cssi/SciSciGPT-SciSciNet huggingface dataset:
Out-of-hospital cardiac arrest volumes and characteristics during the COVID-19 pandemic.

Abstract Aim The COVID-19 pandemic has significantly impacted Emergency Medical Services (EMS) operations throughout the country. Some studies described variation in total volume of out-of-hospital cardiac arrests (OHCA) during the pandemic. We aimed to describe the changes in volume and characteristics of OHCA patients and resuscitations in one urban EMS system. Methods We performed a retrospective cohort analysis of all recorded atraumatic OHCA in Marion County, Indiana, from January 1, 2019 to June 30, 2019 and from January 1, 2020 to June 30, 2020. We described patient, arrest, EMS response, and survival characteristics. We performed paired and unpaired t-tests to evaluate the changes in those characteristics during COVID-19 as compared to the prior year. Data were matched by month to control for seasonal variation. Results The total number of arrests increased from 884 in 2019 to 1034 in 2020 (p = 0.016). Comparing 2019 to 2020, there was little difference in age [median 62 (IQR 59–73) and 60 (IQR 47–72), p = 0.086], gender (38.5% and 39.8% female, p = 0.7466, witness to arrest (44.3% and 39.6%, p = 0.092), bystander AED use (10.1% and 11.4% p = 0.379), bystander CPR (48.7% and 51.4%, p = 0.242). Patients with a shockable initial rhythm (19.2% and 15.4%, p = 0.044) both decreased in 2020, and response time increased by 18 s [6.0 min (IQR 4.5–7.7) and 6.3 min (IQR 4.7–8.0), p = 0.008]. 47.7% and 54.8% (p = 0.001) of OHCA patients died in the field, 19.7% and 19.3% (p = 0.809) died in the Emergency Department, 21.8% and 18.5% (p = 0.044) died in the hospital, 10.8% and 7.4% (p = 0.012) were discharged from the hospital, and 9.3% and 5.9% (p = 0.005) were discharged with Cerebral Performance Category score ≤ 2. Conclusion Total OHCA increased during the COVID-19 pandemic when compared with the prior year. Although patient characteristics were similar, initial shockable rhythm, and proportion of patients who died in the hospital decreased during the pandemic. Further investigation will explore etiologies of those findings.
Acoustic response of turbulent cavity flow using resolvent analysis

Fluid–acoustic interactions are important in a variety of applications and typically result in adverse effects. We analyze the influence of Mach number on such interactions and their input–output characteristics by combining resolvent analysis with Doak's momentum potential theory. The specific problem selected is the flow over an open cavity of L∕D = 6 at Re = 10 000 and M∞ = 0.6 and 1.4, respectively. The resolvent forcing and response modes are decomposed into their hydrodynamic, acoustic, and thermal components. Although the results depend quantitatively on Mach number, some trends remain consistent. In particular, at lower frequencies, the acoustic component appears primarily at the trailing edge of the cavity. When the frequency is increased, the acoustic response moves toward the leading edge and overlaps with its hydrodynamic component. Inspired by actual cavity flow control, the forcing is then localized to two regions—the leading edge and front wall of the cavity—and filtered to consider notional actuators that can separately introduce each component of velocity, density, and temperature forcing, respectively. Among these different types of actuation perturbations, regardless of Mach number, streamwise velocity forcing achieves the largest energy amplification at the leading edge. For both flows, beyond a certain forcing frequency threshold value, the nature of the acoustic vs hydrodynamic response becomes independent of the forcing type; however, the amplification continues to be strongly impacted by the forcing frequency. The present work provides an alternative approach to examine input–output flow–acoustic characteristics and evaluate the relative effectiveness of different types and locations of actuation.
Social conformity under evolving private preferences

Abstract We propose a model of social norms changing in response to evolving privately held preferences. Our aim is to rationalize the tendency for individuals who hold minority preferences to take actions favored by the majority. We do this using a game involving a tension between a desire to act according to one's underlying preferences and a desire to conform to the majority opinion. We find that even after a majority of the population shares what was previously a minority opinion, members of the new majority are slow to change their behavior. The timing and speed with which behavior transitions to match new, majority-held opinions depends on the size of the reward for conformity. When the rewards for conformity are low, the transition is gradual, with considerable periods of costly public disagreement. When the rewards for conformity are high, transitions are slow to start but conclude rapidly once they begin.
Design and Experiments with a Robot-Driven Underwater Holographic Microscope for Low-Cost In Situ Particle Measurements

