cssi/SciSciGPT-SciSciNet huggingface dataset:
Using force data to self-pace an instrumented treadmill and measure self-selected walking speed.

Self-selected speed is an important functional index of walking. A self-pacing controller that reliably matches walking speed without additional hardware can be useful for measuring self-selected speed in a treadmill-based laboratory. We adapted a previously proposed self-pacing controller for force-instrumented treadmills and validated its use for measuring self-selected speeds. We first evaluated the controller’s estimation of subject speed and position from the force-plates by comparing it to those from motion capture data. We then compared five tests of self-selected speed. Ten healthy adults completed a standard 10-meter walk test, a 150-meter walk test, a commonly used manual treadmill speed selection test, a two-minute self-paced treadmill test, and a 150-meter self-paced treadmill test. In each case, subjects were instructed to walk at or select their comfortable speed. We also assessed the time taken for a trial and a survey on comfort and ease of choosing a speed in all the tests. The self-pacing algorithm estimated subject speed and position accurately, with root mean square differences compared to motion capture of 0.023 m s −1 and 0.014 m, respectively. Self-selected speeds from both self-paced treadmill tests correlated well with those from the 10-meter walk test (R>0.93,p<1×10−13). Subjects walked slower on average in the self-paced treadmill tests (1.23±0.27 ms−1) than in the 10-meter walk test (1.32±0.18 ms−1) but the speed differences within subjects were consistent. These correlations and walking speeds are comparable to those from the manual treadmill speed selection test (R=0.89,p=3×10−11;1.18±0.24 ms−1). Comfort and ease of speed selection were similar in the self-paced tests and the manual speed selection test, but the self-paced tests required only about a third of the time to complete. Our results demonstrate that these self-paced treadmill tests can be a strong alternative to the commonly used manual treadmill speed selection test. The self-paced force-instrumented treadmill well adapts to subject walking speed and reliably measures self-selected walking speeds. We provide the self-pacing software to facilitate use by gait researchers and clinicians.
A Narrative Inquiry of Associate Degree Nursing Students' Stories About Their Experience of Academic Misconduct

Abstract Academic misconduct is a growing national and global concern. There is a paucity of literature on academic misconduct in nursing. Utilizing the qualitative method of narrative inquiry, this study gives voice to students’ stories of academic misconduct and reveals intimate stories of their experience. The stories contribute to a deeper understanding of associate degree nursing students' experiences with academic misconduct. The findings of the study have implications for future research, nursing education, and practice.
In Perfect Shape: Certifiably Optimal 3D Shape Reconstruction From 2D Landmarks

We study the problem of 3D shape reconstruction from 2D landmarks extracted in a single image. We adopt the 3D deformable shape model and formulate the reconstruction as a joint optimization of the camera pose and the linear shape parameters. Our first contribution is to apply Lasserre’s hierarchy of convex Sums-of-Squares (SOS) relaxations to solve the shape reconstruction problem and show that the SOS relaxation of minimum order 2 empirically solves the original non-convex problem exactly. Our second contribution is to exploit the structure of the polynomial in the objective function and find a reduced set of basis monomials for the SOS relaxation that significantly decreases the size of the resulting semidefinite program (SDP) without compromising its accuracy. These two contributions, to the best of our knowledge, lead to the first certifiably optimal solver for 3D shape reconstruction, that we name Shape*. Our third contribution is to add an outlier rejection layer to Shape⋆ using a truncated least squares (TLS) robust cost function and leveraging graduated non-convexity to solve TLS without initialization. The result is a robust reconstruction algorithm, named Shape#, that tolerates a large amount of outlier measurements. We evaluate the performance of Shape⋆ and Shape# in both simulated and real experiments, showing that Shape⋆ outperforms local optimization and previous convex relaxation techniques, while Shape# achieves state-of-the-art performance and is robust against 70% outliers in the FG3DCar dataset.
Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance

Real-world applications often involve domain-specific and task-based performance objectives that are not captured by the standard machine learning losses, but are critical for decision making. A key challenge for direct integration of more meaningful domain and task-based evaluation criteria into an end-to-end gradient-based training process is the fact that often such performance objectives are not necessarily differentiable and may even require additional decision-making optimization processing. We propose the Task-Oriented Prediction Network (TOPNet), an end-to-end learning scheme that automatically integrates task-based evaluation criteria into the learning process via a learnable surrogate loss function, which directly guides the model towards the task-based goal. A major benefit of the proposed TOPNet learning scheme lies in its capability of automatically integrating non-differentiable evaluation criteria, which makes it particularly suitable for diversified and customized task-based evaluation criteria in real-world tasks. We validate the performance of TOPNet on two real-world financial prediction tasks, revenue surprise forecasting and credit risk modeling. The experimental results demonstrate that TOPNet significantly outperforms both traditional modeling with standard losses and modeling with hand-crafted heuristic differentiable surrogate losses.
Learning to Segment Actions from Observation and Narration

