the doctrine developed by the Federal Circuit. Since Garcetti, courts have begun applying the job duties exclusion to state statutory whistleblower claims, placing protections for employees at grave risk. This Article examines this developing trend, explaining how Garcetti has had an impact on what is fundamentally a state statutory interpretation issue. It identifies the flaws of the job duties exclusion as first articulated, the reasons why state courts should not apply it to state whistleblower protection statutes, and recommends that legislatures amend statutory protections to ensure that employees who do the right thing and report corporate malfeasance are protected against retaliation.
Insulin-like growth factor-2 does not improve behavioral deficits in mouse and rat models of Angelman Syndrome.

Author(s): Berg, Elizabeth L; Petkova, Stela P; Born, Heather A; Adhikari, Anna; Anderson, Anne E; Silverman, Jill L | Abstract: BackgroundAngelman Syndrome (AS) is a rare neurodevelopmental disorder for which there is currently no cure or effective therapeutic. Since the genetic cause of AS is known to be dysfunctional expression of the maternal allele of ubiquitin protein ligase E3A (UBE3A), several genetic animal models of AS have been developed. Both the Ube3a maternal deletion mouse and rat models of AS reliably demonstrate behavioral phenotypes of relevance to AS and therefore offer suitable in vivo systems in which to test potential therapeutics. One promising candidate treatment is insulin-like growth factor-2 (IGF-2), which has recently been shown to ameliorate behavioral deficits in the mouse model of AS and improve cognitive abilities across model systems.MethodsWe used both the Ube3a maternal deletion mouse and rat models of AS to evaluate the ability of IGF-2 to improve electrophysiological and behavioral outcomes.ResultsAcute systemic administration of IGF-2 had an effect on electrophysiological activity in the brain and on a metric of motor ability; however the effects were not enduring or extensive. Additional metrics of motor behavior, learning, ambulation, and coordination were unaffected and IGF-2 did not improve social communication, seizure threshold, or cognition.LimitationsThe generalizability of these results to humans is difficult to predict and it remains possible that dosing schemes (i.e., chronic or subchronic dosing), routes, and/or post-treatment intervals other than that used herein may show more efficacy.ConclusionsDespite a few observed effects of IGF-2, our results taken together indicate that IGF-2 treatment does not profoundly improve behavioral deficits in mouse or rat models of AS. These findings shed cautionary light on the potential utility of acute systemic IGF-2 administration in the treatment of AS.
Visual Relationship Detection Using Part-and-Sum Transformers With Composite Queries

Computer vision applications such as visual relationship detection and human object interaction can be formulated as a composite (structured) set detection problem in which both the parts (subject, object, and predicate) and the sum (triplet as a whole) are to be detected in a hierarchical fashion. In this paper, we present a new approach, denoted Part-and-Sum detection Transformer (PST), to perform end-to-end visual composite set detection. Different from existing Transformers in which queries are at a single level, we simultaneously model the joint part and sum hypotheses/interactions with composite queries and attention modules. We explicitly incorporate sum queries to enable better modeling of the part-and-sum relations that are absent in the standard Transformers. Our approach also uses novel tensor-based part queries and vector-based sum queries, and models their joint interaction. We report experiments on two vision tasks, visual relationship detection and human object interaction and demonstrate that PST achieves state of the art results among single-stage models, while nearly matching the results of custom designed two-stage models.
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting

Predicting accurate future trajectories of multiple agents is essential for autonomous systems, but is challenging due to the complex agent interaction and the uncertainty in each agent's future behavior. Forecasting multi-agent trajectories requires modeling two key dimensions: (1) time dimension, where we model the influence of past agent states over future states; (2) social dimension, where we model how the state of each agent affects others. Most prior methods model these two dimensions separately; e.g., first using a temporal model to summarize features over time for each agent independently and then modeling the interaction of the summarized features with a social model. This approach is suboptimal since independent feature encoding over either the time or social dimension can result in a loss of information. Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time. To this end, we propose a new Transformer, AgentFormer, that jointly models the time and social dimensions. The model leverages a sequence representation of multi-agent trajectories by flattening trajectory features across time and agents. Since standard attention operations disregard the agent identity of each element in the sequence, AgentFormer uses a novel agent-aware attention mechanism that preserves agent identities by attending to elements of the same agent differently than elements of other agents. Based on AgentFormer, we propose a stochastic multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep when inferring an agent's future position. The latent intent of all agents is also jointly modeled, allowing the stochasticity in one agent's behavior to affect other agents. Our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
Approximation of semiclassical expectation values by symplectic Gaussian wave packet dynamics