Microscopic analysis of microparticles in situ in diverse water environments is necessary for monitoring water quality and localizing contamination sources. Conventional sensors such as optical microscopes and fluorometers often require complex sample preparation, are restricted to small sample volumes, and are unable to simultaneously capture all pertinent details of a sample such as particle size, shape, concentration, and three-dimensional motion. This paper proposes a novel and cost-effective robotic system for mobile microscopic analysis of particles in situ at various depths which are fully controlled by the robot system itself. A miniature underwater digital in-line holographic microscope (DIHM) performs high-resolution imaging of microparticles (e.g., algae cells, plastic debris, sediments) while movement allows measurement of particle distributions covering a large area of water.
Deep neural networks for human’s fall-risk prediction using force-plate time series signal

Abstract Early and accurate identification of the balance deficits could reduce falls, in particular for older adults, a prone population. Our work investigates deep neural networks’ capacity to identify human balance patterns towards predicting fall-risk. Human balance ability can be characterized based on commonly-used balance metrics, such as those derived from the force-plate time series. We hypothesized that low, moderate, and high risk of falling can be characterized based on balance metrics, derived from the force-plate time series, in conjunction with deep learning algorithms. Further, we predicted that our proposed One-One-One Deep Neural Networks algorithm provides a considerable increase in performance compared to other algorithms. Here, an open source force-plate dataset, which quantified human balance from a wide demographic of human participants (163 females and males aged 18–86) for varied standing conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, eyes-closed foam surface) was used. Classification was based on one of the several indicators of fall-risk tied to the fear of falling: the clinically-used Falls Efficacy Scale (FES) assessment. For human fall-risk prediction, the deep learning architecture implemented comprised of: Recurrent Neural Network (RNN), Long-Short Time Memory (LSTM), One Dimensional Convolutional Neural Network (1D-CNN), and a proposed One-One-One Deep Neural Network. Results showed that our One-One-One Deep Neural Networks algorithm outperformed the other aforementioned algorithms and state-of-the-art models on the same dataset. With an accuracy, precision, and sensitivity of 99.9%, 100%, 100%, respectively at the 12th epoch, we found that our proposed One-One-One Deep Neural Network model is the most efficient neural network in predicting human’s fall-risk (based on the FES measure) using the force-plate time series signal. This is a novel methodology for an accurate prediction of human risk of fall.
Task-graph scheduling extensions for efficient synchronization and communication

Task graphs have been studied for decades as a foundation for scheduling irregular parallel applications and incorporated in many programming models including OpenMP. While many high-performance parallel libraries are based on task graphs, they also have additional scheduling requirements, such as synchronization within inner levels of data parallelism and internal blocking communications. In this paper, we extend task-graph scheduling to support efficient synchronization and communication within tasks. Compared to past work, our scheduler avoids deadlock and oversubscription of worker threads, and refines victim selection to increase the overlap of sibling tasks. To the best of our knowledge, our approach is the first to combine gang-scheduling and work-stealing in a single runtime. Our approach has been evaluated on the SLATE high-performance linear algebra library. Relative to the LLVM OMP runtime, our runtime demonstrates performance improvements of up to 13.82%, 15.2%, and 36.94% for LU, QR, and Cholesky, respectively, evaluated across different configurations related to matrix size, number of nodes, and use of CPUs vs GPUs.
Efficient Algorithms for Block-Cyclic Redistribution of Arrays

