cssi/SciSciGPT-SciSciNet huggingface dataset:
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. In this article, we propose the first secure aggregation framework, named Turbo-Aggregate, that in a network with $N$ users achieves a secure aggregation overhead of $O(N\log {N})$ , as opposed to $O(N^{2})$ , while tolerating up to a user dropout rate of 50%. Turbo-Aggregate employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that Turbo-Aggregate achieves a total running time that grows almost linear in the number of users, and provides up to $40\times $ speedup over the state-of-the-art protocols with up to $N=200$ users. Our experiments also demonstrate the impact of model size and bandwidth on the performance of Turbo-Aggregate.
Towards molecular simulations that are transparent, reproducible, usable by others, and extensible (TRUE)

Systems composed of soft matter (e.g., liquids, polymers, foams, gels, colloids, and most biological materials) are ubiquitous in science and engineering, but molecular simulations of such systems pose particular computational challenges, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation. Performing a molecular simulation of a soft matter system involves multiple steps, which have traditionally been performed by researchers in a "bespoke" fashion, resulting in many published soft matter simulations not being reproducible based on the information provided in the publications. To address the issue of reproducibility and to provide tools for computational screening, we have been developing the open-source Molecular Simulation and Design Framework (MoSDeF) software suite. In this paper, we propose a set of principles to create Transparent, Reproducible, Usable by others, and Extensible (TRUE) molecular simulations. MoSDeF facilitates the publication and dissemination of TRUE simulations by automating many of the critical steps in molecular simulation, thus enhancing their reproducibility. We provide several examples of TRUE molecular simulations: All of the steps involved in creating, running and extracting properties from the simulations are distributed on open-source platforms (within MoSDeF and on GitHub), thus meeting the definition of TRUE simulations.
Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks

We present a new convolutional neural network (CNN) based ImageJ plugin for fluorescence microscopy image denoising with an average improvement of 7.5 dB in peak signal-to-noise ratio (PSNR) and denoising instantly within 80 msec.
Transcriptome-guided parsimonious flux analysis improves predictions with metabolic networks in complex environments

The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell's investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities.
Incremental computation and maintenance of temporal aggregates

Abstract.We consider the problems of computing aggregation queries in temporal databases and of maintaining materialized temporal aggregate views efficiently. The latter problem is particularly challenging since a single data update can cause aggregate results to change over the entire time line. We introduce a new index structure called the SB-tree, which incorporates features from both segment-trees and B-trees. SB-trees support fast lookup of aggregate results based on time and can be maintained efficiently when the data change. We extend the basic SB-tree index to handle cumulative (also called moving-window) aggregates, considering separatelycases when the window size is or is not fixed in advance. For materialized aggregate views in a temporal database or warehouse, we propose building and maintaining SB-tree indices instead of the views themselves.
Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping

In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performance between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.
Selective carbon sources influence the end products of microbial nitrate respiration

Respiratory and catabolic genes are differentially distributed across microbial genomes. Thus, specific carbon sources may favor different respiratory processes. We profiled the influence of 94 carbon sources on the end products of nitrate respiration in microbial enrichment cultures from diverse terrestrial environments. We found that some carbon sources consistently favor dissimilatory nitrate reduction to ammonium (DNRA/nitrate ammonification) while other carbon sources favor nitrite accumulation or denitrification. For an enrichment culture from aquatic sediment, we sequenced the genomes of the most abundant strains, matched these genomes to 16S rDNA exact sequence variants (ESVs), and used 16S rDNA amplicon sequencing to track the differential enrichment of functionally distinct ESVs on different carbon sources. We found that changes in the abundances of strains with different genetic potentials for nitrite accumulation, DNRA or denitrification were correlated with the nitrite or ammonium concentrations in the enrichment cultures recovered on different carbon sources. Specifically, we found that either L-sorbose or D-cellobiose enriched for a Klebsiella nitrite accumulator, other sugars enriched for an Escherichia nitrate ammonifier, and citrate or formate enriched for a Pseudomonas denitrifier and a Sulfurospirillum nitrate ammonifier. Our results add important nuance to the current paradigm that higher concentrations of carbon will always favor DNRA over denitrification or nitrite accumulation, and we propose that, in some cases, carbon composition can be as important as carbon concentration in determining nitrate respiratory end products. Furthermore, our approach can be extended to other environments and metabolisms to characterize how selective parameters influence microbial community composition, gene content, and function.
The rate of mass loss and variations in the wind from the Be star Delta Centauri

