cssi/SciSciGPT-SciSciNet huggingface dataset:
Web-Scale Generic Object Detection at Microsoft Bing

In this paper, we present Generic Object Detection (GenOD), one of the largest object detection systems deployed to a web-scale general visual search engine that can detect over 900 categories for all Microsoft Bing Visual Search queries in near real-time. It acts as a fundamental visual query understanding service that provides object-centric information and shows gains in multiple production scenarios, improving upon domain-specific models. We discuss the challenges of collecting data, training, deploying and updating such a large-scale object detection model with multiple dependencies. We discuss a data collection pipeline that reduces per-bounding box labeling cost by 81.5% and latency by 61.2% while improving on annotation quality. We show that GenOD can improve weighted average precision by over 20% compared to multiple domain-specific models. We also improve the model update agility by nearly 2 times with the proposed disjoint detector training compared to joint fine-tuning. Finally we demonstrate how GenOD benefits visual search applications by significantly improving object-level search relevance by 54.9% and user engagement by 59.9%.
A Galois–Dynamics Correspondence for Unicritical Polynomials

In an analogy with the Galois homothety property for torsion points of abelian varieties that was used in the proof of the Mordell–Lang conjecture, we describe a correspondence between the action of a Galois group and the dynamical action of a rational map. For nonlinear polynomials with rational coefficients, the irreducibility of the associated dynatomic polynomial serves as a convenient criterion, although we also verify that the correspondence occurs in several cases when the dynatomic polynomial is reducible. The work of Morton, Morton–Patel, and Vivaldi–Hatjispyros in the early 1990s connected the irreducibility and Galois-theoretic properties of dynatomic polynomials to rational periodic points; from the Galois–dynamics correspondence, we derive similar consequences for quadratic periodic points of unicritical polynomials. This is sufficient to deduce the non-existence of quadratic periodic points of quadratic polynomials with exact period 5 and 6, outside of a specified finite set from Morton and Krumm’s work in explicit Hilbert irreducibility.
Convex hull representations for bounded products of variables

It is well known that the convex hull of $$\{{(x,y,xy)}\}$$ , where (x, y) is constrained to lie in a box, is given by the reformulation-linearization technique (RLT) constraints. Belotti et al. (Electron Notes Discrete Math 36:805–812, 2010) and Miller et al. (SIAG/OPT Views News 22(1):1–8, 2011) showed that if there are additional upper and/or lower bounds on the product $$z=xy$$ , then the convex hull can be represented by adding an infinite family of inequalities, requiring a separation algorithm to implement. Nguyen et al. (Math Progr 169(2):377–415, 2018) derived convex hulls for $$\{(x,y,z)\}$$ with bounds on $$z=xy^b$$ , $$b\ge 1$$ . We focus on the case where $$b=1$$ and show that the convex hull with either an upper bound or lower bound on the product is given by RLT constraints, the bound on z and a single second-order cone (SOC) constraint. With both upper and lower bounds on the product, the convex hull can be represented using no more than three SOC constraints, each applicable on a subset of (x, y) values. In addition to the convex hull characterizations, volumes of the convex hulls with either an upper or lower bound on z are calculated and compared to the relaxation that imposes only the RLT constraints. As an application of these volume results, we show how spatial branching can be applied to the product variable so as to minimize the sum of the volumes for the two resulting subproblems.
Figurative Language in Recognizing Textual Entailment

We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language -- simile, metaphor, and irony -- and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture different aspects of figurative language. Our results and analyses indicate that these models might not sufficiently capture figurative language, struggling to perform pragmatic inference and reasoning about world knowledge. Ultimately, our datasets provide a challenging testbed for evaluating RTE models.
Tangent Space Backpropagation for 3D Transformation Groups

We address the problem of performing backpropagation for computation graphs involving 3D transformation groups SO(3), SE(3), and Sim(3). 3D transformation groups are widely used in 3D vision and robotics, but they do not form vector spaces and instead lie on smooth manifolds. The standard backpropagation approach, which embeds 3D transformations in Euclidean spaces, suffers from numerical difficulties. We introduce a new library, which exploits the group structure of 3D transformations and performs backpropagation in the tangent spaces of manifolds. We show that our approach is numerically more stable, easier to implement, and beneficial to a diverse set of tasks. Our plug-and-play PyTorch library is available at https://github.com/princeton-vl/lietorch.
Experimental Rotordynamic Coefficient Results for Teeth-on-Rotor and Teeth-on-Stator Labyrinth Gas Seals

An experimental test facility is used to measure the rotordynamic coefficients of teeth-on-rotor and teeth-on-stator labyrinth gas seals. Direct damping coefficients are presented for these seals for the first time. The results are presented for the two seal configurations at identical operating conditions, and show that, in a rotordynamic sense, the teeth-on-stator seal is more stable than the teeth-on-rotor seal, for inlet tangential velocity in the direction of rotation.
SARS-CoV-2 Serology Status Detected by Commercialized Platforms Distinguishes Previous Infection and Vaccination Adaptive Immune Responses.

