cssi/SciSciGPT-SciSciNet huggingface dataset:
Mono2Micro: a practical and effective tool for decomposing monolithic Java applications to microservices

In migrating production workloads to cloud, enterprises often face the daunting task of evolving monolithic applications toward a microservice architecture. At IBM, we developed a tool called Mono2Micro to assist with this challenging task. Mono2Micro performs spatio-temporal decomposition, leveraging well-defined business use cases and runtime call relations to create functionally cohesive partitioning of application classes. Our preliminary evaluation of Mono2Micro showed promising results. How well does Mono2Micro perform against other decomposition techniques, and how do practitioners perceive the tool? This paper describes the technical foundations of Mono2Micro and presents results to answer these two questions. To answer the first question, we evaluated Mono2Micro against four existing techniques on a set of open-source and proprietary Java applications and using different metrics to assess the quality of decomposition and tool’s efficiency. Our results show that Mono2Micro significantly outperforms state-of-the-art baselines in specific metrics well-defined for the problem domain. To answer the second question, we conducted a survey of twenty-one practitioners in various industry roles who have used Mono2Micro. This study highlights several benefits of the tool, interesting practitioner perceptions, and scope for further improvements. Overall, these results show that Mono2Micro can provide a valuable aid to practitioners in creating functionally cohesive and explainable microservice decompositions.
Clavicular Metastasis as an Initial Presentation of Papillary Thyroid Cancer.

Objective We present the case of a 44-year-old man with a large neck mass to highlight the unique presentation of papillary thyroid carcinoma (PTC) metastatic to the clavicle. Methods We reviewed the medical record for a detailed history and physical examination findings. Our radiology colleagues examined the diagnostic imaging studies performed. The pathology team reviewed the neck mass biopsy and the confirmatory surgical pathology after total resection of the mass. Results A 44-year-old man presented with an enlarging neck mass. Initial X-rays revealed a large soft tissue density mass that extended to the midline of the right clavicle. A neck ultrasound established a 5.4 × 3.6 cm mass with increased vascularity and calcification extending from the thyroid. A CT scan noted the extension of the mass into the adjacent sternoclavicular junction with osteolysis of the middle third of the clavicle and the superior aspect of the sternal body. Fine-needle aspiration revealed a thyroid neoplasm with follicular features and positive immunostaining consistent with thyroid carcinoma. The patient underwent a composite resection of the tumor, including a segmental osteotomy of approximately two-thirds of the medial clavicle. The pathology report confirmed PTC with extrathyroidal extension and clavicle involvement (staged pT4a pN0), with further genomic findings showing positive KRAS mutation. Conclusion Clavicular metastasis from differentiated thyroid cancer is rare. While the prognosis is generally favorable, various factors, including age greater than 45 years, poor differentiation, follicular thyroid carcinoma, Hurthle cell variant, and extrapulmonary metastasis, have typically been associated with poorer cancer-specific survival.
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End

Comprehending the overall intent of an utterance helps a listener recognize the individual words spoken. Inspired by this fact, we perform a novel study of the impact of explicitly incorporating intent representations as additional information to improve a recurrent neural network-transducer (RNN-T) based automatic speech recognition (ASR) system. An audio-to-intent (A2I) model encodes the intent of the utterance in the form of embeddings or posteriors, and these are used as auxiliary inputs for RNN-T training and inference. Experimenting with a 50k-hour far-field English speech corpus, this study shows that when running the system in non-streaming mode, where intent representation is extracted from the entire utterance and then used to bias streaming RNN-T search from the start, it provides a 5.56% relative word error rate reduction (WERR). On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3.33% relative WERR. A further detailed analysis of the streaming system indicates that our proposed method brings especially good gain on media-playing related intents (e.g. 9.12% relative WERR on PlayMusicIntent).
CHEF: a cheap and fast pipeline for iteratively cleaning label uncertainties

High-quality labels are expensive to obtain for many machine learning tasks, such as medical image classification tasks. Therefore, probabilistic (weak) labels produced by weak supervision tools are used to seed a process in which influential samples with weak labels are identified and cleaned by several human annotators to improve the model performance. To lower the overall cost and computational overhead of this process, we propose a solution called Chef(CHEap and Fast label cleaning), which consists of the following three components. First, to reduce the cost of human annotators, we use Infl, which prioritizes the most influential training samples for cleaning and provides cleaned labels to save the cost of one human annotator. Second, to accelerate the sample selector phase and the model constructor phase, we use Increm-Infl to incrementally produce influential samples, and DeltaGrad-L to incrementally update the model. Third, we redesign the typical label cleaning pipeline so that human annotators iteratively clean smaller batch of samples rather than one big batch of samples. This yields better over all model performance and enables possible early termination when the expected model performance has been achieved. Extensive experiments show that our approach gives good model prediction performance while achieving significant speed-ups.
The impact of make-take fees on market efficiency