Copernicus ultraviolet scans of the Be star delta Centauri obtained in 1976 and 1979, show a significant variation in the Si III lambda 1206 profile, The strong asymmetry that was present in 1976 had disappeared by 1979. The Si IV lambda 1400 doublet was also asymmetric in 1976, but was not observed in 1979. A quantitative fit of the line shapes to theoretical wind profiles shows that the mass-loss rate in 1976 was 2 x 10 to the minus 8th power/yr, and that the rate of mass loss in Si III was at least one order of magnitude less in 1979. It is not possible to determine whether the variation represented an overall change in the lass-loss rate, or whether it was due to a change in the ionization balance. The profile fitting procedure resulted in the adoption of assumed underlying photospheric Si III and Si IV profiles, and the equivalent widths measured from these profiles are most consistent with T sub eff between 30,000 and 35,000 K, somewhat hotter than implied by the spectral classification normally assigned to this star. The ultraviolet photospheric line widths, coupled wit published theoretical analyses of rotational gravitational darkening, imply an intrinsic equatorial velocity of about 310 km/sec and an angle of inclination of the rotational axis to the line of sight of i is less than or equal to 44 deg.
How does the business cycle affect eating habits

As economic expansions raise employment and wages, associated shifts in income and time constraints would be expected to also impact individuals' health. This study utilizes information from the US Behavioral Risk Factor Surveillance System (1990-2009) to explore the relationship between the state unemployment rate and the consumption of various healthy and unhealthy foods in the United States. Estimates, based on fixed effects methodologies, indicate that unemployment is associated with reduced consumption of fruits and vegetables and increased consumption of "unhealthy" foods such as snacks and fast food. Heterogeneous responses are also identified through detailed sample stratifications and by isolating the effect for those predicted to be at highest risk of unemployment based on their socioeconomic characteristics. Among individuals predicted to be at highest risk of being unemployed, a one percentage point increase in the resident state's unemployment rate is associated with a 3-6% reduction in the consumption of fruits and vegetables. The impact is somewhat higher among younger, low-educated, and married adults. Supplementary analyses also explore specific mediating pathways, and point to reduced family income and adverse mental health as significant channels underlying the procyclical nature of healthy food consumption.
Algebraic Manipulation on Computers for Scientists and Engineers

A system for algebraic manipulations using the TYPE OTHER feature of Control Data's F-63 has been developed. The TYPE OTHER feature enables the user to perform algebraic operations without altering the existing FORTRAN while retaining the ability to write statements in the simple FORTRAN language.
Federal competition and economic growth

Abstract This paper exploits exogenous variation in the natural topography of the United States to estimate the causal impact of inter-jurisdictional competition on income growth. We find that doubling the number of county governments in a metropolitan area leads to a 17% increase in the average annual growth rate of earnings per employee over 1969–2006, and a 10% increase in 2006 income per employee. Decomposing income effects using 2000 Census worker-level data, we find that approximately half of the effect stems from making workers more productive, while the other half comes from changing the composition of the workforce and inducing workers to work more hours. We also present evidence that inter-jurisdictional competition leads local governments to raise more in taxes, spend more, and issue more debt (per capita), but does not help them obtain more inter-governmental transfers. However, the additional cost from this increase in expenditures to a median-wage employee is much smaller than the increase in that employee's wages due to greater inter-jurisdictional competition.
An analysis of key indicators of reproducibility in radiology

