cssi/SciSciGPT-SciSciNet huggingface dataset:
Taking Sides on Genetic Modification

Should government fund safe human enhancements for the hereditary contribution to good looks or athletic prowess or IQ? Is there reason to restrict genetic interventions that would create chickens without the nesting instincts that agitate normal chickens confined to life in a battery cage? Or cows with stunted emotions that would feel less fear as they were led off to slaughter? Or pigs with no legs, better suited to a sedentary existence as bacon-to-be? Some philosophers argue that political decisions about such practices, to be legitimate, must be made without reference to reasons that arise from within controversial worldviews. They believe that society is arranged best when governed by principles that leave citizens free to pursue their own views about what gives life value. So the reasons that public officials give to justify state action must not privilege some moral or religious beliefs over others. This is a mistake, for two reasons. First, it is conceptually incoherent for the state to stay neutral toward genetic engineering because any strategy that legislators might choose depends for its plausibility on an answer to the underlying normative questions these practices presuppose. Second, the bracketing of formative moral commitments would unduly diminish the character of public deliberation about these questions. What we should do about offspring enhancement depends on why the natural lottery matters to us. And whether we should re-fashion animals to suit our uses for them depends on whether “animal nature” is worth preserving. But these are precisely the sort of controversial moral questions that a neutral state excludes as legitimate grounds for political debate and policy decisions. It might be replied that it is unrealistic to expect citizens in a diverse society to reach agreement on normative issues as contentious as the value of animal nature or the qualities of character worth promoting in a modern democracy. The truth in this reply is that there is no assurance that agreement will follow from public discourse about the moral status of genetic engineering of humans and animals. Nor might such discourse even lead people to have any greater understanding or respect for those views with which they disagree. But people who hold conflicting values can engage in rational moral dialogue about matters of public policy. And it is possible for people who disagree about controversial technological advances to persuade one another, at least for a time, that some moral views about, for example, genetic engineering, are more reasonable than other views. The price of excluding substantive moral arguments from political debate should not be overlooked. The risk of seeking neutral justification for policy decisions about genetic engineering is that future generations and our own will be subject to a course of action that we cannot plausibly endorse or coherently defend.
The Misallocation of Housing Under Rent Control

The standard analysis of price controls assumes that goods are efficiently allocated, even when there are shortages. But if shortages mean that goods are randomly allocated across the consumers that want them, the welfare costs from misallocation may be greater than the undersupply costs. We develop a framework to empirically test for misallocation. The methodology compares consumption patterns for demographic subgroups in rent-controlled and free-market places. We find that in New York City, which is rent-controlled, an economically and statistically significant fraction of apartments appears to be misallocated across demographic subgroups. (JEL C25, D12, D61, R20)
Robust Implementation in Direct Mechanisms

A social choice function is robustly implementable if there is a mechanism under which the process of iteratively eliminating strictly dominated messages lead to outcomes that agree with the social choice function for all beliefs at every type profile. In an interdependent-value environment with single-crossing preferences, we identify a contraction property on the preferences which together with strict ex post incentive compatibility is sufficient to guarantee robust implementation in the direct mechanism. Strict ex post incentive compatibility and the contraction property are also necessary for robust implementation in any mechanism, including indirect ones. The contraction property requires that the interdependence is not too high. In a linear signal model, the contraction property is equivalent to an interdependence matrix having all eigenvalues smaller than one.
Asymmetric timeliness of earnings, market-to-book and conservatism in financial reporting

Abstract Using an accounting conservatism theory that reflects accounting's role in practice, we investigate the relation between two extensively used measures of conservatism: asymmetric timeliness of earnings and the market-to-book ratio (MTB). We predict and observe that when asymmetric timeliness is measured cumulatively over long periods, its relation with end-of-period MTB is positive. When asymmetric timeliness is measured over short periods, its dependence on beginning-of-period composition of equity value (EV) is responsible for its negative association with MTB. Further, asymmetric timeliness appears to measure conservatism more efficiently when it is estimated cumulatively over multiple periods.
Waiving Disqualification: When Do Securities Violators Receive a Reprieve?

