cssi/SciSciGPT-SciSciNet huggingface dataset:
On the constant factor in several related asymptotic estimates

We establish formulas for the constant factor in several asymptotic estimates related to the distribution of integer and polynomial divisors. The formulas are then used to approximate these factors numerically.
Alternative Fuels for ICF Targets

The first observation of the beta-layering phenomenon showed that it was possible to fabricate inertial confinement fusion (ICF) targets having an outer ablating shell surrounding a symmetric solid...
Vibrational Circular Dichroism

Abstract Since the first discovery by Biot and Pasteur that some molecules rotate the polarization of light, this optical activity has been recognized to be a consequence of molecular structure [1]. In fact, natural optical activity is one of the most structurally sensitive techniques available to the spectroscopist and, hence, has been extensively exploited. Most spectroscopic techniques evidence sensitivity to detailed molecular geometry only via perturbation of energy levels and selection rules. However, molecular optical activity comes about as the direct result of the geometrical arrangement and interaction of the atoms in a molecule [2]. In a somewhat oversimplified manner, the observable optical activity can be viewed to have a first-order dependence on the molecular asymmetry.
Risky business: Publicly insuring against rising tides

ABSTRACTThis article uses the history and current state of publicly subsidized coastal flood insurance policy in the United States to highlight how perceptions of risk can be influenced by past and current policy practices. For the purposes of this article, public flood insurance premiums are used as a proxy for risk; higher premiums suggest greater risk, while lower premiums suggest lowered risk. By using coastal flood insurance as a proxy for flood risk, subjective factors of risk are highlighted and contextualized. The goal is to provide the reader with an example of the importance of considering both objective and subjective risk factors when developing and implementing climate change policies that make investments today to protect against future harm.
Speech-Processing Fatigue in Children: Auditory Event-Related Potential and Behavioral Measures.

Purpose Fatigue related to speech processing is an understudied area that may have significant negative effects, especially in children who spend the majority of their school days listening to clas...
Leakage-Abuse Attacks against Order-Revealing Encryption

Order-preserving encryption and its generalization order-revealing encryption (OPE/ORE) allow sorting, performing range queries, and filtering data — all while only having access to ciphertexts. But OPE and ORE ciphertexts necessarily leak information about plaintexts, and what level of security they provide in practice has been unclear. In this work, we introduce new leakage-abuse attacks that recover plaintexts from OPE/ORE-encrypted databases. Underlying our new attacks is a framework in which we cast the adversary's challenge as a non-crossing bipartite matching problem. This allows easy tailoring of attacks to a specific scheme's leakage profile. In a case study of customer records, we show attacks that recover 99% of first names, 97% of last names, and 90% of birthdates held in a database, despite all values being encrypted with the OPE scheme most widely used in practice. We also show the first attack against the recent frequency-hiding Kerschbaum scheme, to which no prior attacks have been demonstrated. Our attack recovers frequently occurring plaintexts most of the time.
A dynamic load balancing strategy for channel assignment using selective borrowing in cellular mobile environment

We propose a dynamic load balancing scheme for the channel assignment problem in a cellular mobile environment. As an underlying approach, we start with a fixed assignment scheme where each cell is initially allocated a set of channels, each to be assigned on demand to a user in the cell. A cell is classified as ‘hot’, if the degree of coldness of a cell (defined as the ratio of the number of available channels to the total number of channels for that cell), is less than or equal to some threshold value. Otherwise the cell is `cold'. Our load balancing scheme proposes to migrate unused channels from underloaded cells to an overloaded one. This is achieved through borrowing a fixed number of channels from cold cells to a hot one according to a channel borrowing algorithm. A channel assignment strategy is also proposed based on dividing the users in a cell into three broad types—‘new’, ‘departing’, ‘others’—and forming different priority classes of channel demands from these three types of users. Assignment of the local and borrowed channels are performed according to the priority classes. Next, a Markov model for an individual cell is developed, where the state is determined by the number of occupied channels in the cell. The probability for a cell being hot and the call blocking probability in a hot cell are derived, and a method to estimate the value of the threshold is also given. Detailed simulation experiments are carried out in order to evaluate our proposed methodology. The performance of our load balancing scheme is compared with the fixed channel assignment, simple borrowing, and two existing strategies with load balancing (e.g., directed retry and CBWL), and a significant improvement of the system behavior is noted in all cases.
A design cycle for echelle spectrometers

