cssi/SciSciGPT-SciSciNet huggingface dataset:
Model theoretic characterizations of large cardinals

We consider compactness characterizations of large cardinals. Based on results of Benda [Ben78], we study compactness for omitting types in various logics. In $$\mathbb{L}$$κ,κ, this allows us to characterize any large cardinal defined in terms of normal ultrafilters, and we also analyze second-order and sort logic. In particular, we give a compactness for omitting types characterization of huge cardinals, which have consistency strength beyond Vopĕnka’s Principle.
DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis

Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence. Existing work on trajectory clustering often relies on similarity measurements that utilize raw spatial and/or temporal information of trajectories. These measures are incapable of identifying similar moving behaviors that exhibit varying spatiotemporal scales of movement. In addition, the expense of labeling massive trajectory data is a barrier to supervised learning models. To address these challenges, we propose an unsupervised neural approach for mobility behavior clustering, called the Deep Embedded TrajEctory ClusTering network (DETECT). DETECT operates in three parts: first it transforms the trajectories by summarizing their critical parts and augmenting them with context derived from their geographical locality (e.g., using POIs from gazetteers). In the second part, it learns a powerful representation of trajectories in the latent space of behaviors, thus enabling a clustering function (such as k-means) to be applied. Finally, a clustering oriented loss is directly built on the embedded features to jointly perform feature refinement and cluster assignment, thus improving separability between mobility behaviors. Exhaustive quantitative and qualitative experiments on two real-world datasets demonstrate the effectiveness of our approach for mobility behavior analyses.
Would a File by Any Other Name Seem as Malicious

Successful malware attacks on information technology systems can cause millions of dollars in damage, the exposure of sensitive and private information, and the irreversible destruction of data. Anti-virus systems that analyze a file’s contents use a combination of static and dynamic analysis to detect and remove/remediate such malware. However, examining a file’s entire contents is not always possible in practice, as the volume and velocity of incoming data may be too high, or access to the underlying file contents may be restricted or unavailable. If it were possible to obtain estimates of a file’s relative likelihood of being malicious without looking at the file contents, we could better prioritize file processing order and aid analysts in situations where a file is unavailable.In this work, we demonstrate that file names can contain information predictive of the presence of malware in a file. In particular, we show the effectiveness of a character-level convolutional neural network at predicting malware status using file names on Endgame’s EMBER malware detection benchmark dataset.
Multi-contact Phase Change Toggle Logic Device Utilizing Thermal Crosstalk

Phase change memory (PCM) is an emerging high speed, high density, high endurance, and scalable nonvolatile memory technology which utilizes the large resistivity contrast between the amorphous and crystalline phases of chalcogenide materials such as Ge 2 Sb 2 Te 5 , (GST). In addition to being used as a standalone memory, there has been a growing interest in integration of PCM devices on top of the CMOS layer for computation in memory and neuromorphic computing. The large CMOS overhead for memory controllers is a limiting factor for this purpose. Transferring functionality like routing, multiplexing, and logic to the memory layer can substantially reduce the CMOS overhead, making it possible to integrate 100s of GB of PCM storage on top of a conventional CPU [1], [2].
Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing.

An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activi...
Derived invariants of the fixed ring of enveloping algebras of semisimple Lie algebras

Let $${\mathfrak {g}}$$ be a semisimple complex Lie algebra, and let W be a finite subgroup of $${\mathbb {C}}$$ -algebra automorphisms of the enveloping algebra $$U({\mathfrak {g}})$$ . We show that the derived category of $$U({\mathfrak {g}})^W$$ -modules determines isomorphism classes of both $${\mathfrak {g}}$$ and W. Our proofs are based on the geometry of the Zassenhaus variety of the reduction modulo $$p\gg 0$$ of $${\mathfrak {g}}.$$ Specifically, we use non-existence of certain etale coverings of its smooth locus.
Chemical Constituents Involved in E-Cigarette, or Vaping Product Use-Associated Lung Injury (EVALI)

The Centers for Disease Control declared e-cigarette, or vaping, product use-associated lung injury (EVALI) a national outbreak due to the high incidence of emergency department admissions and deaths. We have identified chemical constituents in e-cig counterfeit cartridges and compared these to medical-grade and CBD containing cartridges. Apart from vitamin E acetate (VEA) and tetrahydrocannabinol (THC), other potential toxicants were identified including solvent-derived hydrocarbons, silicon conjugated compounds, various terpenes, pesticides/plasticizers/polycaprolactones, and metals. This study provides additional insights into the chemicals associated with EVALI cartridges and thus may contribute to the underlying disease mechanism of acute lung injury.
A parallel workload model and its implications for processor allocation