The block-cyclic data distribution is commonly used to organize array elements over the processors of a coarse-grained distributed memory parallel computer. In many scientific applications, the data layout must be reorganized at run-time in order to enhance locality and reduce remote memory access overheads. In this paper we present a general framework for developing array redistribution algorithms. Using this framework, we have developed efficient algorithms that redistribute an array from one block-cyclic layout to another. Block-cyclic redistribution consists of index set computation , wherein the destination locations for individual data blocks are calculated, and data communication , wherein these blocks are exchanged between processors. The framework treats both these operations in a uniform and integrated way. We have developed efficient and distributed algorithms for index set computation that do not require any interprocessor communication. To perform data communication in a conflict-free manner, we have developed direct indirect and hybrid algorithms. In the direct algorithm, a data block is transferred directly to its destination processor. In an indirect algorithm, data blocks are moved from source to destination processors through intermediate relay processors. The hybrid algorithm is a combination of the direct and indirect algorithms. Our framework is based on a generalized circulant matrix formalism of the redistribution problem and a general purpose distributed memory model of the parallel machine. Our algorithms sustain excellent performance over a wide range of problem and machine parameters. We have implemented our algorithms using MPI, to allow for easy portability across different HPC platforms. Experimental results on the IBM SP-2 and the Cray T3D show superior performance over previous approaches. When the block size of the cyclic data layout changes by a factor of K , the redistribution can be performed in O( log K) communication steps. This is true even when K is a prime number. In contrast, previous approaches take O(K) communication steps for redistribution. Our framework can be used for developing scalable redistribution libraries, for efficiently implementing parallelizing compiler directives, and for developing parallel algorithms for various applications. Redistribution algorithms are especially useful in signal processing applications, where the data access patterns change significantly between computational phases. They are also necessary in linear algebra programs, to perform matrix transpose operations.
Few-Shot Graph Learning for Molecular Property Prediction

The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery. The existing deep neural network methods usually require large training dataset for each property, impairing their performance in cases (especially for new molecular properties) with a limited amount of experimental data, which are common in real situations. To this end, we propose Meta-MGNN, a novel model for few-shot molecular property prediction. Meta-MGNN applies molecular graph neural network to learn molecular representations and builds a meta-learning framework for model optimization. To exploit unlabeled molecular information and address task heterogeneity of different molecular properties, Meta-MGNN further incorporates molecular structures, attribute based self-supervised modules and self-attentive task weights into the former framework, strengthening the whole learning model. Extensive experiments on two public multi-property datasets demonstrate that Meta-MGNN outperforms a variety of state-of-the-art methods.
No dimension reduction for doubling subsets of $\ell_q$ when $q>2$ revisited

Abstract We revisit the main results from [6] , [7] and [23] about the impossibility of dimension reduction for doubling subsets of l q for q > 2 . We provide an alternative elementary proof of this impossibility result that combines the simplicity of the construction in [6] , [7] with the generality of the approach in [23] (except for L 1 targets). One advantage of this different approach is that it can be naturally generalized to obtain embeddability obstructions into non-positively curved spaces or asymptotically uniformly convex Banach spaces.
How asymmetry helps buffer management: achieving optimal tail size in cup games

The cup game on n cups is a multi-step game with two players, a filler and an emptier. At each step, the filler distributes 1 unit of water among the cups, and then the emptier selects a single cup to remove (up to) 1 unit of water from. There are several objective functions that the emptier might wish to minimize. One of the strongest guarantees would be to minimize tail size, which is defined to be the number of cups with fill 2 or greater. A simple lower-bound construction shows that the optimal tail size for deterministic emptying algorithms is Θ(n), however. We present a simple randomized emptying algorithm that achieves tail size O(logn) with high probability in n for poly n steps. Moreover, we show that this is tight up to doubly logarithmic factors. We also extend our results to the multi-processor cup game, achieving tail size O(logn + p) on p processors with high probability in n. We show that the dependence on p is near optimal for any emptying algorithm that achieves polynomial-bounded backlog. A natural question is whether our results can be extended to give unending guarantees, which apply to arbitrarily long games. We give a lower bound construction showing that no monotone memoryless emptying algorithm can achieve an unending guarantee on either tail size or the related objective function of backlog. On the other hand, we show that even a very small (i.e., 1 / poly n) amount of resource augmentation is sufficient to overcome this barrier.
Minimal volume entropy of free-by-cyclic groups and 2-dimensional right-angled Artin groups

Let G be a free-by-cyclic group or a 2-dimensional right-angled Artin group. We provide an algebraic and a geometric characterization for when each aspherical simplicial complex with fundamental group isomorphic to G has minimal volume entropy equal to 0. In the nonvanishing case, we provide a positive lower bound to the minimal volume entropy of an aspherical simplicial complex of minimal dimension for these two classes of groups. Our results rely upon a criterion for the vanishing of the minimal volume entropy for 2-dimensional groups with uniform uniform exponential growth. This criterion is shown by analyzing the fiber $$\pi _1$$ -growth collapse and non-collapsing assumptions of Babenko–Sabourau (Minimal volume entropy and fiber growth, arXiv:2102.04551 , 2020).
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

