cssi/SciSciGPT-SciSciNet huggingface dataset:
Systematicity theory meets Socratic scientific realism: the systematic quest for truth

Systematicity theory—developed and articulated by Paul Hoyningen-Huene—and scientific realism constitute separate encompassing and empirical accounts of the nature of science. Standard scientific realism asserts the axiological thesis that science seeks truth and the epistemological thesis that we can justifiably believe our successful theories at least approximate that aim. By contrast, questions pertaining to truth are left “outside” systematicity theory’s “intended scope” (21); the scientific realism debate is “simply not” its “focus” (173). However, given the continued centrality of that debate in the general philosophy of science literature, and given that scientific realists also endeavor to provide an encompassing empirical account of science, I suggest that these two contemporary accounts have much to offer one another. Overlap for launching a discussion of their relations can be found in Nicholas Rescher’s work. Following through on a hint from Rescher, I embrace a non-epistemic, purely axiological scientific realism—what I have called, Socratic scientific realism. And, bracketing the realist’s epistemological thesis, I put forward the axiological tenet of scientific realism as a needed supplement to systematicity theory. There are two broad components to doing this. First, I seek to make clear that axiological realism and systematicity theory accord with one another. Toward that end, after addressing Hoyningen-Huene’s concerns about axiological analysis, I articulate a refined axiological realist meta-hypothesis: it is, in short, that the end toward which scientific inquiry is directed is an increase in a specific subclass of true claims. I then identify a key feature of scientific inquiry, not generally flagged explicitly, that I take to stand as shared terrain for the two empirical meta-hypotheses. And I argue that this feature can be informatively accounted for by my axiological meta-hypothesis. The second broad component goes beyond mere compatibility between the two positions: I argue that, in want of a systematic account of science, we are prompted to find an end toward which scientific inquiry is directed that is deeper than what systematicity theory offers. Specifically, I argue that my refined axiological realist meta-hypothesis is required to both explain and justify key dimensions of systematicity in science. To the quick question, what is it that the scientific enterprise is systematically doing? My quick answer is that it is systematically seeking to increase a particular subclass of true claims.
Particle Swarm Optimization for Solving a Class of Type-1 and Type-2 Fuzzy Nonlinear Equations

This paper proposes a modified particle swarm optimization (PSO) algorithm that can be used to solve a variety of fuzzy nonlinear equations, i.e. fuzzy polynomials and exponential equations. Fuzzy nonlinear equations are reduced to a number of interval nonlinear equations using alpha cuts. These equations are then sequentially solved using the proposed methodology. Finally, the membership functions of the fuzzy solutions are constructed using the interval results at each alpha cut. Unlike existing methods, the proposed algorithm does not impose any restriction on the fuzzy variables in the problem. It is designed to work for equations containing both positive and negative fuzzy sets and even for the cases when the support of the fuzzy sets extends across 0, which is a particularly problematic case.
If Higher Pay Is Profitable, Why Is It So Rare? Modeling Competing Strategies in Mass Market Services

Several case studies suggest that firms targeting mass market services can align profitability with jobs offering a living wage, stable schedules, and engaging work. Yet, few do. To understand this...
Current Resources for Evidence‐Based Practice, November/December 2014

Nicole S. Carlson, CNM, is a doctoral candidate in the College of Nursing, University of Colorado Denver, Denver, CO. Accessing Evidence-Based Practice The individual practitioner’s use of evidencedbased practice (EBP) for clinical care is often described in terms of the 5A Model: Ask, Acquire, Appraise, Apply and Assess (Green, 2006). In the September/October issue of this column the discussion focused on the importance of perinatal providers connecting their clinical questions with evidence-based findings, which is the first step of the 5A EBP model (Ask). The second step of this model, Acquire, is crucial for the successful implementation of EBP and often represents a significant stumbling block for providers.
Successful heart transplantation in patients with active Staphylococcus bloodstream infection and suspected mechanical circulatory support device infection