Given the central role of radiology in patient care, it is important that radiological research is grounded in reproducible science. It is unclear whether there is a lack of reproducibility or transparency in radiologic research. To analyze published radiology literature for the presence or lack of key indicators of reproducibility. This cross-sectional retrospective study was performed by conducting a search of the National Library of Medicine (NLM) for publications contained within journals in the field of radiology. Our inclusion criteria were being MEDLINE indexed, written in English, and published from January 1, 2014, to December 31, 2018. We randomly sampled 300 publications for this study. A pilot-tested Google form was used to record information from the publications regarding indicators of reproducibility. Following peer-review, we extracted data from an additional 200 publications in an attempt to reproduce our initial results. The additional 200 publications were selected from the list of initially randomized publications. Our initial search returned 295,543 records, from which 300 were randomly selected for analysis. Of these 300 records, 294 met inclusion criteria and 6 did not. Among the empirical publications, 5.6% (11/195, [3.0–8.3]) contained a data availability statement, 0.51% (1/195) provided clear documented raw data, 12.0% (23/191, [8.4–15.7]) provided a materials availability statement, 0% provided analysis scripts, 4.1% (8/195, [1.9–6.3]) provided a pre-registration statement, 2.1% (4/195, [0.4–3.7]) provided a protocol statement, and 3.6% (7/195, [1.5–5.7]) were pre-registered. The validation study of the 5 key indicators of reproducibility—availability of data, materials, protocols, analysis scripts, and pre-registration—resulted in 2 indicators (availability of protocols and analysis scripts) being reproduced, as they fell within the 95% confidence intervals for the proportions from the original sample. However, materials’ availability and pre-registration proportions from the validation sample were lower than what was found in the original sample. Our findings demonstrate key indicators of reproducibility are missing in the field of radiology. Thus, the ability to reproduce studies contained in radiology publications may be problematic and may have potential clinical implications.
Learning a Probabilistic Strategy for Computational Imaging Sensor Selection

Optimized sensing is important for computational imaging in low-resource environments, when images must be recovered from severely limited measurements. In this paper, we propose a physics-constrained, fully differentiable, autoencoder that learns a probabilistic sensor-sampling strategy for optimized sensor design. The proposed method learns a system's preferred sampling distribution that characterizes the correlations between different sensor selections as a binary, fully-connected Ising model. The learned probabilistic model is achieved by using a Gibbs sampling inspired network architecture, and is trained end-to-end with a reconstruction network for efficient co-design. The proposed framework is applicable to sensor selection problems in a variety of computational imaging applications. In this paper, we demonstrate the approach in the context of a very-long-baseline-interferometry (VLBI) array design task, where sensor correlations and atmospheric noise present unique challenges. We demonstrate results broadly consistent with expectation, and draw attention to particular structures preferred in the telescope array geometry that can be leveraged to plan future observations and design array expansions.
The Environmental Benefits from Transportation Electrification: Urban Buses

Abstract Rapid technological change is opening new possibilities for electrification of the transportation sector. This paper offers novel empirical guidance to policymakers considering investments in electric urban buses. We determine the environmental benefit of using electric buses rather than diesel or Compressed Natural Gas (CNG) for urban transit. For diesel and CNG we calculate air pollution damages by combining emission rates with damage valuations from the AP3 integrated assessment model and the social cost of carbon. For electric buses we calculate air pollution damages by combining the damage valuations with estimates of the marginal increase in emissions from electricity usage. The environmental benefit is positive on average across all counties in the contiguous U.S. when comparing electric to either diesel or CNG. The environmental benefit of operating an electric bus fleet (rather than diesel) is about $65 million per year in Los Angeles and above $10 million per year in six other MSAs. Including the environmental benefit, we calculate the net present value (NPV) of bus investment. Relative to diesel, the NPV benefit of an electric bus is positive in about two thirds of urban counties. Relative to CNG, the NPV benefit is negative in all counties.
Effect of scanning speed on texture-elicited vibrations

To sense the texture of a surface, we run our fingers across it, which leads to the elicitation of skin vibrations that depend both on the surface and on exploratory parameters, particularly scanning speed. The transduction and processing of these vibrations mediate the ability to discern fine surface features. The objective of the present study is to characterize the effect of changes in scanning speed on texture-elicited vibrations to better understand how the exploratory movements shape the neuronal representation of texture. To this end, we scanned a variety of textures across the fingertip of human participants at a variety of speeds (10-160 mm s-1) while measuring the resulting vibrations using a laser Doppler vibrometer. First, we found that the intensity of the vibrations-as indexed by root-mean-square velocity-increases with speed but that the skin displacement remains constant. Second, we found that the frequency composition of the vibrations shifts systematically to higher frequencies with increases in scanning speed. Finally, we show that the speed-dependent shift in frequency composition accounts for the speed-dependent change in intensity.
Do Transformers Need Deep Long-Range Memory?

Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL — a Transformer augmented with a long-range memory of past activations — has been shown to be state-of-the-art across a variety of well-studied benchmarks. The Transformer-XL incorporates a long-range memory at every layer of the network, which renders its state to be thousands of times larger than RNN predecessors. However it is unclear whether this is necessary. We perform a set of interventions to show that comparable performance can be obtained with 6X fewer long range memories and better performance can be obtained by limiting the range of attention in lower layers of the network.
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

A trade-off between accuracy and fairness is almost taken as a given in the existing literature on fairness in machine learning. Yet, it is not preordained that accuracy should decrease with increased fairness. Novel to this work, we examine fair classification through the lens of mismatched hypothesis testing: trying to find a classifier that distinguishes between two ideal distributions when given two mismatched distributions that are biased. Using Chernoff information, a tool in information theory, we theoretically demonstrate that, contrary to popular belief, there always exist ideal distributions such that optimal fairness and accuracy (with respect to the ideal distributions) are achieved simultaneously: there is no trade-off. Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset. To complement our main result, we formulate an optimization to find ideal distributions and derive fundamental limits to explain why a trade-off exists on the given biased dataset. We also derive conditions under which active data collection can alleviate the fairness-accuracy trade-off in the real world. Our results lead us to contend that it is problematic to measure accuracy with respect to data that reflects bias, and instead, we should be considering accuracy with respect to ideal, unbiased data.
Rounding dynamic matchings against an adaptive adversary

We present a new dynamic matching sparsification scheme. From this scheme we derive a framework for dynamically rounding fractional matchings against adaptive adversaries. Plugging in known dynamic fractional matching algorithms into our framework, we obtain numerous randomized dynamic matching algorithms which work against adaptive adversaries. In contrast, all previous randomized algorithms for this problem assumed a weaker, oblivious, adversary. Our dynamic algorithms against adaptive adversaries include, for any constant є >0, a (2+є)-approximate algorithm with constant update time or polylog worst-case update time, as well as (2−δ)-approximate algorithms in bipartite graphs with arbitrarily-small polynomial update time. All these results achieve polynomially better update time to approximation trade-offs than previously known to be achievable against adaptive adversaries.
Black-Box Methods for Restoring Monotonicity

In many practical applications, heuristic or approximation algorithms are used to efficiently solve the task at hand. However their solutions frequently do not satisfy natural monotonicity properties of optimal solutions. In this work we develop algorithms that are able to restore monotonicity in the parameters of interest. Specifically, given oracle access to a (possibly non-monotone) multi-dimensional real-valued function f, we provide an algorithm that restores monotonicity while degrading the expected value of the function by at most e. The number of queries required is at most logarithmic in 1/e and exponential in the number of parameters. We also give a lower bound showing that this exponential dependence is necessary. Finally, we obtain improved query complexity bounds for restoring the weaker property of k-marginal monotonicity. Under this property, every k-dimensional projection of the function f is required to be monotone. The query complexity we obtain only scales exponentially with k.
On the Theoretical Properties of the Network Jackknife

We study the properties of a leave-node-out jackknife procedure for network data. Under the sparse graphon model, we prove an Efron-Stein-type inequality, showing that the network jackknife leads to conservative estimates of the variance (in expectation) for any network functional that is invariant to node permutation. For a general class of count functionals, we also establish consistency of the network jackknife. We complement our theoretical analysis with a range of simulated and real-data examples and show that the network jackknife offers competitive performance in cases where other resampling methods are known to be valid. In fact, for several network statistics, we see that the jackknife provides more accurate inferences compared to related methods such as subsampling.
Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single Image

