cssi/SciSciGPT-SciSciNet huggingface dataset:
Thermo-mechanical Design Optimization of Conformal Cooling Channels using Design of Experiments Approach

Abstract Plastic injection molding is a versatile process and a major part of the present plastic manufacturing industry. Traditional die design is limited to straight (drilled) cooling channels, which don’t impart optimal thermal (or thermo-mechanical) performance. With the advent of additive manufacturing technology, design of injection molding tools with conformal cooling channels is now possible. The incorporation of conformal cooling channels can improve the thermal performance of an injection mold, though it may compromise the structural or mechanical stability of the mold. However, optimum conformal channels based on thermo-mechanical performance are not found in the literature. This paper proposes a design methodology to generate optimized design configurations of such channels in plastic injection molds. Design of experiments (DOEs) technique is used to study the effect of critical design parameters of conformal channels. In addition, a trade-off technique is utilized to obtain optimum design configurations of conformal cooling channels for “best” thermo-mechanical performance of a mold.
Multiphase turbulence mechanisms identification from consistent analysis of direct numerical simulation data

Abstract Direct Numerical Simulation (DNS) serves as an irreplaceable tool to probe the complexities of multiphase flow and identify turbulent mechanisms that elude conventional experimental measurement techniques. The insights unlocked via its careful analysis can be used to guide the formulation and development of turbulence models used in multiphase computational fluid dynamics simulations of nuclear reactor applications. Here, we perform statistical analyses of DNS bubbly flow data generated by Bolotnov (Reτ = 400) and Lu–Tryggvason (Reτ = 150), examining single-point statistics of mean and turbulent liquid properties, turbulent kinetic energy budgets, and two-point correlations in space and time. Deformability of the bubble interface is shown to have a dramatic impact on the liquid turbulent stresses and energy budgets. A reduction in temporal and spatial correlations for the streamwise turbulent stress (uu) is also observed at wall-normal distances of y+ = 15, y/δ = 0.5, and y/δ = 1.0. These observations motivate the need for adaptation of length and time scales for bubble-induced turbulence models and serve as guidelines for future analyses of DNS bubbly flow data.
Investors as Stewards of the Commons

The author makes the case that business generally, not just government, should assume responsibility for social and environmental problems. The Sustainable Development Goals (SDGs) formally recognize the role of the private sector in addressing some of the world's most pressing environmental and social challenges. What started as a corporate social responsibility movement now a focuses on integrating positive social impact into the core mission of the organization. Encouragingly, studies document that improving firm performance on business‐relevant ESG issues has a positive association with future financial performance. Investors can enable better societal outcomes by exercising ‘voice’ and voting rights in corporate governance. He acknowledges that competitive businesses face a “commons” or “free‐rider” problem where a defector avoids the full cost of his actions. Overcoming this problem requires legally sanctioned collaboration between business enterprises and large institutional shareholders, particularly pension funds. He also acknowledges that the corporate level free‐rider problem has a counterpart that at the investor level. Investor engagement with companies involves resources, money and time. It is no simple matter to justify increased costs in the context of asset managers that compete on the basis of low management fees, such as index funds. Collaboration between companies can mitigate some of these free riding problems. Large institutional investors with long time horizons and significant common ownership across different companies may have the best opportunities for collaboration. But, smaller activist funds and retail investors also have an important role in pushing large institutional investors to engage. While it is unlikely that investors will be able to solve all of the pressing societal problems, progress can be made.
A multi-faceted approach of a nursing led education in response to MERS-CoV infection.

Abstract Background The emergence of the Middle East respiratory syndrome-coronavirus (MERS-CoV) resulted in multiple healthcare associated outbreaks. In response, we developed a nurse-led program to screen and triage patients with MERS-CoV infection. Methods A formal educational program was implemented to ensure a standardized approach to care planning and delivery. The essential skills that were included were the use of Personal Protective Equipment (PPE) including gown, gloves, head cover and N95 mask, hand hygiene, the practice of donning and doffing and the collection of MERS-CoV nasopharyngeal specimens. Results A core group of nurses were identified from high-risk units. The education program consisted of four skill-days. Nurses were assigned a pre-course work from Mosby’s Nursing Skills, an online skill module, donning and doffing of PPE, and the process for obtaining a nasopharyngeal swab. The skill lab incorporated multiple methods such group discussion, watching an on-line video, and a simulated demonstration and practice on a mannequin. In total, 450 nurses attended the Nasopharyngeal Skills Day and 1000 nurses received training. Donning and doffing PPE and N95 mask fit testing has become an annual mandatory competency requirement for staff in nursing and clinical services. Conclusion The application of specified protocols minimizes the risks of cross infection, placing emphasis on patient and staff safety as well as expediting the patient to definitive treatment. Nurse educators have an instrumental role in training nurses and other healthcare providers on the specifics needed to identify, contain and manage patient presenting with MERS-CoV.
An Exploratory Multilevel Analysis of Pedestrian Frisks in Philadelphia