Background An active bloodstream infection (BSI) is typically considered a contraindication to heart transplantation (HT). However, in some patients with Staphylococcus bacteremia and mechanical circulatory support device infection, positive blood cultures may persist until removal of the infected device, and eradicating the infection prior to HT may not be possible. We report the outcomes of six patients with active Staphylococcus BSI at the time of HT. Methods All cases of HT performed at The Mount Sinai Hospital from 2009 through 2015 were reviewed. All patients with a mechanical circulatory support device and an active Staphylococcus BSI at the time of HT were included. Results Six patients with active Staphylococcus bacteremia and suspected mechanical circulatory support device infection underwent HT. All patients were bacteremic with Staphylococcus species at the time of HT. All were managed with antimicrobial therapy, radical debridement at the time of HT, and limited use of immunosuppression, and all survived until hospital discharge with no evidence of relapsed Staphylococcus infection. Conclusion These results suggest that some carefully selected patients with active Staphylococcus bacteremia and suspected mechanical circulatory support device infection may safely undergo HT, and that HT may effectively eliminate the underlying infection.
The Many Faces of Link Fraud

Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But is this assumption true? And, in either case, what are the characteristics of such fraudulent behaviors? In this work, we set up honeypots ("dummy" social network accounts), and buy fake followers (after careful IRB approval). We report the signs of such behaviors including oddities in local network connectivity, account attributes, and similarities and differences across fraud providers. Most valuably, we discover and characterize several types of fraud behaviors. We discuss how to leverage our insights in practice by engineering strongly performing entropy-based features and demonstrating high classification accuracy. Our contributions are (a) observations: we analyze our honeypot fraudster ecosystem and give surprising insights into the multifaceted behaviors of these fraudster types, and (b) features: we propose novel features that give strong (>0.95 precision/recall) discriminative power on ground-truth Twitter data.
Ancient multiple-layer solutions to the Allen-Cahn equation

We consider the parabolic one-dimensional Allen–Cahn equationThe steady state connects, as a ‘transition layer’, the stable phases –1 and +1. We construct a solution u with any given number k of transition layers between –1 and +1. Mainly they consist of k time-travelling copies of w, with each interface diverging as t → –∞. More precisely, we findwhere the functions ξj (t) satisfy a first-order Toda-type system. They are given byfor certain explicit constants γjk.
Modeling of a GaN Based Static Induction Transistor

Static induction transistors (SITs) are short channel FET structures which are suitable for high power, high frequency and high temperature applications. GaN has particularly favorable properties for SIT operation. However, such a device has not yet been fabricated. In this paper we report simulation studies on GaN static induction transistors over a range of device structures and operating conditions. The transistor was modeled with coupled drift-diffusion and heat-flow equations. We found that the performance of the device depends sensitively on the thermal boundary conditions, as self-heating effects limit the maximum voltage swing.
Ion Channeling Analysis of Gallium Nitride Implanted with Deuterium

Ion channeling and transmission electron microscopy were used to examine the microstructure of GaN implanted with deuterium (D) at high (>1 at. %) and low (< 0.1 at. %) D concentrations. At high concentrations, bubbles and basal-plane stacking faults were observed. Ion channeling showed the D was disordered relative to the GaN lattice, consistent with precipitation of D2 into bubbles. At low D concentrations, bubbles and stacking faults are absent and ion channeling shows that a large fraction of the D occupies sites near the center of the c-axis channel.
Modeling polypharmacy side effects with graph convolutional networks.

Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity. Results Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies. Availability and implementation Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
Study of substrate noise and techniques for minimization

The effects of substrate noise on analog circuits in a mixed-signal chip are described and analyzed based on measured results. Experimental data from a 0.25-/spl mu/m CMOS test chip reveals that substrate noise generated by digital circuits couples into analog circuits through circuit asymmetries and device nonlinearity, degrading analog circuit performance. In particular, a delta-sigma modulator shows over 20-dB reduction of signal-to-noise-plus-distortion ratio (SNDR) in the presence of large toggling digital inverters. To combat the effects of substrate noise, the technique of active substrate noise shaping is investigated. An implementation of active substrate noise shaping on the same test chip demonstrates over 10-dB improvement in SNDR in the 0-20-kHz band of the delta-sigma modulator in the presence of toggling digital inverters emulating I/O drivers. This technique complements existing substrate noise reduction techniques for integrated circuits without a significant penalty in power and does not require modification of analog nor digital circuits.
Power to the People? The Initiative Process and Fiscal Discipline in City Governments:

Does giving citizens the power to decide budget policies improve fiscal discipline in the local public sector? This study examines the effects of local initiatives on city budgetary solvency or the...
Crisis Informatics in the Anthropocene: Disasters as Matters of Care and Concern