We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling moldable jobs on space-sharing parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, does not perform as well as strategies that adapt better to system load. The best of the strategies we consider is one that explicitly reduces allocations when load is high (a variation of Sevcik’s (1989) A+ strategy).
C(sp3)–H Fluorination with a Copper(II)/(III) Redox Couple

Despite the growing interest in the synthesis of fluorinated organic compounds, few reactions are able to incorporate fluoride ions directly into alkyl C–H bonds. Here, we report the C(sp3)–H fluor...
Drunkenness as a Criminal Offense

Consumption of intoxicating liquor, and effects to control antisocial results are well-nigh universal phenomena. From the laws of the ancient Hebrews and the discourses of Greek philosophers to the Anglo-Saxon dooms and down to the present time, control of the harmful social effects of drunkenness has been a matter of public concern.
Cerebellar Purkinje cell activity modulates aggressive behavior

Although the cerebellum is traditionally associated with balance and motor function, it also plays wider roles in affective and cognitive behaviors. Evidence suggests that the cerebellar vermis may regulate aggressive behavior, though the cerebellar circuits and patterns of activity that influence aggression remain unclear. We used optogenetic methods to bidirectionally modulate the activity of spatially-delineated cerebellar Purkinje cells to evaluate the impact on aggression in mice. Increasing Purkinje cell activity in the vermis significantly reduced the frequency of attacks in a resident-intruder assay. Reduced aggression was not a consequence of impaired motor function, because optogenetic stimulation did not alter motor performance. In complementary experiments, optogenetic inhibition of Purkinje cells in the vermis increased the frequency of attacks. These results suggest Purkinje cell activity in the cerebellar vermis regulates aggression, and further support the importance of the cerebellum in driving affective behaviors that could contribute to neurological disorders.
Chapter 6 – Cognition

Publisher Summary Cognition involves the concept of self as a separate identity from the surrounding environment, and at its highest levels involves the imaginative ability to bring seemingly unrelated facts or ideas together to create a novel solution to a problem or a unique plan for future action. This chapter reveals that when animals interpret each other's personalities, personality becomes a part of the cognitive picture. The concept of the behavioral syndrome has yielded a somewhat broader tool than personality to understand variation in the behavioral tendencies of animals. Emotionality is, in itself, not a cognitive process, but empathy for the emotions of others is such a process, and the discussion of emotions in this chapter helps in understanding impulsivity, impulse control, and empathy. Cognition separates an animal from being a purely impulsive creature, in that cognition allows reflection, planning, and foresight—in other words, nonimpulsive behavior.
Revisiting Legal Foundations of Crisis Standards of Care.

In the decade since the original development of crisis standards of care (CSC) via the National Academies of Science, Engineering, and Medicine, multiple legal/policy concerns have arisen, centered on 2 key questions: (1) what legal “triggers” justify shifts to CSC?; and (2) what evidence substantiates critical medical/ public health choices?
Race, Immigrant Status, and Housing Tenure Choice

Abstract This paper applies Census microdata from 1980 and 1990 to assess the determinants of housing tenure choice among racial and ethnic groups in the Los Angeles metropolitan area. Like previous research, our results indicate that endowment differences (income, education, and immigrant status) largely explain the homeownership gap between Latinos and whites. In contrast to previous work, we find that Asians are as likely to choose homeownership as are whites, and that status as an immigrant did not portend lower homeownership rates among Asians. However, the endowment-adjusted homeownership choice differential between whites and blacks remains sizable; furthermore, that gap more than doubled between 1980 and 1990, to a full 11 percentage points.
A Learning-Based Guidance Selection Mechanism for a Formally Verified Sense and Avoid Algorithm

This paper describes a learning-based strategy for selecting conflict avoidance maneuvers for autonomous unmanned aircraft systems. The selected maneuvers are provided by a formally verified algorithm and they are guaranteed to solve any impending conflict under general assumptions about aircraft dynamics. The decision-making logic that selects the appropriate maneuvers is encoded in a stochastic policy encapsulated as a neural network. The network's parameters are optimized to maximize a reward function. The reward function penalizes loss of separation with other aircraft while rewarding resolutions that result in minimum excursions from the nominal flight plan. This paper provides a description of the technique and presents preliminary simulation results.
Chapter 12 – Relativity II

