cssi/SciSciGPT-SciSciNet huggingface dataset:
A log PSS morphism with applications to Lagrangian embeddings

Let $M$ be a smooth projective variety and $\mathbf{D}$ an ample normal crossings divisor. From topological data associated to the pair $(M, \mathbf{D})$, we construct, under assumptions on Gromov-Witten invariants, a series of distinguished classes in symplectic cohomology of the complement $X = M \backslash \mathbf{D}$. Under further "topological" assumptions on the pair, these classes can be organized into a Log(arithmic) PSS morphism, from a vector space which we term the logarithmic cohomology of $(M, \mathbf{D})$ to symplectic cohomology. Turning to applications, we show that these methods and some knowledge of Gromov-Witten invariants can be used to produce dilations and quasi-dilations (in the sense of Seidel-Solomon [SS]) in examples such as conic bundles. In turn, the existence of such elements imposes strong restrictions on exact Lagrangian embeddings, especially in dimension 3. For instance, we prove that any exact Lagrangian in a complex 3-dimensional conic bundle over $(\mathbb{C}^*)^2$ must be diffeomorphic to $T^3$ or a connect sum $\#^n S^1 \times S^2$.
TextureWGAN: texture preserving WGAN with MLE regularizer for inverse problems

Many algorithms and methods have been proposed for inverse problems particularly with the recent surge of interest in machine learning and deep learning methods. Among all proposed methods, the most popular and effective method is the convolutional neural network (CNN) with mean square error (MSE). This method has been proven effective in super-resolution, image de-noising, and image reconstruction. However, this method is known to over-smooth images due to the nature of MSE. MSE based methods minimize Euclidean distance for all pixels between a baseline image and a generated image by CNN and ignore the spatial information of the pixels such as image texture. In this paper, we proposed a new method based on Wasserstein GAN (WGAN) for inverse problems. We showed that the WGAN-based method was effective to preserve image texture. It also used a maximum likelihood estimation (MLE) regularizer to preserve pixel fidelity. Maintaining image texture and pixel fidelity is the most important requirement for medical imaging. We used Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) to evaluate the proposed method quantitatively. We also conducted the first-order and the second-order statistical image texture analysis to assess image texture.
Scattering amplitudes and simple canonical forms for simple polytopes

We provide an efficient recursive formula to compute the canonical forms of arbitrary d-dimensional simple polytopes, which are convex polytopes such that every vertex lies precisely on d facets. For illustration purposes, we explicitly derive recursive formulae for the canonical forms of Stokes polytopes, which play a similar role for a theory with quartic interaction as the Associahedron does in planar bi-adjoint ϕ3 theory. As a by-product, our formula also suggests a new way to obtain the full planar amplitude in ϕ4 theory by taking suitable limits of the canonical forms of constituent Stokes polytopes.
The tortoise and the hare: a causality puzzle in AdS/CFT

We pose and resolve a holographic puzzle regarding an apparent violation of causality in AdS/CFT. If a point in the bulk of $AdS$ moves at the speed of light, the boundary subregion that encodes it may need to move superluminally to keep up. With $AdS_3$ as our main example, we prove that the finite extent of the encoding regions prevents a paradox. We show that the length of the minimal-size encoding interval gives rise to a tortoise coordinate on $\mathrm{AdS}$ that measures the nonlocality of the encoding. We use this coordinate to explore circular and radial motion in the bulk before passing to the analysis of bulk null geodesics. For these null geodesics, there is always a critical encoding where the possible violation of causality is barely avoided. We show that in any other encoding, the possible violation is subcritical.
A Game Theoretic Analysis for Cooperative Smart Farming

The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms and precision agriculture. The growth in the number of smart farms and potential cooperation between these farms has given rise to the Cooperative Smart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit. This data sharing through CSF has various advantages where individual data from separate farms can be aggregated by ML models and be used to produce actionable outputs which then can be utilized by all the farms in CSFs. This enables farms to gain better insights for enhancing desired outputs, such as crop yield, managing water resources and irrigation schedules, as well as better seed applications. However, complications may arise in CSF when some of the farms do not transfer high-quality data and rather rely on other farms to feed ML models. Another possibility is the presence of rogue farms in CSFs that want to snoop on other farms without actually contributing any data. In this paper, we analyze the behavior of farms participating in CSFs using game theory approach, where each farm is motivated to maximize its profit. We first present the problem of defective farms in CSFs due to lack of better data, and then propose a ML framework that segregates farms and automatically assign them to an appropriate CSF cluster based on the quality of data they provide. Our proposed model rewards the farms supplying better data and penalize the ones that do not provide required data or are malicious in nature, thus, ensuring the model integrity and better performance all over while solving the defective farms problem.
Janus-headed intaglio