The City of Philadelphia has faced significant litigation related to racial and ethnic disparities in stop-and-frisk practices. The Philadelphia Police Department has made much of its stop-and-fris...
Hole pockets in the doped 2D Hubbard model

The electronic momentum distribution n(k) of the two dimensional Hubbard model is studied for different values of the coupling U/t, electronic density , and temperature, using quantum Monte Carlo techniques. A detailed analysis of the data on 8 × 8 clusters shows that features consistent with hole pockets at momenta k = (± π/2, ± π/2) appear as the system is doped away from half-filling. Our results are consistent with recent experimental data for the cuprates discussed by Aebi et al (Phys. Rev. Lett. 72, 2757 (1994)). In the range of couplings studied, the depth of the pockets is maximum at 0.9, and it increases with decreasing temperature. The apparent absence of hole pockets in previous numerical studies of this model is explained
The paradox of educational attitudes: Racial differences in public opinion on school closure

In 2009, the federal government of the United States proposed the “turnaround” of the nation’s 5,000 lowest performing public schools. In 2013, nearly 2,000 public schools were closed, in part due ...
Antithrombotic Reversal Agents

The actively bleeding anticoagulated patient presenting to the emergency department requires rapid evaluation and treatment, which is made increasingly complicated by the ever-evolving antithrombotic treatment options used in medicine. Even with excellent supportive care, the timeliness with which reversal decisions need to be made continues to demand of the emergency practitioner a familiarity with the properties and general characteristics of a variety of antithrombotic agents. Reversal options vary and may include vitamin K, FFP, PCC, rFVIIa, platelets, and desmopressin, among others.
Shareholders valuation of long-term debt and decline in firms' leverage ratio

Abstract The median U.S. non-regulated firm reports a 47% decline in leverage ratio between 1980 and 2010. We investigate whether the cost-benefit tradeoff to shareholders, captured by the valuation impact of an additional dollar of debt on owners' equity, is an explanation for the observed change in leverage. Using Faulkender and Wang (2006) methodology, we find that shareholders view increasing debt to have a negative impact on their wealth, that is, shareholders perceive firms to be over-levered. Further, the net cost of issuing additional debt has increased steadily for three decades beginning in 1980 before declining marginally after 2010. This trend holds for different groups of firms classified on factors known to affect capital structure decisions. Managers respond to the changing cost to shareholders by reducing (increasing) leverage when the cost of debt increases (decreases). The time-series pattern in the marginal cost of debt persists after controlling for firm-specific characteristics. We find that macroeconomic factors, such as federal debt, play a role in explaining the marginal value of debt.
Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks

The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in political and policy sciences, in network dynamics at the actor or relationship levels. Micro-level interpretation of the ERGM has been employed in varied applications in sociology and statistics. We present a comprehensive framework for interpretation of the ERGM at all levels of analysis, which casts network formation as block-wise updating of a network. These blocks can represent, for example, each potential link, each dyad, the out- or in-going ties of each actor, or the entire network. We contrast this interpretive framework with the stochastic actor-based model (SABM) of network dynamics. We present the theoretical differences between the ERGM and the SABM and introduce an approach to comparing the models when theory is not sufficiently strong to make the selection a priori. The alternative models we discuss and the interpretation methods we propose are illustrated on previously published data on estuary policy and governance networks.
Social Insurance and Occupational Mobility

This paper studies how insurance from progressive taxation improves the matching of workers to occupations. We propose an equilibrium dynamic assignment model to illustrate how social insurance encourages mobility. Workers experiment to find their best occupational fit in a process filled with uncertainty. Risk aversion and limited earnings insurance induce workers to remain in unfitting occupations. We estimate the model using microdata from the United States and Germany. Higher earnings uncertainty explains the U.S. higher mobility rate. When workers in the United States enjoy Germany’s higher progressivity, mobility rises. Output and welfare gains are large.
Educational aid policy and inequality: the case for merit- and need-based aid

Using model in which the assignment of skills to tasks is determined by relative productivities and are endogenously determined by ability, access to higher education, and technology, I find the ef...
Patterning III-N Semiconductors by Low Energy Electron Enhanced Etching (LE4)

Fabricating device structures from the III-N wide bandgap semiconductors requires anisotropoic dry etching processes that leave smooth surfaces with stoichiometric composition after transferring high-resolution patterns with vertical sidewalls. The purpose of this article is to describe results obtained by a new low-damage dry etching technique that provides an alternative to the standard ion-enhanced dry etching methods in meeting these demands for processing the III-N materials.
Measurement invariance of the patient health questionnaire-9 (PHQ-9) depression screener in U.S. adults across sex, race/ethnicity, and education level: NHANES 2005-2016.