BACKGROUND: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has infected over 110 million individuals and led to 2.5 million deaths worldwide. As more individuals are vaccinated, the clinical performance and utility of SARS-CoV-2 serology platforms needs to be evaluated. METHODS: The ability of four commercial SARS-CoV-2 serology platforms to detect previous infection or vaccination were evaluated using a cohort of 53 SARS-CoV-2 PCR-positive patients, 89 SARS-CoV-2-vaccinated healthcare workers (Pfizer or Moderna), and 127 SARS-CoV-2 negative patients. Serology results were compared to a cell based SARS-CoV-2 pseudovirus (PSV) neutralizing antibodies assay. RESULTS: The Roche S-(spike) antibody and Diazyme neutralizing antibodies (NAbs) assays detected adaptive immune response in 100.0% and 90.1% of vaccinated individuals who received two-doses of vaccine (initial and booster), respectively. The Roche N-(nucleocapsid) antibody assay and Diazyme IgG assay did not detect adaptive immune response in vaccinated individuals. The Diazyme Nabs assay correlated with the PSV SARS-CoV-2 ID50 neutralization titers (R2= 0.70), while correlation of the Roche S-antibody assay was weaker (R2= 0.39). Median PSV SARS-CoV-2 ID50 titers more than doubled in vaccinated individuals who received two-doses of the Moderna vaccine (ID50: 597) compared to individuals that received a single dose (ID50: 284). CONCLUSIONS: The Roche S-antibody and Diazyme NAbs assays robustly detected adaptive immune responses in SARS-CoV-2 vaccinated individuals and SARS-CoV-2 infected individuals. The Diazyme NAbs assay strongly correlates with the PSV SARS-CoV-2 NAbs in vaccinated individuals. Understanding the reactivity of commercially available serology platforms is important when distinguishing vaccination response versus natural infection.
Semi-Supervised Image Deraining Using Gaussian Processes

Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. More specifically, we model the latent space vectors of unlabeled data using Gaussian Processes, which is then used to compute pseudo-ground-truth for supervising the network on unlabeled data. The pseudo ground-truth is further used to supervise the network at the intermediate level for the unlabeled data. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200L and DDN-SIRR), we show that the proposed method is able to effectively leverage unlabeled data thereby resulting in significantly better performance as compared to labeled-only training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to the existing methods. Code is available at: https://github.com/rajeevyasarla/Syn2Real
Biogenic amines and acute thermal stress in the rat

A study is summarized which demonstrates that depletion of the biogenic amines 5-hydroxytryptamine (5-HT) or norepinephrine (NE) alters the normal thermoregulatory responses to acute temperature stress. Specifically, NE depletion caused a significant depression in equilibrium rectal temperature at 22 C and a greater depression in rectal temperature than controls in response to cold (6 C) stress; NE depletion also resulted in a significantly higher rectal temperature response to acute heat (38 C) stress. Depletion of 5-HT had less severe effects. It remains unclear whether the primary site of action of these agents is central or peripheral.
Escaping Spurious Local Minimum Trajectories in Online Time-varying Nonconvex Optimization

This paper is concerned with solving online nonconvex optimization problems using simple gradient-based algorithms with an arbitrary initialization. The main objective is to understand how the natural data variation of an online optimization problem affects finding its time-varying global minima. To this end, we investigate the properties of a time-varying gradient flow system with inertia, which can be regarded as the continuous-time limit of the online tracking scheme obtained by working through the optimality conditions for a discretized sequential optimization problem with a proximal regularization. We introduce the notion of the dominant trajectory and show that the inherent temporal variation of the problem could re-shape the landscape and help a proximal algorithm escape the spurious local minimum trajectories if the global minimum trajectory is dominant. By studying the three notions of jumping, tracking and escaping for nonlinear dynamical systems, sufficient conditions are derived to guarantee that no matter how the local search method is initialized, it will find and track a time-varying global solution after some time.
Distinguishing Aerial Intruders from Trajectory Data: A Model-Based Hypothesis-Testing Approach