My research examines new ways of approaching the contentious and uncertain knowledge politics surrounding natural disasters and climate change. Using three case studies, focused on different locations and type of hazards, I show how current information systems and technologies reproduce and reinforce long-standing discourses that have been widely shown to be problematic by social science research on disaster. Inspired by research in the area of feminist studies of technoscience, disaster STS and HCI research into critical and participatory design, I explore novel approaches to making sense of flood hazard, earthquake damage, and sea-level rise that seek provide new ways of engaging with the complexities and uncertainties that characterize the Anthropocene.
Omental Vascularized Lymph Node Flap: A Radiographic Analysis

BACKGROUND Vascularized lymph node transfer is an increasingly popular option for the treatment of lymphedema. The omental donor site is advantageous for its copious soft tissue, well-defined collateral circulation, and large number of available nodes, without the risk of iatrogenic lymphedema. The purpose of this study is to define the anatomy of the omental flap in the context of vascularized lymph node harvest. METHODS Consecutive abdominal computed tomography angiography (CTA) images performed at a single institution over a 1-year period were reviewed. Right gastroepiploic artery (RGEA) length, artery caliber, lymph node size, and lymph node location in relation to the artery were recorded. A two-tailed Z-test was used to compare means. A Gaussian Mixture Model confirmed by normalized entropy criterion was used to calculate three-dimensional lymph node cluster locations along the RGEA. RESULTS In total, 156 CTA images met inclusion criteria. The RGEA caliber at its origin was significantly larger in males compared with females (p < 0.001). An average of 3.1 (1.7) lymph nodes were present per patient. There was no significant gender difference in the number of lymph nodes identified. Average lymph node size was significantly larger in males (4.9 [1.9] × 3.3 [0.6] mm in males vs. 4.5 [1.5] × 3.1 [0.5] mm in females; p < 0.001). Three distinct anatomical variations of the RGEA course were noted, each with a distinct lymph node clustering pattern. Total lymph node number and size did not differ among anatomical subgroups. CONCLUSION The omentum is a reliable lymph node donor site with consistent anatomy. This study serves as an aid in preoperative planning for vascularized lymph node transfer using the omental flap.
Methadone as the Initial Long-Acting Opioid in Children with Advanced Cancer.

Abstract Background: Methadone is an attractive medication for treating children with advanced cancer with pain as it is the only long-acting opioid available as a liquid. However, it is not frequently used due to concerns about potential toxicities and side effects. Objective: Evaluate the efficacy and safety of methadone as the first long-acting opioid in children with advanced cancer. Design: Retrospective chart review of 52 consecutive patients referred to Pediatric Supportive Care for pain management started on methadone as their first long-acting opioid. Data collected at baseline, follow-up visits #1 (F1) and #2 (F2) included child and parent-reported outcomes for various physical and psychological symptoms, opioid side effects and other clinical data. Symptoms were rated on a 0 (not at all) to 4 (a lot) scale. Results: Pain (mean ± standard deviation [SD]) scored by the child was 3.6 (±0.6)/4 at baseline and 1.8 (±1.1)/4 at F1 (p < 0.0001). Compared to baseline, pain scored by the child at F2 was ...
Expressed exome capture sequencing: A method for cost‐effective exome sequencing for all organisms

Exome capture is an effective tool for surveying the genome for loci under selection. However, traditional methods require annotated genomic resources. Here, we present a method for creating cDNA probes from expressed mRNA, which are then used to enrich and capture genomic DNA for exon regions. This approach, called "EecSeq," eliminates the need for costly probe design and synthesis. We tested EecSeq in the eastern oyster, Crassostrea virginica, using a controlled exposure experiment. Four adult oysters were heat shocked at 36°C for 1 hr along with four control oysters kept at 14°C. Stranded mRNA libraries were prepared for two individuals from each treatment and pooled. Half of the combined library was used for probe synthesis, and half was sequenced to evaluate capture efficiency. Genomic DNA was extracted from all individuals, enriched via captured probes, and sequenced directly. We found that EecSeq had an average capture sensitivity of 86.8% across all known exons and had over 99.4% sensitivity for exons with detectable levels of expression in the mRNA library. For all mapped reads, over 47.9% mapped to exons and 37.0% mapped to expressed targets, which is similar to previously published exon capture studies. EecSeq displayed relatively even coverage within exons (i.e., minor "edge effects") and even coverage across exon GC content. We discovered 5,951 SNPs with a minimum average coverage of 80×, with 3,508 SNPs appearing in exonic regions. We show that EecSeq provides comparable, if not superior, specificity and capture efficiency compared to costly, traditional methods.
Dynamic inventory and price controls involving unknown demand on discrete nonperishable items