Stock exchanges structure their trading fees to subsidize liquidity by offering “make” rebates for providing liquidity through limit orders and charging “take” fees for consuming liquidity via marketable orders, leading to debate regarding the impact of these fees on market quality. Using an experiment performed by NASDAQ, I employ difference-in-differences analysis and find that a decrease in take fee and make rebate levels on one exchange leads to greater absolute pricing error and larger variance of mispricing for the market as a whole, beyond that expected from widened bid-ask spreads. This occurs because bid-ask spreads widen and fewer informative trades are executed.
CellProfiler Analyst 3.0: Accessible data exploration and machine learning for image analysis.

Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualisation tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. Availability CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample data set is available at https://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection (BBBC).
Endoscopic decompositions and the Hausel–Thaddeus conjecture

We construct natural operators connecting the cohomology of the moduli spaces of stable Higgs bundles with different ranks and genera which, after numerical specialisation, recover the topological mirror symmetry conjecture of Hausel and Thaddeus concerning -Higgs bundles in terms of the tautological classes, and gives a new proof of the Hausel–Thaddeus conjecture, which was also proven recently by Grochenig, Wyss and Ziegler via p-adic integration.Our method is to relate the decomposition theorem for the Hitchin fibration, using vanishing cycle functors, to the decomposition theorem for the twisted Hitchin fibration, whose supports are simpler.
Institutions and Incentives in Antitrust Enforcement

The United States Supreme Court has decided in a number of cases how Section One 1 of the Sherman Act should apply to agreements that potentially harm competition. In recent key cases, the Court stated that the rule of reason is the default rule in antitrust. Second, per se condemnation (or some rebuttable presumption) is reserved for a limited group of practices that economics and experience show that the type of agreement under consideration is inevitably destructive of competition with little or no redeeming features. Third, reasonable people can disagree about the likely competitive effects of a particular type of agreement. Therefore, lower courts going forward should apply the rule of reason on a case by case basis to determine whether there is any likelihood of competitive harm and any likelihood of significant procompetitive benefits. However, lower courts should structure and streamline their analysis by applying one or more rules of thumb. The Supreme Court followed this basic approach in Leegin, applying the rule of reason to resale price maintenance agreements (vertical price fixing), and in Actavis, applying the rule of reason to pay-for-delay agreements involving branded and generic pharmaceutical manufacturers. This essay explores where this strategy has been successful (Actavis) and where it has not (Leegin). I focus not on the substantive law but instead on the institutions and incentives in antitrust enforcement that ensure that the pronouncements of the law on the books by the Supreme Court get translated into the law in action in the lower courts and the real world.
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts

Governments are increasingly turning to algorithmic risk assessments when making important decisions, such as whether to release criminal defendants before trial. Policymakers assert that providing public servants with algorithmic advice will improve human risk predictions and thereby lead to better (e.g., fairer) decisions. Yet because many policy decisions require balancing risk-reduction with competing goals, improving the accuracy of predictions may not necessarily improve the quality of decisions. If risk assessments make people more attentive to reducing risk at the expense of other values, these algorithms would diminish the implementation of public policy even as they lead to more accurate predictions. Through an experiment with 2,140 lay participants simulating two high-stakes government contexts, we provide the first direct evidence that risk assessments can systematically alter how people factor risk into their decisions. These shifts counteracted the potential benefits of improved prediction accuracy. In the pretrial setting of our experiment, the risk assessment made participants more sensitive to increases in perceived risk; this shift increased the racial disparity in pretrial detention by 1.9%. In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8.3%. These results demonstrate the potential limits and harms of attempts to improve public policy by incorporating predictive algorithms into multifaceted policy decisions. If these observed behaviors occur in practice, presenting risk assessments to public servants would generate unexpected and unjust shifts in public policy without being subject to democratic deliberation or oversight.
Chiral anomalies induced by gravitational waves

Chiral symmetries in field theory are typically affected by an anomaly in the quantum theory. This anomaly emerges when one introduces an interaction with a Yang-Mills or gravitational background. Physical applications of this quantum effect have been traditionally connected to topological questions of the background field and the study of instantons. We show here how one can alternatively find situations of physical interest that only involve ordinary, but dynamical solutions of the background field equations. More precisely, we show that solutions to the Einstein (Maxwell) equations are able to trigger the chiral anomaly if and only if they admit a flux of gravitational (electromagnetic) radiation with net circular polarization. As a consequence, astrophysical systems that admit such radiation spontaneously generate a flux of particles with net helicity from the quantum vacuum.
Better Distance Preservers and Additive Spanners