We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.
A Girl Has A Name: Detecting Authorship Obfuscation

Authorship attribution aims to identify the author of a text based on the stylometric analysis. Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text’s style. In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model. An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated – a decision that is key to the adversary interested in authorship attribution. We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87. The reason for the lack of stealthiness is that these obfuscators degrade text smoothness, as ascertained by neural language models, in a detectable manner. Our results highlight the need to develop stealthy authorship obfuscation methods that can better protect the identity of an author seeking anonymity.
Transient Primary Cilia Mediate Robust Hedgehog Pathway-Dependent Cell Cycle Control

The regulation of proliferation is a primary function of Hedgehog (Hh) signaling in development. Hh signal transduction requires the primary cilium for several steps in the pathway [1-5]. Many cells only build a primary cilium upon cell cycle exit, in G0. In those proliferating cells that do make a cilium, it is a transient organelle, being assembled in G1 and disassembled sometime prior to mitosis [6-9]. Thus, the requirement for primary cilia presents a conundrum: how are proliferative signals conveyed through an organelle that is present for only part of the cell cycle? Here, we investigate this question in a mouse medulloblastoma cell line, SMB55, that requires cilium-mediated Hh pathway activity for proliferation [10]. We show that SMB55 cells, and the primary cerebellar granule neuron precursors (GNPs) from which they derive, are often ciliated beyond G1 into S phase, and the presence of the cilium in SMB55 cells determines the periods of Hh pathway activity. Using live imaging over multiple cell cycles, we demonstrate that Hh pathway activity in either G1-S of the previous cell cycle or G1 of the cell cycle in which the decision is made is sufficient for cell cycle entry. We also show that cyclin D1 contributes to the persistent effects of pathway activity over multiple cell cycles. Together, our results reveal that, even though the signaling organelle itself is transient, Hh pathway control of proliferation is remarkably robust. Further, primary cilium transience may have implications for other Hh-mediated events in development.
On Age and Value of Information in Status Update Systems

Motivated by the inherent value of packets arising in many cyber-physical applications (e.g., due to precision of the information content or an alarm message), we consider status update systems with update packets carrying values as well as their generation time stamps. Once generated, a status update packet has a random initial value and a deterministic deadline after which it is not useful (ultimate staleness). In our model, value of a packet decreases in time (even after reception) starting from its generation to ultimate staleness when it vanishes. The value of information (VoI) at the receiver is additive in that the VoI is the sum of the current values of all packets held by the receiver. We investigate various queuing disciplines under potential dependence between value and service time and provide closed form expressions for average VoI at the receiver. Numerical results illustrate the average VoI for different scenarios and the contrast between average age of information (AoI) and average VoI.
Differential SP1 interactions in SV40 chromatin from virions and minichromosomes.

Abstract SP1 binding in SV40 chromatin in vitro and in vivo was characterized in order to better understand its role during the initiation of early transcription. We observed that chromatin from disrupted virions, but not minichromosomes, was efficiently bound by HIS-tagged SP1 in vitro, while the opposite was true for the presence of endogenous SP1 introduced in vivo. Using ChIP-Seq to compare the location of SP1 to nucleosomes carrying modified histones, we found that SP1 could occupy its whole binding site in virion chromatin but only the early side of its binding site in most of the minichromosomes carrying modified histones due to the presence of overlapping nucleosomes. The results suggest that during the initiation of an SV40 infection, SP1 binds to an open region in SV40 virion chromatin but quickly triggers chromatin reorganization and its own removal.
Decision Problems in Information Theory

Constraints on entropies are considered to be the laws of information theory. Even though the pursuit of their discovery has been a central theme of research in information theory, the algorithmic aspects of constraints on entropies remain largely unexplored. Here, we initiate an investigation of decision problems about constraints on entropies by placing several different such problems into levels of the arithmetical hierarchy. We establish the following results on checking the validity over all almost-entropic functions: first, validity of a Boolean information constraint arising from a monotone Boolean formula is co-recursively enumerable; second, validity of "tight" conditional information constraints is in Π⁰₃. Furthermore, under some restrictions, validity of conditional information constraints "with slack" is in Σ⁰₂, and validity of information inequality constraints involving max is Turing equivalent to validity of information inequality constraints (with no max involved). We also prove that the classical implication problem for conditional independence statements is co-recursively enumerable.
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks—Part I: Single-Layer Dynamics and Selective Inhibition

Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control theory as well as insights and structures from neuroscience. Central to this framework is an information-processing hierarchy with two main components: selective inhibition of task-irrelevant activity and top-down recruitment of task-relevant activity. We analyze the internal dynamics of each layer of the hierarchy described as a network with linear-threshold dynamics and derive conditions on its structure to guarantee existence and uniqueness of equilibria, asymptotic stability, and boundedness of trajectories. We also provide mechanisms that enforce selective inhibition using the biologically inspired schemes of feedforward and feedback inhibition. Despite their differences, both lead to the same conclusion: the intrinsic dynamical properties of the (not-inhibited) task-relevant subnetworks are the sole determiner of the dynamical properties that are achievable under selective inhibition.
Rationing the Commons

Common resources may be managed with inefficient policies for the sake of equity. We study how rationing the commons shapes the efficiency and equity of resource use in the context of agricultural ...
Venture Capital’s Role in Financing Innovation: What We Know and How Much We Still Need to Learn

Venture capital is associated with some of the most high-growth and influential firms in the world. Academics and practitioners have effectively articulated the strengths of the venture model. At the same time, venture capital financing also has real limitations in its ability to advance substantial technological change. Three issues are particularly concerning to us: 1) the very narrow band of technological innovations that fit the requirements of institutional venture capital investors; 2) the relatively small number of venture capital investors who hold and shape the direction of a substantial fraction of capital that is deployed into financing radical technological change; and 3) the relaxation in recent years of the intense emphasis on corporate governance by venture capital firms. While our ability to assess the social welfare impact of venture capital remains nascent, we hope that this article will stimulate discussion of and research into these questions.
A homogenization theorem for Langevin systems with an application to Hamiltonian dynamics

This paper studies homogenization of stochastic differential systems. The standard example of this phenomenon is the small mass limit of Hamiltonian systems. We consider this case first from the heuristic point of view, stressing the role of detailed balance and presenting the heuristics based on a multiscale expansion. This is used to propose a physical interpretation of recent results by the authors, as well as to motivate a new theorem proven here. Its main content is a sufficient condition, expressed in terms of solvability of an associated partial differential equation (“the cell problem”), under which the homogenization limit of an SDE is calculated explicitly. The general theorem is applied to a class of systems, satisfying a generalized detailed balance condition with a position-dependent temperature.
Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions

In this paper, we present a data driven approach for approximating dynamical systems. A system of governing equations is approximated using basis functions, which are derived from maximization of the information-theoretic entropy, and can be generated directly from the data provided. This approach has advantages over other methods, where a dictionary of basis functions has to be provided by the user, which is non trivial in some applications. We compare the accuracy of the proposed data-driven modeling approach to existing methods in the literature, and demonstrate that for some applications, the maximum entropy basis functions provide significantly more accurate models.
Real-Time Context-Aware Detection of Unsafe Events in Robot-Assisted Surgery

Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.
Representation through Lived Experience: Expanding Representative Bureaucracy Theory

This study draws on the insights of managers in the behavioral health treatment system to explore the value of persons who bring lived experience to their organizational positions. Within these org...
Computing the Fréchet Gap Distance

Measuring the similarity of two polygonal curves is a fundamental computational task. Among alternatives, the Frechet distance is one of the most well-studied similarity measures. Informally, the Frechet distance is described as the minimum leash length required for a man on one of the curves to walk a dog on the other curve continuously from the starting to the ending points. In this paper we study a variant called the Frechet gap distance. In the man and dog analogy, the Frechet gap distance minimizes the difference of the longest and smallest leash lengths used over the entire walk. This measure in some ways better captures our intuitive notions of curve similarity, for example giving distance zero to translated copies of the same curve. The Frechet gap distance was originally introduced by Filtser and Katz [19] in the context of the discrete Frechet distance. Here we study the continuous version, which presents a number of additional challenges not present in discrete case. In particular, the continuous nature makes bounding and searching over the critical events a rather difficult task. For this problem we give an $$O(n^5\log n)$$ time exact algorithm and a more efficient $$O(n^2\log n +({n^2}/{{\varepsilon }})\log ({1}/{{\varepsilon }}))$$ time $$(1+{\varepsilon })$$ -approximation algorithm, where n is the total number of vertices of the input curves. Note that, ignoring logarithmic factors, for any constant $${\varepsilon }$$ our approximation has quadratic running time, matching the lower bound, assuming SETH [Bringmann, K.: Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails (2014)], for approximating the standard Frechet distance for general curves.
On the Rank-$1$ convex hull of a set arising from a hyperbolic system of Lagrangian elasticity