BACKGROUND Despite its popularity, little is known about the measurement invariance of the Patient Health Questionnaire-9 (PHQ-9) across U.S. sociodemographic groups. Use of a screener shown not to possess measurement invariance could result in under/over-detection of depression, potentially exacerbating sociodemographic disparities in depression. Therefore, we assessed the factor structure and measurement invariance of the PHQ-9 across major U.S. sociodemographic groups. METHODS U.S. population representative data came from the 2005-2016 National Health and Nutrition Examination Survey (NHANES) cohorts. We conducted a measurement invariance analysis of 31,366 respondents across sociodemographic factors of sex, race/ethnicity, and education level. RESULTS Considering results of single-group confirmatory factor analyses (CFAs), depression theory, and research utility, we justify a two-factor structure for the PHQ-9 consisting of a cognitive/affective factor and a somatic factor (RMSEA = 0.034, TLI = 0.985, CFI = 0.989). On the basis of multiple-group CFAs testing configural, scalar, and strict factorial invariance, we determined that invariance held for sex, race/ethnicity, and education level groups, as all models demonstrated close model fit (RMSEA = 0.025-0.025, TLI = 0.985-0.992, CFI = 0.986-0.991). Finally, for all steps ΔCFI was <-0.004, and ΔRMSEA was <0.01. CONCLUSIONS We demonstrate that the PHQ-9 is acceptable to use in major U.S. sociodemographic groups and allows for meaningful comparisons in total, cognitive/affective, and somatic depressive symptoms across these groups, extending its use to the community. This knowledge is timely as medicine moves towards alternative payment models emphasizing high-quality and cost-efficient care, which will likely incentivize behavioral and population health efforts. We also provide a consistent, evidence-based approach for calculating PHQ-9 subscale scores.
Cross-sectional Skewness

This paper evaluates skewness in the cross-section of stock returns in light of predictions from a well-known class of models. Cross-sectional skewness in monthly returns far exceeds what the standard lognormal model of returns would predict. However, skewness in long-run returns substantially understates what the lognormal model would predict. Nonstationary share dynamics imply a breakdown in the distinction between market and idiosyncratic risk in the lognormal model. We present an alternative model that matches the skewness in the data and implies stationary wealth shares. In this model, idiosyncratic risk is the primary driver of growth in the economy.
Successful Treatment of Paradoxical Puborectalis Contraction and Intractable Anorectal Pain With Sacral Neuromodulation.

Background Paradoxical puborectalis contraction (PPC) is a syndrome of obstructed defecation associated with a cluster of complaints including rectal pain, incomplete evacuatory sensation, prolonged repetitive straining with bowel movements, and the need for digital manipulation. Traditional treatment has yielded mixed results. Case We present a case of PPC successfully treated with staged sacral neuromodulation and review her diagnostic features, medical regimen, and prior unsuccessful interventions tried. Symptoms were analyzed using a visual analog scale pain score (0-10). Criteria to progress to implantation of the pulse generator included a pain score less than 3 during test stimulation and/or greater than 50% decrease in the pain score compared to baseline.Our patient had a pain score of 0 (baseline 8) with stage 1 sacral neuromodulation. In addition, she had dramatic relief in her straining with bowel movements and need for digital manipulation. Her pulse generator was implanted after a 2-week trial, and she has experienced a lasting improvement at her follow-up of 2 years. Conclusions Sacral neuromodulation is an established therapy for overactive bladder syndrome, urinary retention, and fecal incontinence. In urology, the use of sacral neuromodulation has been described to benefit some patients with pelvic floor pain. Sacral neuromodulation can be a successful treatment for PPC and functional anorectal pain with resulting improvement in quality of life without the sequelae of an invasive and irreversible surgery.
Advanced glycation end (AGE) product modification of laminin downregulates Kir4.1 in retinal Müller cells

Diabetic retinopathy (DR) is a major cause of adult blindness. Retinal Muller cells maintain water homeostasis and potassium concentration via inwardly rectifying Kir4.1 channels. Accumulation of advanced glycation end products (AGEs) is a major pathologic event in DR. While diabetes leads to a decrease in the Kir4.1 channels, it remains unknown whether AGEs-linked to the basement membrane (BM) affect normal Kir4.1 channels. For this study, we hypothesized that AGE-modification of laminin is detrimental to Kir4.1 channels, therefore, disrupting Muller cell function. The AGE-modified laminin-coated substrates were prepared by incubating Petri-dishes with laminin and methylglyoxal for seven days. The rat Muller cells (rMC-1) were propagated on AGE-modified laminin, and Kir4.1 expression and function were evaluated. Quantification of AGEs using ELISA revealed a dose-dependent increase in methylglyoxal-hydro-imidazolone adducts. The rMC-1 propagated on AGE-modified laminin demonstrated a decrease in Kir4.1 levels in immunofluorescence and western blot studies and a decrease in the Kir4.1 channel function. Kir4.1 decrease on AGE-modified laminin resulted in a disorganization of an actin cytoskeleton and disruption of α-dystroglycan-syntrophin-dystrophin complexes. Our studies suggest that AGE-modification of laminin is detrimental to Kir4.1 channels. By studying the role of AGEs in Kir4.1 channels we have identified a novel mechanism of Muller cell dysfunction and its subsequent involvement in DR.
Executive Decision Making