We study adaptive policies that handle dynamic inventory and price controls when the random demand for discrete nonperishable items is unknown. Pure inventory control is achieved by targeting newsvendor ordering quantities that correspond to empirical demand distributions learned over time. On this basis we conduct the more complex joint inventory-price control, where demand-affecting prices await to be evaluated as well. We identify policies that strive to balance between exploration and exploitation, and measure their performances via regrets, i.e., the prices to pay for not knowing demand distributions a priori over a given horizon. Multiple bounds are derived on regrets' growth rates; they vary with how thoroughly unknown the demand distributions are and whether nonperishability has indeed been accounted for. Our simulation study illustrates order-of-magnitude differences between pure inventory and joint inventory-price controls in terms of regret growth rates.
Self-supervised learning for dense depth estimation in monocular endoscopy

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires sequential data from monocular endoscopic videos and a multi-view stereo reconstruction method, e.g. structure from motion, that supervises learning in a sparse but accurate manner. Consequently, our method requires neither manual interaction, such as scaling or labeling, nor patient CT in the training and application phases. We demonstrate the performance of our method on sinus endoscopy data from two patients and validate depth prediction quantitatively using corresponding patient CT scans where we found submillimeter residual errors. (Link to the supplementary video: https://camp.lcsr.jhu.edu/miccai-2018-demonstration-videos/)
Interaction of Langmuir Turbulence and Inertial Currents in the Ocean Surface Boundary Layer under Tropical Cyclones

AbstractBased on a large-eddy simulation approach, this study investigates the response of the ocean surface boundary layer (OSBL) and Langmuir turbulence (LT) to extreme wind and complex wave forc...
Air pollution, land use, and complications of pregnancy

Abstract Background Mounting evidence suggests that the natural and built environment can affect human health, but relatively few studies have considered links between features of the residential natural and built environment other than air pollution and complications of pregnancy. Objectives To quantify the impact of features of the maternal residential natural and built environments on risk of gestational diabetes mellitus (GDM), gestational hypertension and preeclampsia among 61,640 women who delivered at a single hospital in Rhode Island between 2002 and 2012. Methods We estimated residential levels of ambient fine particulate matter (PM2.5) and black carbon (BC) using spatiotemporal models, neighborhood green space using remote sensing and proximity to recreational facilities, and neighborhood blue space using distance to coastal and fresh water. We used logistic regression to separately estimate the association between each feature and GDM, gestational hypertension, and preeclampsia, adjusting for individual and neighborhood markers of socioeconomic status. Results GDM, gestational hypertension, and preeclampsia were diagnosed in 8.0%, 5.0%, and 3.6% of women, respectively. We found 2nd trimester PM2.5 (OR = 1.08, 95% CI: 1.00, 1.15 per interquartile range increase in PM2.5) and living close to a major roadway (1.09, 95% CI: 1.00, 1.19) were associated with higher odds of GDM, while living Conclusions In this small coastal US state, risk of GDM was positively associated with PM2.5 and proximity to busy roadways, and negatively associated with proximity to blue space, highlighting the importance of the natural and built environment to maternal health.
A Unified Theory of Multiple-Access and Interference Channels via Approximate Capacity Regions for the MAC-IC-MAC