Motivated by security needs in unmanned aerial system (UAS) operations, an algorithm for identifying airspace intruders (e.g., birds vs. drones) is developed. The algorithm is structured to use sensed intruder velocity data from Internet-of-Things platforms together with limited knowledge of physical models. The identification problem is posed as a statistical hypothesis testing or detection problem, wherein inertial feedback-controlled objects subject to stochastic actuation must be distinguished by speed data. The maximum a posteriori probability detector is obtained, and then is simplified to an explicit computation based on two points in the sample autocorrelation of the data. The simplified form allows computationally-friendly implementation of the algorithm, and simplified learning from archived data. Also, the total probability of error of the detector is computed and characterized. Simulations based on synthesized data are presented to illustrate and supplement the formal analyses.
OpenSerDes: An Open Source Process-Portable All-Digital Serial Link

Over the last decade, the growing influence of open source software has necessitated the need to reduce the abstraction levels in hardware design. Open source hardware significantly reduces the development time, increasing the probability of first-pass success and enable developers to optimize software solutions based on hardware features, thereby reducing the design costs. The recent introduction of open source Process Development Kit (OpenPDK) by Skywater technologies in June 2020 has eliminated the barriers to Application-Specific Integrated Circuit (ASIC) design, which is otherwise considered expensive and not easily accessible. The OpenPDK is the first concrete step towards achieving the goal of open source circuit blocks that can be imported to reuse and modify in ASIC design. With process technologies scaling down for better performance, the need for entirely digital designs, which can be synthesized in any standard Automatic Place-and-Route (APR) tool, has increased considerably, for mapping physical design to the new process technology. This work presents a first open source all-digital Serializer/Deserializer (SerDes) for multi-GHz serial links designed using Skywater OpenPDK 130nm process node. To ensure that the design is fully synthesizable, the SerDes uses CMOS inverter based drivers at the transmitter, while the receiver front end comprises a resistive feedback inverter as a sensing element, followed by sampling elements. A fully digital oversampling CDR at the receiver end recovers the transmitter clock for proper decoding of data bits. The physical design flow utilizes OpenLANE, which is an open source end-to-end tool for generating GDS from RTL. Cadence Virtuoso has been used for extracting parasitics for post-layout simulations, which exhibit the SerDes functionality at 2 Gbps for 34 dB channel loss while consuming 438 mW power. The generated GDS and netlist files of the SerDes, along with the required documentation, are uploaded in a GitHub repository for public access.
Association between overcrowded households, multigenerational households, and COVID-19: a cohort study.

Abstract Objectives The role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households and COVID-19 in New York City (NYC). Study design Cohort study. Methods We conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as the proportion of the estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was an adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), the prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering. Results 39,923 suspected COVID-19 cases were presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant (βinteraction = 0.99, 95% CI: 0.99–1.00). Conclusions Overcrowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of the surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.
easyXpress: An R package to analyze and visualize high-throughput C. elegans microscopy data generated using CellProfiler.

High-throughput imaging techniques have become widespread in many fields of biology. These powerful platforms generate large quantities of data that can be difficult to process and visualize efficiently using existing tools. We developed easyXpress to process and review C. elegans high-throughput microscopy data in the R environment. The package provides a logical workflow for the reading, analysis, and visualization of data generated using CellProfiler’s WormToolbox. We equipped easyXpress with powerful functions to customize the filtering of noise in data, specifically by identifying and removing objects that deviate from expected animal measurements. This flexibility in data filtering allows users to optimize their analysis pipeline to match their needs. In addition, easyXpress includes tools for generating detailed visualizations, allowing the user to interactively compare summary statistics across wells and plates with ease. Researchers studying C. elegans benefit from this streamlined and extensible package as it is complementary to CellProfiler and leverages the R environment to rapidly process and analyze large high-throughput imaging datasets.
Law and Social Cognition