We address the questions (P1), (P2) asked in Kirchheim et al. (Studying nonlinear PDE by geometry in matrix space. Geometric analysis and nonlinear partial differential equations, Springer, Berlin, 1986) concerning the structure of the Rank-1 convex hull of a submanifold $$\mathcal {K}_1\subset M^{3\times 2}$$ that is related to weak solutions of the two by two system of Lagrangian equations of elasticity studied by DiPerna (Trans Am Math Soc 292(2):383–420, 1985) with one entropy augmented. This system serves as a model problem for higher order systems for which there are only finitely many entropies. The Rank-1 convex hull is of interest in the study of solutions via convex integration: the Rank-1 convex hull needs to be sufficiently non-trivial for convex integration to be possible. Such non-triviality is typically shown by embedding a $$\mathbb {T}_4$$ (Tartar square) into the set; see for example Muller et al. (Attainment results for the two-well problem by convex integration. Geometric analysis and the calculus of variations, Int. Press, Cambridge, 1996) and Muller and Sverak (Ann Math (2) 157(3):715–742, 2003). We show that in the strictly hyperbolic, genuinely nonlinear case considered by DiPerna (1985), no $$\mathbb {T}_4$$ configuration can be embedded into $$\mathcal {K}_1$$ .
Development of an Online 2D Ultrahigh-Pressure Nano-LC System for High-pH and Low-pH Reversed Phase Separation in Top-Down Proteomics

The development of novel high-resolution separation techniques is crucial for advancing the complex sample analysis necessary for high-throughput top-down proteomics. Recently, our group developed an offline 2D high-pH RPLC/low-pH RPLC separation method and demonstrated good orthogonality between these two RPLC formats. Specifically, ultrahigh-pressure long capillary column RPLC separation has been applied as the second dimensional low-pH RPLC separation for the improvement of separation resolution. To further improve the throughput and sensitivity of the offline approach, we developed an online 2D ultrahigh-pressure nano-LC system for high-pH and low-pH RPLC separations in top-down proteomics. An online microtrap column with a dilution setup was used to collect eluted proteins from the first dimension high-pH separation and inject the fractions for ultrahigh-pressure long capillary column low-pH RPLC separation in the second dimension. This automatic platform enables the characterization of 1000+ intact proteoforms from 5 μg of intact E. coli cell lysate in 10 online-collected fractions. Here, we have demonstrated that our online 2D pH RP/RPLC system coupled with top-down proteomics holds the potential for deep proteome characterization of mass-limited samples because it allows the identification of hundreds of intact proteoforms from complex biological samples at low microgram sample amounts.
Ligand-Controlled Regiodivergence in Nickel-Catalyzed Hydroarylation and Hydroalkenylation of Alkenyl Carboxylic Acids*.

A nickel-catalyzed regiodivergent hydroarylation and hydroalkenylation of unactivated alkenyl carboxylic acids is reported, whereby the ligand environment around the metal center dictates the regiochemical outcome. Markovnikov hydrofunctionalization products are obtained under mild ligand-free conditions, with up to 99 % yield and >20:1 selectivity. Alternatively, anti-Markovnikov products can be accessed with a novel 4,4-disubstituted Pyrox ligand in excellent yield and >20:1 selectivity. Both electronic and steric effects on the ligand contribute to the high yield and selectivity. Mechanistic studies suggest a change in the turnover-limiting and selectivity-determining step induced by the optimal ligand. DFT calculations reveal that in the anti-Markovnikov pathway, repulsion between the ligand and the alkyl group is minimized (by virtue of it being 1° versus 2°) in the rate- and regioselectivity-determining transmetalation transition state.
Inverse ratio ventilation combined with PEEP in infants undergoing thoracoscopic surgery with one lung ventilation for lung cystadenomas: a randomized control trial of 63 cases

