cssi/SciSciGPT-SciSciNet huggingface dataset:
Structural relations of language and cognitive skills, and topic knowledge to written composition: A test of the direct and indirect effects model of writing

Background Writing involves multiple processes, drawing on a number of language, cognitive, and print-related skills, and knowledge. According to the Direct and Indirect Effects model of Writing (DIEW; Kim & Park, 2019, Reading and Writing, 32, 1319), these multiple factors have hierarchical, interactive, and dynamic relations. Aims I examined the hierarchical relations of language and cognitive skills to written composition as well as the relation of topic knowledge to written composition, using DIEW as a theoretical framework. Sample One hundred thirty-two English-speaking students in Grade 4 were assessed on written composition, topic knowledge, oral language (vocabulary, grammatical knowledge, discourse-level oral production), higher order cognitions (inference, perspective taking [theory of mind], monitoring), domain-general cognitions (working memory and attention), and transcription skills (spelling and handwriting fluency). Methods Structural equation modelling was used to compare hierarchical relations models with a direct or flat relations model. Results The hierarchical relations model was supported. Discourse oral language skills and transcription skills completely mediated the relations of the other component skills to written composition, and the included component skills explained 82% of variance in written composition. Substantial total effects were found for discourse language, transcription, attention, working memory, vocabulary, theory of mind, and grammatical knowledge. Topic knowledge was moderately related to writing, but this relation became weak once the other skills were accounted for. Conclusions Component skills have hierarchical structural relations and make direct and indirect contributions to written composition. Furthermore, the role of topic knowledge in written composition appears constrained by language and transcription skills for developing writers.
Respecting Beasts: The Dehumanizing Quality of the Modern Prison and an Unusual Model for Penal Reform

Through a lens carved with unfortunate personal experiences, but mindful that my experiences as an inmate may vary significantly from those of others, I examine the interpersonal climate inside the walls of prison. Focusing on the notion of withholding respect, this Article contends that current prison management practices do not foster a healthy sense of self-respect among inmates. Instead, I argue that the inmate culture, guided by the normative expectations of the convict code, is a prisoner‘s only source of recognition as a human being worthy of respect. Accordingly, I propose that those charged with running modern prisons look to this aspect of the inmate culture for guidance and treat those who exist behind concrete and steel as beings rather than beasts.
An RXTE Survey of Long-Term X-Ray Variability in Seyfert 1 Galaxies

Data from the first three years of RXTE observations have been systematically analyzed to yield a set of 300 day 2-10 keV light curves with similarly uniform, ~5 day sampling, for a total of nine Seyfert 1 galaxies. This is the first X-ray variability survey to probe consistently timescales longer than a few days in a large number of active galactic nuclei (AGNs). Comparison to ASCA data covering a similar band but much shorter (1 day) timescales shows that all the AGNs are more strongly variable on long timescales than on short timescales. This increase is greatest for the highest luminosity sources. The well-known anticorrelation between source luminosity and variability amplitude is both stronger and shallower in power-law slope when measured on long timescales. This is consistent with a picture in which the X-ray variability of Seyfert 1 galaxies can be can be described by a single, universal fluctuation power-density shape for which the cutoff moves to longer timescales for higher luminosity sources. All of the Seyfert 1 galaxies exhibit stronger variability in the relatively soft 2-4 keV band than in the harder 7-10 keV band. This effect is much too pronounced to be explained by simple models based either on the dilution of the power-law continuum by the Compton reflection component or on the hard X-rays being produced in a static, pair-dominated, plane-parallel Comptonizing corona.
On Global Asymptotic Stability for the LSW Model with Subcritical Initial Data

The main result of the paper is a global asymptotic stability result for solutions to the Lifschitz–Slyozov–Wagner (LSW) system of equations. This extends some local asymptotic stability results of Niethammer–Velazquez (Commun Partial Differ Equ 31:1679–1708, 2006). The method of proof is along similar lines to the one used in a previous paper of the authors. This previous paper proves global asymptotic stability for a class of infinite dimensional dynamical systems for which no Lyapunov function is (apparently) available.
The Importance of Political Systems for Trade Union Membership, Coverage and Influence: Theory and Comparative Evidence