The body of research on law and psychology is vast, but the overwhelming proportion of it is on jury decision making, especially in criminal cases. In this chapter for the forthcoming Oxford Handbook on Social Cognition (D. Carlston ed.), we attempt to broaden this research agenda. We survey briefly the existing state of psychological research on jury decision making, but show that, even with respect to factual determinations, the jury is a less important decision maker than most psychologists appear to believe. Thus, further research on factual determination by judges, of which there is some but not much, could substantially enrich our understanding of the psychological dimensions of legal decision making. Moreover, the role of judges in finding, interpreting, and applying the law is itself a task necessarily involving social cognition, and we explain both this connection and how further research on the social cognition dimensions of legal reasoning and legal argument could be highly valuable. Finally, we explain how numerous issues of substantive law – questions of intent, reasonableness, and knowledge, to give just a few examples – are themselves dependent on assumptions about the social and cognitive psychological reasoning of the people affected and governed by the law. There is very little psychology research on such questions, and the agenda of law and psychology could usefully be expanded to include such themes.
Enhancing School-wide Positive Behavioral Interventions and Supports Tier 1 core practices to improve disciplinary equity

Addressing racial disparities in school discipline is an urgent and widespread issue facing US schools. One approach to improve racial equity in school discipline practice is to enhance the context...
Frame Field Operators

Differential operators are widely used in geometry processing for problem domains like spectral shape analysis, data interpolation, parametrization and mapping, and meshing. In addition to the ubiquitous cotangent Laplacian, anisotropic second-order operators, as well as higher-order operators such as the Bilaplacian, have been discretized for specialized applications. In this paper, we study a class of operators that generalizes the fourth-order Bilaplacian to support anisotropic behavior. The anisotropy is parametrized by a symmetric frame field, first studied in connection with quadrilateral and hexahedral meshing, which allows for fine-grained control of local directions of variation. We discretize these operators using a mixed finite element scheme, verify convergence of the discretization, study the behavior of the operator under pullback, and present potential applications.
The Meaning of the Victim–Offender Overlap for Criminological Theory and Crime Prevention Policy

Criminological theory developed without an expectation of a victim–offender overlap. Among most crime theorists and policymakers, to solve crime it is necessary to solve the criminal offender. Modern choice theories took a different view by evolving from victim data, treating target vulnerability as essential to the criminal act and with full awareness of the overlap. Here, we discuss the emphasis on offenders in criminology as being inconsistent with the facts of the overlap. The evidence shows that the victim–offender overlap is consistently found, implying that offending and victimization arise for similar substantive reasons and that offenders act principally in response to targets. This conclusion has important implications. First, any theory of crime that cannot logically predict the overlap as a fact may be subject to falsification. Second, the choice perspective suggests a theory of precautionary behavior, which urges a policy agenda that encourages actions against crime by potential targets.
Accounting as a Normalizing Tool for Transitional Dirtiness: The Case of the US Adult-Use Cannabis Industry†

While prior accounting research documents normalizing strategies within the accounting profession and in instances of accounting adoption, the potential of accounting itself as a strategic tool toward normalizing that which is considered socially abnormal (i.e., dirty) remains an important and unexamined area of inquiry. In this study, we conduct in-depth interviews to examine the role accounting plays in the development of the US cannabis industry as it transitions from the illicit market in which formal accounting was systematically avoided, to a state-legal market in which participants are subject to conventional business processes. Facing impediments to traditional operating practices and pressures to increase normative conformity for industry survival, cannabis operators incorporated the use of accounting in three normalizing strategies (creative concession, collaborative facilitation, and experimentation), seemingly influenced by the incongruencies between prior illicit-market culture and experiences, and the state-legal operating environment. In response to what operators perceived to be coercive regulation, they employed creative concession strategies, including influencing, bargaining, challenging, escaping, and cessation tactics. However, in response to pressures to adopt more commonly accepted forms of accounting, cannabis operators instead deployed two different strategies, one focused on acquiescence to normalizing pressures when doing so facilitated essential relationship building (i.e., collaborative facilitation strategies), and one deployed as strategic experimentation, working to normalize industry activities in areas of perceived threats to industry acceptance and continuity. Given cannabis operator accounting naivete, its usefulness was often introduced by peripheral industry parties attempting to normalize their own participation with the cannabis industry. Notably, we also find that normalizing pressures occasionally resulted in unintended consequences, including reversion to the illicit market and forgoing normalizing strategies in favor of retaining some level of dirtiness to fend off pending competition, both of which threaten to reemphasize the industry’s dirtiness. Our study, therefore, points to accounting itself as a central mechanism in the complex, multidirectional strategy to normalize transitional dirtiness.
Identifying the Support of Rectangular Signals in Gaussian Noise

We consider the problem of identifying the support of the block signal in a sequence when both the length and the location of the block signal are unknown. The multivariate version of this problem ...
JEM++: Improved Techniques for Training JEM