This paper concerns an approximation of the expectation values of the position and momentum of the solution to the semiclassical Schrodinger equation with a Gaussian as the initial condition. Of particular interest is the approximation obtained by our symplectic/Hamiltonian formulation of the Gaussian wave packet dynamics that introduces a correction term to the conventional formulation using the classical Hamiltonian system by Hagedorn and others. The main result is a proof that our formulation gives a higher-order approximation than the classical formulation does to the expectation value dynamics under certain conditions on the potential function. Specifically, as the semiclassical parameter $$\varepsilon $$ approaches 0, our dynamics gives an $$O(\varepsilon ^{3/2})$$ approximation of the expectation value dynamics, whereas the classical one gives an $$O(\varepsilon )$$ approximation.
A weight initialization based on the linear product structure for neural networks

Abstract Weight initialization plays an important role in training neural networks and also affects tremendous deep learning applications. Various weight initialization strategies have already been developed for different activation functions with different neural networks. These initialization algorithms are based on minimizing the variance of the parameters between layers and might still fail when neural networks are deep, e.g., dying ReLU. To address this challenge, we study neural networks from a nonlinear computation point of view and propose a novel weight initialization strategy that is based on the linear product structure (LPS) of neural networks. The proposed strategy is derived from the polynomial approximation of activation functions by using theories of numerical algebraic geometry to guarantee to find all the local minima. We also provide a theoretical analysis that the LPS initialization has a lower probability of dying ReLU comparing to other existing initialization strategies. Finally, we test the LPS initialization algorithm on both fully connected neural networks and convolutional neural networks to show its feasibility, efficiency, and robustness on public datasets.
K-fiber bundles in the mitotic spindle are mechanically reinforced by Kif15.

The mammalian kinetochore fiber (k-fiber) connects chromosomes to the spindle and supports segregation. We cut k-fibers by laser ablation to probe mechanical connections of microtubules within the ...
Optimized hip-knee-ankle exoskeleton assistance at a range of walking speeds.

Background Autonomous exoskeletons will need to be useful at a variety of walking speeds, but it is unclear how optimal hip-knee-ankle exoskeleton assistance should change with speed. Biological joint moments tend to increase with speed, and in some cases, optimized ankle exoskeleton torques follow a similar trend. Ideal hip-knee-ankle exoskeleton torque may also increase with speed. The purpose of this study was to characterize the relationship between walking speed, optimal hip-knee-ankle exoskeleton assistance, and the benefits to metabolic energy cost. Methods We optimized hip-knee-ankle exoskeleton assistance to reduce metabolic cost for three able-bodied participants walking at 1.0 m/s, 1.25 m/s and 1.5 m/s. We measured metabolic cost, muscle activity, exoskeleton assistance and kinematics. We performed Friedman's tests to analyze trends across walking speeds and paired t-tests to determine if changes from the unassisted conditions to the assisted conditions were significant. Results Exoskeleton assistance reduced the metabolic cost of walking compared to wearing the exoskeleton with no torque applied by 26%, 47% and 50% at 1.0, 1.25 and 1.5 m/s, respectively. For all three participants, optimized exoskeleton ankle torque was the smallest for slow walking, while hip and knee torque changed slightly with speed in ways that varied across participants. Total applied positive power increased with speed for all three participants, largely due to increased joint velocities, which consistently increased with speed. Conclusions Exoskeleton assistance is effective at a range of speeds and is most effective at medium and fast walking speeds. Exoskeleton assistance was less effective for slow walking, which may explain the limited success in reducing metabolic cost for patient populations through exoskeleton assistance. Exoskeleton designers may have more success when targeting activities and groups with faster walking speeds. Speed-related changes in optimized exoskeleton assistance varied by participant, indicating either the benefit of participant-specific tuning or that a wide variety of torque profiles are similarly effective.
Small Propeller and Rotor Testing Capabilities of the NASA Langley Low Speed Aeroacoustic Wind Tunnel