Echelle spectroscopy is a mature technique that was introduced over 60 years ago. However, the process of determining all of the physical parameters of an echelle spectrometer, especially the main parameters of the echelle grating itself (the groove density, the blaze angle, and the grating’s size), is not easily found in the literature. This paper takes a systematic approach to the problem of designing an echelle spectrometer and develops a design cycle that starts with the main requirements of an echelle spectrometer: the wavelength range, the spectral resolution, and the geometrical optical throughput. It sorts all of the equations that relate the requirements to the physical parameters of the spectrometer, identifies the design parameters (those that are directly selected by the designer), and develops a tool that determines all of the physical parameters of an echelle spectrometer for any given set of requirements.
Women don't ask: an investigation of start-up financing and gender

AbstractAre women less likely to ask for help financing their businesses? This study investigates whether gender is a factor that impacts the propensity to ask for financing among nascent entrepreneurs. We also investigate if start-up helpers, who do not have an ownership share, have an impact on the likelihood of asking for financing, specifically between men and women. Our findings suggest that being female significantly decreases the probability of asking for financing and the presence of start-up helpers significantly increases the incidence of asking for financing in the nascent stage. In addition, among those who created new firms or were still in the start-up process, the number of start-up helpers exponentially increased the incidence of asking for financing among female founders. We use the Panel Study of Entrepreneurial Dynamics II data, the largest, nationally representative, and longitudinal database on nascent entrepreneurs for the United States.
Geometry-oblivious FMM for compressing dense SPD matrices

We present GOFMM (geometry-oblivious FMM), a novel method that creates a hierarchical low-rank approximation, or "compression," of an arbitrary dense symmetric positive definite (SPD) matrix. For many applications, GOFMM enables an approximate matrix-vector multiplication in N log N or even N time, where N is the matrix size. Compression requires N log N storage and work. In general, our scheme belongs to the family of hierarchical matrix approximation methods. In particular, it generalizes the fast multipole method (FMM) to a purely algebraic setting by only requiring the ability to sample matrix entries. Neither geometric information (i.e., point coordinates) nor knowledge of how the matrix entries have been generated is required, thus the term "geometry-oblivious." Also, we introduce a shared-memory parallel scheme for hierarchical matrix computations that reduces synchronization barriers. We present results on the Intel Knights Landing and Haswell architectures, and on the NVIDIA Pascal architecture for a variety of matrices.
Audit Firms as Networks of Offices

SUMMARY Prior audit research suggests that most, if not all, audit quality can be explained at the office level. However, the question remains of whether office-level audit quality is contingent on how individual offices relate to the firm as a whole. Motivated by theories of knowledge management, organizational learning, and networks, we posit that individual offices are connected to their audit network through partner knowledge sharing and oversight, which impact office-level audit quality. We interview Big 4 audit partners and learn that knowledge sharing between partners in different offices is common and intended to aid in the provision of audit services. Using network connectedness to proxy for knowledge sharing and oversight between offices of the same firm, we document that more connected offices are associated with fewer client restatements and lower discretionary accruals. We additionally find that network effects are magnified when accounting treatments are more complex and require greater audi...
Optimal Penalized Function-on-Function Regression under a Reproducing Kernel Hilbert Space Framework.

Many scientific studies collect data where the response and predictor variables are both functions of time, location, or some other covariate. Understanding the relationship between these functiona...
Chemical and Isotopic Evidence for CO2 Charge and Migration within Bravo Dome and Potential CO2 Leakage to the Southwest

Abstract Gas analyses from northeastern New Mexico, USA indicate that previous interpretations of the location of gas charge into the northeastern portion of Bravo Dome are likely correct, and that there may be multiple migration pathways from the same source for different regions in northeastern New Mexico.
Transportation Policy at the Ballot Box

Through direct democracy, the voting public can engage in ballot box planning, by which voters themselves, rather than decision makers, set policy and make project decisions. Previous studies found that ballot box planning has a growing influence on land use policy as well as transportation and land use finance. However, the impact of ballot box planning on transportation policy is relatively unexplored. This paper examines 148 local transportation policy–related ballot measures in California from 1995 to 2015. Most transportation policy measures passed, but measures that could be classified as proalternative or anticar passed at a slightly higher rate than did procar or antialternative measures and showed some agreement between voters and transportation professionals toward multimodalism. Nonetheless, there are numerous examples of voters constraining the options of transportation planners by restricting or even prohibiting contemporary planning strategies. For example, several successful ballot measures...
Univariate Tests for Phase Capacity: Tools for Identifying When to Modify a Survey’s Data Collection Protocol