We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Since no existing dataset provides ground truth high quality spatially-varying material and spatially-varying lighting, we propose novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Experiments show that our framework outperforms previous methods and enables various novel applications like photorealistic object insertion and material editing.
Defect Modes for Dislocated Periodic Media

We study defect modes in a one-dimensional periodic medium perturbed by an adiabatic dislocation of amplitude $$\delta \ll 1$$ . If the periodic background admits a Dirac point—a linear crossing of dispersion curves—then the dislocated operator acquires a gap in its essential spectrum. For this model (and its honeycomb analog) Fefferman et al. (Proc Natl Acad Sci USA 111(24):8759–8763, 2014, Mem Am Math Soc 247(1173):118, 2017, Ann PDE 2(2):80, 2016, 2D Mater 3:1, 2016) constructed (at leading order in $$\delta $$ ) defect modes with energies within the gap. These bifurcate from the eigenmodes of an effective Dirac operator. Here we address the following open problems: We respond positively to both questions. Our approach relies on (a) resolvent estimates for the bulk operators; (b) scattering theory for highly oscillatory potentials [Dr18a, Dr18b, Dr18c]. It has led to an understanding of the topological stability of defect states in continuous dislocated systems—in connection with the bulk-edge correspondence [Dr18d].
Adversarially Robust Learning Could Leverage Computational Hardness.

Over recent years, devising classification algorithms that are robust to adversarial perturbations has emerged as a challenging problem. In particular, deep neural nets (DNNs) seem to be susceptible to small imperceptible changes over test instances. However, the line of work in provable robustness, so far, has been focused on information-theoretic robustness, ruling out even the existence of any adversarial examples. In this work, we study whether there is a hope to benefit from algorithmic nature of an attacker that searches for adversarial examples, and ask whether there is any learning task for which it is possible to design classifiers that are only robust against polynomial-time adversaries. Indeed, numerous cryptographic tasks can only be secure against computationally bounded adversaries, and are indeed impossible for computationally unbounded attackers. Thus, it is natural to ask if the same strategy could help robust learning. We show that computational limitation of attackers can indeed be useful in robust learning by demonstrating the possibility of a classifier for some learning task for which computational and information theoretic adversaries of bounded perturbations have very different power. Namely, while computationally unbounded adversaries can attack successfully and find adversarial examples with small perturbation, polynomial time adversaries are unable to do so unless they can break standard cryptographic hardness assumptions. Our results, therefore, indicate that perhaps a similar approach to cryptography (relying on computational hardness) holds promise for achieving computationally robust machine learning. On the reverse directions, we also show that the existence of such learning task in which computational robustness beats information theoretic robustness requires computational hardness by implying (average-case) hardness of NP.
Cooperative foraging during larval stage affects fitness in Drosophila

Cooperative behavior can confer advantages to animals. This is especially true for cooperative foraging which provides fitness benefits through more efficient acquisition and consumption of food. While examples of group foraging have been widely described, the principles governing formation of such aggregations and rules that determine group membership remain poorly understood. Here, we take advantage of an experimental model system featuring cooperative foraging behavior in Drosophila. Under crowded conditions, fly larvae form coordinated digging groups (clusters), where individuals are linked together by sensory cues and group membership requires prior experience. However, fitness benefits of Drosophila larval clustering remain unknown. We demonstrate that animals raised in crowded conditions on food partially processed by other larvae experience a developmental delay presumably due to the decreased nutritional value of the substrate. Intriguingly, same conditions promote the formation of cooperative foraging clusters which further extends larval stage compared to non-clustering animals. Remarkably, this developmental retardation also results in a relative increase in wing size, serving an indicator of adult fitness. Thus, we find that the clustering-induced developmental delay is accompanied by fitness benefits. Therefore, cooperative foraging, while delaying development, may have evolved to give Drosophila larvae benefits when presented with competition for limited food resources.
Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites.

Direct sampling of building wastewater has the potential to enable “precision public health” observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic “features”, but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features—providing orthogonal information to physicochemical properties—illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.
Traumatic abdominal wall hernias in children: A case for early exploration.