Four-vectors are defined representing the velocity and momentum of a particle. The energy is defined and found to be related to the momentum of a particle by the equation E2=p2c2+m2c4. The same kind of arguments that lead to the Scrodinger equation is found to lead to relativistic wave equations. The relativistic wave equation for the electron, which is called the Dirac equation, is useful for describing scattering processes at high energy. For high-energy scattering processes in which particles are created and destroyed, the field describing the interaction of particles must be treated quantum mechanically. This leads to a new kind of theory called quantum field theory which is based on the ideas of relativity and quantum mechanics.
Block Coordinate Regularization by Denoising

We consider the problem of reconstructing an image from its noisy measurements using a prior specified only with an image denoiser. Recent work on plug-and-play priors (PnP) and regularization by denoising (RED) has shown the state-of-the-art performance of image reconstruction algorithms under such priors in a range of imaging problems. In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables. We theoretically analyze the convergence of the algorithm and discuss its relationship to the traditional proximal optimization. Our analysis complements and extends recent theoretical results for RED-based estimation methods. We numerically validate our method using several denoising priors, including those based on deep neural nets.
A mixture theory-based finite element formulation for the study of biodegradation of poroelastic scaffolds

Surgical implants, known as Tissue Engineered Nerve Guides (TENGs) are often inserted to provide alignment and mechanical support to broken or damaged nerves. These implants are designed to be poroelastic and biodegradable, and the ideal rate of degradation would be that equal to the rate of regrowth of the recovering nerve. Inspired by the design of these TENGs, we develop a mixture theory based mathematical model to simulate the degradation of a poroelastic solid immersed in a fluid bath. The temporal evolution of the solid’s mechanical and transport properties is also modeled. The model comprises of the degrading solid, the degradation reaction products, and the fluid in which the solid is immersed. The resultant governing equations are formulated in an Arbitrary Lagrangian–Eulerian (ALE) framework. The weak formulation of the partial differential equations (PDEs) so derived is numerically implemented using a finite element method (FEM). The numerical model is studied for stability and convergence rates using the Method of Manufactured Solutions.
Orthogonal Convolutional Neural Networks

Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix representation of the convolutional kernel, instead of the common kernel orthogonality approach, which we show is only necessary but not sufficient for ensuring orthogonal convolutions. Our proposed orthogonal convolution requires no additional parameters and little computational overhead. It consistently outperforms the kernel orthogonality alternative on a wide range of tasks such as image classification and inpainting under supervised, semi-supervised and unsupervised settings. It learns more diverse and expressive features with better training stability, robustness, and generalization. Our code is publicly available.
Berkshire's Blemishes: Lessons for Buffett's Successors, Peers, and Policy

Berkshire Hathaway’s unique managerial model is lauded for its great value; this Article highlights its costs. Most costs stem from the same features that yield such great value, which boil down, ironically, to Berkshire trying to be something it isn’t: it is a massive industrial conglomerate run as an old-fashioned investment partnership. An advisory board gives unchecked power to a single manager (Warren Buffett); Buffett makes huge capital allocations and pivotal executive hiring-and-firing decisions with modest investigation and scant oversight; Berkshire’s autonomous and decentralized structure grants operating managers enormous discretion with limited second-guessing; its trustbased culture relies on a cultivated vision of integrity more than internal controls; and its thrifty anti-bureaucracy means no central departments, such as public relations or general counsel. Delineating the visible costs of Berkshire’s model confirms the desirability of tolerating many of them, given the value concurrently generated, but also reveals ways to improve the model—a few while Buffett is at the helm, but mostly for successors. Current reform suggestions include hiring a fulltime public relations professional at headquarters and more systematically developing senior executives; suggestions for future reform include enhanced subsidiary compliance resources and separating the identity and personal opinions of top executives from the corporation and its official policy. Besides helping Berkshire, the review and suggestions will help managers of other companies inspired by Buffett’s unique managerial model and policymakers who should study it. Implications for peers and policymakers include highlighting flexibility in corporate governance, the efficacy of the conglomerate form, and especially the value of strategies that produce long-term thinking among shareholders and managers alike.
Assessing the Performance of Density Functional Theory Methods on the Prediction of Low-Frequency Vibrational Spectra

