cssi/SciSciGPT-SciSciNet huggingface dataset:
Higher curvature gravities, unlike GR, cannot be bootstrapped from their (usual) linearizations

We show that higher curvature order gravities, in particular the propagating quadratic curvature models, cannot be derived by self-coupling from their linear, flat space, forms, except through an unphysical version of linearization; only GR can. Separately, we comment on an early version of the self-coupling bootstrap.
Production Process Moves: Template Use and the Need to Adapt

We use a behavioral experiment to examine whether the use of templates facilitates or hinders a faster production ramp-up after a process move. If the environment in which a template is applied closely resembles the environment in which the template was developed, template use promotes knowledge transfer, resulting in increased performance while ramping up the process. However, when the environments at the origin and destination of the template differ, these benefits do not occur. We further show that making the template too rigid, i.e., not allowing any changes to the template after a production start, decreases its performance benefits. These findings help characterize when and how the encoding of existing knowledge through a template is a worthwhile investment of time and resources to facilitate a production process move. This article is protected by copyright. All rights reserved.
The mutational decay of male-male and hermaphrodite-hermaphrodite competitive fitness in the androdioecious nematode C. elegans.

Androdioecious Caenorhabditis have a high frequency of self-compatible hermaphrodites and a low frequency of males. The effects of mutations on male fitness are of interest for two reasons. First, when males are rare, selection on male-specific mutations is less efficient than in hermaphrodites. Second, males may present a larger mutational target than hermaphrodites because of the different ways in which fitness accrues in the two sexes. We report the first estimates of male-specific mutational effects in an androdioecious organism. The rate of male-specific inviable or sterile mutations is ⩽5 × 10−4/generation, below the rate at which males would be lost solely due to those kinds of mutations. The rate of mutational decay of male competitive fitness is ~ 0.17%/generation; that of hermaphrodite competitive fitness is ~ 0.11%/generation. The point estimate of ~ 1.5X faster rate of mutational decay of male fitness is nearly identical to the same ratio in Drosophila. Estimates of mutational variance (VM) for male mating success and competitive fitness are not significantly different from zero, whereas VM for hermaphrodite competitive fitness is similar to that of non-competitive fitness. Two independent estimates of the average selection coefficient against mutations affecting hermaphrodite competitive fitness agree to within two-fold, 0.33–0.5%.
Payments, promotion, and the purple pill.

SUMMARY Understanding competition in the US drug market requires knowing how sensitive demand is to prices. The relevant prices for insured consumers are copayments. There are many studies of copayment elasticity in the health literature, but they are of limited applicability for studies of competition. Because of a paucity of data, such studies typically control for neither competitor copayment nor advertising. Whereas previous studies examined copayment sensitivity when copayments for branded drugs move in unison, this study examines copayment sensitivity when copayments diverge. This study uses unique panel data of insurance copayments and utilization for 77 insurance groups, as well as data on advertising. The results indicate that demand can be much more sensitive to copayment than previously recognized. Manufacturers selling drugs with higher copayments than branded competitors can lose substantial market share. Manufacturers can offset the loss of demand by increasing advertising to physicians, but it is costly. Copyright © 2013 John Wiley & Sons, Ltd.
Multivariate pattern dependence

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.
The Law and Economics of Redistribution

Should legal rules be used to redistribute income? Or should income taxation be the exclusive means for reducing income inequality? This article reviews the legal scholarship on this question. Firs...
The Determination of Protoplanetary Disk Masses

In this article we introduce methods used to determine the gas and dust masses of protoplanetary disks, with an emphasis on the lesser characterized total gas mass . Our review encompasses all the indirect tracers and the methodology that is being used to isolate the hidden H2 via dust, CO, and HD emission . We discuss the overall calibration of gaseous tracers which is based on decades of study of the dense phases of the interstellar medium . At present, disk gas masses determined via CO and HD are (in a few instances) different by orders of magnitude, hinting at either significant evolution in total disk mass or in the CO abundance . Either of these would represent a fundamental physical or chemical process that appears to dominate the system on ∼ million year timescales. Efforts to reconcile these differences using existing and future facilities are discussed.
Elephants, safety nets and agrarian culture: understanding human-wildlife conflict and rural livelihoods around Chobe National Park, Botswana