Approximate capacity regions are established for a class of interfering multiple access channels consisting of two multiple-access channels (MACs), each with an arbitrary number of transmitters, with one transmitter in each MAC causing interference to the receiver of the other MAC, a channel we refer to henceforth as the MAC-IC-MAC. For the discrete memory-less (DM) MAC-IC-MAC, two inner bounds are obtained that are generalizations of prior inner bounds for the two-user DM interference channel (IC) due to Chong et al. For the semi-deterministic MAC-IC-MAC, it is shown that single-user coding at the non-interfering transmitters and superposition coding at the interfering transmitter of each MAC achieves a rate region that is within a quantifiable gap of the capacity region, thereby extending such a result for the two-user semi-deterministic IC by Telatar and Tse. For the Gaussian MAC-IC-MAC, an approximate capacity region that is within a constant gap of the capacity region is obtained, generalizing such a result for the two-user Gaussian IC by Etkin et al. On contrary to the aforementioned approximate capacity results for the two-user IC, whose achievability requires the union of all admissible input distributions, our gap results on the semi-deterministic and the Gaussian MAC-IC-MAC are achievable by only a subset and one of all admissible coding distributions, respectively. The symmetric generalized degrees of freedom (GDoFs) of the symmetric Gaussian MAC-IC-MAC with more than one user per cell, which is a function of the interference strength (the ratio of INR to SNR at high SNR, both expressed in dB) and the numbers of users in each cell, are V-shaped with flat shoulders. An analysis based on signal-level partitions shows that the non-interfering transmitters utilize the signal-level partitions at the receiver where they are intended that cannot be accessed by the interfering transmitters (due to the restriction of superposition coding), thereby improving the sum symmetric GDoF of up to one degree of freedom per cell under a range of SINR exponent levels, which in turn becomes wider as the number of transmitters in each cell increases. Consequently, time-sharing between interfering and non-interfering transmitters is GDoF-suboptimal in general, as is time-sharing between the two embedded MAC-Z-MACs.
Benefit of self-stabilizing protocols in eventually consistent key-value stores: a case study

In this paper, we focus on the implementation of distributed programs in using a key-value store where the state of the nodes is stored in a replicated and partitioned data store to improve performance and reliability. Applications of such algorithms occur in weather monitoring, social media, etc. We argue that these applications should be designed to be stabilizing so that they recover from an arbitrary state to a legitimate state. Specifically, if we use a stabilizing algorithm then we can work with more efficient implementations that provide eventual consistency rather than sequential consistency where the data store behaves as if there is just one copy of the data. We find that, although the use of eventual consistency results in consistency violation faults (cvf) where some node executes its action incorrectly because it relies on an older version of the data, the overall performance of the resulting protocol is better. We use experimental analysis to evaluate the expected improvement. We also identify other variations of stabilization that can provide additional guarantees in the presence of eventual consistency. Finally, we note that if the underlying algorithm is not stabilizing, even a single cvf may cause the algorithm to fail completely, thereby making it impossible to benefit from this approach.
LCD: A Fast Contrastive Divergence Based Algorithm for Restricted Boltzmann Machine

Restricted Boltzmann Machine (RBM) is the building block of Deep Belief Nets and other deep learning tools. Fast learning and prediction are both essential for practical usage of RBM-based machine learning techniques. This paper proposes Lean Contrastive Divergence (LCD), a modified Contrastive Divergence (CD) algorithm, to accelerate RBM learning and prediction without changing the results. LCD avoids most of the required computations with two optimization techniques. The first is called bounds-based filtering, which, through triangle inequality, replaces expensive calculations of many vector dot products with fast bounds calculations. The second is delta product, which effectively detects and avoids many repeated calculations in the core operation of RBM, Gibbs Sampling. The optimizations are applicable to both the standard contrastive divergence learning algorithm and its variations. In addition, this paper presents how to implement these optimizations effectively on massively parallel processors. Results show that the optimizations can produce several-fold (up to 3X for training and 5.3X for prediction) speedups.
Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.
Coordinating China's economic growth strategy via its government-controlled association for private firms

Abstract Scholars in economics and political science argue that one major function of government is to overcome coordination failure in economic development, especially during times of rapid environmental changes. But, how and through what means does the state coordinate firms to follow the changing directions of its economic objectives? This paper focuses on the case of a government-controlled business association, namely the All-China Federation of Industry and Commerce (ACFIC), and shows that the ACFIC may be serving, at least partially, as a means of avoiding the kind of coordination failures that are often associated with policy reform programs in authoritarian regimes like China when the government deems it necessary to radically and suddenly change its policy objectives. It does so by comparing the activities of firms that are members of ACFIC with non-members before and after the world financial crisis of 2008–09 which induced a significant change in government objectives. Before that crisis when priority in government objectives was “outward” (to stave off balance of payments crises that had befallen many other developing countries), ACFIC member firms were able to engage in exports and foreign investment to a greater extent than non-member firms, and even than those whose owners are members of the Congresses or Chinese Communist Party. After viewing the crises in international markets, and government objectives had turned more “inward”, ACFIC members were more likely to focus on domestic sales and investments.
Interpretable convolutional neural networks via feedforward design