The traditional use of research methods is to provide a framework for constructing a project to address particular questions or interests. The idea behind this is that, by utilising these frameworks, research will be assessable by other researchers who are then able to judge any given research output through a common framework, while still allowing each project to stand (or fall) on its own merits. At risk of getting lost, or silenced, in this process is any attention to how the individual researcher understands their own project. This article explores the potential for developing a self-reflective research method. How does the researcher engage with, and understand, their own work? How does this provide a new pathway for disseminating research to others within the academic community? How does an object-centred approach to this self-reflection aid in engaging both the researcher and the audience in a shared experience?
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs: Part I: Deterministic Inversion and Linearized Bayesian Inference

We present an extensible software framework, hIPPYlib, for solution of large-scale deterministic and Bayesian inverse problems governed by partial differential equations (PDEs) with (possibly) infinite-dimensional parameter fields (which are high-dimensional after discretization). hIPPYlib overcomes the prohibitively expensive nature of Bayesian inversion for this class of problems by implementing state-of-the-art scalable algorithms for PDE-based inverse problems that exploit the structure of the underlying operators, notably the Hessian of the log-posterior. The key property of the algorithms implemented in hIPPYlib is that the solution of the inverse problem is computed at a cost, measured in linearized forward PDE solves, that is independent of the parameter dimension. The mean of the posterior is approximated by the MAP point, which is found by minimizing the negative log-posterior with an inexact matrix-free Newton-CG method. The posterior covariance is approximated by the inverse of the Hessian of the negative log posterior evaluated at the MAP point. The construction of the posterior covariance is made tractable by invoking a low-rank approximation of the Hessian of the log-likelihood. Scalable tools for sample generation are also discussed. hIPPYlib makes all of these advanced algorithms easily accessible to domain scientists and provides an environment that expedites the development of new algorithms.
Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study

Twitter data has been shown broadly applicable for public health surveillance. Previous public heath studies based on Twitter data have largely relied on keyword-matching or topic models for clustering relevant tweets. However, both methods suffer from the short-length of texts and unpredictable noise that naturally occurs in user-generated contexts. In response, we introduce a deep learning approach that uses hashtags as a form of supervision and learns tweet embeddings for extracting informative textual features. In this case study, we address the specific task of estimating state-level obesity from dietary-related textual features. Our approach yields an estimation that strongly correlates the textual features to government data and outperforms the keyword-matching baseline. The results also demonstrate the potential of discovering risk factors using the textual features. This method is general-purpose and can be applied to a wide range of Twitter-based public health studies.
BOLD signal Changes in resting state networks are related to performance on a vigilance task

Purpose and Background: Recent research has shown that spatially separated brain regions often display functional synchrony that relates to brain state and human performance. Two important anti-correlated functional networks that are seen with resting state functional magnetic resonance imaging (fMRI) are the default mode network (DMN) and the task positive network (TPN). Analytically defining these two networks to better understand their behavior may have a critical impact on understanding higher level function and human performance. Methods: 17 participants were scanned using fMRI in two different states: while performing the psychomotor vigilance task (PVT) and while in resting state. Using seed based correlation defined networks, the behavior of the TPN and DMN were tested for dynamic behavior after the onset of the task and the shifts in the magnitude of the signal in each network was compared to reaction time on the PVT using a linear regression. Results and conclusion: The signal in each network changed significantly in response to the task (TPN increased with a peak at 6 seconds, DMN decreased with a peak at 12 seconds). The magnitude of the increase in the signal within the TPN was significantly related to response time on the PVT. This study validates a network generation technique that can be used in future studies to further investigate the behavior of functional networks, and it shows a relationship between shifts within the TPN and behavior on this vigilance task.
The Bricklayer Ecosystem - Art, math, and code