Explores some of the elements of decision making that are common to all decisions. Examples of business problems; Types of executive decisions; Management alternatives.
Placenta Accreta Spectrum

Key Clinical Points Placenta Accreta Spectrum The incidence of placenta accreta spectrum has increased by a factor of approximately 8 since the 1970s, probably owing to increases in cesarean delivery. Women with major risk factors, such as placenta previa, previous cesarean delivery, endometrial ablation, or other uterine surgery, should undergo obstetrical sonography in the middle-to-late second trimester to assess for possible placenta accreta spectrum. Patients with suspected placenta accreta spectrum should be referred to a center with multidisciplinary expertise and experience. Recommended management of suspected placenta accreta spectrum is planned cesarean hysterectomy with the placenta left in situ. However, surgical management may be individualized. In most cases, planned preterm delivery at 34 weeks of gestation is recommended to best balance maternal and neonatal risks. Earlier delivery may be warranted in women with labor, bleeding, or other complications.
Zeno: A Straggler Diagnosis System for Distributed Computing Using Machine Learning

Modern distributed computing frameworks for cloud computing and high performance computing typically accelerate job performance by dividing a large job into small tasks for execution parallelism. Some tasks, however, may run far behind others, which jeopardize the job completion time. In this paper, we present Zeno, a novel system which automatically identifies and diagnoses stragglers for jobs by machine learning methods. First, the system identifies stragglers with an unsupervised clustering method which groups the tasks based on their execution time. It then uses a supervised rule learning algorithm to learn diagnosis rules inferring the stragglers with their resource assignment and usage data. Zeno is evaluated on traces from a Google’s Borg system and an Alibaba’s Fuxi system. The results demonstrate that our system is able to generate simple and easy-to-read rules with both valuable insights and decent performance in predicting stragglers.
The Microbiome and Metabolome of Preterm Infant Stool Are Personalized and Not Driven by Health Outcomes, Including Necrotizing Enterocolitis and Late-Onset Sepsis.

The assembly and development of the gut microbiome in infants have important consequences for immediate and long-term health. Preterm infants represent an abnormal case for bacterial colonization because of early exposure to bacteria and frequent use of antibiotics. To better understand the assembly of the gut microbiota in preterm infants, fecal samples were collected from 32 very low birth weight preterm infants over the first 6 weeks of life. Infant health outcomes included health, late-onset sepsis, and necrotizing enterocolitis (NEC). We characterized bacterial compositions by 16S rRNA gene sequencing and metabolomes by untargeted gas chromatography-mass spectrometry. Preterm infant fecal samples lacked beneficial Bifidobacterium spp. and were dominated by Enterobacteriaceae, Enterococcus, and Staphylococcus organisms due to nearly uniform antibiotic administration. Most of the variance between the microbial community compositions could be attributed to the baby from which the sample derived (permutational multivariate analysis of variance [PERMANOVA] R2 = 0.48, P < 0.001), while clinical status (health, NEC, or late-onset sepsis) and overlapping times in the neonatal intensive care unit (NICU) did not explain a significant amount of variation in bacterial composition. Fecal metabolomes were also found to be unique to the individual (PERMANOVA R2 = 0.43, P < 0.001) and weakly associated with bacterial composition (Mantel statistic r = 0.23 ± 0.05, P < 0.05). No measured metabolites were found to be associated with necrotizing enterocolitis, late-onset sepsis, or a healthy outcome. Overall, preterm infant gut microbial communities were personalized and reflected antibiotic usage.IMPORTANCE Preterm infants face health problems likely related to microbial exposures, including sepsis and necrotizing enterocolitis. However, the role of the gut microbiome in preterm infant health is poorly understood. Microbial colonization differs from that of healthy term babies because it occurs in the NICU and is often perturbed by antibiotics. We measured bacterial compositions and metabolomic profiles of 77 fecal samples from 32 preterm infants to investigate the differences between microbiomes in health and disease. Rather than finding microbial signatures of disease, we found that both the preterm infant microbiome and the metabolome were personalized and that the preterm infant gut microbiome is enriched in microbes that commonly dominate in the presence of antibiotics. These results contribute to the growing knowledge of the preterm infant microbiome and emphasize that a personalized view will be important to disentangle the health consequences of the preterm infant microbiome.
Distributed Self-Paced Learning in Alternating Direction Method of Multipliers