This paper introduces MediaSum, a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. To create this dataset, we collect interview transcripts from NPR and CNN and employ the overview and topic descriptions as summaries. Compared with existing public corpora for dialogue summarization, our dataset is an order of magnitude larger and contains complex multi-party conversations from multiple domains. We conduct statistical analysis to demonstrate the unique positional bias exhibited in the transcripts of televised and radioed interviews. We also show that MediaSum can be used in transfer learning to improve a model’s performance on other dialogue summarization tasks.
Semantic Frame Forecast

This paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of “story blocks,” where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame’s inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the feasibility of the task. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers

We propose Predict then Interpolate (PI), a simple algorithm for learning correlations that are stable across environments. The algorithm follows from the intuition that when using a classifier trained on one environment to make predictions on examples from another environment, its mistakes are informative as to which correlations are unstable. In this work, we prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes. Since the oracle interpolation coefficients are not accessible, we use group distributionally robust optimization to minimize the worst-case risk across all such interpolations. We evaluate our method on both text classification and image classification. Empirical results demonstrate that our algorithm is able to learn robust classifiers (outperforms IRM by 23.85% on synthetic environments and 12.41% on natural environments). Our code and data are available at this https URL.
Making Pre-trained Language Models Better Few-shot Learners

The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.
DLion: Decentralized Distributed Deep Learning in Micro-Clouds

Deep learning (DL) is a popular technique for building models from large quantities of data such as pictures, videos, messages generated from edges devices at rapid pace all over the world. It is often infeasible to migrate large quantities of data from the edges to centralized data center(s) over WANs for training due to privacy, cost, and performance reasons. At the same time, training large DL models on edge devices is infeasible due to their limited resources. An attractive alternative for DL training distributed data is to use micro-clouds---small-scale clouds deployed near edge devices in multiple locations. However, micro-clouds present the challenges of both computation and network resource heterogeneity as well as dynamism. In this paper, we introduce DLion, a new and generic decentralized distributed DL system designed to address the key challenges in micro-cloud environments, in order to reduce overall training time and improve model accuracy. We present three key techniques in DLion: (1) Weighted dynamic batching to maximize data parallelism for dealing with heterogeneous and dynamic compute capacity, (2) Per-link prioritized gradient exchange to reduce communication overhead for model updates based on available network capacity, and (3) Direct knowledge transfer to improve model accuracy by merging the best performing model parameters. We build a prototype of DLion on top of TensorFlow and show that DLion achieves up to 4.2X speedup in an Amazon GPU cluster, and up to 2X speed up and 26% higher model accuracy in a CPU cluster over four state-of-the-art distributed DL systems.
On the inconsistency of matching without replacement

The paper shows that matching without replacement on propensity scores produces estimators that generally are inconsistent for the average treatment effect of the treated. To achieve consistency, practitioners must either assume that no units exist with propensity scores greater than one-half or assume that there is no confounding among such units. The result is not driven by the use of propensity scores, and similar artifacts arise when matching on other scores as long as it is without replacement.
Notes on equivariant bundles

Abstract We compare two notions of G -fiber bundles and G -principal bundles in the literature, with an aim to clarify early results in equivariant bundle theory that are needed in current work of equivariant algebraic topology. We also give proofs of some equivariant generalizations of well-known non-equivariant results involving the classifying space.
Desensitizing structural-control design

A new method for targeted robustness optimization is presented. This method allows the analyst to 'target' the most critical performance parameter (or family of design requirements with almost the same criticality) for enhanced protection from uncertainties in the model. To create this protection, this method uses knowledge about the uncertainties in some of the parameters of the model - this information is usually available to the structural analyst. The power of the targeted robustness method is demonstrated numerically using an active damping example. The results show that the most critical damping ratio is protected - its sensitivity to the uncertainties is far below those of non-critical damping ratios.
Every Bite Is an Experience: Key Point Analysis of Business Reviews

Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary. These approaches provide only a partial view of the data: aspect-based sentiment summaries lack sufficient explanation or justification for the aspect rating, while textual summaries do not quantify the significance of each element, and are not well-suited for representing conflicting views. Recently, Key Point Analysis (KPA) has been proposed as a summarization framework that provides both textual and quantitative summary of the main points in the data. We adapt KPA to review data by introducing Collective Key Point Mining for better key point extraction; integrating sentiment analysis into KPA; identifying good key point candidates for review summaries; and leveraging the massive amount of available reviews and their metadata. We show empirically that these novel extensions of KPA substantially improve its performance. We demonstrate that promising results can be achieved without any domain-specific annotation, while human supervision can lead to further improvement.
Projection-based iterative mode scheduling for switched systems

Abstract This paper describes a method for scheduling the events of a switched system to achieve an optimal performance. The approach has guarantees on convergence and computational complexity that parallel derivative-based iterative optimization but in the infinite dimensional, integer constrained setting of mode scheduling. In comparison to methods relying on mixed integer programming, the presented approach does not require a priori discretizations of time or state. Furthermore, in comparison to embedding and relaxation methods, every iteration of the algorithm returns a dynamically feasible solution. A large class of problems call for optimal mode scheduling. This paper considers a vehicle tracking problem and a high dimensional multimachine power network synchronization problem. For the power network example, both single horizon and receding horizon approaches prevent instability of the network, and the receding horizon approach does so at near real-time speeds on a single processor.
Switching from Face-to-Face to Online Instruction Midsemester: Impacts and Challenges for Student Learning and Success

The COVID-19 pandemic disrupted higher education during Spring 2020 by forcing all face-to-face classes to unexpectedly transition to online learning. To better understand how switching to remote learning affected students and the factors that impacted their ability to successfully complete classes, 168 undergraduate students in three different psychology classes (six sections total) were asked in the last week of the semester about their experiences from before and after the switch. Students reported some decreased access to technology, changes in work responsibilities, some amount of physical illness, and the need to care for others who were physically ill. Notably, students consistently reported increased stress and decreased ability to focus. Students varied in how much they prioritized classes after the switch, which predicted their performance in the class, measured by exam grade, overall grade, and completion of attendance before and after the switch. Importantly, survey respondents significantly differed from non-respondents in their class performance, which suggests that results from voluntary surveys may capture a limited perspective and possibly underestimate the detrimental effects of the shift to online instruction. Implications for planning for future online classes in a global pandemic are discussed.
Abstraction and analogy-making in artificial intelligence

Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite a long history of research on constructing artificial intelligence (AI) systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress in this area.
Laser spectroscopy of small molecules

Studies of alkaline earth oxide and monohalide molecules using laser induced photolumi-nescence spectroscopy (Mg0), tunable laser excitation spectroscopy (CaCl), laser-laser double resonance spectroscopy (CaCl) and microwave-laser double resonance spectroscopy (Ba0) are discussed and summarized. The advantages of these laser techniques over conventional methods of obtaining and assigning optical spectra are emphasized.
In-situ monitoring of Direct Energy Deposition via Structured Light System and its application in remanufacturing industry

Abstract The Direct Energy Deposition (DED) process utilizes laser energy to melt metal powders and deposit them on the substrate layer to manufacture complex metal parts. This study was applied as a remanufacturing and repair process to fix used parts, which reduced unnecessary waste in the manufacturing industry. However, there could be defects generated during the repair, such as porosity or bumpy morphological defects. Traditionally, the operator would use a design of experiment (DOE) or simulation method to understand the printing parameters’ influence on the printed part. There are several influential factors: laser power, scanning speed, powder feeding rate, and standoff distance. Each DED machine has a different setup in practice, which results in some uncertainties for the printing results. For example, the nozzle diameter and laser type could be varied in different DED machines. Thus, it was hypothesized that a repair could be more effective if the printing process could be monitored in real-time. In this study, a structured light system (SLS) was used to capture the printing process’s layerwise information. The SLS system is capable of performing 3D surface scanning with a high-resolution of 10 µm. To determine how much material needs to be deposited, given the initial scanning of the part and allowing the real-time observation of each layer’s information. Once a defect was found in-situ, the DED machine (hybrid machine) would change the tool and remove the flawed layer. After the repair, the non-destructive approach computed tomography (CT) was applied to examine its interior features. In this research, a DED machine using 316L stainless steel was used to perform the repairing process to demonstrate its effectiveness. The lab-built SLS system was used to capture each layer’s information, and CT data was provided for the quality evaluation. The novel manufacturing approach could improve the DED repair quality, reduce the repair time, and promote repair automation. In the future, it has a great potential to be used in the manufacturing industry to repair used parts and avoid the extra cost involved in buying a new part.
FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result's metadata. An ontology for Evidence Graphs, EVI ( https://w3id.org/EVI ), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.
Moves relating C-complexes: A correction to Cimasoni's “A geometric construction of the Conway potential function”