The Low Speed Aeroacoustic Wind Tunnel (LSAWT) at NASA Langley Research Center has recently undergone a configuration change. This change incorporates an inlet nozzle extension meant to serve the dual purposes of achieving lower free-stream velocities as well as a larger core flow region. The LSAWT, part of the NASA Langley Jet Noise Laboratory, had historically been utilized to simulate realistic forward flight conditions of commercial and military aircraft engines in an anechoic environment. The facility was modified starting in 2016 in order to expand its capabilities for the aerodynamic and acoustic testing of small propeller and unmanned aircraft system (UAS) rotor configurations. This paper describes the modifications made to the facility, its current aerodynamic and acoustic capabilities, the propeller and UAS rotor-vehicle configurations to be tested, and some preliminary predictions and experimental data for isolated propeller and UAS rotor con figurations, respectively. Isolated propeller simulations have been performed spanning a range of advance ratios to identify the theoretical propeller operational limits of the LSAWT. Performance and acoustic measurements of an isolated UAS rotor in hover conditions are found to compare favorably with previously measured data in an anechoic chamber and blade element-based acoustic predictions.
Strong Bidentate Coordination for Surface Passivation and Ligand-Shell Engineering of Lead Halide Perovskite Nanocrystals in the Strongly Quantum-Confined Regime

The surface of lead halide perovskite nanocrystals (PNCs) is unique compared to conventional metal chalcogenide or pnictogenide semiconductor nanoparticles for its ionic character and the dynamic ligand layer, which makes them unstable in stock solutions and hinders the development of surface engineering strategies. This work employs a chelating strategy to form stable coordination on the PNC surface. Through screening a series of heterocyclic aromatic carboxylates, we found the best ligand, picolinate (PIC), with exceptional passivation effect to the surface traps of CsPbBr3 PNCs in the strongly quantum confined regime, resulting in > 0.8 photoluminescence quantum yields. The exciton lifetime in the passivated PNC approaches the radiative decay limit in various solvents. From an NMR titration experiment, the binding affinity of PIC is estimated to be at least 15 to 30 folds stronger than the original ligand from synthesis. The NMR and FTIR spectroscopic data and first-principles calculations elucidate the bidentate nature of the PIC coordination at the surface Pb site and the coadsorption of the ammonium-PIC ion pair. In apolar solvents, such as cyclohexane, the binding of PIC is stoichiometric to the available surface sites, suggesting the structure as a potent candidate for anchoring functional molecular structures to the PNC surface. In polar solvents, the strong affinity of PIC on the PNC surface provides protection for carrying out the precipitation-redissolution purification procedure that removes synthetic residual from the as-synthetic PNC samples. By modifying the purification procedure, we also develop a cation exchange procedure to replace the original oleylammonium cation with desired structures that consist of an ammonium anchoring group. Our results provide a direction for constructing strong interactions to protect the vulnerable surface of PNCs and pave the road for developing surface engineering strategies to functionalize these nanoparticles.
Effects of surface treatments on flux tunable transmon qubits

One of the main limitations in state-of-the art solid-state quantum processors is qubit decoherence and relaxation due to noise from adsorbates on surfaces, impurities at interfaces, and material defects. For the field to advance towards full fault-tolerant quantum computing, a better understanding of these microscopic noise sources is therefore needed. Here, we use an ultra-high vacuum package to study the impact of vacuum loading, UV-light exposure, and ion irradiation treatments on relaxation and coherence times, as well as slow parameter fluctuations of flux tunable superconducting transmon qubits. The treatments studied do not significantly impact the relaxation rate Γ1 and the echo decay rate $${{{\Gamma }}}_{2,{{{\rm{SS}}}}}^{{{{\rm{e}}}}}$$ at the sweet spot, except for Ne ion bombardment which reduces Γ1. In contrast, flux noise parameters are improved by removing magnetic adsorbates from the chip surfaces with UV-light and NH3 treatments. Additionally, we demonstrate that SF6 ion bombardment can be used to adjust qubit frequencies in situ and post-fabrication without affecting qubit relaxation and coherence times at the sweet spot.
Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction.

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.
The modes and competing rates of cartilage fluid loss and recovery.