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset. In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large-scale datasets. Specifically, both the model and instance weights can be optimized in parallel for each batch based on a consensus alternating direction method of multipliers. We also prove the convergence of our algorithm under mild conditions. Extensive experiments on both synthetic and real datasets demonstrate that our approach is superior to those of existing methods.
Learning Time Series Detection Models from Temporally Imprecise Labels

In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class events. Such temporally imprecise labels commonly occur in areas like mobile health research where human annotators are tasked with labeling the occurrence of very short duration events. We propose a general learning framework for this problem that can accommodate different base classifiers and noise models. We present results on real mobile health data showing that the proposed framework significantly outperforms a number of alternatives including assuming that the label time stamps are noise-free, transforming the problem into the multiple instance learning framework, and learning on labels that were manually re-aligned.
Substitutional mechanism for growth of hexagonal boron nitride on epitaxial graphene

Monolayer-thick hexagonal boron nitride (h-BN) is grown on graphene on SiC(0001), by exposure of the graphene to borazine, (BH)3(NH)3, at 1100 °C. The h-BN films form ∼2-μm size grains with a preferred orientation of 30° relative to the surface graphene. Low-energy electron microscopy is employed to provide definitive signatures of the number and composition of two-dimensional planes across the surface. These grains are found to form by substitution for the surface graphene, with the C atoms produced by this substitution, then being incorporated below the h-BN (at the interface between the existing graphene and the SiC) to form a new graphene plane.
Cyber law and espionage law as communicating vessels

Existing legal literature would have us assume that espionage operations and "below-the-threshold" cyber operations are doctrinally distinct. Whereas one is subject to the scant, amorphous, and under-developed legal framework of espionage law, the other is subject to an emerging, ever-evolving body of legal rules, known cumulatively as cyber law. This dichotomy, however, is erroneous and misleading. In practice, espionage and cyber law function as communicating vessels, and so are better conceived as two elements of a complex system, Information Warfare (IW). This paper therefore first draws attention to the similarities between the practices - the fact that the actors, technologies, and targets are interchangeable, as are the knee-jerk legal reactions of the international community. In light of the convergence between peacetime Low-Intensity Cyber Operations (LICOs) and peacetime Espionage Operations (EOs) the two should be subjected to a single regulatory framework, one which recognizes the role intelligence plays in our public world order and which adopts a contextual and consequential method of inquiry. The paper proceeds in the following order: Part 2 provides a descriptive account of the unique symbiotic relationship between espionage and cyber law, and further explains the reasons for this dynamic. Part 3 places the discussion surrounding this relationship within the broader discourse on IW, making the claim that the convergence between EOs and LICOs, as described in Part 2, could further be explained by an even larger convergence across all the various elements of the informational environment. Parts 2 and 3 then serve as the backdrop for Part 4, which details the attempt of the drafters of the Tallinn Manual 2.0 to compartmentalize espionage law and cyber law, and the deficits of their approach. The paper concludes by proposing an alternative holistic understanding of espionage law, grounded in general principles of law, which is more practically transferable to the cyber realm.
Actor-Centric Relation Network

Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. Here, we go one step further and model spatio-temporal relations to capture the interactions between human actors, relevant objects and scene elements essential to differentiate similar human actions. Our approach is weakly supervised and mines the relevant elements automatically with an actor-centric relational network (ACRN). ACRN computes and accumulates pair-wise relation information from actor and global scene features, and generates relation features for action classification. It is implemented as neural networks and can be trained jointly with an existing action detection system. We show that ACRN outperforms alternative approaches which capture relation information, and that the proposed framework improves upon the state-of-the-art performance on JHMDB and AVA. A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.
Entity Tracking Improves Cloze-style Reading Comprehension

Recent work has improved on modeling for reading comprehension tasks with simple approaches such as the Attention Sum-Reader; however, automatic systems still significantly trail human performance. Analysis suggests that many of the remaining hard instances are related to the inability to track entity-references throughout documents. This work focuses on these hard entity tracking cases with two extensions: (1) additional entity features, and (2) training with a multi-task tracking objective. We show that these simple modifications improve performance both independently and in combination, and we outperform the previous state of the art on the LAMBADA dataset by 8 pts, particularly on difficult entity examples. We also effectively match the performance of more complicated models on the named entity portion of the CBT dataset.
Is privacy privacy

This position paper observes how different technical and normative conceptions of privacy have evolved in parallel and describes the practical challenges that these divergent approaches pose. Notably, past technologies relied on intuitive, heuristic understandings of privacy that have since been shown not to satisfy expectations for privacy protection. With computations ubiquitously integrated in almost every aspect of our lives, it is increasingly important to ensure that privacy technologies provide protection that is in line with relevant social norms and normative expectations. Similarly, it is also important to examine social norms and normative expectations with respect to the evolving scientific study of privacy. To this end, we argue for a rigorous analysis of the mapping from normative to technical concepts of privacy and vice versa. We review the landscape of normative and technical definitions of privacy and discuss specific examples of gaps between definitions that are relevant in the context of privacy in statistical computation. We then identify opportunities for overcoming their differences in the design of new approaches to protecting privacy in accordance with both technical and normative standards.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.
Minimal Realization Problems for Jump Linear Systems