This paper describes the Bricklayer Ecosystem - a freely-available online educational ecosystem created for people of all ages and coding backgrounds. Bricklayer is designed in accordance with a "low-threshold infinite ceiling" philosophy and has been successfully used to teach coding to primary school students, middle school students, university freshmen, and in-service secondary math teachers. Bricklayer programs are written in the functional programming language SML and, when executed, create 2D and 3D artifacts. These artifacts can be viewed using a variety of third-party tools such as LEGO Digital Designer (LDD), LDraw, Minecraft clients, Brickr, as well as STereoLithography viewers.
Sulfated zirconia and iron- and manganese-promoted sulfated zirconia: do they protonate alkanes?

Because of their high activities for alkane conversion, sulfated zirconia and iron- and manganese-promoted sulfated zirconia have been the objects of much recent attention as a possible next generation of solid acid catalysts for alkane conversion. These catalysts have been suggested to be superacidic on the basis of measurements with adsorbed Hammett indicator bases, but published data determined with other adsorbed bases indicate only moderately strong acid sites. The indicator methods are limited by the opaqueness of the materials and by the inability of the methods to probe a possible set of minority sites that might be responsible for the reactivity and catalytic activity for alkane conversions. Another approach to the challenge of estimating the acid strengths of the reactive and catalytic sites is to investigate the reactivities and catalytic activities of the materials for reactions which, for initiation, require donation of protons from a solid acid to a very weakly basic reactant such as an alkane. Such a test reaction is the acid-catalyzed dehydrogenation of alkanes proceeding by the Haag–Dessau mechanism (Olah type chemistry). This review includes a summary of results for conversion of ethane, propane, and n-butane that are consistent with the postulate that iron- and manganese-promoted sulfated zirconia and sulfated zirconia are capable of protonating light alkanes to give carbonium-ion transition states at temperatures as low as 200°C. The data support the postulate that these proton-donation reactions are important at low alkane conversions and in initiating alkane conversions, although conventional carbenium ion reactions predominate at high conversions.
Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines.

Background Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined. Results We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality. Conclusions Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.
Bootstrapping Multilingual AMR with Contextual Word Alignments.

We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.
Impact of COVID-19 shelter-in-place order on transmission of gastrointestinal pathogens in Northern California.

In response to the COVID-19 pandemic, California was the first state to impose a strict shelter-in-place (SIP) order in March 2020. .
10 Years Later: Cloud Computing is Closing the Performance Gap

Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing high-performance computing. We present a multi-level approach to investigate the performance gap between HPC and cloud computing, isolating different variables that contribute to this gap. Our experiments are divided into (i) hardware and system microbenchmarks and (ii) user application proxies. The results show that today's high-end cloud computing can deliver HPC-competitive performance not only for computationally intensive applications, but also for memory- and communication-intensive applications -- at least at modest scales -- thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning

The milestone improvements brought about by deep representation learning and pre-training techniques have led to large performance gains across downstream NLP, IR and Vision tasks. Multimodal modeling techniques aim to leverage large high-quality visio-linguistic datasets for learning complementary information across image and text modalities. In this paper, we introduce the Wikipedia-based Image Text (WIT) Dataset to better facilitate multimodal, multilingual learning. WIT is composed of a curated set of 37.5 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal models, as we show when applied to downstream tasks such as image-text retrieval. WIT has four main and unique advantages. First, WIT is the largest multimodal dataset by the number of image-text examples by 3x (at the time of writing). Second, WIT is massively multilingual (first of its kind) with coverage over 100+ languages (each of which has at least 12K examples) and provides cross-lingual texts for many images. Third, WIT represents a more diverse set of concepts and real world entities relative to what previous datasets cover. Lastly, WIT provides a very challenging real-world test set, as we empirically illustrate using an image-text retrieval task as an example. WIT Dataset is available for download and use via a Creative Commons license here: https://github.com/google-research-datasets/wit.
Reinforcement Learning for Mean Field Games with Strategic Complementarities