We study two popular ways to sketch the shortest path distances of an input graph. The first is distance preservers, which are sparse subgraphs that agree with the distances of the original graph on a given set of demand pairs. Prior work on distance preservers has exploited only a simple structural property of shortest paths, called consistency, stating that one can break shortest path ties such that no two paths intersect, split apart, and then intersect again later. We prove that consistency alone is not enough to understand distance preservers, by showing both a lower bound on the power of consistency and a new general upper bound that polynomially surpasses it. Specifically, our new upper bound is that any p demand pairs in an n-node undirected unweighted graph have a distance preserver on O(n2/3p2/3 + np1/3 edges. We leave a conjecture that the right bound is O(n2/3p2/3 + n) or better. The second part of this paper leverages these distance preservers in a new construction of additive spanners, which are subgraphs that preserve all pairwise distances up to an additive error function. We give improved error bounds for spanners with relatively few edges; for example, we prove that all graphs have spanners on O(n) edges with +O(n3/7 + e ) error. Our construction can be viewed as an extension of the popular path-buying framework to clusters of larger radii.
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

Self-supervised depth estimation for indoor environments is more challenging than its outdoor counterpart in at least the following two aspects: (i) the depth range of indoor sequences varies a lot across different frames, making it difficult for the depth network to induce consistent depth cues, whereas the maximum distance in outdoor scenes mostly stays the same as the camera usually sees the sky; (ii) the indoor sequences contain much more rotational motions, which cause difficulties for the pose network, while the motions of outdoor sequences are pre-dominantly translational, especially for driving datasets such as KITTI. In this paper, special considerations are given to those challenges and a set of good practices are consolidated for improving the performance of self-supervised monocular depth estimation in indoor environments. The proposed method mainly consists of two novel modules, \ie, a depth factorization module and a residual pose estimation module, each of which is designed to respectively tackle the aforementioned challenges. The effectiveness of each module is shown through a carefully conducted ablation study and the demonstration of the state-of-the-art performance on three indoor datasets, \ie, EuRoC, NYUv2, and 7-scenes.
Kinetics of carbon condensation in detonation of high explosives: First-order phase transition theory perspective

The kinetics of carbon condensation, or carbon clustering, in detonation of carbon-rich high explosives is modeled by solving a system of rate equations for concentrations of carbon particles. Unlike previous efforts, the rate equations account not only for the aggregation of particles but also for their fragmentation in a thermodynamically consistent manner. Numerical simulations are performed, yielding the distribution of particle concentrations as a function of time. In addition to that, analytical expressions are obtained for all the distinct steps and regimes of the condensation kinetics, which facilitates the analysis of the numerical results and allows one to study the sensitivity of the kinetic behavior to the variation of system parameters. The latter is important because the numerical values of many parameters are not reliably known at present. The theory of the kinetics of first-order phase transitions is found adequate to describe the general kinetic trends of carbon condensation, as described by the rate equations. Such physical phenomena and processes as the coagulation, nucleation, growth, and Ostwald ripening are observed, and their dependence on various system parameters is studied and reported. It is believed that the present work will become useful when analyzing the present and future results for the kinetics of carbon condensation, obtained from experiments or atomistic simulations.
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs

Both geometric and semantic information of the search space are imperative for a good plan. We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color) and propose a generalized A∗ to find the shortest path among the set of paths with minimal inclusion of low-ranked color edges. We prove the completeness and optimality of this Class-Ordered A∗ (COA∗ ) algorithm with respect to the hereto defined notion of optimality. The utility of COA∗ is numerically validated in a ternary graph with feasible, infeasible, and unknown vertices and edges for the cases of a 2D mobile robot, a 3D robotic arm, and a 5D robotic arm with limited sensing capabilities. We compare the results of COA∗ to that of the regular A∗ algorithm, the latter of which finds a shortest path regardless of the semantic information, and we show that the COA∗ dominates the A∗ solution in terms of finding less uncertain paths.
Distinct regulation of tonic GABAergic inhibition by NMDA receptor subtypes.

Tonic inhibition mediated by extrasynaptic GABAARs regulates various brain functions. However, the mechanisms that regulate tonic inhibition remain largely unclear. Here, we report distinct actions of GluN2A- and GluN2B-NMDA receptors (NMDARs) on tonic inhibition in hippocampal neurons under basal and high activity conditions. Specifically, overexpression of GluN2B, but not GluN2A, reduces α5-GABAAR surface expression and tonic currents. Additionally, knockout of GluN2A and GluN2B decreases and increases tonic currents, respectively. Mechanistically, GluN2A-NMDARs inhibit and GluN2B-NMDARs promote α5-GABAAR internalization, resulting in increased and decreased surface α5-GABAAR expression, respectively. Furthermore, GluN2A-NMDARs, but not GluN2B-NMDARs, are required for homeostatic potentiation of tonic inhibition induced by prolonged increase of neuronal activity. Last, tonic inhibition decreases during acute seizures, whereas it increases 24 h later, involving GluN2-NMDAR-dependent signaling. Collectively, these data reveal an NMDAR subunit-specific regulation of tonic inhibition in physiological and pathological conditions and provide mechanistic insight into activity-dependent modulation of tonic inhibition.
Optimization Models for Estimating Transit Network Origin-Destination Flows with AVL/APC Data