In addition to considerable sanctions, criminal and civil securities enforcement actions trigger an array of collateral consequences. This Article studies automatic “bad actor” and “ineligible issuer” disqualifications, which bar disqualified firms from relying on relaxed disclosure and reporting requirements when raising external capital. First adopted in 1940, the disqualifications were primarily intended to reduce the risk of future violations related to the sale of securities. The SEC has the authority to waive disqualifications on showing of good cause, in particular where the defendant poses low risk of fraud in a disqualified offering. This Article reviews the population of 201 bad actor and ineligible issuer waivers granted between July 2003 and December 2014, and identifies three significant trends in the SEC’s exercise of waiver authority. First, large financial firms received a large majority of waivers, 81.6 percent. Smaller financial firms and nonfinancial companies rarely received waivers. The relative share of waivers issued to large financial firms is not necessarily a reason for concern if such firms are less likely to commit securities fraud than smaller and nonfinancial firms. This could be due to alternative risk-reduction mechanisms such as external oversight or superior self-policing. Second, the study finds that the SEC has developed unwritten criteria for granting waiver requests but has lacked transparency. Most significantly, firms charged with accounting fraud rarely receive a waiver, regardless of their size. But large firms charged with fraud related to structured mortgage securities do tend to receive waivers, even though such misconduct signals fraud risk. Moreover, waivers are often granted to repeat violators whose track record suggests problems with legal compliance. And third, the study reveals a shift in waiver practices over time. The number of granted waivers has declined since 2010, as scrutiny of the SEC’s waiver practices has increased. Also, the SEC recently shifted from granting waivers in full to limited and conditional waivers, a practice that is consistent with the purpose of disqualifications.This Article proposes that automatic disqualifications can be useful tools if deployed consistently and transparently. The SEC should articulate clear and justifiable criteria for automatic disqualifications based on indicators for future fraud. It should make principled and transparent decisions on whether, when, and under what conditions to grant waivers. Disqualifications can be blunt instruments. Rather than waive disqualifications in full, the SEC should expand its practice of granting limited and conditional waivers where waiver denial would be excessive.
Ego depletion: Applications and implications for auditing research

In this paper, I synthesize the prior psychology literature on ego depletion and apply this literature to an auditing setting. Ego depletion refers to a reduced desire or ability to use self-control in task performance due to using self-control on prior tasks. I focus on the likely causes and consequences of depletion in an auditing setting, as well as means of mitigating depletion and recovering self-control resources. While ego depletion theory is prevalent in the psychology literature, little is known about whether or how ego depletion affects professionals on meaningful task performance. As a result, this synthesis is aimed at stimulating future ego depletion research in accounting, and specifically auditing, by surveying existing literature and applying this literature to an auditing setting. Further, I develop 13 questions for future research to investigate. My synthesis reveals that ego depletion likely has a pervasive effect in an auditing setting, and can hinder auditors’ judgment and decision-making (JDM) quality. Therefore, this synthesis helps to provide a greater understanding of the impact of auditing tasks on individuals, and refines both auditor JDM and ego depletion theories.
Organizational Management of Conflicting Professional Identities

Professionals in the military have suffered criticism for their failure to counter military excess in the so-called "War on Terror" - especially in the area of torture and maltreatment of detainees. Much of the criticism leveled against such professionals has assumed that they were bad actors who were making a conscious choice to avoid the strictures of their code of ethics. This Article counters that narrative by applying identity theory to offer a more situationist explanation. It argues that some of these professional failures arise from the cognitive incentives faced by individuals in an organization that rewards organizational deference over independent professional advice. Medical, legal, and other professionals in large organizations must deal with two competing identities - one tied to their membership in a profession, and one tied to their role in supporting the organizational mission. Identity theory predicts that when both identities are activated, individuals are more likely to act in accordance with the more salient identity. The Article recommends that military leaders and other organizational managers create formal mechanisms to recognize and reward professional competence even when professional responsibilities diverge from organizational expediency. Such policies may heighten the salience of the professional identity, thereby reducing the risk that individuals will subordinate their professional responsibilities in favor of deference to organizational aims.
Awareness Reduces Racial Bias

Can raising awareness of racial bias subsequently reduce that bias? We address this question by exploiting the widespread media attention highlighting racial bias among professional basketball refe...
Patent and Antitrust Differing Shades of Meaning