Mean Field Games (MFG) are the class of games with a very large number of agents and the standard equilibrium concept is a Mean Field Equilibrium (MFE). Algorithms for learning MFE in dynamic MFGs are unknown in general. Our focus is on an important subclass that possess a monotonicity property called Strategic Complementarities (MFG-SC). We introduce a natural refinement to the equilibrium concept that we call Trembling-Hand-Perfect MFE (T-MFE), which allows agents to employ a measure of randomization while accounting for the impact of such randomization on their payoffs. We propose a simple algorithm for computing T-MFE under a known model. We also introduce a model-free and a model-based approach to learning T-MFE and provide sample complexities of both algorithms. We also develop a fully online learning scheme that obviates the need for a simulator. Finally, we empirically evaluate the performance of the proposed algorithms via examples motivated by real-world applications.
Representational Pattern Similarity of Electrical Brain Activity Reveals Rapid and Specific Prediction during Language Comprehension

Predicting upcoming events is a critical function of the brain, and language provides a fertile testing ground for studying prediction, as comprehenders use context to predict features of upcoming words. Many aspects of the mechanisms of prediction remain elusive, partly due to a lack of methodological tools to probe prediction formation in the moment. To elucidate what features are neurally preactivated and when, we used representational similarity analysis on previously collected sentence reading data. We compared EEG activity patterns elicited by expected and unexpected sentence final words to patterns from the preceding words of the sentence, in both strongly and weakly constraining sentences. Pattern similarity with the final word was increased in an early time window following the presentation of the pre-final word, and this increase was modulated by both expectancy and constraint. This was not seen at earlier words, suggesting that predictions were precisely timed. Additionally, pre-final word activity-the predicted representation-had negative similarity with later final word activity, but only for strongly expected words. These findings shed light on the mechanisms of prediction in the brain: rapid preactivation occurs following certain cues, but the predicted features may receive reduced processing upon confirmation.
Computing moment inequality models using constrained optimization

Inference for moment inequality models is computationally demanding and often involves time-consuming grid search. By exploiting the equivalent formulations between unconstrained and constrained optimization, we establish new ways to compute the identified set and its confidence set in moment inequality models which overcome some of these computational hurdles. In simulations, using both linear and nonlinear moment inequality models, we show that our methods significantly improve the solution quality and save considerable computing resources relative to conventional grid search. Our methods are user-friendly and can be implemented using a variety of canned software packages.
Constructing subspace packings from other packings

Abstract The desirable properties when constructing collections of subspaces often include the algebraic constraint that the projections onto the subspaces yield a resolution of the identity like the projections onto lines spanned by vectors of an orthonormal basis (the so-called tightness condition) and the geometric constraint that the subspaces form an optimal packing of the Grassmannian, again like the one-dimensional subspaces spanned by vectors in an orthonormal basis. In this article a generalization of related constructions which use known packings to build new configurations and which appear in numerous forms in the literature is given, as well as the characterization of a long list of desirable algebraic and geometric properties which the construction preserves. Another construction based on subspace complementation is similarly analyzed. While many papers on subspace packings focus only on so-called equiisoclinic or equichordal arrangements, attention is also given to other configurations like those which saturate the orthoplex bound and thus are optimal but lie outside of the parameter regime where equiisoclinic and equichordal packings can occur.
Feedback Coding for Active Learning

The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise. While this high-level overlap has been previously noted, there remain open questions on how to best formulate active learning as a communications system to leverage existing analysis and algorithms in feedback coding. In this work, we formally identify and leverage the structural commonalities between the two problems, including the characterization of encoder and noisy channel components, to design a new algorithm. Specifically, we develop an optimal transport-based feedback coding scheme called Approximate Posterior Matching (APM) for the task of active example selection and explore its application to Bayesian logistic regression, a popular model in active learning. We evaluate APM on a variety of datasets and demonstrate learning performance comparable to existing active learning methods, at a reduced computational cost. These results demonstrate the potential of directly deploying concepts from feedback channel coding to design efficient active learning strategies.
Cardiomyocyte contractile impairment in heart failure results from reduced BAG3-mediated sarcomeric protein turnover