Ideological connections between the state, political systems and industrial relations have long been important. But the influence of the structural nature of a country's political system on trade union membership, coverage and influence has been largely overlooked. We uniquely theorize three channels through which a political system can shape unionization in the workplace independent of ideology: incentives for inclusionary governance, legislative body composition and policy enactment. Empirically, we use multiple European datasets to test the relationship between political and employee representation using multivariate analyses across more than 25 countries. We find that increased political representativeness, measured by lower disproportionality and the presence of multiparty coalitions, is a statistically significant predictor of a greater likelihood of individual trade union membership, coverage and influence, while competitive fragmentation, measured by greater numbers of political parties, is associated with weakened collective voice.
On local tameness of certain graphs of groups

Let $G$ be the fundamental group of a finite graph of groups with Noetherian edges and locally tame vertices. We prove that $G$ is locally tame. It follows that if a finitely presented group $H$ has a non-trivial $JSJ$-decomposition over the class of its $VPC(k)$ subgroups for $k=1$ or $k=2$, and all the vertex groups in the decomposition are flexible, then $H$ is locally tame.
Hemoglobin A1c as a marker to stratify diabetes risk following pancreaticoduodenectomy.

Abstract Background Pancreatic cancer has been shown to cause diabetes mellitus, and diabetes mellitus has been shown to be a risk factor for pancreatic cancer. The effect of pancreaticoduodenectomy on risk for development of diabetes mellitus is unclear. This study used hemoglobin A1c to determine the incidence of diabetes mellitus development following pancreaticoduodenectomy based on preoperative risk of diabetes mellitus. Methods Retrospective review of patients undergoing pancreaticoduodenectomy was performed with comparison of preoperative diabetes mellitus status and hemoglobin A1c with development of diabetes mellitus postoperatively. Risk ratios were calculated to determine the risk for diabetes mellitus development. Results Among 90 patients who met inclusion criteria, 26.7% developed new-onset or worsening diabetes mellitus following pancreaticoduodenectomy. Of those with hemoglobin A1c ≤ 5.6%, only 7.7% of patients developed diabetes mellitus. Patients at risk for diabetes mellitus preoperatively had 4.0 times greater risk for development of diabetes mellitus following pancreaticoduodenectomy. Conclusion Hemoglobin A1c levels should be used to identify patients at risk for new-onset or worsening diabetes mellitus following pancreaticoduodenectomy.
GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter

Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter.
On the Number of Integer Points in Translated and Expanded Polyhedra

We prove that the problem of minimizing the number of integer points in parallel translations of a rational convex polytope in $${\mathbb {R}}^6$$ is NP-hard. We apply this result to show that given a rational convex polytope $$P\subset {\mathbb {R}}^6$$ , finding the largest integer t s.t. the expansion tP contains fewer than k integer points is also NP-hard. We conclude that the Ehrhart quasi-polynomials of rational polytopes can have arbitrary fluctuations.
An Information Theoretic Approach to Originality and Bias in Science

We introduce an information theoretic framework for a quantitative measure of originality to model the impact of various classes of biases, errors and error corrections on scientific research. Some...
Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth- limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.
US State Tort Liability Reform and Entrepreneurship

Tort and civil liability reform has been a hotly debated issue across US states. This has spawned a large theoretical and empirical academic literature that evaluates the implications of such reform and the impact that it can have on various aspects of an economy. This study adds to that literature by considering how various tort reforms affect entrepreneurial activity across states. The study employs the Database of State Tort Law Reforms (6th Edition) and utilizes the Kauffman Index – an index of entrepreneurial startup activity – between 1996 and 2016, finding that liability reform is generally positively associated with increased entrepreneurial activity. These results are largely driven by joint and several liability, caps on noneconomic damages, limits on contingency fees, and collateral source rules though this latter result is somewhat less robust, while the effects of joint and several liability do not persist through time.
Radiative AGN feedback on a moving mesh: the impact of the galactic disc and dust physics on outflow properties