Objective To investigate the effect of inverse ratio ventilation (IRV) combined with positive end-expiratory pressure (PEEP) in infants undergoing thoracoscopic surgery with single lung ventilation (OLV) for lung cystadenomas. Methods A total of 66 infants undergoing thoracoscopic surgery with OLV for lung cystadenomas in our hospital from February, 2018 to February, 2019 were randomized into conventional ventilation groups (group N, n=33) and inverse ventilation group (group R, n=33). Hemodynamics and respiratory parameters of the infants were recorded and arterial blood gas analysis was performed at 15 min after two lung ventilation (TLV) (T1), OLV30 min (T2), OLV60 min (T3), and 15 min after recovery of TLV (T4). Bronchoalveolar lavage fluid was collected before and after surgery to detect the expression level of advanced glycation end product receptor (RAGE). Results Sixty-three infants were finally included in this study. At T2 and T3, Cdyn, PaO2 and OI in group R were significantly higher (P l 0.05) and Ppeak, PaCO2 and PA-aO2 were significantly lower than those in group N (P l 0.05). There was no significant difference in HR or MAP between the two groups at T2 and T3 (P g 0.05). The level of RAGE significantly increased after the surgery in both groups (P l 0.05), and was significantly lower in R group than in N group (P l 0.05). Conclusions In infants undergoing thoracoscopic surgery with OLV for pulmonary cystadenoma, appropriate IRV combined with PEEP does not affect hemodynamic stability and can increases pulmonary compliance, reduce the peak pressure, and improve oxygenation to provide pulmonary protection.
Covariance Matrix Estimation under Total Positivity for Portfolio Selection

Selecting the optimal Markowitz porfolio depends on estimating the covariance matrix of the returns of $N$ assets from $T$ periods of historical data. Problematically, $N$ is typically of the same order as $T$, which makes the sample covariance matrix estimator perform poorly, both empirically and theoretically. While various other general purpose covariance matrix estimators have been introduced in the financial economics and statistics literature for dealing with the high dimensionality of this problem, we here propose an estimator that exploits the fact that assets are typically positively dependent. This is achieved by imposing that the joint distribution of returns be multivariate totally positive of order 2 ($\text{MTP}_2$). This constraint on the covariance matrix not only enforces positive dependence among the assets, but also regularizes the covariance matrix, leading to desirable statistical properties such as sparsity. Based on stock-market data spanning over thirty years, we show that estimating the covariance matrix under $\text{MTP}_2$ outperforms previous state-of-the-art methods including shrinkage estimators and factor models.
Ideal memristor based on viscous magnetization dynamics driven by spin torque

We show that ideal memristors—devices whose resistance is proportional to the charge that flows through them—can be realized using spin torque-driven viscous magnetization dynamics. The latter can be accomplished in the spin liquid state of thin-film heterostructures with frustrated exchange, where the memristive response is tunable by proximity to the glass transition, while current-induced Joule heating facilitates non-volatile operation and second-order memristive functionality beneficial for neuromorphic applications. Ideal memristive behaviors can be achieved in other systems characterized by viscous dynamics of physical, electronic, or magnetic degrees of freedom.
Design of a Heterogeneous Cellular Network With a Wireless Backhaul

The downlink of a two-layered heterogeneous network is studied with macro basestations (MBs), small-cell basestations (SBs) that act as half-duplex analog relays, and mobile terminals (MTs). The first layer is a wireless backhaul layer between MBs and SBs, and the second is the transmission layer between SBs and MTs. The layers use the same time/frequency resources for communication, limiting the maximum per user degrees of freedom (puDoF) to half, due to the half-duplex nature of the SBs. For linear network models, it is established that the optimal puDoF can be achieved by cooperation with an appropriate number of antennas that depends on the connectivity of the network. The proposed zero-forcing schemes achieve cooperation without overloading the backhaul, through each MB sending an appropriate linear combination of MTs’ message signals to the SBs in the backhaul layer. The achievable schemes exploit the half-duplexity of the SBs, and schedule the SBs and MTs to be active in different time-slots to manage interference. These results are then extended to a more realistic hexagonal cellular network and it is shown that the optimal puDoF of half can be approached using only zero-forcing schemes.
V-Speech: Noise-Robust Speech Capturing Glasses Using Vibration Sensors

Smart glasses are often used in noisy public spaces or industrial settings. Voice commands and automatic speech recognition (ASR) are good user interfaces for such a form factor, but the background noise and interfering speakers pose important challenges. Typical signal processing techniques have limitations in performance and/or hardware resources. V-Speech is a novel solution that captures the voice signal with a vibration sensor located in the nasal pads of smart glasses. Although signal-to-noise ratio (SNR) is much higher with vibration sensor capture, it introduces a "nasal distortion," which must be dealt with. The second part of our proposed solution involves a voice transformation of the vibration signal using a neural network to produce an output that mimics the characteristics of a conventional microphone. We evaluated V-Speech in noise-free and very noisy conditions with 30 volunteer speakers uttering 145 phrases each, and validated its performance on ASR engines, with assessments of voice quality using the Perceptual Evaluation of Speech Quality (PESQ) metric, and with subjective listeners to determine intelligibility, naturalness and overall quality. The results show, in extreme noise conditions, a mean improvement of 50% for Word Error Rate (WER), 1.0 on a scale of 5.0 for PESQ, and speech regarded intelligible, with naturalness rated as fair to good. The output of V-Speech has low noise, sounds natural, and enables clear voice communication in challenging environments.
Clinical Ordering Practices of the SARS-CoV-2 Antibody Test at a Large Academic Medical Center