This paper addresses two fundamental problems in the context of jump linear systems (JLS). The first problem is concerned with characterizing the minimal state space dimension solely from input-output pairs and without any knowledge of the number of mode switches. The second problem is concerned with characterizing the number of discrete modes of the JLS. For the first problem, we develop a linear system theory based approach and construct an appropriate Hankel–like matrix. The rank of this matrix gives us the state space dimension. For the second problem we show that minimal number of modes corresponds to the minimal rank of a positive semi–definite matrix obtained via a non–convex formulation.
Unlocking Amenities: Estimating Public-Good Complementarity

Abstract Public goods may exhibit complementarities that are essential for determining their individual value. Our results indicate that improving safety near parks can turn them from public bads to goods. Ignoring complementarities may lead to i) undervaluing the potential value of public goods; ii) overestimating heterogeneity in preferences; and iii) understating the value of public goods to minority households. Recent reductions in crime have “unlocked” almost $7 billion in property value in Chicago, New York and Philadelphia. Still, two-fifths of the potential value of park proximity, $10 billion, remains locked-in.
On Achieving Zero Delay with Power-of- $d$ -Choices Load Balancing

Power-of- $d$ -choices is a popular load balancing algorithm for many-server systems such as large-scale data centers. For each incoming job, the algorithm probes $d$ servers, chosen uniformly at random from a total of $N$ servers ( $N$ is the number of servers in the system), and routes the job to the least loaded one. It is well known that power-of- $d$ -choices reduces queueing delays by orders of magnitude compared to the policy that routes each incoming job to a randomly selected server. The question to be addressed in this paper is how large $d$ needs to be so that power-of- $d$ -choices achieves asymptotic zero delay like the join-the-shortest-queue (JSQ) algorithm, which is a special case of power-of- $d$ -choices with $d=N.$ We are interested in the heavy-traffic regime where the load of the system, denoted by $\lambda, $ approaches to one as $N$ increases, and assume $\lambda =1-\gamma N^{-\alpha }$ for $0 This paper establishes that when $d=\Omega \left(\frac{\log N}{1-\lambda }\right)$ , finite buffer size $b$ and $0\leq \alpha the probability that an incoming job is routed to a busy server is asymptotically zero, i.e., a job experiences zero queueing delay with probability one asymptotically; and when $d=O\left(\frac{1}{1-\lambda }\right)$ and infinite buffer size $b=\infty$ , the probability that a job is routed to a busy server is lower bounded by a positive constant independent of $N.$ Therefore, our results show that $d=\Omega \left(\frac{\log N}{1-\lambda }\right)$ is sufficient and almost necessary for achieving zero delay with the power-of- $d$ -choices policy.
Poisoning deaths among late-middle aged and older adults: comparison between suicides and deaths of undetermined intent

Background Given the rapid increase in prescription and illicit drug poisoning deaths in the 50+ age group, we examined precipitating/risk factors and toxicology results associated with poisoning deaths classified as suicides compared to intent-undetermined death (UnD) among decedents aged 50+. Methods Data were from the 2005-2015 US National Violent Death Reporting System (N = 15,453). χ2 tests and multinomial logistic regression models were used to compare three groups of decedents: suicide decedent who left a suicide note, suicide decedent who did not leave a note, and UnD cases. Results Compared to suicide decedents without a note (37.7% of the sample), those with a note (29.4%) were more likely to have been depressed and had physical health problems and other life stressors, while UnD cases (32.9%) were less likely to have had mental health problems and other life stressors but more likely to have had substance use and health problems. UnD cases were also more likely to be opioid (RRR = 2.65, 95% CI = 2.42-2.90) and cocaine (RRR = 2.59, 95% CI = 2.09-3.21) positive but less likely to be antidepressant positive. Blacks were more than twice as likely as non-Hispanic Whites to be UnDs. Results from separate regression models in the highest UnD states (Maryland and Utah) and in states other than Maryland/Utah were similar. Conclusions Many UnDs may be more correctly classified as unintentional overdose deaths. Along with more accurate determination processes for intent/manner of death, substance use treatment and approaches to curbing opioid and other drug use problems are needed to prevent intentional and unintentional poisoning deaths.
Dissipative shock waves generated by a quantum-mechanical piston.