Abstract In work from 2004, Cimasoni gave a geometric computation of the multivariable Conway potential function in terms of a generalization of a Seifert surface for a link called a C-complex [2] . These surfaces were introduced by Cooper [9] , [10] for 2-component links and were used to compute Alexander invariants, signatures and nullities. Cooper presents a sequence of geometric moves which relate any two C-complexes for a 2-component link. Cimasoni gives an extension of Cooper's moves, providing a family of moves which relates any two C-complexes for a fixed link. This allows for an approach to studying links from the point of view of C-complexes and in following papers it has been used to derive invariants. Cimasoni's result is incorrect. We present counterexamples, a correction with detailed proof, and check that subsequent results relying on this lemma still hold.
Physics-informed Spline Learning for Nonlinear Dynamics Discovery

Dynamical systems are typically governed by a set of linear/nonlinear differential equations. Distilling the analytical form of these equations from very limited data remains intractable in many disciplines such as physics, biology, climate science, engineering and social science. To address this fundamental challenge, we propose a novel Physics-informed Spline Learning (PiSL) framework to discover parsimonious governing equations for nonlinear dynamics, based on sparsely sampled noisy data. The key concept is to (1) leverage splines to interpolate locally the dynamics, perform analytical differentiation and build the library of candidate terms, (2) employ sparse representation of the governing equations, and (3) use the physics residual in turn to inform the spline learning. The synergy between splines and discovered underlying physics leads to the robust capacity of dealing with high-level data scarcity and noise. A hybrid sparsity-promoting alternating direction optimization strategy is developed for systematically pruning the sparse coefficients that form the structure and explicit expression of the governing equations. The efficacy and superiority of the proposed method have been demonstrated by multiple well-known nonlinear dynamical systems, in comparison with two state-of-the-art methods.
A simple real-space scheme for periodic Dirac operators

We address in this work the question of the discretization of two-dimensional periodic Dirac Hamiltonians. Standard finite differences methods on rectangular grids are plagued with the so-called Fermion doubling problem, which creates spurious unphysical modes. The classical way around the difficulty used in the physics community is to work in the Fourier space, with the inconvenience of having to compute the Fourier decomposition of the coefficients in the Hamiltonian and related convolutions. We propose in this work a simple real-space method immune to the Fermion doubling problem and applicable to all two-dimensional periodic lattices. The method is based on spectral differentiation techniques. We apply our numerical scheme to the study of flat bands in graphene subject to periodic magnetic fields and in twisted bilayer graphene.
Moving-resting process with measurement error in animal movement modeling

Statistical modeling of animal movement is of critical importance. The continuous trajectory of an animal's movements is only observed at discrete, often irregularly spaced time points. Most existing models cannot handle the unequal sampling interval naturally and/or do not allow inactivity periods such as resting or sleeping. The recently proposed moving-resting (MR) model is a Brownian motion governed by a telegraph process, which allows periods of inactivity in one state of the telegraph process. The MR model shows promise in modeling the movements of predators with long inactive periods such as many felids, but the lack of accommodation of measurement errors seriously prohibits its application in practice. Here we incorporate measurement errors in the MR model and derive basic properties of the model. Inferences are based on a composite likelihood using the Markov property of the chain composed by every other observed increments. The performance of the method is validated in finite sample simulation studies. Application to the movement data of a mountain lion in Wyoming illustrates the utility of the method.
Bias in machine learning software: why? how? what to do?