To mitigate the potentially harmful effects of nonresponse, most surveys repeatedly follow up with nonrespondents, often targeting a response rate or predetermined number of completes. Each additional recruitment attempt generally brings in a new wave of data, but returns gradually diminish over the course of a fixed data collection protocol, as each subsequent wave tends to consist of fewer responses than the last. Consequently, point estimates begin to stabilize. This is the notion of phase capacity, suggesting some form of design change is in order, such as switching modes, increasing the incentive, or, as is considered exclusively in this research, discontinuing the nonrespondent follow-up campaign altogether. A previously proposed test for phase capacity calls for multiply imputing nonrespondents’ missing data to assess, retrospectively, whether the most recent wave of data significantly altered a key, nonresponse-adjusted point estimate. This study introduces a more flexible adaptation amenable to surveys that instead reweight the observed data to compensate for nonresponse. Results from a simulation study and application indicate that, all else equal, the weighting version of the test is more sensitive to point estimate changes, thereby dictating more follow-up attempts are warranted.
Limit theory for explosive autoregression under conditional heteroskedasticity

Abstract This paper studies an explosive autoregression with conditionally heteroskedastic innovations. The asymptotic distributions of LS, GLS, t -statistics, heteroskedasticity-consistent t -statistics and GLS t -statistics are derived for nonstationary local-to-unity and mildly explosive roots, in which ρ n satisfies n 1 − ρ n → − ∞ , 0 . Combined with the existing results, we can cover the range of ρ n such that n 1 − ρ n → − ∞ , ∞ . Some results on pure explosive cases are also discussed.
Physical Examination is the Best Predictor of the Need for Abdominal Surgery in Children Following Motor Vehicle Collision.

Abstract Background Exploratory laparotomy in children after motor vehicle collision (MVC) is rare. In the absence of definitive hemorrhage or free abdominal air on radiographic imaging, predictors for operative exploration are conflicting. Objective The purpose of this study was to explore objective findings that may aid in determining which children require operative abdominal exploration after MVC. Methods Data from 2010–2014 at an American College of Surgeons–certified level 1 pediatric trauma center were retrospectively reviewed. Demographics, vital signs, laboratory data, radiologic studies, operative records, associated injuries, and outcomes were analyzed and p Results Eight hundred sixty-two patients 0–18 years of age presented to the hospital after an MVC during the study period. Seventeen patients (2.0%) required abdominal exploration and all were found to have intraabdominal injuries. Respiratory rate was the only vital sign that was significantly altered (p = 0.04) in those who required abdominal surgery compared with those who did not. Physical examination findings, such as the seat belt sign, abdominal bruising, abdominal wound, and abdominal tenderness, were present significantly more frequently in those requiring abdominal surgery (p Conclusion Data from this study solidify the relationship between specific physical examination findings and the need for abdominal exploration after MVC in children. In addition, these data suggest that a lack of the seat belt sign, abdominal bruising, abdominal wounds, or abdominal tenderness are individually predictive of patients who will not require surgical intervention.
Optimal Deployment of Dynamic Wireless Charging Facilities for an Electric Bus System

Diesel engine buses still make up the majority of the bus fleet in the United States, even with the problem of diesel exhaust and greenhouse gas emissions. Electric buses, which generate no emissions, are a promising green alternative for bus fleets. However, electric buses have a limited travel range and a time-consuming recharging process. Dynamic wireless charging, which allows electric buses to charge while traveling, could alleviate the drawbacks of electric buses. With dynamic wireless charging technology, electric buses can operate with smaller batteries, and the stationary recharging time at the base station can be shortened. The key design variables in deploying dynamic wireless charging facilities for an electric bus system are battery size and the location of the wireless charging facilities. This paper addresses the problem of simultaneously selecting the optimal locations for the wireless charging facilities and designing the battery size for an electric bus system. A mixed integer linear pro...
Choosing a media outlet when seeking public approval