The association between reduced myofilament force-generating capacity (Fmax) and heart failure (HF) is clear, however the underlying molecular mechanisms are poorly understood. Here, we show impaired Fmax arises from reduced BAG3-mediated sarcomere turnover. Myofilament BAG3 expression decreases in human HF and positively correlates with Fmax. We confirm this relationship using BAG3 haploinsufficient mice, which display reduced Fmax and increased myofilament ubiquitination, suggesting impaired protein turnover. We show cardiac BAG3 operates via chaperone-assisted selective autophagy (CASA), conserved from skeletal muscle, and confirm sarcomeric CASA complex localization is BAG3/proteotoxic stress-dependent. Using mass spectrometry, we characterize the myofilament CASA interactome in the human heart and identify eight clients of BAG3-mediated turnover. To determine if increasing BAG3 expression in HF can restore sarcomere proteostasis/Fmax, HF mice were treated with rAAV9-BAG3. Gene therapy fully rescued Fmax and CASA protein turnover after four weeks. Our findings indicate BAG3-mediated sarcomere turnover is fundamental for myofilament functional maintenance.
Image Classification with CondenseNeXt for ARM-Based Computing Platforms

In this paper, we demonstrate the implementation of our ultra-efficient deep convolutional neural network architecture: CondenseNeXt on NXP BlueBox, an autonomous driving development platform developed for self-driving vehicles. We show that CondenseNeXt is remarkably efficient in terms of FLOPs, designed for ARM-based embedded computing platforms with limited computational resources and can perform image classification without the need of a CUDA enabled GPU. CondenseNeXt utilizes the state-of-the-art depthwise separable convolution and model compression techniques to achieve a remarkable computational efficiency. Extensive analyses are conducted on CIFAR-10, CIFAR-100 and ImageNet datasets to verify the performance of Con-denseNeXt Convolutional Neural Network (CNN) architecture. It achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error). CondenseNeXt achieves final trained model size improvement of 2.9+ MB and up to 59.98% reduction in forward FLOPs compared to CondenseNet and can perform image classification on ARM-Based computing platforms without needing a CUDA enabled GPU support, with outstanding efficiency.
Riemann–Hilbert approach to the elastodynamic equation: half plane

We show, how the Riemann–Hilbert approach to the elastodynamic equations, which have been suggested in our preceding papers, works in the half plane case. We pay a special attention to the emergence of the Rayleigh waves within the scheme.
Manifolds of Amphiphilic Bilayers: Stability up to the Boundary

Abstract We consider the mass preserving L 2 -gradient flow of the strong scaling of the functionalized Cahn Hilliard gradient flow and establish the nonlinear stability of a manifold comprised of quasi-equilibrium bilayer distributions up to the manifold's boundary. In the limit of thin but non-zero interfacial width, e ≪ 1 , the bilayer manifold is parameterized by meandering modes that describe the interfacial evolution. The normal coercivity of the manifold is limited by “pearling” modes that control the structure of the profile near the interface, these are weakly damped and can lead to the dynamic rupture of the interface. Amphiphilic interfaces may decrease energy by either lengthening or shortening, depending upon the amphiphilic mass distributed in the bulk. We introduce an implicitly defined parameterization of the interfacial shape that uncouples the length change from the parameters describing the shape and introduce a nonlinear projection onto the manifold from a surrounding neighborhood. The bilayer manifold has asymptotically large but finite dimension tuned to maximize normal coercivity while preserving the wave-number gap between the meandering and the pearling modes. Modulo a pearling stability assumption, we show that the manifold attracts nearby orbits into a tubular neighborhood about itself so long as the interfacial shape remains sufficiently smooth and far from self-intersection. In a companion paper, [8] , we identify open sets of initial data whose orbits converge to circular equilibrium after a significant transient, and derive a singularly perturbed interfacial evolution comprised of motion against curvature regularized by an asymptotically weak Willmore term.
Phase precession in the human hippocampus and entorhinal cortex

Knowing where we are, where we have been, and where we are going is critical to many behaviors, including navigation and memory. One potential neuronal mechanism underlying this ability is phase precession, in which spatially tuned neurons represent sequences of positions by activating at progressively earlier phases of local network theta oscillations. Based on studies in rodents, researchers have hypothesized that phase precession may be a general neural pattern for representing sequential events for learning and memory. By recording human single-neuron activity during spatial navigation, we show that spatially tuned neurons in the human hippocampus and entorhinal cortex exhibit phase precession. Furthermore, beyond the neural representation of locations, we show evidence for phase precession related to specific goal states. Our findings thus extend theta phase precession to humans and suggest that this phenomenon has a broad functional role for the neural representation of both spatial and non-spatial information.
Economic Recessions and Congressional Preferences for Redistribution