BACKGROUND: The novel severe acute respiratory coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) originated in December 2019 and has now infected almost 5 million people in the United States In the spring of 2020, private laboratories and some hospitals began antibody testing despite limited evidence-based guidance METHODS: We conducted a retrospective chart review of patients who received SARS-CoV-2 antibody testing from May 14, 2020, to June 15, 2020, at a large academic medical center, 1 of the first in the United States to provide antibody testing capability to individual clinicians in order to identify clinician-described indications for antibody testing compared with current expert-based guidance from the Infectious Diseases Society of America (IDSA) and the Centers for Disease Control and Prevention (CDC) RESULTS: Of 444 individual antibody test results, the 2 most commonly described testing indications, apart from public health epidemiology studies (n = 223), were for patients with a now resolved COVID-19-compatible illness (n = 105) with no previous molecular testing and for asymptomatic patients believed to have had a past exposure to a person with COVID-19-compatible illness (n = 60) The rate of positive SARS-CoV-2 antibody testing among those indications consistent with current IDSA and CDC guidance was 17% compared with 5% (P < 0001) among those indications inconsistent with such guidance Testing inconsistent with current expert-based guidance accounted for almost half of testing costs CONCLUSIONS: Our findings demonstrate a dissociation between clinician-described indications for testing and expert-based guidance and a significantly different rate of positive testing between these 2 groups Clinical curiosity and patient preference appear to have played a significant role in testing decisions and substantially contributed to testing costs
Byzantine Ordered Consensus without Byzantine Oligarchy

The specific order of commands agreed upon when running state machine replication (SMR) is immaterial to fault-tolerance: all that is required is for all correct deterministic replicas to follow it. In the permissioned blockchains that rely on Byzantine fault tolerant (BFT) SMR, however, nodes have a stake in the specific sequence that ledger records, as well as in preventing other parties from manipulating the sequencing to their advantage. The traditional specification of SMR correctness, however, has no language to express these concerns. This paper introduces Byzantine ordered consensus, a new primitive that augments the correctness specification of BFT SMR to include specific guarantees on the total orders it produces; and a new architecture for BFT SMR that, by factoring out ordering from consensus, can enforce these guarantees and prevent Byzantine nodes from controlling ordering decisions (a Byzantine oligarchy). These contributions are instantiated in Pompe, a BFT SMR protocol that is guaranteed to order commands in a way that respects a natural extension of linearizability.
Diffuse Interplanetary Radio Emission from a Polar Coronal Mass Ejection

We report on the first detection of nonthermal radio emission associated with a polar coronal mass ejection. We call the radio emission as diffuse interplanetary radio emission (DIRE), which occurs in the decameter-hectometric wavelengths. The radio emission originates from the shock flanks that interact with nearby streamers.
New Pathways of Mutational Change in SARS-CoV-2 Proteomes Involve Regions of Intrinsic Disorder Important for Virus Replication and Release

The massive worldwide spread of the SARS-CoV-2 virus is fueling the COVID-19 pandemic. Since the first whole-genome sequence was published in January 2020, a growing database of tens of thousands o...
The strong circular law: A combinatorial view

Let Nn be an n × n complex random matrix, each of whose entries is an independent copy of a centered complex random variable z with finite nonzero variance σ2. The strong circular law, proved by Ta...
Prevalence of SARS-CoV-2 infections in a pediatric orthopedic hospital.

Montreal has the highest incidence of coronavirusdisease (SARS-CoV-2) in Canadarepresenting about one quarter of all reported cases nationwide (1).At the start of the pandemic, March 2020,the Shriners Hospital for Children, Canada,implemented a set of measures to minimize the risk of infection, such as screening each person entering the hospital, mandatory temperature checks at the entrance, wearing face masks within the building and reducing the number of clinic visits. The aim of this project was to assess whether the institution remained free of SARS-CoV-2 infections after these countermeasures were implemented.
Hyperthrombotic Milieu in COVID-19 Patients