Resolving conflict between agricultural livelihoods and wildlife conservation requires a sophisticated understanding of both wildlife ecology and human livelihood decision-making. This case study extends the literature on human-wildlife conflict in Africa by using a political ecology framework to understand how and why farmers in areas of high wildlife disturbance make their farming decisions, and how their strategies are affected by a broader socio-political context that includes, but is not restricted to, wildlife conservation policy. Specifically, this article chronicles the livelihood strategies of smallholder farmers in a village on the edge of Chobe National Park in northern Botswana. This is a place where the state has prioritized wildlife conservation but also supports residents' livelihoods. Because of disturbance from wildlife, especially elephants, protected under conservation law, agricultural production in Chobe is becoming increasingly challenging, even as the government increases its agricultural subsidies and support to small farmers. This results in unexpected farming strategies that reflect the interactive effects of conservation policy and other relevant macro-economic policies that structure the livelihood strategies of rural communities living near protected areas. Future human-wildlife conflict studies must take into account these multi-scalar and multi-dimensional dynamics in order to accurately explain the livelihood strategies of people living in wildlife-populated areas, so that appropriate conservation and development policies can be designed. Keywords: Botswana, wildlife conservation, rural livelihoods, human-wildlife conflict, political ecology
Optimal Control for Generalized Network-Flow Problems

We consider the problem of throughput-optimal packet dissemination, in the presence of an arbitrary mix of unicast, broadcast, multicast, and anycast traffic, in an arbitrary wireless network. We propose an online dynamic policy, called Universal Max-Weight (UMW), which solves the problem efficiently. To the best of our knowledge, UMW is the first known throughput-optimal policy of such versatility in the context of generalized network flow problems. Conceptually, the UMW policy is derived by relaxing the precedence constraints associated with multi-hop routing and then solving a min-cost routing and max-weight scheduling problem on a virtual network of queues . When specialized to the unicast setting, the UMW policy yields a throughput-optimal cycle-free routing and link scheduling policy. This is in contrast with the well-known throughput-optimal back-pressure (BP) policy which allows for packet cycling, resulting in excessive latency. Extensive simulation results show that the proposed UMW policy incurs a substantially smaller delay as compared with the BP policy. The proof of throughput-optimality of the UMW policy combines ideas from the stochastic Lyapunov theory with a sample path argument from adversarial queueing theory and may be of independent theoretical interest.
Mimo versus conventional radar performance against jammers

It has been claimed that MIMO radars perform better than conventional radars against repeater and hot clutter jammers (jammer signals reflected from the ground into the radar). It is shown here that conventional radars can perform as well if not better than MIMO radars against these jammers as well as against barrage noise jammers. The results are presented in tutorial form without heavy math. Instead physical explanations are given for these results. Applied here to reject the barrage jammer and hot clutter is the Adaptive-Adaptive Array Processing (AAAP) technique which makes use of the information available as to where the jammers are rather than assuming there location is not known as done for the classical sample matrix inversion (SMI) method. This is reminiscent of the KA-STAP technique used by DARPA. The method reduces the transient time (the number of time samples needed to calculate the interference covariance matrix) by orders of magnitude. Also the interference covariance matrix size is reduced by orders of magnitude and in turn the computation of its matrix inverse. Finally this method reduces the sidelobe degradation usually resulting from using the SMI method. The AAAP technique lends itself well to both the MIMO and conventional array systems when digital beam forming is used.
Differential Privacy for Information Retrieval

The concern for privacy is real for any research that uses user data. Information Retrieval (IR) is not an exception. Many IR algorithms and applications require the use of users' personal information, contextual information and other sensitive and private information. The extensive use of personalization in IR has become a double-edged sword. Sometimes, the concern becomes so overwhelming that IR research has to stop to avoid privacy leaks. The good news is that recently there have been increasing attentions paid on the joint field of privacy and IR -- privacy-preserving IR. As part of the effort, this tutorial offers an introduction to differential privacy (DP), one of the most advanced techniques in privacy research, and provides necessary set of theoretical knowledge for applying privacy techniques in IR. Differential privacy is a technique that provides strong privacy guarantees for data protection. Theoretically, it aims to maximize the data utility in statistical datasets while minimizing the risk of exposing individual data entries to any adversary. Differential privacy has been successfully applied to a wide range of applications in database (DB) and data mining (DM). The research in privacy-preserving IR is relatively new, however, research has shown that DP is also effective in supporting multiple IR tasks. This tutorial aims to lay a theoretical foundation of DP and explains how it can be applied to IR. It highlights the differences in IR tasks and DB and DM tasks and how DP connects to IR. We hope the attendees of this tutorial will have a good understanding of DP and other necessary knowledge to work on the newly minted joint research field of privacy and IR.
Environmental Determinants of Malaria Transmission Around the Koka Reservoir in Ethiopia