The piston shock problem is a prototypical example of strongly nonlinear fluid flow that enables the experimental exploration of fluid dynamics in extreme regimes. Here we investigate this problem for a nominally dissipationless, superfluid Bose-Einstein condensate and observe rich dynamics including the formation of a plateau region, a non-expanding shock front, and rarefaction waves. Many aspects of the observed dynamics follow predictions of classical dissipative—rather than superfluid dispersive—shock theory. The emergence of dissipative-like dynamics is attributed to the decay of large amplitude excitations at the shock front into turbulent vortex excitations, which allow us to invoke an eddy viscosity hypothesis. Our experimental observations are accompanied by numerical simulations of the mean-field, Gross-Pitaevskii equation that exhibit quantitative agreement with no fitting parameters. This work provides an avenue for the investigation of quantum shock waves and turbulence in channel geometries, which are currently the focus of intense research efforts. There is increasing interest in understanding the non-equilibrium phenomena in quantum fluids. Here, the authors show dissipative, viscous shock and rarefaction wave dynamics emerging from the turbulent, superfluid flow of an elongated BEC of ultracold Rb atoms driven by a quantum-mechanical piston.
Money, Millennials and Human Rights: Sustaining ‘Sustainable Investing’

The first part of this paper examines the rise and current state of environmental, social and governance (ESG) investing. The second addresses the conceptual and statistical weakness of the S domain. The third describes how drawing on internationally recognized human rights standards can strengthen the S and thereby improve the robustness and comparability of ESG aggregations. This should interest investors, issuers, and human rights advocates alike.
Substance Use Issues Among the Underserved: United States and International Perspectives

Substance use affects people of all ages, cultures, and socioeconomic levels. Most underserved populations have lower rates of substance use than the general population in a given society, excluding tobacco use. The impact of substance use is more severe, however, in the underserved, with higher rates of incarceration, job loss, morbidity, and mortality. Innovative solutions are being developed to address these differences. Working together, underserved patients with substance use problems can be helped on their journeys toward health and wholeness.
The effects of medicare advantage on opioid use

Despite a vast literature on the determinants of prescription opioid use, the role of health insurance plans has received little attention. We study how the form of Medicare beneficiaries' drug coverage affects the volume of opioids they consume. We find that enrollment in Medicare Advantage, which integrates drug coverage with other medical benefits, significantly reduces beneficiaries' likelihood of filling an opioid prescription, as compared to enrollment in a stand-alone drug plan. Approximately half of this effect was due to fewer fills from prescribers who write a very large number of opioid prescriptions.
Diversity and the starting salaries of undergraduate business school students

The authors treat racial diversity of the student body at both the college of business and the institutional level as an input into the undergraduate business school productive process, and find th...
Multi-Shot Sensitivity-Encoded Diffusion MRI Using Model-Based Deep Learning (Modl-Mussels)

We propose a model-based deep learning architecture for the correction of phase errors in multishot diffusion-weighted echo-planar MRI images. This work is a generalization of MUSSELS, which is a structured low-rank algorithm. We show that an iterative reweighted least-squares implementation of MUSSELS resembles the model-based deep learning (MoDL) framework. We propose to replace the self-learned linear filter bank in MUSSELS with a convolutional neural network, whose parameters are learned from exemplary data. The proposed algorithm reduces the computational complexity of MUSSELS by several orders of magnitude, while providing comparable image quality.
Work and occupational profile of Eastern African immigrants in the United States: a historical overview

This paper discusses the trends and patterns of emigration from Eastern Africa, otherwise known as the Horn of Africa. In particular, the paper focuses on the countries that collectively account fo...
Analysis of a structured intronic region of the LMP2 pre-mRNA from EBV reveals associations with human regulatory proteins and nuclear actin.

The pre-mRNA of the Epstein–Barr virus LMP2 (latent membrane protein 2) has a region of unusual RNA structure that partially spans two consecutive exons and the entire intervening intron; suggesting RNA folding might affect splicing—particularly via interactions with human regulatory proteins. To better understand the roles of protein associations with this structured intronic region, we undertook a combined bioinformatics (motif searching) and experimental analysis (biotin pulldowns and RNA immunoprecipitations) of protein binding. Characterization of the ribonucleoprotein composition of this region revealed several human proteins as interactors: regulatory proteins hnRNP A1 (heterogeneous nuclear ribonucleoprotein A1), hnRNP U, HuR (human antigen R), and PSF (polypyrimidine tract-binding protein-associated splicing factor), as well as, unexpectedly, the cytoskeletal protein actin. Treatment of EBV-positive cells with drugs that alter actin polymerization specifically showed marked effects on splicing in this region. This suggests a potentially novel role for nuclear actin in regulation of viral RNA splicing.
Association Between Randall's Plaque Stone Anchors and Renal Papillary Pits.

Abstract Introduction: Renal papillary pits are commonly encountered during ureteroscopy. The mechanism by which such pits arise is unclear. One hypothesis is that pits represent sites where stones...
Cultural memories of the conquest of al-Andalus between the ninth and twelfth centuries, C.E.