This paper considers the media outlet choice of a politician who seeks public approval for a political agenda in a broadcast interview. The available media outlets differ in their “toughness” towards the politician. An interview with a tougher media outlet is more informative, but is also more likely to yield a negative outcome. The choice of the media outlet determines the accuracy of the information that flows to the public and the volume of citizens who consume that information. The analysis shows that (1) politicians who enjoy sufficient popularity are likely to avoid tough media outlets, (2) when seeking approval for controversial agendas, politicians are more likely to appear in tougher outlets.
Sensor Fault Detection and Isolation for a Wireless Sensor Network-Based Remote Wind Turbine Condition Monitoring System

To improve the reliability of wind turbines, various condition monitoring systems (CMSs) have been developed and most of them transmit data using wired communication channels. Recently, wireless sensor networks (WSNs) have been used to transmit data in wind turbine CMS due to the low cost and easy deployment feature of WSNs. However, since wind turbines are installed in harsh environments, the sensors and sensor nodes used in the WSN-based wind turbine CMSs are easily subject to faults, leading to corruption of the signals used for condition monitoring, which decreases the reliability of the CMS. This paper proposes a three-stage method for the detection and isolation of three most common sensor faults, i.e., SHORT fault, CONSTANT fault, and NOISE fault, in WSN-based wind turbine CMS. The proposed sensor fault detection and isolation greatly increases the accuracy and reliability of wind turbine CMSs. Data collected from wind turbines in the field are used to validate the effectiveness of the proposed method.
A Psychological Profile of the Alt-Right:

The 2016 U.S. presidential election coincided with the rise of the "alternative right," or alt-right. Alt-right associates have wielded considerable influence on the current administration and on social discourse, but the movement's loose organizational structure has led to disparate portrayals of its members' psychology and made it difficult to decipher its aims and reach. To systematically explore the alt-right's psychology, we recruited two U.S. samples: An exploratory sample through Amazon's Mechanical Turk (N = 827, alt-right n = 447) and a larger, nationally representative sample through the National Opinion Research Center's Amerispeak panel (N = 1,283, alt-right n = 71-160, depending on the definition). We estimate that 6% of the U.S. population and 10% of Trump voters identify as alt-right. Alt-right adherents reported a psychological profile more reflective of the desire for group-based dominance than economic anxiety. Although both the alt-right and non-alt-right Trump voters differed substantially from non-alt-right, non-Trump voters, the alt-right and Trump voters were quite similar, differing mainly in the alt-right's especially high enthusiasm for Trump, suspicion of mainstream media, trust in alternative media, and desire for collective action on behalf of Whites. We argue for renewed consideration of overt forms of bias in contemporary intergroup research.
Development and evaluation of a novel user interface for reviewing clinical microbiology results.

Background Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review. Methods We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design. Results The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale. Conclusions The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).
A Phylogenetic Analysis of Normal Modes Evolution in Enzymes and its Relationship to Enzyme Function

Enzyme function and catalysis is often dictated by the vibrational dynamics and flexibility of the protein. Since the catalytic functions of enzymes are necessary in nearly all biochemical pathways essential for life, evolutionary conservation of the dynamics that aid enzymatic function is an expected outcome. In this study, a novel phylogenetic approach is implemented to explore the relationship between enzyme dynamics and function as it relates to evolutionary history. Protein dynamics and flexibility are described by normal mode analysis based on a simplified harmonic potential force field applied to the Cα backbone while enzymatic function is described by the Enzyme Commission (EC) numbers in the ENZYME database. Analyzing the comparative normal mode analysis of a catalytic domain family with respects to its phylogenetic tree revealed a negative correlation in dynamic conservation as evolutionary distance increases, which has not been accounted for in prior studies. By correcting the overlap scores of the normal modes for the natural negative relationship, a new score dependent on evolution can be obtained. This novel approach exhibited potential for predicting functional divergence in a protein family's phylogeny and adds a new layer of insight in regards to the role of protein dynamics in enzyme evolution.
Bibliometric Analysis of Gender Authorship Trends and Collaboration Dynamics Over 30 Years of Spine 1985 to 2015.