New dam construction is known to exacerbate malaria transmission in Africa as the vectors of malaria-Anopheles mosquitoes-use bodies of water as breeding sites. Precise environmental mechanisms of how reservoirs exacerbate malaria transmission are yet to be identified. Understanding of these mechanisms should lead to a better assessment of the impacts of dam construction and to new prevention strategies. Combining extensive multiyear field surveys around the Koka Reservoir in Ethiopia and rigorous model development and simulation studies, environmental mechanisms of malaria transmission around the reservoir were examined. Most comprehensive and detailed malaria transmission model, Hydrology, Entomology, and Malaria Transmission Simulator, was applied to a village adjacent to the reservoir. Significant contributions to the dynamics of malaria transmission are shaped by wind profile, marginal pools, temperature, and shoreline locations. Wind speed and wind direction influence Anopheles populations and malaria transmission during the major and secondary mosquito seasons. During the secondary mosquito season, a noticeable influence was also attributed to marginal pools. Temperature was found to play an important role, not so much in Anopheles population dynamics, but in malaria transmission dynamics. Change in shoreline locations drives malaria transmission dynamics, with closer shoreline locations to the village making malaria transmission more likely. Identified environmental mechanisms help in predicting malaria transmission seasons and in developing village relocation strategies upon dam construction to minimize the risk of malaria.
TRANS-NEPTUNIAN OBJECTS WITH HUBBLE SPACE TELESCOPE ACS/WFC*

We introduce a novel search technique that can identify trans-Neptunian objects in three to five exposures of a pointing within a single Hubble Space Telescope (HST) orbit. The process is fast enough to allow the discovery of candidates soon after the data are available. This allows sufficient time to schedule follow-up observations with HST within a month. We report the discovery of 14 slow-moving objects found within 5° of the ecliptic in archival data taken with the Wide Field Channel of the Advanced Camera for Surveys. The luminosity function of these objects is consistent with previous ground-based and space-based results. We show evidence that the size distribution of both high and low inclination populations is similar for objects smaller than 100 km, as expected from collisional evolution models, while their size distribution differs for brighter objects. We suggest that the two populations formed in different parts of the protoplanetary disk and after being dynamically mixed have collisionally evolved together. Among the objects discovered there is an equal-mass binary with an angular separation ~053.
Finding Subcube Heavy Hitters in Analytics Data Streams

Modern data streams typically have high dimensionality. For example, digital analytics streams consist of user online activities (e.g., web browsing activity, commercial site activity, apps and social behavior, and response to ads). An important problem is to find frequent joint values (heavy hitters) of subsets of dimensions. Formally, the data stream consists of d-dimensional items and a \em k-dimensional subcube T is a subset of k distinct coordinates. Given a theshold γ, a \em subcube heavy hitter query $\rm Query (T,v)$ outputs YES if $f_T(v) \geq γ$ and NO if $f_T(v) ∠ γ/4$ where $f_T$ is the ratio of the number of stream items whose coordinates T have joint values v. The \em all subcube heavy hitters query $\rm AllQuery(T)$ outputs all joint values v that return YES to $\rm Query (T,v)$. The problem is to answer these queries correctly for all T and v. We present a simple one-pass sampling algorithm to solve the subcube heavy hitters problem in $\tildeO (kd/γ)$ space. $\tildeO(\cdot)$ suppresses polylogarithmic factors. This is optimal up to polylogarithmic factors based on the lower bound of Liberty et al. In the worst case, this bound becomes Θ(d^2/γ)$ which is prohibitive for large d. Our main contribution is to circumvent this quadratic bottleneck via a model-based approach. In particular, we assume that the dimensions are related to each other via the Naive Bayes model. We present a new two-pass, $\tildeO (d/γ)$-space algorithm for our problem, and a fast algorithm for answering $\rm AllQuery (T)$ in $\tO((k/γ)^2)$ time. We demonstrate the effectiveness of our approach on a synthetic dataset as well as real datasets from Adobe and Yandex. Our work shows the potential of model-based approach to data streams.
Whole egg consumption and cortical bone in healthy children