Abstract The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP. The FF design adopts a data-centric approach. It derives network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner. To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace approximation with adjusted bias) transform. It is a variant of the principal component analysis with an added bias vector to annihilate activation’s nonlinearity. Multiple Saab transforms in cascade yield multiple convolutional layers. As to fully-connected layers, we construct them using a cascade of multi-stage linear least squared regressors. The classification and robustness performances of BP- and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are compared. Finally, we comment on the relationship between BP and FF designs.
Metacognition, Personal Distress, and Performance-Based Empathy in Schizophrenia.

Background People with schizophrenia experience significant deficits in the kinds of empathic skills that are the foundation for interpersonal relationships. Researchers have speculated that deficits in empathic skills in schizophrenia may be related to disturbances in metacognition and heightened levels of personal distress. To explore this issue, this study examined whether better metacognition and reduced personal distress would be associated with improved performance on cognitive and affective empathy tasks. Further, we tested whether metacognition moderated the relationship between personal distress and empathy. Method Fifty-eight participants with schizophrenia-spectrum disorders receiving community-based treatment completed a self-report questionnaire of personal distress, a performance-based measure of empathy, and an observer-rated interview to assess metacognitive capacity. Results Correlation analyses revealed that metacognitive capacity, but not personal distress, was significantly associated with cognitive and affective empathy performance. Moderation results suggest the relationship between personal distress and affective empathy performance was significant for those with low metacognition, but that the relationship was the opposite of hypotheses-increased personal distress predicted better performance. This relationship changed at higher levels of metacognition, when increased personal distress became associated with reduced performance. Conclusions This study is the first of its kind to examine performance-based empathy with metacognition and personal distress. Results suggest interventions targeted to improve metacognition may be useful in enhancing empathic skills.
Analyzing Bank Overdraft Fees with Big Data

In 2012, consumers paid $32 billion in overdraft fees, representing the single largest source of revenue for banks from demand deposit accounts during this period. Owing to consumer attrition cause...
Top Indian wealth shares and inheritances 1966–1985

Between 1953 and 1985, India implemented various progressive taxes on personal wealth. I use estate tax returns to compute top wealth shares (top 1%, top 0.1%, and top 0.01%) over 1966–1985; a period marked explicitly by a dirigiste policy environment. These new series suggest that wealth concentration in India reduced substantially during the 1970s. Although the decline affected the entire top 1%, the losses faced by the top 0.01% were especially large. Combined with identical trends in top income shares, it appears that the 1950–1980 expropriations of India’s rich had similarities to institutional transitions and shocks faced by European elites in the early to mid twentieth century.
Risk of Maternal Morbidity with Increasing Number of Cesareans.

Objective To estimate the risk of perioperative morbidity with increasing number of cesareans. Study Design We conducted a retrospective cohort study from 2004 to 2010. Patients delivered by cesarean were included. Outcome measures were a composite organ injury (bowel or bladder), hysterectomy, hemorrhage requiring transfusion, severe morbidity, or surgical site complications. The Cochran–Armitage's test of trend was used to assess increasing incidence of each morbidity with number of prior cesareans. Multivariable logistic regression was used to estimate adjusted risks for each morbidity with increasing number of cesareans compared with primary cesarean. Results Of the 15,872 women in the cohort, 5,144 had cesarean delivery: 3,113 primary, 1,310 one prior, 510 two prior, and 211 three or more prior cesareans. There was a significant increase in organ injury, hysterectomy, and surgical site complications with increasing number of cesareans. In multivariable analysis, the risk of organ injury and hysterectomy was increased compared with primary cesarean after two prior cesareans, and after three or more cesareans for hemorrhage requiring transfusion and surgical site complications. Conclusion The risks of organ injury and hysterectomy are increased after two or more prior cesareans, and risks of hemorrhage and surgical site complications are increased after three or more cesareans.
The paradox of minority attorney satisfaction