STUDY DESIGN A bibliometric analysis. OBJECTIVE The aim of this article was to study bibliometric changes over the last 30 years of Spine. These trends are important regarding academic publication productivity. SUMMARY OF BACKGROUND DATA Inflation in authorship number and other bibliometric variables has been described in the scientific literature. The issue of author gender is taking on increasing importance, as efforts are being made to close the gender gap. METHODS From 1985 to 2015, 10-year incremental data for several bibliometric variables were collected, including author gender. Standard bivariate statistical analyses were performed. Trends over time were assessed by the Cochran linear trend. A P < 0.05 was considered statistically significant. RESULTS Inclusion criteria were met for 1566 manuscripts. The majority of the manuscripts were from North America (51.2%), Europe (25.2%), and Asia (20.8%). The number of manuscripts, authors, countries, pages, and references all increased from 1985 to 2015. There was a slight increase in female first authors over time (17.5% to 18.4%, P = 0.048). There was no gender change over time for corresponding authors (14.3% to 14.0%, P = 0.29). There was an 88% increase in the percentage of female first authors having male corresponding authors (P = 0.00004), and a 123% increase in male first authors having female corresponding authors (P = 0.0002). The 14% to 18% of female authors in Spine is higher than the ∼5% female membership of the Scoliosis Research Society and North American Spine Society. CONCLUSION Manuscripts in Spine over the past 30 years have shown a significant increase in the number of authors, collaborating institutions and countries, printed pages, references, and number of times each manuscript was cited. There has been a mild increase in female first authorship, but none in corresponding authorship. Increases in female authorship will likely require recruitment of more females into the discipline rather than providing females in the discipline with authorship opportunities. LEVEL OF EVIDENCE N/A.
Identification of human vaccinees that possess antibodies targeting the egg-adapted hemagglutinin receptor binding site of an H1N1 influenza vaccine strain

Human influenza viruses passaged in eggs often acquire mutations in the hemagglutinin (HA) receptor binding site (RBS). To determine if egg-adapted H1N1 vaccines commonly elicit antibodies targeting the egg-adapted RBS of HA, we completed hemagglutinin-inhibition assays with A/California/7/2009 HA and egg-adapted A/California/7/2009-X-179A HA using sera collected from 159 humans vaccinated with seasonal influenza vaccines during the 2015-16 season. We found that ∼5% of participants had ≥4-fold higher antibody titers to the egg-adapted viral strain compared to wild type viral strain. We used reverse-genetics to demonstrate that a single egg-adapted HA RBS mutation (Q226R) was responsible for this phenotype.
Institutional Theory and the Evolution of Marketing Channels in Emerging Economies: Setting a Research Agenda

Abstract The story of the growth of emerging economies is about the growth of their marketing institutions. Marketing channels are the key marketing institution in enabling the flow of goods and services. However, marketing scholars typically rely on models and theories devised in the developed economies to study the marketing channels in emerging economies, and these include theories based on political economy-based frameworks that emphasize power and dependence. We argue that often the institutional drives to gain legitimacy provide a powerful lens to understand the evolution of marketing channels. In this conceptual paper, we illustrate our arguments through a few examples, and provide an agenda for future research. Keywords: Marketing Channels, Emerging Economies, Institutional Theory Introduction Emerging economies are often characterized by their relatively higher and sustainable growth rates. The attendant economic conditions usually include increase in productivity and GDP at rates higher than those in the developed economies. These developments affect many facets of economy. For example, the increased variety and flow of goods and services result in the development and evolution of marketing channels. This is an expected outcome because growing economy and the increase in consumption go hand in hand and the marketing channels are made of interdependent organizations (channel members) that link the marketer with the end consumer. Wholesalers, agents, brokers, insurers, retailers, warehouses, transporters, specialized delivery vehicles, road, rail, ports, waterways, and the electronic data network – these all are vital ingredients of a marketing channel system that keeps the economy humming. Despite the central role of marketing channels (or simply channels) in connecting the marketers and consumers, surprisingly little attention is paid to understanding how marketing channels evolve in emerging economies and if the dominant frameworks and model used to understand the channels in developed economies are appropriate in understanding the evolution of channels in emerging economies. Usually, the models from the mature economies are deemed relevant for the emerging economies as well (e.g., Dong, Tse, and Hung 2010; Kale 1986). It is our thesis that the neoclassical theory based efficiency centered framework or the political economy-based models, the dominant theoretical frameworks through which the functioning of channels is examined in developed markets,
Analysis of an Anti-reflecting Nanowire Transparent Electrode for Solar Cells