COVID-19 infection has protean systemic manifestations. Experience from previous coronavirus outbreaks, including the current SARS-CoV-2, has shown an augmented risk of thrombosis of both macrovasculature and microvasculature. The former involves both arterial and venous beds manifesting as stroke, acute coronary syndrome and venous thromboembolic events. The microvascular thrombosis is an underappreciated complication of SARS-CoV-2 infection with profound implications on the development of multisystem organ failure. The telltale signs of perpetual on-going coagulation and fibrinolytic cascades underscore the presence of diffuse endothelial damage in the patients with COVID-19. These parameters serve as strong predictors of mortality. While summarizing the alterations of various components of thrombosis in patients with COVID-19, this review points to the emerging evidence that implicates the prominent role of the extrinsic coagulation cascade in COVID-19-related coagulopathy. These mechanisms are triggered by widespread endothelial cell damage (endotheliopathy), the dominant driver of macro- and micro-vascular thrombosis in these patients. We also summarize other mediators of thrombosis, clinically relevant nuances such as the occurrence of thromboembolic events despite thromboprophylaxis (breakthrough thrombosis), current understanding of systemic anticoagulation therapy and its risk-benefit ratio. We conclude by emphasizing a need to probe COVID-19-specific mechanisms of thrombosis to develop better risk markers and safer therapeutic targets.
Variable Frame Rate-Based Data Augmentation to Handle Speaking-Style Variability for Automatic Speaker Verification.

The effects of speaking-style variability on automatic speaker verification were investigated using the UCLA Speaker Variability database which comprises multiple speaking styles per speaker. An x-vector/PLDA (probabilistic linear discriminant analysis) system was trained with the SRE and Switchboard databases with standard augmentation techniques and evaluated with utterances from the UCLA database. The equal error rate (EER) was low when enrollment and test utterances were of the same style (e.g., 0.98% and 0.57% for read and conversational speech, respectively), but it increased substantially when styles were mismatched between enrollment and test utterances. For instance, when enrolled with conversation utterances, the EER increased to 3.03%, 2.96% and 22.12% when tested on read, narrative, and pet-directed speech, respectively. To reduce the effect of style mismatch, we propose an entropy-based variable frame rate technique to artificially generate style-normalized representations for PLDA adaptation. The proposed system significantly improved performance. In the aforementioned conditions, the EERs improved to 2.69% (conversation -- read), 2.27% (conversation -- narrative), and 18.75% (pet-directed -- read). Overall, the proposed technique performed comparably to multi-style PLDA adaptation without the need for training data in different speaking styles per speaker.
SCINTILLATIONS AND LEVY FLIGHTS THROUGH THE INTERSTELLAR MEDIUM

Temporal broadening of pulsar signals results from electron density fluctuations in the interstellar medium that cause the radiation to travel along paths of different lengths. The theory of Gaussian fluctuations predicts that the pulse temporal broadening should scale with the wavelength as λ4 and with the dispersion measure (DM; proportional to the distance to the pulsar) as DM2. However, for large dispersion measures, DM > 20 pc cm-3, the observed scaling is λ4DM4, contradicting the conventional theory. Although the problem has existed for 30 years, there has been no resolution to this paradox. We suggest that scintillations for distant pulsars are caused by non-Gaussian, spatially intermittent density fluctuations with a power-law-like probability distribution. Such a probability distribution does not have a second moment, and therefore the previously applied conventional Fokker-Planck theory does not hold. Instead, we propose to apply the theory of Levy distributions (so-called Levy flights). We show that the observed scaling is recovered for large DM if the density differences, ΔN, have Levy distribution decaying as |ΔN|-5/3. In the thin-screen approximation, the corresponding tail of the time-profile of the arriving signal is estimated to be I(τ) ∝ τ-4/3.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.
Hyperspherical Asymptotics of a System of Four Charged Particles

We present a detailed analysis of the charged four-body system in hyperspherical coordinates in the large hyperradial limit. In powers of R−1 for any masses and charges, the adiabatic Hamiltonian is expanded to third order in the dimer–dimer limit and to first order in the particle–trimer limit.
Comparing the PYTHIA Monte Carlo to a two-component (soft + hard) model of hadron production in high-energy p–p collisions

The PYTHIA Monte Carlo (PMC), first introduced more than 30 years ago, remains a popular simulation tool both for analysis of p–p collision dynamics and for detector design and calibration. The PMC...
Reconnection Outflows and Current Sheet Observed with Hinode/XRT in the 2008 April 9 "Cartwheel CME" Flare