Feedback from accreting supermassive black holes, active galactic nuclei (AGN), is now a cornerstone of galaxy formation models. In this work, we present radiation-hydrodynamic simulations of radiative AGN feedback using the novel Arepo-RT. A central black hole emits radiation at a constant luminosity and drives an outflow via radiation pressure on dust grains. Utilising an isolated NFW halo we validate our setup in the single and multi-scattering regimes, with the simulated shock front propagation in excellent agreement with the expected analytic result. For a spherically symmetric NFW halo, an examination of the simulated outflow properties generated by radiative feedback demonstrates that they are lower than typically observed at a fixed AGN luminosity, regardless of the collimation of the radiation. We then explore the impact of a central disc galaxy and the assumed dust model on the outflow properties. The contraction of the halo during the galaxy's formation and modelling the production of dust grains results in a factor $100$ increase in the halo's optical depth. Radiation is then able to couple momentum more efficiently to the gas, driving a stronger shock and producing a mass-loaded $\sim10^{3}\,\mathrm{M}_{\odot}\,\mathrm{yr}^{-1}$ outflow with a velocity of $\sim2000\,\mathrm{km}\,\mathrm{s}^{-1}$, in agreement with observations. However, the inclusion of dust destruction mechanisms, like thermal sputtering, leads to the rapid destruction of dust grains within the outflow, reducing its properties below typically observed values. We conclude that radiative AGN feedback can drive outflows, but a thorough numerical and physical treatment is required to assess its true impact.
An odd Khovanov homotopy type

Abstract For each link L ⊂ S 3 and every quantum grading j, we construct a stable homotopy type X o j ( L ) whose cohomology recovers Ozsvath-Rasmussen-Szabo's odd Khovanov homology, H ˜ i ( X o j ( L ) ) = Kh o i , j ( L ) , following a construction of Lawson-Lipshitz-Sarkar of the even Khovanov stable homotopy type. Furthermore, the odd Khovanov homotopy type carries a Z / 2 action whose fixed point set is a desuspension of the even Khovanov homotopy type. We also construct a potentially new even Khovanov homotopy type with a Z / 2 action, with fixed point set a desuspension of X o j ( L ) .
RIVAROXABAN VERSUS WARFARIN IN AFRICAN AMERICAN PATIENTS WITH NONVALVULAR ATRIAL FIBRILLATION

African American patients were under-represented in randomized trials evaluating non-vitamin K oral anticoagulants for the management of non-valvular atrial fibrillation (NVAF). We evaluated the effectiveness and safety of rivaroxaban vs. warfarin in African American patients with NVAF managed in
Unique equilibrium states, large deviations and Lyapunov spectra for the Katok map

We study the thermodynamic formalism of a non-uniformly hyperbolic diffeomorphism on the 2-torus, known as the Katok map. We prove for a Holder continuous potential with one additional condition, or geometric -potential with , the equilibrium state exists and is unique. We derive the level-2 large deviation principle for the equilibrium state of . We study the multifractal spectra of the Katok map for the entropy and dimension of level sets of Lyapunov exponents.
The Influence of Late-stage Nuclear Burning on Red Supergiant Supernova Light Curves

Many Type II supernovae (SNe) show hot early (~30 days) emission, and a diversity in their light curves extending from the Type IIP to the Type IIL, which can be explained by interaction with dense and confined circumstellar material (CSM). We perform hydrodynamical simulations of red supergiants to model the ejection of CSM caused by wave heating during late-stage nuclear burning. Even a small amount of deposited energy (10⁴⁶–10⁴⁷ erg), which is roughly that expected due to waves excited by convection in the core, is sufficient to change the shapes of SN light curves and bring them into better agreement with observations. As a test case, we consider the specific example of supernova (SN) 2017eaw, which shows that a nuclear burning episode is able to explain the light curve if it occurs ~150–450 days prior to core collapse. Due to the long timescale that it takes for the low-energy shock to traverse the star, this would manifest as a pre-SN outburst ~50–350 days prior to the full-fledged SN. Applying work like this to other SNe will provide a direct connection between the SN and pre-SN outburst properties, which can be tested by future wide field surveys. In addition, we show that our models can qualitatively explain the short-lived "flash-ionization" lines seen in the early spectra of many Type II SNe.
Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using behavioral modeling based on CPT. This dynamic pricing strategy together with dynamic routing algorithms that we have developed earlier, provide a complete solution customized for the SMoDS of multi-passenger transport. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing for the SMoDS are delineated using computational experiments involving passenger preferences and risk attitudes. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.
Estrogen Attenuates the Growth of Human Papillomavirus-Positive Epithelial Cells