We investigate a nanowire transparent electrode that also functions as an anti-reflection coating for solar cells. To provide physical insights into this broadband anti-reflection, we introduce a generalized Fabry-Perot model.
Nickel-Catalyzed Regioselective 1,4-Hydroboration of N-Heteroarenes

Combinations of Ni(acac)2 with phosphine ligands were found to catalyze the regioselective hydroboration of N-heteroarenes with pinacolborane, affording N-borylated 1,4-reduction products. Preliminary mechanistic studies have focused on the isolation and study of potential intermediates in the catalytic cycle.
Comment on “Computerizing industries and routinizing jobs: Explaining trends in aggregate productivity” by Sangmin Aum, Sang Yoon (Tim) Lee and Yongseok Shin

Abstract Aum et al. (2018) quantify the impact of production complementarities and differential productivity growth across occupations and sectors on the slowdown of aggregate productivity growth. This note expands their work to study substitutability between new computer equipment and labor in individual occupations as opposed to all occupations combined. Preliminary empirical evidence suggests (1) significantly different elasticities of substitution between computers and labor across occupations and (2) a strong correlation between productivity growth of computers and labor in occupations where these two inputs are complementary. When they are substitutes, however, their productivity growth rates appear uncorrelated. These findings have the potential to amplify or weaken the magnitude of the aggregate productivity slowdown explained by Aum et al. (2018) making their approach a promising avenue for future research.
Statistical Implications of the Revenue Transfer Methodology in the Affordable Care Act

The Affordable Care Act (ACA) includes a permanent revenue transfer methodology that provides financial incentives to health insurance plans that have higher than average actuarial risk. In this ar...
Physiological dynamic compression regulates central energy metabolism in primary human chondrocytes.

Chondrocytes use the pathways of central metabolism to synthesize molecular building blocks and energy for cartilage homeostasis. An interesting feature of the in vivo chondrocyte environment is the cyclical loading generated in various activities (e.g., walking). However, it is unknown whether central metabolism is altered by mechanical loading. We hypothesized that physiological dynamic compression alters central metabolism in chondrocytes to promote production of amino acid precursors for matrix synthesis. We measured the expression of central metabolites (e.g., glucose, its derivatives, and relevant co-factors) for primary human osteoarthritic chondrocytes in response to 0–30 minutes of compression. To analyze the data, we used principal components analysis and ANOVA-simultaneous components analysis, as well as metabolic flux analysis. Compression-induced metabolic responses consistent with our hypothesis. Additionally, these data show that chondrocyte samples from different patient donors exhibit different sensitivity to compression. Most importantly, we find that grade IV osteoarthritic chondrocytes are capable of synthesizing non-essential amino acids and precursors in response to mechanical loading. These results suggest that further advances in metabolic engineering of chondrocyte mechanotransduction may yield novel translational strategies for cartilage repair.
On a pursuit for perfecting an undergraduate requirements engineering course

Abstract Requirements Engineering (RE) is an essential component of any software development cycle. Understanding and satisfying stakeholder needs and wants is the difference between the success and failure of a product. However, RE is often perceived as a “soft” skill by students and is often ignored by students who prioritize the learning of coding, testing, and algorithmic thinking. This view contrasts with the industry, where “soft” skills are instead valued equal to any other engineering ability. A key challenge in teaching RE is that students who are accustomed to technical work have a hard time relating to something that is non-technical. Furthermore, students are rarely afforded the opportunity to practice requirements elicitation and management skills in a meaningful way while learning the RE concepts as an adjunct to other content. At Rose-Hulman, several project-based approaches have been experimented with in teaching RE, and these have evolved over time. In this paper, the progress of teaching methodologies is documented to capture the pros and cons of these varied approaches, and to reflect on what worked and what did not in teaching RE to undergraduate engineering students.
Deep Learning Based Tumor Type Classification Using Gene Expression Data

The differential analysis is the most significant part of RNA-Seq analysis. Conventional methods of the differential analysis usually match the tumor samples to the normal samples, which are both from the same tumor type. Such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. The Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%. Furthermore, based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply a convolutional neural network on Pan-Cancer Atlas for the classification of tumor types, and we are also the first to use gene's contribution in classification to the importance of genes to identify candidate biomarkers. Our experiment results show that our method has a good performance and could also apply to other genomics data.
Cellular Controlled Delay TCP (C2TCP)