The relationship between patent law and antitrust law has challenged legal minds since the emergence of antitrust law in the late nineteenth century. In reductionist form, the two concepts pose a natural contradiction: One encourages monopoly, while the other restricts it. To avoid uncomfortable dissonance, the trend across time has been to try to harmonize patent and antitrust law. In particular, harmonization efforts in recent decades have led Congress and the courts to engage in a series of attempts, some aborted and some half-formed, to graft antitrust doctrines onto patent law. These efforts have failed to resolve the conflicts. This piece argues that the deviations between patent law and antitrust law run far deeper than courts and commentators recognize. The problem is not just that one encourages monopoly while the other limits it. Rather, patent law and antitrust law often use the same concepts and terminology with differing meanings and contexts. In other words, it may appear that they are talking about the same things, and yet, they are not. Our tendency to assume parallel meanings threatens any attempt to reconcile the two bodies of law. Most important, ignoring asymmetries can lead to both underprotection and overprotection of patent rights, as well as the improper application of antitrust laws. To highlight the problem, this article explores a number of examples of differing meanings in hopes of promoting a subtler understanding of the patent-antitrust terrain. © 2008 Virginia Journal of Law & Technology Association, at http://www.vjolt.net. ? Professor of Law; Director, Law & Bioscience Project, U.C. Hastings College of the Law. I wish to thank Mark Lemley, Christopher Leslie, and Scott Hemphill for their comments on prior drafts. I also wish to thank William Newsom for his research assistance. VIRGINIAJOURNAL OF LAW & TECHNOLOGY Vol. 13 No. 5
Beyond Presidentialism and Parliamentarism

The presidential-parliamentary distinction is foundational to comparative politics and at the center of a large theoretical and empirical literature. However, an examination of constitutional texts suggests a fair degree of heterogeneity within these categories with respect to important institutional attributes. These observations indicate that the classic presidential-parliamentary distinction, and the semi-presidential category, may not be systemic. This article investigates whether the defining attributes that separate presidential and parliamentary constitutions predict other attributes that are stereotypically associated with these institutional models. The results suggest the need for considerable skepticism of the ‘systemic’ nature of the classification. Indeed, the results imply that in order to predict the powers of a country's executive and legislature, it is more useful to know where and when the constitution was written than whether the country has a presidential or parliamentary system.
Political economists or political economists? The role of political environments in the formation of fed policy under burns, Greenspan, and Bernanke

Abstract How do political environments influence the behavior of economists who transition from academic and business environments to policymaking positions? And, more specifically, are an economist’s preexisting beliefs and principles congruent with their policy stances and actions once they transition into a policy role? These questions are particularly relevant when it comes to central banking because central bankers, who wield substantial economic influence, are often selected for policy roles based partly on their stated principles and beliefs. To address these questions, we analyze the writings and speeches of three economists, Arthur Burns, Alan Greenspan, and Ben Bernanke, as they transitioned to becoming chairmen of the Fed. The tension between their previously stated views and their subsequent policy stances as Fed chairmen suggest that operation within political institutions impelled them to alter their views. Our findings suggest it is important to take a realistic view of politics and the political process when discussing central banking and monetary policy.
Case Selection and the Study of Judicial Politics

One complication in studying the Supreme Court and the judicial hierarchy is that the Court’s docket is now nearly completely discretionary. Although the Justices’ strategies in picking cases affect the observations we make and the inferences we draw, this is rarely taken into account in studies of judicial politics. In this article, we study how case selection can affect our inferences within judicial politics, including those about decision making in the Supreme Court itself (such as whether law constrains the Justices) and throughout the judicial hierarchy (such as whether lower courts comply with Supreme Court doctrine). We use simulation analysis to show that the inferential problems raised by the Court’s case selection range from moderate to severe. At stake are substantive conclusions within some of the most important and controversial debates in judicial politics.
Cash crisis in newly public Internet-based firms: an empirical analysis

This study examines why, even when financial resource constraints are significantly relaxed, some new ventures struggle to survive while others prosper. Using the data of approximately 200 new Internet ventures that went public during the years 1997 through 1999, we propose that the performance of new ventures is a function of pre-initial public offering (IPO) characteristics. We determined that firm-level characteristics, including top management team (TMT), financial position, networks, and location, are related to the performance of struggling new ventures. We found strong evidence of agency relationships, so that a substantial reduction in equity holdings by the entrepreneurial team is a strong signal of impending crisis. Interestingly, similar reductions by venture capital (VC) backers did not serve as a signal of crisis.
3-braid knots do not admit purely cosmetic surgeries

A pair of surgeries on a knot is called purely cosmetic if the pair of resulting 3-manifolds are homeomorphic as oriented manifolds. An outstanding conjecture predicts that no nontrivial knots admit any purely cosmetic surgeries. Recent work of Hanselman [5] uses Heegaard Floer homology to obtain new obstructions for the existence of such surgeries. In this work, we apply those obstructions to show that (nontrivial) knots which arise as the closure of a 3-stranded braid do not admit any purely cosmetic surgeries.
Mobile element warfare via CRISPR and anti-CRISPR in Pseudomonas aeruginosa.