Abstract Cartilage loses, recovers, and maintains its thickness, hydration, and biomechanical functions based on competing rates of fluid loss and recovery under varying joint-use conditions. While the mechanics and implications of load-induced fluid loss have been studied extensively, those of fluid recovery have not. This study isolates, quantifies, and compares rates of cartilage recovery from three known modes: (1) passive swelling – fluid recovery within a static unloaded contact area; (2) free swelling – unrestricted fluid recovery by an exposed surface; (3) tribological rehydration – fluid recovery within a loaded contact area during sliding. Following static loading of adult bovine articular cartilage to between 100 and 500 μm of compression, passive swelling, free swelling, and tribological rehydration exhibited average rates of 0.11 ± 0.04, 0.71 ± 0.15, and 0.63 ± 0.22 μm/s, respectively, over the first 100 seconds of recovery; for comparison, the mean exudation rate just prior to sliding was 0.06 ± 0.04 μm/s. For this range of compressions, we detected no significant difference between free swelling and tribological rehydration rates. However, free swelling and tribological rehydration rates, those associated with joint articulation, were ∼7-fold faster than passive swelling rates. While previous studies show how joint articulation prevents fluid loss indefinitely, this study shows that joint articulation reverses fluid loss following static loading at >10-fold the preceding exudation rate. These competitive recovery rates suggest that joint space and function may be best maintained throughout an otherwise sedentary day using brief but regular physical activity. Statement of Significance : Cartilage loses, recovers, and maintains its thickness, hydration, and biomechanical functions based on competing rates of fluid loss and recovery under varying joint-use conditions. While load-induced fluid loss is extremely well studied, this is the first to define the competing modes of fluid recovery and to quantify their rates. The results show that the fluid recovery modes associated with joint articulation are 10-fold faster than exudation during static loading and passive swelling during static unloading. The results suggest that joint space and function are best maintained throughout an otherwise sedentary day using brief but regular physical activities.
Adaptive Circuit Learning for Quantum Metrology

Quantum sensing is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use optimization techniques to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics.Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the "parameter-shift" rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate up to 13.12x SNR improvement over existing fixed protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over the classical limit on 15 qubits using IBM quantum computer. More notably, our algorithm overcomes the decreasing performance of existing entanglement-based protocols with increased system sizes.
Isolation of a cDNA encoding the B isozyme of human phosphoglycerate mutase (PGAM) and characterization of the PGAM gene family.

Abstract We previously reported the isolation of a full-length cDNA specifying the muscle-specific isozyme of human phosphoglycerate mutase (PGAM-M). We now report the isolation of a full-length cDNA specifying the non-muscle-specific, or brain (B), isozyme of human PGAM (PGAM-B). The PGAM-B cDNA encodes a deduced protein 254 amino acids long, 79% identical to PGAM-M, and contains a 913-nucleotide 3'-untranslated region, as compared to the unusually short 37-nucleotide 3'-untranslated region of PGAM-M. Northern analysis demonstrates the non-muscle-specific nature of PGAM-B transcription, while genomic Southern analysis implies the presence of a large PGAM family in the human genome. Most of the PGAM-hybridizing sequences in both the human and mouse genomes seem to be related to the B-isozyme gene; many members of the PGAM-B gene family in humans are apparently processed genes. These results agree with the evolutionary analysis, which indicates that the PGAM-B gene is the progenitor of the PGAM-M gene.
Giving Meaning to “Meaningful Enough”: Why Trevino Requires New Counsel on Appeal

Generally, defendants cannot raise new claims in a writ of habeas corpus unless they can accomplish the difficult task of showing that they could not have raised the claims earlier. In 2012, the U.S. Supreme Court laid out an equitable exception that allows defendants to claim—for the first time in a writ of habeas corpus—that they had an ineffective trial attorney if their failure to make a timely claim was due to a second ineffective attorney or no attorney whatsoever. The exception, however, only applied to defendants in states that required ineffective assistance claims to be brought in collateral proceedings, as opposed to allowing the claims on direct appeal. However, a year later, when faced with inequity in Texas, the Court broadened the exception, applying it to any state that does not provide a defendant with a meaningful opportunity to initially raise that claim, regardless of the forum they chose. In doing so, the Court neglected to explain how “a meaningful opportunity” should be measured. This Note seeks to provide that explanation, arguing that it must depend on whether a defendant is provided with a new, unconflicted attorney on appeal. If the same attorney represents a defendant at trial and on appeal, a defendant cannot meaningfully challenge his lawyer’s performance at trial. If a defendant does not receive new counsel on appeal, habeas courts should consider claims of ineffective assistance regardless of the procedural history of the case.