Eggs contain bioactive compounds thought to benefit pediatric bone. This cross-sectional study shows a positive link between childhood egg intake and radius cortical bone. If randomized trials confirm our findings, incorporating eggs into children’s diets could have a significant impact in preventing childhood fractures and reducing the risk of osteoporosis. This study examined the relationships between egg consumption and cortical bone in children. The cross-sectional study design included 294 9–13-year-old black and white males and females. Three-day diet records determined daily egg consumption. Peripheral quantitative computed tomography measured radius and tibia cortical bone. Body composition and biomarkers of bone turnover were assessed using dual-energy X-ray absorptiometry and ELISA, respectively. Egg intake was positively correlated with radius and tibia cortical bone mineral content (Ct.BMC), total bone area, cortical area, cortical thickness, periosteal circumference, and polar strength strain index in unadjusted models (r = 0.144–0.224, all P < 0.050). After adjusting for differences in race, sex, maturation, fat-free soft tissue mass (FFST), and protein intakes, tibia relationships were nullified; however, egg intake remained positively correlated with radius Ct.BMC (r = 0.138, P = 0.031). Egg intake positively correlated with total body bone mineral density, BMC, and bone area in the unadjusted models only (r = 0.119–0.224; all P < 0.050). After adjusting for covariates, egg intake was a positive predictor of radius FFST (β = 0.113, P < 0.050) and FFST was a positive predictor of Ct.BMC (β = 0.556, P < 0.050) in path analyses. There was a direct influence of egg on radius Ct.BMC (β = 0.099, P = 0.035), even after adjusting for the mediator, FFST (β = 0.137, P = 0.020). Egg intake was positively correlated with osteocalcin in both the unadjusted (P = 0.005) and adjusted (P = 0.049) models. If the positive influence of eggs on Ct.BMC observed in this study is confirmed through future randomized controlled trials, whole eggs may represent a viable strategy to promote pediatric bone development and prevent fractures.
Interest-Growth Differentials and Debt Limits in Advanced Economies

Do persistently low nominal interest rates mean that governments can safely borrow more? To addresses this question, I extend the model of Ghosh et al. [2013] to allow for persistent stochastic changes in nominal interest and growth rates. The key model parameter is the long-run difference between nominal interest and growth rates; if negative, maximum sustainable debts (debt limits) are unbounded. I show how both VAR- and spectral-based methods produce negative point estimates of this long-run differential, but cannot reject positive values at standard significance levels. I calibrate the model to the UK using positive but statistically plausible average interest-growth differentials. This produces debt limits which increase by only around 5% GDP as interest rates fall after 2008. In contrast, only a tiny change in the long-run average interest-growth differential â from the 95th to the 97.5th percentile of the distribution â is required to move average debt limits by the same amount.
Prescription Opioids and Labor Market Pains: The Effect of Schedule II Opioids on Labor Force Participation and Unemployment

We examine the effect of prescription opioids on county labor market outcomes, using data from the Prescription Drug Monitoring Programs of ten U.S. states and labor data from the Bureau of Labor Statistics. We achieve causal identofication by exploiting plausibly exogenous variation in the concentration of high-volume prescribers as instruments (using Medicare Part D prescriber data). We find strong adverse effects on labor force participation rates, employment- to-population ratios, and unemployment rates. Notably, a 10 percent increase in prescriptions causes a 0.56 percentage point reduction in labor force participation, similar to the drop attributed to the 1984 liberalization of Disability Insurance.
Generalized coherent state approach to star products and applications to the fuzzy sphere

Abstract We construct a star product associated with an arbitrary two-dimensional Poisson structure using generalized coherent states on the complex plane. From our approach one easily recovers the star product for the fuzzy torus, and also one for the fuzzy sphere. For the latter we need to define the ‘fuzzy’ stereographic projection to the plane and the fuzzy sphere integration measure, which in the commutative limit reduce to the usual formulae for the sphere.
tableone: An open source Python package for producing summary statistics for research papers.