Bacteria deploy multiple defenses to prevent mobile genetic element (MGEs) invasion. CRISPR-Cas immune systems use RNA-guided nucleases to target MGEs, which counter with anti-CRISPR (Acr) proteins. Our understanding of the biology and co-evolutionary dynamics of the common Type I-C CRISPR-Cas subtype has lagged because it lacks an in vivo phage-host model system. Here, we show the anti-phage function of a Pseudomonas aeruginosa Type I-C CRISPR-Cas system encoded on a conjugative pKLC102 island, and its Acr-mediated inhibition by distinct MGEs. Seven genes with anti-Type I-C function (acrIC genes) were identified, many with highly acidic amino acid content, including previously described DNA mimic AcrIF2. Four of the acr genes were broad spectrum, also inhibiting I-E or I-F P. aeruginosa CRISPR-Cas subtypes. Dual inhibition comes at a cost, however, as simultaneous expression of Type I-C and I-F systems renders phages expressing the dual inhibitor AcrIF2 more sensitive to targeting. Mutagenesis of numerous acidic residues in AcrIF2 did not impair anti-I-C or anti-I-F function per se but did exacerbate inhibition defects during competition, suggesting that excess negative charge may buffer DNA mimics against competition. Like AcrIF2, five of the Acr proteins block Cascade from binding DNA, while two function downstream, likely preventing Cas3 recruitment or activity. One such inhibitor, AcrIC3, is found in an 'anti-Cas3' cluster within conjugative elements, encoded alongside bona fide Cas3 inhibitors AcrIF3 and AcrIE1. Our findings demonstrate an active battle between an MGE-encoded CRISPR-Cas system and its diverse MGE targets.
Evolutionary transcriptomics implicates HAND2 in the origins of implantation and regulation of gestation length

The developmental origins and evolutionary histories of cell types, tissues, and organs contribute to the ways in which their dysfunction produces disease. In mammals, the nature, development and evolution of maternal-fetal interactions likely influence diseases of pregnancy. Here we show genes that evolved expression at the maternal-fetal interface in Eutherian mammals play essential roles in the evolution of pregnancy and are associated with immunological disorders and preterm birth. Among these genes is HAND2, a transcription factor that suppresses estrogen signaling, a Eutherian innovation allowing blastocyst implantation. We found dynamic HAND2 expression in the decidua throughout the menstrual cycle and pregnancy, gradually decreasing to a low at term. HAND2 regulates a distinct set of genes in endometrial stromal fibroblasts including IL15, a cytokine also exhibiting dynamic expression throughout the menstrual cycle and gestation, promoting migration of natural killer cells and extravillous cytotrophoblasts. We demonstrate that HAND2 promoter loops to an enhancer containing SNPs implicated in birth weight and gestation length regulation. Collectively, these data connect HAND2 expression at the maternal-fetal interface with evolution of implantation and gestational regulation, and preterm birth.
Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers

Variational algorithms are a promising paradigm for utilizing near-term quantum devices for modeling electronic states of molecular systems. However, previous bounds on the measurement time required have suggested that the application of these techniques to larger molecules might be infeasible. We present a measurement strategy based on a low-rank factorization of the two-electron integral tensor. Our approach provides a cubic reduction in term groupings over prior state-of-the-art and enables measurement times three orders of magnitude smaller than those suggested by commonly referenced bounds for the largest systems we consider. Although our technique requires execution of a linear-depth circuit prior to measurement, this is compensated for by eliminating challenges associated with sampling nonlocal Jordan–Wigner transformed operators in the presence of measurement error, while enabling a powerful form of error mitigation based on efficient postselection. We numerically characterize these benefits with noisy quantum circuit simulations for ground-state energies of strongly correlated electronic systems.
Automated rip current detection with region based convolutional neural networks

Abstract This paper presents a machine learning approach for the automatic identification of rip currents with breaking waves. Rip currents are dangerous fast moving currents of water that result in many deaths by sweeping people out to sea. Most people do not know how to recognize rip currents in order to avoid them. Furthermore, efforts to forecast rip currents are hindered by lack of observations to help train and validate hazard models. The presence of web cams and smart phones have made video and still imagery of the coast ubiquitous and provide a potential source of rip current observations. These same devices could aid public awareness of the presence of rip currents. What is lacking is a method to detect the presence or absence of rip currents from coastal imagery. This paper provides expert labeled training and test data for rip currents. We use Faster R–CNN and a custom temporal aggregation stage to make detections from still images or videos with higher measured accuracy than both humans and other methods of rip current detection previously reported in the literature.
Deficient spermiogenesis in mice lacking Rlim