This essay takes up the subject of the conquest of al-Andalus as a conceptually originative act in a discussion of group identity and memory. The focus is not on Umayyad rule and memory, except in so far as Umayyad rule structured the historical perspectives of Andalusi writers, but on Andalusi Mālikī jurists and their social memory. The way Andalusi Mālikī jurists represented their community, its origins, and their relation to the past changed and varied over the period of the rise and fall of Umayyad rule in Iberia and the establishment of Almoravid rule, as their own structures of power developed with the systematization of legal learning and practice. The essay demonstrates these developments through analysis of biographical texts written by jurists in the ninth, tenth, and twelfth centuries and suggests how the conquest of al-Andalus, real and imagined, fits into a history of Mālikī community, “textual polity,” and “empire.”
Mechanism of damped oscillation in microbubble coalescence

Abstract This work is part of our continuous research effort to reveal the underlying physics of bubble coalescence in microfluidics through the GPU-accelerated lattice Boltzmann method. We numerically explore the mechanism of damped oscillation in microbubble coalescence characterized by the Ohnesorge (Oh) number. The focus is to address when and how a damped oscillation occurs during a coalescence process. Sixteen cases with a range of Oh numbers from 0.039 to 1.543, varying in liquid viscosity from 0.002 to 0.08kg/(m · s) correspondingly, are systematically studied. First, a criterion of with or without damped oscillation has been established. It is found that a larger Oh enables faster/slower bubble coalescence with/without damped oscillation when (Oh 0.477) and the fastest coalescence falls at Oh ≈ 0.477. Second, the mechanism behind damped oscillation is explored in terms of the competition between driving and resisting forces. When Oh is small in the range of Oh
Competition Policy and Antitrust Law: Implications of Developments in Supply Chain Management

Building on research in supply chain management (SCM) that aids in the workings of society, the authors illustrate how SCM research can advance public policy and law. Using competition policy and antitrust law as an example, they consider how developments in SCM thought and practice augment economic understanding of vertical restraints involving minimum resale price maintenance (RPM). Developments affecting the organization of supply chains, firm‐level strategies for the management of retail distribution, and the interactions of supply chain participants are investigated. The findings advance knowledge of the primary procompetitive and anticompetitive theories of RPM found in competition policy and antitrust law. They also illustrate the potential of SCM to expand its reach and impact through studies that address the interplay of SCM and public policy and law.
A Graphical Characterization of Structurally Controllable Linear Systems With Dependent Parameters

One version of the concept of structural controllability defined for single-input systems by Lin and subsequently generalized to multi-input systems by others, states that a parameterized matrix pair $(A, B)$ whose nonzero entries are distinct parameters, is structurally controllable if values can be assigned to the parameters that cause the resulting matrix pair to be controllable. In this paper, the concept of structural controllability is broadened to allow for the possibility that a parameter may appear in more than one location in the pair $(A, B)$ . Subject to a certain condition on the parameterization called the “binary assumption,” an explicit graph-theoretic characterization of such matrix pairs is derived.
Competition with Three-Tier Spectrum Access and Spectrum Monitoring

The Citizens Broadband Radio Service (CBRS) recently adopted in the U.S. enables two tiers of commercial users to share spectrum with a third tier of incumbent users. This sharing can be further assisted by Environmental Sensing Capability operators (ESCs), that monitor the spectrum occupancy to determine when use of the spectrum will not harm incumbents. Two key aspects of this framework that impact how firms may compete are the differences in information provided by different ESCs and the different tiers in which a firm may access the spectrum. We develop a game theoretic model that captures both of these features and analyze it to gain insight into their impact. Specifically, we consider a priority access (PA) tier firm has access to the both a licensed band and an unlicensed band, and a general authorized access (GAA) tier firm has access only to the unlicensed band. The PA tier and GAA tier firms compete for users. Our analysis reveals that the amount of unlicensed and licensed bandwidth must be chosen judiciously in order to maximize the social welfare. We also show that a limited amount of unlicensed access by the PA tier firm is beneficial to the user's surplus as well as to the social welfare.
On the Inapproximability of the Discrete Witsenhausen Problem

We consider a discrete version of the Witsenhausen problem where all random variables are bounded and take on integer values. Our main goal is to understand the complexity of computing good strategies given the distributions for the initial state and second-stage noise as inputs to the problem. Following Papadimitriou and Tsitsiklis, who showed that computing the optimal solution is NP-complete, we construct a sequence of problem instances with the initial state uniform over a set of size ${n}$ and the noise uniform over a set of size at most ${n} {^ 2}$ , such that finding a strategy whose cost is a multiplicative ${n} ^{{ 2-\epsilon }}$ approximation to the optimal cost is NP-hard for any $ {\epsilon > \text 0}$ .