Objectives In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table ("Table 1") of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers. Materials and methods The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged. Results The tableone software package automatically compiles summary statistics into publishable formats such as CSV, HTML, and LaTeX. An executable Jupyter Notebook demonstrates application of the package to a subset of data from the MIMIC-III database. Tests such as Tukey's rule for outlier detection and Hartigan's Dip Test for modality are computed to highlight potential issues in summarizing the data. Discussion and conclusion We present open source software for researchers to facilitate carrying out reproducible studies in Python, an increasingly popular language in scientific research. The toolkit is intended to mature over time with community feedback and input. Development of a common tool for summarizing data may help to promote good practice when used as a supplement to existing guidelines and recommendations. We encourage use of tableone alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. We also suggest seeking guidance from a statistician when using tableone for a research study, especially prior to submitting the study for publication.
Generating Image Captions in Arabic Using Root-Word Based Recurrent Neural Networks and Deep Neural Networks.

Automatic caption generation of an image requires both computer vision and natural language processing techniques. Despite of advanced research in English caption generation, research on generating Arabic descriptions of an image is extremely limited. Semitic languages like Arabic are heavily influenced by root-words. We leverage this critical dependency of Arabic and in this paper are the first to generate captions of an image directly in Arabic using root-word based Recurrent Neural Networks and Deep Neural Networks. We report the first BLEU score for direct Arabic caption generation. Experimental results confirm that generating image captions using root-words directly in Arabic significantly outperforms the English-Arabic translated captions using state-of-the-art methods.
Bounds on Differential and Linear Branch Number of Permutations

Nonlinear permutations (S-boxes) are key components in block ciphers. The differential branch number measures the diffusion power of a permutation, whereas the linear branch number measures resistance against linear cryptanalysis. There has not been much analysis done on the differential branch number of nonlinear permutations of \(\mathbb {F}_2^n\), although it has been well studied in case of linear permutations. Similarly upper bounds for the linear branch number have also not been studied in general. In this paper we obtain bounds for both the differential and the linear branch number of permutations (both linear and nonlinear) of \(\mathbb {F}_2^n\). We also prove that in the case of \(\mathbb {F}_2^4\), the maximum differential branch number can be achieved only by affine permutations.
Foodways at a Colonial Military Frontier Outpost in Northern New Spain: The Faunal Assemblage from Presidio San Sabá, 1757–1772

An 18th-century colonial settlement, Presidio San Saba was the largest and, indeed, the most remote military frontier outpost within the Spanish Borderlands of northern New Spain in Texas. Garrisoned with 100 Spanish soldiers who resided there with their civilian families, the presidio numbered nearly 400 people. Historical records reveal that this resident population lived under adverse conditions, suffering from malnutrition, disease, and chronic shortages of food and other supplies. Analysis of the faunal assemblage recovered during archaeological excavations conducted at the presidio site indicates that the San Saba people managed to survive by subsisting primarily upon the food products of their livestock herds. Moreover, they secured some additional animal protein for their diet by occasionally trading, fishing, hunting, and collecting locally available natural resources.
Rethinking the importance of the structure of ecological networks under an environment-dependent framework

A major quest in network and community ecology has been centered on understanding the importance of structural patterns in species interaction networks-the synthesis of who interacts with whom in a given location and time. In the past decades, much effort has been devoted to infer the importance of a particular structure by its capacity to tolerate an external perturbation on its structure or dynamics. Here, we demonstrate that such a perspective leads to inconsistent conclusions. That is, the importance of a network structure changes as a function of the external perturbations acting on a community at any given point in time. Thus, we discuss a research agenda to investigate the relative importance of the structure of ecological networks under an environment-dependent framework. We hypothesize that only by studying systematically the link between network structure and community dynamics under an environment-dependent framework, we can uncover the limits at which communities can tolerate environmental changes.
Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection

The low resolution of objects of interest in aerial images makes pedestrian detection and action detection extremely challenging tasks. Furthermore, using deep convolutional neural networks to process large images can be demanding in terms of computational requirements. In order to alleviate these challenges, we propose a two-step, yes and no question answering framework to find specific individuals doing one or multiple specific actions in aerial images. First, a deep object detector, Single Shot Multibox Detector (SSD), is used to generate object proposals from small aerial images. Second, another deep network, is used to learn a latent common sub-space which associates the high resolution aerial imagery and the pedestrian action labels that are provided by the human-based sources.
Ethnic Enclaves and Immigrant Outcomes: Norwegian Immigrants during the Age of Mass Migration