The X-linked gene Rlim plays major roles in female mouse development and reproduction, where it is crucial for the maintenance of imprinted X chromosome inactivation in extraembryonic tissues of embryos. However, while females carrying a systemic Rlim knockout (KO) die around implantation, male Rlim KO mice appear healthy and are fertile. Here, we report an important role for Rlim in testis where it is highly expressed in post-meiotic round spermatids as well as in Sertoli cells. Systemic deletion of the Rlim gene results in lower numbers of mature sperm that contains excess cytoplasm, leading to decreased sperm motility and in vitro fertilization rates. Targeting the conditional Rlim cKO specifically to the spermatogenic cell lineage largely recapitulates this phenotype. These results reveal functions of Rlim in male reproduction specifically in round spermatids during spermiogenesis.
High-Fidelity Measurement of a Superconducting Qubit Using an On-Chip Microwave Photon Counter

Fast, high-fidelity measurement of a superconducting qubit with a microwave photon counter offers a strong foundation for error correction in future, robust, large-scale quantum computers.
A comprehensive computational model of animal biosonar signal processing

Computational models of animal biosonar seek to identify critical aspects of echo processing responsible for the superior, real-time performance of echolocating bats and dolphins in target tracking and clutter rejection. The Spectrogram Correlation and Transformation (SCAT) model replicates aspects of biosonar imaging in both species by processing wideband biosonar sounds and echoes with auditory mechanisms identified from experiments with bats. The model acquires broadband biosonar broadcasts and echoes, represents them as time-frequency spectrograms using parallel bandpass filters, translates the filtered signals into ten parallel amplitude threshold levels, and then operates on the resulting time-of-occurrence values at each frequency to estimate overall echo range delay. It uses the structure of the echo spectrum by depicting it as a series of local frequency nulls arranged regularly along the frequency axis of the spectrograms after dechirping them relative to the broadcast. Computations take place entirely on the timing of threshold-crossing events for each echo relative to threshold-events for the broadcast. Threshold-crossing times take into account amplitude-latency trading, a physiological feature absent from conventional digital signal processing. Amplitude-latency trading transposes the profile of amplitudes across frequencies into a profile of time-registrations across frequencies. Target shape is extracted from the spacing of the object's individual acoustic reflecting points, or glints, using the mutual interference pattern of peaks and nulls in the echo spectrum. These are merged with the overall range-delay estimate to produce a delay-based reconstruction of the object's distance as well as its glints. Clutter echoes indiscriminately activate multiple parts in the null-detecting system, which then produces the equivalent glint-delay spacings in images, thus blurring the overall echo-delay estimates by adding spurious glint delays to the image. Blurring acts as an anticorrelation process that rejects clutter intrusion into perceptions.
Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning

We present a pipeline for geomorphological analysis that uses structure from motion (SfM) and deep learning on close-range aerial imagery to estimate spatial distributions of rock traits (size, roundness, and orientation) along a tectonic fault scarp. The properties of the rocks on the fault scarp derive from the combination of initial volcanic fracturing and subsequent tectonic and geomorphic fracturing, and our pipeline allows scientists to leverage UAS-based imagery to gain a better understanding of such surface processes. We start by using SfM on aerial imagery to produce georeferenced orthomosaics and digital elevation models (DEM). A human expert then annotates rocks on a set of image tiles sampled from the orthomosaics, and these annotations are used to train a deep neural network to detect and segment individual rocks in the entire site. The extracted semantic information (rock masks) on large volumes of unlabeled, high-resolution SfM products allows subsequent structural analysis and shape descriptors to estimate rock size, roundness, and orientation. We present results of two experiments conducted along a fault scarp in the Volcanic Tablelands near Bishop, California. We conducted the first, proof-of-concept experiment with a DJI Phantom 4 Pro equipped with an RGB camera and inspected if elevation information assisted instance segmentation from RGB channels. Rock-trait histograms along and across the fault scarp were obtained with the neural network inference. In the second experiment, we deployed a hexrotor and a multispectral camera to produce a DEM and five spectral orthomosaics in red, green, blue, red edge, and near infrared. We focused on examining the effectiveness of different combinations of input channels in instance segmentation.
Minimizing closed geodesics on polygons and disks

In this paper we study 1/k geodesics, those closed geodesics that minimize on all subintervals of length $L/k$, where $L$ is the length of the geodesic. We develop new techniques to study the minimizing properties of these curves on doubled polygons, and demonstrate a sequence of doubled polygons whose closed geodesics exhibit unbounded minimizing properties. We also compute the length of the shortest closed geodesic on doubled odd-gons and show that this length approaches 4 times the diameter.
Nearly tight frames of spin wavelets on the sphere