We analyze the roots of politicians' preferences for redistribution by exploring whether early life experiences have persistent, long-run effects on U.S. Members of Congress' voting records. We study whether having experienced an economic recession during early adulthood affected their positions on redistribution-specific bills during the period 1957-2012. We find that politicians who experienced a recession hold more conservative positions on redistribution, even compared to members of the same party in the same legislature. We rule out alternative accounts and show that experiencing a recession directly affects future politicians' personal preferences. In light of recent empirical evidence showing that voters become more supportive of redistribution following a recession, our findings suggest that macroeconomic shocks have a polarizing effect: recessions can create an ideological wedge between voters and their future representatives.
Methods for Detecting Pediatric Adverse Drug Reactions From the Electronic Medical Record

Adverse drug reactions (ADRs) are common yet are often underreported, making them difficult to track and study. Prospective pharmacovigilance programs significantly increase detection and reporting of ADRs. The aim of this pilot study was to apply triggers used by a prospective pharmacovigilance program at a freestanding children's hospital to retrospectively detect ADRs at our institution, therefore determining if these methods could be replicated and provide the basis for implementation of a prospective pharmacovigilance program. In 2019, our institution had 22 000 inpatient admissions and 51 000 emergency room visits and had 21 ADRs voluntarily reported in an electronic medication safety tracking system. Additional ADRs were identified by methods including new or modified entries to a patient's allergy profile in the electronic medical record (EMR) and International Classification of Disease (ICD) codes. We identified 754 unique patients with changes to allergy profile and 5719 ICD codes in 3966 unique patients to evaluate. These triggers prompted screening of the EMR to validate the ADR, and we identified 280 ADRs occurring in 2019. Eight (2.8%) were identified solely by the electronic medication safety tracking system, 64 (23%) were identified by the allergy list, 110 (39%) were identified only by ICD coding, and the remaining 98 (35%) were identified by multiple methods. The use of triggers followed by review of the EMR identified 13-fold more ADRs than were voluntarily reported, illustrating the need for an active pharmacovigilance service and the successful use of multimodal methods to detect and track ADRs.
pyrpipe: a Python package for RNA-Seq workflows

The availability of terabytes of RNA-Seq data and continuous emergence of new analysis tools, enable unprecedented biological insight. There is a pressing requirement for a framework that allows for fast, efficient, manageable, and reproducible RNA-Seq analysis. We have developed a Python package, (pyrpipe), that enables straightforward development of flexible, reproducible and easy-to-debug computational pipelines purely in Python, in an object-oriented manner. pyrpipe provides access to popular RNA-Seq tools, within Python, via high-level APIs. Pipelines can be customized by integrating new Python code, third-party programs, or Python libraries. Users can create checkpoints in the pipeline or integrate pyrpipe into a workflow management system, thus allowing execution on multiple computing environments, and enabling efficient resource management. pyrpipe produces detailed analysis, and benchmark reports which can be shared or included in publications. pyrpipe is implemented in Python and is compatible with Python versions 3.6 and higher. To illustrate the rich functionality of pyrpipe, we provide case studies using RNA-Seq data from GTEx, SARS-CoV-2-infected human cells, and Zea mays. All source code is freely available at https://github.com/urmi-21/pyrpipe; the package can be installed from the source, from PyPI (https://pypi.org/project/pyrpipe), or from bioconda (https://anaconda.org/bioconda/pyrpipe). Documentation is available at (http://pyrpipe.rtfd.io).
The complementarity of drug monitoring programs and health IT for reducing opioid-related mortality and morbidity

In response to the opioid crisis, each US state has implemented a prescription drug monitoring program (PDMP) to collect data on controlled substances prescribed and dispensed in the state. I study whether health information technology (HIT) complements patient prescription data in PDMPs to reduce opioid-related mortality and morbidity. A novel dataset is constructed that records state policies that integrate PDMP with HIT and facilitate interstate data sharing. Using difference-in-differences models, I find that PDMP-HIT integration policies reduce opioid-related inpatient morbidity. The reductions are substantial in states that established integration without ever mandating the use of a PDMP. A mechanism test suggests that PDMP integration works mainly through the hospital system while a mandate affects legal opioids prescription. The impacts from integration are strongest for the vulnerable groups-middle-aged, low-to middle-income patients, and those with public insurance. There is suggestive evidence that interstate data sharing further complements integration despite not having a significant impact independently. The results are robust to a set of tests using alternative specifications and measures. The total benefits from integration far exceed the associated costs.
On-board propulsion system analysis of high density propellants