This paper examines the effect of ethnic enclaves on economic outcomes of Norwegian immigrants in 1910 and 1920, the later part of the Age of Mass Migration. Using different identification strategies, including county fixed effects and an instrumental variables strategy based on chain migration, I consistently find that Norwegians living in larger enclaves in the United States had lower occupational earnings, were more likely to be in farming occupations, and were less likely to be in white-collar occupations. Results are robust to matching method and choice of occupational score. This earnings disadvantage is partly passed on to the second generation.
The Antiparticle Filter : an Adaptive Nonlinear Estimator

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. W ...
Time-resolved detection of vaporization during laser metal processing with laser-induced fluorescence

Abstract Element loss during high-power laser processing of metals can lead to deleterious chemical and mechanical effects that negatively impact the processed material’s quality. Currently, there is no suitable way to measure time-resolved element loss in situ during laser processing. In this work, we show that laser-induced fluorescence (LIF) can be used to temporally resolve individual element vaporization during a laser spot welding of 316L stainless steel. As a proof-of-concept, we measured iron loss during a 500 µs laser spot weld with a time resolution of 5 microseconds. We found that keyhole formation could clearly be identified by a dramatic increase in iron emission from the weld pool. This conclusion was validated by independent, time-resolved measurements of laser absorptance during laser spot welding from our previous work.
A Sampling Wattmeter With Extended Frequency Range

This paper describes the implementation of a sampling wattmeter for the measurement of active and reactive power over the 50-Hz–6-kHz frequency range, 20–600-V voltage range, and 0.05–80-A current range. The wattmeter is capable of measuring sinusoidal and distorted voltage and current signals with up to 100 harmonics of the fundamental frequency component. Details of the system design, measurement techniques, and significant sources of uncertainty are presented.
The Role of Inclusive Innovation in Promoting Social Sustainability

This special issue implores us to address sustainability from the lens of emerging economies and the role that innovation can play in this context. We posit that, for sustainable operations research to be relevant in the context of emerging economies, it needs to incorporate social concerns and conditions of underserved populations, with an emphasis on inclusion and equity. Especially important for addressing social concerns of these populations will be product/service innovation, process/business model innovation, and supply chain innovation. We further posit that successful innovation in these areas will require collaboration of for‐profit firms with the public sector, civil society organizations, and communities. In thisstudy, we put forward “inclusive innovation” as a unifying approach that enables the collaborative integration of social issues of relevance to underserved populations in operations management decisions. We then focus on contemporary sectoral challenges in services, manufacturing, and agriculture, highlight the relevant social sustainability issues with an emphasis on those relevant to underserved populations, and point to new opportunities for research.
Novel therapies for relapsed/refractory aggressive lymphomas

Most patients with aggressive non-Hodgkin lymphoma will be cured with initial chemoimmunotherapy; however, most patients with relapsed disease will not be cured and will die as a result of their disease. In these cases, continued treatment with conventional chemotherapy is typically not of benefit and can contribute to significant toxicities and decreased quality of life for patients. Fortunately, a number of therapies are currently available or under investigation for this group of patients, ranging from oral tyrosine kinase inhibitors targeting multiple pathways within the malignant cells to adoptive cellular therapies that harness the patient's immune system to fight disease. Additionally, many agents that are modestly effective as monotherapies can be safely combined with additional novel and conventional therapies to improve response rates and duration. Chimeric antigen receptor T cells are among the most promising group of therapies and provide the potential for cure for patients with relapsed/refractory lymphoma. In this chapter, we will review the currently available novel treatments as well as those still under investigation and discuss the most appropriate approach to patients with relapsed/refractory aggressive lymphoma. We will highlight the challenges associated with these therapies, as well as potential toxicities, and the need for additional clinical trials evaluating combinations and newer treatments.
Persuasion under costly learning

Abstract A Sender (seller) tries to persuade a rationally inattentive Receiver (buyer) to take a particular action (e.g., buying). Learning is costly for the Receiver who can choose to process strictly less information than what the sender provides. In a binary-action binary-state model, we show that optimal disclosure involves information distortion, but to a lesser extent than the case without learning costs; meanwhile, the Receiver processes less information than what he would under full disclosure. We also find that the Receiver can leverage his potential inattention to attain a higher equilibrium payoff than the perfectly attentive case. While the Sender is always worse off when facing a less attentive Receiver, the amount of information processed in equilibrium varies with learning costs in a non-monotone fashion.
Towards a Comprehensive Standard Model of Human-Like Minds.