Abstract Purpose Traumatic abdominal wall hernia (TAWH) is a rare consequence of blunt abdominal trauma (BAT). We examined a series of patients suffering TAWH to evaluate its frequency, rate of associated concurrent intraabdominal injuries (CAI) and correlation with CT, management and outcomes. Methods A Level 1 pediatric trauma center trauma registry was queried for children less than 18 years old suffering TAWH from BAT between 2009 and 2019. Results 9370 patients were admitted after BAT. TAWH was observed in 11 children, at incidence 0.1%. Eight children (73%) were male, at mean age 10 years, and mean ISS of 16. Six cases (55%) were because of MVC, three (27%) impaled by a handlebar or pole, and two (18%) dragged under large machinery. Seven (64%) had a CAI requiring operative or interventional management. Patients with CAI were similar to those without other injury, with 20% and 50% CT scan sensitivity and specificity for detection of associated injury, respectively. Five patients had immediate hernia repair with laparotomy for repair of intraabdominal injury, three had delayed repair, two have asymptomatic unrepaired TAWH, and one resolved spontaneously. Conclusions Children with TAWH have high rates of CAI requiring operative repair. CT scans have low sensitivity and specificity for detecting associated injuries. A high suspicion of injury and low threshold for exploration must be maintained in TAWH cases. Level of evidence IV
First-time Diabetic Ketoacidosis in Type 2 Diabetics With Covid-19 Infection: A Novel Case Series.

Abstract Background Severe acute respiratory syndrome coronavirus 2 is a novel coronavirus first diagnosed in U.S. hospitals in January 2020. Typical presenting symptoms include fever, dry cough, dyspnea, and hypoxia. However, several other symptoms have been reported, including fatigue, weakness, diarrhea, and abdominal pain. We have identified a series of patients with diabetic ketoacidosis (DKA) likely precipitated by coronavirus disease 2019 (COVID-19). Case Series We describe 5 patients with previously known type 2 diabetes and no history of DKA, who presented to the emergency department with new-onset DKA and COVID-19. Why Should an Emergency Physician Be Aware of This? Diabetes mellitus is a known risk factor for poor outcomes in viral respiratory illnesses, including COVID-19. Infection may precipitate DKA in patients with type 2 diabetes. Aggressive management of these patients is recommended; however, management guidelines have not yet been put forth for this unique subset of patients.
Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. Here, we develop a transformational spatial analytics computational and systems biology platform (SpAn) that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. We apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naive colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of 55 fluorescently tagged antibodies. We show that SpAn predicts the 5-year risk of CRC recurrence with a mean AUROC of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. Additionally, SpAn infers the emergent network biology of tumor microenvironment spatial domains revealing a spatially-mediated role of CRC consensus molecular subtype features with the potential to inform precision medicine.
Accounting Research and Problem Solving

Accounting and management practice as well as accounting research would benefit by shifting more of the scholarly attention towards external matters — solving problems of practice — while retaining its emphasis on rigor and validity of research claims. The scope of natural sciences extends beyond the boundaries of time or space, but social sciences are contingent on both. Compared with the laws of mechanics, electricity or sound, any regularities we observe in social domains tend to have limited, and noisier predictive power and practical applicability. Moreover, regularities observed in social data are often unstable because of the reflexivity of such phenomena: once human beings recognize a pattern in past data, they tend to adjust their behavior so the pattern in the data changes. In other words, many social phenomena tend not to be robust to their own discovery.
Scheduling for Weighted Flow and Completion Times in Reconfigurable Networks

New optical technologies offer the ability to recon-figure network topologies dynamically, rather than setting them once and for all. This is true in both optical wide area networks (optical WANs) and in datacenters, despite the many differences between these two settings. Because of these new technologies, there has been a surge of both practical and theoretical research on algorithms to take advantage of them. In particular, Jia et al. [INFOCOM '17] designed online scheduling algorithms for dynamically reconfigurable topologies for both the makespan and sum of completion times objectives. In this paper, we work in the same setting but study an objective that is more meaningful in an online setting: the sum of flow times. The flow time of a job is the total amount of time that it spends in the system, which may be considerably smaller than its completion time if it is released late. We provide competitive algorithms for the online setting with speed augmentation, and also give a lower bound proving that speed augmentation is in fact necessary. As a side effect of our techniques, we also improve and generalize the results of Jia et al. on completion times by giving an O(1)-competitive algorithm for arbitrary sizes and release times even when nodes have different degree bounds, and moreover allow for the weighted sum of completion times (or flow times).
Finding patterns in objects and numbers: Repeating patterning in pre-K predicts kindergarten mathematics knowledge