The increasing adoption of automatic vehicle location and automatic passenger count technologies by transit agencies produces passenger boarding and alighting count data on a continuous basis. This data provides new opportunities for origin–destination (O–D) flow estimation. However, the state-of-the-art methodologies generated flows within routes and barely considered linked trips. This paper proposes optimization models to identify transfers and approximate network-level O–D flows by: a quadratic integer program (QIP), a feasible rounding procedure for the quadratic convex programming (QCP) relaxation of the QIP, and an integer program (IP). A case study for Ann Arbor-Ypsilanti area in Michigan suggests that: The IP model outperforms the QCP in terms of accuracy and remains tractable from an efficiency standpoint, contrary to the QIP. Its O–D estimation achieves an R-Squared metric of $$95.57\%$$ at the traffic analysis zone level and $$92.39\%$$ at the stop level, compared to the ground-truths inferred from the state-of-the-practice trip-chaining methods.
CHIP: CHannel Independence-based Pruning for Compact Neural Networks

Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-channel information. In this paper, starting from an inter-channel perspective, we propose to perform efficient filter pruning using Channel Independence, a metric that measures the correlations among different feature maps. The less independent feature map is interpreted as containing less useful information$/$knowledge, and hence its corresponding filter can be pruned without affecting model capacity. We systematically investigate the quantification metric, measuring scheme and sensitiveness$/$reliability of channel independence in the context of filter pruning. Our evaluation results for different models on various datasets show the superior performance of our approach. Notably, on CIFAR-10 dataset our solution can bring $0.75\%$ and $0.94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models, respectively, and meanwhile the model size and FLOPs are reduced by $42.8\%$ and $47.4\%$ (for ResNet-56) and $48.3\%$ and $52.1\%$ (for ResNet-110), respectively. On ImageNet dataset, our approach can achieve $40.8\%$ and $44.8\%$ storage and computation reductions, respectively, with $0.15\%$ accuracy increase over the baseline ResNet-50 model. The code is available at https://github.com/Eclipsess/CHIP_NeurIPS2021.
Mad1's ability to interact with Mad2 is essential to regulate and monitor meiotic synapsis in C. elegans.

Meiotic homolog synapsis is essential to ensure accurate segregation of chromosomes during meiosis. In C. elegans, proper regulation of synapsis and a checkpoint that monitors synapsis relies on the spindle checkpoint components, Mad1 and Mad2, and Pairing Centers (PCs), cis-acting loci that interact with the nuclear envelope to mobilize chromosomes within the nucleus. Here, we test what specific functions of Mad1 and Mad2 are required to regulate and monitor synapsis. We find that a mutation that prevents Mad1’s localization to the nuclear periphery abolishes the synapsis checkpoint but has no effect on Mad2’s localization to the nuclear periphery or synapsis. By contrast, a mutation that prevents Mad1’s interaction with Mad2 abolishes the synapsis checkpoint, delays synapsis and fails to localize Mad2 to the nuclear periphery. These data indicate that Mad1’s primary role in regulating synapsis is through control of Mad2 and that Mad2 can bind other factors at the nuclear periphery. We also tested whether Mad2’s ability to adopt a specific conformation associated with its activity during spindle checkpoint function is required for its role in meiosis. A mutation that prevents Mad2 from adopting its active conformer fails to localize to the nuclear periphery, abolishes the synapsis checkpoint and exhibits substantial defects in meiotic synapsis. Thus, Mad2, and its regulation by Mad1, is an important regulator of meiotic synapsis in C. elegans.
Energy balance and energy correction in dynamics of classical spin systems

An energy-correction method is proposed as an addition to mainstream integrators for equations of motion of systems of classical spins. This solves the problem of nonconservation of energy in long computations and makes mainstream integrators competitive with symplectic integrators for spin systems that for different-site interactions conserve the energy explicitly. The proposed method is promising for spin systems with single-site interactions for which symplectic integrators do not conserve energy and thus have no edge against mainstream integrators. From the energy balance in the spin system with a phenomenological damping and Langevin fields, a formula for the dynamical spin temperature in the presence of single-site anisotropy is obtained.
Neural Representations of Food-Related Attributes in the Human Orbitofrontal Cortex During Choice Deliberation in Anorexia Nervosa.