The impact of the performance and density of on-board propellants on science payload mass of Discovery Program class missions is evaluated. A propulsion system dry mass model, anchored on flight-weight system data from the Near Earth Asteroid Rendezvous mission is used. This model is used to evaluate the performance of liquid oxygen, hydrogen peroxide, hydroxylammonium nitrate, and oxygen difluoride oxidizers with hydrocarbon and metal hydride fuels. Results for the propellants evaluated indicate that the state-of-art, Earth Storable propellants with high performance rhenium engine technology in both the axial and attitude control systems has performance capabilities that can only be exceeded by liquid oxygen/hydrazine, liquid oxygen/diborane and oxygen difluoride/diborane propellant combinations. Potentially lower ground operations costs is the incentive for working with nontoxic propellant combinations.
Open-Domain Question Answering with Pre-Constructed Question Spaces.

Open-domain question answering aims at locating the answers to user-generated questions in massive collections of documents. Retriever-readers and knowledge graph approaches are two big families of solutions to this task. A retriever-reader first applies information retrieval techniques to locate a few passages that are likely to be relevant, and then feeds the retrieved text to a neural network reader to extract the answer. Alternatively, knowledge graphs can be constructed and queried to answer users’ questions. We propose an algorithm with a novel reader-retriever design that differs from both families. Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way. We further combine one retriever-reader and two reader-retrievers into a hybrid model called R6 for the best performance. Experiments with two large-scale public datasets show that R6 achieves state-of-the-art accuracy.
Whatever Happened to the ‘Goodwin Pattern’? Profit Squeeze Dynamics in the Modern American Labour Market

The ‘Goodwin pattern’ — an anti-clockwise rotation in real activity × wage share space recurring at intervals that correspond roughly to the duration of business cycles — is an enduring feature of ...
Normalized hodographic mapping for constrained trajectory families

Normalized hodographic mapping for constrained trajectory families, discussing mapping concepts, information content and applications
Probing Contextual Language Models for Common Ground with Visual Representations.

The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model that evaluates how effective are text-only representations in distinguishing between matching and non-matching visual representations. Our findings show that language representations alone provide a strong signal for retrieving image patches from the correct object categories. Moreover, they are effective in retrieving specific instances of image patches; textual context plays an important role in this process. Visually grounded language models slightly outperform text-only language models in instance retrieval, but greatly under-perform humans. We hope our analyses inspire future research in understanding and improving the visual capabilities of language models.
Vapor-liquid equilibrium of water with the MB-pol many-body potential.

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water’s solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor–liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor–liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor–liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. These results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol’s application across a large swath of water’s phase diagram.
Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer

The performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) cell-free massive multiple-input multiple-output (MIMO) is investigated. Tight approximations to the DL harvested energy and the DL/uplink (UL) achievable rates are derived for two practical channel state information (CSI) cases by using a non-linear energy harvesting model for time-switching and power-splitting protocols. Max-min fairness-based transmit power control policies are employed to mitigate the deleterious near-far effects caused by distributed transmissions/receptions in cell-free massive MIMO. The achievable common DL energy-rate trade-off is derived, and thereby, it is shown that the proposed max-min power control guarantees user-fairness regardless of near-far effects in terms of both harvested energy and achievable rate. The benefits of user estimated DL CSI to boost the SWIPT performance are explored. These performance metrics are compared against those of the conventional co-located massive MIMO, and thereby, it is revealed that the reduction of path-losses and lower average transmit powers offered by cell-free massive MIMO can be exploited to boost the energy-rate trade-off of SWIPT at the expense of increased backhaul requirements.
A relationship between network chain concentration and time dependence of rupture for SBR vulcanizates