The Standard Model of particle physics has been an effective framework for describing entities of the domain relative to each other and thereby predicting missing particles. In this sense, it plays a similar role to the periodic table of the elements. Could something analogous work for AI? In this paper, we propse a dozen elements of a standard model of the mind and characterize their interrelationships. Naturally, this set reflects our own experience and biases and is not exhaustive. We also describe some of the ways in which this apporach to a standard model differs from that in physics and suggest some limitations.
Expressions of Sentiments during Code Reviews: Male vs. Female

As most of the software development organizations are male-dominated, many female developers encounter negative workplace experiences and report feeling like they “do not belong”. Exposures to discriminatory expletives or negative critiques from their male colleagues may further exacerbate those feelings. The primary goal of this study is to identify the differences in expressions of sentiments between male and female developers during various software engineering tasks. On this goal, we mined the code review repositories of six popular open source projects. We used a semi-automated approach leveraging the name as well as multiple social networks to identify the gender of a developer. Using SentiSE, a customized and state-of-the-art sentiment analysis tool for the software engineering domain, we classify each communication as negative, positive, or neutral. We also compute the frequencies of sentiment words, emoticons, and expletives used by each developer.Our results suggest that the likelihood of using sentiment words, emoticons, and expletives during code reviews varies based on the gender of a developer, as females are significantly less likely to express sentiments than males. Although female developers were more neutral to their male colleagues than to another female, male developers from three out of the six projects were not only writing more frequent negative comments but also withholding positive encouragements from their female counterparts. Our results provide empirical evidence of another factor behind the negative work place experiences encountered by the female developers that may be contributing to the diminishing number of females in the SE industry.
A patient-oriented clinical decision support system for CRC risk assessment and preventative care

Colorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management. We implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences. A prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty). This CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.
The FDA and Regulatory Issues

A brief outline of information that is available on the Web pertinent to the FDA and regulatory issues. The process of new drug approval and its continuous modification reflects both the progress of science and the globalization of the world’s economy. The reader is encouraged to follow the changes by constant monitoring of the FDA and the industry sites dedicated to these issues. The history of drug regulation in the United States, the evolving structure of the FDA, the steps to be taken in the process of obtaining the approval to market the new molecular entity, and the post-approval requirements are briefly described. Keywords: the FDC Act; FDA; FDAMA; PDUFA; CRF; MAPP; USP; PhRMA; ICH; CDT; IND; clinical hold; parallel track; IRB; NDA; priorities; standard review; accelerated approval; post-approval
Coverage in Downlink Heterogeneous mmWave Cellular Networks With User-Centric Small Cell Deployment

A $K$ -tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being distributed according to a Poisson cluster process (PCP). Distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight and non-line-of-sight transmissions, are taken into account. Initially, we determine general expressions for the association probabilities of different tier BSs. Using tools from stochastic geometry, we then characterize the Laplace transform of the interference and derive a general expression for the signal-to-interference-plus-noise ratio coverage probability. While these expressions are applicable to any PCP, we later specialize the results to two popular PCPs, namely, first, Thomas cluster process, where the UEs are clustered around the base stations (BSs) according to a Gaussian distribution, and second, Matern cluster process, where the UEs are scattered according to a uniform distribution. Subsequently, upper and lower bounds for the coverage probability are provided. Special cases are also addressed, providing the insight that when the cluster size grows without bound, our PCP-based model specializes to a Poisson point process-based model. Area spectral efficiency is investigated as well. Moreover, we have discussed extensions to cases in which more practical antenna gain patterns are taken into account and also the shadowing is considered. Via numerical and simulation results, we explore the effects of the beamwidth of the main lobe, the main lobe directivity gain, the biasing factor and the transmit power of the small-cell BSs to get insight on the performance in practical scenarios. Performance in dense networks is also analyzed numerically.
Vulnerable Boys: Short-term and Long-term Gender Differences in the Impacts of Adolescent Disadvantage