Human papillomaviruses (HPVs) are small, double-stranded DNA viruses that are significant risk factors in the development of cancer, and HPV accounts for approximately 5% of all worldwide cancers. Recent studies using data from The Cancer Genome Atlas (TCGA) have demonstrated that elevated levels of estrogen receptor alpha (ERα) are associated with improved survival in oropharyngeal cancers, and these elevated receptor levels were linked with human papillomavirus-positive cancers (HPV+cancers). There has been a dramatic increase in HPV-related head and neck squamous cell carcinomas (HPV+HNSCCs) over the last 2 decades, and therapeutic options for this ongoing health crisis are a priority; currently, there are no antiviral therapeutics available for combatting HPV+cancers. During our TGCA studies on head and neck cancer, we had also discovered the overexpression of ERα in HPV+cancers. Here, we demonstrate that 17β-estradiol (estrogen) attenuates the growth/cell viability of HPV+cancers in vitro, but not HPV-negative cancer cells. In addition, N/Tert-1 cells (foreskin keratinocytes immortalized with human telomerase reverse transcriptase [hTERT]) containing human papillomavirus 16 (HPV16) have elevated levels of ERα and growth sensitivity after estrogen treatment compared with parental N/Tert-1 cells. Finally, we demonstrate that there are potentially two mechanisms contributing to the attenuation of HPV+ cell growth following estrogen treatment. First, estrogen represses the viral transcriptional long control region (LCR) downregulating early gene expression, including E6/E7. Second, expression of E6 and E7 by themselves sensitizes cells to estrogen. Overall, our results support the recent proposal that estrogen could be exploited therapeutically for the treatment of HPV-positive oral cancers.IMPORTANCE Human papillomaviruses cause around 5% of all human cancers, yet there are no specific antiviral therapeutic approaches available for combatting these cancers. These cancers are currently treated with standard chemoradiation therapy (CRT). Specific antiviral reagents are desperately required, particularly for HPV+HNSCC whose incidence is increasing and for which there are no diagnostic tools available for combatting this disease. Using data from The Cancer Genome Atlas (TCGA), we and others determined that the estrogen receptor alpha (ERα) is overexpressed in HPV+HNSCC and that elevated levels are associated with an improved disease outcome. This has led to the proposal that estrogen treatment could be a novel therapeutic approach for combatting HPV+cancers. Here, we demonstrate that estrogen attenuates the growth of HPV+epithelial cells using multiple mechanisms, supporting the idea that estrogen has potential as a therapeutic agent for the treatment of HPV+HNSCC.
Rational Compositional Control of Electrodeposited Ag-Fe films.

Some alloys are very difficult to electrodeposit, due to problems such as the large difference in the equilibrium potentials and/or deposition kinetics of the alloy components, as well as the bath ...
Quad-Rotor Flight Simulation in Realistic Atmospheric Conditions

In trajectory planning and control design for unmanned air vehicles, highly simplified models are typically used to represent the vehicle dynamics and the operating environment. The goal of this wo...
Molecular bond stabilization in the strong-field dissociation of O 2 +

We theoretically examine the rotational and vibrational dynamics of ${\mathrm{O}}_{2}{}^{+}$ molecular ions exposed to intense, short laser pulses for conditions realized in contemporary pump-probe experiments. We solve the time-dependent Schr\"odinger equation within the Born-Oppenheimer approximation for an initial distribution of randomly aligned molecular ions. For fixed peak intensities, our numerical results show that total, angle-integrated ${\mathrm{O}}_{2}{}^{+}\ensuremath{\rightarrow}\mathrm{O}(^{3}P)+\mathrm{O}{}^{+}(^{4}S^{0})$ dissociation yields do not monotonically increase with increasing infrared-probe pulse duration. We find this pulse-duration-dependent stabilization to be consistent with the transient trapping of nuclear probability density in a light-induced (bond-hardening) potential-energy surface and robust against rotational excitation. We analyze this stabilization effect and its underlying bond-hardening mechanism (i) in the time domain, by following the evolution of partial nuclear probability densities associated with the dipole-coupled ${\mathrm{O}}_{2}{}^{+}(a\phantom{\rule{0.16em}{0ex}}^{4}\mathrm{\ensuremath{\Pi}}_{u})$ and ${\mathrm{O}}_{2}{}^{+}(f\phantom{\rule{0.16em}{0ex}}^{4}\mathrm{\ensuremath{\Pi}}_{g})$ cationic states, and (ii) in the frequency domain, by examining rovibrational quantum-beat spectra for the evolution of the partial nuclear probability densities associated with these states. Our analysis reveals the characteristic timescale for the bond-hardening mechanism in ${\mathrm{O}}_{2}{}^{+}$ and explains the onset of bond stabilization for sufficiently long pulse durations.
Is This the Right Time to Post My Task? An Empirical Analysis on a Task Similarity Arrival in TopCoder