Abstract Both recent evidence and research-based early mathematics curricula indicate that repeating patterns—predictable sequences that follow a rule—are a topic of major importance for mathematics development. The purpose of the current study was to help build a theory for how early repeating patterning knowledge contributes to early math development, focusing on development in children aged 4–6 years. The current study examined the relation between 65 preschool children’s repeating patterning knowledge (via a fast, teacher-friendly measure) and their end-of-kindergarten broad math and numeracy knowledge, controlling for verbal and visual-spatial working memory (WM) skills as well as end-of-pre-K (pre-kindergarten) broad math knowledge. Relations were also examined between repeating patterning and specific aspects of numeracy knowledge—knowledge of the count sequence to 100 and the successor principle. Children’s repeating patterning knowledge was significantly predictive of their broad math and general numeracy knowledge, as well as one specific aspect of their numeracy knowledge (counting to 100), even after controlling for verbal and visual-spatial WM skills. Further, repeating patterning knowledge remained a unique predictor of general numeracy knowledge and counting to 100 after controlling for end-of-pre-K broad math knowledge. The relation between repeating patterning and mathematics may be explained by the central role that identifying predictable sequences based on underlying rules plays in both. Theories of math development and early math instruction standards should thus give even greater attention to the role of children’s repeating patterning knowledge.
Whole‐brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals

MRI-based neuroimaging techniques have been used to investigate brain injury associated with HIV-infection. Whole-brain cortical mean-field dynamic modeling provides a way to integrate structural and functional imaging outcomes, allowing investigation of microscale brain dynamics. In this study, we adopted the relaxed mean-field dynamic modeling to investigate structural and functional connectivity in 42 HIV-infected subjects before and after 12-week of combination antiretroviral therapy (cART) and compared them with 46 age-matched healthy subjects. Microscale brain dynamics were modeled by a set of parameters including two region-specific microscale brain properties, recurrent connection strengths, and subcortical inputs. We also analyzed the relationship between the model parameters (i.e., the recurrent connection and subcortical inputs) and functional network topological characterizations, including smallworldness, clustering coefficient, and network efficiency. The results show that untreated HIV-infected individuals have disrupted local brain dynamics that in part correlate with network topological measurements. Notably, after 12 weeks of cART, both the microscale brain dynamics and the network topological measurements improved and were closer to those in the healthy brain. This was also associated with improved cognitive performance, suggesting that improvement in local brain dynamics translates into clinical improvement.
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features Relate

In time-to-event prediction problems, a standard approach to estimating an interpretable model is to use Cox proportional hazards, where features are selected based on lasso regularization or stepwise regression. However, these Cox-based models do not learn how different features relate. As an alternative, we present an interpretable neural network approach to jointly learn a survival model to predict time-to-event outcomes while simultaneously learning how features relate in terms of a topic model. In particular, we model each subject as a distribution over “topics”, which are learned from clinical features as to help predict a time-to-event outcome. From a technical standpoint, we extend existing neural topic modeling approaches to also minimize a survival analysis loss function. We study the effectiveness of this approach on seven healthcare datasets on predicting time until death as well as hospital ICU length of stay, where we find that neural survival-supervised topic models achieves competitive accuracy with existing approaches while yielding interpretable clinical “topics” that explain feature relationships.
Utility of three-dimensional transesophageal echocardiography to guide transseptal positioning of a single multistage venous cannula to provide both venous drainage and indirect left ventricular venting in veno-arterial extracorporeal membrane oxygenation