Joint Energy-based Model (JEM) is a recently proposed hybrid model that retains strong discriminative power of modern CNN classifiers, while generating samples rivaling the quality of GAN-based approaches. In this paper, we propose a variety of new training procedures and architecture features to improve JEM's accuracy, training stability, and speed altogether. 1) We propose a proximal SGLD to generate samples in the proximity of samples from the previous step, which improves the stability. 2) We further treat the approximate maximum likelihood learning of EBM as a multi-step differential game, and extend the YOPO framework to cut out redundant calculations during backpropagation, which accelerates the training substantially. 3) Rather than initializing SGLD chain from random noise, we introduce a new informative initialization that samples from a distribution estimated from training data. 4) This informative initialization allows us to enable batch normalization in JEM, which further releases the power of modern CNN architectures for hybrid modeling. Code: this https URL
Heralding on the detection of zero photons

This work was supported by the National Science Foundation under grant number PHY-2013464.
The Weakly-Labeled Rand Index

Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there are many approaches to quantify performance in standard crisp segmentation schemes, drawing hard boundaries in remote sensing imagery where gradients and regions of uncertainty exist is inappropriate. These cases warrant weak labels and an associated appropriate scoring approach. In this paper, a labeling approach and associated modified version of the Rand index for weakly-labeled data is introduced to address these issues. Results are evaluated with the new index and compared to traditional segmentation evaluation methods. Experimental results on a SAS data set containing must-link and cannot-link labels show that our Weakly-Labeled Rand index scores segmentations appropriately in reference to qualitative performance and is more suitable than traditional quantitative metrics for scoring weakly-labeled data.
Verbal Focus-of-Attention System for Learning-from-Observation

The learning-from-observation (LfO) framework aims to map human demonstrations to a robot to reduce programming effort. To this end, an LfO system encodes a human demonstration into a series of execution units for a robot, which are referred to as task models. Although previous research has proposed successful task-model encoders, there has been little discussion on how to guide a task-model encoder in a scene with spatio-temporal noises, such as cluttered objects or unrelated human body movements. Inspired by the function of verbal instructions guiding an observer’s visual attention, we propose a verbal focus-of-attention (FoA) system (i.e., spatiotemporal filters) to guide a task-model encoder. For object manipulation, the system first recognizes the name of a target object and its attributes from verbal instructions. The information serves as a where-to-look FoA filter to confine the areas in which the target object existed in the demonstration. The system then detects the timings of grasp and release that occurred in the filtered areas. The timings serve as a when-to-look FoA filter to confine the period of object manipulation. Finally, a task-model encoder recognizes the task models by employing the FoA filters. We demonstrate the robustness of the verbal FoA in attenuating spatio-temporal noises by comparing it with an existing action localization network. The contributions of this study are as follows: (1) to propose a verbal FoA for LfO, (2) to design an algorithm to calculate FoA filters from verbal input, and (3) to demonstrate the effectiveness of a verbal FoA in localizing an action by comparing it with a state-of-the-art vision system.
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions

Abstract We propose a new type of neural networks, Kronecker neural networks (KNNs), that form a general framework for neural networks with adaptive activation functions. KNNs employ the Kronecker product, which provides an efficient way of constructing a very wide network while keeping the number of parameters low. Our theoretical analysis reveals that under suitable conditions, KNNs induce a faster decay of the loss than that by the feed-forward networks. This is also empirically verified through a set of computational examples. Furthermore, under certain technical assumptions, we establish global convergence of gradient descent for KNNs. As a specific case, we propose the Rowdy activation function that is designed to get rid of any saturation region by injecting sinusoidal fluctuations, which include trainable parameters. The proposed Rowdy activation function can be employed in any neural network architecture like feed-forward neural networks, Recurrent neural networks, Convolutional neural networks etc. The effectiveness of KNNs with Rowdy activation is demonstrated through various computational experiments including function approximation using feed-forward neural networks, solution inference of partial differential equations using the physics-informed neural networks, and standard deep learning benchmark problems using convolutional and fully-connected neural networks.
Tempered fractional Brownian motion on finite intervals

Diffusive transport in many complex systems features a crossover between anomalous diffusion at short times and normal diffusion at long times. This behavior can be mathematically modeled by cutting off (tempering) beyond a mesoscopic correlation time the power-law correlations between the increments of fractional Brownian motion. Here, we investigate such tempered fractional Brownian motion confined to a finite interval by reflecting walls. Specifically, we explore how the tempering of the long-time correlations affects the strong accumulation and depletion of particles near reflecting boundaries recently discovered for untempered fractional Brownian motion. We find that exponential tempering introduces a characteristic size for the accumulation and depletion zones but does not affect the functional form of the probability density close to the wall. In contrast, power-law tempering leads to more complex behavior that differs between the superdiffusive and subdiffusive cases.
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records