Cellular networks have special characteristics including highly variable channels, fast fluctuating capacities, deep per user buffers, self-inflicted queuing delays, radio uplink/downlink scheduling delays, etc. These distinguishing properties make the problem of achieving low latency and high throughput in cellular networks more challenging than in wired networks. That’s why in this environment, TCP and its flavors, which are generally designed for wired networks, perform poorly.To cope with these challenges, we present C2TCP, a flexible end-to-end solution targeting interactive applications requiring high throughput and low delay in cellular networks. C2TCP stands on top of loss-based TCP and brings it delay sensitivity without requiring any network state profiling, channel prediction, or complicated rate adjustment mechanisms. The key idea behind C2TCP is to absorb dynamics of unpredictable cellular channels by investigating local minimum delay of packets in a moving time window and react to the cellular network’s capacity changes very fast.Through extensive trace-based evaluations using traces from five commercial LTE and 3G networks, we have compared performance of C2TCP with various TCP variants, and state-of-the-art schemes including BBR, Verus, and Sprout. Results show that on average, C2TCP outperforms these schemes and achieves lower average and 95th percentile delay for packets.
Insulin and glucose homeostasis in childhood cancer survivors treated with abdominal radiation: A pilot study.

Background Childhood cancer survivors exposed to abdominal radiation (abdRT) are at increased risk for both insulin-dependent and non-insulin-dependent diabetes. We sought to clarify the pathophysiology of diabetes after abdRT by performing dynamic studies of insulin and glucose and testing for type 1 diabetes-associated autoantibodies. Procedure Cross-sectional analysis of 2-year childhood cancer survivors treated with abdRT at age ≤21 years who underwent oral glucose tolerance testing and assessment of diabetes-related autoantibodies from December 2014 to September 2016. Prevalence of insulin/glucose derangements, indices of insulin sensitivity/secretion (homeostatic model assessment of insulin resistance [HOMA-IR], whole-body insulin sensitivity, insulinogenic index), autoantibody positivity, and treatment/demographic factors associated with adverse metabolic outcomes were assessed. Results Among 40 participants previously exposed to abdRT (57.5% male; median age at cancer diagnosis, 3.3 years [range, 0.5-20.1]; median age at study 14.3 years [range, 8.3-49.8]; none with obesity), 9 (22.5%) had glucose derangements (n = 4 with impaired fasting glucose [≥100 mg/dL]; n = 4 with impaired glucose tolerance [2-hour glucose 140-199 mg/dL]; n = 1 with previously unrecognized diabetes [2-hour glucose ≥200 mg/dL]). Three of the four individuals with impaired fasting glucose also had insulin resistance, as measured by HOMA-IR; an additional four subjects with normal glucose tolerance were insulin resistant. The subject with diabetes had normal HOMA-IR. No participant had absolute insulinopenia or >1 positive diabetes-related autoantibody. Conclusions This study suggests that radiation-induced damage to the insulin-producing β-cells is an unlikely explanation for the early derangements in glucose metabolism observed after abdRT. Research into alternative pathways leading to diabetes after abdRT is needed.
Structural Behavioral Economics

Abstract What is the role of structural estimation in behavioral economics? I discuss advantages, and limitations, of the work in Structural Behavioral Economics. I also cover common modeling choices and how to get started. Among the advantages, I argue that structural estimation builds on, and expands, a classical behavioral tool, simple calibrations, and that it benefits from the presence of a few parsimonious behavioral models which can be taken to the data. Estimation is also well suited for experimental work, common in behavioral economics, as it can lead to improvements in the experimental design. In addition, at a time where policy implications of behavioral work are increasingly discussed, it is important to ground these policy implications in (estimated) models. Structural work, however, has important limitations, which are relevant to its behavioral applications. Estimation takes much longer and the extra degree of complexity can make it difficult to know which of a series of assumptions is driving the results. For related reasons, it is also easy to over-reach with the welfare implications. Taking this into account, I provide a partial how-to guide to structural behavioral economics, covering: (i) the choice of estimation method; (ii) the modeling of heterogeneity; (iii) identification and sensitivity. Finally, I discuss common issues for the estimation of leading behavioral models. I illustrate this discussion with selected coverage of existing work in the literature.
Learning to classify from impure samples with high-dimensional data