The failure properties of crosslinked elastomers are influenced by variables such as the test rate, test temperature, chemical nature of the elastomers, degree of crosslinking, etc. For an elastomer with a fixed degree of crosslinkingthe time dependence of both the stress- and strain-at-break, when determined as a function of the test rate and test speed, can be correlated using a time–temperature shift such as the WLF aT shift factor. When a similar study is carried out with a sample having a different degree of crosslinking, the time dependence of both the stress- and strain-at-break is shifted along the time scale. It is demonstrated here that the time dependence of failure obtained as a function of the degree of crosslinking can be superposed on a master curve by the use of a time shift factor whose value depends directly on the degree of crosslinking.
The Mitotic Crosslinking Protein PRC1 Acts as a Mechanical Dashpot to Resist Microtubule Sliding

Cell division in eukaryotes requires the regulated assembly of the spindle apparatus. The proper organization of microtubules within the spindle is driven by motor proteins that exert forces to push and slide filaments, while non-motor proteins can crosslink filaments into higher order motifs such as overlapping bundles. It has not been clear how active and passive forces are integrated to produce regulated mechanical outputs within spindles. Here we employ a combined optical tweezers and TIRF microscopy instrument to directly measure the resistive forces produced by the mitotic crosslinking protein PRC1. We observe that PRC1 generates frictional forces that resist microtubule sliding. These forces scale with microtubule sliding velocity and the number of PRC1 crosslinks, but do not depend on overlap length or PRC1 density within overlaps. Our results suggest that PRC1 ensembles act like a mechanical dashpot, producing significant resistance against fast motions, but minimal resistance against slow motions, allowing for the integration of diverse motor activities into a single mechanical outcome.
Stochastic integrals and Brownian motion on abstract nilpotent Lie groups

We construct a class of iterated stochastic integrals with respect to Brownian motion on an abstract Wiener space which allows for the definition of Brownian motions on a general class of infinite-dimensional nilpotent Lie groups based on abstract Wiener spaces. We then prove that a Cameron–Martin type quasi-invariance result holds for the associated heat kernel measures in the non-degenerate case, and give estimates on the associated Radon–Nikodym derivative. We also prove that a log Sobolev estimate holds in this setting.
How to design robust algorithms using noisy comparison Oracle

Metric based comparison operations such as finding maximum, nearest and farthest neighbor are fundamental to studying various clustering techniques such as k-center clustering and agglomerative hie...
Learning to Trust: Understanding Editorial Authority and Trust in Recommender Systems for Education

Trust in a recommendation system (RS) is often algorithmically incorporated using implicit or explicit feedback of user-perceived trustworthy social neighbors, and evaluated using user-reported trustworthiness of recommended items. However, real-life recommendation settings can feature group disparities in trust, power, and prerogatives. Our study examines a complementary view of trust which relies on the editorial power relationships and attitudes of all stakeholders in the RS application domain. We devise a simple, first-principles metric of editorial authority, i.e., user preferences for recommendation sourcing, veto power, and incorporating user feedback, such that one RS user group confers trust upon another by ceding or assigning editorial authority. In a mixed-methods study at Virginia Tech, we surveyed faculty, teaching assistants, and students about their preferences of editorial authority, and hypothesis-tested its relationship with trust in algorithms for a hypothetical ‘Suggested Readings’ RS. We discover that higher RS editorial authority assigned to students is linked to the relative trust the course staff allocates to RS algorithm and students. We also observe that course staff favors higher control for the RS algorithm in sourcing and updating the recommendations long-term. Using content analysis, we discuss frequent staff-recommended student editorial roles and highlight their frequent rationales, such as perceived expertise, scaling the learning environment, professional curriculum needs, and learner disengagement. We argue that our analyses highlight critical user preferences to help detect editorial power asymmetry and identify RS use-cases for supporting teaching and research.
Tight bounds for Katona’s shadow intersection theorem

Abstract A fundamental result in extremal set theory is Katona’s shadow intersection theorem, which extends the Kruskal–Katona theorem by giving a lower bound on the size of the shadow of an intersecting family of k -sets in terms of its size. We improve this classical result and a related result of Ahlswede, Aydinian and Khachatrian by proving tight bounds for families that can be quite small. For example, when k = 3 our result is sharp for all families with n points and at least 3 n − 7 triples. Katona’s theorem was extended by Frankl to families with matching number s . We improve Frankl’s result by giving tight bounds for large n .