Clinical documentation can be transformed by Electronic Health Records, yet the documentation process is still a tedious, time-consuming, and error-prone process. Clinicians are faced with multi-faceted requirements and fragmented interfaces for information exploration and documentation. These challenges are only exacerbated in the Emergency Department—clinicians often see 35 patients in one shift, during which they have to synthesize an often previously unknown patient’s medical records in order to reach a tailored diagnosis and treatment plan. To better support this information synthesis, clinical documentation tools must enable rapid contextual access to the patient’s medical record. MedKnowts is an integrated note-taking editor and information retrieval system which unifies the documentation and search process and provides concise synthesized concept-oriented slices of the patient’s medical record. MedKnowts automatically captures structured data while still allowing users the flexibility of natural language. MedKnowts leverages this structure to enable easier parsing of long notes, auto-populated text, and proactive information retrieval, easing the documentation burden.
Furin cleavage of the SARS-CoV-2 spike is modulated by O -glycosylation.

The SARS-CoV-2 coronavirus responsible for the global pandemic contains a novel furin cleavage site in the spike protein (S) that increases viral infectivity and syncytia formation in cells. Here, we show that O-glycosylation near the furin cleavage site is mediated by members of the GALNT enzyme family, resulting in decreased furin cleavage and decreased syncytia formation. Moreover, we show that O-glycosylation is dependent on the novel proline at position 681 (P681). Mutations of P681 seen in the highly transmissible alpha and delta variants abrogate O-glycosylation, increase furin cleavage, and increase syncytia formation. Finally, we show that GALNT family members capable of glycosylating S are expressed in human respiratory cells that are targets for SARS-CoV-2 infection. Our results suggest that host O-glycosylation may influence viral infectivity/tropism by modulating furin cleavage of S and provide mechanistic insight into the role of the P681 mutations found in the highly transmissible alpha and delta variants.
Zero-shot Relation Classification from Side Information

We propose a zero-shot learning relation classification (ZSLRC) framework that improves on state-of-the-art by its ability to recognize novel relations that were not present in training data. The zero-shot learning approach mimics the way humans learn and recognize new concepts with no prior knowledge. To achieve this, ZSLRC uses advanced prototypical networks that are modified to utilize weighted side (auxiliary) information. ZSLRC's side information is built from keywords, hypernyms of name entities, and labels and their synonyms. ZSLRC also includes an automatic hypernym extraction framework that acquires hypernyms of various name entities directly from the web. ZSLRC improves on state-of-the-art few-shot learning relation classification methods that rely on labeled training data and is therefore applicable more widely even in real-world scenarios where some relations have no corresponding labeled examples for training. We present results using extensive experiments on two public datasets (NYT and FewRel) and show that ZSLRC significantly outperforms state-of-the-art methods on supervised learning, few-shot learning, and zero-shot learning tasks. Our experimental results also demonstrate the effectiveness and robustness of our proposed model.
Adjusting for Autocorrelated Errors in Neural Networks for Time Series

An increasing body of research focuses on using neural networks to model time series. A common assumption in training neural networks via maximum likelihood estimation on time series is that the errors across time steps are uncorrelated. However, errors are actually autocorrelated in many cases due to the temporality of the data, which makes such maximum likelihood estimations inaccurate. In this paper, in order to adjust for autocorrelated errors, we propose to learn the autocorrelation coefficient jointly with the model parameters. In our experiments, we verify the effectiveness of our approach on time series forecasting. Results across a wide range of real-world datasets with various state-of-the-art models show that our method enhances performance in almost all cases. Based on these results, we suggest empirical critical values to determine the severity of autocorrelated errors. We also analyze several aspects of our method to demonstrate its advantages. Finally, other time series tasks are also considered to validate that our method is not restricted to only forecasting.
Infinite Products and Elementary Functions

The objective in this chapter is to lay out a working background for dealing with infinite products and their possible applications. The reader will be familiarized with a specific topic that is not often included in traditional texts on related courses of mathematical analysis, namely the infinite product representation of elementary functions.
The Economics of Cost Recovery in Transport