Abstract The growing gender gap in educational attainment between men and women has raised concerns that the skill development of boys may be more sensitive to family disadvantage than that of girls. Using the National Longitudinal Study of Adolescent to Adult Health (Add Health) data we find, as do previous studies, that boys are more likely to experience increased problems in school relative to girls, including suspensions and reduced educational aspirations, when they are in poor quality schools, less-educated neighborhoods, and father-absent households. Following these cohorts into young adulthood, however, we find no evidence that adolescent disadvantage has stronger negative impacts on long-run economic outcomes such as college graduation, employment, or income for men, relative to women. We do find that father absence is more strongly associated with men’s marriage and childbearing and weak support for greater male vulnerability to disadvantage in rates of high school graduation. An investigation of adult outcomes for another recent cohort from the National Longitudinal Survey of Youth, 1997 produces a similar pattern of results. We conclude that focusing on gender differences in behavior in school may not lead to valid inferences about the effects of disadvantage on adult skills.
Effect of the Doping and the Al Content on the Microstructure and Morphology of Thin AlxGa1−xN Layers Grown by MOCVD

AlxGa1−xN {x=30% (doped and undoped), 45% (doped)} thin films were grown by MOCVD on ∼2 μm thick GaN layer using Al2O3 substrate. These films were designed to be the active parts of HFETs with nsμ product of about 1016(Vs)-1. The layers were then studied by means of transmission electron microscopy (TEM) techniques. In this paper, it is shown that the AlxGa1−xN layer thickness was non-uniform due to the presence of V-shaped defects within the AlxGa1−xN films. The nucleation of these V-shaped defects has taken place about 20 nm above the AlxGa1−xN/GaN interface. Many of these V-shaped defects were associated with the presence of the threading dislocations propagating from the GaN/Al2O3 interface. We show that the density of these V-shaped defects increases with the doping level and also with the Al mole fraction in the films. The formation mechanism of the V-shaped defects seems to be related to the concentration of dopants or other impurities at the ledges of the growing film. This suggestion is supported by high resolution TEM analysis. The growth front between the V-shaped defects in the lower Al concentration thin films was planar as compared with the three-dimensional growth in the doped, higher Al concentration film. This interpretation of the origin of the V-shaped defects is consistent with the observed lowering of the Schottky barrier height in n-doped AlGaN/Ni Schottky diodes.
The development and characterization of patient derived xenografts from non-small cell lung cancer brain metastases

Introduction: NSCLC brain metastasis cell lines and in vivo brain metastasis models are not widely accessible. The purpose of this study was to establish and characterize a direct-from patient-derived xenograft (PDX) model of non-small cell lung cancer (NSCLC) brain metastases. Methods: Surgically obtained tissue was implanted subcutaneously and as orthotopic intracranial implants into immunodeficient mice. Histology and DNA loci were compared between original tumor and subsequent PDX passages. Tumors underwent RNA and DNA sequencing and relevant therapeutic targets were identified. Tumor growth rates were assessed following treatment with radiation, MEK inhibitor selumetinib, or MET inhibitor savolitinib. Cell lines were established. Results: Nine NSCLC brain metastases PDXs were established. Morphologically, strong retention of cytoarchitectural features was observed between original patient tumor and subcutaneous and intracranial tumors. Preservation of thyroid transcription factor 1 expression was seen in all xenografts. Short tandem repeat analysis demonstrated strong concordance between patient tumors and subsequent PDX passages. RNA sequencing analysis revealed high correlation between matched patient and PDX samples. Significant growth inhibition was shown with radiation, with selumetinib in tumors harboring KRAS G12C mutation and with savolitinib in tumors with MET exon 14 skipping mutation. Significant tumor growth delay was observed with the combination of radiation and savolitinib compared to radiation or savolitinib alone in the PDX harboring MET exon 14 skipping mutation. Early passage cell strains showed high consistency between patient and PDX tumors. Conclusion: We have established a robust human xenograft model system for investigating NSCLC brain metastases. These PDXs and cell lines show strong phenotypic and molecular correlation with the original patient tumors and provide a valuable resource for testing preclinical therapeutics.