Decisions about what to eat recruit the orbitofrontal cortex (OFC) and involve the evaluation of food-related attributes, such as taste and health. These attributes are utilized differently by healthy individuals and patients with disordered eating behavior, but it is unclear whether these attributes are decodable from activity in the OFC in both groups and whether neural representations of these attributes are differentially related to decisions about food. We used fMRI combined with behavioral tasks to investigate the representation of taste and health attributes in the human OFC and the role of these representations in food choices in healthy women and women with anorexia nervosa (AN). We found that subjective ratings of tastiness and healthiness could be decoded from patterns of activity in the OFC in both groups. However, health-related patterns of activity in the OFC were more related to the magnitude of choice preferences among patients with AN than healthy individuals. These findings suggest that maladaptive decision-making in AN is associated with more consideration of health information represented by the OFC during deliberation about what to eat. SIGNIFICANCE STATEMENT An open question about the orbitofrontal cortex (OFC) is whether it supports the evaluation of food-related attributes during deliberation about what to eat. We found that healthiness and tastiness information were decodable from patterns of neural activity in the OFC in both patients with anorexia nervosa (AN) and healthy controls. Critically, neural representations of health were more strongly related to choices in patients with AN, suggesting that maladaptive over-consideration of healthiness during deliberation about what to eat is related to activity in the OFC. More broadly, these results show that activity in the human OFC is associated with the evaluation of relevant attributes during value-based decision-making. These findings may also guide future research into the development of treatments for AN.
Cesarean Birth Morbidity among Women with SARS-CoV-2.

Objective The objective of this study was to compare maternal outcomes of women with and without severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections who underwent cesarean births. Study design This was a matched cohort study of pregnant women who had a cesarean birth between March 15, 2020, and May 20, 2020. Cases included women who tested positive for SARS-CoV-2. For every case, two patients who tested negative for SARS-CoV-2 were matched by maternal age, gestational age, body mass index, primary or repeat cesarean birth, and whether the procedure was scheduled or unscheduled. We compared rates of adverse postcesarean complications (intraoperative bladder or bowel injury, estimated blood loss more than or equal to 1,000 mL, hemoglobin drop more than 3 g/dL, hematocrit drop more than 10%, need for blood transfusion, need for hysterectomy, maternal intensive care unit admission, postoperative fever, and development of surgical site infection), with the primary outcome being a composite of those outcomes. We also assessed duration of postoperative stay. Fisher's exact tests were performed to compare the primary outcome between both groups. Results Between March and May 2020, 202 women who subsequently underwent cesarean birth were tested for SARS-CoV-2. Of those 202, 43 (21.3%) patients were positive. They were matched to 86 patients who tested negative. There was no significant difference in the rate of composite adverse surgical outcomes between the groups (SARS-CoV-2 infected 27.9%, SARS-CoV-2 uninfected 25.6%; p = 0.833). There was a higher rate of postoperative fevers (20.9 vs. 5.8%; p = 0.015), but that did not result in a longer length of stay (p = 0.302). Conclusion Pregnant women with SARS-CoV-2 who underwent a cesarean birth did not have an increased risk of adverse surgical outcomes, other than fever, compared with pregnant women without SARS-CoV-2. Key points · Women with SARS-CoV-2 had more postoperative fevers.. · Length of stay did not differ based on SARS-CoV-2 status.. · Composite postoperative outcome did not differ based on SARS-CoV-2 status..
Constructing germline research cohorts from the discarded reads of clinical tumor sequences.

BACKGROUND Hundreds of thousands of cancer patients have had targeted (panel) tumor sequencing to identify clinically meaningful mutations. In addition to improving patient outcomes, this activity has led to significant discoveries in basic and translational domains. However, the targeted nature of clinical tumor sequencing has a limited scope, especially for germline genetics. In this work, we assess the utility of discarded, off-target reads from tumor-only panel sequencing for the recovery of genome-wide germline genotypes through imputation. METHODS We developed a framework for inference of germline variants from tumor panel sequencing, including imputation, quality control, inference of genetic ancestry, germline polygenic risk scores, and HLA alleles. We benchmarked our framework on 833 individuals with tumor sequencing and matched germline SNP array data. We then applied our approach to a prospectively collected panel sequencing cohort of 25,889 tumors. RESULTS We demonstrate high to moderate accuracy of each inferred feature relative to direct germline SNP array genotyping: individual common variants were imputed with a mean accuracy (correlation) of 0.86, genetic ancestry was inferred with a correlation of > 0.98, polygenic risk scores were inferred with a correlation of > 0.90, and individual HLA alleles were inferred with a correlation of > 0.80. We demonstrate a minimal influence on the accuracy of somatic copy number alterations and other tumor features. We showcase the feasibility and utility of our framework by analyzing 25,889 tumors and identifying the relationships between genetic ancestry, polygenic risk, and tumor characteristics that could not be studied with conventional on-target tumor data. CONCLUSIONS We conclude that targeted tumor sequencing can be leveraged to build rich germline research cohorts from existing data and make our analysis pipeline publicly available to facilitate this effort.
Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training