There have periodically been sea changes in the way in which the economic regulation of transport has been viewed. While the traditional divide between the 'Continental' philosophy of regulation ? whereby regulation is seen as the norm to achieve wide socio-political goals and markets only adopted when this is coincidental with these goals ? and the 'Anglo-Saxon' philosophy ? with its neo-classical economic emphasis on the need to ensure markets are allowed to work efficiently and unencumbered by regulators ? still exists, the focus has shifted very much towards the latter. We have moved into what Denis Swann and I (Button and Swann, 1989) once called the Age of Deregulation. Since the deregulation of the US domestic airline industry in 1978, there has been a global trend towards market liberalisation and privatisation with the transport sector being particularly affected. The changes that have occurred have, most economists would seem to agree, been broadly beneficial. There has been a general downward trend in transport costs and the use of transport services has risen indicating the revealed prefer ences of potential users. The US term 'deregulation', although quite widely used, is, however, something of an exaggeration. There remain many generic regulations covering such things as mergers policy, labour conditions, as well as a plethora of safety, security, and environmental regulations. In addition, in many countries reforms to transport regimes have been slower in their introduction, and even in those that are consid
Behavioral Risk Reduction in a Declining HIV Epidemic: Injection Drug Users in New York City, 1990-1997.

OBJECTIVES: This study assessed trends in HIV risk behaviors among injection drug users in New York City from 1990 to 1997. METHODS: Injection drug users were recruited continuously from a large drug detoxification treatment program (N = 2588) and a research storefront located in a high-drug-use area (N = 2701). Informed consent was obtained, and a trained interviewer administered a structured interview covering sociodemographics, drug use history, HIV risk behavior, and participation in syringe exchange. RESULTS: Trends were assessed for 5 risk behaviors in the 6-month period before the interview. The 3 injection risk behaviors declined significantly over time at each site (all P < .01). When data were pooled across sites, all 5 risk behaviors declined significantly over time (all P < .01). Participation in syringe exchange programs and in HIV counseling and testing increased greatly from 1990 to 1997. CONCLUSIONS: The continuing risk reduction among injection drug users indicates a "declining phase" in the large HIV epidemic in New York City. HIV prevention programs appear to be making an important contribution to the declining phase.
Cavity Quantum Electrodynamics

In Chap. 14, we have interpreted spontaneous emission in terms of the irreversible emission of a photon into the free space modes of the electromagnetic field, accompanied by a transition of the atom from an upper to a lower electronic state. As already hinted at in that chapter, this description of spontaneous emission is not general, and spontaneous emission is not an intrinsic atomic property: rather, it can be modified by tailoring the electromagnetic environment that the atom radiates into. This was first realized by Purcell, who noted that the spontaneous emission rate can be enhanced for an atom placed inside a cavity with one of its modes resonant with the transition under consideration. Kleppner later on discussed the opposite case of inhibited spontaneous emission. It is also recognized that spontaneous emission can be replaced by a periodic exchange of energy between the atom and the electromagnetic field, as we have seen in the context of the Jaynes-Cummings model. Indeed, the Schrodinger equation always leads to reversible dynamics. It is when the electromagnetic field is treated as a Markovian reservoir with no memory that irreversibility is achieved in the derivations of Chaps. 14,15. But qualitatively different types of dynamics can be achieved if the electromagnetic field modes cannot be described in this simple way.
From Einstein Nonlocality to Von Neumann Reality

Recent nonlocality results support a new picture of reality built on the ideas of John von Neumanm
Efficient sampling of RNA secondary structures from the Boltzmann ensemble of low-energy: the boustrophedon method.

We adapt here a surprising technique, the boustrophedon method, to speed up the sampling of RNA secondary structures from the Boltzmann low-energy ensemble. This technique is simple and its implementation straight-forward, as it only requires a permutation in the order of some operations already performed in the stochastic traceback stage of these algorithms. It nevertheless greatly improves their worst-case complexity from \({\mathcal{O}}({n^2})\) to \({\mathcal{O}}({n\log(n)})\) , for n the size of the original sequence. Moreover the average-case complexity of the generation is shown to be improved from \({\mathcal{O}}({n\sqrt{n}})\) to \({\mathcal{O}}({n\log(n)})\) in an Boltzmann-weighted homopolymer model based on the Nussinov–Jacobson free-energy model. These results are extended to the more realistic Turner free-energy model through experiments performed on both structured (Drosophilia melanogaster mRNA 5S) and hybrid (Staphylococcus aureus RNAIII) RNA sequences, using a boustrophedon modified version of the popular software UnaFold. This improvement allows for the sampling of greater and more significant sets of structures in a given time.
Role of HOXA9 in leukemia: dysregulation, cofactors and essential targets