Supra-arcade downflows (SADs) have been observed with Yohkoh/SXT (soft X-rays (SXR)), TRACE (extreme ultraviolet (EUV)), SOHO/LASCO (white light), SOHO/SUMER (EUV spectra), and Hinode/XRT (SXR). Characteristics such as low emissivity and trajectories, which slow as they reach the top of the arcade, are consistent with post-reconnection magnetic flux tubes retracting from a reconnection site high in the corona until they reach a lower-energy magnetic configuration. Viewed from a perpendicular angle, SADs should appear as shrinking loops rather than downflowing voids. We present X-ray Telescope (XRT) observations of supra-arcade downflowing loops (SADLs) following a coronal mass ejection (CME) on 2008 April 9 and show that their speeds and decelerations are consistent with those determined for SADs. We also present evidence for a possible current sheet observed during this flare that extends between the flare arcade and the CME. Additionally, we show a correlation between reconnection outflows observed with XRT and outgoing flows observed with LASCO.
Electrical control of coherent spin rotation of a single-spin qubit

Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, have attracted tremendous interest for quantum sensing, network, and computing applications due to their excellent quantum coherence and remarkable versatility in a real, ambient environment. One of the critical challenges to develop NV-based quantum operation platforms results from the difficulty in locally addressing the quantum spin states of individual NV spins in a scalable, energy-efficient manner. Here, we report electrical control of the coherent spin rotation rate of a single-spin qubit in NV-magnet based hybrid quantum systems. By utilizing electrically generated spin currents, we are able to achieve efficient tuning of magnetic damping and the amplitude of the dipole fields generated by a micrometer-sized resonant magnet, enabling electrical control of the Rabi oscillation frequency of NV spins. Our results highlight the potential of NV centers in designing functional hybrid solid-state systems for next-generation quantum-information technologies. The demonstrated coupling between the NV centers and the propagating spin waves harbored by a magnetic insulator further points to the possibility to establish macroscale entanglement between distant spin qubits.
Hofstadter’s Cocoon

Hofstadter showed that the energy levels of electrons on a lattice plotted as a function of magnetic field form an beautiful structure now referred to as “Hofstadter’s butterfly”. We study a non-Hermitian continuation of Hofstadter’s model; as the non-Hermiticity parameter g increases past a sequence of critical values the eigenvalues successively go complex in a sequence of “double-pitchfork bifurcations” wherein pairs of real eigenvalues degenerate and then become complex conjugate pairs. The associated wavefunctions undergo a spontaneous symmetry breaking transition that we elucidate. Beyond the transition a plot of the real parts of the eigenvalues against magnetic field resembles the Hofstadter butterfly; a plot of the imaginary parts plotted against magnetic fields forms an intricate structure that we call the Hofstadter cocoon. The symmetries of the cocoon are described. Hatano and Nelson have studied a non-Hermitian continuation of the Anderson model of localization that has close parallels to the model studied here. The relationship of our work to that of Hatano and Nelson and to PT transitions studied in PT quantum mechanics is discussed.
Neutrino Zero Modes and Stability of Electroweak Strings

We discuss massive and massless neutrino zero modes on electroweak strings. We argue that the eventual absence of the neutrino zero mode implies the existence of topogically stable strings where the required non-trivial topology has been induced by the fermionic sector.
Derived smooth manifolds

We define a simplicial category called the category of derived manifolds. It contains the category of smooth manifolds as a full discrete subcategory, and it is closed under taking arbitrary intersections in a manifold. A derived manifold is a space together with a sheaf of local C∞-rings that is obtained by patching together homotopy zero sets of smooth functions on Euclidean spaces. We show that derived manifolds come equipped with a stable normal bundle and can be imbedded into Euclidean space. We define a cohomology theory called derived cobordism, and use a Pontrjagin-Thom argument to show that the derived cobordism theory is isomorphic to the classical cobordism theory. This allows us to define fundamental classes in cobordism for all derived manifolds. In particular, the intersection A∩B of submanifolds A,B⊂X exists on the categorical level in our theory, and a cup product formula [A]⌣[B]=[A∩B] holds, even if the submanifolds are not transverse. One can thus consider the theory of derived manifolds as a categorification of intersection theory.
360-degree videos: a new visualization technique for astrophysical simulations

360-degree videos are a new type of movie that renders over all 4π steradian. Video sharing sites such as YouTube now allow this unique content to be shared via virtual reality (VR) goggles, hand-held smartphones/tablets, and computers. Creating 360° videos from astrophysical simulations is not only a new way to view these simulations as you are immersed in them, but is also a way to create engaging content for outreach to the public. We present what we believe is the first 360° video of an astrophysical simulation: a hydrodynamics calculation of the central parsec of the Galactic centre. We also describe how to create such movies, and briefly comment on what new science can be extracted from astrophysical simulations using 360° videos.
Strings in the near plane wave background and AdS / CFT

We study the AdS/CFT correspondence for string states which flow into plane wave states in the Penrose limit. Leading finite radius corrections to the string spectrum are compared with scaling dimensions of finite R-charge BMN-like operators. We find agreement between string and gauge theory results.