We study the problem of training named entity recognition (NER) models using only distantly-labeled data, which can be automatically obtained by matching entity mentions in the raw text with entity types in a knowledge base. The biggest challenge of distantly-supervised NER is that the distant supervision may induce incomplete and noisy labels, rendering the straightforward application of supervised learning ineffective. In this paper, we propose (1) a noise-robust learning scheme comprised of a new loss function and a noisy label removal step, for training NER models on distantly-labeled data, and (2) a self-training method that uses contextualized augmentations created by pre-trained language models to improve the generalization ability of the NER model. On three benchmark datasets, our method achieves superior performance, outperforming existing distantly-supervised NER models by significant margins.
Factors associated with police shooting mortality: A focus on race and a plea for more comprehensive data

Objectives To quantify nonfatal injurious police shootings of people and examine the factors associated with victim mortality. Methods We gathered victim-level data on fatal and nonfatal injurious police shootings from four states that have such information publicly available: Florida (2009-14), Colorado (2010-19), Texas (2015-19), and California (2016-19). For each state, we examined bivariate associations between mortality and race/ethnicity, gender, age, weapon, and access to trauma care. We also estimated logistic regression models predicting victim mortality in each state. Results Forty-five percent of these police shooting victims (N = 1,322) did not die. Black-white disparities were more pronounced in nonfatal injurious police shootings than in fatal police shootings. Overall, Black victims were less likely than white victims to die from their wound(s). Younger victims were less likely to die from their wound(s), as well as those who were unarmed. Conclusions Racial and age disparities in police shootings are likely more pronounced than previous estimates suggest. Policy implications Other states should strongly consider compiling data like that which is currently being gathered in California. Absent data on nonfatal injurious police shootings-which account for a large share of deadly force incidents-researchers and analysts must be cautious about comparing and/or ranking jurisdictions in terms of their police-involved fatality rates.
The birth-and-death evolution of cytochrome P450 genes in bees.

The birth-and-death model of multigene family evolution describes how gene families evolve and diversify through duplication and deletion. The cytochrome P450s are one of the most diverse and well-studied multigene families, involved in both physiological and xenobiotic functions. Extensive studies of insect P450 genes have demonstrated their role in insecticide resistance. Bees are thought to experience toxin exposure through their diet of nectar and pollen, as well as the resin-collecting behavior exhibited by some species. Here, we describe the repertoire of P450 genes in the orchid bee Euglossa dilemma. Male orchid bees form perfume bouquets used in courtship displays by collecting volatile compounds, resulting in exposure to compounds known to be toxic. In addition, we conducted phylogenetic and selection analyses across ten bee species encompassing three bee families. We find that social behavior and resin collection are not correlated with the repertoire of P450 present in a bee species. However, our analyses revealed that P450 clades can be classified as stable and unstable, and that genes involved in xenobiotic metabolism are more likely to belong to unstable clades. Furthermore, we find that unstable clades are under more dynamic evolutionary pressures, and exhibit signals of adaptive evolution. This work highlights the complexity of multigene family evolution, revealing that multiple factors contribute to the diversification, stability and dynamics of this gene family. Furthermore, we provide a resource for future detailed studies investigating the function of different P450s in economically important bee species.
Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks

Generalizability of machine-learning (ML) based turbulence closures to accurately predict unseen practical flows remains an important challenge. It is well recognized that the neural network (NN) architecture and training protocol profoundly influence the generalizability characteristics. At the Reynolds-averaged Navier–Stokes level, NN–based turbulence closure modeling is rendered difficult due to two important reasons: inherent complexity of the constitutive relation arising from flow-dependent non-linearity and bifurcations; and, inordinate difficulty in obtaining high-fidelity data covering the entire parameter space of interest. Thus, a predictive turbulence model must be robust enough to perform reasonably outside the domain of training. In this context, the objective of the work is to investigate the approximation capabilities of standard moderate‐sized fully connected NNs. We seek to systematically investigate the effects of (i) intrinsic complexity of the solution manifold; (ii) sampling procedure (interpolation vs extrapolation); and (iii) optimization procedure. To overcome the data acquisition challenges, three proxy-physics turbulence surrogates of different degrees of complexity (yet significantly simpler than turbulence physics) are employed to generate the parameter-to-solution maps. Lacking a strong theoretical basis for finding the globally optimal NN architecture and hyperparameters in the presence of non-linearity and bifurcations, a “brute‐force” parameter-space sweep is performed to determine a locally optimal solution. Even for this simple proxy-physics system, it is demonstrated that feed-forward NNs require more degrees of freedom than the original proxy-physics model to accurately approximate the true model even when trained with data over the entire parameter space (interpolation). Additionally, if deep fully connected NNs are trained with data only from part of the parameter space (extrapolation), their approximation capability reduces considerably and it is not straightforward to find an optimal architecture. Overall, the findings provide a realistic perspective on the utility of ML turbulence closures for practical applications and identify areas for improvement.
Multi-Dimensional Diffusions