Existed studies have shown that crowd workers are more interested in taking similar tasks in terms of context, field and required technology, rather than tasks from the same project. Therefore, it is important for task owners to not only be able to plan “when the new task should arrive?” but also, to justify “what the strategic task arrival plan should be?” in order to receive a valid submission for the posted task. To address these questions this research reports an empirical analysis on the impact of similar task arrival in the platform, on both tasks’ success level and workers’ performance. Our study supports that 1) higher number of arrival tasks with similarity level greater than 70% will negatively impact on task competition level, 2) Bigger pool of similar open and arrival tasks would lead to lower worker attraction and elasticity, and 3) Workers who register for tasks with lower similarity level are more reliable to make a valid submission and 4) arriving task to the pool of 60% similar task will provide the highest chance of receiving valid submission.
Internal and External Effects of Social Distancing in a Pandemic

We develop a quantitative framework for exploring how individuals trade off the utility benefit of social activity against the internal and external health risks that come with social interactions during a pandemic. We calibrate the model to external targets and then compare its predictions with daily data on social activity, fatalities, and the estimated effective reproduction number R(t) from the COVID-19 pandemic in March-June 2020. While the laissez- faire equilibrium is consistent with much of the decline in social activity that we observed in US data, optimal policy further imposes immediate and highly persistent social distancing. Notably, neither equilibrium nor optimal social distancing is extremely restrictive, in the sense that the effective reproduction number never falls far below 1. The expected cost of COVID-19 in the US is substantial, $12,700 in the laissez-faire equilibrium and $8,100 per person under an optimal policy. Optimal policy generates this large welfare gain by shifting the composition of costs from fatalities to persistent social distancing.
The Evolution of Endogenous Business Cycles

This paper distinguishes two kinds of endogenous business cycle models: EBC1 models, which display dynamic indeterminacy, and EBC2 models, which display steady-state indeterminacy. Both strands of the literature have their origins in the sunspot literature that developed at the University of Pennsylvania in the 1980s. I argue that EBC1 models are part of the evolution of modern macroeconomics that has classical roots dating back to the 1920s. EBC2 models provide a microfoundation for one of the most important ideas to emerge from Keynes's (1936) General Theory of Employment, Interest and Money: that high involuntary unemployment can persist as part of the steady-state equilibrium of a market economy.
Creating value in the face of declining performance: firm strategies and organizational recovery

Firms that have failed to meet the performance expectations of investors must seek new ways of creating value or face the loss of financial support. Using resource-based arguments, we find that valuable and difficult-to-imitate strategies that recombine the firm's existing stock of resources to create new products, processes, or technologies have a positive effect on organizational recovery as measured by investors' expectations. Similarly, acquiring new resources through mergers or acquisitions also has positive effects on investors' expectations. In contrast, valuable and difficult-to-imitate strategies that provide the firm with access to new resources through alliances or joint ventures do not affect investors' expectations of performance. We also find that taking actions that are not valuable and difficult-to-imitate either have no effect on performance or may lead to further performance declines. Lastly, our results show that valuable and difficult-to-imitate strategic actions that use existing resources in new ways contribute the most to organizational recovery. Copyright © 2007 John Wiley & Sons, Ltd.
Short Directed Cycles in Bipartite Digraphs