The low-frequency (terahertz) dynamics of condensed phase materials provide valuable insight into numerous bulk phenomena. However, the assignment and interpretation of experimental results require computational methods due to the complex mode types that depend on weak intermolecular forces. Solid-state density functional theory has been used in this regard with great success, yet the selection of specific computational parameters, namely the chosen basis set and density functional, has a profound influence on the accuracy of predicted spectra. In this work, the role of these two parameters is investigated in a series of organic molecular crystals, in order to assess the ability of various methods to reproduce intermolecular forces, and subsequently experimental terahertz spectra. Specifically, naphthalene, oxalic acid, and thymine were chosen based on the varied intermolecular interactions present in each material. The results highlight that unconstrained geometry optimizations can be used as an initial proxy for the accuracy of interatomic forces, with errors in the calculated geometries indicative of subsequent errors in the calculated low-frequency vibrational spectra, providing a powerful metric for the validation of theoretical results. Finally, the origins of the observed shortcomings are analyzed, providing a basic framework for further studies on related materials.
Individual Calibration with Randomized Forecasting

Machine learning applications often require calibrated predictions, e.g. a 90\% credible interval should contain the true outcome 90\% of the times. However, typical definitions of calibration only require this to hold on average, and offer no guarantees on predictions made on individual samples. Thus, predictions can be systematically over or under confident on certain subgroups, leading to issues of fairness and potential vulnerabilities. We show that calibration for individual samples is possible in the regression setup if the predictions are randomized, i.e. outputting randomized credible intervals. Randomization removes systematic bias by trading off bias with variance. We design a training objective to enforce individual calibration and use it to train randomized regression functions. The resulting models are more calibrated for arbitrarily chosen subgroups of the data, and can achieve higher utility in decision making against adversaries that exploit miscalibrated predictions.
Dynamical Systems Theory for Causal Inference with Application to Synthetic Control Methods

In this paper, we adopt results in nonlinear time series analysis for causal inference in dynamical settings.~Our motivation is policy analysis with panel data, particularly through the use of "synthetic control" methods. These methods regress pre-intervention outcomes of the treated unit to outcomes from a pool of control units, and then use the fitted regression model to estimate causal effects post-intervention. In this setting, we propose to screen out control units that have a weak dynamical relationship to the treated unit. In simulations, we show that this method can mitigate bias from "cherry-picking" of control units, which is usually an important concern. We illustrate on real-world applications, including the tobacco legislation example of \citet{Abadie2010}, and Brexit.
A direct prediction of the shape parameter—A purely scattered data approach

Abstract In this paper we present an approach which predicts directly without search the optimal choice of the shape parameter c contained in the multiquadrics ( − 1 ) ⌈ β 2 ⌉ ( c 2 + ∥ x ∥ 2 ) β 2 , β > 0 , and the inverse multiquadrics ( c 2 + ∥ x ∥ 2 ) β 2 , β 0 . Unlike the simplex scheme where the data points are required to be evenly spaced, as in a recent paper of the author, here we allow them to be arbitrarily scattered in the simplex, making it much more useful. Besides this, we aim at helping non-mathematicians use our approach and hence remove some complicated requirements for the domain. The drawback is that its theoretical ground is not so strong as in the evenly spaced data setting. However, experiments show that it works well. The experimentally optimal value of c coincides with the theoretically predicted one. Since the fill distance, which reflects the amount of data points needed, involved is always of reasonable size, this approach is supposed to be practically useful.
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality

Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated. All smell report data are publicly accessible online. These reports are also sent to the local health department and visualized on a map along with air quality data from monitoring stations. This visualization provides a comprehensive overview of the local pollution landscape. Additionally, with these reports and air quality data, we developed a model to predict upcoming smell events and send push notifications to inform communities. We also applied regression analysis to identify statistically significant effects of push notifications on user engagement. Our evaluation of this system demonstrates that engaging residents in documenting their experiences with pollution odors can help identify local air pollution patterns and can empower communities to advocate for better air quality. All citizen-contributed smell data are publicly accessible and can be downloaded from https://smellpgh.org.
Functional Architecture of Motion Direction in the Mouse Superior Colliculus

Summary Motion vision is important in guiding animal behavior. Both the retina and the visual cortex process object motion in largely unbiased fashion: all directions are represented at all locations in the visual field. We investigate motion processing in the superior colliculus of the awake mouse by optically recording neural responses across both hemispheres. Within the retinotopic map, one finds large regions of ∼500 μm size where neurons prefer the same direction of motion. This preference is maintained in depth to ∼350 μm. The scale of these patches, ∼30 degrees of visual angle, is much coarser than the animal’s visual resolution (∼2 degrees). A global map of motion direction shows approximate symmetry between the left and right hemispheres and a net bias for upward-nasal motion in the upper visual field.
Model-based Randomness Monitor for Stealthy Sensor Attacks.