Abstract A substantial literature documents the challenges faced by minority attorneys in the legal profession, ranging from underrepresentation in prestigious practice settings and lower incomes to discrimination from fellow lawyers, clients, and judges. In light of the foregoing, one would expect minority attorneys to regret their decisions to attend law school and become lawyers. Yet, empirical research indicates that minority attorneys are predominately satisfied with their decision to become attorneys and that their satisfaction is on par with that of white attorneys. How to account for this seeming paradox? Drawing on data from a large cross-section of Texas lawyers, this is the first study to address and assess empirically the paradox of minority attorney satisfaction. We provide evidence that the drivers of white and minority attorneys’ satisfaction differ and may be rooted in different value systems. In particular, minority attorneys’ satisfaction is unaffected by their academic performance in law school or earnings from legal practice. Moreover, they are especially satisfied in nominally low status solo practitioner positions. This article concludes by advancing possible explanations for these differences.
Pane in the Glass: A Review of the Accounting Cycle

ABSTRACT This case employs a realistic scenario in which students apply for internships with Pane in the Glass, Inc.'s financial reporting group. As part of the interview process, students are requ...
RN-to-BSN Education. Global and Population Health Project: Think Global, Act Local

Abstract Purpose An associate degree in nursing (ADN) is an established pathway to becoming a Registered Nurse (RN) in the United States (US) however there is a push for ADN trained RNs to pursue further training and obtain a Bachelor of Science in Nursing (BSN). Within the AACN Essentials, Essential VII: Clinical Prevention and Population Health a Community Health and Population-Focused Health Promotion Activity is required to be met for all BSN trained RNs. This manuscript outlines a novel way of meeting this requirement which allows students to develop knowledge and skills around public health, health promotion and disease prevention at the individual and population level. Methods The Community Health and Population-Focused Health Promotion Activity which the RN to BSN students undertook required them to choose a topic that addresses the National Health Initiatives, Strategies & Action Plans. From these broad health topics groups of students (3-4) developed a health promotion activity (HPA) on a public university campus which included a poster during their HPA. The poster presented the health topic and addressed why it was important globally, nationally, locally, and specifically to the university community using a variety of resources specific to the question. Prior to development, students were required to do an abbreviated community assessment on how the health promotion activity task would be presented to interact and engage fellow university students. An evaluation of the health promotion exercise was undertaken through a simple online survey using Qualitrics © accessed either by computers or tablets provided. Results After the health promotion activity, the planning for the project, the poster and syntheses of the data which was collected on the HPA was presented to their peers in class. Further analysis of the project was done on an individual basis requiring the students to submit an individual assignment on their health promotion topic, the activity and their individual analysis of the HPA. Conclusion This activity aligns with the Community Health and Population-Focused Health Promotion Activity for RN to BSN which is a requirement for the AACN The Essentials of Baccalaureate Education for Professional Nursing Practice and is a model for alternative practice for RN to BSN curriculum.
foxF-1 Controls Specification of Non-body Wall Muscle and Phagocytic Cells in Planarians.

Summary Planarians are flatworms capable of regenerating any missing body part in a process requiring stem cells and positional information. Muscle is a major source of planarian positional information and consists of several types of fibers with distinct regulatory roles in regeneration. The transcriptional regulatory programs used to specify different muscle fibers are poorly characterized. Using single-cell RNA sequencing, we define the transcriptomes of planarian dorsal-ventral muscle (DVM), intestinal muscle (IM), and pharynx muscle. This analysis identifies foxF-1, which encodes a broadly conserved Fox-family transcription factor, as a master transcriptional regulator of all non-body wall muscle. The transcription factors encoded by nk4 and gata4/5/6-2 specify two different subsets of DVM, lateral and medial, respectively, whereas gata4/5/6-3 specifies IM. These muscle types all express planarian patterning genes. Both lateral and medial DVM are required for medial-lateral patterning in regeneration, whereas medial DVM and IM have a role in maintaining and regenerating intestine morphology. In addition to the role in muscle, foxF-1 is required for the specification of multiple cell types with transcriptome similarities, including high expression levels of cathepsin genes. These cells include pigment cells, glia, and several other cells with unknown function. cathepsin+ cells phagocytose E. coli, suggesting these are phagocytic cells. In conclusion, we describe a regulatory program for planarian muscle cell subsets and phagocytic cells, both driven by foxF-1. FoxF proteins specify different mesoderm-derived tissues in other organisms, suggesting that FoxF regulates formation of an ancient and broadly conserved subset of mesoderm derivatives in the Bilateria.
Comparative Responsiveness of the PROMIS Pain Interference Short Forms With Legacy Pain Measures: Results From Three Randomized Clinical Trials