The Caccetta-Haggkvist conjecture implies that for every integer k ≥ 1, if G is a bipartite digraph, with n vertices in each part, and every vertex has out-degree more than n/(k+1), then G has a directed cycle of length at most 2k. If true this is best possible, and we prove this for k = 1, 2, 3, 4, 6 and all k ≥ 224,539. More generally, we conjecture that for every integer k ≥ 1, and every pair of reals α,β > 0 with kα + β > 1, if G is a bipartite digraph with bipartition (A, B), where every vertex in A has out-degree at least β|B|, and every vertex in B has out-degree at least α|A|, then G has a directed cycle of length at most 2k. This implies the Caccetta-Haggkvist conjecture (set β > 0 and very small), and again is best possible for infinitely many pairs (α,β). We prove this for k = 1,2, and prove a weaker statement (that α + β > 2/(k + 1) suffices) for k = 3,4.
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems

The main challenge for adaptive regulation of linear-quadratic systems is the tradeoff between identification and control. An adaptive policy needs to address both the estimation of unknown dynamics parameters (exploration), as well as the regulation of the underlying system (exploitation). To this end, optimism-based methods that bias the identification in favor of optimistic approximations of the true parameter are employed in the literature. A number of asymptotic results have been established, but their finite-time counterparts are few, with important restrictions. This article establishes results for the worst-case regret of optimism-based adaptive policies. The presented high probability upper bounds are optimal up to logarithmic factors. The nonasymptotic analysis of this article requires the following very mild assumptions: stabilizability of the system's dynamics, and limiting the degree of heaviness of the noise distribution. To establish such bounds, certain novel techniques are developed to comprehensively address the probabilistic behavior of dependent random matrices with heavy-tailed distributions.
Clinical Perspectives on Hospitals' Role in the Opioid Epidemic

Policymakers, legislators, and clinicians have raised concerns that hospital-based clinicians may be incentivized to inappropriately prescribe and administer opioids when addressing pain care needs of their patients, thus potentially contributing to the ongoing opioid epidemic in the United States. Given the need to involve all healthcare settings, including hospitals, in joint efforts to curb the opioid epidemic, it is essential to understand if clinicians perceive hospitals as contributors to the problem. Therefore, we examined clinical perspectives on the role of hospitals in the opioid epidemic. We conducted individual semi-structured interviews with 23 clinicians from 6 different acute care hospitals that are part of a single healthcare system in the Midwestern United States. Our participants were hospitalists (N = 12), inpatient registered nurses (N = 9), and inpatient adult nurse practitioners (N = 2). In the interviews, we asked clinicians whether hospitals play a role in the opioid epidemic, and if so, how hospitals may contribute to the epidemic. We used a qualitative thematic analysis approach to analyze coded text for patterns and themes and examined potential differences in themes by respondent type using Dedoose software. The majority of clinicians believed hospitals contribute to the opioid epidemic. Multiple clinicians cited Center for Medicare and Medicaid Services’ (CMS) reimbursement policy and the Joint Commission’s report as drivers of inappropriate opioid prescribing in hospitals. Furthermore, numerous clinicians stated that opioids are inappropriately administered in the emergency department (ED), potentially as a mechanism to facilitate discharge and prevent re-admission. Many clinicians also described how overreliance on pre-populated pain care orders for surgical (orthopedic) patients, may be contributing to inappropriate opioid use in the hospital. Finally, clinicians suggested the following initiatives for hospitals to help address the crisis: 1) educating patients about negative consequences of using opioids long-term and setting realistic pain expectations; 2) educating medical staff about appropriate opioid prescribing practices, particularly for patients with complex chronic conditions (chronic pain; opioid use disorder (OUD)); and 3) strengthening the hospital leadership efforts to decrease inappropriate opioid use. Our findings can inform efforts at decreasing inappropriate opioid use in hospitals.
Scalable sim-to-real transfer of soft robot designs