HOXA9 is a homeodomain-containing transcription factor that has an important role in hematopoietic stem cell expansion and is commonly deregulated in acute leukemias. A variety of upstream genetic alterations in acute myeloid leukemia lead to overexpression of HOXA9, which is a strong predictor of poor prognosis. In many cases, HOXA9 has been shown to be necessary for maintaining leukemic transformation; however, the molecular mechanisms through which it promotes leukemogenesis remain elusive. Recent work has established that HOXA9 regulates downstream gene expression through binding at promoter distal enhancers along with a subset of cell-specific cofactor and collaborator proteins. Increasing efforts are being made to identify both the critical cofactors and target genes required for maintaining transformation in HOXA9-overexpressing leukemias. With continued advances in understanding HOXA9-mediated transformation, there is a wealth of opportunity for developing novel therapeutics that would be applicable for greater than 50% of AML with overexpression of HOXA9.
Hospital readmission and healthcare utilization following sepsis in community settings

Background Sepsis, the most expensive cause of hospitalization in the US, is associated with high morbidity and mortality. However, healthcare utilization patterns following sepsis are poorly understood.
Sentinel node dissection in the treatment of melanoma. Report of three cases and review of the literature.

background Elective lymph node dissection for treatment of cutaneous malignant melanoma is controversial. Sentinel node dissection involves removing the primary lymph node in a nodal basin that drains a particular cutaneous lesion. Theoretically, this node would collect regional metastases first. Therefore, if this node is negative, the chances are low that the melanoma would have spread either systemically or to other nodes within this basin. Removing one node would decrease the morbidity associated with radical lymph node dissection. However, the actual risk of widespread metastases in sentinel node–negative patients is yet to be determined, pending results of large, multi–center studies currently under investigation. objective To present three cases of intermediate thickness cutaneous melanoma treated with selective lymph node dissection and to review the techniques of selective lymphadenectomy and lymphoscintigraphy. methods In a nonrandomized prospective evaluation, patients with intermediate depth melanomas or in transit metastases without signs of systemic disease were given the opportunity for further investigation by sentinel node dissection to determine if additional lymph node dissection or adjunctive therapies would be advantageous. results All three patients had negative sentinel node examinations. Two are without visceral or nodal metastases 1 year after the procedure. The third had in–transit metastases from the outset, had in–transit metastases on sentinel node/lymphatic examination, and now has systemic cutaneous metastases. Complications of sentinel node dissection were limited to transient postoperative iymphedema of the extremities and transient seroma formation. No postoperative wound infections or permanent were damage were noted. conclusions Our preliminary findings in this limited series suggests that sentinel node dissection appears to be a procedure of low morbidity and relatively high predictive value.
The emergence and reduction of bias in letters of recommendation

Despite the widespread use of letters of recommendation (LORs) in selection systems, research has largely failed to consider the potential emergence of bias in interpretations of LORs. The present study fills this void by examining both race and gender bias in evaluations of LORs and assessing the efficacy of elaboration as a strategy for reducing such bias. Undergraduate students (n = 423) rated four LORs that varied with regard to applicant race and gender. Results suggest that bias does exist in evaluations of LORs, but that requiring raters to expand on their evaluations (i.e., elaborate) reduces this bias. Implications include elaboration as a strategy organizations can implement to reduce bias from emerging when relying on LORs as a selection tool.
Mutational Analysis of Copper Binding by Human Tyrosinase

Tyrosinase (EC 1.14.18.1) is a copper-containing enzyme that catalyzes several reactions in the biosynthesis of melanin pigments and is deficient in patients with type I oculocutaneous albinism (OCA1). Tyrosinase is thought to bind two copper ions, one at each of two conserved sequence motifs, termed CuA and CuB, but to date this has been directly proved only for the Neurospora and mushroom enzyme. Here, we demonstrate that mammalian tyrosinase directly binds copper, and that the CuA and CuB sites are both required for copper binding and for catalytic activity. We show that in human tyrosinase, copper binding by the CuB site is most likely coordinated by residues His363, His367, and His389, and that copper binding may be cooperative, with copper binding at one site facilitating copper binding by the other site. Furthermore, correct folding of the tyrosinase polypeptide appears to be necessary for copper binding, and a number of human OCA1 mutations disrupt copper binding and thus catalytic function of tyrosinase.