Malicious attacks on modern autonomous cyber-physical systems (CPSs) can leverage information about the system dynamics and noise characteristics to hide while hijacking the system toward undesired states. Given attacks attempting to hide within the system noise profile to remain undetected, an attacker with the intent to hijack a system will alter sensor measurements, contradicting with what is expected by the system's model. To deal with this problem, in this paper we present a framework to detect non-randomness in sensor measurements on CPSs under the effect of sensor attacks. Specifically, we propose a run-time monitor that leverages two statistical tests, the Wilcoxon Signed-Rank test and Serial Independence Runs test to detect inconsistent patterns in the measurement data. For the proposed statistical tests we provide formal guarantees and bounds for attack detection. We validate our approach through simulations and experiments on an unmanned ground vehicle (UGV) under stealthy attacks and compare our framework with other anomaly detectors.
Quantization for a Mixture of Uniform Distributions Associated with Probability Vectors

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus approximation of a continuous probability distribution by a discrete distribution. Mixtures of probability distributions, also known as mixed distributions, are an exciting new area for optimal quantization. In this paper, we investigate the optimal quantization for three different mixed distributions generated by uniform distributions associated with probability vectors.
High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes

This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction code for intractable nonlinear channels. The LDPC encoder shrinks the input space of the autoencoder, which enables the autoencoder to learn more easily. The proposed error correction code shows promising results for one-bit quantization, a challenging case of a nonlinear channel. Specifically, our design gives a waterfall slope bit error rate even with high order modulation formats such as 16-QAM and 64-QAM despite one-bit quantization. This gain is theoretically grounded by proving that the trained autoencoder provides approximately Gaussian distributed data to the LDPC decoder even though the received signal has non-Gaussian statistics due to the one-bit quantization.
Geometric limits of Julia sets for sums of power maps and polynomials

For maps of one complex variable, f, given as the sum of a degree n power map and a degree d polynomial, we provide necessary and sufficient conditions that the geometric limit as n approaches infinity of the set of points that remain bounded under iteration by f is the closed unit disk or the unit circle. We also provide a general description, for many cases, of the limiting set.
Time‐domain sparsity promoting least‐squares reverse time migration with source estimation

Least-squares reverse time migration is well-known for its capability to generate artifact-free true-amplitude subsurface images through fitting observed data in the least-squares sense. However, when applied to realistic imaging problems, this approach is faced with issues related to overfitting and excessive computational costs induced by many wave-equation solves. The fact that the source function is unknown complicates this situation even further. Motivated by recent results in stochastic optimization and transform-domain sparsity-promotion, we demonstrate that the computational costs of inversion can be reduced significantly while avoiding imaging artifacts and restoring amplitudes. While powerful, these new approaches do require accurate information on the source-time function, which is often lacking. Without this information, the imaging quality deteriorates rapidly. We address this issue by presenting an approach where the source-time function is estimated on the fly through a technique known as variable projection. Aside from introducing negligible computational overhead, the proposed method is shown to perform well on imaging problems with noisy data and problems that involve complex settings such as salt. In either case, the presented method produces high resolution high-amplitude fidelity images including an estimates for the source-time function. In addition, due to its use of stochastic optimization, we arrive at these images at roughly one to two times the cost of conventional reverse time migration involving all data.
Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts

Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.
Modeling and Characterization of Metastability in Single Flux Quantum (SFQ) Synchronizers

Despite the promises of low-power and high-frequency of single-flux quantum (SFQ) technology, scaling these circuits remains a serious challenge that motivates the support of multiple SFQ clock domains. Towards this end, this paper analyzes the impact of setup time violations and metastability in SFQ circuits comparing the derived analytical models to their CMOS counterparts. It then extends this model to estimate the Mean Time Between Failure (MTBF) of flip-flop-based synchronizers and curve fits this model to simulations in the state-of-the-art SFQ5ee process. Interestingly, we find a two-flop SFQ synchronizer has an estimated MTBF of ∼106 years.
Fully-Automated Packaging Structure Recognition in Logistics Environments