The manual design of soft robots and their controllers is notoriously challenging, but it could be augmented—or, in some cases, entirely replaced—by automated design tools. Machine learning algorithms can automatically propose, test, and refine designs in simulation, and the most promising ones can then be manufactured in reality (sim2real). However, it is currently not known how to guarantee that behavior generated in simulation can be preserved when deployed in reality. Although many previous studies have devised training protocols that facilitate sim2real transfer of control polices, little to no work has investigated the simulation-reality gap as a function of morphology. This is due in part to an overall lack of tools capable of systematically designing and rapidly manufacturing robots. Here we introduce a low cost, open source, and modular soft robot design and construction kit, and use it to simulate, fabricate, and measure the simulation-reality gap of minimally complex yet soft, locomoting machines. We prove the scalability of this approach by transferring an order of magnitude more robot designs from simulation to reality than any other method. The kit and its instructions can be found here: github.com/skriegman/sim2real4designs
Posterior Control of Blackbox Generation

Text generation often requires high-precision output that obeys task-specific rules. This fine-grained control is difficult to enforce with off-the-shelf deep learning models. In this work, we consider augmenting neural generation models with discrete control states learned through a structured latent-variable approach. Under this formulation, task-specific knowledge can be encoded through a range of rich, posterior constraints that are effectively trained into the model. This approach allows users to ground internal model decisions based on prior knowledge, without sacrificing the representational power of neural generative models. Experiments consider applications of this approach for text generation. We find that this method improves over standard benchmarks, while also providing fine-grained control.
A Simple Framework for Contrastive Learning of Visual Representations

This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Limiting the oscillations in queues with delayed information through a novel type of delay announcement

Many service systems use technology to notify customers about their expected waiting times or queue lengths via delay announcements. However, in many cases, either the information might be delayed or customers might require time to travel to the queue of their choice, thus causing a lag in information. In this paper, we construct a neutral delay differential equation model for the queue length process and explore the use of velocity information in our delay announcement. Our results illustrate that using velocity information can have either a beneficial or detrimental impact on the system. Thus, it is important to understand how much velocity information a manager should use. In some parameter settings, we show that velocity information can eliminate oscillations created by delays in information. We derive a fixed point equation for determining the optimal amount of velocity information that should be used and find closed-form upper and lower bounds on its value. When the oscillations cannot be eliminated altogether, we identify the amount of velocity information that minimizes the amplitude of the oscillations. However, we also find that using too much velocity information can create oscillations in the queue lengths that would otherwise be stable.
Control-Aware Scheduling for Low Latency Wireless Systems with Deep Learning

We consider the problem of scheduling transmissions over low-latency wireless communication links to control various control systems. Low-latency requirements are critical in developing wireless technology for industrial control, but are inherently challenging to meet while also maintaining reliable performance. An alternative to ultra reliable low latency communications is a framework in which reliability is adapted to control system demands. We formulate the control-aware scheduling problem as a constrained statistical optimization problem in which the optimal scheduler is a function of current control and channel states. The scheduler is parameterized with a deep neural network, and the constrained problem is solved using techniques from primal-dual learning, which have a necessary model-free property in that they do not require explicit knowledge of channels models, performance metrics, or system dynamics to execute. The resulting control-aware deep scheduler is evaluated in empirical simulations and strong performance is shown relative to other model-free heuristic scheduling methods.
Genomic Patterns of Introgression in Interspecific Populations Created by Crossing Wheat with Its Wild Relative

Introgression from wild relatives is a valuable source of novel allelic diversity for breeding. We investigated the genomic patterns of introgression from Aegilops tauschii, the diploid ancestor of the wheat D genome, into winter wheat (Triticum aestivum) cultivars. The population of 351 BC1F3:5 lines was selected based on phenology from crosses between six hexaploid wheat lines and 21 wheat-Ae. tauschii octoploids. SNP markers developed for this population and a diverse panel of 116 Ae. tauschii accessions by complexity-reduced genome sequencing were used to detect introgression based on the identity-by-descent analysis. Overall, introgression frequency positively correlated with recombination rate, with a high incidence of introgression at the ends of chromosomes and low in the pericentromeric regions, and was negatively related to sequence divergence between the parental genomes. Reduced introgression in the pericentromeric low-recombining regions spans nearly 2/3 of each chromosome arm, suggestive of the polygenic nature of introgression barriers that could be associated with multilocus negative epistasis between the alleles of wild and cultivated wheat. On the contrary, negative selection against the wild allele of Tg, controlling free-threshing trait and located in the high-recombining chromosomal region, led to reduced introgression only within ∼10 Mbp region around Tg These results are consistent with the effect of selection on linked variation described by the Hill-Robertson effect, and offer insights into the introgression population development for crop improvement to maximize retention of introgressed diversity across entire genome.
Predictive Scheduling for Virtual Reality