Applied to multi-dimensional diffusions, the heuristic helps with theoretical questions such as the distribution of extreme values, and applied questions such as the rate of escape from potential wells. Sections I1–I7 contain some basic definitions and facts we’ll need in the examples.
Direct and indirect relationships between Factor 2 psychopathy, behavioral activation, positive alcohol expectancies, and alcohol use

Abstract There is a well-established relationship between Factor 2 psychopathy and heavy alcohol use. Very few studies, however, have examined potential mediators of this relationship. As a result, it is difficult to develop intervention strategies that target early steps in the psychopathy/alcohol use pathway. The present study tested two structural equation models linking Factor 2 psychopathy to alcohol use through the influence of both behavioral activation and positive alcohol expectancies in a sample of college undergraduates ( N = 196). The study was also one of the first to utilize both self-report and laboratory measures of behavioral activation. Findings indicate that both behavioral activation (as assessed by self-report but not laboratory measures) and positive alcohol expectancies fully account for the relationship between Factor 2 psychopathy and alcohol use.
Assessment of M867, a selective caspase-3 inhibitor, in an orthotopic mouse model for non-small cell lung carcinoma

Radiation-induced lung injury (RILI) is a significant dose limiting complication of thoracic radiation for lung, breast, and esophageal cancer. Strategies for increasing the therapeutic index of radiation involve the use of radiosensitizing agents. We investigated the potential of M867 to sensitize non-small cell lung cancer (NSCLC) to radiation in vivo, while assessing its protective effects in normal lung parenchyma. H460-Luc2 cells were implanted into the mediastinum of athymic nude mice, which were separated into four treatment groups: control, M867, radiation therapy (RT) or combination. H460-Luc2 cell cultures were treated in parallel. Tumor growth was followed using bioluminescence imaging. Immunohistochemistry staining was used to detect phospho-Smad2/3 and cleaved caspase-3 expression. Western blot was done for the detection of cleaved caspase-3 and phospho-Smad2/3. TUNEL assays were used to measure apoptosis. M867+RT group had significantly increased tumor growth inhibition relative to either treatment alone (p=0.02). M867+RT was associated with increased levels of apoptosis (p<0.01), but combination treatment was associated with a decrease in caspase-dependent apoptosis relative to RT alone (p<0.01). We found that this increase in apoptosis in the M867+RT group was due to caspase-independent cell death. Based on early biomarker analyses of phospho-Smad 2/3 and cleaved caspase-3, M867+RT had a radio-protective effect on normal lung parenchyma. M867 may increase the therapeutic ratio of RT by enhancing the radiosensitivity of NSCLC while mitigating RILI. Further research is warranted to examine the late effects of lung injury and to study differences in the mechanism of action of M867 on lung cancer and normal tissue.
Effect of oral felodipine on ocular circulation.

Purpose: To evaluate the effects of felodipine, a calcium channelblocker, on ocular circulation. Methods: In a double blind, randomized,crossover design, 10 volunteers received placebo or felodipine on 2 separatedays. Bidirectional laser Doppler velocimetry (BLDV), laser Doppler flowmetry(LDF), and monochromatic fundus photography (MFP) were employed to assessretinal, choroidal, and optic nerve rim circulatory parameters. Measurements wereobtained at baseline, 1.5, and 3 hours after dosing. Blood pressure, intraocularpressure (IOP), and heart rate were monitored, and perfusion pressure (PP) wascalculated. Results: In comparison to placebo, significant average percentagedecreases in diastolic blood pressure (BPd; p = 0.001), mean blood pressure (BPm;p = 0.003), and perfusion pressure (PP; p = 0.003) were observed 1.5 hours following felodipine. No significant differences were observed following placebo. Retinal, choroidal and optic nerve rim circulatory parameters showed no significant changes after placebo or felodipine. A significant positive correlation between BPd and optic nerve velocity (ONvel) was observed 3 hours following felodipine (R = +0.654; p = 0.040). In contrast, no significant correlation between BPd and ONvel was noted (R = +0.198; p = 0.583) following placebo. Conclusions: No significant change in retinal, optic nerve rim, or foveolar choroidal blood flow were observed, a factor that may be important in the treatment of systemic hypertension in patients with glaucoma. Abbreviations:BLDV – Bidirectional laser Doppler velocimetry;BPd – diastolic blood pressure;BPm – mean blood pressure;BPs – systolic blood pressure;CHflow – choroidal flow;CHvel – choroidal velocity;CHvol – choroidal volume;D – vessel diameter;LDF – Laser Doppler flowmetry;MFP – monochromatic fundus photography;ONflow – optic nerve flow;ONvel – optic nerve velocity;ONvol – optic nerve volume;PP – perfusion pressure;Q – retinal volumetric blood flow rate;Vmax – maximum velocity of whole blood;Vmean – mean velocity of whole blood.
The Cost of Free Looks—Ruminations on Steiner v Thexton