Within a logistics supply chain, a large variety of transported goods need to be handled, recognized and checked at many different network points. Often, huge manual effort is involved in recognizing or verifying packet identity or packaging structure, for instance to check the delivery for completeness. We propose a method for complete automation of packaging structure recognition: Based on a single image, one or multiple transport units are localized and, for each of these transport units, the characteristics, the total number and the arrangement of its packaging units is recognized. Our algorithm is based on deep learning models, more precisely convolutional neural networks for instance segmentation in images, as well as computer vision methods and heuristic components. We use a custom data set of realistic logistics images for training and evaluation of our method. We show that the solution is capable of correctly recognizing the packaging structure in approximately 85% of our test cases, and even more (91%) when focusing on most common package types.
A mathematical framework for estimating risk of airborne transmission of COVID-19 with application to face mask use and social distancing

A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19 is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries but it can also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model is used to assess the protection from transmission afforded by face coverings made from a variety of fabrics. The reduction in the transmission risk associated with increased physical distance between the host and susceptible is also quantified by coupling the model with available and new large eddy simulation data on scalar dispersion in canonical flows. Finally, the effect of the level of physical activity (or exercise intensity) of the host and the susceptible in enhancing the transmission risk is also assessed.
Comparing DNA replication programs reveals large timing shifts at centromeres of endocycling cells in maize roots

Plant cells undergo two types of cell cycles-the mitotic cycle in which DNA replication is coupled to mitosis, and the endocycle in which DNA replication occurs in the absence of cell division. To investigate DNA replication programs in these two types of cell cycles, we pulse labeled intact root tips of maize (Zea mays) with 5-ethynyl-2'-deoxyuridine (EdU) and used flow sorting of nuclei to examine DNA replication timing (RT) during the transition from a mitotic cycle to an endocycle. Comparison of the sequence-based RT profiles showed that most regions of the maize genome replicate at the same time during S phase in mitotic and endocycling cells, despite the need to replicate twice as much DNA in the endocycle and the fact that endocycling is typically associated with cell differentiation. However, regions collectively corresponding to 2% of the genome displayed significant changes in timing between the two types of cell cycles. The majority of these regions are small with a median size of 135 kb, shift to a later RT in the endocycle, and are enriched for genes expressed in the root tip. We found larger regions that shifted RT in centromeres of seven of the ten maize chromosomes. These regions covered the majority of the previously defined functional centromere, which ranged between 1 and 2 Mb in size in the reference genome. They replicate mainly during mid S phase in mitotic cells but primarily in late S phase of the endocycle. In contrast, the immediately adjacent pericentromere sequences are primarily late replicating in both cell cycles. Analysis of CENH3 enrichment levels in 8C vs 2C nuclei suggested that there is only a partial replacement of CENH3 nucleosomes after endocycle replication is complete. The shift to later replication of centromeres and possible reduction in CENH3 enrichment after endocycle replication is consistent with a hypothesis that centromeres are inactivated when their function is no longer needed.
Does maternal race or ethnicity modify the association between maternal psychiatric disorders and preterm birth

Abstract Purpose Preterm birth risk has been linked to maternal racial and ethnic background, particularly African American heritage; however, the association of maternal race and ethnicity with psychiatric disorders and preterm birth has received relatively limited attention. Methods The Consortium on Safe Labor (2002–2008) is a nationwide U.S. cohort study with 223,394 singleton pregnancies. Clinical data were obtained from electronic medical records, including maternal diagnoses of psychiatric disorders. Relative risk (RR) and 95% confidence intervals (CI) were estimated for the association between maternal psychiatric disorders and preterm birth ( Results Non-Hispanic White (RR, 1.42; 95% CI, 1.35–1.49), Hispanic (RR, 1.44; 95% CI, 1.29–1.60), and non-Hispanic Black (RR, 1.21, 95% CI, 1.13–1.29) women with any psychiatric disorder were at increased risk for delivering preterm infants, compared with women without any psychiatric disorder. However, non-Hispanic Black women with any psychiatric disorder, depression, bipolar disorder, and schizophrenia had a significantly lower increase in preterm birth risk than non-Hispanic White women. Conclusions Despite the significant association between maternal psychiatric disorders and preterm birth risk, psychiatric disorders did not appear to contribute to racial and ethnic disparities in preterm birth.
Attorney Discipline, the Quality of Legal Systems and Economic Growth within the United States