A persistent challenge in practical classification tasks is that labeled training sets are not always available. In particle physics, this challenge is surmounted by the use of simulations. These simulations accurately reproduce most features of data, but cannot be trusted to capture all of the complex correlations exploitable by modern machine learning methods. Recent work in weakly supervised learning has shown that simple, low-dimensional classifiers can be trained using only the impure mixtures present in data. Here, we demonstrate that complex, high-dimensional classifiers can also be trained on impure mixtures using weak supervision techniques, with performance comparable to what could be achieved with pure samples. Using weak supervision will therefore allow us to avoid relying exclusively on simulations for high-dimensional classification. This work opens the door to a new regime whereby complex models are trained directly on data, providing direct access to probe the underlying physics.
Generative Adversarial Estimation of Channel Covariance in Vehicular Millimeter Wave Systems

Enabling highly-mobile millimeter wave (mmWave) systems is challenging because of the huge training overhead associated with acquiring the channel knowledge or designing the narrow beams. Current mmWave beam training and channel estimation techniques do not normally make use of the prior beam training or channel estimation observations. Intuitively, though, the channel matrices are functions of the various elements of the environment. Learning these functions can dramatically reduce the training overhead needed to obtain the channel knowledge. In this paper, a novel solution that exploits machine learning tools, namely conditional generative adversarial networks (GAN), is developed to learn these functions between the environment and the channel covariance matrices. More specifically, the proposed machine learning model treats the covariance matrices as 2D images and learns the mapping function relating the uplink received pilots, which act as RF signatures of the environment, and these images. Simulation results show that the developed strategy efficiently predicts the covariance matrices of the large-dimensional mmWave channels with negligible training overhead.
The Caching Broadcast Channel with a Wire and Cache Tapping Adversary of Type II

This paper introduces the notion of cache-tapping into information theoretic models of coded caching. In particular, the wiretap II model with two receivers equipped with fixed-size cache memories is considered. The adversary chooses a set of symbols from cache placement, delivery, or both to tap into. The legitimate parties know neither whether cache placement, delivery, or both transmissions are tapped, nor the positions of tapped symbols. Only the size of overall tapped set is known. The strong secrecy capacity, i.e., the maximum achievable file rate while keeping the overall library strongly secure, is identified for the instance of two library files. Achievability is established using a code design which combines wiretap coding, security embedding codes, one-time pad keys, and coded caching. The study overall demonstrates that information theoretic security guarantees are possible against a powerful adversary which optimizes its attack over both phases of a cache-aided communication system.
Adaptive Affinity Fields for Semantic Segmentation

Semantic segmentation has made much progress with increasingly powerful pixel-wise classifiers and incorporating structural priors via Conditional Random Fields (CRF) or Generative Adversarial Networks (GAN). We propose a simpler alternative that learns to verify the spatial structure of segmentation during training only. Unlike existing approaches that enforce semantic labels on individual pixels and match labels between neighbouring pixels, we propose the concept of Adaptive Affinity Fields (AAF) to capture and match the semantic relations between neighbouring pixels in the label space. We use adversarial learning to select the optimal affinity field size for each semantic category. It is formulated as a minimax problem, optimizing our segmentation neural network in a best worst-case learning scenario. AAF is versatile for representing structures as a collection of pixel-centric relations, easier to train than GAN and more efficient than CRF without run-time inference. Our extensive evaluations on PASCAL VOC 2012, Cityscapes, and GTA5 datasets demonstrate its above-par segmentation performance and robust generalization across domains.
Interpretable Spiculation Quantification for Lung Cancer Screening

Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman’s rank correlation coefficient \(\rho = 0.48\)) with the radiologist’s spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.
Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data

Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Second, compared to the number of involved molecules and system complexity, the number of available samples for studying complex disease, such as cancer, is often limited, especially considering disease heterogeneity. The key question is whether we may integrate available data from all different sources or domains to achieve reproducible disease prognosis based on NGS count data. In this paper, we develop a Bayesian Multi-Domain Learning (BMDL) model that derives domain-dependent latent representations of overdispersed count data based on hierarchical negative binomial factorization for accurate cancer subtyping even if the number of samples for a specific cancer type is small. Experimental results from both our simulated and NGS datasets from The Cancer Genome Atlas (TCGA) demonstrate the promising potential of BMDL for effective multi-domain learning without ``negative transfer'' effects often seen in existing multi-task learning and transfer learning methods.