A significant challenge for future virtual reality (VR) applications is to deliver high quality-of-experience, both in terms of video quality and responsiveness, over wireless networks with limited bandwidth. This paper proposes to address this challenge by leveraging the predictability of user movements in the virtual world. We consider a wireless system where an access point (AP) serves multiple VR users. We show that the VR application process consists of two distinctive phases, whereby during the first (proactive scheduling) phase the controller has uncertain predictions of the demand that will arrive at the second (deadline scheduling) phase. We then develop a predictive scheduling policy for the AP that jointly optimizes the scheduling decisions in both phases. In addition to our theoretical study, we demonstrate the usefulness of our policy by building a prototype system. We show that our policy can be implemented under Furion, a Unity-based VR gaming software, with minor modifications. Experimental results clearly show visible difference between our policy and the default one. We also conduct extensive simulation studies, which show that our policy not only outperforms others, but also maintains excellent performance even when the prediction of future user movements is not accurate.
Fundamental Challenges of Cyber-Physical Systems Security Modeling

Systems modeling practice lacks security analysis tools that can interface with modeling languages to facilitate security by design. Security by design is a necessity in the age of safety critical cyber-physical systems, where security violations can cause hazards. Currently, the overlap between security and safety is narrow. But deploying cyber-physical systems means that today's adversaries can intentionally trigger accidents. By implementing security assessment tools for modeling languages we are better able to address threats earlier in the system's lifecycle and, therefore, assure their safe and secure behavior in their eventual deployment. We posit that cyber-physical systems security modeling is practiced insufficiently because it is still addressed similarly to information technology systems.
Multimodal Neuroimaging Data Integration and Pathway Analysis

With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.
Statistics of Square-Tiled Surfaces: Symmetry and Short Loops

Square-tiled surfaces are a class of translation surfaces that are of particular interest in geometry and dynamics because, as covers of the square torus, they share some of its simplicity and stru...
Testing for Anomalies: Active Strategies and Non-asymptotic Analysis

The problem of verifying whether a multi-component system has anomalies or not is addressed. Each component can be probed over time in a data-driven manner to obtain noisy observations that indicate whether the selected component is anomalous or not. The aim is to minimize the probability of incorrectly declaring the system to be free of anomalies while ensuring that the probability of correctly declaring it to be safe is sufficiently large. This problem is modeled as an active hypothesis testing problem in the Neyman-Pearson setting. Component selection and inference strategies are designed and analyzed in the non-asymptotic regime. For a specific class of homogeneous problems, stronger (with respect to prior work) non-asymptotic converse and achievability bounds are provided.
Accident Detection System for Bicycle Riders

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders’ injuries is higher compared to the risk of injuries for motorists. Crashes, riders’ errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions ( ${X}$ , ${Y}$ , and ${Z}$ ). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system.
Global landscape of phenazine biosynthesis and biodegradation reveals species-specific colonization patterns in agricultural soils and crop microbiomes

Phenazines are natural bacterial antibiotics that can protect crops from disease. However, for most crops it is unknown which producers and specific phenazines are ecologically relevant, and whether phenazine biodegradation can counter their effects. To better understand their ecology, we developed and environmentally-validated a quantitative metagenomic approach to mine for phenazine biosynthesis and biodegradation genes, applying it to >800 soil and plant-associated shotgun-metagenomes. We discover novel producer-crop associations and demonstrate that phenazine biosynthesis is prevalent across habitats and preferentially enriched in rhizospheres, whereas biodegrading bacteria are rare. We validate an association between maize and Dyella japonica, a putative producer abundant in crop microbiomes. D. japonica upregulates phenazine biosynthesis during phosphate limitation and robustly colonizes maize seedling roots. This work provides a global picture of phenazines in natural environments and highlights plant-microbe associations of agricultural potential. Our metagenomic approach may be extended to other metabolites and functional traits in diverse ecosystems.