Abstract The quality of the legal system is of fundamental importance within any society, but the economics of attorney discipline (AD) have not been studied empirically. We rank states by AD resources, complaints, and convictions, and explore the impact of AD on economic growth. The American Bar Association’s annual Survey on Lawyer Discipline (SOLD) reports complaints and charges regarding lawyer misconduct, the caseload per disciplinary attorney, and each state’s budget for attorney discipline. We develop five measures of attorney discipline and a composite measure. We consider low levels of AD to indicate less effective legal systems. Since prior literature finds a relation between corruption and state economic growth, we assess the ability of our AD variables to explain growth, finding that states with lower levels of AD experience slower growth. To our knowledge, no other study has considered the SOLD data to evaluate the quality of attorney discipline, or its impact on growth. One implication is that legal ethics training adds value to society.
Spo0A Suppresses sin Locus Expression in Clostridioides difficile.

ABSTRACT Clostridioides difficile is the leading cause of nosocomial infection and is the causative agent of antibiotic-associated diarrhea. The severity of the disease is directly associated with toxin production, and spores are responsible for the transmission and persistence of the organism. Previously, we characterized sin locus regulators SinR and SinR′ (we renamed it SinI), where SinR is the regulator of toxin production and sporulation. The SinI regulator acts as its antagonist. In Bacillus subtilis, Spo0A, the master regulator of sporulation, controls SinR by regulating the expression of its antagonist, sinI. However, the role of Spo0A in the expression of sinR and sinI in C. difficile had not yet been reported. In this study, we tested spo0A mutants in three different C. difficile strains, R20291, UK1, and JIR8094, to understand the role of Spo0A in sin locus expression. Western blot analysis revealed that spo0A mutants had increased SinR levels. Quantitative reverse transcription-PCR (qRT-PCR) analysis of its expression further supported these data. By carrying out genetic and biochemical assays, we show that Spo0A can bind to the upstream region of this locus to regulates its expression. This study provides vital information that Spo0A regulates the sin locus, which controls critical pathogenic traits such as sporulation, toxin production, and motility in C. difficile. IMPORTANCEClostridioides difficile is the leading cause of antibiotic-associated diarrheal disease in the United States. During infection, C. difficile spores germinate, and the vegetative bacterial cells produce toxins that damage host tissue. In C. difficile, the sin locus is known to regulate both sporulation and toxin production. In this study, we show that Spo0A, the master regulator of sporulation, controls sin locus expression. Results from our study suggest that Spo0A directly regulates the expression of this locus by binding to its upstream DNA region. This observation adds new detail to the gene regulatory network that connects sporulation and toxin production in this pathogen.
Transition Probabilities of the Bethe Ansatz Solvable Interacting Particle Systems

This paper presents the exact expressions of the transition probabilities of some non-determinantal Bethe ansatz solvable interacting particle systems: the two-sided PushASEP, the asymmetric avalanche process and the asymmetric zero range process. The time-integrated currents of the asymmetric avalanche process and the asymmetric zero range process are immediate from the results of the asymmetric simple exclusion process.
A more natural composite Higgs model

Composite Higgs models provide an attractive solution to the hierarchy problem. However, many realistic models suffer from tuning problems in the Higgs potential. There are often large contributions from the UV dynamics of the composite resonances to the Higgs potential, and tuning between the quadratic term and the quartic term is required to separate the electroweak breaking scale and the compositeness scale. We consider a composite Higgs model based on the SU(6)/Sp(6) coset, where an enhanced symmetry on the fermion resonances can minimize the Higgs quadratic term. Moreover, a Higgs quartic term from the collective symmetry breaking of the little Higgs mechanism can be realized by the partial compositeness couplings between elementary Standard Model fermions and the composite operators, without introducing new elementary fields beyond the Standard Model and the composite sector. The model contains two Higgs doublets, as well as several additional pseudo-Nambu-Goldstone bosons. To avoid tuning, the extra Higgs bosons are expected to be relatively light and may be probed in the future LHC runs. The deviations of the Higgs couplings and the weak gauge boson couplings also provide important tests as they are expected to be close to the current limits in this model.
Generating comments from source code with CCGs