We show that the spin wavelets on the sphere S 2 , which were constructed by the first author and Marinucci in [3], can be chosen so as to form a nearly tight frame. These spin wavelets can be applied (see [1] and [2]) to the study of the polarization of cosmic microwave background radiation. For certain of these frames, there is a positive C such that each frame element at scale a j is supported in a geodesic ball of radius Ca j .
Fair Classification with Group-Dependent Label Noise

This work examines how to train fair classifiers in settings where training labels are corrupted with random noise, and where the error rates of corruption depend both on the label class and on the membership function for a protected subgroup. Heterogeneous label noise models systematic biases towards particular groups when generating annotations. We begin by presenting analytical results which show that naively imposing parity constraints on demographic disparity measures, without accounting for heterogeneous and group-dependent error rates, can decrease both the accuracy and the fairness of the resulting classifier. Our experiments demonstrate these issues arise in practice as well. We address these problems by performing empirical risk minimization with carefully defined surrogate loss functions and surrogate constraints that help avoid the pitfalls introduced by heterogeneous label noise. We provide both theoretical and empirical justifications for the efficacy of our methods. We view our results as an important example of how imposing fairness on biased data sets without proper care can do at least as much harm as it does good.
Spin-polarized two-dimensional electron/hole gases on LiCoO$_2$ layers.

First-principles calculations show the formation of a 2D spin polarized electron (hole) gas on the Li (CoO$_2$) terminated surfaces of finite slabs down to a monolayer of LiCoO$_2$ in remarkable contrast with the bulk band structure stabilized by Li donating its electron to the CoO$_2$ layer forming a Co-$d-t_{2g}^6$ insulator. By mapping the first-principles computational results to a minimal tight-binding models corresponding to a non-chiral 3D generalization of the quadripartite Su-Schriefer-Heeger (SSH4) model, we show that these surface states have topological origin.
Aerosol and droplet generation from orbital repair: Surgical risk in the pandemic era.

Abstract Introduction The highly contagious COVID-19 has resulted in millions of deaths worldwide. Physicians performing orbital procedures may be at increased risk of occupational exposure to the virus due to exposure to secretions. The goal of this study is to measure the droplet and aerosol production during repair of the inferior orbital rim and trial a smoke-evacuating electrocautery handpiece as a mitigation device. Material and methods The inferior rim of 6 cadaveric orbits was approached transconjunctivally using either standard or smoke-evacuator electrocautery and plated using a high-speed drill. Following fluorescein inoculation, droplet generation was measured by counting under ultraviolet-A (UV-A) light against a blue background. Aerosol generation from 0.300–10.000 μm was measured using an optical particle sizer. Droplet and aerosol generation was compared against retraction of the orbital soft tissue as a negative control. Results No droplets were observed following the orbital approach using electrocautery. Visible droplets were observed after plating with a high-speed drill for 3 of 6 orbits. Total aerosol generation was significantly higher than negative control following the use of standard electrocautery. Use of smoke-evacuator electrocautery was associated with significantly lower aerosol generation in 2 of 3 size groups and in total. There was no significant increase in total aerosols associated with high-speed drilling. Discussion and conclusions Droplet generation for orbital repair was present only following plating with high-speed drill. Aerosol generation during standard electrocautery was significantly reduced using a smoke-evacuating electrocautery handpiece. Aerosols were not significantly increased by high-speed drilling.
Containers on the Parallelization of General-Purpose Java Programs

Static parallelization of general-purpose programs is still impossible, in general, due to their common use of pointers, irregular data structures, and complex control-flows. One promising strategy is to exploit parallelism at runtime. Runtime parallelization schemes, particularly data speculations, alleviate the need to statically prove independent computations at compile-time. However, studies show that many real-world applications exhibit limited speculative parallelism to offset the overhead and penalty of speculation schemes. This paper addresses this issue by using compiler analyses to compensate for speculative parallelizations. We focus on general-purpose Java programs with extensive use of Java container classes. In our scheme, compilers serve as a guideline of where to speculate by “lazily” detecting dependences that are mostly static, while leaving those that are more dynamic to runtime. We also propose techniques to enhance speculative parallelism in the programs. The experimental results show that, after eliminating static dependences, the four applications we study exhibit significant parallelism that can be gainfully exploited by a speculative parallelization system.
Exact and Approximation Algorithms for Clustering

In this paper we present an n^ O(k1-1/d) -time algorithm for solving the k -center problem in \realsd , under Lźfty - and L2 -metrics. The algorithm extends to other metrics, and to the discrete k -center problem. We also describe a simple (1+ź) -approximation algorithm for the k -center problem, with running time O(nlog k) + (k/ź)^ O(k1-1/d) . Finally, we present an n^ O(k1-1/d) -time algorithm for solving the L -capacitated k -center problem, provided that L=Ω(n/k1-1/d) or L=O(1) .
Variationally regularized graph-based representation learning for electronic health records

Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of diseases and lab-disease correlations can be informative when only a subset of these variables is documented by the clinician. A feasible approach to improving the representation learning of EHR data is to associate relevant medical concepts and utilize these connections. Existing medical ontologies can be the reference for EHR structures, but they place numerous constraints on the data source. Recent progress on graph neural networks (GNN) enables end-to-end learning of topological structures for non-grid or non-sequential data. However, there are problems to be addressed on how to learn the medical graph adaptively and how to understand the effect of medical graph on representation learning. In this paper, we propose a variationally regularized encoder-decoder graph network that achieves more robustness in graph structure learning by regularizing node representations. Our model outperforms the existing graph and non-graph based methods in various EHR predictive tasks based on both public data and real-world clinical data. Besides the improvements in empirical experiment performances, we provide an interpretation of the effect of variational regularization compared to standard graph neural network, using singular value analysis.
A many-server functional strong law for a non-stationary loss model

Abstract The purpose of this note is to show that it is possible to establish a many-server functional strong law of large numbers (FSLLN) for the fraction of occupied servers (i.e., the scaled number-in-system) without explicitly tracking through a measure valued process either the age or the residual service times of the jobs in a non-Markovian, non-stationary loss model. This considerable analytical simplification is achieved by exploiting a semimartingale representation. The fluid limit is shown to be the unique solution of a Volterra integral equation.
Topos Semantics for a Higher-Order Temporal Logic of Actions

TLA is a popular temporal logic for writing stuttering-invariant specifications of digital systems. However, TLA lacks higher-order features useful for specifying modern software written in higher-order programming languages. We use categorical techniques to recast a real-time semantics for TLA in terms of the actions of a group of time dilations, or "stutters," and an extension by a monoid incorporating delays, or "falters." Via the geometric morphism of the associated presheaf topoi induced by the inclusion of stutters into falters, we construct the first model of a higher-order TLA.
Unsupervised and interpretable domain adaptation to rapidly filter tweets for emergency services

During the onset of a natural or man-made crisis event, public often share relevant information for emergency services on social web platforms such as Twitter. However, filtering such relevant data in real-time at scale using social media mining is challenging due to the short noisy text, sparse availability of relevant data, and also, practical limitations in collecting large labeled data during an ongoing event. In this paper, we hypothesize that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events for training efficient information filtering models during the sudden onset of a new crisis. We present a novel method to classify relevant social posts during an ongoing crisis without seeing any new data from this event (fully unsupervised domain adaptation). Specifically, we construct a customized multi-task architecture with a multi-domain discriminator for crisis analytics: multi-task domain adversarial attention network (MT-DAAN). This model consists of dedicated attention layers for each task to provide model interpretability; critical for real-word applications. As deep networks struggle with sparse datasets, we show that this can be improved by sharing a base layer for multitask learning and domain adversarial training. The framework is validated with the public datasets of TREC incident streams that provide labeled Twitter posts (tweets) with relevant classes (Priority, Factoid, Sentiment) across 10 different crisis events such as floods and earthquakes. Evaluation of domain adaptation for crisis events is performed by choosing one target event as the test set and training on the rest. Our results show that the multi-task model outperformed its single-task counterpart. For the qualitative evaluation of interpretability, we show that the attention layer can be used as a guide to explain the model predictions and empower emergency services for exploring accountability of the model, by showcasing the words in a tweet that are deemed important in the classification process. Finally, we show a practical implication of our work by providing a use-case for the COVID-19 pandemic.
Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis

Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.
Jackknife Empirical Likelihood Approach for K-sample Tests

The categorical Gini correlation is an alternative measure of dependence between a categorical and numerical variables, which characterizes the independence of the variables. A nonparametric test for the equality of K distributions has been developed based on the categorical Gini correlation. By applying the jackknife empirical likelihood approach, the standard limiting chi-square distribution with degree freedom of $K-1$ is established and is used to determine critical value and $p$-value of the test. Simulation studies show that the proposed method is competitive to existing methods in terms of power of the tests in most cases. The proposed method is illustrated in an application on a real data set.
The Antiviral Enzyme, Viperin, Activates Protein Ubiquitination by the E3 Ubiquitin Ligase, TRAF6