ABSTRACT The Patient-Reported Outcomes Measurement Information System Pain Interference (PROMIS-PI) scales are reliable and publicly accessible; however, little is known about how responsive they are to detect change in clinical trials and how their responsiveness compares with legacy measures. The study purpose was to evaluate responsiveness for the PROMIS-PI scales and to compare their responsiveness with legacy pain measures. We used data from 3 clinical trials totaling 759 participants. The clinical trials included patients with chronic low back pain (n = 261), chronic back or osteoarthritis pain (n = 240), and a history of stroke (n = 258). At both baseline and follow-up, participants completed PROMIS-PI scales and legacy pain measures (Brief Pain Inventory Interference scale; Pain, Enjoyment, General Activity scale; 36-Item Short Form Survey (SF-36) Bodily Pain scale; and Roland-Morris Disability Questionnaire). We measured global ratings of pain change, both prospectively and retrospectively, as anchors to identify patients as improved, unchanged, or worsened. Responsiveness was assessed with standardized response means, statistical tests comparing change groups, and area under the curve analysis. The PROMIS-PI scales had largely comparable responsiveness with the Brief Pain Inventory Interference and Pain, Enjoyment, General Activity scales. The 4 PROMIS-PI short forms had comparable responsiveness. For all pain questionnaires, responsiveness varied based on the study population and whether pain improved or worsened. Perspective This article presents 1) how responsive the PROMIS-PI scales were to detect change over time in the context of 3 clinical trials and 2) how their responsiveness compared with legacy pain measures. The findings can help researchers and clinicians choose between different patient-reported pain outcome measures.
Design of Platform-Based HF Direction-Finding Antennas Using the Characteristic Mode Theory

A systematic method for utilizing the characteristic modes (CMs) of a platform to design a direction finding (DF) system operating at the high-frequency (HF) band is presented. In this band, due to the large wavelengths, practical antennas used for DF are electrically small and have limited bandwidths. When such a DF array is mounted on a platform, however, the platform’s presence can be exploited to considerably improve the performance of the platform-based DF system compared to that of a stand-alone DF system. Our proposed approach relies upon selecting and exciting a group of the platform’s CMs that provides the lowest Cramer–Rao bound for the DF system’s accuracy. This approach allows for designing DF systems that can accurately detect the direction of arrival and the state of polarization of the wave over wide fields of views. This approach is used to design and simulate an HF DF system for an airborne platform. A scaled-model prototype of this platform-based DF array is fabricated and characterized. The measurement results of this array are used to perform DF experiments in an emulated environment. Measured results are found to be in good agreement with simulations, confirming the efficacy of the proposed design approach.
Chromatography, Radial Flow

Downstream processing is a key component of a process for the production of biomolecules. Liquid chromatograph often plays a central role. Radial flow chromatography (RFC) offers an alternative to the conventional axial flow chromatography (AFC) in preparative and large-scale applications. This article reviews the configurations and operating conditions for RFC, and provides comparisons between RFC and AFC. Several application examples and a mathematical model for RFC also are presented. Keywords: chromatography; radial flow; scale-up; protein purification; separation
Sexual agency and the unfinished work of rape law reform

For half a century, feminist legal scholars and activists have worked to reconstruct the retrograde foundations of rape law. In place of chastity protection, I argue that sexual agency underpins the modern law of rape. I begin by describing the construct of sexual agency, emphasizing how feminist insights have enriched a more familiar ideal of autonomy in ways that bear directly on sexual violence. I then apply the theory of sexual agency to three areas of relatively successful law reform efforts—substantive, evidentiary and procedural. I also consider the limits of rape law’s progress, again by reference to sexual agency. The agency frame recasts how a set of necessary reforms has fallen short of its intended target. I consider one final challenge to women’s agency that has become increasingly salient in the current #MeToo moment—acquiesced-to (unwanted consensual) sex. I conclude that, while the promise of female sexual agency remains unfulfilled, it should endure as the desideratum of reform.
Spectral Inference Networks: Unifying Deep and Spectral Learning

We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics. As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data. We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions. We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets. Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.