Good comments help developers understand software faster and provide better maintenance. However, comments are often missing, generally inaccurate, or out of date. Many of these problems can be avoided by automatic comment generation. This paper presents a method to generate informative comments directly from the source code using general-purpose techniques from natural language processing. We generate comments using an existing natural language model that couples words with their individual logical meaning and grammar rules, allowing comment generation to proceed by search from declarative descriptions of program text. We evaluate our algorithm on several classic algorithms implemented in Python.
Regolith behavior under asteroid-level gravity conditions: low-velocity impacts into mm- and cm-sized grain targets

Context. In situ observations of small asteroids, such as Itokawa, Ryugu, and Bennu, show that surfaces covered by boulders and coarse terrain are frequent on such bodies. Regolith grain sizes have distributions on approximately mm and cm scales, and the behavior of such large grains in the very low-gravity environments of small body surfaces dictates their morphology and evolution.Aims. In order to support the understanding of natural processes (e.g., the recapturing of impact ejecta) or spacecraft-induced interactions (e.g., the fate of a small lander), we aim to experimentally investigate the response of coarse-grained target surfaces to very-low-speed impacts (below 2 m s−1 ).Methods. We present the outcome of 86 low-speed impacts of a cm-sized spherical projectile into a bed of simulated regolith, composed of irregular mm- and cm-sized grains. These impacts were performed under vacuum and microgravity conditions. Our results include measurements for the projectile coefficient of restitution and penetration depth, as well as ejecta production, speed, and mass estimation. As part of our data analysis, we compared our data set with impacts performed in similar conditions with fine grain regolith targets to determine the dependence of our measurements on the target grain size.Results. We find that impact outcomes include the frequent occurrence of projectile bouncing and tangential rolling on the target surface upon impact. Ejecta is produced for impact speeds higher than about 12 cm s−1 , and ejecta speeds scale with the projectile to target the grain size ratio and the impact speed. Ejected mass estimations indicate that ejecta is increasingly difficult to produce for increasing grain sizes. Coefficients of restitution of rebounding projectiles do not display a dependency on the target grain size, unlike their maximum penetration depth, which can be scaled with the projectile to target grain size ratio. Finally, we compare our experimental measurements to spacecraft data and numerical work on Hayabusa 2’s MASCOT landing on the surface of the asteroid Ryugu.
Three-temperature plasma shock solutions with gray radiation diffusion

The effects of radiation on the structure of shocks in a fully ionized plasma are investigated by solving the steady-state fluid equations for ions, electrons, and radiation. The electrons and ions are assumed to have the same bulk velocity but separate temperatures, and the radiation is modeled with the gray diffusion approximation. Both electron and ion conduction are included, as well as ion viscosity. When the material is optically thin, three-temperature behavior occurs. When the diffusive flux of radiation is important but radiation pressure is not, two-temperature behavior occurs, with the electrons strongly coupled to the radiation. Since the radiation heats the electrons on length scales that are much longer than the electron–ion Coulomb coupling length scale, these solutions resemble radiative shock solutions rather than plasma shock solutions that neglect radiation. When radiation pressure is important, all three components are strongly coupled. Results with constant values for the transport and coupling coefficients are compared to a full numerical simulation with a good match between the two, demonstrating that steady shock solutions constitute a straightforward and comprehensive verification test methodology for multi-physics numerical algorithms.
Searching the landscape of flux vacua with genetic algorithms

In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider a symmetric T6 as well as the conifold region of a Calabi-Yau hypersurface. We argue that in both cases genetic algorithms are powerful tools for finding flux vacua with interesting phenomenological properties. We also compare genetic algorithms to algorithms based on different breeding mechanisms as well as random walk approaches.
Future computing platforms for science in a power constrained era

Power consumption will be a key constraint on the future growth of Distributed High Throughput Computing (DHTC) as used by High Energy Physics (HEP). This makes performance-per-watt a crucial metric for selecting cost-efficient computing solutions. For this paper, we have done a wide survey of current and emerging architectures becoming available on the market including x86-64 variants, ARMv7 32-bit, ARMv8 64-bit, Many-Core and GPU solutions, as well as newer System-on-Chip (SoC) solutions. We compare performance and energy efficiency using an evolving set of standardized HEP-related benchmarks and power measurement techniques we have been developing. We evaluate the potential for use of such computing solutions in the context of DHTC systems, such as the Worldwide LHC Computing Grid (WLCG).