A patient with heart failure due to dilated ischemic cardiomyopathy presented in cardiogenic shock for institution of veno-arterial extracorporeal membrane oxygenation as a bridge to cardiac transplantation. To provide adequate venous drainage and simultaneous decompression of the left atrium (indirect left ventricular venting), a single venous cannula was placed across the interatrial septum so that the distal orifice and side ports were located within the left atrium and the proximal set of side ports were positioned at the cavoatrial junction. Three-dimensional transesophageal echocardiography demonstrated utility in guiding cannula placement and appropriate positioning within the left atrium.
Analysis of Micropolar Fluids: Existence of Potential Microflow Solutions, Nearby Global Well-Posedness, and Asymptotic Stability

In this paper we concern ourselves with an incompressible, viscous, isotropic, and periodic micropolar fluid. We find that in the absence of forcing and microtorquing there exists an infinite family of well-behaved solutions, which we call potential microflows, in which the fluid velocity vanishes identically, but the angular velocity of the microstructure is conservative and obeys a linear parabolic system. We then prove that nearby each potential microflow, the nonlinear equations of motion are well-posed globally-in-time, and solutions are stable. Finally, we prove that in the absence of force and microtorque, solutions decay exponentially, and in the presence of force and microtorque obeying certain conditions, solutions have quantifiable decay rates.
Election Control by Manipulating Issue Significance

Integrity of elections is vital to democratic systems, but it is frequently threatened by malicious actors. The study of algorithmic complexity of the problem of manipulating election outcomes by changing its structural features is known as election control. One means of election control that has been proposed is to select a subset of issues that determine voter preferences over candidates. We study a variation of this model in which voters have judgments about relative importance of issues, and a malicious actor can manipulate these judgments. We show that computing effective manipulations in this model is NP-hard even with two candidates or binary issues. However, we demonstrate that the problem is tractable with a constant number of voters or issues. Additionally, while it remains intractable when voters can vote stochastically, we exhibit an important special case in which stochastic voting enables tractable manipulation.
Laser spectroscopy of metastable palladium at 340 and 363 nm

We report measurements of the isotope shift of the mass 104, 105, 106, 108, and 110 stable palladium isotopes for transitions from the metastable state $4{d}^{9}{(}^{2}{D}_{5/2})5s,{\phantom{\rule{0.16em}{0ex}}}^{2}{[5/2]}_{3}\ensuremath{\rightarrow}4{d}^{9}{(}^{2}{D}_{5/2})5p,{\phantom{\rule{0.16em}{0ex}}}^{2}{[7/2]}_{4}^{o}$ at 340 nm, and $4{d}^{9}{(}^{2}{D}_{5/2})5s,{\phantom{\rule{0.16em}{0ex}}}^{2}{[5/2]}_{3}\ensuremath{\rightarrow}4{d}^{9}{(}^{2}{D}_{5/2})5p,{\phantom{\rule{0.16em}{0ex}}}^{2}{[3/2]}_{2}^{o}$ at 363 nm. For $^{105}\mathrm{Pd}$, we determine the hyperfine structure of both transitions. The measurements were performed by saturation absorption spectroscopy in a custom-built hollow cathode lamp and will serve as a reference for collinear laser spectroscopy of short-lived neutron-rich palladium isotopes. We also report on the observation of a population inversion for the 363-nm transition under certain discharge conditions.
Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via Alignment Ensemble

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population. Screening for PDACs in dynamic contrast-enhanced CT is beneficial for early diagnosis. In this paper, we investigate the problem of automated detecting PDACs in multi-phase (arterial and venous) CT scans. Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture, making it difficult to combine cross-phase information seamlessly. We study multiple phase alignment strategies, i.e., early alignment (image registration), late alignment (high-level feature registration), and slow alignment (multi-level feature registration), and suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection. We provide an extensive empirical evaluation on two PDAC datasets and show that the proposed alignment ensemble significantly outperforms previous state-of-the-art approaches, illustrating the strong potential for clinical use.
Bridge trisections in rational surfaces

We study smooth isotopy classes of complex curves in complex surfaces from the perspective of the theory of bridge trisections, with a special focus on curves in ℂℙ2 and ℂℙ1 × ℂℙ1. We are especiall...