Viperin is a broadly conserved radical SAM enzyme that synthesizes the antiviral nucleotide ddhCTP. In higher animals, viperin expression also accelerates the degradation of various cellular and viral proteins necessary for viral replication; however, the details of this process remain largely unknown. Here, we show that viperin activates a component of the protein ubiquitination machinery, which plays an important role in both protein degradation and immune signaling pathways. We demonstrate that viperin binds the E3 ubiquitin ligase, TRAF6, which catalyzes K63-linked ubiquitination associated with immune signaling pathways. Viperin activates ubiquitin transfer by TRAF6-2.5-fold and causes a significant increase in polyubiquitinated forms of TRAF6 that are important for mediating signal transduction. Our observations both imply a role for viperin as an agonist of immune signaling and suggest that viperin may activate other K48-linked E3-ligases involved in targeting proteins for proteasomal degradation.
Flexoelectricity and the entropic force between fluctuating fluid membranes

Biological membranes undergo noticeable thermal fluctuations at physiological temperatures. When two membranes approach each other, they hinder the out-of-plane fluctuations of the other. This hind...
Biochemistry of methanogenesis

The recent and unexpected finding that methanogenic bacteria occupy an isolated biochemical island in the sea of procaryotes has added a touch of excitement to the study of these organisms1. This island is defined by such diverse biochemical qualities as: a very restricted range of oxidizable substrates coupled to the biosynthesis of methane; synthesis of an unusual range of cell-wall components; synthesis of biphytanyl glycerol ethers as well as high amounts of squalene; synthesis of unusual coenzymes and growth factors; synthesis of rRNA that is distantly related to that of typical bacteria; possession of a genome size (DNA) approaching 1/3 that of E. coli.
Orientation stabilization in a bioinspired bat-robot using integrated mechanical intelligence and control

Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats wings exhibit fast wing articulation and can mobilize as many as 40 joints within a single wingbeat. Mimicking bat flight can be a significant ordeal and the current design paradigms have failed as they assume only closed-loop feedback roles through sensors and conventional actuators while ignoring the computational role carried by morphology. In this paper, we propose a design framework called Morphing via Integrated Mechanical Intelligence and Control (MIMIC) which integrates a small and low energy actuators to control the robot through a change in morphology. In this paper, using the dynamic model of Northeastern University’s Aerobat, which is designed to test the effectiveness of the MIMIC framework, it will be shown that computational structures and closed-loop feedback can be successfully used to mimic bats stable flight apparatus.
Exploiting Symmetry Reduces the Cost of Training QAOA

A promising approach to the practical application of the quantum approximate optimization algorithm (QAOA) is finding QAOA parameters classically in simulation and sampling the solutions from QAOA with optimized parameters on a quantum computer. Doing so requires repeated evaluations of QAOA energy in simulation. In this article, we propose a novel approach for accelerating the evaluation of QAOA energy by leveraging the symmetry of the problem. We show a connection between classical symmetries of the objective function and the symmetries of the terms of the cost Hamiltonian with respect to the QAOA energy. We show how by considering only the terms that are not connected by symmetry, we can significantly reduce the cost of evaluating the QAOA energy. Our approach is general and applies to any known subgroup of symmetries and is not limited to graph problems. Our results are directly applicable to nonlocal QAOA generalization recursive QAOA. We outline how available fast graph automorphism solvers can be leveraged for computing the symmetries of the problem in practice. We implement the proposed approach on the MaxCut problem using a state-of-the-art tensor network simulator and a graph automorphism solver on a benchmark of 48 graphs with up to 10 000 nodes. Our approach provides an improvement for $p=1$ on 71.7% of the graphs considered, with a median speedup of 4.06, on a benchmark, where 62.5% of the graphs are known to be hard for automorphism solvers.
On the Power of Randomization for Scheduling Real-Time Traffic in Wireless Networks

In this paper, we consider the problem of scheduling real-time traffic in wireless networks under a conflict-graph interference model and single-hop traffic. The objective is to guarantee that at least a certain fraction of packets of each link are delivered within their deadlines, which is referred to as delivery ratio . This problem has been studied before under restrictive frame-based traffic models, or greedy maximal scheduling schemes like LDF (Largest-Deficit First) that can lead to poor delivery ratio for general traffic patterns. In this paper, we pursue a different approach through randomization over the choice of maximal links that can transmit at each time. We design randomized policies in collocated networks, multi-partite networks, and general networks, that can achieve delivery ratios much higher than what is achievable by LDF. Further, our results apply to any traffic (arrival and deadline) process that evolves as an unknown positive recurrent Markov chain. Hence, this work is an improvement with respect to both efficiency and traffic assumptions compared to the past work. We further present extensive simulation results over various traffic patterns and interference graphs to illustrate the gains of our randomized policies over LDF variants.