Increasingly, software is making autonomous decisions in case of criminal sentencing, approving credit cards, hiring employees, and so on. Some of these decisions show bias and adversely affect certain social groups (e.g. those defined by sex, race, age, marital status). Many prior works on bias mitigation take the following form: change the data or learners in multiple ways, then see if any of that improves fairness. Perhaps a better approach is to postulate root causes of bias and then applying some resolution strategy. This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples. Our Fair-SMOTE algorithm removes biased labels; and rebalances internal distributions such that based on sensitive attribute, examples are equal in both positive and negative classes. On testing, it was seen that this method was just as effective at reducing bias as prior approaches. Further, models generated via Fair-SMOTE achieve higher performance (measured in terms of recall and F1) than other state-of-the-art fairness improvement algorithms. To the best of our knowledge, measured in terms of number of analyzed learners and datasets, this study is one of the largest studies on bias mitigation yet presented in the literature.
Strategic Complements or Substitutes? The Case of Adopting Health Information Technology by U.S. Hospitals

This paper explores the adoption choice of electronic medical records by U.S. hospitals, which could exhibit strategic complements or substitutes. I find complementarities in adoption through a reduced-form analysis with instruments for unobserved market characteristics. I further develop a dynamic oligopoly model to allow for strategic timing incentives that are missing in the static model. Adopting a dominant local vendor could increase per-period profits from adoption by 9.2% over choosing a marginal vendor. A counterfactual analysis suggests an incentive program rewarding coordination, not just adoption, is more effective in achieving interoperability, especially before the widespread adoption of the technology.
Differential effects of endogenous and exogenous attention on sensory tuning

Covert spatial attention (without concurrent eye movements) improves performance in many visual tasks (e.g., orientation discrimination and visual search). However, both covert attention systems, endogenous (voluntary) and exogenous (involuntary), exhibit differential effects on performance in tasks mediated by spatial and temporal resolution suggesting an underlying mechanistic difference. We investigated whether these differences manifest in sensory tuning by assessing whether and how endogenous and exogenous attention differentially alter the representation of two basic visual dimensions: orientation and spatial frequency (SF). The same human observers detected a vertical grating embedded in noise in two separate experiments (with cues to elicit either endogenous or exogenous attention) and reverse correlation was used to infer the underlying neural representation from behavioral responses. Both endogenous and exogenous attention similarly improved performance at the attended location by enhancing the gain of all orientations without changing tuning width. In the SF dimension, endogenous attention enhanced the gain of SFs above and below the target SF, whereas exogenous attention only enhanced those above. Additionally, exogenous attention shifted peak sensitivity to SFs above the target SF, whereas endogenous attention did not. Both covert attention systems modulated sensory tuning via the same computation (gain changes). However, there were differences in the strength of the gain. Compared to endogenous attention, exogenous attention had a stronger orientation gain enhancement but a weaker overall SF gain enhancement. These differences in sensory tuning may underlie differential effects of endogenous and exogenous attention on performance.
Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia.

Summary Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Despite overlap between genetic risk loci for ALL and hematologic traits, the etiological relevance of dysregulated blood-cell homeostasis remains unclear. We investigated this question in a genome-wide association study (GWAS) of childhood ALL (2,666 affected individuals, 60,272 control individuals) and a multi-trait GWAS of nine blood-cell indices in the UK Biobank. We identified 3,000 blood-cell-trait-associated (p
Increased Excited State Metallicity in Neutral Cr2On Clusters (n < 5) upon Sequential Oxidation.

Excited state lifetimes of neutral Cr2On (n 2.5 ps) response is recorded only in clusters with significant d-electron character, suggesting that adiabatic relaxation back to the ground state is efficient in heavily oxidized clusters, due to the presence of terminal O atoms. The simple picture of sequential oxidation of Cr2On reveals a linear variation in the contributions of each relaxation component to the total transient signals, therefore opening possibilities for the design of new molecular spintronic materials.
Nucleon clustering at kinetic freezeout of heavy-ion collisions via path-integral Monte Carlo

Abstract Clustering of the four-nucleon system at kinetic freezeout conditions is studied using path-integral Monte Carlo techniques. This method seeks to improve upon previous calculations which relied on approximate semiclassical methods or few-body quantum mechanics. Estimates are given for the decay probabilities of the 4N system into various light nuclei decay channels and the strength of spatial correlations is characterized. Additionally, a simple model is presented to describe the impact of this clustering on nucleon multiplicity distributions. The effects of a possible modification of the inter-nucleon interaction due to the close critical line (and hypothetical QCD critical point) on the clustering are also studied.
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning

Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior. However, these interventions can impose significant burden on a human supervisor, as each intervention interrupts other work the human is doing, incurs latency with each context switch between supervisor and autonomous control, and requires time to perform. We present LazyDAgger, which extends the interactive imitation learning (IL) algorithm SafeDAgger to reduce context switches between supervisor and autonomous control. We find that LazyDAgger improves the performance and robustness of the learned policy during both learning and execution while limiting burden on the supervisor. Simulation experiments suggest that LazyDAgger can reduce context switches by an average of 60% over SafeDAgger on 3 continuous control tasks while maintaining state-of-the-art policy performance. In physical fabric manipulation experiments with an ABB YuMi robot, LazyDAgger reduces context switches by 60% while achieving a 60% higher success rate than SafeDAgger at execution time.
Computational Design and Fabrication of Corrugated Mechanisms from Behavioral Specifications

Orthogonally assembled double-layered corrugated (OADLC) mechanisms are a class of foldable structures that harness origami-inspired methods to enhance the structural stiffness of resulting devices; these mechanisms have extensive applications due to their lightweight, compact nature as well as their high strength-to-weight ratio. However, the design of these mechanisms remains challenging. Here, we propose an efficient method to rapidly design OADLC mechanisms from desired behavioral specifications, i.e. in-plane stiffness and out-of-plane stiffness. Based on an equivalent plate model, we develop and validate analytical formulas for the behavioral specifications of OADLC mechanisms; the analytical formulas can be described as expressions of design parameters. On the basis of the analytical expressions, we formulate the design of OADLC mechanisms from behavioral specifications into an optimization problem that minimizes the weight with given design constraints. The 2D folding patterns of the optimized OADLC mechanisms can be generated automatically and directly delivered for fabrication. Our rapid design method is demonstrated by developing stiffness-enhanced mechanisms with a desired out-of-plane stiffness for a foldable gripper that enables a blimp to perch steadily under air disturbance and weight limit.
PointAcc: Efficient Point Cloud Accelerator

Deep learning on point clouds plays a vital role in a wide range of applications such as autonomous driving and AR/VR. These applications interact with people in real time on edge devices and thus require low latency and low energy. Compared to projecting the point cloud to 2D space, directly processing 3D point cloud yields higher accuracy and lower #MACs. However, the extremely sparse nature of point cloud poses challenges to hardware acceleration. For example, we need to explicitly determine the nonzero outputs and search for the nonzero neighbors (mapping operation), which is unsupported in existing accelerators. Furthermore, explicit gather and scatter of sparse features are required, resulting in large data movement overhead. In this paper, we comprehensively analyze the performance bottleneck of modern point cloud networks on CPU/GPU/TPU. To address the challenges, we then present PointAcc, a novel point cloud deep learning accelerator. PointAcc maps diverse mapping operations onto one versatile ranking-based kernel, streams the sparse computation with configurable caching, and temporally fuses consecutive dense layers to reduce the memory footprint. Evaluated on 8 point cloud models across 4 applications, PointAcc achieves 3.7 × speedup and 22 × energy savings over RTX 2080Ti GPU. Co-designed with light-weight neural networks, PointAcc rivals the prior accelerator Mesorasi by 100 × speedup with 9.1% higher accuracy running segmentation on the S3DIS dataset. PointAcc paves the way for efficient point cloud recognition.
A Conjectural Extension of the Kazhdan–Lusztig Equivalence

A theorem of Kazhdan and Lusztig establishes an equivalence between the category of G(CO)-integrable representations of the Kac-Moody algebra \hat{g}_{-\kappa} at a negative level -\kappa and the category \Rep_q(G) of (algebraic) representations of the "big" (a.k.a. Lusztig's) quantum group. In this paper we propose a conjecture that describes the category of Iwahori-integrable Kac-Moody modules. The corresponding object on the quantum group side, denoted Rep^{mxd}_q(G), involves Lusztig's version of the quantum group for the Borel and the De Concini-Kac version for the negative Borel.