This article examines the the California Supreme Court case Steiner v. Thexton as to illusory promises and critiques the various standard broker forms Realtors Residential Purchase Agreements.
On sample size and classification accuracy: a performance comparison

We investigate the dependency between sample size and classification accuracy of three classification techniques: Naive Bayes, Support Vector Machines and Decision Trees over a set of 8500 text excerpts extracted automatically from narrative reports from the Brigham & Women's Hospital, Boston, USA. Each excerpt refers to the smoking status of a patient as: current, past, never a smoker or, denies smoking. Our empirical results, consistent with [1], confirm that size of the training set and the classification rate are indeed correlated. Even though these algorithms perform reasonably well with small datasets, as the number of cases increases, both SMV and Decision Trees show a substantial improvement in performance, suggesting a more consistent learning process. Unlike the majority of evaluations, ours were carried out specifically in a medical domain where the limited amount of data is a common occurrence [13][14]. This study is part of the I2B2 project, Core 2.
Two Cases of Pneumatosis Intestinalis during Cetuximab Therapy for Advanced Head and Neck Cancer

Pneumatosis intestinalis is a rare but known potential complication of treatment with cetuximab. Here we present two cases of pneumatosis intestinalis occurring in patients who were receiving cetuximab as treatment for advanced head and neck cancer. In both cases, cetuximab was discontinued after discovery of the pneumatosis intestinalis.
Homoconjugation and homoaromaticity—XVII

Abstract A more extensive investigation of the solvolysis of the 3-bicyclo-[3.1.0]hexyl tosylates has been carried out using kinetic, NMR and VPC techniques not employed previously. For labeled starting materials, 6,6-dideuterated species have been used. The trishomocyclopropenyl cation is here discussed on the basis of the new information. In line with the presence of a special salt effect in acetolysis of the cis -tosylate, the latter exhibits equilibration of the deuterium label, originally at C 6 , over positions 2, 4 and 6 during acetolysis. The sum of the first order equilibration rate constant, k eq , and the titrimetric rate constant, k t , represents a minimum value for the ionization rate constant, k 1 . Lithium perchlorate reduces considerably the gap between k t and ( k eq + k t ), but it does not eliminate it entirely. Thus, the special salt effect eliminates only part of the ion pair return during the acetolysis. With respect to salt effects, ion pair behavior and lack of common ion rate depression, the cis -3-bicyclo[3.1.0]hexyl system is analogous to the 3-anisyl-2-butyl analog. As regards mechanism of solvolysis of cis -tosylate, the essentially exclusive formation of cis -acetate with retention of configuration and complete equilibration of the label, the absence of elimination, the lack of rearrangement to 2-bicyclo[3.1.0]hexyl and monocyclic structures, and the special salt effect and ion pair return phenomena provide a complete contrast with the behavior of the trans -tosylate. All of the results are explicable on the basis of the trishomocyclopropenyl intermediate formed by anchimerically assisted ionization ( k Δ ) of cis -tosylate. Rate constant k Δ evidently exceeds the rate constant for anchimerically unassisted solvolysis, k s , by a factor of ca. 50 in acetolysis and by a somewhat smaller factor in aqueous acetone. The most striking aspect of the solvolysis of trans -tosylate is the complete absence of equilibration of the deuterium label in the cis -acetate part of the acetolysis product. Leakage from the classical cation to the nonclassical trishomocyclopropenyl structure competes unusually poorly with its other reactions. Probably the most important reason for this very inefficient leakage is that the geometry of the classical ion is unfavorable for the change. The reason for this goes back to the preferred boat conformation of 3-bicyclo[3.1.0]hexyl derivatives. This preferred boat conformation also provides part of the reason for the relatively low ( k Δ / k s ) ratios in solvolysis of the cis -tosylate. Corey's alternative suggestion of three rapidly equilibrating “almost classical” nonclassical ions instead of the trishomocyclopropenyl species is discussed. None of the data require this modification, so the application of “Ockham's razor” to the three species and the retention of the simpler interpretation are recommended.